PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images.

OBJECTIVE: To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning. METHODS: We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility. RESULTS: Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89. CONCLUSIONS: In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.

RBMS1 promotes gastric cancer metastasis through autocrine IL-6/JAK2/STAT3 signaling.

Metastasis is the most important reason for the poor prognosis of gastric cancer (GC) patients, and the mechanism urgently needs to be clarified. Here, we explored a prognostic model for the estimation of tumor-associated mortality in GC patients and revealed the RNA-binding protein RBMS1 as a candidate promoter gene for GC metastasis by analyzing GOBO and Oncomine high-throughput sequencing datasets for 408 GC patients. Additionally, RBMS1 was observed with overexpression in 85 GC patient clinical specimens by IHC staining and further be verified its role in GC metastasis via inducing EMT process both in in vitro and in vivo experiments. Moreover, we identified that IL-6 was predicted to be one of the most significant upstream cytokines in the RBMS1 overexpression gene set based on the Ingenuity Pathway Analysis (IPA) algorithm. Most importantly, we also revealed that RBMS1 could promote migration and invasion through IL6 transactivation and JAK2/STAT3 downstream signaling pathway activation by influencing histone modification in the promoter regions after binding with the transcription factor MYC in the HGC-27 and SGC-7901 GC cell lines. Hence, we shed light on the potential molecular mechanisms of RBMS1 in the promotion of GC metastasis, which suggests that RBMS1 may be a potential therapeutic target for GC patients.