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Updated: Jan 12, 2026
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Artificial intelligence-based lesion characterization and outcome prediction of prostate cancer on [18F]DCFPyL PSMA
Linmei Zhao1, Maliha R Imami2, Yuli Wang2
1Department of Radiology, University of Colorado School of Medicine, Aurora, CO, USA; Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Background And Purpose:
Prostate cancer remains a significant clinical challenge, particularly in characterizing lesions and predicting patient outcomes. With the growing availability of advanced imaging techniques like [18F]DCFPyL PET/CT, there is an urgent need for intelligent tools that can facilitate clinical decision-making. This study aimed to develop artificial intelligence (AI) models for lesion characterization and outcome prediction in prostate cancer (PCa) patients.
Materials And Methods:
PCa patients who underwent [18F]DCFPyL PET/CT imaging were divided into training and internal test sets (n = 238) and a prospective test set (n = 36). Lesions were scored using the PSMA-Reporting and Data System (RADS) and assessed for malignancy, treatment response, and survival outcomes. Single- and multi-modality deep learning models were trained for four tasks: PSMA-RADS scoring, malignancy classification, treatment response prediction, and survival prediction.
Results:
The input concatenation model, which combined PET and CT modalities, demonstrated superior performance across all tasks. For the internal test set, the area under the receiver operating characteristic curves (AUROCs) were 0.81 (95 % CI: 0.80-0.81) for PSMA-RADS scoring, 0.79 (95 % CI: 0.78-0.80) for malignancy classification, and 0.74 (95 % CI: 0.73-0.77) for treatment response prediction. In the prospective test set, the AUROCs were 0.72 (95 % CI: 0.69-0.75) for PSMA-RADS scoring, 0.70 (95 % CI: 0.68-0.71) for malignancy classification, and 0.70 (95 % CI: 0.67-0.72) for treatment response prediction. The C-indices for survival predictions were 0.58 (95 % CI: 0.57-0.59) and 0.60 (95 % CI: 0.60-0.63) for the internal and prospective test sets, respectively.
Conclusion:
Our study highlights the potential of AI to improve lesion characterization and identify patients at high risk of disease progression.
