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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A

Liwei Wei1, Yongdi Huang2, Zheng Chen1

  • 1Department of Urology, the First Affiliated Hospital of Jinan University, Guangzhou, China.

Frontiers in Oncology
|November 1, 2021
PubMed
Summary

Machine learning accurately predicts lymph node involvement in prostate cancer, potentially reducing unnecessary surgeries by 50%. This approach improves treatment strategies for prostate cancer patients.

Keywords:
SEER databaselymph node involvementmachine learningpredictive modelprostate cancer

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Area of Science:

  • Oncology
  • Urology
  • Medical Informatics

Background:

  • Accurate preoperative prediction of lymph node involvement (LNI) in prostate cancer (PCa) is crucial for optimizing clinical management and follow-up.
  • Current strategies necessitate improvement for better patient outcomes.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model utilizing big data for predicting LNI in PCa.
  • To enhance the accuracy of preoperative LNI assessment.

Main Methods:

  • Utilized clinicopathological data from 2,884 PCa patients from the SEER database (2010-2015).
  • Developed an ML model incorporating eight key variables.
  • Evaluated model performance using ROC curves, calibration plots, and decision curve analysis (DCA).

Main Results:

  • Identified 11.9% of patients with LNI.
  • Gleason score, T stage, positive core percentage, tumor size, and PSA levels were key predictors.
  • The XGBoost (XGB) model demonstrated superior predictive accuracy (AUC=0.883) and clinical utility via DCA.
  • The XGB model achieved a high net benefit, reducing omissions and overtreatment, with a lower false-negative rate.

Conclusions:

  • An ML model effectively predicts LNI in PCa using big data.
  • The model can potentially reduce extended pelvic lymph node dissection (ePLND) by approximately 50%.
  • A cutoff value (5%-20%) resulted in ≤3% misdiagnosis, warranting further prospective validation.