Objectives. This study aims to develop a computer-aided diagnosis (CADx) scheme to classify between benign and malignant ground glass nodules (GGNs), and fuse deep leaning and radiomics imaging features to improve the classification performance. Methods. We first retrospectively collected 513 surgery histopathology confirmed GGNs from two centers. Among these GGNs, 100 were benign and 413 were malignant. All malignant tumors were stage I lung adenocarcinoma. To segment GGNs, we applied a deep convolutional neural network and residual architecture to train and build a 3D U-Net. Then, based on the pre-trained U-Net, we used a transfer learning approach to build a deep neural network (DNN) to classify between benign and malignant GGNs. With the GGN segmentation results generated by 3D U-Net, we also developed a CT radiomics model by adopting a series of image processing techniques, i.e. radiomics feature extraction, feature selection, synthetic minority over-sampling technique, and support vector machine classifier training/testing, etc. Finally, we applied an information fusion method to fuse the prediction scores generated by DNN based CADx model and CT-radiomics based model. To evaluate the proposed model performance, we conducted a comparison experiment by testing on an independent testing dataset. Results. Comparing with DNN model and radiomics model, our fusion model yielded a significant higher area under a receiver operating characteristic curve (AUC) value of 0.73 ± 0.06 (P < 0.01). The fusion model generated an accuracy of 75.6%, F1 score of 84.6%, weighted average F1 score of 70.3%, and Matthews correlation coefficient of 43.6%, which were higher than the DNN model and radiomics model individually. Conclusions. Our experimental results demonstrated that (1) applying a CADx scheme was feasible to diagnosis of early-stage lung adenocarcinoma, (2) deep image features and radiomics features provided complementary information in classifying benign and malignant GGNs, and (3) it was an effective way to build DNN model with limited dataset by using transfer learning. Thus, to build a robust image analysis based CADx model, one can combine different types of image features to decode the imaging phenotypes of GGN.

This study aims to develop a deep neural network (DNN)-based two-stage risk stratification model for early lung adenocarcinomas in CT images, and investigate the performance compared with practicing radiologists. A total of 2393 GGNs were retrospectively collected from 2105 patients in four centers. All the pathologic results of GGNs were obtained from surgically resected specimens. A two-stage deep neural network was developed based on the 3D residual network and atrous convolution module to diagnose benign and malignant GGNs (Task1) and classify between invasive adenocarcinoma (IA) and non-IA for these malignant GGNs (Task2). A multi-reader multi-case observer study with six board-certified radiologists’ (average experience 11 years, range 2–28 years) participation was conducted to evaluate the model capability. DNN yielded area under the receiver operating characteristic curve (AUC) values of 0.76 ± 0.03 (95% confidence interval (CI): (0.69, 0.82)) and 0.96 ± 0.02 (95% CI: (0.92, 0.98)) for Task1 and Task2, which were equivalent to or higher than radiologists in the senior group with average AUC values of 0.76 and 0.95, respectively (p > 0.05). With the CT image slice thickness increasing from 1.15 mm ± 0.36 to 1.73 mm ± 0.64, DNN performance decreased 0.08 and 0.22 for the two tasks. The results demonstrated (1) a positive trend between the diagnostic performance and radiologist’s experience, (2) the DNN yielded equivalent or even higher performance in comparison with senior radiologists, and (3) low image resolution decreased model performance in predicting the risks of GGNs. Once tested prospectively in clinical practice, the DNN could have the potential to assist doctors in precision diagnosis and treatment of early lung adenocarcinoma.

Objective: The aim of the present Bayesian network meta-analysis (NMA) was to explore the comparative effectiveness and safeaty of different Chinese Medicine injections (CMIs) combined with the XELOX regimen versus XELOX alone for colorectal cancer (CRC).Methods: A comprehensive search for randomized controlled trials (RCTs) was performed with regard to different CMIs for the treatment of CRC in several electronic databases up to April 2022. The quality assessment of the included RCTs was conducted according to the Cochrane risk of bias tool. Standard pair-wise and Bayesian NMA were designed to comparethe effectiveness and safety of different CMIs combined with the XELOX regimen by utilizing R 4.0.3 software and Stata 15.1 software simultaneously.Results: Initially, a total of 4296 citations were retrieved through comprehensive searching, and 32 eligible articles involving 2847 participants and 11 CMIs were ultimately included. CMIs combined with XELOX were superior to the XELOX regimen alone, and a total of ten Observation Indicators were included in the study, with the following results. Among all the injections, Shengmaiyin, Shenmai, and Kanglaite combined with the XELOX regimen were the three CMIs with the highest clinical efficiency. The top three in terms of improving CD3+ values were Shengmaiyin, Shenqifuzheng, and Cinobufacini injections. Shenqifuzheng, Shengmaiyin, and BruceaJavanica oil injections combined with the XELOX regimen performed best at raising CD4+ values. Kanglaite, Cinobufacini, and Matrine injections combined with the XELOX regimen performed best in improving CD4+/CD8+ rates. The top three in terms of improving performance status were Xiaoaiping, Shenmai, and Kanglaite injections. Cinobufacini and Brucea Javanica oil injections combined with the XELOX regimen performed best at raising CD8+ values. Shenqifuzheng, Kangai, and Matrine injections combined with the XELOX regimen performed best in improving Gastrointestinal reactions.The top threein terms of improving Leukopenia were Shenqifuzheng, Compound Kushen and Kanglaite injections. The top three in terms of improving Platelet decline were Compound Kushen, Cinobufacini and Shenqifuzheng injections. Additionally, those that were best at improving nausea and vomitting were Cinobufacini, Compound Kushen and Aidi injections.Conclusion: The results of the analysis demonstrated thatShengmaiyin, Kanglaite, and Cinobufacini injections and the XELOX regimen were associated with morepreferable and beneficial outcomes than other CMI groups. Nevertheless, additional results from multicenter trials and high-quality studies will bevital to support our findings.Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=326097, CRD42022326097.