Clinical implementation of deep learning robust IMPT planning in oropharyngeal cancer patients: A blinded clinical study.

Background And Purpose: This study aimed to evaluate the plan quality of our deep learning-based automated treatment planning method for robustly optimized intensity-modulated proton therapy (IMPT) plans in patients with oropharyngeal carcinoma (OPC). The assessment was conducted through a retrospective and prospective study, blindly comparing manual plans with deep learning plans.

Materials And Methods: A set of 95 OPC patients was split into training (n = 60), configuration (n = 10), test retrospective study (n = 10), and test prospective study (n = 15).

Automated Robust Planning for IMPT in Oropharyngeal Cancer Patients Using Machine Learning.

Purpose: The aim of this study was to evaluate an automated treatment planning method for robustly optimized intensity modulated proton therapy (IMPT) plans for oropharyngeal carcinoma patients and to compare the results with manually optimized robust IMPT plans.

Methods And Materials: An atlas regression forest-based machine learning (ML) model for dose prediction was trained on CT scans, contours, and dose distributions of robust IMPT plans of 88 oropharyngeal cancer (OPC) patients. The ML model was combined with a robust voxel and dose volume histogram-based dose mimicking optimization algorithm for 21 perturbed scenarios to generate a machine-deliverable plan from the predicted dose distribution.

Diffusion weighted imaging of the breast: Performance of standardized breast tumor tissue selection methods in clinical decision making.

Objectives: In breast diffusion weighted imaging (DWI) protocol standardization, it is recently shown that no breast tumor tissue selection (BTTS) method outperformed the others. The purpose of this study is to analyze the feasibility of three fixed-size breast tumor tissue selection (BTTS) methods based on the reproducibility, accuracy and time-measurement in comparison to the largest oval and manual delineation in breast diffusion weighted imaging data.

Methods: This study is performed with a consecutive dataset of 116 breast lesions (98 malignant) of at least 1.

A Subset of Malignant Mesothelioma Tumors Retain Osteogenic Potential.

Malignant mesothelioma (MM) is an aggressive serosal tumor associated with asbestos exposure. We previously demonstrated that mesothelial cells differentiate into cells of different mesenchymal lineages and hypothesize that osseous tissue observed in a subset of MM patients is due to local differentiation of MM cells. In this study, the capacity of human and mouse MM cells to differentiate into osteoblast-like cells was determined in vitro using a functional model of bone nodule formation and in vivo using an established model of MM.

CD8alpha+ DC are not the sole subset cross-presenting cell-associated tumor antigens from a solid tumor.

One of the clear paradoxes in tumor immunology is the fact that cross-presentation of cell-associated tumor antigens to CD8(+) T cells is efficient, yet CTL generation is weak, and tumors continue to grow. We examined, for the first time whether this may be due to alterations in the phenotype or function of cross-presenting DC using a solid tumor model expressing a membrane bound neo-antigen (hemagglutinin, HA). Tumor antigen was constitutively cross-presented in the tumor-draining LN throughout tumor progression by CD11c(+) DC.