Quantitative ultrasound radiomics analysis to evaluate lymph nodes in patients with cancer: a systematic review

Antonio Guerrisi; Ludovica Miseo; Italia Falcone; Claudia Messina; Sara Ungania; Fulvia Elia; Flora Desiderio; Fabio Valenti; Vito Cantisani; Antonella Soriani; Mauro Caterino

doi:10.1055/a-2275-8342

Ultraschall in der Medizin - European Journal of Ultrasound, Table of Contents

Ultraschall Med 2024; 45(06): 586-596
DOI: 10.1055/a-2275-8342

Review

Quantitative ultrasound radiomics analysis to evaluate lymph nodes in patients with cancer: a systematic review

Quantitative Ultraschall-Radiomics-Analyse zur Beurteilung von Lymphknoten bei Krebspatienten: Eine systematische Übersicht

Antonio Guerrisi

¹Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Roma, Italy (Ringgold ID: RIN159149)

,

Ludovica Miseo

¹Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Roma, Italy (Ringgold ID: RIN159149)

,

Italia Falcone

²SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, Roma, Italy (Ringgold ID: RIN18658)

,

Claudia Messina

³Library, San Gallicano Dermatological Institute IRCCS, Roma, Italy (Ringgold ID: RIN159149)

,

Sara Ungania

⁴Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, IRCCS-Regina Elena National Cancer Institute, Roma, Italy (Ringgold ID: RIN18658)

,

Fulvia Elia

¹Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Roma, Italy (Ringgold ID: RIN159149)

,

Flora Desiderio

¹Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Roma, Italy (Ringgold ID: RIN159149)

,

Fabio Valenti

⁵UOC Oncological Translational Research, IRCCS-Regina Elena National Cancer Institute, Roma, Italy (Ringgold ID: RIN18658)

,

Vito Cantisani

⁶Department of Radiology, "Sapienza" University of Rome, Roma, Italy

,

Antonella Soriani

⁴Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, IRCCS-Regina Elena National Cancer Institute, Roma, Italy (Ringgold ID: RIN18658)

,

Mauro Caterino

¹Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Roma, Italy (Ringgold ID: RIN159149)

› Author Affiliations

Abstract

This systematic review aims to evaluate the role of ultrasound (US) radiomics in assessing lymphadenopathy in patients with cancer and the ability of radiomics to predict metastatic lymph node involvement. A systematic literature search was performed in the PubMed (MEDLINE), Cochrane Central Register of Controlled Trials (CENTRAL), and EMBASE (Ovid) databases up to June 13, 2023. 42 articles were included in which the lymph node mass was assessed with a US exam, and the analysis was performed using radiomics methods. From the survey of the selected articles, experimental evidence suggests that radiomics features extracted from US images can be a useful tool for predicting and characterizing lymphadenopathy in patients with breast, head and neck, and cervical cancer. This noninvasive and effective method allows the extraction of important information beyond mere morphological characteristics, extracting features that may be related to lymph node involvement. Future studies are needed to investigate the role of US-radiomics in other types of cancers, such as melanoma.

Zusammenfassung

Ziel dieser systematischen Übersicht ist es, die Rolle von Ultraschall-Radiomics (US-Radiomics) in der Beurteilung der Lymphadenopathie bei Krebspatienten zu bewerten, sowie die Fähigkeit von Radiomics, einen metastasierenden Lymphknotenbefall vorherzusagen. Es wurde eine systematische Literatursuche in den Datenbanken PubMed (MEDLINE), Cochrane Central Register of Controlled Trials (CENTRAL) und EMBASE (Ovid) bis zum 13. Juni 2023 durchgeführt. Es wurden 42 Artikel eingeschlossen, in denen die Lymphknoten-Raumforderung mittels US-Untersuchung beurteilt wurde, und die Analyse wurde mithilfe von Radiomics-Methoden durchgeführt. Aus der Untersuchung der ausgewählten Artikel geht hervor, dass die aus US-Bildern extrahierten Radiomics-Merkmale ein nützliches Instrument zur Vorhersage und Charakterisierung der Lymphadenopathie bei Patienten mit Brust-, Kopf- und Hals- sowie Gebärmutterhalskrebs sein können. Diese nicht invasive und effektive Methode ermöglicht die Gewinnung wichtiger Informationen über bloße morphologische Merkmale hinaus – und extrahiert Merkmale, die mit dem Lymphknotenbefall in Zusammenhang stehen können. Zukünftige Studien sind erforderlich, um die Rolle der US-Radiomics bei anderen Krebsarten wie Melanomen zu untersuchen.

