Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples. (October 2021)

• Record Type: Journal Article
• Authors: Ubaldi, L.; Valenti, V.; Borgese, R.F.; Collura, G.; Fantacci, M.E.; Ferrera, G.; Iacoviello, G.; Abbate, B.F.; Laruina, F.; Tripoli, A.; Retico, A.; Marrale, M.
• Is Part Of: Physica medica. Volume 90(2021)
• Page Start: 13
• Page End: 22
• Publication Date: 2021-10
• Subjects: Radiomics -- Machine learning -- Cross validation -- Non-small cell lung cancer; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.08.015
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: We evaluated the potential of radiomics and machine leaning on small data samples. Training predictive models is challenging when small data samples are available. Small proprietary samples may be integrated with larger publicly available cases. Predictive performances were evaluated on each sample and on the merged one. Inter-sample cross validation is feasible when samples have similar composition. Abstract: Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets. We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC = 0.72 ± 0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC = 0.72 ± 0.04 for Random Forest and AUC = 0.84 ± 0.03 for linear-kernel Support Vector Machine). According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML.