Implementation and clinical evaluation of an in-house thoracic auto-segmentation model for 0.35 T magnetic resonance imaging guided radiotherapy.

Background And Purpose: Magnetic resonance imaging-guided radiotherapy (MRgRT) facilitates high accuracy, small margins treatments at the cost of time-consuming and labor-intensive manual delineation of organs-at-risk (OARs). Auto-segmentation models show promise in streamlining this workflow. This study investigates the clinical applicability of a set of thoracic OAR segmentation models for baseline treatment planning in lung tumor patients.

Deep learning-based contour propagation in magnetic resonance imaging-guided radiotherapy of lung cancer patients.

Fast and accurate organ-at-risk (OAR) and gross tumor volume (GTV) contour propagation methods are needed to improve the efficiency of magnetic resonance (MR) imaging-guided radiotherapy. We trained deformable image registration networks to accurately propagate contours from planning to fraction MR images.Data from 140 stage 1-2 lung cancer patients treated at a 0.

Evaluating large language models as an educational tool for meningioma patients: patient and clinician perspectives.

Background: The study explores the potential of ChatGPT, an advanced large language model (LLM) by OpenAI, in educating patients about meningioma, a common type of brain tumor. While ChatGPT has generated significant debate regarding its utility and ethics, its growing popularity suggests that patients may increasingly use such tools for medical information. The study specifically examines how patients who have undergone radiation therapy for meningioma perceive the information generated by ChatGPT, integrating both patient feedback and clinical assessment.

Prospective Assessment of MR Guided Single-Fraction Stereotactic Ablative Radiation Therapy for Peripheral Lung Metastases: Dosimetric and Clinical Outcomes.

Objectives: This study aimed to evaluate the feasibility and safety of online MR guided single-fraction stereotactic ablative radiation therapy (SABR) and to assess acute changes in pulmonary function tests (PFTs), inflammatory markers, patient-reported quality of life (QoL), and dosimetric outcomes.

Methods And Materials: Patients with lung metastases were included in this single-center prospective study. Patients underwent MR guided single-fraction SABR.

Online adaptive stereotactic body radiotherapy for localized prostate cancer in patients with lower urinary tract symptoms and/or prostate hyperplasia (X-SMILE).

Background: Stereotactic body radiotherapy (SBRT) for localized prostate cancer offers non-inferior oncological outcomes and toxicity profiles compared to conventionally or moderately hypofractioned radiotherapy regimens, with shorter treatment durations. However, SBRT may not be suitable for all patients, particularly those with lower urogenital tract symptoms and/or prostatic hyperplasia.

Methods: This study aims to evaluate the safety and efficacy of weekly computed tomography (CT) or magnetic resonance image (MRI)-guided online adaptive SBRT in patients with intermediate to high-risk localized prostate cancer (i.

Patient-specific uncertainty calibration of deep learning-based autosegmentation networks for adaptive MRI-guided lung radiotherapy.

Uncertainty assessment of deep learning autosegmentation (DLAS) models can support contour corrections in adaptive radiotherapy (ART), e.g. by utilizing Monte Carlo Dropout (MCD) uncertainty maps.

Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy.

Background: This study aims to evaluate the capabilities and limitations of large language models (LLMs) for providing patient education for men undergoing radiotherapy for localized prostate cancer, incorporating assessments from both clinicians and patients.

Methods: Six questions about definitive radiotherapy for prostate cancer were designed based on common patient inquiries. These questions were presented to different LLMs [ChatGPT‑4, ChatGPT-4o (both OpenAI Inc.

Predicting cisplatin tolerability in older adults with head and neck cancer - Insights for improved chemoradiation outcomes.

Purpose: Cumulative cisplatin doses of ≥ 200 mg/m improve survival in adults with head-and-neck squamous cell carcinoma (HNSCC) undergoing chemoradiation, but many older adults with HNSCC cannot receive this prognostically relevant dose due to toxicities. This study aims to develop predictive models to assess the likelihood of older adults with HNSCC receiving ≥ 200 mg/m cisplatin during chemoradiation.

