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Predicting Response to [177Lu]Lu-PSMA Therapy in mCRPC Using Machine Learning.

Kaiyuan Gong1,2, Baptiste Magnier2,3, Salomé L'hostis4

  • 1Department of Computer Science & Artificial Intelligence, IMT Mines Ales, 30100 Ales, France.

Journal of Personalized Medicine
|November 26, 2024
PubMed
Summary

Machine learning models predict outcomes for radioligand therapy (RLT) in metastatic castration-resistant prostate cancer (mCRPC). Integrating imaging and blood data improves prediction of fully beneficial treatment patients (FBTP) versus not fully beneficial treatment patients (NFBTP).

Keywords:
CholineFDG (fluorodeoxyglucose)Lu-177machine learning (ML)prostate cancer (PC)prostate specific membrane antigen (PSMA)radioligand therapy (RLT)response prediction

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Area of Science:

  • Oncology
  • Nuclear Medicine
  • Medical Imaging

Background:

  • Metastatic castration-resistant prostate cancer (mCRPC) treatment with [177Lu]Lu-PSMA radioligand therapy (RLT) is emerging.
  • Patient response and tolerance to RLT vary, necessitating identification of predictive factors.
  • Distinguishing fully beneficial treatment patients (FBTP) from not fully beneficial treatment patients (NFBTP) is crucial for optimizing RLT outcomes.

Purpose of the Study:

  • To identify predictive factors for distinguishing FBTP from NFBTP undergoing RLT for mCRPC.
  • To evaluate the role of machine learning in predicting RLT treatment effectiveness.
  • To enhance patient selection for personalized RLT treatment planning.

Main Methods:

  • Analysis of clinical, imaging, and biological data from 25 mCRPC patients (11 FBTP, 14 NFBTP).
  • Application of exploratory data analysis and feature engineering for machine learning model development.
  • Integration of Choline PET imaging and blood parameters (neutrophils, leukocytes, alkaline phosphatase) to improve predictive accuracy.

Main Results:

  • Significant differences in renal Choline uptake intensity observed between FBTP and NFBTP.
  • Discordance between FDG PET and PSMA imaging (FDG+/PSMA-) identified as a potential indicator for NFBTP.
  • Machine learning model integrating imaging and biological data achieved 0.92 accuracy, 0.96 sensitivity, and 0.96 precision.

Conclusions:

  • An integrated approach combining imaging (especially Choline PET) and biological data significantly enhances predictive accuracy for RLT outcomes in mCRPC.
  • Machine learning effectively refines patient selection for RLT, optimizing treatment planning.
  • Identifying predictive factors improves understanding of RLT efficacy and patient stratification.