Automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this article provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: First, the proposed region of interest extraction implements patch mechanism strategy to satisfy the applicability of 3-D network and remove irrelevant background. Second, 3-D network is established to extract spatial features, where 3-D attention model promotes network to enhance target area. Then, to improve the convergence of network, a combination loss function is introduced to lead gradient optimization and training direction. Finally, data augmentation and conditional random field are applied to realize data resampling and binary segmentation. This method was assessed with some comparative experiment. By comparison, the proposed method reached the highest performance. Therefore, it has potential clinical applications.

Retinal blood vessels are the basis for clinical diagnosis of some diseases. Achieving automatic retinal vessel segmentation from fundus images is an important and challenging work. In this paper, a neural network architecture based on the Dense U-net using Inception module is proposed for retinal vessel segmentation. First, the skip connections in traditional U-net are replaced with Dense Block to achieve full fusion of features from shallow layers to deep layers. Then, the Inception module is applied to supersede the traditional convolution operation. Thus, vessel features corresponding to the convolution kernels of different sizes can be extracted. Finally, Generative Adversarial Networks (GAN) are adopted in the training phase. The Dense U-net using Inception module is treated as the generator of GAN, and a multilayer neural network is created as the discriminator of GAN. The generator and discriminator are trained alternately. The loss function is a combination of segmentation loss and GAN loss. So that segmentation results can be fitted to the ground truth from both pixel value and pixel distribution. The algorithm proposed in this paper is verified on the public Digital Retinal Images for Vessel Extraction (DRIVE) dataset, where the Dice rate reaches 82.15%, and the AU-ROC and AU-PR reach 0.9772 and 0.9058, respectively. Experiments show that the proposed algorithm is effective in realizing automatic retinal vessel segmentation.

BACKGROUND: The tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of immune context and investigate their associations with prognosis and immunotherapy response in gastric cancer (GC). METHODS: This study involved 2,600 patients with GC from 9 independent cohorts. We developed and validated 2 CT imaging biomarkers (lymphoid radiomics score [LRS] and myeloid radiomics score [MRS]) for evaluating the IHC-derived lymphoid and myeloid immune context respectively, and integrated them into a combined imaging biomarker [LRS/MRS: low(−) or high(+)] with 4 radiomics immune subtypes: 1 (−/−), 2 (+/−), 3 (−/+), and 4 (+/+). We further evaluated the imaging biomarkers’ predictive values on prognosis and immunotherapy response. RESULTS: The developed imaging biomarkers (LRS and MRS) had a high accuracy in predicting lymphoid (AUC range: 0.765–0.773) and myeloid (AUC range: 0.736–0.750) immune context. Further, similar to the IHC-derived immune context, 2 imaging biomarkers (HR range: 0.240–0.761 for LRS; 1.301–4.012 for MRS) and the combined biomarker were independent predictors for disease-free and overall survival in the training and all validation cohorts (all P < 0.05). Additionally, patients with high LRS or low MRS may benefit more from immunotherapy ( P < 0.001). Further, a highly heterogeneous outcome on objective response rate was observed in 4 imaging subtypes: 1 (−/−) with 27.3%, 2 (+/−) with 53.3%, 3 (−/+) with 10.2%, and 4 (+/+) with 30.0% ( P < 0.0001). CONCLUSION: The noninvasive imaging biomarkers could accurately evaluate the immune context and provide information regarding prognosis and immunotherapy for GC.

Background: Peritoneal recurrence (PR) is the predominant pattern of relapse after curative-intent surgery in gastric cancer (GC) and indicates a dismal prognosis. Accurate prediction of PR is crucial for patient management and treatment. We aimed to develop a noninvasive imaging biomarker from computed tomography (CT) for PR evaluation, and investigate its associations with prognosis and chemotherapy benefit. Methods: In this multicenter study including five independent cohorts of 2005 GC patients, we extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced CT images. The artificial intelligence algorithms were used to select significant PR-related features, and then integrated into a radiomic imaging signature. And improvements of diagnostic accuracy for PR by clinicians with the signature assistance were quantified. Using Shapley values, we determined the most relevant features and provided explanations to prediction. We further evaluated its predictive performance in prognosis and chemotherapy response. Results: The developed radiomics signature had a consistently high accuracy in predicting PR in the training cohort (area under the curve: 0.732), internal and SYSUCC validation cohorts (0.721 and 0.728). The radiomics signature was the most important feature in Shapley interpretation. The diagnostic accuracy of PR with the radiomics signature assistance was improved by 10.13% to 18.86% for clinicians (P<0.001). Furthermore, it was also applicable in the survival prediction. In multivariable analysis, the radiomics signature remained an independent predictor for PR and prognosis (P<0.001 for all). Importantly, patients with predicting high risk of PR from radiomics signature, could gain survival benefit from adjuvant chemotherapy. By contrast, chemotherapy had no impact on survival for patients with a predicted low risk of PR. Conclusion: The noninvasive and explainable model developed from preoperative CT images could accurately predict PR and chemotherapy benefit in patients with GC, which will allow the optimization of individual decision-making. Export

Background: Tertiary lymphoid structures (TLSs) are associated with favorable prognosis and enhanced response to anticancer therapy. A digital assessment of TLSs could provide an objective alternative that mitigates variability inherent in manual evaluation. This study aimed to develop and validate a digital gene panel based on biological prior knowledge for assessment of TLSs, and further investigate its associations with survival and multiple anticancer therapies. Materials and methods: The present study involved 1704 patients with gastric cancer from seven cancer centers. TLSs were identified morphologically through hematoxylin-and-eosin staining. The authors further developed a digital score based on targeted gene expression profiling to assess TLSs status, recorded as gene signature of tertiary lymphoid structures (gsTLS). For enhanced interpretability, we employed the SHapley Additive exPlanation (SHAP) analysis to elucidate its contribution to the prediction. The authors next evaluated the signature’s associations with prognosis, and investigated its predictive accuracy for multiple anticancer therapies, including adjuvant chemotherapy and immunotherapy. Results: The gsTLS panel with nine gene features achieved high accuracies in predicting TLSs status in the training, internal, and external validation cohorts (area under the curve, range: 0.729–0.791). In multivariable analysis, gsTLS remained an independent predictor of disease-free and overall survival (hazard ratio, range: 0.346–0.743, all P<0.05) after adjusting for other clinicopathological variables. SHAP analysis highlighted gsTLS as the strongest predictor of TLSs status compared with clinical features. Importantly, patients with high gsTLS (but not those with low gsTLS) exhibited substantial benefits from adjuvant chemotherapy (P<0.05). Furthermore, the authors found that the objective response rate to antiprogrammed cell death protein 1 (anti-PD-1) immunotherapy was significantly higher in the high-gsTLS group (40.7%) versus the low-gsTLS group (5.6%, P=0.036), and the diagnosis was independent from Epstein–Barr virus, tumor mutation burden, and programmed cell death-ligand 1 (PD-L1) expression. Conclusion: The gsTLS digital panel enables accurate assessment of TLSs status, and provides information regarding prognosis and responses to multiple therapies for gastric cancer.