Computer-aided diagnosis of cervical dysplasia using colposcopic images.

Background: computer-aided diagnosis of medical images is becoming more significant in intelligent medicine. Colposcopy-guided biopsy with pathological diagnosis is the gold standard in diagnosing CIN and invasive cervical cancer. However, it struggles with its low sensitivity in differentiating cancer/HSIL from LSIL/normal, particularly in areas with a lack of skilled colposcopists and access to adequate medical resources. Methods: the model used the auto-segmented colposcopic images to extract color and texture features using the T-test method. It then augmented minority data using the SMOTE method to balance the skewed class distribution. Finally, it used an RBF-SVM to generate a preliminary output. The results, integrating the TCT, HPV tests, and age, were combined into a naïve Bayes classifier for cervical lesion diagnosis. Results: the multimodal machine learning model achieved physician-level performance (sensitivity: 51.2%, specificity: 86.9%, accuracy: 81.8%), and it could be interpreted by feature extraction and visualization. With the aid of the model, colposcopists improved the sensitivity from 53.7% to 70.7% with an acceptable specificity of 81.1% and accuracy of 79.6%. Conclusion: using a computer-aided diagnosis system, physicians could identify cancer/HSIL with greater sensitivity, which guided biopsy to take timely treatment.

An 8-gene DNA methylation signature predicts the recurrence risk of cervical cancer.

OBJECTIVE: This study examined the predictive utility of DNA methylation for cervical cancer recurrence. METHODS: DNA methylation and RNA expression data for patients with cervical cancer were downloaded from The Cancer Genome Atlas. Differentially methylated genes (DMGs) and differentially expressed genes were screened and extracted via correlation analysis. A support vector machine (SVM)-based recurrence prediction model was established using the selected DMGs. Cox regression analysis and receiver operating characteristic curve analysis were used for self-evaluation. The Gene Expression Omnibus (GEO) database was applied for external validation. Functional enrichment was determined using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. RESULTS: An eight-gene DNA methylation signature identified patients with a high risk of recurrence (area under the curve = 0.833). The SVM score was an independent risk factor for recurrence (hazard ratio [HR] = 0.418; 95% confidence interval [CI] = 0.26-0.67). The independent GEO database analysis further supported the result. CONCLUSION: An eight-gene DNA methylation signature predictive of cervical cancer recurrence was identified in this study, and this signature may help identify patients at high risk of recurrence and improve clinical treatment.

Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks.

OBJECTIVE: To explore an effective algorithm based on artificial neural network to pick correctly the minority of pregnant women with SLE suffering fetal loss outcomes from the majority with live birth and train a well behaved model as a clinical decision assistant. METHODS: We integrated the thoughts of comparative and focused study into the artificial neural network and presented an effective algorithm aiming at imbalanced learning in small dataset. RESULTS: We collected 469 non-trivial pregnant patients with SLE, where 420 had live-birth outcomes and the other 49 patients ended in fetal loss. A well trained imbalanced-learning model had a high sensitivity of 19/21 ([Formula: see text]) for the identification of patients with fetal loss outcomes. DISCUSSION: The misprediction of the two patients was explainable. Algorithm improvements in artificial neural network framework enhanced the identification in imbalanced learning problems and the external validation increased the reliability of algorithm. CONCLUSION: The well-trained model was fully qualified to assist healthcare providers to make timely and accurate decisions.