PDF(Pubmed)
Abstract：
UNASSIGNED: Urine cytology is an important non-invasive examination for urothelial carcinoma (UC) diagnosis and follow-up. We aimed to explore whether artificial intelligence (AI) can enhance the sensitivity of urine cytology and help avoid unnecessary endoscopy.
UNASSIGNED: In this multicentre diagnostic study, consecutive patients who underwent liquid-based urine cytology examinations at four hospitals in China were included for model development and validation. Patients who declined surgery and lacked associated histopathology results, those diagnosed with rare subtype tumours of the urinary tract, or had low-quality images were excluded from the study. All liquid-based cytology slides were scanned into whole-slide images (WSIs) at 40 × magnification and the WSI-labels were derived from the corresponding histopathology results. The Precision Urine Cytology AI Solution (PUCAS) was composed of three distinct stages (patch extraction, features extraction, and classification diagnosis) and was trained to identify important WSI features associated with UC diagnosis. The diagnostic sensitivity was mainly used to validate the performance of PUCAS in retrospective and prospective validation cohorts. This study is registered with the ChiCTR, ChiCTR2300073192.
UNASSIGNED: Between January 1, 2018 and October 31, 2022, 2641 patients were retrospectively recruited in the training cohort, and 2335 in retrospective validation cohorts; 400 eligible patients were enrolled in the prospective validation cohort between July 7, 2023 and September 15, 2023. The sensitivity of PUCAS ranged from 0.922 (95% CI: 0.811-0.978) to 1.000 (0.782-1.000) in retrospective validation cohorts, and was 0.896 (0.837-0.939) in prospective validation cohort. The PUCAS model also exhibited a good performance in detecting malignancy within atypical urothelial cells cases, with a sensitivity of over 0.84. In the recurrence detection scenario, PUCAS could reduce 57.5% of endoscopy use with a negative predictive value of 96.4%.
UNASSIGNED: PUCAS may help to improve the sensitivity of urine cytology, reduce misdiagnoses of UC, avoid unnecessary endoscopy, and reduce the clinical burden in resource-limited areas. The further validation in other countries is needed.
UNASSIGNED: National Natural Science Foundation of China; Key Program of the National Natural Science Foundation of China; the National Science Foundation for Distinguished Young Scholars; the Science and Technology Planning Project of Guangdong Province; the National Key Research and Development Programme of China; Guangdong Provincial Clinical Research Centre for Urological Diseases.
UNASSIGNED: In this multicentre diagnostic study, consecutive patients who underwent liquid-based urine cytology examinations at four hospitals in China were included for model development and validation. Patients who declined surgery and lacked associated histopathology results, those diagnosed with rare subtype tumours of the urinary tract, or had low-quality images were excluded from the study. All liquid-based cytology slides were scanned into whole-slide images (WSIs) at 40 × magnification and the WSI-labels were derived from the corresponding histopathology results. The Precision Urine Cytology AI Solution (PUCAS) was composed of three distinct stages (patch extraction, features extraction, and classification diagnosis) and was trained to identify important WSI features associated with UC diagnosis. The diagnostic sensitivity was mainly used to validate the performance of PUCAS in retrospective and prospective validation cohorts. This study is registered with the ChiCTR, ChiCTR2300073192.
UNASSIGNED: Between January 1, 2018 and October 31, 2022, 2641 patients were retrospectively recruited in the training cohort, and 2335 in retrospective validation cohorts; 400 eligible patients were enrolled in the prospective validation cohort between July 7, 2023 and September 15, 2023. The sensitivity of PUCAS ranged from 0.922 (95% CI: 0.811-0.978) to 1.000 (0.782-1.000) in retrospective validation cohorts, and was 0.896 (0.837-0.939) in prospective validation cohort. The PUCAS model also exhibited a good performance in detecting malignancy within atypical urothelial cells cases, with a sensitivity of over 0.84. In the recurrence detection scenario, PUCAS could reduce 57.5% of endoscopy use with a negative predictive value of 96.4%.
UNASSIGNED: PUCAS may help to improve the sensitivity of urine cytology, reduce misdiagnoses of UC, avoid unnecessary endoscopy, and reduce the clinical burden in resource-limited areas. The further validation in other countries is needed.
