Pathology: Diagnostics, Reporting and Artificial Intelligence

Abstract

Breast pathology poses a particular diagnostic challenge due to the broad spectrum of functional, reactive and neoplastic changes in the breast. Objectifiable and reproducible criteria are the key to a valid diagnosis. In addition to the diagnostic classification of lesions, it is the task of pathologists to identify and document all tumor characteristics that are relevant for clinical management. Modern personalized medicine is based on up-to-date, valid pathomorphological and molecular diagnostics. Reports of findings should be written comprehensibly, completely and quickly. Structured pathology reports are ideal for this purpose. Before artificial intelligence can fulfil the hopes placed in it regarding the acceleration and objectification of reporting, technical and financial limitations must be resolved in addition to the explainability of AI-generated decisions.

Keywords

Breast pathology - biomarkers - precision medicine - pathology reporting - artificial intelligence

Introduction

Pathology has advanced rapidly over the last two decades. Its focus on descriptive diagnosis was expanded by the possibilities opened up by molecular pathology. This development has significantly contributed to our understanding of the cancerogenesis and progression of various tumour diseases, including breast cancer, refined the differentiation of tumour entities and enabled the identification of pathogenic or likely pathogenic gene variants, which are today used in precision medicine to predict the response to targeted drugs. The amount of information that can be obtained from tissue specimens today has multiplied as a result of this. In the context of innovative treatment strategies building on mRNA sequencing and proteomics, it will increase even further.

Thanks to the willingness of German pathologists to innovate and to their quality awareness, the new methods and the necessary quality assurance measures were quickly established throughout Germany across the ambulatory and hospital care sectors. The Quality Assurance Initiative Pathology (QuIP, Qualitätssicherungs-Initiative Pathologie GmbH) that was initiated by the German Society of Pathology (DGP, Deutsche Gesellschaft für Pathologie e.V.) and the Professional Association of German Pathologists (BDP, Berufsverband Deutscher Pathologinnen und Pathologen e.V.) started in 2004 with immunohistochemical Round Robin tests; today, QuIP offers a broad spectrum of Round Robin tests, including some addressing molecular pathology questions. Compared to the rest of Europe, Germany today enjoys a leading position in terms of the availability of quality-assured biomarker diagnostics [1].

Other forward-looking development themes are digitalization and artificial intelligence, both of which are viewed as building blocks of a modern Next Generation Pathology (the motto of the Annual Meeting of the German Society of Pathology in 2024) and have increasingly been implemented and advanced in recent years. In pathology, there are numerous options for digitalization: laboratory workflow, digital scanning of slides for assessment on the monitor and automated quantification of immunohistological markers, reporting using speech recognition, creation of structured, standardized pathology reports, electronic transfer of pathology reports to practices and hospitals via defined interfaces, and cancer registry reporting. The areas of application of artificial intelligence for optimizing processes and supporting diagnostic assessments are therefore broad and varied, ranging from prioritizing cases for diagnosis to pre-screening of biopsies and quantification of expression profiles to creating standardized pathology reports and systematically interpreting extensive data sets [2] [3] [4].

The aim of this article is to highlight the special demands on modern, future-oriented breast pathology reporting and to describe the possibilities and limitations of artificial intelligence in this context.

The special features of breast pathology

Pathology is the critical link between diagnosis and treatment in the management of women with suspicious breast lesions – whether as part of mammography screening or otherwise. On the one hand, it is important to identify the morphological correlate for the suspicious imaging findings or clinical signs and symptoms, on the other, to set the course for the provision of appropriate treatment.

The diversity of breast lesions is high in terms of clinical imaging presentation, morphology, molecular characteristics, and biological behaviour and thus represents a particular challenge. This applies both to routine diagnostics and the context of AI-assisted reporting. From a morphological perspective, the breast is an exceptionally colourful organ that is subject to hormonal effects. Accordingly, the already broad histological spectrum of benign and malignant neoplasms is expanded by physiological functional changes, such as mastopathy, which are common and occur in a wide variety of forms, potentially in various combinations with neoplasms. Benign changes can be precursors for malignant transformation, other lesions entail the risk of misinterpretation because of their close morphological resemblance to invasive breast cancer (so-called mimickers of cancer). In breast pathology, intraductal epithelial proliferations account for a large number of conditions. Thus, there is a need for diagnostic stratification and assessment of progression potential based on qualitative and quantitative criteria. The invasive types of breast cancer also present with an unusually broad morphological spectrum, determined by the intrinsic properties of the tumour cells, their architectural patterns and the extent of stromal reaction.

The task is to identify the various entities and establish a prognostic stratification based on qualitative and quantitative criteria. Quantifiable criteria for histological grading of breast cancer were defined and introduced at a relatively early point [5] [6].

The WHO classification provides the basis for criteria-based diagnostics which takes molecular pathology aspects in addition to pathomorphological properties into account and is updated on a regular basis [7]. Diagnostic criteria are particularly useful where it is important to identify mimickers of cancer and not misinterpret them as carcinoma. This risk is especially high when various non-invasive changes coincide in a way that there appears to be an invasive cancer lesion. For example, the colonisation of a sclerosing adenosis lesion by a ductal carcinoma in situ (DCIS) or a lobular carcinoma in situ (LCIS) can be misinterpreted as an invasive carcinoma when the myoepithelial lining of the acini is not recognized.

Immunohistochemistry (IHC) and molecular pathology have expanded the array of diagnostic methods and were utilised early on, especially in breast pathology and hematopathology. Immunohistochemistry plays a key role in the differential diagnosis of intraductal epithelial proliferations, the differentiation between non-invasive and invasive lesions, the histological typing of breast cancer, and the differential diagnosis of spindle cell lesions. While structural changes and architectural abnormalities allow the pathologist to suspect the presence of cancer already in the overview magnification, in case of doubt it is the immunohistochemistry-based detection of the absence of myoepithelial cells and the destruction of the basement membrane that permits a reliable differentiation between invasive and non-invasive lesions.

