Utility of pre-treatment FDG PET/CT–derived machine learning models for outcome prediction in classical Hodgkin lymphoma

Frood, R; Clark, M; Burton, C; Tsoumpas, C; Frangi, AF; Gleeson, F; Patel, C; Scarsbrook, A

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Journal article

Utility of pre-treatment FDG PET/CT–derived machine learning models for outcome prediction in classical Hodgkin lymphoma

Abstract:: Objectives Relapse occurs in similar to 20% of patients with classical Hodgkin lymphoma (cHL) despite treatment adaption based on 2dcoxy-2[F-18]fluoro-D-glucose positron emission tomography/computed tomography response. The objective was to evaluate pre-treatment FDG PET/CT-derived machine learning (ML) models for predicting outcome in patients with cHL.Methods All cHL patients undergoing pre-treatment PET/CT at our institution between 2008 and 2018 were retrospectively identified. A 1.5 x mean liver standardised uptake value (SUV) and a fixed 4.0 SUV threshold were used to segment PET/CT data. Feature extraction was performed using PyRadiomics with ComBat harmonisation. Training (80%) and test (20%) cohorts stratified around 2-year event-free survival (EFS), age, sex, ethnicity and disease stage were defined. Seven ML models were trained and hyperparameters tuned using stratified 5-fold cross-validation. Area under the curve (AUC) from receiver operator characteristic analysis was used to assess performance.Results A total of 289 patients (153 males), median age 36 (range 16-88 years), were included. There was no significant difference between training (n = 231) and test cohorts (n = 58) (p value > 0.05). A ridge regression model using a 1.5 x mean liver SUV segmentation had the highest performance, with mean training, validation and test AUCs of 0.82 +/- 0.002, 0.79 +/- 0.01 and 0.81 4- 0.12. However, there was no significant difference between a logistic model derived from metabolic tumour volume and clinical features or the highest performing radiomic model.Conclusions Outcome prediction using pre-treatment FDG PET/CT-derived ML models is feasible in cHL patients. Further work is needed to determine optimum predictive thresholds for clinical use.

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Frood, R., Clark, M., Burton, C., Tsoumpas, C., Frangi, A. F., Gleeson, F., Patel, C., & Scarsbrook, A. (2022). Utility of pre-treatment FDG PET/CT–derived machine learning models for outcome prediction in classical Hodgkin lymphoma. European Radiology, 32(10), 7237–7247.

MLA Style

Frood, R, et al. “Utility of Pre-Treatment FDG PET/CT–Derived Machine Learning Models for Outcome Prediction in Classical Hodgkin Lymphoma.” European Radiology, vol. 32, no. 10, 2022, pp. 7237–47.

Chicago Style

Frood, R, M Clark, C Burton, et al. 2022. “Utility of Pre-Treatment FDG PET/CT–Derived Machine Learning Models for Outcome Prediction in Classical Hodgkin Lymphoma.” European Radiology 32 (10): 7237–47.
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