Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring

Abstract

Background Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths. The development of therapies targeting molecular alterations has significantly improved the treatment of NSCLC patients. To identify these targets, tumor phenotyping is required, with tissue biopsies and molecular pathology being the gold standard. Some patients do not respond to targeted therapies and many patients suffer from tumor recurrence, which can in part be explained by tumor heterogeneity. This points out the need for new biomarkers allowing for better tumor phenotyping and monitoring during treatment to assess patient outcome.

Method The contents of this review are based on a literature search conducted using the PubMed database in March 2021 and the authors’ experience.

Results and Conclusion The use of radiomics and artificial intelligence-based approaches allows for the identification of imaging biomarkers in NSCLC patients for tumor phenotyping. Several studies show promising results for models predicting molecular alterations, with the best results being achieved by combining structural and functional imaging. Radiomics could help solve the pressing clinical need for assessing and predicting therapy response. To reach this goal, advanced tumor phenotyping, considering tumor heterogeneity, is required. This could be achieved by integrating structural and functional imaging biomarkers with clinical data sources, such as liquid biopsy results. However, to allow for radiomics-based approaches to be introduced into clinical practice, further standardization using large, multi-center datasets is required.

Key points: 

• Some NSCLC patients do not benefit from targeted therapies, and many patients suffer from tumor recurrence, pointing out the need for new biomarkers allowing for better tumor phenotyping and monitoring during treatment.

• The use of radiomics-based approaches allows for the identification of imaging biomarkers in NSCLC patients for tumor phenotyping.

• A multi-omics approach integrating not only structural and functional imaging biomarkers but also clinical data sources, such as liquid biopsy results, could further enhance the prediction and assessment of therapy response.

Citation Format

• Kroschke J, von Stackelberg O, Heußel CP et al. Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring. Fortschr Röntgenstr 2022; 194: 720 – 727

Zusammenfassung

Hintergrund Nichtkleinzelliger Lungenkrebs ist die häufigste Krebstodesursache. Durch die Entwicklung neuer gezielter Therapien konnten erhebliche Fortschritte in der Behandlung von Lungenkrebspatienten erzielt werden. Gewebebiopsien und molekulare Pathologie sind der Goldstandard zur Identifikation der molekularen Therapieziele. Nicht alle Patienten profitieren von diesen neuen Therapieformen und viele Patienten leiden an Rezidiven, was z. T. durch die Tumorheterogenität erklärt werden kann. Die Identifikation weiterer Biomarker ist nötig, um das Therapieansprechen besser beurteilen zu können.

Methode Der Inhalt dieser Übersichtarbeit basiert auf einer Literaturrecherche in PubMed aus dem März 2021 und den Erfahrungen der Autoren.

Ergebnisse und Schlussfolgerung Mithilfe von Radiomics und maschinellem Lernen können Bildgebungsbiomarker zur Tumorphänotypisierung bei Lungenkrebspatienten identifiziert werden. Einige Studien zeigen gute Ergebnisse der Prädiktionsmodelle für das Vorliegen verschiedener molekularer Alterationen, wobei die Verwendung von struktureller und funktionaler Bildgebung die besten Ergebnisse liefert. Durch die Integration struktureller und funktionaler Bildgebung mit weiteren klinischen Datenquellen, wie den Ergebnissen von liquid biopsies, könnte Radiomics ein Lösungsansatz für die klinische Notwendigkeit zur besseren Beurteilung des Therapieverlaufs sein. Damit Radiomics-Ansätze Einzug in die klinische Routine halten können, sind weitere Studien mit großen, multizentrischen Datensätzen zur Validierung nötig.

Kernaussagen: 

• Nicht alle Patienten mit nichtkleinzelligem Lungenkrebs profitieren von gezielten Therapien und viele Patienten entwickeln Rezidive, was die Notwendigkeit der Identifikation neuer Biomarker zur besseren Phänotypisierung und Verlaufskontrolle von Tumoren aufzeigt.

• Mithilfe von Radiomics können Bildgebungsbiomarker zur Tumorphänotypisierung bei Lungenkrebspatienten identifiziert werden.

• Durch die Integration struktureller und funktionaler Bildgebung mit weiteren klinischen Datenquellen, wie den Ergebnissen von liquid biopsies, könnte Radiomics ein Lösungsansatz für die klinische Notwendigkeit zur besseren Beurteilung des Therapieverlaufs sein.

Key words

radiomics - artificial intelligence - imaging biomarkers - non-small cell lung cancer - tumor heterogeneity

Publication History

Received: 30 April 2021

Accepted: 20 December 2021

Article published online:
24 February 2022

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