Focus on therapy

Apart from the diversity of breast lesions, the rapidly expanding array of therapeutic options poses a particular challenge. Recognising and documenting all tissue characteristics that are relevant to clinical management is one of the tasks pathologists are responsible for. Knowledge of the parameters that entail specific therapeutic consequences or recommendations is a prerequisite for this. Here, the evidence based interdisciplinary guideline (S3 Guideline Breast Cancer) edited by the Association of Scientific Medical Societies in Germany (Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften e. V., AWMF), the German Cancer Society (Deutsche Krebsgesellschaft e.V., DKG), and German Cancer Aid (Deutsche Krebshilfe, DKH) and the recommendations of the Breast Committee of the German Gynecological Oncology Group (AGO, Arbeitsgemeinschaft Gynäkologische Onkologie) offer guidance for pathologists, too [8] [9] [10].

Today, guideline-adherent testing for oestrogen (ER) and progesterone receptor (PR) expression, HER2 status and Ki67 proliferation index is performed almost like a reflex in patients with primary breast cancer. The results of the semi-quantitative analysed tests are essential for determining the (neo-)adjuvant systemic therapy and also have an impact on the timing of surgery. In metastatic breast cancer, the determination of ER, PR and HER2 is supplemented by the analysis of other parameters, which are selected on the basis of the resulting receptor status, to identify potential targets for new targeted therapy. The number of addressable target structures has been increasing rapidly in the last few years in breast cancer, too. Besides immunohistochemistry (PD-L1) and in situ hybridization (HER2), DNA-based molecular analysis methods, such as PCR and NGS (Next Generation Sequencing), are also used to detect changes in addressable signalling pathways (e.g., PIK3CA, AKT1, PTEN). Tissue-based analyses were supplemented by the analysis of cell-free, circulating DNA (cfDNA) in blood plasma (liquid biopsy; ESR1, PIK3CA).

In order to help pathologists to keep pace with the rapid growth in knowledge and the increasing workload, QuIP GmbH offers an information portal on breast cancer [11]. This covers the current state of knowledge on biomarker diagnostics and the resulting treatment options.

Due to the accelerated development in the field of biomarkers, a shift away from single-gene analyses towards parallel high-throughput analyses is becoming increasingly necessary. The NGS-based techniques will enjoy a further boost from the model project of section 64e of the German Healthcare Development Act (GVWG, Gesundheitsversorgungsweiterentwicklungsgesetz) of July 11^th, 2021 (BGBl. I, 2754). Apart from whole exome sequencing (WES) and whole genome sequencing (WGS), it also includes the shift to multi-omics techniques, such as whole transcriptome sequencing (WTS), proteomics and epigenetic analyses, among others.

Only an innovation-ready pathology can enable modern personalized medicine, pursuing new treatment options for patients who have exploited all available means of therapy. Nevertheless, the pathology budget is not being adapted to the requirements of increasingly sophisticated diagnostics, but is, in fact, being reduced. Consequently, there is a risk that the ability of pathology to function and to innovate could be restricted in the long term, and, ultimately, compromise patient care.

Reporting

Structuring and standardising pathology reports

Essentially, pathology reports should be written comprehensibly, completely and quickly.

This implies that a generally recognised terminology is used and that all therapy-relevant criteria are included.

Meeting these requirements prevents misunderstandings, which may lead to incorrect treatment recommendations, reduces queries and allows timely initiation of necessary treatments.

The current WHO classification provides the generally accepted medical terminology for tumours of the breast [7]. It is advisable to name the source if deviating terms are used, for example, for rare entities or variants not mentioned in the WHO classification.

Protocols for structured or synoptic pathology reporting are used to ensure uniform nomenclature and standardised documentation. In contrast to “narrative” pathology reports, the respective organ- and/or tumour type-specific criteria (e.g., histological tumour grade) are presented in form of a list [12]. Likewise, the level of the criteria (e.g. grade 1, grade 2, grade 3) and any specifications (e.g., tubule formation, nuclear pleomorphism, mitotic count; 1–3 points each) are stated using clear terminology. In the German mammography screening programme, the results of the pathomorphological evaluation of core needle biopsies as well as of surgical specimens have been documented electronically in predefined protocols right from the start of the programme. Forms for standardised documentation of findings have already been provided in the appendix to the S3 Guideline Breast Cancer for a long time [8]. In Germany, however, pathology reports in daily routine are usually still written in narrative form.

Unlike in Germany, the use of structured protocols has been common, particularly in the USA for some time. The College of American Pathologists (CAP) makes almost 100 different protocols available online, free of charge for various organs [13]. There are protocols for biopsies, surgical specimens and biomarkers. However, their use without a license is subject to certain restrictions. Furthermore, to the authors’ knowledge, the electronic versions of these protocols are not available in German, and also not compatible with the German pathology information systems.

The International Collaboration on Cancer Reporting (ICCR) now wants to remove these language and electronic restrictions. The ICCR is a non-profit organization that integrates standard sources (current WHO classifications, UICC/AJCC TNM classification) and internationally validated and evidence-based pathology datasets for cancer reporting [14]. These can be used globally. There is a broad cooperation between national pathology societies, interdisciplinary associations and major international cancer organisations. Authorised translations into French, Spanish and Portuguese are already available for some protocols. The DGP and BDP are involved on the German side. The ICCR allows a largely free use of the protocols for diagnostic reports, but not for commercial research.

In the datasets, a distinction is made between required essential parameters (CORE elements) and recommended additional information (NON-CORE elements) ([Table 1]). The categories for the various parameters are predefined in a form. A detailed explanation and illustration of the parameters is provided in an appendix, so that not only the documentation of the criteria, but also their collection and interpretation is standardised.

