MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI.

IF 3.8 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Tao Lian, Mengting Zhou, Yangyang Shao, Xiaqing Chen, Yinghua Zhao, Qianjin Feng
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引用次数: 0

Abstract

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.

MRMS-CNNFormer:多序列MRI预测前列腺癌生化复发的新框架。
准确预测前列腺癌(PCa)术前生化复发(BCR)对优化治疗至关重要,需要明确关注肿瘤微环境(TME)。为了解决这个问题,我们开发了MRMS-CNNFormer,这是一个创新的框架,将二维多区域(肿瘤内、肿瘤周围和前列腺周围)和多序列磁共振成像(MRI)图像(脂肪抑制的t2加权成像(T2WI-FS)和弥散加权成像(DWI))与临床特征结合起来。该框架使用基于cnn的编码器进行图像特征提取,然后使用基于变压器的编码器进行多模态特征集成,最后使用全连接(FC)层进行最终的BCR预测。在这项多中心研究中(46例bcr阳性病例,186例bcr阴性病例),A中心和B中心的患者被分配到训练组(n = 146)和验证组(n = 36),而C中心的患者(n = 50)组成外部测试组。基于多区域mri的模型表现出优越的性能(AUC, 0.825;95% CI, 0.808-0.852)。临床数据的整合进一步增强了模型的预测能力(AUC 0.835;95% CI, 0.818-0.869),显著优于单纯临床模型(AUC 0.612;95% ci, 0.574-0.646)。MRMS-CNNFormer为BCR预测提供了一个强大的、无创的方法,为PCa管理的个性化治疗计划和临床决策提供了有价值的见解。
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来源期刊
Bioengineering
Bioengineering Chemical Engineering-Bioengineering
CiteScore
4.00
自引率
8.70%
发文量
661
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
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