AUTOMATED IN VIVO HIGH-RESOLUTION IMAGING TO DETECT HPV-ASSOCIATED ANAL PRECANCER IN PERSONS LIVING WITH HIV

BACKGROUND 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 interepithelial 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 coupled with a novel machine learning algorithm for the automated, in vivo diagnosis of anal precancer. METHODS The high-resolution microendoscope (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% w/v proflavine, a topical contrast agent. HRME images were analyzed by a multi-task deep learning network (MTN) that computed the probability of AIN 2+ for each HRME image. RESULTS 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. CONCLUSIONS When used in combination with HRA, this system could facilitate more selective biopsies and promote same-day “see and treat” AIN2+ treatment options by enabling real-time diagnosis.