Table 1 CORE elements and NON-CORE elements for the pathology reporting of resection specimens with invasive carcinoma of the breast based on the ICCR recommendations [15].

Parameter	CORE element	NON-CORE element	Values
Clinical information	√		Screening vs symptomatic presentation, clinical findings, prior treatment, imaging, family history, genetic predisposition
Operative procedure	√		Type of excision or mastectomy
Specimen laterality	√		Right or left
Size/weight/details of tissue specimen		√	Free text
Tumour site	√		Distance to the nipple, quadrant or clock face
Tumour focality	√		Unifocal or multifocal
		√	Number and size of each focus
Tumour dimensions	√		Maximum dimension of largest focus
		√	Other dimensions
Histological tumour type	√		According to the WHO classification
Histological tumour grade	√		Grade 1, 2 or 3
		√	Specification: tubule, nuclear pleomorphism and mitosis scores
Carcinoma in situ	√		Histological type and nuclear grading (in DCIS), necroses
		√	Architectural pattern (DCIS), extensive intraductal component (EIC)
Tumour extension	√		Skin, nipple, skeletal muscle
Margin status	√		Involved by invasive carcinoma/DCIS or distance to closest margin
		√	Extent of involvement, distance to all margins
Lymphovascular invasion	√		Present or not detectable
		√	Site if detectable elsewhere
Coexistent pathology		√	Free text
Microcalcifications		√	Present, associated lesion
Oestrogen receptor	√		Negative/positive/low positive, % positive nuclei, average intensity
Progesterone receptor	√		Negative/positive, % positive nuclei, average intensity
HER2	√		IHC score, ISH negative/positive, cells counted, HER2- and CEP17 signals/nuclei, HER2/CEP17 ratio
		√	IHC % 3+ cells, ISH aneusomy, heterogeneity
Ancillary studies		√	e.g. Ki67, representative block for ancillary studies
Pathological staging	√		TNM classification

For the documentation of breast cancer, there are already 4 datasets available in English [14] [15]:

1. Ductal Carcinoma in Situ, Variants of Lobular Carcinoma in Situ and Low Grade Lesions

2. Invasive Carcinoma of the Breast

3. Invasive Carcinoma of the Breast in the Setting of Neoadjuvant Therapy

4. Surgically Removed Lymph Nodes for Breast Tumours

Work is currently being carried out on the German translations as well as on the integration of the templates and data records into the German pathology information systems. The use of such protocols would be a significant step towards international pathology report standardisation and scientific utilisation of the documented data.

However, one must not underestimate the effort and costs for pathology facilities that would be involved in implementing a wide range of authorised synoptic report templates and having them continuously updated. Collaborating specialties and hospital administrations would also benefit. Thus, it would be desirable that all beneficiaries contribute to the costs.

The speed of reporting

Given its direct impact on the possible start of appropriate treatment, the speed of reporting plays an important role in patient care. Finding the right balance between speed and accuracy is key. The turnaround time (TAT) is the period of time from receipt of the tissue specimen to completion of the pathology report. It has an impact on the following aspects of patient care:

• Stress and uncertainty of the patients

• Start of treatment

• Efficiency of hospital processes

The aim is to keep the period of uncertainty for patients as short as possible. Of course, this must not affect the precision of the diagnosis. It is important that the appropriate therapy can be initiated as early as possible to optimize a patient’s chances of survival [16]. This also applies to molecular pathological testing, especially in the metastatic situation. Time requirements can therefore also be found in some of the international recommendations on molecular diagnostics [17]. Delays in diagnosis can prolong the patient’s length of hospital stay and thereby reduce hospital efficiency.

Germany plays a pioneering role by setting a TAT for pathology services in certified breast centres and time requirements in the mammography screening programme.

In the German mammography screening programme, the period between the start of the diagnostic assessment and the notification of the result should not exceed one week [18].

The time windows for the TAT Pathology in the certified breast centres are as follows [19]:

• Histology results of core needle biopsies: within 2 working days,

• Routine histology results incl. immunohistochemistry: max. 5 working days

These requirements have also been incorporated into the Manual for Breast Cancer Services of the European Commission Initiative on Breast Cancer (ECIBC), albeit in a somewhat less stringent form: Pathology results incl. immunohistochemistry: max. 5 working days for non-surgical biopsies and 10 working days for surgical specimens [20].

Digitization of histopathology slides

Histological diagnostics requires a special, time-consuming and standardized processing of the tissue to be examined. This involves tissue fixation, grossing, dehydration and degreasing, immersion with paraffin, cutting with special microtomes, and finally staining and mounting a cover slip over the tissue section on the slide. While some of the tissue processing steps are automated, the grossing as a medical task and the sectioning with a microtome, requiring a high level of fine motor skills, have not yet been automated for routine application. Pathologists then examine the resulting histological slides under the microscope and describe, report and classify their findings. AI-supported diagnostics require that slides are digitized. The easiest way to do this is by using digital microphotography directly at the microscope of the reporting pathologist. A number of scanners with different features is commercially available for the digitization of whole slides. Some models can be loaded with up to a thousand slides which can, however, only be processed sequentially. The resulting whole slide images (WSIs) have a size of 0.5 to 4 GB, depending on the amount of tissue and the desired resolution; this image size is by several orders of magnitude higher than that of standard images. A surgical case with 25 slides takes up 50 to 75 GB of hard disc space. A few surgical specimens already exceed the amount of data that a large, well-utilised radiography device (CT/MRI) produces in a year.

