Abstract

Objective:Non-invasive distinction between squamous cell carcinoma and adenocarcinoma subtypes of non-small-cell lung cancer (NSCLC) may be beneficial to patients unfit for invasive diagnostic procedures or when tissue is insufficient for diagnosis. The purpose of our study was to compare the performance of random forest algorithms utilizing CT radiomics and/or semantic features in classifying NSCLC.Methods:Two thoracic radiologists scored 11 semantic features on CT scans of 106 patients with NSCLC. A set of 115 radiomics features was extracted from the CT scans. Random forest models were developed from semantic (RM-sem), radiomics (RM-rad), and all features combined (RM-all). External validation of models was performed using an independent test data set (n = 100) of CT scans. Model performance was measured with out-of-bag error and area under curve (AUC), and compared using receiver-operating characteristics curve analysis on the test data set.Results:The median (interquartile-range) error rates of the models were: RF-sem 24.5 % (22.6 – 37.5 %), RF-rad 35.8 % (34.9 – 38.7 %), and RM-all 37.7 % (37.7 – 37.7). On training data, both RF-rad and RF-all gave perfect discrimination (AUC = 1), which was significantly higher than that achieved by RF-sem (AUC = 0.78; p < 0.0001). On test data, however, RM-sem model (AUC = 0.82) out-performed RM-rad and RM-all (AUC = 0.5 and AUC = 0.56; p < 0.0001), neither of which was significantly different from random guess ( p = 0.9 and 0.6 respectively).Conclusion:Non-invasive classification of NSCLC can be done accurately using random forest classification models based on well-known CT-derived descriptive features. However, radiomics-based classification models performed poorly in this scenario when tested on independent data and should be used with caution, due to their possible lack of generalizability to new data.Advances in knowledge:Our study describes novel CT-derived random forest models based on radiologist-interpretation of CT scans (semantic features) that can assist NSCLC classification when histopathology is equivocal or when histopathological sampling is not possible. It also shows that random forest models based on semantic features may be more useful than those built from computational radiomic features.

Highlights

  • Non-small cell lung cancers (NSCLC) comprise 85% of all primary lung malignancies 1

  • Random forest models were developed from semantic (RM-sem), radiomics (RMrad), and all features combined (RM-all)

  • RM-sem model (AUC=0.82) out-performed RM-rad and RM-all (AUC=0.5 and area under curve (AUC)=0.56; p

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Summary

Introduction

Non-small cell lung cancers (NSCLC) comprise 85% of all primary lung malignancies 1. Approximately 60% are adenocarcinomas (ADCA) and 35-40% are squamous cell carcinomas (SCCA), with large cell cancers accounting for less than 5%1. NSCLC is diagnosed on sputum cytology or clinical and radiological features, but adequate tissue is not available to perform histological subtyping and molecular analysis, requiring a multidisciplinary approach for decision-making 2. An accurate non-invasive test for NSCLC classification could serve as a valuable alternative for prognostication and choosing targeted agents in patients unsuitable for surgical resection. Radiomics involves computational analysis of a grey-scale image to derive features (e.g., mean, mode, kurtosis, and skewness) which are expected to quantify the tumour pathophysiology 5. CT radiomics has been shown to be moderately to highly accurate in predicting NSCLC subtype, with reported performance of 68% to

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