Non-Invasive Detection of PROState Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914063885271040 |
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| author | Ramos, Baltasar Garrido, Cristian Narv'aez, Paulette Claro, Santiago Gelerstein Li, Haotian Salvador, Rafael V'asquez-Venegas, Constanza Gallegos, Iv'an Zhang, Yi Casta~neda, V'ictor Acevedo, Cristian Wu, Dan C'ardenas, Gonzalo Sotomayor, Camilo G. |
| author_facet | Ramos, Baltasar Garrido, Cristian Narv'aez, Paulette Claro, Santiago Gelerstein Li, Haotian Salvador, Rafael V'asquez-Venegas, Constanza Gallegos, Iv'an Zhang, Yi Casta~neda, V'ictor Acevedo, Cristian Wu, Dan C'ardenas, Gonzalo Sotomayor, Camilo G. |
| contents | Prostate cancer (PCa) is the most frequently diagnosed malignancy in men and the eighth leading cause of cancer death worldwide. Multiparametric MRI (mpMRI) has become central to the diagnostic pathway for men at intermediate risk, improving de-tection of clinically significant PCa (csPCa) while reducing unnecessary biopsies and over-diagnosis. However, mpMRI remains limited by false positives, false negatives, and moderate to substantial interobserver agreement. Time-dependent diffusion (TDD) MRI, a novel sequence that enables tissue microstructure characterization, has shown encouraging preclinical performance in distinguishing clinically significant from insignificant PCa. Combining TDD-derived metrics with machine learning may provide robust, zone-specific risk prediction with less dependence on reader training and improved accuracy compared to current standard-of-care. This study protocol out-lines the rationale and describes the prospective evaluation of a home-developed AI-enhanced TDD-MRI software (PROSTDAI) in routine diagnostic care, assessing its added value against PI-RADS v2.1 and validating results against MRI-guided prostate biopsy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24227 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Non-Invasive Detection of PROState Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI Ramos, Baltasar Garrido, Cristian Narv'aez, Paulette Claro, Santiago Gelerstein Li, Haotian Salvador, Rafael V'asquez-Venegas, Constanza Gallegos, Iv'an Zhang, Yi Casta~neda, V'ictor Acevedo, Cristian Wu, Dan C'ardenas, Gonzalo Sotomayor, Camilo G. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Prostate cancer (PCa) is the most frequently diagnosed malignancy in men and the eighth leading cause of cancer death worldwide. Multiparametric MRI (mpMRI) has become central to the diagnostic pathway for men at intermediate risk, improving de-tection of clinically significant PCa (csPCa) while reducing unnecessary biopsies and over-diagnosis. However, mpMRI remains limited by false positives, false negatives, and moderate to substantial interobserver agreement. Time-dependent diffusion (TDD) MRI, a novel sequence that enables tissue microstructure characterization, has shown encouraging preclinical performance in distinguishing clinically significant from insignificant PCa. Combining TDD-derived metrics with machine learning may provide robust, zone-specific risk prediction with less dependence on reader training and improved accuracy compared to current standard-of-care. This study protocol out-lines the rationale and describes the prospective evaluation of a home-developed AI-enhanced TDD-MRI software (PROSTDAI) in routine diagnostic care, assessing its added value against PI-RADS v2.1 and validating results against MRI-guided prostate biopsy. |
| title | Non-Invasive Detection of PROState Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2509.24227 |