Machine-learning with region-level radiomic and dosimetric features for predicting radiotherapy-induced rectal toxicities in prostate cancer patients

Zhuolin Yang (Lead / Corresponding author), David J. Noble, Leila Shelley, Thomas Berger, Raj Jena, Duncan B. McLaren, Neil G. Burnet, William H. Nailon

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

Background and Purpose: This study aims to build machine learning models to predict radiation-induced rectal toxicities for three clinical endpoints and explore whether the inclusion of radiomic features calculated on radiotherapy planning computerised tomography (CT) scans combined with dosimetric features can enhance the prediction performance.

Materials and Methods: 183 patients recruited to the VoxTox study (UK-CRN-ID-13716) were included. Toxicity scores were prospectively collected after 2 years with grade ≥ 1 proctitis, haemorrhage (CTCAEv4.03); and gastrointestinal (GI) toxicity (RTOG) recorded as the endpoints of interest. The rectal wall on each slice was divided into 4 regions according to the centroid, and all slices were divided into 4 sections to calculate region-level radiomic and dosimetric features. The patients were split into a training set (75%, N = 137) and a test set (25%, N = 46). Highly correlated features were removed using four feature selection methods. Individual radiomic or dosimetric or combined (radiomic + dosimetric) features were subsequently classified using three machine learning classifiers to explore their association with these radiation-induced rectal toxicities.

Results: The test set area under the curve (AUC) values were 0.549, 0.741 and 0.669 for proctitis, haemorrhage and GI toxicity prediction using radiomic combined with dosimetric features. The AUC value reached 0.747 for the ensembled radiomic-dosimetric model for haemorrhage.

Conclusions: Our preliminary results show that region-level pre-treatment planning CT radiomic features have the potential to predict radiation-induced rectal toxicities for prostate cancer. Moreover, when combined with region-level dosimetric features and using ensemble learning, the model prediction performance slightly improved.

Original languageEnglish
Article number109593
Number of pages6
JournalRadiotherapy and Oncology
Volume183
Early online date2 Mar 2023
DOIs
Publication statusPublished - Jun 2023

Keywords

  • toxicity
  • radiotherapy
  • prostate cancer
  • machine learning
  • radiomics
  • Toxicity
  • Machine learning
  • Radiotherapy
  • Prostate cancer
  • Radiomics

ASJC Scopus subject areas

  • Hematology
  • Oncology
  • Radiology Nuclear Medicine and imaging

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