Performance Comparison of 10 State-of-the-Art Machine Learning Algorithms for Outcome Prediction Modeling of Radiation-Induced Toxicity.

Purpose: To evaluate the efficacy of prominent machine learning algorithms in predicting normal tissue complication probability using clinical data obtained from 2 distinct disease sites and to create a software tool that facilitates the automatic determination of the optimal algorithm to model any given labeled data set.

Methods And Materials: We obtained 3 sets of radiation toxicity data (478 patients) from our clinic: gastrointestinal toxicity, radiation pneumonitis, and radiation esophagitis. These data comprised clinicopathological and dosimetric information for patients diagnosed with non-small cell lung cancer and anal squamous cell carcinoma.

An automated treatment planning portfolio for whole breast radiotherapy.

Background: Automation in radiotherapy presents a promising solution to the increasing cancer burden and workforce shortages. However, existing automated methods for breast radiotherapy lack a comprehensive, end-to-end solution that meets varying standards of care.

Purpose: This study aims to develop a complete portfolio of automated radiotherapy treatment planning for intact breasts, tailored to individual patient factors, clinical approaches, and available resources.

Health System Factors Influencing the Integration of Pre-Exposure Prophylaxis into Antenatal and Postnatal Clinic Services in Cape Town, South Africa.

Introduction: Oral pre-exposure prophylaxis (PrEP) is an effective and safe option to prevent HIV acquisition and vertical HIV transmission in pregnant and breastfeeding women. Understanding health system factors influencing the integration of PrEP into care for pregnant and breastfeeding women is key to increasing access. We explored managers' and health care workers' (HCWs) experiences with integrating PrEP into antenatal care and postnatal care services in primary health care clinics in Cape Town, South Africa.

Effect of the social environment on olfaction and social skills in wild-type and a mouse model of autism.

Autism spectrum disorders (ASD) are complex, polygenic and heterogenous neurodevelopmental conditions. The severity of autism-associated variants is influenced by environmental factors, particularly social experiences during the critical neurodevelopmental period. While early behavioral interventions have shown efficacy in some children with autism, pharmacological support for core features - impairments in social interaction and communication, and stereotyped or restricted behaviors - is currently lacking.

Chromosome Structural Rearrangements in Invasive Haplodiploid Ambrosia Beetles Revealed by the Genomes of Euwallacea fornicatus (Eichhoff) and Euwallacea similis (Ferrari) (Coleoptera, Curculionidae, Scolytinae).

Bark and ambrosia beetles are among the most ecologically and economically damaging introduced plant pests worldwide. Life history traits including polyphagy, haplodiploidy, inbreeding polygyny, and symbiosis with fungi contribute to their dispersal and impact. Species vary in their interactions with host trees, with many attacking stressed or recently dead trees, such as the globally distributed Euwallacea similis (Ferrari).

Autocontouring of primary lung lesions and nodal disease for radiotherapy based only on computed tomography images.

Background And Purpose: In many clinics, positron-emission tomography is unavailable and clinician time extremely limited. Here we describe a deep-learning model for autocontouring gross disease for patients undergoing palliative radiotherapy for primary lung lesions and/or hilar/mediastinal nodal disease, based only on computed tomography (CT) images.

Materials And Methods: An autocontouring model (nnU-Net) was trained to contour gross disease in 379 cases (352 training, 27 test); 11 further test cases from an external centre were also included.

Global Workforce and Access: Demand, Education, Quality.

There has long existed a substantial disparity in access to radiotherapy globally. This issue has only been exacerbated as the growing disparity of cancer incidence between high-income countries (HIC) and low and middle-income countries (LMICs) widens, with a pronounced increase in cancer cases in LMICs. Even within HICs, iniquities within local communities may lead to a lack of access to care.

Deep learning-based statistical robustness evaluation of intensity-modulated proton therapy for head and neck cancer.

. Previous methods for robustness evaluation rely on dose calculation for a number of uncertainty scenarios, which either fails to provide statistical meaning when the number is too small (e.g.

Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues.

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of D (D0.

Landmark-based auto-contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer.

Background: The delineation of clinical target volumes (CTVs) for radiotherapy for nasopharyngeal cancer is complex and varies based on the location and extent of disease.

Purpose: The current study aimed to develop an auto-contouring solution following one protocol guidelines (NRG-HN001) that can be adjusted to meet other guidelines, such as RTOG-0225 and the 2018 International guidelines.

Methods: The study used 2-channel 3-dimensional U-Net and nnU-Net framework to auto-contour 27 normal structures in the head and neck (H&N) region that are used to define CTVs in the protocol.

A Visualization and Radiation Treatment Plan Quality Scoring Method for Triage in a Population-Based Context.

Purpose: Our purpose was to develop a clinically intuitive and easily understandable scoring method using statistical metrics to visually determine the quality of a radiation treatment plan.

Methods And Materials: Data from 111 patients with head and neck cancer were used to establish a percentile-based scoring system for treatment plan quality evaluation on both a plan-by-plan and objective-by-objective basis. The percentile scores for each clinical objective and the overall treatment plan score were then visualized using a daisy plot.

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.

Background: Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography (PET), are used in the routine clinic to assist radiation oncologists for accurate GTV delineation. However, the availability of PET imaging may not always be guaranteed.

METRIC-GUIDED IMAGE RECONSTRUCTION BOUNDS VIA CONFORMAL PREDICTION.

Recent advancements in machine learning have led to the development of novel medical imaging systems and algorithms that address ill-posed problems. Assessing their trustworthiness and understanding how to deploy them safely at test time remains an important and open problem. In this work, we propose using conformal prediction to compute valid and distribution-free bounds on downstream metrics given reconstructions generated by one algorithm, and retrieve upper/lower bounds and inlier/outlier reconstructions according to the adjusted bounds.

Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning.

Purpose: Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool.

Improved reference quality genome sequence of the plastic-degrading greater wax moth, Galleria mellonella.

Galleria mellonella is a pest of honeybees in many countries because its larvae feed on beeswax. However, G. mellonella larvae can also eat various plastics, including polyethylene, polystyrene, and polypropylene, and therefore, the species is garnering increasing interest as a tool for plastic biodegradation research.

Metabolic disruptions and impaired reproductive fitness in wild-caught freshwater turtles (Emydura macquarii macquarii) exposed to elevated per- and polyfluoroalkyl substances (PFAS).

Per- and poly-fluoroalkyl substances (PFAS) pose a threat to organisms and ecosystems due to their persistent nature. Ecotoxicology endpoints used in regulatory guidelines may not reflect multiple, low-level but persistent stressors. This study examines the biological effects of PFAS on Eastern short-necked turtles in Queensland, Australia.

Artificial Intelligence-Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care.

Purpose: Increased automation has been identified as one approach to improving global cancer care. The Radiation Planning Assistant (RPA) is a web-based tool offering automated radiotherapy (RT) contouring and planning to low-resource clinics. In this study, the RPA workflow and clinical acceptability were assessed by physicians around the world.

SC-GAN: Structure-completion generative adversarial network for synthetic CT generation from MR images with truncated anatomy.

Creating synthetic CT (sCT) from magnetic resonance (MR) images enables MR-based treatment planning in radiation therapy. However, the MR images used for MR-guided adaptive planning are often truncated in the boundary regions due to the limited field of view and the need for sequence optimization. Consequently, the sCT generated from these truncated MR images lacks complete anatomic information, leading to dose calculation error for MR-based adaptive planning.