Detecting MRI-Invisible Prostate Cancers Using a Weakly Supervised Deep Learning Model.

IF 3.3 Q2 ENGINEERING, BIOMEDICAL

International Journal of Biomedical Imaging Pub Date : 2024-03-19 eCollection Date: 2024-01-01 DOI:10.1155/2024/2741986

Yao Zheng, Jingliang Zhang, Dong Huang, Xiaoshuo Hao, Weijun Qin, Yang Liu

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引用次数: 0

Abstract

Background: MRI is an important tool for accurate detection and targeted biopsy of prostate lesions. However, the imaging appearances of some prostate cancers are similar to those of the surrounding normal tissue on MRI, which are referred to as MRI-invisible prostate cancers (MIPCas). The detection of MIPCas remains challenging and requires extensive systematic biopsy for identification. In this study, we developed a weakly supervised UNet (WSUNet) to detect MIPCas.

Methods: The study included 777 patients (training set: 600; testing set: 177), all of them underwent comprehensive prostate biopsies using an MRI-ultrasound fusion system. MIPCas were identified in MRI based on the Gleason grade (≥7) from known systematic biopsy results.

Results: The WSUNet model underwent validation through systematic biopsy in the testing set with an AUC of 0.764 (95% CI: 0.728-0.798). Furthermore, WSUNet exhibited a statistically significant precision improvement of 91.3% (p < 0.01) over conventional systematic biopsy methods in the testing set. This improvement resulted in a substantial 47.6% (p < 0.01) decrease in unnecessary biopsy needles, while maintaining the same number of positively identified cores as in the original systematic biopsy.

Conclusions: In conclusion, the proposed WSUNet could effectively detect MIPCas, thereby reducing unnecessary biopsies.

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利用弱监督深度学习模型检测核磁共振成像看不见的前列腺癌

背景：磁共振成像是准确检测前列腺病变并进行有针对性活检的重要工具。然而，一些前列腺癌在核磁共振成像上的表现与周围正常组织相似，被称为核磁共振成像不可见前列腺癌（MIPCas）。MIPCas 的检测仍具有挑战性，需要进行广泛的系统活检才能识别。在这项研究中，我们开发了一种弱监督前列腺癌检测网络（WSUNet）来检测前列腺癌：研究纳入了 777 例患者（训练集：600 例；测试集：177 例），所有患者均使用核磁共振成像-超声波融合系统进行了全面的前列腺活检。根据已知系统活检结果中的格里森分级（≥7级）在核磁共振成像中识别出MIPCas：WSUNet模型通过测试集中的系统活检进行了验证，其AUC为0.764（95% CI：0.728-0.798）。此外，在测试集中，WSUNet 比传统的系统活检方法在统计学上显著提高了 91.3%（p < 0.01）。这一改进使不必要的活检针大幅减少了 47.6% (p < 0.01)，同时保持了与原始系统性活检相同的阳性鉴定核心数量：总之，拟议的 WSUNet 能有效检测 MIPCas，从而减少不必要的活检。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biomedical Imaging ENGINEERING, BIOMEDICAL-

CiteScore

12.00

自引率

0.00%

发文量

审稿时长

20 weeks

期刊介绍： The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics