Author: Yufeng Cao, Luigi Marchionni, William Silva Mendes, Cem Onal, Lei Ren, Amit Sawant, Nicole L Simone, Philip Sutera, Phuoc Tran 👨🔬
Affiliation: University of Maryland School of Medicine, 9Department of Radiation Oncology, Thomas Jefferson University, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, University of Maryland, Baltimore, Baskent University Faculty of Medicine, Department of Radiation Oncology, Department of Radiation Oncology, University of Maryland School of Medicine, Maryland University Baltimore, 8Department of Pathology and Laboratory Medicine, Weill Cornell Medicine 🌍
Purpose: This study aims to predict 2-yr Metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) patients treated by metastasis-directed therapy (MDT) by developing a novel auto-classification network using pre-treatment multi-modality imaging. This prediction will allow for treatment customization based on the prediction to improve outcomes.
Materials/Methods: This multi-institutional study involved 118 omCSPC patients treated by MDT, comprising 34 patients from an institution in US and 84 from another institution in Europe. A novel interpretable 3D convolutional-neural-network (CNN) architecture was designed with two encoding paths for CT and PET images and one encoding path for clinical parameters. Each path used a 3×3×3 kernel with the same filters to independently weigh the spatial features of each image. Five clinical parameters—Age, Gleason Score, Number of Total Lesions, Untreated Lesions, and pre-MDT Prostate-specific Antigen (PSA)—were included as inputs for the models. The model was trained to predict 2-yr MFS with the actual patient outcome as the ground-truth. The model's performance was evaluated through 10-fold cross-validation.
Results: Among the 93 patients selected from 118, 44 (47%) were confirmed to have experienced distant metastases within the two-year timeframe, while 49(53%) were confirmed without metastases for the same duration. The AI model predicted correctly for 77 (83%) patients, including 34 patients with MFS and 43 patients without MFS. Additionally, clinical parameters improved the overall prediction accuracy by 11%. The weighting for each input in the final model provides insights into its importance in the prediction.
Conclusion: A novel fusion CNN model with three encoding paths was successfully developed for predicting metastasis-free survival (MFS). Our study highlighted the potential of using multi-modality imaging biomarkers (CT and PET) for 2-year MFS prediction in patients with omCSPC. This finding presents a unique opportunity for targeted treatment interventions to improve outcomes for patients identified as having a poor prognosis.