OpenGray is a new benchmark that evaluates radiotherapy planning agents on their decision-making rather than just dose outcomes. It extends the OpenKBP-Opt head-and-neck dataset with tasks that have known correct decisions, including contradictions with dose goals and a feasible control plan. Four systems—ranging from a rule-based script to full LLM agents—were tested on 30 patients, revealing that high decision accuracy can mask specific failures such as missed hard goals and inadequate stopping actions.
By Humza Nusrat, Kundan Thind
arXiv:2608.30419v1 Announce Type: new
Abstract: Healthcare workforce scheduling is an NP-hard optimization problem requiring simultaneous satisfaction of labor regulations, coverage requirements, emp...
By Vipul Patel, Anirudh Deodhar, Dagnachew Birru
RadOnc-Agent is an AI framework that organizes radiotherapy into four clinical phases and offers 26 callable functions via a conversational interface. A large‑language‑model controller maps clinical intent to schema‑constrained calls, maintains patient and workflow context, and routes requests to specialist services. In evaluations, the system achieved high accuracy in single‑function calls (98.79%) and cross‑stage workflow completions (96.50% scripted, 96.67% real‑patient), demonstrating technical feasibility of LLM‑orchestrated coordination across heterogeneous radiotherapy capabilities.
By Caiwen Jiang, Shuoyang Wei, Songlin Zhao, Junyu Li, Jingyuan Chen, Wei Liu
The paper presents a generative AI model that predicts 3‑D radiation dose distributions based solely on user‑defined preference settings, allowing planners to balance trade‑offs between organs‑at‑risk and target volumes. This approach offers greater flexibility and personalization compared to traditional deep‑learning models that rely on institutional reference plans. The authors report that their method can outperform the Varian RapidPlan model in adaptability and plan quality in certain scenarios, and it is designed for easy integration with clinical treatment planning systems.
By Riqiang Gao, Simon Arberet, Martin Kraus, Han Liu, Wilko FAR Verbakel, Dorin Comaniciu, Florin-Cristian Ghesu, Ali Kamen
arXiv:2607. 13877v1 Announce Type: new Abstract: Brain tumor progression exhibits spatially heterogeneous growth, patient-specific treatment response, and complex interactions with surrounding anatomy, making accurate long-term prediction challenging.
By Wenxi Liu, Michael Trimboli, Xianqi Li
Dose-PlanNet is a physics-guided 3D deep learning architecture that predicts radiotherapy dose distributions for prostate cancer. In a prospective trial, the model achieved target coverage comparable to clinical plans while slightly reducing target homogeneity, yet it significantly improved high‑dose organ‑at‑risk sparing. Automated plans met clinical acceptance criteria in 11 of 14 Moderate Hypofraction and 9 of 12 SBRT plans, demonstrating that physics-informed deep learning can accelerate radiotherapy workflows without compromising dosimetric quality.
By Ankit Bhattacharjee, Sougata Maity, Santam Chakraborty, Indranil Mallick
The study presents a virtual imaging trial framework that uses reinforcement learning to optimize computed tomography (CT) protocols, balancing liver lesion detectability against radiation dose. By training a Proximal Policy Optimization agent on 63 computational human models across 468 parameter combinations, the authors demonstrate that evaluating only eight protocols per patient—about 2% of exhaustive testing—recovers 98.2% of the optimal objective. Conditioning the agent on patient‑specific CT localizer embeddings further improves zero‑simulation recovery by 10.7 percentage points compared to a localizer‑blind policy.
By Jiaqi Zou, David Fenwick, Vahid Tarokh, Nicholas Felice, Jayasai Rajagopal, Anuj Kapadia, Ehsan Samei, Navid NaderiAlizadeh, Ehsan Abadi
Dose-PlanNet is a physics-guided 3D deep learning model that predicts radiotherapy dose distributions for prostate cancer, aiming to automate complex treatment planning. In a prospective trial, the model achieved comparable target coverage while slightly reducing target homogeneity, yet it significantly improved high-dose organ‑at‑risk sparing. Automated plans met clinical acceptance criteria in most cases across both moderate hypofraction and stereotactic body radiation therapy arms.
arXiv:2607. 03761v1 Announce Type: new Abstract: Computed Tomography (CT) is indispensable in clinical diagnostics, yet minimizing radiation dose without compromising image quality remains a critical challenge.
By Shunta Nonaga, Koji Tabata, Junya Honda, Hiroyuki Kudo, Wataru Yashiro, Tamiki Komatsuzaki
arXiv:2608. 09343v1 Announce Type: new Abstract: Simulation-based optimization (SBO) evaluates executable policies under stochastic dynamics, but most methods treat the simulator as a black box: aggregate scores rank candidates without revealing why they fail or which policy logic should change.
By Jinbo Li, Chuanhao Li
arXiv:2608. 03086v1 Announce Type: cross Abstract: Microwave ablation (MWA) is a promising minimally invasive treatment for liver tumors, but its therapeutic outcome strongly depends on patient-specific planning of antenna insertion trajectory, power, and treatment duration.
By Seonaeng Cho, Minjee Seo, Minju Seol, Juil Park, Joon Ho Kwon, Kyungho Yoon
arXiv:2609.14418v1 Announce Type: cross
Abstract: Dynamic multi-mode resource-constrained project scheduling requires decisions to be made under precedence constraints, limited resources, multiple ex...
By Yuan Tian, Yi Mei, Mengjie Zhang