In contrast to radiology, the digitization of slides is always a secondary step in histology. Scanning of a slide takes between 1 and 3 minutes. While in radiology, primary digital image capture offers a speed advantage when taking plain radiographs and is the only option of processing in diagnostic cross-sectional imaging, in histology, digitization is an additional, time-consuming and very cost-intensive factor. The costs result from the acquisition of slide scanners, most of which have to be purchased as redundant equipment for capacity and downtime reasons, the high costs of storage space, the digital transformation of workplaces, and the additional space, staff and energy cost for devices and servers. These costs are significant and, as the costs are not matched by compensation in any service area, it is almost impossible to provide care in a way that covers costs, especially in standard outpatient care.

[Table 2] provides a comparison of digitized radiological and histological image data. Despite the effort and high costs, many pathology facilities in Germany are now working on the digitization of image data and are preparing for it by changing processes in the run-up to image data digitization. These include the digitization of accompanying documents for submitted specimens, the use of barcode printers for paraffin blocks and slides, and the primary database recording of examinations that have been performed. Aside from digital microscope cameras taking high-resolution, colour-balanced and standardised images, many facilities have software suitable for the partial digitization of slides. The pathologist films the whole slide or the region of interest in a meandering motion under the microscope and one image is then assembled from suitable images of the video stream in which overlapping image content is recognised (so-called stitching). In this way, the pathologist can perform a part digitization of slides without spending a significant amount of time; these images can then be used in particular for a cloud-based low-threshold second opinion procedure. In addition, many facilities have a low-capacity scanner available. Already today, these allow the digitization of selected slides and the application of AI-based measurement and evaluation methods as well as the internal development of custom models and algorithms.

Table 2 Comparison of the characteristics of radiological and histopathological image data.

	Radiological image data	Histopathological image data
Resolution	Coarse	Microscopically fine
Colour channels	Usually one channel	Three channels (or more)
Digitization of image information	Primary, state of the art	Secondary, by no means everywhere as yet routine
Image data size	Moderate (MB range)	Very large (GB range)
Image data storage	2-dimensional	Pyramidal in various resolution levels
Image data format	Standardized, DICOM	Manufacturer-dependent proprietary
Digital workflow	Established	Available
Information density of the image data	Moderate	Very high
Information redundancy	Low	Very high
Interinstitutional variations	Lower	Higher

Artificial intelligence (AI) in breast pathology

Breast pathology does offer a host of potential areas of applications of artificial intelligence to support diagnostics. Despite the fact that, due to the complexity of breast pathology, fully automated comprehensive histological diagnostics are still a long way off, AI support for pathologists in individual tasks is conceivable. The relevant key aspect of AI-supported reporting assistance is to improve quality through objectifiable and more precise results in the evaluation of semi-quantitative individual diagnostic parameters that are part of pre-operative core needle biopsy diagnosis, immunohistochemical examinations of diagnostic and predictive factors and processing of surgical specimens. The potential applications are numerous and cover almost all aspects of routine diagnostics which have been summarised in extensive reviews [21] [22] [23]. Individual application fields are discussed below, together with the challenges that arise when transferring promising AI approaches into routine practice.

AI-supported evaluation of immunohistochemical staining (Ki67, ER/PR, HER2/neu, PD-L1)

Software for the automated quantitative evaluation of immunohistochemical examinations for predictive factors was already introduced about 10 years ago. The programmes were originally based on image processing routines, but these can only be described as artificial intelligence in a very broad sense. Today, however, there are neural network-based software solutions on the market that can apply artificial intelligence in the narrower sense. The task of quantitatively evaluating nuclear immunohistochemical staining, as it is carried out for Ki67, the oestrogen receptor and the progesterone receptor, is nowadays an entry-level task for companies positioning themselves on the market. The potential advantage of an automated evaluation is a higher degree of objectivity and accuracy. However, a common feature of the currently available software programmes is that they can only evaluate a limited field of view, or that there is a considerable time delay (several hours!) until the result becomes available. The task of determining the proportion of labelled relevant cells is much more complex than it may appear at first glance. The algorithms used not only have to distinguish between labelled and unlabelled cells, but also be able to differentiate between the nuclei of tumour cells and those of connective tissue cells, normal epithelial cells and inflammatory cells, so that only tumour cells are included in the evaluation. This is a particular challenge in sections that are only counter-stained with haematoxylin. There are also similar commercial models on the market for HER2 diagnostics, including some which are capable of differentiating between the HER2 scores 0 and 1+, an important feature when specific antibody-drug conjugates are to be administered. For PD-L1, such models are desirable, too. However, this is currently still hampered by the relatively complex design of the various relevant scores for breast cancer.

In routine diagnostics, these methods for automated IHC evaluation will only become widely adopted if the considerable costs for such software solutions are offset by a tangible gain in quality. Especially in the area of hormone receptors, however, in the case of Ki67 and HER2, the spectrum is distributed in such a way that an evaluation focussed on the exact percentage value only achieves therapeutic relevance in a very small proportion of cases. In the vast majority of cases, the pathologist’s results, which are classified in increments of 5%, is perfectly adequate. AI-supported diagnostics in borderline cases is associated with a significant time expenditure for the reporting pathologist. At present, neither the additional time involved for the pathologist nor the investment and maintenance costs for the AI programmes are funded at any point in the care provision chain.

Detection of specific target structures (nodal metastases, microcalcifications)

Different organ systems have different requirements for the detection of small structures which are difficult to identify in overview magnification. In breast pathology, these challenges include the detection of microcalcifications in vacuum assisted biopsies and surgical specimens. These are difficult to recognise due to the fact that histological sections are essentially two-dimensional; as the result, microcalcifications can be distributed across several step sections at different locations. By using haematoxylin staining alone as an additional step section, detection can be improved as calcifications usually stand out as more intensely stained structures compared to nuclei. Segmentation of calcifications in the histological sections, ideally with automated measurement, would be desirable. In contrast to automated microcalcification detection in mammography which has slowed down the radiologists by showing them, especially in the earlier years, far too many irrelevant microcalcifications, in the histological evaluation, each microcalcification identified in the section is primarily important und must be documented and correlated. However, oxalate crystals are not visible in the digitized H&E section; polarisation microscopy has to be used to detect them.

