Dataset Information

Comparison of Colposcopic Impression Based on Live Colposcopy and Evaluation of Static Digital Images.

ABSTRACT: OBJECTIVE:The aim of the study was to evaluate the agreement and compare diagnostic accuracy of colposcopic impressions from live colposcopy versus evaluation of static digital images. MATERIALS AND METHODS:Live impressions and corresponding static images obtained during colposcopy of 690 women were independently compared. Diagnostic accuracy was calculated for colposcopic impressions from both methods, varying hypothetical thresholds for colposcopically directed cervical biopsies (acetowhitening or worse, low grade or worse, high grade or worse). Stratified analyses investigated the impact of referral cytology, human papillomavirus 16 infection, and age on colposcopic impression. RESULTS:Overall agreement between live and static colposcopic visualization was 43.0% (? = 0.20; 95% CI = 0.14-0.26) over normal, acetowhitening, low-grade, and high-grade impressions. Classification of acetowhitening or worse impressions showed the highest agreement (92.2%; ? = 0.39; 95% CI = 0.21-0.57); both methods achieved more than 95% sensitivity for CIN 2+. Agreement between live and static colposcopic visualization was 69.3% for rating low-grade or worse impressions (? = 0.23; 95% CI = 0.14-0.33) and 71% when rating high-grade impressions (? = 0.33; 95% CI = 0.24-0.42). Live colposcopic impressions were more likely to be rated low grade or worse (p < .01; odds ratio = 3.5; 95% CI = 2.4-5.0), yielding higher sensitivity for CIN 2+ at this threshold than static image assessment (95.4% vs 79.8%, p < .01). Overall, colposcopic impressions were more likely rated high grade on live assessment among women referred with high-grade cytology (odds ratio = 3.3; 95% CI = 1.8-6.4), significantly improving the sensitivity for CIN 2+ (66.3% vs 48.5%, p < .01). CONCLUSIONS:Colposcopic impressions of acetowhitening or worse are highly sensitive for identifying cervical precancers and reproducible on static image-based pattern recognition.

SUBMITTER: Liu AH

PROVIDER: S-EPMC4808516 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Comparison of Colposcopic Impression Based on Live Colposcopy and Evaluation of Static Digital Images.

Liu Angela H AH Liu Angela H AH Liu Angela H AH Gold Michael A MA Schiffman Mark M Smith Katie M KM Zuna Rosemary E RE Dunn S Terence ST Gage Julia C JC Walker Joan L JL Wentzensen Nicolas N

Journal of lower genital tract disease 20160401 2

<h4>Objective</h4>The aim of the study was to evaluate the agreement and compare diagnostic accuracy of colposcopic impressions from live colposcopy versus evaluation of static digital images.<h4>Materials and methods</h4>Live impressions and corresponding static images obtained during colposcopy of 690 women were independently compared. Diagnostic accuracy was calculated for colposcopic impressions from both methods, varying hypothetical thresholds for colposcopically directed cervical biopsies ...[more]

PMID: 27015261

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Project description:BackgroundThis cross-sectional pilot study evaluates diagnostic accuracy of live colposcopy versus static image Swede-score evaluation for detecting significant precancerous cervical lesions greater than, or equal to grade 2 severity (CIN2+).MethodsVIA or HrHPV positive women were examined using a mobile colposcope, in a rural clinic in Kolkata, India. Live versus static Swede-score colposcopy assessments were made independently. All assessments were by gynecologists, junior or expert. Static image assessors were blinded to live scoring, patient information and final histopathology result. Primary outcome was the ability to detect CIN2+ lesions verified by directed biopsies. Diagnostic accuracy was calculated for live versus static Swede-score in detecting CIN2+ lesions, as well as for interclass correlation.Results495 images from 94 VIA positive women were evaluated in this study. Thirteen women (13.9%) had CIN2+ on biopsy. No significant difference was found in the detection of CIN2+ lesions between live and static assessors (area under curve = 0.69 versus 0.71, p = 0.63). A Swede-score of 4+, had a sensitivity of 76.9% (95% CI 46.2-95.0%) and 84.6% (95% CI 54.6-98.1%), for live- and static-image assessment respectively. The corresponding positive predictive values were found to be 90.9% (95% CI 75.7-98.1%) and 92.6% (95% CI 75.7-99.1%). The interclass correlation was good (kappa statistic = 0.60) for the senior static assessors.ConclusionsSwede-score evaluation of static colposcopy images was found to reliably detect CIN2+ lesions in this study. Larger studies are needed to further develop the colposcopy telemedicine concept which may offer reliable guidance in management where direct specialist input is not available.Trial registrationEthical approval of the study was obtained by the Chittaranjan National Cancer Institute (CNCI) Human Research Ethics Committee (4.311/27/2014). The trial was retrospectively registered in the Clinical Trails Registry of India CTRI/2018/03/012470 .

