Generalizable Magnetic Resonance Imaging-based Nasopharyngeal Carcinoma Delineation: Bridging Gaps Across Multiple Centers and Raters With Active Learning.

Purpose: To develop a deep learning method exploiting active learning and source-free domain adaptation for gross tumor volume delineation in nasopharyngeal carcinoma (NPC), addressing the variability and inaccuracy when deploying segmentation models in multicenter and multirater settings.

Methods And Materials: One thousand fifty-seven magnetic resonance imaging scans of patients with NPC from 5 hospitals were retrospectively collected and annotated by experts from the same medical group with consensus for multicenter adaptation evaluation. One data set was used for model development (source domain), with the remaining 4 for adaptation testing (target domains).

Multicriteria optimization achieves superior normal tissue sparing in volumetric modulated arc therapy for gastric cancer.

Objective: To evaluate the benefits of volumetric modulated arc therapy (VMAT) based on multicriteria optimization (MCO) for gastric cancer patients, particularly the protection of serial organs at risk (OARs) that overlap with the target volume.

Methods: MCO and single-criterion optimization (SCO) VMAT plans were conducted among 30 gastric cancer patients, with a prescription dose of 50.4 Gy delivered in 28 fractions.

Pipeline multi-type high consequence area identification based on mask R-CNN with fused attention mechanisms.

Identification of high consequence areas is an important task in pipeline integrity management. However, traditional identification methods are generally characterized by low efficiency, high cost and low accuracy. For this reason, this paper proposes a recognition method based on the improved algorithm Mask Region-based Convolutional Neural Network.

Optimization of collimator angle combined island blocking with parked gap achieves superior normal tissue sparing in SBRT planning of multiple liver lesions.

Purpose: To propose an efficient collimator angle optimization method by combining island blocking (IB) and parked gap (PG) problem to reduce the radiotherapy dose for normal tissue. The reduction will be done with single-isocenter multi-lesion volumetric modulated arc therapy (VMAT) for the stereotactic body radiation therapy (SBRT) of liver cancer.

Methods: A novel collimator angle optimization algorithm was developed based on the two-dimensional projection of targets on a beam's eye view (BEV) plane as a function of gantry and collimator angle.

Dosimetric comparison in sparing normal tissue dosage by using auto-SBRT planning in oligo liver tumors.

Purpose: The study aimed to compare the dosimetric distribution of VMAT plans by increasing the number of half arcs in liver SBRT and investigate the effect by using automatic plan software in plan optimization.

Method: Thirty-one patients with oligo liver tumors were randomly selected. VMAT treatment plans with different numbers of coplanar half arcs were generated.

Unsupervised Domain Adaptive Dose Prediction via Cross-Attention Transformer and Target-Specific Knowledge Preservation.

Radiotherapy is one of the leading treatments for cancer. To accelerate the implementation of radiotherapy in clinic, various deep learning-based methods have been developed for automatic dose prediction. However, the effectiveness of these methods heavily relies on the availability of a substantial amount of data with labels, i.

TransDose: Transformer-based radiotherapy dose prediction from CT images guided by super-pixel-level GCN classification.

Radiotherapy is a mainstay treatment for cancer in clinic. An excellent radiotherapy treatment plan is always based on a high-quality dose distribution map which is produced by repeated manual trial-and-errors of experienced experts. To accelerate the radiotherapy planning process, many automatic dose distribution prediction methods have been proposed recently and achieved considerable fruits.

A Transformer-Embedded Multi-Task Model for Dose Distribution Prediction.

Radiation therapy is a fundamental cancer treatment in the clinic. However, to satisfy the clinical requirements, radiologists have to iteratively adjust the radiotherapy plan based on experience, causing it extremely subjective and time-consuming to obtain a clinically acceptable plan. To this end, we introduce a transformer-embedded multi-task dose prediction (TransMTDP) network to automatically predict the dose distribution in radiotherapy.

Comprehensive Evaluation of a Deep Learning Model for Automatic Organs-at-Risk Segmentation on Heterogeneous Computed Tomography Images for Abdominal Radiation Therapy.

