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

Russell Frood, Matt Clark, Cathy Burton, Charalampos Tsoumpas, Alejandro F. Frangi, Fergus Gleeson, Chirag Patel, Andrew Scarsbrook

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: Relapse occurs in ~20% of patients with classical Hodgkin lymphoma (cHL) despite treatment adaption based on 2-deoxy-2-[18F]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 × 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 × 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 ± 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. Key points: • A fixed threshold segmentation method led to more robust radiomic features. • A radiomic-based model for predicting 2-year event-free survival in classical Hodgkin lymphoma patients is feasible. • A predictive model based on ridge regression was the best performing model on our dataset.

Original languageEnglish
Pages (from-to)7237-7247
Number of pages11
JournalEuropean Radiology
Volume32
Issue number10
DOIs
Publication statusPublished - 1 Oct 2022

Keywords

  • Hodgkin disease: positron emission tomography computed tomography
  • Machine learning, progression-free survival
  • Humans
  • Middle Aged
  • Neoplasm Recurrence, Local
  • Male
  • Machine Learning
  • Hodgkin Disease/diagnostic imaging
  • Young Adult
  • Adolescent
  • Aged, 80 and over
  • Adult
  • Fluorodeoxyglucose F18/metabolism
  • Aged
  • Retrospective Studies
  • Positron Emission Tomography Computed Tomography/methods
  • Positron-Emission Tomography/methods

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