Prediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models.

PURPOSE: This study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI). METHODS: Pre-treatment MRI images of patients with cervical cancer were collected retrospectively. An HPV DNA oncogene analysis was performed based on cervical biopsy specimens. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1) and T2-weighted images (T2WI). A third feature subset was created as a combined group by concatenating the CE-T1 and T2WI subsets. Feature selection was performed using Pearson's correlation coefficient and wrapper- based sequential-feature selection. Two models were built with each feature subset, using support vector machine (SVM) and logistic regression (LR) classifiers. The models were validated using a five-fold cross-validation technique and compared using Wilcoxon's signed rank and Friedman's tests. RESULTS: Forty-one patients were enrolled in the study (26 were positive for carcinogenic HPV oncogenes, and 15 were negative). A total of 851 features were extracted from each imaging sequence. After feature selection, 5, 17, and 20 features remained in the CE-T1, T2WI, and combined groups, respectively. The SVM models showed 83%, 95%, and 95% accuracy scores, and the LR models revealed 83%, 81%, and 92.5% accuracy scores in the CE-T1, T2WI, and combined groups, respectively. The SVM algorithm performed better than the LR algorithm in the T2WI feature subset (P = 0.005), and the feature sets in the T2WI and the combined group performed better than CE-T1 in the SVM model (P = 0.033 and 0.006, respectively). The combined group feature subset performed better than T2WI in the LR model (P = 0.023). CONCLUSION: Machine learning-based radiomics models based on pre-treatment MRI can detect carcinogenic HPV status with discriminative accuracy.

Effect of Adjuvant Chemotherapy in Stage III Cervical Cancer Patients Treated with Concurrent Chemoradiation: A Multicenter Study.

INTRODUCTION: A significant proportion of cervical cancer (CC) patients are diagnosed at a locally advanced stage. Concurrent chemoradiotherapy (CCRT) is the cornerstone of treatment for patients with locally advanced CC. However, the role of adjuvant chemotherapy (AC) after CCRT is controversial. In this study, we analyzed the efficacy of AC after CCRT in stage III CC patients. METHODS: We performed a multicenter, retrospective analysis of 139 International Federation of Gynecology and Obstetrics stage III CC patients treated with CCRT of whom 45.3% received AC. Our goal was to determine the impact of AC on survival in these patients. RESULTS: Five-year progression-free survival (PFS) was 37.5% and 16% in patients receiving CCRT with and without AC, respectively (p = 0.008). Median PFS was 30.9 months (CI 95% 14.8-46.9) and 16.6 months (CI 95% 9.3-23.9) in patients receiving CCRT with and without AC, respectively. Five-year overall survival (OS) was 78.2% and 28.4% in patients receiving CCRT with and without AC, respectively (p < 0.001). Median OS was 132.2 months (CI 95, %66.5-197.8) and 34.9 months (CI 95% 23.1-46.7) in patients receiving CCRT with and without AC, respectively. CONCLUSION: Our study suggests that AC provides OS and PFS benefit in stage III CC patients. Larger studies are needed to identify subgroups of patients who would benefit from AC.