Purpose: Our purpose was to develop a deep learning model (AbsegNet) that produces accurate contours of 16 organs at risk (OARs) for abdominal malignancies as an essential part of fully automated radiation treatment planning.

Methods And Materials: Three data sets with 544 computed tomography scans were retrospectively collected. Data set 1 was split into 300 training cases and 128 test cases (cohort 1) for AbsegNet.

Automated treatment planning for liver cancer stereotactic body radiotherapy.

Purpose: To evaluate the quality of fully automated stereotactic body radiation therapy (SBRT) planning based on volumetric modulated arc therapy, which can reduce the reliance on historical plans and the experience of dosimetrists.

Methods: Fully automated re-planning was performed on twenty liver cancer patients, automated plans based on automated SBRT planning (ASP) program and manual plans were conducted and compared. One patient was randomly selected and evaluate the repeatability of ASP, ten automated and ten manual SBRT plans were generated based on the same initial optimization objectives.

Efficient Multi-Organ Segmentation From 3D Abdominal CT Images With Lightweight Network and Knowledge Distillation.

Accurate segmentation of multiple abdominal organs from Computed Tomography (CT) images plays an important role in computer-aided diagnosis, treatment planning and follow-up. Currently, 3D Convolution Neural Networks (CNN) have achieved promising performance for automatic medical image segmentation tasks. However, most existing 3D CNNs have a large set of parameters and huge floating point operations (FLOPs), and 3D CT volumes have a large size, leading to high computational cost, which limits their clinical application.

Construction of cuproptosis-related gene signature to predict the prognosis and immunotherapy efficacy of patients with bladder cancer through bioinformatics analysis and experimental validation.

A new form of cell death, copper-dependent cell death (termed cuproptosis), was illustrated in a recent scientific study. However, the biological function or prognostic value of cuproptosis regulators in bladder cancer (BLCA) remains unknown. Sequencing data obtained from BLCA samples in TCGA and GEO databases were preprocessed for analysis.

WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image.

Whole abdominal organ segmentation is important in diagnosing abdomen lesions, radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from 3D volumes is time-consuming and very expensive. Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation.

CT-Only Radiotherapy: An Exploratory Study for Automatic Dose Prediction on Rectal Cancer Patients Deep Adversarial Network.

Purpose: Current deep learning methods for dose prediction require manual delineations of planning target volume (PTV) and organs at risk (OARs) besides the original CT images. Perceiving the time cost of manual contour delineation, we expect to explore the feasibility of accelerating the radiotherapy planning by leveraging only the CT images to produce high-quality dose distribution maps while generating the contour information automatically.

Materials And Methods: We developed a generative adversarial network (GAN) with multi-task learning (MTL) strategy to produce accurate dose distribution maps without manually delineated contours.

Treatment plan comparison of volumetric-modulated arc therapy to intensity-modulated radiotherapy in lung stereotactic body radiotherapy using either 6- or 10-MV photon energies.

Purpose: The aim of this study was to dosimetrically compare volumetric-modulated arc therapy (VMAT) with intensity-modulated radiotherapy (IMRT) techniques using either 6- or 10-MV photon beam energies in lung stereotactic body radiation therapy (SBRT) plans.

Methods: Thirty patients with primary or metastatic lung tumors eligible for SBRT were randomly selected. VMAT and IMRT treatment plans using either 6- or 10-MV photon energies were generated through automatic SBRT planning software in the RayStation treatment planning system.

Automatic Delineation of Gross Tumor Volume Based on Magnetic Resonance Imaging by Performing a Novel Semisupervised Learning Framework in Nasopharyngeal Carcinoma.

Purpose: We aimed to validate the accuracy and clinical value of a novel semisupervised learning framework for gross tumor volume (GTV) delineation in nasopharyngeal carcinoma.

Methods And Materials: Two hundred fifty-eight patients with magnetic resonance imaging data sets were divided into training (n = 180), validation (n = 20), and testing (n = 58) cohorts. Ground truth contours of nasopharynx GTV (GTVnx) and node GTV (GTVnd) were manually delineated by 2 experienced radiation oncologists.

