Therapeutic Radiology

Internal validation of THINKERS-PA for outcome prediction after pituitary adenoma radiosurgery.

BACKGROUND: Outcome prediction after Gamma Knife radiosurgery (GKRS) for pituitary adenomas remains guided by tumor anatomy, functional status, prior treatment, optic-apparatus proximity, and physician experience rather than individualized estimates of tumor response, biochemical remission, and endocrine morbidity. We developed THINKERS-PA, a mixture-of-experts (MoE) framework for outcome predicti...

Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS (THINKERS): a mixture-of-experts framework for gastrointestinal brain metastases.

BACKGROUND: Brain metastases from gastrointestinal (GI) malignancies are heterogeneous, while stereotactic radiosurgery dose selection remains guided by lesion size, location, prior treatment, and physician judgment rather than individualized estimates of local failure and toxicity. We developed THINKERS-GI, a three-expert mixture-of-experts framework for outcome prediction and dose-policy evaluat...

Improving portability of knowledge-based planning using an LLM-driven plan refinement framework in lung radiotherapy.

BACKGROUND: Knowledge-based planning (KBP) has improved the quality and efficiency of radiotherapy treatment planning. However, its broader clinical a...

Deep learning auto-contouring of target and organs-at-risk on post-catheter implant CT images for prostate HDR brachytherapy.

BACKGROUND: Accurate delineation of the prostate and surrounding organs-at-risk (OARs) is essential for HDR prostate brachytherapy. Manual contouring ...

Deep learning augmented pathological grading and intraductal carcinoma of the prostate signatures improve recurrence prediction after intensity-modulated radiation therapy.

Locally advanced prostate cancer (PCa) is associated with a high recurrence rate even after curative treatment. We aimed to develop a precise risk mod...

Machine Learning Prediction of Local Failure in GI Brain Metastases on the Day of Gamma Knife Radiosurgery.

BACKGROUND: Time to local failure after stereotactic radiosurgery (SRS), including Gamma Knife radiosurgery (GKRS), for gastrointestinal (GI) brain me...

Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS (THINKERS): an AI model for lung cancer brain metastases.

BACKGROUND: Prescription dose selection for lung brain metastases treated with stereotactic radiosurgery (SRS) remains largely guided by generalized p...

Automatic treatment planning using reinforcement learning for high-dose-rate prostate brachytherapy.

Purpose.In high-dose-rate (HDR) prostate brachytherapy procedures, needle placement solely relies on physician experience. We investigated the feasibi...

Volumetric Assessment of Vestibular Schwannomas Exhibiting Internal Growth After Stereotactic Radiosurgery: Application of Artificial Intelligence.

PURPOSE: Vestibular schwannomas (VS) are monitored for growth after stereotactic radiosurgery (SRS) using serial MRI. Conventional measurements often ...

A simple parameter to guide plan optimization for robotic pancreas SBRT.

BACKGROUND AND PURPOSE: Pancreatic SBRT planning is challenging due to the proximity of highly sensitive organs at risk. Since automated planning is c...

UNetrDose: a fast and accurate transformer-based dose prediction model for radiotherapy.

To develop and validate UNetrDose, a Transformer-based deep learning model designed for fast and accurate photon beamlet dose prediction. The study ai...

Reference-Free large language model agents for physician-guided radiotherapy treatment planning.

BACKGROUND: Large language models (LLMs) have recently demonstrated exceptional capabilities, offering the potential to streamline workflows and enhan...

Personalized prediction of local control after stereotactic radiosurgery for craniopharyngioma: a multicenter machine learning survival model.

BACKGROUND: Stereotactic radiosurgery (SRS) is used in selected patients with craniopharyngioma, yet counseling and follow-up planning often rely on p...

Era-specific prognostic factors and an interpretable machine-learning survival model for renal cell carcinoma brain metastases.

BACKGROUND: Brain metastases (BM) in renal cell carcinoma (RCC) are associated with poor prognosis and limited clinical guidance. We aimed to identify...

Quantifying the spatial information loss of Dose-Volume Histograms in Gamma Knife radiosurgery via unsupervised 3D dose geometry embedding.

Dose-Volume Histograms (DVH) discard spatial information by summarizing 3D distributions into 1D curves. This study quantifies this loss in Gamma Knif...

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of mana...

Predicting local control of brain metastases after Gamma Knife radiosurgery: a multimodal deep learning-radiomics approach.

BACKGROUND: Accurate prediction of local control (LC) following Gamma Knife radiosurgery (GKRS) remains clinically challenging for patients with brain...

Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS (THINKERS): an AI model for meningioma.

BACKGROUND: Prescription dose selection for intracranial meningiomas treated with stereotactic radiosurgery remains guided by tumor volume, anatomical...

Evaluating Risk Factors Associating With Late Recurrence After Low-Dose-Rate Brachytherapy for Prostate Cancer Using Machine Learning.

BACKGROUND: The Phoenix definition is widely used to define biochemical recurrence (BCR) after radiation therapy for prostate cancer; however, a defin...

Dual AI models for gamma knife radiosurgery in craniopharyngioma: prescription dose modeling and outcome risk prediction.

BACKGROUND: Gamma Knife radiosurgery (GKRS) is an established adjunct for residual or recurrent craniopharyngioma, yet prescription dose selection rem...