Another diagnostic challenge in breast pathology is the detection of metastases in lymph nodes. Several step sections have to be examined to detect metastases in sentinel lymph nodes. Detection of metastases of lobular breast cancer is notoriously difficult – due to that fact that a desmoplastic reaction is usually missing and that a dissociated growth pattern is typically found in the metastatic lesion, too. Another common finding is the marked sinus histiocytosis in the lymph node. In this case, there is less of a risk of sinus histiocytosis cells being misinterpreted as metastases of the breast cancer but rather the risk of small metastases being overlooked in the presence of sinus histiocytosis. A strong AI innovation driver in medicine are international competitions where annotated data sets are provided, and the time allowed for solving specific tasks is usually limited to a few months. The detection of lymph node metastases of breast cancer was the topic of the challenges CAMELYON16 and CAMELYON17 [24]. The particular difficulty of these Challenges was the processing of metastasis detection on the level of whole slide images (WSIs). These Challenges have significantly advanced the development of processing routines that enable the integrative processing of entire WSIs and several WSIs for one patient. In the CAMELYON17 Challenge, the task was to evaluate 5 WSIs of lymph nodes, which were grouped to virtual patients, by an automated routine on slide level and on patient level, and to provide a nodal stage as integrating diagnosis. The best algorithm classified 86.6% of WSIs correctly; however, among the incorrectly classified slides there were 10 micrometastases and 4 macrometastases that were not recognized.

Grading assistance by AI

The grading of breast cancer has an extremely important prognostic and stratifying function. This applies to both invasive and pre-invasive lesions. Since the 1990s, this procedure has been part of histopathological routine diagnostics. Invasive breast cancer is graded by adding the scores for three criteria reflecting architectural, cytological and functional features [5]. This is different from other entities. In prostate cancer, where grading has a similarly strong prognostic significance, grading is based solely on the tissue architecture of the cancer. In breast cancer, the architectural feature assessed is the proportion of area of tubular (and cribriform) differentiation. As the cytological criterion, nuclear pleomorphism is stratified, and as the tumour functional feature the proliferation activity is quantified as the number of mitoses per tumour area. In the core needle diagnostics of breast cancer, the mitosis criterion is increasingly supplemented by the growth fraction determined by Ki67 immunohistochemistry which allows a more accurate and definite classification at the boundary between G2 and G3.

The use of artificial intelligence to support grading promises mathematical accuracy, reproducibility and objectivity. In addition, computer-based diagnostics enable in principle a diagnostic evaluation across the entire tumour at the highest magnification; thus, it is better suited to show intratumoural heterogeneity. Using AI-based semantic segmentation, it is possible to detect, virtually in real-time, the tumour epithelium and with object segmentation several ten thousands of tumour nuclei. With subsequent imaging processing routines, numerous measurement parameters can be recorded, and the results can be aggregated to a graph representation. The parameters that can be measured automatically include maximum and minimum nuclear diameter, nuclear area and circumference, eccentricity and contour features as well as the orientation of the main axis. In addition, the position in the image can, for example, be determined as coordinates of the geometric centre of gravity. [Fig. 1] and [Fig. 2] show, as an example, the evaluation of the nuclear morphology of 2 breast cancers, using a self-developed processing routine, and illustrate the dilemma in the implementation of these detailed measurement results in the diagnostic routine: The three-stage classification of nuclear pleomorphism is based on the comparison of the tumour cell nuclear area with the area of normal epithelial nuclei as a means of normalizing hormonally induced functional differences. The class limits were defined at 1.5-fold and 2.0-fold difference, so that a score of 3 points is awarded for a nuclear pleomorphism with at least twice the area compared to normal epithelial nuclei, and a score of 2 points for tumour cell nuclei with 1.5 to 2 times the area. More than 30 years ago, this was likely set as a rough guideline for operationalizing the term “nuclear pleomorphism” which was not based on systematic measurements. However, the distribution of the diameters of the nuclei of cancer cells, determined based on exact computer-aided measurements shows such a high variability of the nuclear areas, but also of the other measured variables, that these initial definitions cannot be applied to reality. As shown by this special problem, various definitions in pathology must be further operationalized before AI can be meaningfully integrated into the reporting process; doing so will enable pathologists to keep pace with the possibilities of objective and simultaneous measurement of numerous parameters.

Fig. 1 AI-supported analysis of the area, diameter and circumference of tumour and stromal nuclei in breast cancer with nuclear grade 1. The prototype software demonstrated here contains several modules for the automated measurement and data aggregation of nuclei from cancer cells and stromal cells. Based on whole slide images (here from the TCGA dataset [25]), the software analyses the regions of interest and automatically selects representative sub-regions that are analysed in detail. These are marked with green squares in the overview magnification (A). In B, the enlargement of a subregion is shown. The subregion is analysed with a neural network for epithelial segmentation (D) and a network for instance segmentation of the nuclei (C [26] [27] [28]). The nuclei are measured using image processing routines; the compartment to which they belong can be determined from the position of the centroid in comparison with the epithelial segmentation. The data (on average approx. 25 000 nuclei/case) are output graphically (E: nuclear area, F: nuclear diameter, G: nuclear circumference). The entire processing routine takes approx. 50 seconds to complete.