Multi-constraint generative adversarial network for dose prediction in radiotherapy.

Radiation therapy (RT) is regarded as the primary treatment for cancer in the clinic, aiming to deliver an accurate dose to the planning target volume (PTV) while protecting the surrounding organs at risk (OARs). To improve the effectiveness of the treatment planning, deep learning methods are widely adopted to predict dose distribution maps for clinical treatment planning. In this paper, we present a novel multi-constraint dose prediction model based on generative adversarial network, named Mc-GAN, to automatically predict the dose distribution map from the computer tomography (CT) images and the masks of PTV and OARs.

A novel dosimetric metrics-based risk model to predict local recurrence in nasopharyngeal carcinoma patients treated with intensity-modulated radiation therapy.

Background: To develop a risk model based on dosimetric metrics to predict local recurrence in nasopharyngeal carcinoma (NPC) patients treated with intensive modulated radiation therapy (IMRT).

Methods: 493 consecutive patients were included, among whom 44 were with local recurrence. One-to-two propensity score matching (PSM) was used to balance variables between recurrent and non-recurrent groups.

Influence of target dose heterogeneity on dose sparing of normal tissue in peripheral lung tumor stereotactic body radiation therapy.

Objective: To evaluate the influence of target dose heterogeneity on normal tissue dose sparing for peripheral lung tumor stereotactic body radiation therapy (SBRT).

Methods: Based on the volumetric-modulated arc therapy (VMAT) technique, three SBRT plans with homogeneous, moderate heterogeneous, and heterogeneous (HO, MHE, and HE) target doses were compared in 30 peripheral lung tumor patients. The prescription dose was 48 Gy in 4 fractions.

MRF-RFS: A Modified Random Forest Recursive Feature Selection Algorithm for Nasopharyngeal Carcinoma Segmentation.

Background: An accurate and reproducible method to delineate tumor margins is of great importance in clinical diagnosis and treatment. In nasopharyngeal carcinoma (NPC), due to limitations such as high variability, low contrast, and discontinuous boundaries in presenting soft tissues, tumor margin can be extremely difficult to identify in magnetic resonance imaging (MRI), increasing the challenge of NPC segmentation task.

Objectives: The purpose of this work is to develop a semiautomatic algorithm for NPC image segmentation with minimal human intervention, while it is also capable of delineating tumor margins with high accuracy and reproducibility.

Description and evaluation of a new volumetric-modulated arc therapy plan complexity metric.

This study describes a new plan complexity metric for volumetric-modulated arc therapy (VMAT) and evaluates the relationship of this metric with the VMAT dosimetric accuracy. The new modulation complexity score for VMAT (NMCSv) that is based on the aperture shape and multi-leaf collimator (MLC) leaf travel is described. Its performance is evaluated through correlation and receiver operating characteristic (ROC) analyses with patient-specific gamma passing rates using 2 3-dimensional diode arrays.

Toxicity and dosimetric analysis of nasopharyngeal carcinoma patients undergoing radiotherapy with IMRT or VMAT: A regional center's experience.

Objectives: To observe the differences of dosimetric parameters and late toxicities in Nasopharyngeal Carcinoma (NPC) patients treated with intensity-modulated radiotherapy (IMRT) or volumetric modulated arc therapy (VMAT), which may provide the selective basis about radiation technology in clinical practices.

Methods And Materials: Dosimetric parameters and late toxicities were collected and retrospectively analyzed from 627 NPC patients (stage as I-IVA/IVB) between January 2010 and December 2015.

Results: The median D of all targets and D of PGTVnd (regional lymph nodes) were lower in VAMT than those in IMRT, while the median D and D of PGTVnx (primary lesions) were higher in VMAT than those in IMRT (p < 0.

Endothelial Autophagy: an Effective Target for Radiation-induced Cerebral Capillary Damage.

Toxicity to central nervous system tissues is the common side effects for radiotherapy of brain tumor. The radiation toxicity has been thought to be related to the damage of cerebral endothelium. However, because of lacking a suitable high-resolution vivo model, cellular response of cerebral capillaries to radiation remained unclear.