Zheng Yao, Zhang Jingliang, Huang Dong, Hao Xiaoshuo, Qin Weijun, Liu Yang
School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an, Shaanxi, China.
Department of Urology, Xijing Hospital, Air Force Medical University, No. 127 Changle West Road, Xi'an, Shaanxi Province, China.
Int J Biomed Imaging. 2024 Mar 19;2024:2741986. doi: 10.1155/2024/2741986. eCollection 2024.
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.
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.
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% ( < 0.01) over conventional systematic biopsy methods in the testing set. This improvement resulted in a substantial 47.6% ( < 0.01) decrease in unnecessary biopsy needles, while maintaining the same number of positively identified cores as in the original systematic biopsy.
In conclusion, the proposed WSUNet could effectively detect MIPCas, thereby reducing unnecessary biopsies.
磁共振成像（MRI）是准确检测前列腺病变并进行靶向活检的重要工具。然而，一些前列腺癌在MRI上的影像表现与周围正常组织相似，这些被称为MRI隐匿性前列腺癌（MIPCas）。MIPCas的检测仍然具有挑战性，需要进行广泛的系统活检来识别。在本研究中，我们开发了一种弱监督UNet（WSUNet）来检测MIPCas。
该研究纳入了777例患者（训练集：600例；测试集：177例），所有患者均使用MRI-超声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的Gleason分级（≥7）在MRI中识别MIPCas。
WSUNet模型在测试集中通过系统活检进行了验证，AUC为0.764（95%CI：0.728 - 0.798）。此外，在测试集中，WSUNet相对于传统系统活检方法在统计学上表现出显著的精度提高，提高了91.3%（<0.01）。这种提高使得不必要的活检针数大幅减少了47.6%（<0.01），同时保持与原始系统活检中阳性识别核心数量相同。
总之，所提出的WSUNet可以有效检测MIPCas，从而减少不必要的活检。
