Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with 18F-FDG PET Radiomics Based Machine Learning Classification

Roelof J Beukinga; Floris B Poelmann; Gursah Kats-Ugurlu; Alain R Viddeleer; Ronald Boellaard; Robbert J De Haas; John Th M Plukker; Jan Binne Hulshoff

doi:10.3390/diagnostics12051070

Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with ¹⁸F-FDG PET Radiomics Based Machine Learning Classification

Diagnostics (Basel). 2022 Apr 24;12(5):1070. doi: 10.3390/diagnostics12051070.

Authors

Roelof J Beukinga¹, Floris B Poelmann², Gursah Kats-Ugurlu³, Alain R Viddeleer⁴, Ronald Boellaard^{1

5}, Robbert J De Haas⁴, John Th M Plukker², Jan Binne Hulshoff^{1

4}

Affiliations

¹ Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands.
² Department of Surgical Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands.
³ Department of Pathology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands.
⁴ Department of Radiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands.
⁵ Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.

Abstract

Background: Approximately 26% of esophageal cancer (EC) patients do not respond to neoadjuvant chemoradiotherapy (nCRT), emphasizing the need for pre-treatment selection. The aim of this study was to predict non-response using a radiomic model on baseline ¹⁸F-FDG PET.

Methods: Retrospectively, 143 ¹⁸F-FDG PET radiomic features were extracted from 199 EC patients (T1N1-3M0/T2-4aN0-3M0) treated between 2009 and 2019. Non-response (n = 57; 29%) was defined as Mandard Tumor Regression Grade 4-5 (n = 44; 22%) or interval progression (n = 13; 7%). Randomly, 139 patients (70%) were allocated to explore all combinations of 24 feature selection strategies and 6 classification methods towards the cross-validated average precision (AP). The predictive value of the best-performing model, i.e AP and area under the ROC curve analysis (AUC), was evaluated on an independent test subset of 60 patients (30%).

Results: The best performing model had an AP (mean ± SD) of 0.47 ± 0.06 on the training subset, achieved by a support vector machine classifier trained on five principal components of relevant clinical and radiomic features. The model was externally validated with an AP of 0.66 and an AUC of 0.67.

Conclusion: In the present study, the best-performing model on pre-treatment ¹⁸F-FDG PET radiomics and clinical features had a small clinical benefit to identify non-responders to nCRT in EC.

Keywords: esophageal neoplasms; neoadjuvant therapy; positron-emission tomography.