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Background: We aimed to investigate whether pre-therapeutic radiomic features based on magnetic resonance imaging (MRI) can predict the clinical response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods: A total of 275 patients with LACC receiving NACT were enrolled in this study from eight hospitals, and allocated to training and testing sets (2:1 ratio). Three radiomic feature sets were extracted from the intratumoural region of T1-weighted images, intratumoural region of T2-weighted images, and peritumoural region of T2-weighted images before NACT for each patient. With a feature selection strategy, three single sequence radiomic models were constructed, and three additional combined models were constructed by combining the features of different regions or sequences. The performance of all models was assessed using receiver operating characteristic curve. Findings: The combined model of the intratumoural zone of T1-weighted images, intratumoural zone of T2-weighted images,and peritumoural zone of T2-weighted images achieved an AUC of 0.998 in training set and 0.999 in testing set, which was significantly better (p b .05) than the other radiomic models. Moreover, no significant variation in performance was found if different training sets were used. Interpretation: This study demonstrated that MRI-based radiomic features hold potential in the pretreatment prediction of response to NACT in LACC, which could be used to identify rightful patients for receiving NACT avoiding unnecessary treatment.

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PurposeTo study the therapeutic effects of uterine artery embolization (UAE) on adenomyosis and to investigate the association between uterine blood supply and artery embolization treatment outcomes.MethodsUsing digital subtraction angiography (DSA) imaging data, we retrospectively evaluated the vascular features of 252 adenomyosis patients treated with UAE. The cases were classified based on the equality of uterine blood supply (equal and unequal subgroups) and the degree of vascularity at the adenomyosis lesion site (hypervascular, isovascular and hypovascular subgroups). Patients were followed-up for 5 years after UAE. Improvements in dysmenorrhea and menorrhagia were evaluated based on the relief of the patients’ symptoms. The improvement rates among the different subgroups were analyzed and compared.ResultsThe improvement rates of dysmenorrhea and menorrhagia were 74.0% and 70.9%, respectively, at the short-term (12-month) follow-up and 70.4% and 68.8%, respectively, at the long-term (5-year) follow-up. No statistically significant differences were observed in the improvement rates for dysmenorrhea or menorrhagia between the equal and unequal blood supply subgroups at either the short- or long-term follow-up. The improvement rates for dysmenorrhea among the hypervascular, isovascular and hypovascular subgroups were 86.5%, 71.8% and 58.8%, respectively, at the short-term follow-up (p = 0.002) and 83.6%, 67.3% and 52.8%, respectively, at the long-term follow-up (p = 0.005). The improvement rates for menorrhagia in the hypervascular, isovascular and hypovascular subgroups were 81.0%, 68.3% and 60.7%, respectively, at the short-term follow-up (p = 0.024) and 79.4%, 61.4% and 62.2%, respectively, at the long-term follow-up (p = 0.052).ConclusionUAE is effective in treating patients with adenomyosis in both the short and long term. The outcomes of patients with adenomyosis were significantly correlated with lesion vascularity.

• Nerve-sparing radical hysterectomy is a developing trend in cervical cancer surgery • MRI allows reconstructions of pelvic autonomic nerves and their related organs • The 3D reconstructions provide detailed 3D anatomical information on nerves.

Objective: To investigate whether pre-treatment CT-derived radiomic features could be applied for prediction of clinical response to neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC).Patients and Methods: Two hundred and seventy-seven LACC patients treated with NACT followed by surgery/radiotherapy were included in this multi-institution retrospective study. One thousand and ninety-four radiomic features were extracted from venous contrast enhanced and non-enhanced CT imaging for each patient. Five combined methods of feature selection were used to reduce dimension of features. Radiomics signature was constructed by Random Forest (RF) method in a primary cohort of 221 patients. A combined model incorporating radiomics signature with clinical factors was developed using multivariable logistic regression. Prediction performance was then tested in a validation cohort of 56 patients.Results: Radiomics signature containing pre-and post-contrast imaging features can adequately distinguish chemotherapeutic responders from non-responders in both primary and validation cohorts [AUCs: 0.773 (95% CI, 0.701-0.845) and 0.816 (95% CI, 0.690-0.942), respectively] and remain relatively stable across centers. The combined model has a better predictive performance with an AUC of 0.803 (95% CI, 0.734-0.872) in the primary set and an AUC of 0.821 (95% CI, 0.697-0.946) in the validation set, compared to radiomics signature alone. Both models showed good discrimination, calibration.Conclusion: Newly developed radiomic model provided an easy-to-use predictor of chemotherapeutic response with improved predictive ability, which might facilitate optimal treatment strategies tailored for individual LACC patients.