Project description:BackgroundColposcopy is widely used to detect cervical cancer, but developing countries lack the experienced colposcopists necessary for accurate diagnosis. Artificial intelligence (AI) is being widely used in computer-aided diagnosis (CAD) systems. In this study, we developed and validated a CAD model based on deep learning to classify cervical lesions on colposcopy images.MethodsPatient data, including clinical information, colposcopy images, and pathological results, were collected from Qilu Hospital. The study included 15,276 images from 7,530 patients. We performed two tasks in this study: normal cervix (NC) vs. low grade squamous intraepithelial lesion or worse (LSIL+) and high-grade squamous intraepithelial lesion (HSIL)- vs. HSIL+. The residual neural network (ResNet) probability was calculated for each patient to reflect the probability of lesions through a ResNet model. Next, a combination model was constructed by incorporating the ResNet probability and clinical features. We divided the dataset into a training set, validation set, and testing set at a ratio of 7:1:2. Finally, we randomly selected 300 patients from the testing set and compared the results with the diagnosis of a senior colposcopist and a junior colposcopist.ResultsThe model that combines ResNet and clinical features performs better than ResNet alone. In the classification of NC and LSIL+, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.953, 0.886, 0.932, 0.846, 0.838, and 0.936, respectively. In the classification of HSIL- and HSIL+, the AUC, accuracy, sensitivity, specificity, PPV, and NPV were 0.900, 0.807, 0.823, 0.800, 0.618, and 0.920, respectively. In the two classification tasks, the diagnostic performance of the model was determined to be comparable to that of the senior colposcopist and exhibited a stronger diagnostic performance than the junior colposcopist.ConclusionsThe CAD system for cervical lesion diagnosis based on deep learning performs well in the classification of cervical lesions and can provide an objective diagnostic basis for colposcopists.

Project description:Background Deep learning has presented considerable potential and is gaining more importance in computer assisted diagnosis. As the gold standard for pathologically diagnosing cervical intraepithelial lesions and invasive cervical cancer, colposcopy-guided biopsy faces challenges in improving accuracy and efficiency worldwide, especially in developing countries. To ease the heavy burden of cervical cancer screening, it is urgent to establish a scientific, accurate and efficient method for assisting diagnosis and biopsy. Methods The data were collected to establish three deep-learning-based models. For every case, one saline image, one acetic image, one iodine image and the corresponding clinical information, including age, the results of human papillomavirus testing and cytology, type of transformation zone, and pathologic diagnosis, were collected. The dataset was proportionally divided into three subsets including the training set, the test set and the validation set, at a ratio of 8:1:1. The validation set was used to evaluate model performance. After model establishment, an independent dataset of high-definition images was collected to further evaluate the model performance. In addition, the comparison of diagnostic accuracy between colposcopists and models weas performed. Results The sensitivity, specificity and accuracy of the classification model to differentiate negative cases from positive cases were 85.38%, 82.62% and 84.10% respectively, with an AUC of 0.93. The recall and DICE of the segmentation model to segment suspicious lesions in acetic images were 84.73% and 61.64%, with an average accuracy of 95.59%. Furthermore, 84.67% of high-grade lesions were detected by the acetic detection model. Compared to colposcopists, the diagnostic system performed better in ordinary colposcopy images but slightly unsatisfactory in high-definition images. Implications The deep learning-based diagnostic system could help assist colposcopy diagnosis and biopsy for HSILs.

Dataset Information

Comparison of Colposcopic Impression Based on Live Colposcopy and Evaluation of Static Digital Images.

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Comparison of Colposcopic Impression Based on Live Colposcopy and Evaluation of Static Digital Images.

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