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.

Development of a multimodal mobile colposcope for real-time cervical cancer detection.

Cervical cancer remains a leading cause of cancer death among women in low-and middle-income countries. Globally, cervical cancer prevention programs are hampered by a lack of resources, infrastructure, and personnel. We describe a multimodal mobile colposcope (MMC) designed to diagnose precancerous cervical lesions at the point-of-care without the need for biopsy. The MMC integrates two complementary imaging systems: 1) a commercially available colposcope and 2) a high speed, high-resolution, fiber-optic microendoscope (HRME). Combining these two image modalities allows, for the first time, the ability to locate suspicious cervical lesions using widefield imaging and then to obtain co-registered high-resolution images across an entire lesion. The MMC overcomes limitations of high-resolution imaging alone; widefield imaging can be used to guide the placement of the high-resolution imaging probe at clinically suspicious regions and co-registered, mosaicked high-resolution images effectively increase the field of view of high-resolution imaging. Representative data collected from patients referred for colposcopy at Barretos Cancer Hospital in Brazil, including 22,800 high resolution images and 9,900 colposcope images, illustrate the ability of the MMC to identify abnormal cervical regions, image suspicious areas with subcellular resolution, and distinguish between high-grade and low-grade dysplasia.

Algorithm to quantify nuclear features and confidence intervals for classification of oral neoplasia from high-resolution optical images.

Purpose: In vivo optical imaging technologies like high-resolution microendoscopy (HRME) can image nuclei of the oral epithelium. In principle, automated algorithms can then calculate nuclear features to distinguish neoplastic from benign tissue. However, images frequently contain regions without visible nuclei, due to biological and technical factors, decreasing the data available to and accuracy of image analysis algorithms. Approach: We developed the nuclear density-confidence interval (ND-CI) algorithm to determine if an HRME image contains sufficient nuclei for classification, or if a better image is required. The algorithm uses a convolutional neural network to exclude image regions without visible nuclei. Then the remaining regions are used to estimate a confidence interval (CI) for the number of abnormal nuclei per mm 2 , a feature used by a previously developed algorithm (called the ND algorithm), to classify images as benign or neoplastic. The range of the CI determines whether the ND-CI algorithm can classify an image with confidence, and if so, the predicted category. The ND and ND-CI algorithm were compared by calculating their positive predictive value (PPV) and negative predictive value (NPV) on 82 oral biopsies with histopathologically confirmed diagnoses. Results: After excluding the images that could not be classified with confidence, the ND-CI algorithm had higher PPV (65% versus 59%) and NPV (78% versus 75%) than the ND algorithm. Conclusions: The ND-CI algorithm could improve the real-time classification of HRME images of the oral epithelium by informing the user if an improved image is required for diagnosis.

Prospective evaluation of oral premalignant lesions using a multimodal imaging system: a pilot study.

BACKGROUND: Multimodal optical imaging, incorporating reflectance and fluorescence modalities, is a promising tool to detect oral premalignant lesions in real-time. METHODS: Images were acquired from 171 sites in 66 patient visits for clinical evaluation of oral lesions. An automated algorithm was used to classify lesions as high- or low-risk for neoplasia. Biopsies were acquired at clinically indicated sites and those classified as high-risk by imaging, at the surgeon's discretion. RESULTS: Twenty sites were biopsied based on clinical examination or imaging. Of these, 12 were indicated clinically and by imaging; 58% were moderate dysplasia or worse. Four biopsies were indicated by imaging evaluation only; 75% were moderate dysplasia or worse. Finally, four biopsies were indicated by clinical evaluation only; 75% were moderate dysplasia or worse. CONCLUSION: Multimodal imaging identified more cases of high-grade dysplasia than clinical evaluation, and can improve detection of high grade precancer in patients with oral lesions.

Autofluorescence Imaging to Monitor the Progression of Oral Potentially Malignant Disorders.

