PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | You, Suhang Pitarch-Abaigar, Carla Kachole, Sanket Sonawane, Sumedh Ha, Juhyung Gada, Anish Sudarshan Crandall, David Shiradkar, Rakesh Bakas, Spyridon |
| author_facet | You, Suhang Pitarch-Abaigar, Carla Kachole, Sanket Sonawane, Sumedh Ha, Juhyung Gada, Anish Sudarshan Crandall, David Shiradkar, Rakesh Bakas, Spyridon |
| contents | Almost 30% of prostate cancer (PCa) patients undergoing radical prostatectomy (RP) experience biochemical recurrence (BCR), characterized by increased prostate specific antigen (PSA) and associated with increased mortality. Accurate early prediction of BCR, at the time of RP, would contribute to prompt adaptive clinical decision-making and improved patient outcomes. In this work, we propose prostate cancer BCR prediction via fused multi-modal embeddings (PROFUSEme), which learns cross-modal interactions of clinical, radiology, and pathology data, following an intermediate fusion configuration in combination with Cox Proportional Hazard regressors. Quantitative evaluation of our proposed approach reveals superior performance, when compared with late fusion configurations, yielding a mean C-index of 0.861 ($σ=0.112$) on the internal 5-fold nested cross-validation framework, and a C-index of 0.7107 on the hold out data of CHIMERA 2025 challenge validation leaderboard. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings You, Suhang Pitarch-Abaigar, Carla Kachole, Sanket Sonawane, Sumedh Ha, Juhyung Gada, Anish Sudarshan Crandall, David Shiradkar, Rakesh Bakas, Spyridon Computer Vision and Pattern Recognition Almost 30% of prostate cancer (PCa) patients undergoing radical prostatectomy (RP) experience biochemical recurrence (BCR), characterized by increased prostate specific antigen (PSA) and associated with increased mortality. Accurate early prediction of BCR, at the time of RP, would contribute to prompt adaptive clinical decision-making and improved patient outcomes. In this work, we propose prostate cancer BCR prediction via fused multi-modal embeddings (PROFUSEme), which learns cross-modal interactions of clinical, radiology, and pathology data, following an intermediate fusion configuration in combination with Cox Proportional Hazard regressors. Quantitative evaluation of our proposed approach reveals superior performance, when compared with late fusion configurations, yielding a mean C-index of 0.861 ($σ=0.112$) on the internal 5-fold nested cross-validation framework, and a C-index of 0.7107 on the hold out data of CHIMERA 2025 challenge validation leaderboard. |
| title | PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.14051 |