Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks

Chain length dependence of anomalous swelling in multilamellar lipid vesicles

Using small-angle X-ray scattering, the repeat distance vs. temperature is measured for a homologous series of multilamellar vesicles of lecithins with varying acyl chain length in excess water condition around the lipid main transition. A systematic chain length dependence is found which is in accordance with a bending rigidity renormalization and critical unbinding of the lamellae close to the transition, as previously suggested in Hønger et al. [Phys. Rev. Lett. 72, 3911 (1994)]

The effect of editing clinical contours on deep-learning segmentation accuracy of the gross tumor volume in glioblastoma

Background and purpose: Deep-learning (DL) models for segmentation of the gross tumor volume (GTV) in radiotherapy are generally based on clinical delineations which suffer from inter-observer variability. The aim of this study was to compare performance of a DL-model based on clinical glioblastoma GTVs to a model based on a single-observer edited version of the same GTVs. Materials and methods: The dataset included imaging data (Computed Tomography (CT), T1, contrast-T1 (T1C), and fluid-attenuated-inversion-recovery (FLAIR)) of 259 glioblastoma patients treated with post-operative radiotherapy between 2012 and 2019 at a single institute. The clinical GTVs were edited using all imaging data. The dataset was split into 207 cases for training/validation and 52 for testing.GTV segmentation models (nnUNet) were trained on clinical and edited GTVs separately and compared using Surface Dice with 1 mm tolerance (sDSC1mm). We also evaluated model performance with respect to extent of resection (EOR), and different imaging combinations (T1C/T1/FLAIR/CT, T1C/FLAIR/CT, T1C/FLAIR, T1C/CT, T1C/T1, T1C). A Wilcoxon test was used for significance testing. Results: The median (range) sDSC1mm of the clinical-GTV-model and edited-GTV-model both evaluated with the edited contours, was 0.76 (0.43–0.94) vs. 0.92 (0.60–0.98) respectively (p < 0.001). sDSC1mm was not significantly different between patients with a biopsy, partial, and complete resection. T1C as single input performed as good as use of imaging combinations. Conclusions: High segmentation accuracy was obtained by the DL-models. Editing of the clinical GTVs significantly increased DL performance with a relevant effect size. DL performance was robust for EOR and highly accurate using only T1C

Feasibility of dose painting using volumetric modulated arc optimization and delivery

PURPOSE: Dose painting strategies are limited by optimization algorithms in treatment planning systems and physical constraints of the beam delivery. We investigate dose conformity using the RapidArc optimizer and beam delivery technique. Furthermore, robustness of the plans with respect to positioning uncertainties are evaluated. METHODS: A head&neck cancer patient underwent a [(61)Cu]Cu-ATSM PET/CT-scan. PET-SUVs were converted to prescribed dose with a base dose of 60Gy, and target mean dose 90Gy. The voxel-based prescription was converted into 3, 5, 7, 9, and 11 discrete prescription levels. Optimization was performed in Eclipse, varying the following parameters: MLC leaf width (5mm and 2.5mm), number of arcs (1 and 2) and collimator rotation (0, 15, 30 and 45 degrees). Dose conformity was evaluated using quality volume histograms (QVHs), and relative volumes receiving within ±5% of prescribed dose (Q(0.95–1.05)). Deliverability was tested using a Delta4 phantom. Robustness was tested by shifting the isocenter 1mm and 2mm in all directions, and recalculating the dose. RESULTS: Good conformity was obtained using MLC leaf width 2.5mm, two arcs, and collimators 45/315 degrees, with Q(0.95–1.05)=92.8%, 91.6%, 89.7% and 84.6%. Using only one arc or increasing the MLC leaf width had a small deteriorating effect of 2–5%. Small changes in collimator angle gave small changes, but large changes in collimator angle gave a larger decrease in plan conformity; for angles of 15 and 0 degrees (two arcs, 2.5 mm leaf width), Q(0.95–1.05) decreased by up to 15%. Consistency between planned and delivered dose was good, with ~90% of gamma values <1. For 1mm shift, Q(0.95–1.05) was decreased by 5–15%, while for 2mm shift, Q(0.95–1.05) was decreased to 55–60%. CONCLUSIONS: Results demonstrate feasibility of planning of prescription doses with multiple levels for dose painting using RapidArc, and plans were deliverable. Robustness to positional error was low