Patients with oral potentially malignant disorders (OPMD) must undergo regular clinical surveillance to ensure that any progression to malignancy is detected promptly. Autofluorescence imaging (AFI) is an optical modality that can assist clinicians in detecting early cancers and high-grade dysplasia. Patients with OPMD undergoing surveillance for the development of oral cancer were examined using AFI at successive clinic visits. Autofluorescence images acquired at 133 clinical visits from sites in 15 patients who met inclusion criteria were analyzed quantitatively using an algorithm to calculate the red-to-green pixel intensity (RG ratio). A quantitative AFI threshold for high risk of progression was defined based on the RG ratio and was compared with expert clinical impression and with histopathology when available. Patients were divided into two groups based on their endpoint: surveillance (n = 6) or surgery (n = 9). In the surveillance group, 0 of 6 (0%) of patients were clinically identified as high risk for progression prior to the study endpoint, whereas 1 of 6 (17%) of patients were deemed at high risk for progression based on AFI during the same time period. In the surgery group, 9 of 9 (100%) of patients were clinically identified as high risk prior to the study endpoint, whereas 8 of 9 (89%) of patients were at high risk for progression based on AFI during the same time period. AFI results tracked over time were comparable with expert clinical impression in these patient groups. AFI has the potential to aid clinicians in noninvasively monitoring oral precancer and evaluating OPMDs that require increased surveillance.

Development of an integrated multimodal optical imaging system with real-time image analysis for the evaluation of oral premalignant lesions.

Oral premalignant lesions (OPLs), such as leukoplakia, are at risk of malignant transformation to oral cancer. Clinicians can elect to biopsy OPLs and assess them for dysplasia, a marker of increased risk. However, it is challenging to decide which OPLs need a biopsy and to select a biopsy site. We developed a multimodal optical imaging system (MMIS) that fully integrates the acquisition, display, and analysis of macroscopic white-light (WL), autofluorescence (AF), and high-resolution microendoscopy (HRME) images to noninvasively evaluate OPLs. WL and AF images identify suspicious regions with high sensitivity, which are explored at higher resolution with the HRME to improve specificity. Key features include a heat map that delineates suspicious regions according to AF images, and real-time image analysis algorithms that predict pathologic diagnosis at imaged sites. Representative examples from ongoing studies of the MMIS demonstrate its ability to identify high-grade dysplasia in OPLs that are not clinically suspicious, and to avoid unnecessary biopsies of benign OPLs that are clinically suspicious. The MMIS successfully integrates optical imaging approaches (WL, AF, and HRME) at multiple scales for the noninvasive evaluation of OPLs.

In Vivo Multimodal Optical Imaging: Improved Detection of Oral Dysplasia in Low-Risk Oral Mucosal Lesions.

Early detection of oral cancer and oral premalignant lesions (OPL) containing dysplasia could improve oral cancer outcomes. However, general dental practitioners have difficulty distinguishing dysplastic OPLs from confounder oral mucosal lesions in low-risk populations. We evaluated the ability of two optical imaging technologies, autofluorescence imaging (AFI) and high-resolution microendoscopy (HRME), to diagnose moderate dysplasia or worse (ModDys+) in 56 oral mucosal lesions in a low-risk patient population, using histopathology as the gold standard, and in 46 clinically normal sites. AFI correctly diagnosed 91% of ModDys+ lesions, 89% of clinically normal sites, and 33% of benign lesions. Benign lesions with severe inflammation were less likely to be correctly diagnosed by AFI (13%) than those without (42%). Multimodal imaging (AFI+HRME) had higher accuracy than either modality alone; 91% of ModDys+ lesions, 93% of clinically normal sites, and 64% of benign lesions were correctly diagnosed. Photos of the 56 lesions were evaluated by 28 dentists of varied training levels, including 26 dental residents. We compared the area under the receiver operator curve (AUC) of clinical impression alone to clinical impression plus AFI and clinical impression plus multimodal imaging using k-Nearest Neighbors models. The mean AUC of the dental residents was 0.71 (range: 0.45-0.86). The addition of AFI alone to clinical impression slightly lowered the mean AUC (0.68; range: 0.40-0.82), whereas the addition of multimodal imaging to clinical impression increased the mean AUC (0.79; range: 0.61-0.90). On the basis of these findings, multimodal imaging could improve the evaluation of oral mucosal lesions in community dental settings. Cancer Prev Res; 11(8); 465-76. ©2018 AACR.