UNASSIGNED: National Natural Science Foundation of China; Key Program of the National Natural Science Foundation of China; the National Science Foundation for Distinguished Young Scholars; the Science and Technology Planning Project of Guangdong Province; the National Key Research and Development Programme of China; Guangdong Provincial Clinical Research Centre for Urological Diseases.
摘要：
■尿细胞学检查是尿路上皮癌（UC）诊断和随访的重要非侵入性检查。我们旨在探索人工智能（AI）是否可以提高尿液细胞学的敏感性并帮助避免不必要的内窥镜检查。
■在这项多中心诊断研究中，纳入了在中国4家医院接受液基尿液细胞学检查的连续患者进行模型开发和验证.拒绝手术且缺乏相关组织病理学结果的患者，那些被诊断患有罕见的泌尿道亚型肿瘤的人，或低质量图像被排除在研究之外.以40倍放大倍数将所有基于液体的细胞学载玻片扫描成全载玻片图像（WSI），并从相应的组织病理学结果得出WSI标记。精确尿液细胞学AI解决方案（PUCAS）由三个不同的阶段组成（贴片提取，特征提取,和分类诊断），并接受培训以识别与UC诊断相关的重要WSI特征。诊断敏感性主要用于验证PUCAS在回顾性和前瞻性验证队列中的表现。这项研究在ChiCTR注册，ChiCTR2300073192。
■在2018年1月1日至2022年10月31日之间，对2641例患者进行了回顾性招募。和2335例回顾性验证队列;在2023年7月7日至2023年9月15日期间,400例符合条件的患者被纳入前瞻性验证队列.在回顾性验证队列中，PUCAS的敏感性范围为0.922（95％CI：0.811-0.978）至1.000（0.782-1.000），在前瞻性验证队列中，为0.896（0.837-0.939）。PUCAS模型在检测非典型尿路上皮细胞病例中的恶性肿瘤方面也表现出良好的性能，灵敏度超过0.84。在复发检测方案中，PUCAS可以减少57.5％的内窥镜检查使用，阴性预测值为96.4％。
■PUCAS可能有助于提高尿细胞学的敏感性，减少UC的误诊，避免不必要的内窥镜检查，减少资源有限地区的临床负担。需要在其他国家进行进一步验证。
■国家自然科学基金；国家自然科学基金重点项目；国家杰出青年科学基金；广东省科技规划项目；国家重点研究发展计划；广东省泌尿外科疾病临床研究中心。
■在这项多中心诊断研究中，纳入了在中国4家医院接受液基尿液细胞学检查的连续患者进行模型开发和验证.拒绝手术且缺乏相关组织病理学结果的患者，那些被诊断患有罕见的泌尿道亚型肿瘤的人，或低质量图像被排除在研究之外.以40倍放大倍数将所有基于液体的细胞学载玻片扫描成全载玻片图像（WSI），并从相应的组织病理学结果得出WSI标记。精确尿液细胞学AI解决方案（PUCAS）由三个不同的阶段组成（贴片提取，特征提取,和分类诊断），并接受培训以识别与UC诊断相关的重要WSI特征。诊断敏感性主要用于验证PUCAS在回顾性和前瞻性验证队列中的表现。这项研究在ChiCTR注册，ChiCTR2300073192。
■在2018年1月1日至2022年10月31日之间，对2641例患者进行了回顾性招募。和2335例回顾性验证队列;在2023年7月7日至2023年9月15日期间,400例符合条件的患者被纳入前瞻性验证队列.在回顾性验证队列中，PUCAS的敏感性范围为0.922（95％CI：0.811-0.978）至1.000（0.782-1.000），在前瞻性验证队列中，为0.896（0.837-0.939）。PUCAS模型在检测非典型尿路上皮细胞病例中的恶性肿瘤方面也表现出良好的性能，灵敏度超过0.84。在复发检测方案中，PUCAS可以减少57.5％的内窥镜检查使用，阴性预测值为96.4％。
■PUCAS可能有助于提高尿细胞学的敏感性，减少UC的误诊，避免不必要的内窥镜检查，减少资源有限地区的临床负担。需要在其他国家进行进一步验证。
■国家自然科学基金；国家自然科学基金重点项目；国家杰出青年科学基金；广东省科技规划项目；国家重点研究发展计划；广东省泌尿外科疾病临床研究中心。
