Deep Learning for Automated Contouring of Gross Tumor Volumes in Esophageal Cancer

PurposeThe aim of this study was to propose and evaluate a novel three-dimensional (3D) V-Net and two-dimensional (2D) U-Net mixed (VUMix-Net) architecture for a fully automatic and accurate gross tumor volume (GTV) in esophageal cancer (EC)–delineated contours.MethodsWe collected the computed tomography (CT) scans of 215 EC patients. 3D V-Net, 2D U-Net, and VUMix-Net were developed and further applied simultaneously to delineate GTVs. The Dice similarity coefficient (DSC) and 95th-percentile Hausdorff distance (95HD) were used as quantitative metrics to evaluate the performance of the three models in ECs from different segments. The CT data of 20 patients were randomly selected as the ground truth (GT) masks, and the corresponding delineation results were generated by artificial intelligence (AI). Score differences between the two groups (GT versus AI) and the evaluation consistency were compared.ResultsIn all patients, there was a significant difference in the 2D DSCs from U-Net, V-Net, and VUMix-Net (p=0.01). In addition, VUMix-Net showed achieved better 3D-DSC and 95HD values. There was a significant difference among the 3D-DSC (mean ± STD) and 95HD values for upper-, middle-, and lower-segment EC (p<0.001), and the middle EC values were the best. In middle-segment EC, VUMix-Net achieved the highest 2D-DSC values (p<0.001) and lowest 95HD values (p=0.044).ConclusionThe new model (VUMix-Net) showed certain advantages in delineating the GTVs of EC. Additionally, it can generate the GTVs of EC that meet clinical requirements and have the same quality as human-generated contours. The system demonstrated the best performance for the ECs of the middle segment

The markers to predict the response to neoadjuvant therapy in patients with rectal cancer

Locally advanced rectal cancer is currently treated with preoperative radiochemotherapy, but the response is not uniform. Most patients benefit from preoperative CRT, however, a small proportion of a patient population is less likely to respond to the treatment. The purpose of this study was to measure neoadjuvant therapy combined with Ki-67 and VEGF expression in pretreatment biopsies and postoperative specimens，serum carcinoembryonic antigen (CEA) and CA19-9 level from patients with locally advanced rectal cancer receiving intensive neoadjuvant treatment and to correlate the findings with clinical outcome

Image1_TKI or TKI combined with PD-1 inhibitors as second-line treatment for HCC patients after sorafenib failure.jpeg

Background: Tyrosine kinase inhibitors (TKI) in combination with programmed cell death-1 (PD-1) inhibitors become the potential treatment modality for patients undergoing unresectable hepatocellular carcinoma (uHCC) in the first-line setting. However, the efficacy and safety of this combination regimen in patients after sorafenib failure remains unclear.Methods: Participants in this study included patients with uHCC after sorafenib failure who received TKI monotherapy (TKI group) or TKI combined with PD-1 inhibitors therapy (combination group) in our center from July 2018 to July 2021. The overall survival (OS) was used to be the primary efficacy endpoint, while progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR) were applied to be secondary endpoints. In addition, the adverse events are recorded and evaluated.Results: Among the 92 patients contained in this work, 50 patients were categorized into the TKI group, while 42 patients were in the combination group. There existed no evident differences between the two groups concerning the ORR (8.0% vs. 9.5%, p = 1.000). However, the DCR in the combined group was better in relative to that in the TKI group (71.4% vs. 50.0%, p = 0.037). In comparison with the TKI group, it was found that the combination group presented notably better median PFS (8.1 months vs. 4.7 months, p = 0.005) and median OS (21.9 months vs. 16.6 months, p = 0.042). According to multivariate analysis, PFS (HR 0.5, 95% CI: 0.3–0.8, p = 0.005) and OS (HR 0.5, 95% CI: 0.3–1.0, p = 0.051) were improved in the combination group in relative to the TKI group after the adjustment for some risk factors. Additionally, the incidence rates of grade ≥1 adverse event in the TKI group and the combination group were 96.0% and 97.6%, respectively. The most normal adverse event in the TKI group was neutropenia (n = 24,48.0%) and the combination group was hypoalbuminemia (n = 23,54.8%). All of these adverse events improved after symptomatic treatment, and no new toxic events were found to occur.Conclusion: TKI combined with PD-1 inhibitors showed better prognosis with manageable toxicity in uHCC patients after sorafenib failure compared with TKI monotherapy.</p

Characterization and identification of the powdery mildew resistance gene in wheat breeding line ShiCG15–009

Powdery mildew, caused by Blumeria graminis f. sp. tritici ( Bgt ), is a serious fungal disease that critically threatens the yield and quality of wheat. Utilization of host resistance is the most effective and economical method to control this disease. In our study, a wheat breeding line ShiCG15–009, released from Hebei Province, was highly resistant to powdery mildew at all stages. To dissect its genetic basis, ShiCG15–009 was crossed with the susceptible cultivar Yannong 21 to produce F 1 , F 2 and F 2:3 progenies. After genetic analysis, a single dominant gene, tentatively designated PmCG15–009 , was proved to confer resistance to Bgt isolate E09. Further molecular markers analysis showed that PmCG15–009 was located on chromosome 2BL and flanked by markers XCINAU130 and XCINAU143 with the genetic distances 0.2 and 0.4?cM, respectively, corresponding to a physic interval of 705.14–723.48?Mb referred to the Chinese Spring reference genome sequence v2.1. PmCG15–009 was most likely a new gene differed from the documented Pm genes on chromosome 2BL since its different origin, genetic diversity, and physical position. To analyze and identify the candidate genes, six genes associated with disease resistance in the candidate interval were confirmed to be associated with PmCG15–009 via qRT-PCR analysis using the parents ShiCG15–009 and Yannong 21 and time-course analysis post-inoculation with Bgt isolate E09. To accelerate the transfer of PmCG15–009 using marker-assisted selection (MAS), 18 closely or co-segregated markers were evaluated and confirmed to be suitable for tracing PmCG15–009 , when it was transferred into different wheat cultivars