Department of Bioengineering, Rice University, Houston, Texas, USA.
Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Clin Transl Gastroenterol. 2023 Feb 1;14(2):e00558. doi: 10.14309/ctg.0000000000000558.
In the United States, the effectiveness of anal cancer screening programs has been limited by a lack of trained professionals proficient in high-resolution anoscopy (HRA) and a high patient lost-to-follow-up rate between diagnosis and treatment. Simplifying anal intraepithelial neoplasia grade 2 or more severe (AIN 2+) detection could radically improve the access and efficiency of anal cancer prevention. Novel optical imaging providing point-of-care diagnoses could substantially improve existing HRA and histology-based diagnosis. This work aims to demonstrate the potential of high-resolution microendoscopy (HRME) coupled with a novel machine learning algorithm for the automated, in vivo diagnosis of anal precancer.
The HRME, a fiber-optic fluorescence microscope, was used to capture real-time images of anal squamous epithelial nuclei. Nuclear staining is achieved using 0.01% wt/vol proflavine, a topical contrast agent. HRME images were analyzed by a multitask deep learning network (MTN) that computed the probability of AIN 2+ for each HRME image.
The study accrued data from 77 people living with HIV. The MTN achieved an area under the receiver operating curve of 0.84 for detection of AIN 2+. At the AIN 2+ probability cutoff of 0.212, the MTN achieved comparable performance to expert HRA impression with a sensitivity of 0.92 ( P = 0.68) and specificity of 0.60 ( P = 0.48) when using histopathology as the gold standard.
When used in combination with HRA, this system could facilitate more selective biopsies and promote same-day AIN2+ treatment options by enabling real-time diagnosis.
在美国，由于缺乏精通高分辨率肛门镜检查（HRA）的专业人员，以及在诊断和治疗之间存在较高的患者失访率，肛门癌筛查计划的效果受到限制。简化高级别肛门上皮内瘤变（AIN 2+）的检测方法可以极大地提高肛门癌预防的可及性和效率。新型光学成像技术提供即时诊断，可大大改善现有的 HRA 和基于组织学的诊断。本研究旨在展示高分辨率显微内镜（HRME）结合新型机器学习算法在自动、体内诊断肛门癌前病变方面的潜力。
HRME 是一种光纤荧光显微镜，用于实时采集肛门鳞状上皮细胞核的图像。细胞核染色是通过使用 0.01%wt/vol 吖啶橙实现的，这是一种局部对比剂。HRME 图像由一个多任务深度学习网络（MTN）进行分析，该网络计算每个 HRME 图像发生 AIN 2+的概率。
该研究共纳入了 77 名 HIV 感染者。MTN 在检测 AIN 2+方面的曲线下面积为 0.84。当 MTN 将 AIN 2+的概率截止值设定为 0.212 时，与专家 HRA 印象相比，该模型的表现相当，以组织病理学为金标准，其敏感性为 0.92（P=0.68），特异性为 0.60（P=0.48）。
当与 HRA 结合使用时，该系统可以通过实时诊断来促进更有针对性的活检，并提供当天的 AIN2+治疗选择。
