Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC

Sumeet Hindocha; Thomas G Charlton; Kristofer Linton-Reid; Benjamin Hunter; Charleen Chan; Merina Ahmed; Emily J Greenlay; Matthew Orton; Catey Bunce; Jason Lunn; Simon J Doran; Shahreen Ahmad; Fiona McDonald; Imogen Locke; Danielle Power; Matthew Blackledge; Richard W Lee; Eric O Aboagye

doi:10.1038/s41698-022-00322-3

Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC

NPJ Precis Oncol. 2022 Oct 27;6(1):77. doi: 10.1038/s41698-022-00322-3.

Authors

Sumeet Hindocha^{1

2

3

4}, Thomas G Charlton⁵, Kristofer Linton-Reid⁴, Benjamin Hunter^{1

3

4}, Charleen Chan³, Merina Ahmed⁶, Emily J Greenlay⁷, Matthew Orton⁸, Catey Bunce⁷, Jason Lunn³, Simon J Doran³, Shahreen Ahmad⁵, Fiona McDonald^{1

3}, Imogen Locke⁶, Danielle Power⁹, Matthew Blackledge¹⁰, Richard W Lee^{1

11}, Eric O Aboagye¹²

Affiliations

¹ Lung Unit, The Royal Marsden NHS Foundation Trust, London, UK.
² AI for Healthcare Centre for Doctoral Training, Imperial College London, London, UK.
³ Institute of Cancer Research NIHR Biomedical Research Centre, London, UK.
⁴ Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, London, UK.
⁵ Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK.
⁶ Lung Unit, The Royal Marsden NHS Foundation Trust, Sutton, UK.
⁷ Clinical Trials Unit, Royal Marsden NHS Foundation Trust, Sutton, UK.
⁸ Artificial Intelligence Imaging Hub, Royal Marsden NHS Foundation Trust, Sutton, UK.
⁹ Department of Clinical Oncology, Charing Cross Hospital, London, UK.
¹⁰ Radiotherapy and Imaging, Institute of Cancer Research, London, UK.
¹¹ Early Diagnosis and Detection Team, Institute of Cancer Research, London, UK.
¹² Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, London, UK. eric.aboagye@imperial.ac.uk.

Abstract

Recurrence occurs in up to 36% of patients treated with curative-intent radiotherapy for NSCLC. Identifying patients at higher risk of recurrence for more intensive surveillance may facilitate the earlier introduction of the next line of treatment. We aimed to use radiotherapy planning CT scans to develop radiomic classification models that predict overall survival (OS), recurrence-free survival (RFS) and recurrence two years post-treatment for risk-stratification. A retrospective multi-centre study of >900 patients receiving curative-intent radiotherapy for stage I-III NSCLC was undertaken. Models using radiomic and/or clinical features were developed, compared with 10-fold cross-validation and an external test set, and benchmarked against TNM-stage. Respective validation and test set AUCs (with 95% confidence intervals) for the radiomic-only models were: (1) OS: 0.712 (0.592-0.832) and 0.685 (0.585-0.784), (2) RFS: 0.825 (0.733-0.916) and 0.750 (0.665-0.835), (3) Recurrence: 0.678 (0.554-0.801) and 0.673 (0.577-0.77). For the combined models: (1) OS: 0.702 (0.583-0.822) and 0.683 (0.586-0.78), (2) RFS: 0.805 (0.707-0.903) and 0·755 (0.672-0.838), (3) Recurrence: 0·637 (0.51-0.·765) and 0·738 (0.649-0.826). Kaplan-Meier analyses demonstrate OS and RFS difference of >300 and >400 days respectively between low and high-risk groups. We have developed validated and externally tested radiomic-based prediction models. Such models could be integrated into the routine radiotherapy workflow, thus informing a personalised surveillance strategy at the point of treatment. Our work lays the foundations for future prospective clinical trials for quantitative personalised risk-stratification for surveillance following curative-intent radiotherapy for NSCLC.