The future of radiomics in lung cancer
Authors: Rajat Thawani, Syed Atif Mustafa
Journal: The Lancet Digital Health (2020)

Radiomics refers to the extraction of quantitative, subvisual image features to create mineable databases from radiological images.1Lambin P Rios-Velazquez E Leijenaar R et al.Radiomics: extracting more information from medical images using advanced feature analysis.Eur J Cancer. 2012; 48: 441-446Summary Full Text Full Text PDF PubMed Scopus (1911) Google Scholar These radiomic features have been shown to correlate with pathogenesis of diseases, especially malignancies. There has been a lot of interest in the use of radiomics in lung cancer screenings with the goal of maximising sensitivity and specificity. However, radiomics is not only being used in diagnosis, but also to predict prognosis and response to therapies.2Thawani R McLane M Beig N et al.Radiomics and radiogenomics in lung cancer: a review for the clinician.Lung Cancer. 2018; 115: 34-41Summary Full Text Full Text PDF PubMed Scopus (182) Google Scholar Pranjal Vaidya and colleagues3Vaidya P Bera K Gupta A et al.CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction.Lancet Digital Health. 2020; (published online Feb 13. https://doi.org/10.1016/S2589-7500(20)30002-9.)Scopus (19) Google Scholar developed a radiomics-based nomogram to this aim. The need of adjuvant therapy in non-small cell lung carcinoma (NSCLC) is a debated topic, and although the National Comprehensive Cancer Network has supported its use, there is some controversy. Selection of the appropriate patient population is key to preserve survival benefit, but also to avoid unnecessary toxicities from chemotherapy.4Cortés ÁA Urquizu LC Cubero JH Adjuvant chemotherapy in non-small cell lung cancer: state-of-the-art.Transl Lung Cancer Res. 2015; 4: 191PubMed Google Scholar Vaidya and colleagues found that radiomics might be a promising tool to answer clinical questions, such as the identification of the patient population who would benefit most from adjuvant therapy. However, the study may have been more useful if done in patients with stage Ib NSCLC, for whom the question of is the most relevant, although for many reasons—including the small sample size available for analysis—this would have be difficult to achieve. Despite this limitation, Vaidya and colleagues3Vaidya P Bera K Gupta A et al.CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction.Lancet Digital Health. 2020; (published online Feb 13. https://doi.org/10.1016/S2589-7500(20)30002-9.)Scopus (19) Google Scholar successfully developed a nomogram that was prognostic and predictive of benefit of adjuvant chemotherapy following curative resection in stage I and II NSCLC. Furthermore, the study indicates that, when combined with clinical and genomic data, radiomics could help decrease overuse of adjuvant therapy and optimise patient treatment. In an additional analysis, the authors investigated the correlation of radiomic imaging features with multimodality, multiscale data—including the spatial architecture of immune and cancer cells on tissue pathology slides and biological pathways of cancer progression through mRNA sequencing data—in a small subset of their patient population. The correlations they found suggest a possible a relation between radiomic features and tumour biology. Experts have emphasised that radiomic features associated with tumour heterogeneity are predictive of poor prognosis, indicating that they might be indicative of tumour characteristics that radiologists are unable to detect.5Gillies RJ Kinahan PE Hricak H Radiomics: images are more than pictures, they are data.Radiol. 2016; 278: 563-577Crossref PubMed Scopus (2549) Google Scholar As most studies that use radiomics, Vaidya and colleagues' work has some limitations, which the authors highlighted. The most important is that biomarkers identified from these studies are not reproducible,2Thawani R McLane M Beig N et al.Radiomics and radiogenomics in lung cancer: a review for the clinician.Lung Cancer. 2018; 115: 34-41Summary Full Text Full Text PDF PubMed Scopus (182) Google Scholar possibly because of the variability in parameter acquisition and in the way radiomic features are generated. In addition, multiple biases and variables deriving from the retrospective nature of most studies using radiomics are likely to hinder their generalisability. It might be too early to include radiomics in routine clinical practice owing to these limitations, but as image acquisition and experimentation become standardised and interpretation of radiomics is validated in clinical trials, radiomics might start playing a powerful role in the future management of patients. We declare no competing interests. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome predictionQuRiS and QuRNom were validated as being prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy, especially in clinically defined low-risk groups. Since QuRiS is based on routine chest CT imaging, with additional multisite independent validation it could potentially be employed for decision management in non-invasive treatment of resectable lung cancer. Full-Text PDF Open Access