Hua Yang

Background: This study aimed to develop a model that automatically predicts the neoadjuvant chemoradiotherapy (nCRT) response for patients with locally advanced cervical cancer (LACC) based on T2-weighted MR images and clinical parameters. Methods: A total of 138 patients were enrolled, and T2-weighted MR images and clinical information of the patients before treatment were collected. Clinical information included age, stage, pathological type, squamous cell carcinoma (SCC) level, and lymph node status. A hybrid model extracted the domain-specific features from the computational radiomics system, the abstract features from the deep learning network, and the clinical parameters. Then, it employed an ensemble learning classifier weighted by logistic regression (LR) classifier, support vector machine (SVM) classifier, K-Nearest Neighbor (KNN) classifier, and Bayesian classifier to predict the pathologic complete response (pCR). The area under the receiver operating characteristics curve (AUC), accuracy (ACC), true positive rate (TPR), true negative rate (TNR), and precision were used as evaluation metrics. Results: Among the 138 LACC patients, 74 were in the pCR group, and 64 were in the non-pCR group. There was no significant difference between the two cohorts in terms of tumor diameter (p = 0.787), lymph node (p = 0.068), and stage before radiotherapy (p = 0.846), respectively. The 109-dimension domain features and 1472-dimension abstract features from MRI images were used to form a hybrid model. The average AUC, ACC, TPR, TNR, and precision of the proposed hybrid model were about 0.80, 0.71, 0.75, 0.66, and 0.71, while the AUC values of using clinical parameters, domain-specific features, and abstract features alone were 0.61, 0.67 and 0.76, respectively. The AUC value of the model without an ensemble learning classifier was 0.76. Conclusions: The proposed hybrid model can predict the radiotherapy response of patients with LACC, which might help radiation oncologists create personalized treatment plans for patients.

Background Distant metastasis remained the major cause of mortality in nasopharyngeal carcinoma (NPC) receiving radiotherapy despite favorable locoregional control in the past decades. Tumor microenvironment plays a critical role in tumor progression by secreting factors that regulate cancer cell metastasis. We explored the role of secreted protein Slit2 in NPC metastasis and the underlying mechanisms. Methods Colony formation assay were determined the opitimal dose. Using real-time PCR, western blotting, and ELISA, we detected the expression of Slit2 in NPC cells and in supernatant. Cell migration assay and cell invasion assay were examined the role of Slit2 in promoting metastasis of NPC cell lines. Western blotting, immunofluorescence staining, transwell and Co-IP assay et al. were performed to explore the detailed molecular mechanism of Slit2 in NPC. Finally, we estimated the effects on Slit2-shRNA NPC cells in vivo tumor metastasis experiment Results We found that irradiated dying NPC cells could secrete Slit2 to enhance the metastatic ability of surviving NPC cells. Mechanistic study showed that Slit2 interacted with its cognate Robo1 receptor, which activated PI3K/Akt pathway and enhanced the nuclear translocation of β-catenin to promote NPC metastasis. Finally, we demonstranted that knockdown of Slit2 in NPC cells significantly suppressed lung and liver metastasis. Conclusion Slit2, a secreted factor released from irradiated dying NPC cells could promote metastasis of living/resistent NPC cells through PI3K/AKT/β-catenin pathway. Tumor microenvironment altered by irradiated dying cancer cells may be promising target to treat cancer metastasis.

Purpose The objectives of this study were to analyze the prognostic factors of patients with distant metastasis after intensity-modulated radiotherapy (IMRT) for nasopharyngeal carcinoma (NPC) and to provide a reference for the clinical treatment of these patients. Methods A retrospective analysis was conducted among 152 patients with distant metastasis after IMRT for NPC from January 2006 to December 2017 (median follow-up, 43 months). The survival rates were calculated and compared using the Kaplan-Meier method and log-rank tests, respectively. The Cox risk ratio model was used for univariateand multivariate analyses. Results Among all patients, the median interval from treatment completion to distant metastasis was 11.3 months. The median post-metastasis survival was 14 months, and the 1-, 2-, and 3-year survival rates were 60.4%, 40.2%, and 27.6%, respectively. Upon univariate analysis, overall survival was found to be related to the N stage and induction chemotherapy at initial diagnosis, time from initial radiotherapy completion to distant metastasis, liver metastasis, and chemotherapy and palliative radiotherapy after metastasis. Liver metastasis, multi-organ metastasis, chemotherapy after metastasis, and the time from radiotherapy completion to distant metastasis were independent prognostic factors for patient survival in the Cox regression analysis. Conclusions The prognosis of patients with distant metastasis after IMRT for NPC was related to the time from radiotherapy completion to distant metastasis, regardless of liver or multiple organ metastasis and adjuvant chemotherapy after metastasis. Adequate adjuvant chemotherapy and local palliative radiotherapy could potentially prolong the survival of patients with distant metastasis of NPC.