{"title":"MRMS-CNNFormer:多序列MRI预测前列腺癌生化复发的新框架。","authors":"Tao Lian, Mengting Zhou, Yangyang Shao, Xiaqing Chen, Yinghua Zhao, Qianjin Feng","doi":"10.3390/bioengineering12050538","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 5","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12109125/pdf/","citationCount":"0","resultStr":"{\"title\":\"MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI.\",\"authors\":\"Tao Lian, Mengting Zhou, Yangyang Shao, Xiaqing Chen, Yinghua Zhao, Qianjin Feng\",\"doi\":\"10.3390/bioengineering12050538\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":8874,\"journal\":{\"name\":\"Bioengineering\",\"volume\":\"12 5\",\"pages\":\"\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2025-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12109125/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioengineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/bioengineering12050538\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioengineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/bioengineering12050538","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
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 multi-region (intratumoral, peritumoral, and periprostatic) and multi-sequence 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 CNN-based encoder for imaging feature extraction, followed by a transformer-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 (n = 146) and validation (n = 36) sets, while center C patients (n = 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.
期刊介绍:
Aims
Bioengineering (ISSN 2306-5354) provides an advanced forum for the science and technology of bioengineering. It publishes original research papers, comprehensive reviews, communications and case reports. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. All aspects of bioengineering are welcomed from theoretical concepts to education and applications. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. There are, in addition, four key features of this Journal:
● We are introducing a new concept in scientific and technical publications “The Translational Case Report in Bioengineering”. It is a descriptive explanatory analysis of a transformative or translational event. Understanding that the goal of bioengineering scholarship is to advance towards a transformative or clinical solution to an identified transformative/clinical need, the translational case report is used to explore causation in order to find underlying principles that may guide other similar transformative/translational undertakings.
● Manuscripts regarding research proposals and research ideas will be particularly welcomed.
● Electronic files and software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
● We also accept manuscripts communicating to a broader audience with regard to research projects financed with public funds.
Scope
● Bionics and biological cybernetics: implantology; bio–abio interfaces
● Bioelectronics: wearable electronics; implantable electronics; “more than Moore” electronics; bioelectronics devices
● Bioprocess and biosystems engineering and applications: bioprocess design; biocatalysis; bioseparation and bioreactors; bioinformatics; bioenergy; etc.
● Biomolecular, cellular and tissue engineering and applications: tissue engineering; chromosome engineering; embryo engineering; cellular, molecular and synthetic biology; metabolic engineering; bio-nanotechnology; micro/nano technologies; genetic engineering; transgenic technology
● Biomedical engineering and applications: biomechatronics; biomedical electronics; biomechanics; biomaterials; biomimetics; biomedical diagnostics; biomedical therapy; biomedical devices; sensors and circuits; biomedical imaging and medical information systems; implants and regenerative medicine; neurotechnology; clinical engineering; rehabilitation engineering
● Biochemical engineering and applications: metabolic pathway engineering; modeling and simulation
● Translational bioengineering