Diagnostic test of bioimpedance-based neural network algorithm in early cervical cancer.

Background: Colposcopy is a critical component of cervical cancer screening services, but the accuracy of colposcopy varies greatly due to the lack of standardized training for colposcopists and pathologists. Thus, to improve the accuracy of colposcopy in the detection of cervical lesions intelligently is urgent. Here, we explored the sensitivity and specificity of a bioimpedance-based neural network algorithm in distinguishing normal and precancerous cervical tissues.

Methods: Bioimpedance data were collected using a bioimpedance analyzer (Mscan1.0B, Sealand Technology, Chengdu, China) from the cervices of 102 female patients with abnormal cervical cytology (≥atypical squamous cells of undetermined significance) who required further colposcopy. Finally, the data of 106 samples from 37 patients were included, among which 85were used as the training set and 21 as the validation set. Using the biopsy pathology at each locus as the gold standard, the sensitivity, specificity, predictive value, likelihood ratio, and false positive and false negative rates of the bioimpedance-based neural network in identifying the normal and precancerous cervical tissues were calculated.

Results: The bioimpedance method had a sensitivity of 0.90 [95% confidence interval (CI): 0.54 to 0.99], specificity of 0.82 (95% CI: 0.48 to 0.97), positive predictive value of 0.82 (95% CI: 0.48 to 0.97), and a negative predictive value of 0.90 (95% CI: 0.54 to 0.99) in distinguishing normal and precancerous cervical tissues. The Kappa value was 0.72.

Conclusions: The bioimpedance method was an intelligent method with relative good sensitivity and specificity in distinguishing benign cervical tissue and precancerous lesions and can therefore be used as an adjunctive test to colposcopy to improve the detection of cervical lesions.