IMPORTANCE Concurrent chemoradiotherapy is the standard-of-care curative treatment for many cancers but is associated with substantial morbidity. Concurrent chemoradiotherapy administered with proton therapy might reduce toxicity and achieve comparable cancer control outcomes compared with conventional photon radiotherapy by reducing the radiation dose to normal tissues.OBJECTIVE To assess whether proton therapy in the setting of concurrent chemoradiotherapy is associated with fewer 90-day unplanned hospitalizations (Common Terminology Criteria for Adverse Events, version 4 [CTCAEv4], grade Ն3) or other adverse events and similar disease-free and overall survival compared with concurrent photon therapy and chemoradiotherapy.

Shigella flexneri, an intracellular Gram-negative bacterium causative for shigellosis, employs a type III secretion system to deliver virulence effectors into host cells. One such effector, IcsB, is critical for S. flexneri intracellular survival and pathogenesis, but its mechanism of action is unknown. Here, we discover that IcsB is an 18-carbon fatty acyltransferase catalysing lysine N-fatty acylation. IcsB disrupted the actin cytoskeleton in eukaryotes, resulting from N-fatty acylation of RhoGTPases on lysine residues in their polybasic region. Chemical proteomic profiling identified about 60 additional targets modified by IcsB during infection, which were validated by biochemical assays. Most IcsB targets are membrane-associated proteins bearing a lysine-rich polybasic region, including members of the Ras, Rho and Rab families of small GTPases. IcsB also modifies SNARE proteins and other non-GTPase substrates, suggesting an extensive interplay between S. flexneri and host membrane trafficking. IcsB is localized on the Shigella-containing vacuole to fatty-acylate its targets. Knockout of CHMP5-one of the IcsB targets and a component of the ESCRT-III complex-specifically affected S. flexneri escape from host autophagy. The unique N-fatty acyltransferase activity of IcsB and its altering of the fatty acylation landscape of host membrane proteomes represent an unprecedented mechanism in bacterial pathogenesis.

A model that accurately predicts toxicities may be used to support clinical decisions for personalized treatment planning. An automated xerostomia prediction model was developed using a 3-dimensional residual convolutional neural network and demonstrated promising performance. This novel model uses computed tomography planning, 3-dimensional dose distributions, and contours as inputs and toxicity probability as output.Purpose: Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a 3-dimensional (3D) residual convolutional neural network (rCNN). The model can be used to guide radiation therapy to reduce toxicity. Methods and Materials: A total of 784 patients with head and neck squamous cell carcinoma enrolled in the Radiation Therapy Oncology Group 0522 clinical trial were included in this study. Late xerostomia is defined as xerostomia of grade !2 occurring in the 12th month of radiation therapy. The computed tomography (CT) planning images, 3D dose distributions, and contours of the parotid and submandibular glands were included as 3D rCNN inputs. Comparative experiments were performed for the 3D rCNN model without 1 of the 3 inputs and for the logistic regression model. Accuracy, sensitivity, specificity, F-score, and area under the receiver operator characteristic curve were evaluated. Results: The proposed model achieved promising prediction results. The performance metrics for 3D rCNN model with contour, CT images, and radiation therapy dose; 3D rCNN without contour; 3D rCNN without CT images; 3D rCNN without the dose; logistic regression with the dose and clinical parameters; and logistic regression without clinical parameters were as follows:

Purpose: Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve the segmentation accuracy and efficiency with a novel network named Convolutional Neural Networks (CNN) Cascades. Methods: CNN Cascades was a two-step, coarse-to-fine approach that consisted of a Simple Region Detector (SRD) and a Fine Segmentation Unit (FSU). The SRD first used a relative shallow network to define the region of interest (ROI) where the organ was located, and then the FSU took the smaller ROI as input and adopted a deep network for fine segmentation. The imaging data (14,651 slices) of 100 head-and-neck patients with segmentations were used for this study. The performance was compared with the state-of-the-art single CNN in terms of accuracy with metrics of Dice similarity coefficient (DSC) and Hausdorff distance (HD) values. Results: The proposed CNN Cascades outperformed the single CNN on accuracy for each OAR. Similarly, for the average of all OARs, it was also the best with mean DSC of 0.90 (SRD: 0.86, FSU: 0.87, and U-Net: 0.85) and the mean HD of 3.0 mm (SRD: 4.0, FSU: 3.6, and U-Net: 4.4). Meanwhile, the CNN Cascades reduced the mean segmentation time per patient by 48% (FSU) and 5% (U-Net), respectively. Conclusions: The proposed two-step network demonstrated superior performance by reducing the input region. This potentially can be an effective segmentation method that provides accurate and consistent delineation with reduced clinician interventions for clinical applications as well as for quality assurance of a multi-center clinical trial.

PurposeTo evaluate the reproducibility of radiomics features by repeating computed tomographic (CT) scans in rectal cancer. To choose stable radiomics features for rectal cancer.ResultsVolume normalized features are much more reproducible than unnormalized features. The average value of all slices is the most reproducible feature type in rectal cancer. Different filters have little effect for the reproducibility of radiomics features. For the average type features, 496 out of 775 features showed high reproducibility (ICC ≥ 0.8), 225 out of 775 features showed medium reproducibility (0.8 > ICC ≥ 0.5) and 54 out of 775 features showed low reproducibility (ICC < 0.5).Methods40 rectal cancer patients with stage II were enrolled in this study, each of whom underwent two CT scans within average 8.7 days. 775 radiomics features were defined in this study. For each features, five different values (value from the largest slice, maximum value, minimum value, average value of all slices and value from superposed intermediate matrix) were extracted. Meanwhile a LOG filter with different parameters was applied to these images to find stable filter value. Concordance correlation coefficients (CCC) and inter-class correlation coefficients (ICC) of two CT scans were calculated to assess the reproducibility, based on original features and volume normalized features.ConclusionsFeatures are recommended to be normalized to volume in radiomics analysis. The average type radiomics features are the most stable features in rectal cancer. Further analysis of these features of rectal cancer can be warranted for treatment monitoring and prognosis prediction.