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Machine learning-based MRI radiomics to predict postoperative complications following peripheral nerve sheath tumour
Jifeng Wang1,2, Jia Hao Liu1,2, Yinuo Sun1,2
1Wound Repair Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Abstract:
This study sought to establish and validate a machine learning-based multi-sequence MRI radiomics model for predicting postoperative complications in patients with peripheral nerve sheath tumours. We conducted a retrospective analysis of 303 patients with pathologically confirmed tumours, extracting features from T1-weighted and T2-weighted MRI scans. Relevant radiomic features were identified through interclass correlation coefficient analysis, t-tests and least absolute shrinkage and selection operator techniques. A multi-sequence radiomics model was developed using the Light Gradient Boosting Machine classifier, alongside a clinical-radiomics model that incorporated clinical features. The models exhibited robust diagnostic performance, with areas under the receiver operating characteristic curve reaching 0.95 in the training cohort. These findings underscore the model's potential to accurately predict postoperative complications, providing crucial support for clinicians in devising personalized treatment strategies for patients with peripheral nerve sheath tumours.Level of evidence: Prognostic III.
Insights
This study developed a machine learning model using MRI radiomics to predict postoperative complications in peripheral nerve sheath tumors. The model shows high accuracy, aiding personalized treatment strategies.
Area of Science:
- Radiology
- Machine Learning
- Oncology
Background:
- Peripheral nerve sheath tumors (PNST) can lead to postoperative complications.
- Accurate prediction of these complications is crucial for patient management.
- Current predictive methods may lack precision.
Purpose of the Study:
- To develop and validate a machine learning-based multi-sequence MRI radiomics model.
- To predict postoperative complications in patients with PNST.
- To enhance personalized treatment strategies.
Main Methods:
- Retrospective analysis of 303 patients with pathologically confirmed PNST.
- Extraction of radiomic features from T1-weighted and T2-weighted MRI scans.
- Development of a Light Gradient Boosting Machine classifier for the radiomics model, incorporating clinical features.
Main Results:
- Identification of relevant radiomic features using statistical and selection techniques.
- The multi-sequence radiomics model achieved an area under the receiver operating characteristic curve of 0.95 in the training cohort.
- The model demonstrated robust diagnostic performance for predicting postoperative complications.
Conclusions:
- The developed machine learning-based radiomics model accurately predicts postoperative complications in PNST patients.
- This tool can assist clinicians in personalizing treatment plans.
- Further validation may support clinical integration for improved patient outcomes.