Methods: 366 patients from the SENIOR database, an international cohort of adults ≥ 65 years with HNSCC, received definitive chemoradiation with single-agent cisplatin and were analyzed.

Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen.

Background: Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since they do not incorporate manual segmentation from treatment planning and previous fractions.

Purpose: In this work, we investigate patient-specific (PS) auto-segmentation methods leveraging expert-segmented planning and prior fraction MR images (MRIs) to improve auto-segmentation on consecutive treatment days.

MRgRT real-time target localization using foundation models for contour point tracking and promptable mask refinement.

. This study aimed to evaluate two real-time target tracking approaches for magnetic resonance imaging (MRI) guided radiotherapy (MRgRT) based on foundation artificial intelligence models..

Patient-Specific Deep Learning Tracking Framework for Real-Time 2D Target Localization in Magnetic Resonance Imaging-Guided Radiation Therapy.

Purpose: We propose a tumor tracking framework for 2D cine magnetic resonance imaging (MRI) based on a pair of deep learning (DL) models relying on patient-specific (PS) training.

Methods And Materials: The chosen DL models are: (1) an image registration transformer and (2) an auto-segmentation convolutional neural network (CNN). We collected over 1,400,000 cine MRI frames from 219 patients treated on a 0.

Proton dose calculation with LSTM networks in presence of a magnetic field.

To present a long short-term memory (LSTM) network-based dose calculation method for magnetic resonance (MR)-guided proton therapy.35 planning computed tomography (CT) images of prostate cancer patients were collected for Monte Carlo (MC) dose calculation under a perpendicular 1.5 T magnetic field.

Adjuvant (chemo)radiotherapy for patients with head and neck cancer: can comorbidity risk scores predict outcome?

Purpose: This study compares the objective American Society of Anesthesiologists (ASA) and Adult Comorbidity Evaluation-27 (ACE-27) scores with the subjective Eastern Cooperative Oncology Group performance status (ECOG PS) for patient outcome prediction.

Methods: We retrospectively analyzed head and neck squamous cell carcinoma patients treated with adjuvant (chemo)radiotherapy at the LMU Munich from June 2008 to June 2015. The study focused on associations between patient outcomes; treatment failures; known risk factors (including human papillomavirus [HPV] status and tumor stage); and the comorbidity indices ECOG-PS, ASA score, and ACE-27.

Targeted RT study: results on early toxicity of targeted therapies and radiotherapy.

Purpose/objective: Currently, there are few prospective data on the tolerability of combining targeted therapies (TT) with radiation therapy (RT). The objective of this prospective study was to assess the feasibility and toxicity of pairing RT with concurrent TT in cancer patients. The aim was to enhance the existing evidence base for the simultaneous administration of targeted substances together with radiotherapy.

MRI-based ventilation and perfusion imaging to predict radiation-induced pneumonitis in lung tumor patients at a 0.35T MR-Linac.

Background And Purpose: Radiation-induced pneumonitis (RP), diagnosed 6-12 weeks after treatment, is a complication of lung tumor radiotherapy. So far, clinical and dosimetric parameters have not been reliable in predicting RP. We propose using non-contrast enhanced magnetic resonance imaging (MRI) based functional parameters acquired over the treatment course for patient stratification for improved follow-up.

Re-irradiation treatment regimens for patients with recurrent glioma - Evaluation of the optimal dose and best concurrent therapy.

Purpose: Re-irradiation (reRT) is an effective treatment modality for patients with recurrent glioma. Data on dose escalation, the use of simulated integrated boost and concomitant therapy to reRT are still scarce. In this monocentric cohort of n = 223 patients we investigated the influence of reRT dose escalation as well as the concomitant use of bevacizumab (BEV) with regard to post-recurrence survival (PRS) and risk of radionecrosis (RN).