Keywords

ultrasound - cancer - quantitative analysis - lymph nodes - precision medicine

Full Text

References

References
1 van Timmeren JE, Cester D, Tanadini-Lang S. et al. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging 2020; 11: 91
2 Qiu X, Jiang Y, Zhao Q. et al. Could ultrasound-based radiomics noninvasively predict axillary lymph node metastasis in breast cancer?. J Ultrasound Med 2020; 39: 1897-1905
3 Yu FH, Wang JX, Ye XH. et al. Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer. Eur J Radiol 2019; 119: 108658
4 Gao Y, Luo Y, Zhao C. et al. Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients. Eur Radiol 2021; 31: 928-937
5 Lee SE, Sim Y, Kim S. et al. Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer. Ultrasonography 2021; 40: 93-102
6 Zheng X, Yao Z, Huang Y. et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun 2020; 11: 1236
7 Sun Q, Lin X, Zhao Y. et al. Deep learning vs. radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images: don't forget the peritumoral region. Front Oncol 2020; 10: 53
8 Jiang M, Li CL, Luo XM. et al. Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer. Eur Radiol 2022; 32: 2313-2325
9 Guo X, Liu Z, Sun C. et al. Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer. EBioMedicine 2020; 60: 103018
10 Zha HL, Zong M, Liu XP. et al. Preoperative ultrasound-based radiomics score can improve the accuracy of the Memorial Sloan Kettering Cancer Center nomogram for predicting sentinel lymph node metastasis in breast cancer. Eur J Radiol 2021; 135: 109512
11 Zhang H, Zhao T, Zhang S. et al. Prediction of axillary lymph node metastatic load of breast cancer based on ultrasound deep learning radiomics nomogram. Technol Cancer Res Treat 2023; 22
12 Chen Y, Xie Y, Li B. et al. Automated breast ultrasound (ABUS)-based radiomics nomogram: an individualized tool for predicting axillary lymph node tumor burden in patients with early breast cancer. BMC Cancer 2023; 23: 340
13 Park VY, Han K, Kim HJ. et al. Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma. PLoS One 2020; 15: e0227315
14 Kim SY, Lee E, Nam SJ. et al. Ultrasound texture analysis: Association with lymph node metastasis of papillary thyroid microcarcinoma. PLoS One 2017; 12: e0176103
15 Zhou SC, Liu TT, Zhou J. et al. An Ultrasound radiomics nomogram for preoperative prediction of central neck lymph node metastasis in papillary thyroid carcinoma. Front Oncol 2020; 10: 1591
16 Jiang M, Li C, Tang S. et al. Nomogram based on shear-wave elastography radiomics can improve preoperative cervical lymph node staging for papillary thyroid carcinoma. Thyroid 2020; 30: 885-897
17 Liu T, Ge X, Yu J. et al. Comparison of the application of B- mode and strain elastography ultrasound in the estimation of lymph node metastasis of papillary thyroid carcinoma based on a radiomics approach. Int J Comput Assist Radiol Surg 2018; 13: 1617-1627
18 Liu T, Zhou S, Yu J. et al. Prediction of lymph node metastasis in patients with papillary thyroid carcinoma: a radiomics method based on preoperative ultrasound images. Technol Cancer Res Treat 2019; 18: 1533033819831713
19 Tong Y, Sun P, Yong J. et al. Radiogenomic analysis of papillary thyroid carcinoma for prediction of cervical lymph node metastasis: a preliminary study. Front Oncol 2021; 11: 682998
20 Tong Y, Li J, Huang Y. et al. Ultrasound-Based Radiomic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma. Acad Radiol 2021; 28: 1675-1684
21 Tong Y, Zhang J, Wei Y. et al. Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study. BMC Med Imaging 2022; 22: 82
22 Abbasian Ardakani A, Reiazi R. et al. A clinical decision support system using ultrasound textures and radiologic features to distinguish metastasis from tumor-free cervical lymph nodes in patients with papillary thyroid carcinoma. J Ultrasound Med 2018; 37: 2527-2535
23 Ardakani AA, Rasekhi A, Mohammadi A. et al. Differentiation between metastatic and tumour-free cervical lymph nodes in patients with papillary thyroid carcinoma by grey-scale sonographic texture analysis. Pol J Radiol 2018; 83: e37-e46
24 Li F, Pan D, He Y. et al. Using ultrasound features and radiomics analysis to predict lymph node metastasis in patients with thyroid cancer. BMC Surg 2020; 20: 315
25 Kwon MR, Shin JH, Park H. et al. Radiomics based on thyroid ultrasound can predict distant metastasis of follicular thyroid carcinoma. J Clin Med 2020; 9: 2156
26 Teng Y, Ai Y, Liang T. et al. the effects of automatic segmentations on preoperative lymph node status prediction models with ultrasound radiomics for patients with early stage cervical cancer. Technol Cancer Res Treat 2022; 21
