Multiscale Optical Imaging Fusion for Cervical Precancer Diagnosis: Integrating Widefield Colposcopy and High-Resolution Endomicroscopy.

OBJECTIVE: Early detection and treatment of cervical precancers can prevent disease progression. However, in low-resource communities with a high incidence of cervical cancer, high equipment costs and a shortage of specialists hinder preventative strategies. This manuscript presents a low-cost multiscale in vivo optical imaging system coupled with a computer-aided diagnostic system that could enable accurate, real-time diagnosis of high-grade cervical precancers. METHODS: The system combines portable colposcopy and high-resolution endomicroscopy (HRME) to acquire spatially registered widefield and microscopy videos. A multiscale imaging fusion network (MSFN) was developed to identify cervical intraepithelial neoplasia grade 2 or more severe (CIN 2+). The MSFN automatically identifies and segments the ectocervix and lesions from colposcopy images, extracts nuclear morphology features from HRME videos, and integrates the colposcopy and HRME information. RESULTS: With a threshold value set to achieve sensitivity equal to clinical impression (0.98 [p = 1.0]), the MSFN achieved a significantly higher specificity than clinical impression (0.75 vs. 0.43, p = 0.000006). CONCLUSION: Our findings show that multiscale optical imaging of the cervix allows the highly sensitive and specific detection of high-grade precancers. SIGNIFICANCE: The multiscale imaging system and MSFN could facilitate the accurate, real-time diagnosis of cervical precancers in low-resource settings.

Cervical lesion assessment using real-time microendoscopy image analysis in Brazil: The CLARA study.

We conducted a prospective evaluation of the diagnostic performance of high-resolution microendoscopy (HRME) to detect cervical intraepithelial neoplasia (CIN) in women with abnormal screening tests. Study participants underwent colposcopy, HRME and cervical biopsy. The prospective diagnostic performance of HRME using an automated morphologic image analysis algorithm was compared to that of colposcopy using histopathologic detection of CIN as the gold standard. To assess the potential to further improve performance of HRME image analysis, we also conducted a retrospective analysis assessing performance of a multi-task convolutional neural network to segment and classify HRME images. One thousand four hundred eighty-six subjects completed the study; 435 (29%) subjects had CIN Grade 2 or more severe (CIN2+) diagnosis. HRME with morphologic image analysis for detection of CIN Grade 3 or more severe diagnoses (CIN3+) was similarly sensitive (95.6% vs 96.2%, P = .81) and specific (56.6% vs 58.7%, P = .18) as colposcopy. HRME with morphologic image analysis for detection of CIN2+ was slightly less sensitive (91.7% vs 95.6%, P < .01) and specific (59.7% vs 63.4%, P = .02) than colposcopy. Images from 870 subjects were used to train a multi-task convolutional neural network-based algorithm and images from the remaining 616 were used to validate its performance. There were no significant differences in the sensitivity and specificity of HRME with neural network analysis vs colposcopy for detection of CIN2+ or CIN3+. Using a neural network-based algorithm, HRME has comparable sensitivity and specificity to colposcopy for detection of CIN2+. HRME could provide a low-cost, point-of-care alternative to colposcopy and biopsy in the prevention of cervical cancer.

Diagnosing Cervical Neoplasia in Rural Brazil Using a Mobile Van Equipped with In Vivo Microscopy: A Cluster-Randomized Community Trial.

Cervical cancer is a leading cause of death in underserved areas of Brazil. This prospective randomized trial involved 200 women in southern/central Brazil with abnormal Papanicolaou tests. Participants were randomized by geographic cluster and referred for diagnostic evaluation either at a mobile van upon its scheduled visit to their local community, or at a central hospital. Participants in both arms underwent colposcopy, in vivo microscopy, and cervical biopsies. We compared rates of diagnostic follow-up completion between study arms, and also evaluated the diagnostic performance of in vivo microscopy compared with colposcopy. There was a 23% absolute and 37% relative increase in diagnostic follow-up completion rates for patients referred to the mobile van (102/117, 87%) compared with the central hospital (53/83, 64%; P = 0.0001; risk ratio = 1.37, 95% CI, 1.14-1.63). In 229 cervical sites in 144 patients, colposcopic examination identified sites diagnosed as cervical intraepithelial neoplasia grade 2 or more severe (CIN2+; 85 sites) with a sensitivity of 94% (95% CI, 87%-98%) and specificity of 50% (95% CI, 42%-58%). In vivo microscopy with real-time automated image analysis identified CIN2+ with a sensitivity of 92% (95% CI, 84%-97%) and specificity of 48% (95% CI, 40%-56%). Women referred to the mobile van were more likely to complete their diagnostic follow-up compared with those referred to a central hospital, without compromise in clinical care. In vivo microscopy in a mobile van provides automated diagnostic imaging with sensitivity and specificity similar to colposcopy. Cancer Prev Res; 11(6); 359-70. ©2018 AACR.