(UroToday.com) The 2024 Society of Nuclear Medicine & Molecular Imaging (SNMMI) annual meeting featured a session on prostate cancer, and a presentation by Dr. Yongxiang Tang discussing an assessment of adverse pathological risk and proteomic biomarker correlations in prostate cancer.
Prostate cancer is a highly heterogeneous malignant disease. Therefore, it is crucial to investigate markers to facilitate early identification of adverse pathological features of prostate cancer thereby improve patient prognosis. In this study, Dr. Tang and colleagues applied radiomics machine learning models to predict the aggressiveness of prostate cancer and identify quantitative radiomic features and protein biomarkers associated with poor pathological traits. The aim of the study was to construct a multi-omics marker model to optimize clinical risk stratification.
This was a retrospective study on 191 patients who were diagnosed with prostate cancer or benign prostatic hyperplasia and were pathologically confirmed after undergoing 68Ga-PSMA-617 PET/CT scan. CT imaging was utilized for anatomical localization, while PET/CT scans were employed for image fusion, and manual contouring of the prostate gland was performed. Radiomic features were then extracted from the contours to analyze the imaging characteristics. Six machine learning algorithms were applied to construct radiomics models for predicting malignancies and combinations of adverse pathological features (Gleason score, ISUP group, pathological stage, lymph node infiltration, and perineural invasion). Two methods, minimum redundancy maximum relevance, and LASSO, were utilized to conduct feature selection and identify quantitative radiomic features with high predictive ability. Moreover, proteomics analyses were performed on 39 patients to identify protein biomarkers associated with adverse pathological features at the molecular level in prostate cancer. Correlation analysis was performed to determine the association of quantitative radiomic features with protein biomarkers.
The optimal radiomics model constructed using machine learning methods showed an AUC of 0.938 (95% CI 0.893 to 0.983) for predicting malignant prostate lesions and an AUC of 0.916 (95% CI 0.854 to 0.977) for adverse pathological feature combinations in the test set. Results of the validation set obtained AUC values of 0.918 (95% CI 0.848 to 0.989) for predicting malignancy and 0.855 (95% CI 0.728 to 0.983) for adverse feature combinations. Three quantitative radiomic features and ten protein molecules associated with adverse pathological characteristics were identified:
Moreover, a significant correlation was observed between quantitative radiomic features and protein biomarkers:
The radioproteomic analysis demonstrated that molecular changes in protein molecules could affect the imaging biomarkers.
Dr. Tang concluded this presentation by discussing an assessment of adverse pathological risk and proteomic biomarker correlations in prostate cancer with the following take home messages:
- This study underscored the efficacy of radiomics machine learning models using 68Ga-PSMA-617 PET/CT in stratifying prostate cancer risks
- These models demonstrated high predictive accuracy for malignancy and adverse pathological features, evidenced by robust AUC values
- Notably, critical quantitative radiomic features and protein biomarkers were identified, revealing significant correlations between imaging and molecular changes in prostate cancer
- The integration of these findings into a multi-omics marker model marked a significant stride in optimizing clinical risk stratification, potentially enhancing personalized treatment strategies and improving patient outcomes in prostate cancer management
Presented by: Yongxiang Tang, MD, Xiangya Hospital, Central South University, Hunan, China
Written by: Zachary Klaassen, MD, MSc – Urologic Oncologist, Associate Professor of Urology, Georgia Cancer Center, Wellstar MCG Health, @zklaassen_md on Twitter during the 2024 Society of Nuclear Medicine & Molecular Imaging (SNMMI) Annual Meeting, Toronto, Ontario, Canada, Sat, June 8 – Tues, June 11, 2024.