FET PET-based target volume delineation for the radiotherapy of glioblastoma: A pictorial guide to help overcome methodological pitfalls.

PET is increasingly used for target volume definition in the radiotherapy of glioblastoma, as endorsed by the 2023 ESTRO-EANO guidelines. In view of its growing adoption into clinical practice and upcoming PET-based multi-center trials, this paper aims to assist in overcoming common pitfalls of FET PET-based target delineation in glioblastoma.

Metabolic pathway-based subtypes associate glycan biosynthesis and treatment response in head and neck cancer.

Head and Neck Squamous Cell Carcinoma (HNSCC) is a heterogeneous malignancy that remains a significant challenge in clinical management due to frequent treatment failures and pronounced therapy resistance. While metabolic dysregulation appears to be a critical factor in this scenario, comprehensive analyses of the metabolic HNSCC landscape and its impact on clinical outcomes are lacking. This study utilized transcriptomic data from four independent clinical cohorts to investigate metabolic heterogeneity in HNSCC and define metabolic pathway-based subtypes (MPS).

The role of online MR-guided multi-fraction stereotactic ablative radiotherapy in lung tumours.

Background: The aim of this prospective observational study was to evaluate the dosimetry benefits, changes in pulmonary function, and clinical outcome of online adaptive MR-guided SBRT.

Methods: From 11/2020-07/2022, 45 consecutive patients with 59 lesions underwent multi-fraction SBRT (3-8 fractions) at our institution. Patients were eligible if they had biopsy-proven NSCLC or lung cancer/metastases diagnosed via clinical imaging.

A Multicenter Evaluation of Different Chemotherapy Regimens in Older Adults With Head and Neck Squamous Cell Carcinoma Undergoing Definitive Chemoradiation.

Purpose: The number of older adults with head and neck squamous cell carcinoma (HNSCC) is increasing, and treatment of these patients is challenging. Although cisplatin-based chemotherapy concomitantly with radiation therapy is considered the standard regimen for patients with locoregionally advanced HNSCC, there is substantial real-world heterogeneity regarding concomitant chemotherapy in older patients with HNSCC.

Methods And Materials: The SENIOR study is an international multicenter cohort study including older patients (≥65 years) with HNSCC treated with definitive radiation therapy at 13 academic centers in the United States and Europe.

Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors.

Background And Purpose: Magnetic resonance imaging guided radiotherapy (MRgRT) offers treatment plan adaptation to the anatomy of the day. In the current MRgRT workflow, this requires the time consuming and repetitive task of manual delineation of organs-at-risk (OARs), which is also prone to inter- and intra-observer variability. Therefore, deep learning autosegmentation (DLAS) is becoming increasingly attractive.

A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in F]FDG PET/CT.

Purpose: PET-derived metabolic tumor volume (MTV) and total lesion glycolysis of the primary tumor are known to be prognostic of clinical outcome in head and neck cancer (HNC). Including evaluation of lymph node metastases can further increase the prognostic value of PET but accurate manual delineation and classification of all lesions is time-consuming and prone to interobserver variability. Our goal, therefore, was development and evaluation of an automated tool for MTV delineation/classification of primary tumor and lymph node metastases in PET/CT investigations of HNC patients.

Evaluation of Concomitant Systemic Treatment in Older Adults With Head and Neck Squamous Cell Carcinoma Undergoing Definitive Radiotherapy.

Importance: The number of older adults with head and neck squamous cell carcinoma (HNSCC) is increasing, and these patients are underrepresented in clinical trials. It is unclear whether the addition of chemotherapy or cetuximab to radiotherapy is associated with improved survival in older adults with HNSCC.

Objective: To examine whether the addition of chemotherapy or cetuximab to definitive radiotherapy is associated with improved survival in patients with locoregionally advanced (LA) HNSCC.