Fig. 2 AI-supported analysis of the area, diameter and circumference of tumour and stromal nuclei in breast cancer with nuclear grade 3. The prototype software demonstrated here contains several modules for the automated measurement and data aggregation of nuclei from cancer cells and stromal cells. Based on whole slide images (here from the TCGA dataset [25]), the software analyses the regions of interest and automatically selects representative sub-regions that are analysed in detail. These are marked with green squares in the overview magnification (A). In B, the enlargement of a subregion is shown. The subregion is analysed with a neural network for epithelial segmentation (D) and a network for instance segmentation of the nuclei (C [26] [27] [28]). The nuclei are measured using image processing routines; the compartment to which they belong can be determined from the position of the centroid in comparison with the epithelial segmentation. The data (on average approx. 25 000 nuclei/case) are output graphically (E: nuclear area, F: nuclear diameter, G: nuclear circumference). The entire processing routine takes approx. 50 seconds to complete.

Prediction of prognostic and predictive markers on the basis of H&E histology

In cancer diagnostics, an astonishing amount of activity in recent years has focused on using artificial intelligence to predict prognostic and predictive factors on the basis of the H&E section. In breast cancer, these factors are hormone receptors and the HER2 status in particular. In Germany, the period between the initial reporting of an H&E section and the immunohistochemical determination of oestrogen and progesterone receptors, HER2/neu and Ki67 amounts to no more than a few working days. Technically, organisationally and without loss of quality, it would in principle also be possible to complete the conventional reporting in the morning and the determination, evaluation and reporting of the above-mentioned factors in the afternoon of the same day. In structured health care programmes, such as the mammography screening, the completion of histological diagnostics within one week until the next multidisciplinary meeting is common practice. The fact that in other countries such a tightly timed diagnostic regime is rather unusual makes the desire for having predictive information already available at the time of reporting the H&E section understandable. Here, the HER2 status is of particular interest, as in the event of an inconclusive immunohistochemical result (HER2-Score 2+), it is necessary to carry out in situ hybridisation, which is considerably more time-consuming than immunohistochemistry. The international HEROHE Challenge (HER2 on HE) addressed this issue in 2021/22 [29]. A total of 25 valid models were submitted and approved for evaluation. Even in the top group, however, the quality of the predictions can at best be described as moderately good on sober reflection. However, this is hardly surprising: One could not expect that the HER2 positivity of a tumour is associated with specific or even defining morphological characteristics in the image or that the information of HER2 positivity is hidden somewhere in the pixel noise. The model submitted by the participant with the highest ranking achieved a precision of 0.75 and a recall of 0.84, based on a receiver operator characteristics area under the curve (ROC AUC) of 0.84. In other words, the prediction of HER2 positivity is incorrect in 1 in 4 cases and 1 in 6 HER2-positive cases is not recognised. Unfortunately, these figures show the unsuitability of such an approach for patient-specific diagnostics and the indispensability of direct determination of predictive factors using established methods. Due to the lack of precision and insufficient recall, such an approach is also unsuitable for potential applications in quality assurance or for the selection of patient populations that potentially do not need to be tested.

Yet it is still possible that other molecular parameters have a closer correlation with conventional histology. For example, a certain correlation with conventional H&E morphology has recently been shown for BRCA1/2 mutation status in breast cancer [30]. Even if the correlation in this case is far from perfect, an AI-generated prediction of the mutation status could be beneficial in particular to those patients for whom a corresponding mutation analysis is considered not often enough, e.g., patients with hormone receptor-positive breast cancer.

Comprehensive AI-supported diagnostics

Histopathological evaluation is the key step in the diagnosis of cancer: Pre-operative clinical and imaging diagnostics are regularly followed by pre-operative histopathological diagnostics; the treatment modalities and the treatment sequence then primarily follow the various individual histopathological aspects of the tumour. Accordingly, there is a need for the diagnostics to offer the greatest possible legal certainty, in addition to the medical, technical and quality assurance aspects. Modern pathology meets this legal aspect in particular by relying on criteria-based diagnostics. Diagnosis is a systematic and step-by-step process in which defining criteria are established for entities and differential diagnostic boundaries are drawn between similar entities. This is done on the basis of qualitative and quantitative criteria for H&E histology which are further refined by immunohistochemical and molecular pathological criteria. Accordingly, the training of pathologists is focused on recognising deviations from the norm as a region of interest in a first step, describing and analysing these changes using predefined criteria, and then deriving a well-founded, rule-based diagnosis in a final step. The diagnosis is established with full understanding of the clinical consequences. It is virtually impossible to replicate such a systematic approach with the currently available AI methods. Using annotated data sets, neural networks are trained towards a desired outcome. The criteria that a neural network finds during training are a pure matrix of numbers and cannot be influenced by canonical knowledge of diagnostic criteria. The numerous explainable AI approaches have also not yet been able to adequately evaluate whether and how a trained neural network applies the desired criteria. Complex diagnostic tasks can be performed using AI by breaking them down into clearly defined individual tasks of low complexity. Neural networks can be trained for each individual task, and their intermediate results can be checked, as already described above for nuclear grading ([Fig. 1] and [Fig. 2]). The complexity of this approach is similar to that associated with the implementation of fully autonomous driving which is based on the carefully orchestrated interaction of numerous neural networks for information abstraction of the traffic environment and the obligatory software implementation of the traffic rules as a so-called traffic rule engine to control the individual agents of the software algorithm. Given its complexity and limited market potential, it is questionable whether pathology can be modelled with rule engines in such comprehensive expert systems.

Consultation and reference pathology

The purpose of consultation pathology is to obtain a second opinion from a specialised pathologist if there is uncertainty about a diagnosis or if the question is particularly complex. Breast cancer is the most common carcinoma in women. Consequently, evaluating breast tissue specimens is part of the daily routine of many pathologists.

