Radiomics is an evolving non-invasive technology using quantitative data from imaging to provide phenotypic tumour characteristics and has been evaluated in diagnostics, genetic profiling and predicting tumour outcomes (1-4). Locally advanced non-small cell lung cancer (NSCLC) outcomes remain poor despite advances in treatment. The aim of this pilot study is to correlate radiomic analyses to clinical outcomes and develop a model to predict recurrence in a cohort of patients with Stage III NSCLC undergoing definitive chemoradiation.
Methods and materials
The pre-therapeutic diagnostic CT images of patients with Stage III NSCLC treated with definitive chemo-radiotherapy +/- maintenance immunotherapy were identified. The tumour volume was manually delineated as a region of interest (ROI), this included the gross tumour volume (GTV) of the primary (T) and nodal disease (N). Radiomics features such as first order, gray-level-co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), gray-level size zone matrix (GLSZM) and neighbourhood gray-tone difference matrix (NGTDM) were extracted from the ROI. Primary outcome was any recurrence defined as local (within...
Seventy-one patients were included in the study. Patient demographics are presented in figure 1. [Fig 1] Most patients were stage IIIa (46%) or stage IIIb (51%). All completed radiotherapy, 97% receiving 66-60Gy in 30-33 fractions and 97% concurrent carboplatin and taxol.
Fifty-two patients (73.2%) patients developed recurrence at a median 6.1 months (IQR 3.3-11.5). Sites of first failure were local in 15 (21%), nodal or distant in 37 (52%). Kaplan Meier estimates for failure free survival (FFS) are given in figure 2. The 1 year...
Models utilizing radiomic features have the potential to predict treatment outcomes using diagnostic image datasets. These models may have a role, in combination with clinicopathological and genetic features, in personalising treatments in Stage III NSCLC to improve outcomes. Our study identified higher clinical stage and radiomic feature of kurtosis, to have a significant association with failure free survival. Age, stage and features of range and GLCM autocorrelation were significant for influencing overall survival. The accuracy of machine learning models was only modestly affected by using...
Nothing to disclose
Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.
Chetan MR, Gleeson FV. Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol. 2021;31(2):1049-58.
Hong D, Xu K, Zhang L, Wan X, Guo Y. Radiomics Signature as a Predictive Factor for EGFR Mutations in Advanced Lung Adenocarcinoma. Front Oncol. 2020;10:28.
Coroller TP, Agrawal V, Huynh E, Narayan V, Lee...