Hu Jia-Ying, Lin Zhen-Zhe, Ding Li, Zhang Zhi-Xing, Huang Wan-Ling, Huang Sha-Sha, Li Bin, Xie Xiao-Yan, Lu Ming-De, Deng Chun-Hua, Lin Hao-Tian, Gao Yong, Wang Zhu
Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China.
Asian J Androl. 2025 Mar 1;27(2):254-260. doi: 10.4103/aja202480. Epub 2024 Oct 4.
Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
基于睾丸活检的睾丸组织学是确定无精子症患者合适的睾丸精子提取手术以及预测精子获取结果的重要因素。因此，我们开发了一种深度学习（DL）模型，以建立睾丸灰度超声图像与睾丸组织学之间的关联。我们回顾性纳入了来自无精子症患者的二维睾丸灰度超声（2017年7月至2021年12月期间在中国广州中山大学附属第一医院的353名男性，共4357张图像），以开发DL模型。我们在常规睾丸精子提取过程中获取了睾丸组织学信息。我们的DL模型基于超声图像或融合数据（超声图像与相应的睾丸体积融合）进行训练，以区分病理状态下精子存在（SPP）和病理状态下精子缺失（SAP），并对SAP患者的成熟停滞（MA）和唯支持细胞综合征（SCOS）进行分类。使用受试者操作特征曲线下面积（AUC）、准确性、敏感性和特异性来分析模型性能。基于图像的DL在预测SPP（包括正常精子发生和精子发生低下）和SAP（包括MA和SCOS）方面，AUC为0.922（95%置信区间[CI]：0.908 - 0.935），敏感性为80.9%，特异性为84.6%，准确性为83.5%。在识别SCOS和MA方面，融合数据上的DL具有更好的诊断性能，AUC为0.979（95%CI：0.969 - 0.989），敏感性为89.7%，特异性为97.1%，准确性为92.1%。我们的研究为无精子症患者提供了一种预测睾丸组织学的非侵入性方法，这将避免不必要的睾丸活检。