The German Mammography Screening Program (MSP) provided proof that an obligatory second-opinion or reference evaluation of breast tissue specimens is unnecessary. In analogy to mammography reporting, a second-opinion evaluation of all core needle biopsies by reference pathologists was required during the initial phase of the German MSP, i.e., during the first 2 years after the start of the screening programme. At that time, this was a worldwide unique demand on screening pathologists. Three of the involved 5 German reference centres pooled and analysed their data then. Based on almost 10 000 duplicate evaluations, a concordance rate of 94% to 98% was found. For carcinomas, the degree of concordance between the first and second evaluating pathologist was above 99% [31]. Somewhat lower concordance rates were found for lesions with unclear biological potential (B3/B4), which are significantly less common. These borderline categories include a wide variety of lesions such as spindle cell lesions, phylloid tumours, papillary lesions, and atypical intraductal proliferations. With certain lesions, such as atypical ductal hyperplasia (ADH), the diagnostic concordance is only moderate – even between experts [32]. Thus, the increased interobserver variability noted in these cases should not be interpreted as an indication of inadequate qualification of the pathologists, but rather of suboptimal objectifiability and reproducibility of the available diagnostic criteria, as also suggested by the results of the diagnostic Round Robin tests in the UK [33] [34]. Based on the results presented above, the second opinion evaluation by pathologists was limited to the first 50 cases in the German MSP. At the same time, the German MSP, as a structured care programme, goes one step further with regard to quality assurance. Participation in the MSP requires regular specialist continuing education for pathologists, too, which is interlinked with interdisciplinary continuing education. The fact that in B3 lesions, despite the methodological challenges, the concordance rate between the first and the second evaluating pathologist is quite high in Germany (with 75%–90%) by international standards may be attributable to this continuing education effort.

Thus, obtaining a second opinion or reference evaluation is limited to specific questions in breast pathology. The most common request is the classification of unusual and rare changes. Other reasons for obtaining a second opinion include discrepancies between clinical findings and primary pathology evaluation and the wish of a patient to hear a second opinion before treatment is started. A separate field is reference pathology for studies which is dedicated to establishing and standardising new histomorphological parameters or molecular biomarkers, which can then be established and used in a decentralised manner, too [35].

The requirements for a second opinion or reference pathology thus comprise specialist expertise with experience in the evaluation of complex, challenging cases, access to modern diagnostic methods, certification or accreditation with regular participation in external quality assurance measures, and timely reporting.

Conclusions

• Given the comparatively broad spectrum of functional, reactive and neoplastic changes in the breast, the systematic analysis of the architectural and cytological characteristics of a lesion in order to validly classify it on the basis of reproducible criteria poses a particular challenge in breast pathology.

• Recognising and documenting all tumour characteristics that are relevant to clinical management is part of the scope of pathology.

• Pathology reports should be written comprehensibly, completely and quickly.

• The utilisation of structured protocols to document findings supports this and facilitates international comparability.

• There are hopes that the increasing digitalization of pathology and the use of artificial intelligence (AI) will speed up the preparation and reporting process in pathology and make diagnostics more objective. However, apart from the lack of transparency of AI-generated decisions, technical and financial limitations still need to be overcome before using AI can really contribute to faster and better reporting in pathology.

Conflict of Interest

A.L. states that within the last 3 years she has served on advisory boards for from AstraZeneca, Daiichi Sankyo, Diaceutics PLC, Eurobio Scientific Service, Gilead, Inflection Point Medical Advisors, MSD Sharp&Dohme and Roche Pharma, lecture fees from AstraZeneca, Daiichi Sankyo, if-kongress management, MSD Sharp&Dohme, Myriad Genetics, Novartis Pharma, Roche Romania, Veracyte Inc. and Menarini Stemline and honoraria for authorship from QuIP GmbH and Barmer Institut für Gesundheitssystemforschung. A.L. and A.T. are co-founders of PathoPlan GbR. A.T. has conducted application testing for PathoPlan over the last 3 years on behalf of EMPAIA (EcosysteM for Pathology Diagnostics with AI Assistance).

