Evaluation the benefits of additional radiotherapy for gastric cancer patients after D2 resection using CT based radiomics.

OBJECTIVES: The value of adding radiotherapy (RT) is still unclear for patients with gastric cancer (GC) after D2 lymphadenectomy. The purpose of this study is to predict and compare the overall survival (OS) and disease-free survival (DFS) of GC patients treated by chemotherapy and chemoradiation based on contrast-enhanced CT (CECT) radiomics. METHODS: A total of 154 patients treated by chemotherapy and chemoradiation in authors' hospital were retrospectively reviewed and randomly divided into the training and testing cohorts (7:3). Radiomics features were extracted from contoured tumor volumes in CECT using the pyradiomics software. Radiomics score and nomogram with integrated clinical factors were developed to predict the OS and DFS and evaluated with Harrell's Consistency Index (C-index). RESULTS: Radiomics score achieved a C index of 0.721(95%CI: 0.681-0.761) and 0.774 (95%CI: 0.738-0.810) in the prediction of DFS and OS for GC patients treated by chemotherapy and chemoradiation, respectively. The benefits of additional RT only demonstrated in subgroup of GC patients with Lauren intestinal type and perineural invasion (PNI). Integrating clinical factors further improved the prediction ability of radiomics models with a C-index of 0.773 (95%CI: 0.736-0.810) and 0.802 (95%CI: 0.765-0.839) for DFS and OS, respectively. CONCLUSIONS: CECT based radiomics is feasible to predict the OS and DFS for GC patients underwent chemotherapy and chemoradiation after D2 resection. The benefits of additional RT only observed in GC patients with intestinal cancer and PNI.

Integrating plan complexity and dosiomics features with deep learning in patient-specific quality assurance for volumetric modulated arc therapy.

PURPOSE: To investigate the feasibility and performance of deep learning (DL) models combined with plan complexity (PC) and dosiomics features in the patient-specific quality assurance (PSQA) for patients underwent volumetric modulated arc therapy (VMAT). METHODS: Total of 201 VMAT plans with measured PSQA results were retrospectively enrolled and divided into training and testing sets randomly at 7:3. PC metrics were calculated using house-built algorithm based on Matlab. Dosiomics features were extracted and selected using Random Forest (RF) from planning target volume (PTV) and overlap regions with 3D dose distributions. The top 50 dosiomics and 5 PC features were selected based on feature importance screening. A DL DenseNet was adapted and trained for the PSQA prediction. RESULTS: The measured average gamma passing rate (GPR) of these VMAT plans was 97.94% ± 1.87%, 94.33% ± 3.22%, and 87.27% ± 4.81% at the criteria of 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. Models with PC features alone demonstrated the lowest area under curve (AUC). The AUC and sensitivity of PC and dosiomics (D) combined model at 2%/2 mm were 0.915 and 0.833, respectively. The AUCs of DL models were improved from 0.943, 0.849, 0.841 to 0.948, 0.890, 0.942 in the combined models (PC + D + DL) at 3%/3 mm, 3%/2 mm and 2%/2 mm, respectively. A best AUC of 0.942 with a sensitivity, specificity and accuracy of 100%, 81.8%, and 83.6% was achieved with combined model (PC + D + DL) at 2%/2 mm. CONCLUSIONS: Integrating DL with dosiomics and PC metrics is promising in the prediction of GPRs in PSQA for patients underwent VMAT.

Contrast-enhanced CT based radiomics in the preoperative prediction of perineural invasion for patients with gastric cancer.

PURPOSE: To investigate the feasibility and accuracy of radiomics models based on contrast-enhanced CT (CECT) in the prediction of perineural invasion (PNI), so as to stratify high-risk recurrence and improve the management of patients with gastric cancer (GC) preoperatively. METHODS: Total of 154 GC patients underwent D2 lymph node dissection with pathologically confirmed GC and preoperative CECT from an open-label, investigator-sponsored trial (NCT01711242) were enrolled. Radiomics features were extracted from contoured images and selected using Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) after inter-class correlation coefficient (ICC) analysis. Models based on radiomics features (R), clinical factors (C) and combined parameters (R + C) were built and evaluated using Support Vector Machine (SVM) and logistic regression to predict the PNI for patients with GC preoperatively. RESULTS: Total of 11 radiomics features were selected for final analysis, along with two clinical factors. The area under curve (AUC) of models based on R, C, and R + C with logistic regression and SVM were 0.77 vs. 0.83, 0.71 vs.0.70, 0.86 vs. 0.90, and 0.73 vs.0.80, 0.62 vs. 0.64, 0.77 vs. 0.82 in the training and testing cohorts, respectively. SVM(R + C) achieved a best AUC of 0.82(0.69-0.94) in the test cohorts with a sensitivity, specificity and accuracy of 0.63, 0.91, and 0.77, respectively. CONCLUSIONS: The performance of these models indicates that radiomics features alone or combined with clinical factors provide a feasible way to classify patients preoperatively and improve the management of patients with GC.

Multipodal mesoporous silica hollow spheres: Branched hierarchical nanostructure by region-selective self-assembly.

HYPOTHESIS: Hollow nanostructures, known as nanocapsules, have been the preferable candidates in the drug-delivery and control-release applications. To enhance the adherence and penetration into biological hosts for efficient drug delivery, constructing multiple pods on the hollow structure to form a tribulus-like branched architecture has been proven an effective strategy. However, the synthesis is challenging due to the simultaneous control of the branched podal morphology, the hollow architecture and the mesophase structures at the nanometer scale. EXPERIMENTS: Polymer spheres with surface carboxyl moieties were first prepared by emulsion polymerization, which were partly coated by a type of basic silane. The left carboxyl moieties formed some seperated acid spots on the surface of polymer spheres, which could lead to the subsequent self-assembly of surfactant and silica within these acidic spots to grow a branched nanostructure. FINDINGS: Radiolarian-like organic-inorganic hybrid hollow architecture with branched ordered mesoporous pods were obtained after removing the organic templates of the polymer spheres and surfactants by calcination. The ordered cylindrical mesoporous channels were along the central axis direction of the hexagonal-prism-like pods, which connected inside and outside of the hollow spheres. The number of the branched pods could be easily tuned at the range of one to four.