27 Liu Y, Chen J, Zhang C. et al. Ultrasound-based radiomics can classify the etiology of cervical lymphadenopathy: a multi-center retrospective study. FrontOncol 2022; 12: 856605
28 Jin X, Ai Y, Zhang J. et al. Non-invasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images. Eur Radiol 2020; 30: 4117-4124
29 Dasgupta A, Fatima K, DiCenzo D. et al. Quantitative ultrasound radiomics in predicting recurrence for patients with node-positive head-neck squamous cell carcinoma treated with radical radiotherapy. Cancer Med 2021; 10: 2579-2589
30 Fatima K, Dasgupta A, DiCenzo D. et al. Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma. Clin Transl Radiat Oncol 2021; 28: 62-70
31 Tran WT, Suraweera H, Quiaoit K. et al. Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies. Future Sci OA 2020; 6: FSO624
32 Shi Y, Zou Y, Liu J. et al. Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP. Front Oncol 2022; 12: 897596
33 Li N, Song C, Huang X. et al. Optimized radiomics nomogram based on automated breast ultrasound system: a potential tool for preoperative prediction of metastatic lymph node burden in breast cancer. Breast Cancer (Dove Med Press) 2023; 15: 121-132
34 Zhang H, Cao W, Liu L. et al. Non-invasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound. J Transl Med 2023; 21: 337
35 Wen Q, Wang Z, Traverso A. et al. A radiomics nomogram for the ultrasound-based evaluation of central cervical lymph node metastasis in papillary thyroid carcinoma. Front Endocrinol (Lausanne) 2022; 13: 1064434
36 Jin P, Chen J, Dong Y. et al. Ultrasound-based radiomics nomogram combined with clinical features for the prediction of central lymph node metastasis in papillary thyroid carcinoma patients with Hashimoto's thyroiditis. Front Endocrinol (Lausanne) 2022; 13: 993564
37 Chung HJ, Han K, Lee E. et al. radiomics analysis of grayscale ultrasonographic images of papillary thyroid carcinoma > 1 cm: potential biomarker for the prediction of lymph node metastasis. J Korean Soc Radiol 2023; 84: 185-196
38 Xue J, Li S, Qu N. et al. Value of clinical features combined with multimodal ultrasound in predicting lymph node metastasis in cervical central area of papillary thyroid carcinoma. J Clin Ultrasound 2023; 51: 908-918
39 Gu J, Tong T, Xu D. et al. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study. Cancer 2023; 129: 356-366
40 Wang YG, Xu FJ, Agyekum EA. et al. Radiomic model for determining the value of elasticity and grayscale ultrasound diagnoses for predicting BRAFV600E mutations in papillary thyroid carcinoma. Front Endocrinol (Lausanne) 2022; 13: 872153
41 Tang YL, Wang B, Ou-Yang T. et al. Ultrasound radiomics based on axillary lymph nodes images for predicting lymph node metastasis in breast cancer. Front Oncol 2023; 13: 1217309
42 Yao J, Zhou W, Xu S. et al. Machine learning-based breast tumor ultrasound radiomics for pre-operative prediction of axillary sentinel lymph node metastasis burden in early-stage invasive breast cancer. Ultrasound Med Biol 2024; 50: 229-236
43 Zhou J, Liu C, Shi Z. et al. Application of ultrasound-based radiomics models of breast masses to predict invasive components of encapsulated papillary carcinoma. Quant Imaging Med Surg 2023; 13: 6887-6898
44 Safakish A, Sannachi L, DiCenzo D. et al. Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of-texture features. Front Oncol 2023; 13: 1258970
45 Wang C, Zhao Y, Wan M. et al. Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images. Medicine (Baltimore) 2023; 102: e35868
46 Shen B, Zhou C, Xu C. et al. Ultrasound-based radiomics for predicting metastasis in the lymph nodes posterior to the right recurrent laryngeal nerve in patients with papillary thyroid cancer. Curr Med Imaging 2023;
47 Yan X, Mou X, Yang Y. et al. Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis. BMC Med Imaging 2023; 23: 111
48 Wei T, Wei W, Ma Q. et al. Development of a clinical-radiomics nomogram that used contrast-enhanced ultrasound images to anticipate the occurrence of preoperative cervical lymph node metastasis in papillary thyroid carcinoma patients. Int J Gen Med 2023; 16: 3921-3932
49 Maksimovic S, Jakovljevic B, Gojkovic Z. Lymph node metastases papillary thyroid carcinoma and their importance in recurrence of disease. Med Arch 2018; 72: 108-111
50 Liu Z, Duan T, Zhang Y. et al. Radiogenomics: a key component of precision cancer medicine. Br J Cancer 2023; 129: 741-753

Supplementary Material

Supplementary Material