• References

• 1 IQNPath, ECPC, EFPIA. Unlocking the potential of precision medicine in Europe. 2021 Accessed January 08, 2025 at: https://www.efpia.eu/media/589727/unlocking-the-potential-of-precision-medicine-in-europe.pdf
• 2 Campanella G, Hanna MG, Geneslaw L. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature medicine 2019; 25 (08) 1301-1309
• 3 Litjens G, Kooi T, Bejnordi BE. et al. A survey on deep learning in medical image analysis. Medical Image Analysis 2017; 42: 60-88
• 4 Bera K, Schalper KA, Rimm DL. et al. Artificial intelligence in digital pathology – new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology 2019; 16 (11) 703-715
• 5 Elston CW, Ellis IO. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 1991; 19 (05) 403-410
• 6 Bloom HJG, Richardson WW. Histological Grading and Prognosis in Breast Cancer. British Journal of Cancer 1957; 11 (03) 359-377
• 7 5th ed, Vol. 2. Lyon (France): International Agency for Research on Cancer; 2019
• 8 Deutsche Krebsgesellschaft; Deutsche Krebshilfe; AWMF. S3-Leitlinie Früherkennung, Diagnose, Therapie und Nachsorge des Mammakarzinoms, Version 4.4 2021 [updated Mai 2021;14.01.2022]. Available from. Accessed January 08, 2025 at: http://www.leitlinienprogramm-onkologie.de/leitlinien/mammakarzinom/
• 9 Park-Simon TW, Müller V, Albert US. et al. Arbeitsgemeinschaft Gynakologische Onkologie Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2024. Breast Care (Basel) 2024; 19 (03) 165-182
• 10 Thill M, Janni W, Albert US. et al. Arbeitsgemeinschaft Gynakologische Onkologie Recommendations for the Diagnosis and Treatment of Patients with Locally Advanced and Metastatic Breast Cancer: Update 2024. Breast Care (Basel) 2024; 19 (03) 183-191
• 11 Qualitätssicherungs-Initiative Pathologie (QuIP). Mammakarzinomportal 2024 [updated August 2024]. Available from. Accessed January 08, 2025 at: https://www.mammakarzinomportal.eu/de/
• 12 Hewer E, Rump A, Langer R. Standardisierte strukturierte Befundberichte gastrointestinaler Tumoren. Der Pathologe 2022; 43 (01) 57-62
• 13 College of American Pathologists (CAP). Cancer Protocol Templates [updated June 2024;]. Available from. Accessed January 08, 2025 at: https://www.cap.org/protocols-and-guidelines/cancer-reporting-tools/cancer-protocol-templates#protocols
• 14 International Collaboration on Cancer Reporting (ICCR). Published Datasets 2024. Available from. Accessed January 08, 2025 at: https://www.iccr-cancer.org/datasets/published-datasets/
• 15 Ellis I, Webster F, Allison KH. et al. Dataset for reporting of the invasive carcinoma of the breast: recommendations from the International Collaboration on Cancer Reporting (ICCR). Histopathology 2024; 85 (03) 418-436
• 16 López-Pineda A, Rodríguez-Moran MF, Álvarez-Aguilar C. et al. Data mining of digitized health records in a resource-constrained setting reveals that timely immunophenotyping is associated with improved breast cancer outcomes. BMC Cancer 2018; 18 (01)
• 17 Sepulveda AR, Hamilton SR, Allegra CJ. et al. Molecular Biomarkers for the Evaluation of Colorectal Cancer: Guideline From the American Society for Clinical Pathology, College of American Pathologists, Association for Molecular Pathology, and the American Society of Clinical Oncology. Journal of Clinical Oncology 2017; 35 (13) 1453-1486
• 18 Versorgung im Rahmen des Programms zur Früherkennung von Brustkrebs durch Mammographie-Screening, (2024). Anlage 9.2 BMV-Ä. Version 1.7.2024. Available from. Accessed October 06, 2024 at: https://www.kbv.de/media/sp/09.2_Mammographie.pdf
• 19 Deutsche Krebsgesllschaft (DKG). Zertifizierung der Deutschen Krebsgesellschaft: Dokumente im Überblick 2024. Available from. Accessed January 08, 2025 at: https://www.krebsgesellschaft.de/zertdokumente.html
• 20 European Commission, Joint Research Centre. European Commission Initiative on Breast Cancer – European Quality Assurance Scheme for Breast Cancer Services. Luxembourg: Publications Office of the European Union; 2025. https://data.europa.eu/doi/10.2760/7067385 , JRC140425
• 21 Soliman A, Li Z, Parwani AV. Artificial intelligence’s impact on breast cancer pathology: a literature review. Diagnostic Pathology 2024; 19 (01)
• 22 Sandbank J, Bataillon G, Nudelman A. et al. Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies. npj Breast Cancer 2022; 8 (01)
• 23 Abdelsamea MM, Zidan U, Senousy Z. et al. A survey on artificial intelligence in histopathology image analysis. WIREs Data Mining and Knowledge Discovery 2022; 12 (06)
• 24 Litjens G, Bandi P, Babak E. et al. 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset. GigaScience 2018; 7 (06)
• 25 National Cancer Institute. National Cancer Institute CDG Data Portal 2024 Available from. Accessed January 08, 2025 at: https://portal.gdc.cancer.gov/analysis_page?app=Downloads
• 26 Weigert M, Schmidt U. Nuclei Instance Segmentation and Classification in Histopathology Images with Stardist. The IEEE International Symposium on Biomedical Imaging Challenges (ISBIC). 2022
• 27 Weigert M, Schmidt U, Haase R. et al. Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy. The IEEE Winter Conference on Applications of Computer Vision (WACV) 2020;
• 28 Schmidt U, Weigert M, Broaddus C. et al. Cell Detection with Star-Convex Polygons. Medical Image Computing and Computer Assisted Intervention (MICCAI), 21st International Conference 2018 September 16–20, 2018; Granada, Spain. 2018
• 29 Conde-Sousa E, Vale J, Feng M. et al. HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin–Eosin Whole-Slide Imaging. Journal of Imaging 2022; 8 (08) 213
• 30 Li Y, Xiong X, Liu X. et al. An interpretable deep learning model for detecting BRCA pathogenic variants of breast cancer from hematoxylin and eosin-stained pathological images. PeerJ 2024; 12: e18098
• 31 Kreipe HH, Höfler H, Lebeau A. et al. Ergebnisse der Referenzpathologie im Mammographie-Screening. Der Pathologe 2008; 29 (Suppl. 2) 178-180
• 32 Amendoeira I, Bianchi S, Boecker W. et al. Consistency achieved by 23 European pathologists from 12 countries in diagnosing breast disease and reporting prognostic features of carcinomas. Virchows Archiv 1999; 434 (01) 3-10
• 33 Ellis IO, Coleman D, Wells C. et al. Impact of a national external quality assessment scheme for breast pathology in the UK. Journal of Clinical Pathology 2006; 59 (02) 138-145
• 34 Rakha EA, Ahmed MA, Aleskandarany MA. et al. Diagnostic concordance of breast pathologists: lessons from the National Health Service Breast Screening Programme Pathology External Quality Assurance Scheme. Histopathology 2017; 70 (04) 632-642
• 35 Kreipe HH. Zweitmeinungs- und Referenzpathologie beim Mammakarzinom. Die Pathologie 2022; 43 (Suppl. 1) 74-80
• 36 Walker M, Mayr E-M, Koppermann M-L. et al. Molekularpathologische Untersuchungen im Wandel der Zeit. Die Pathologie 2024; 45 (03) 173-179

Publication History

Article published online:
10 March 2025