MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI
Accurate preoperative prediction of biochemical recurrence (BCR) in prostate cancer (PCa) is essential for treatment optimization, and demands an explicit focus on tumor microenvironment (TME). To address this, we developed MRMS-CNNFormer, an innovative framework integrating 2D <b>m</b>u...
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MDPI AG
2025-05-01
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| author | Tao Lian Mengting Zhou Yangyang Shao Xiaqing Chen Yinghua Zhao Qianjin Feng |
| author_facet | Tao Lian Mengting Zhou Yangyang Shao Xiaqing Chen Yinghua Zhao Qianjin Feng |
| author_sort | Tao Lian |
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| description | Accurate preoperative prediction of biochemical recurrence (BCR) in prostate cancer (PCa) is essential for treatment optimization, and demands an explicit focus on tumor microenvironment (TME). To address this, we developed MRMS-CNNFormer, an innovative framework integrating 2D <b>m</b>ulti-<b>r</b>egion (intratumoral, peritumoral, and periprostatic) and <b>m</b>ulti-<b>s</b>equence magnetic resonance imaging (MRI) images (T2-weighted imaging with fat suppression (T2WI-FS) and diffusion-weighted imaging (DWI)) with clinical characteristics. The framework utilizes a <b>CNN</b>-based encoder for imaging feature extraction, followed by a trans<b>former</b>-based encoder for multi-modal feature integration, and ultimately employs a fully connected (FC) layer for final BCR prediction. In this multi-center study (46 BCR-positive cases, 186 BCR-negative cases), patients from centers A and B were allocated to training (<i>n</i> = 146) and validation (<i>n</i> = 36) sets, while center C patients (<i>n</i> = 50) formed the external test set. The multi-region MRI-based model demonstrated superior performance (AUC, 0.825; 95% CI, 0.808–0.852) compared to single-region models. The integration of clinical data further enhanced the model’s predictive capability (AUC 0.835; 95% CI, 0.818–0.869), significantly outperforming the clinical model alone (AUC 0.612; 95% CI, 0.574–0.646). MRMS-CNNFormer provides a robust, non-invasive approach for BCR prediction, offering valuable insights for personalized treatment planning and clinical decision making in PCa management. |
| format | Article |
| id | doaj-art-08b67a33a250481db05b39fb42340367 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-08b67a33a250481db05b39fb423403672025-08-20T03:47:53ZengMDPI AGBioengineering2306-53542025-05-0112553810.3390/bioengineering12050538MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRITao Lian0Mengting Zhou1Yangyang Shao2Xiaqing Chen3Yinghua Zhao4Qianjin Feng5School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, ChinaDepartment of Radiology, The Third Affiliated Hospital, Southern Medical University, Guangzhou 510630, ChinaDepartment of Radiology, The Third Affiliated Hospital, Southern Medical University, Guangzhou 510630, ChinaDepartment of Radiology, The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou 510210, ChinaDepartment of Radiology, The Third Affiliated Hospital, Southern Medical University, Guangzhou 510630, ChinaSchool of Biomedical Engineering, Southern Medical University, Guangzhou 510515, ChinaAccurate preoperative prediction of biochemical recurrence (BCR) in prostate cancer (PCa) is essential for treatment optimization, and demands an explicit focus on tumor microenvironment (TME). To address this, we developed MRMS-CNNFormer, an innovative framework integrating 2D <b>m</b>ulti-<b>r</b>egion (intratumoral, peritumoral, and periprostatic) and <b>m</b>ulti-<b>s</b>equence magnetic resonance imaging (MRI) images (T2-weighted imaging with fat suppression (T2WI-FS) and diffusion-weighted imaging (DWI)) with clinical characteristics. The framework utilizes a <b>CNN</b>-based encoder for imaging feature extraction, followed by a trans<b>former</b>-based encoder for multi-modal feature integration, and ultimately employs a fully connected (FC) layer for final BCR prediction. In this multi-center study (46 BCR-positive cases, 186 BCR-negative cases), patients from centers A and B were allocated to training (<i>n</i> = 146) and validation (<i>n</i> = 36) sets, while center C patients (<i>n</i> = 50) formed the external test set. The multi-region MRI-based model demonstrated superior performance (AUC, 0.825; 95% CI, 0.808–0.852) compared to single-region models. The integration of clinical data further enhanced the model’s predictive capability (AUC 0.835; 95% CI, 0.818–0.869), significantly outperforming the clinical model alone (AUC 0.612; 95% CI, 0.574–0.646). MRMS-CNNFormer provides a robust, non-invasive approach for BCR prediction, offering valuable insights for personalized treatment planning and clinical decision making in PCa management.https://www.mdpi.com/2306-5354/12/5/538prostate cancerbiochemical recurrencemagnetic resonance imagingdeep learningpreoperative prognostic prediction |
| spellingShingle | Tao Lian Mengting Zhou Yangyang Shao Xiaqing Chen Yinghua Zhao Qianjin Feng MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI Bioengineering prostate cancer biochemical recurrence magnetic resonance imaging deep learning preoperative prognostic prediction |
| title | MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI |
| title_full | MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI |
| title_fullStr | MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI |
| title_full_unstemmed | MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI |
| title_short | MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI |
| title_sort | mrms cnnformer a novel framework for predicting the biochemical recurrence of prostate cancer on multi sequence mri |
| topic | prostate cancer biochemical recurrence magnetic resonance imaging deep learning preoperative prognostic prediction |
| url | https://www.mdpi.com/2306-5354/12/5/538 |
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