Aining Zhang

CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer

Background Radiomics nomogram analysis is widely preoperatively used to assess gene mutations in various tumors. Purpose To explore the value of computed tomography (CT)-based radiomics nomogram analysis for assessing BRCA gene mutation status of patients with high-grade serous ovarian cancer (HGSOC). Material and Methods In total, 96 patients with HGSOC were retrospectively screened and randomly divided into primary (n = 68) and validation cohorts (n = 28). The clinical model was constructed based on clinical features and CT morphological features using univariate and multivariate logistic analyses. Maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were performed for feature dimensionality reduction and radiomics score was calculated. The nomogram model combining the clinical model and the radiomics score was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curves were generated to assess models’ performance. The calibration analysis and decision curve analysis (DCA) were also performed. Results The clinical model consisted of CA125 level and supradiaphragmatic lymphadenopathy and yielded an area under the curve (AUC) of 0.69 (primary cohort) and 0.81 (validation cohort). The radiomics model was built with seven selected features and showed an AUC of 0.87 (primary cohort) and 0.81 (validation cohort). The nomogram finally showed the highest AUC of 0.89 (primary cohort) and 0.87 (validation cohort). The nomogram presented favorable calibrations in both the primary and validation cohorts. DCA further confirmed the clinical benefits of the constructed nomogram. Conclusion CT-based radiomics nomogram provides a non-invasive method to discriminate BRCA gene mutation status of HGSOC and potentially helps develop precise medical strategies.

Application of apparent diffusion coefficient values derived from diffusion-weighted imaging for assessing different sized metastatic lymph nodes in cervical cancers

Background Lymph nodes metastasis is an important factor affecting survival rate and recurrence in cervical cancer patients. Currently, diagnosis of metastatic lymph nodes is mainly based on morphological changes on imaging. However, it is difficult to differentiate normal-sized metastatic lymph nodes with short axis of 5-10mm. Purpose To assess the diagnostic value of apparent diffusion coefficient (ADC) for discriminating different-sized metastatic lymph nodes in patients with cervical cancers. Material and Methods Pathologically confirmed cervical cancer patients were documented from January 2013 to July 2018 in our hospital. A total of 133 patients who underwent conventional MRI and diffusion-weighted imaging with complete pathology were finally enrolled. A total of 157 lymph nodes were harvested and analyzed. All lymph nodes were divided into three groups according to pathology and their short axis (S) measured on axial T2-weighted imaging: normal-sized (5 mm<S<10 mm) benign lymph nodes (Group 1); normal-sized (5 mm<S<10 mm) metastatic lymph nodes (Group 2); enlarged (S≥10 mm) metastatic lymph nodes (Group 3). Mean ADC (ADCmean), minimum ADC (ADCmin), and maximum ADC (ADCmax) values of lymph nodes were analyzed and compared among the three groups. Results ADCmean of Groups 1 and 2 were significantly larger than those of Group 3 ( P<0.001, P=0.005, respectively). ADCmin of Group 1 were significantly larger than those of Groups 2 and 3 ( P<0.001, P<0.001, respectively). ADCmax was not statistically different among the three groups. ADCmean had the relatively highest area under the curve (AUC) of 0.644 for assessing enlarged metastatic lymph nodes, with a sensitivity of 64.4% and specificity of 67.9%. ADCmin had the highest AUC of 0.758 for assessing normal-sized metastatic lymph nodes, with a sensitivity of 84.7% and specificity of 60.7%. Conclusion Diffusion-weighted imaging can be used to differentiate enlarged metastatic lymph nodes from benign lymph nodes, and ADCmin can be further used to identify micro-metastasis in normal-sized lymph nodes.