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Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case

Michela Destito1, Aldo Marzullo2, Riccardo Leone3

  • 1Department of Experimental and Clinical Medicine, University of Catanzaro, 88100 Catanzaro, Italy.

Bioengineering (Basel, Switzerland)
|March 29, 2023
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Summary
This summary is machine-generated.

Radiomics features from MRI scans significantly improve outcome prediction for primary central nervous system lymphoma (PCNSL). This approach enhances prognosis stratification, potentially guiding treatment decisions for this aggressive brain cancer.

Keywords:
MRIPCNSLimage normalizationradiomicsrare tumor

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging Analysis

Background:

  • Primary Central Nervous System Lymphoma (PCNSL) is an aggressive brain cancer with a poor prognosis.
  • Current treatments like HD-MTX chemotherapy have limitations, with 15-25% of patients not responding and 25-50% relapsing.
  • There is a critical need for improved predictive models to guide PCNSL patient management.

Purpose of the Study:

  • To investigate the utility of radiomics features in improving outcome prediction for PCNSL patients.
  • To evaluate the stability and reproducibility of radiomics features extracted from MRI scans.
  • To compare the predictive performance of radiomics-based models against established clinical prognostic factors.

Main Methods:

  • Radiomics features were extracted from MRI scans of 80 PCNSL patients.
  • Feature stability was assessed using images from 23 patients scanned at three time points, calculating Interclass Correlation Coefficient (ICC).
  • Machine learning models were trained to predict Overall Survival (OS) and Progression-free Survival (PFS) using selected radiomics features.

Main Results:

  • Z-score normalized MRI images yielded significantly more stable radiomics features (38% improvement).
  • Radiomics-based prediction models demonstrated superior performance compared to clinical factors.
  • The area under the ROC curve (AUC) showed a 23% improvement for OS and a 50% improvement for PFS prediction.

Conclusions:

  • Radiomics features extracted from normalized MRI scans can enhance prognosis stratification for PCNSL patients.
  • This approach shows potential for improving treatment selection and patient outcomes in PCNSL.
  • Further research is warranted to explore the role of radiomics in guiding clinical decisions for PCNSL.