Prediction of Lesion-Based Treatment Response after Two Cycles of Lu-177 Prostate Specific Membrane Antigen Treatment in Metastatic Castration-Resistant Prostate Cancer Using Machine Learning

Bülbül O., Nak D., Göksel S.

UROLOGIA INTERNATIONALIS, sa.Online ahead of print, ss.1-7, 2024 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1159/000541628
  • Dergi Adı: UROLOGIA INTERNATIONALIS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Gender Studies Database
  • Sayfa Sayıları: ss.1-7
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

Introduction: Lutetium-177 (Lu-177) prostate-specific membrane antigen (PSMA) therapy is a radionuclide treatment that prolongs overall survival in metastatic castration-resistant prostate cancer (MCRPC). We aimed to predict lesion-based treatment response after Lu-177 PSMA treatment using machine learning with texture analysis data obtained from pretreatment Gallium-68 (Ga-68) PSMA positron emission tomography/computed tomography (PET/CT).

Methods: Eighty-three progressed, and 91 nonprogressed malignant foci on pretreatment Ga-68 PSMA PET/CT of 9 patients were used for analysis. Malignant foci with at least a 30% increase in Ga-68 PSMA uptake after two cycles of treatment were considered progressed lesions. All other changes in Ga-68 PSMA uptake of the lesions were considered nonprogressed lesions. The classifiers tried to predict progressed lesions.

Results: Logistic regression, Naive Bayes, and k-nearest neighbors' area under the ROC curve (AUC) values in detecting progressed lesions in the training group were 0.956, 0.942, and 0.950, respectively, and their accuracy was 87%, 85%, and 89%, respectively. The AUC values of the classifiers in the testing group were 0.937, 0.954, and 0.867, respectively, and their accuracy was 85%, 88%, and 79%, respectively.

Conclusion: Using machine learning with texture analysis data obtained from pretreatment Ga-68 PSMA PET/CT in MCRPC predicted lesion-based treatment response after two cycles of Lu-177 PSMA treatment.

Keywords: Ga-68 PSMA positron emission tomography/computed tomography; Lutetium-177 PSMA; Machine learning; Prediction; Texture analysis.