The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning.

Background: Promptable foundation auto-segmentation models like Segment Anything (SA, Meta AI, New York, USA) represent a novel class of universal deep learning auto-segmentation models that could be employed for interactive tumor auto-contouring in RT treatment planning.

Methods: Segment Anything was evaluated in an interactive point-to-mask auto-segmentation task for glioma brain tumor auto-contouring in 16,744 transverse slices from 369 MRI datasets (BraTS 2020 dataset). Up to nine interactive point prompts were automatically placed per slice.

German radiation oncology's next generation: a web-based survey of young biologists, medical physicists, and physicians-from problems to solutions.

Background: Radiation science is of utmost significance not only due to its growing importance for clinical use, but also in everyday life such as in radiation protection questions. The expected increase in cancer incidence due to an aging population combined with technical advancements further implicates this importance and results in a higher need for sufficient highly educated and motivated personnel. Thus, factors preventing young scientists and medical personnel from entering or remaining in the field need to be identified.

Assessing the practice of total neoadjuvant therapy for rectal cancer: an online survey among radiation oncology departments in Germany and German-speaking regions of Austria and Switzerland.

Total neoadjuvant therapy (TNT) of rectal cancer improves rates of pathological complete remission and progression-free survival. With improved clinical response rates, interest grew in a non-operative approach/watch and wait (WaW) for this disease. In 2020, the working groups of ACO/AIO/ARO published a consensus statement on the use of TNT, including a non-operative approach.

Effects of Climate, Soil, Topography and Disturbance on Liana Prevalence.

Lianas (woody vines and climbing monocots) are increasing in abundance in many tropical forests with uncertain consequences for forest functioning and recovery following disturbances. At a global scale, these increases are likely driven by disturbances and climate change. Yet, our understanding of the environmental variables that drive liana prevalence at regional scales is incomplete and geographically biased towards Latin America.

Utility of Ultrasound-Guided Attenuation Parameter (UGAP) in Renal Angiomyolipoma (AML): First Results.

Angiomyolipoma (AML) are the most common benign solid renal mass. Differentiation from malignant tumours is essential. Imaging features in ultrasound may overlap between malignant lesions, especially between renal cell carcinoma (RCC) and AML.

Impact of Cancer Localization on Symptom Burden and Quality of Life in Head and Neck Cancers: A Comparative Study.

Head and neck cancer (HNC) is a critical concern in oncology, with notable disparities in survival rates. While the long-term symptom burden in HNC survivors and its impact on quality of life (QoL) has been explored, there is limited understanding of the influence of cancer localizations on these aspects. This study aims to elucidate the role of cancer localizations in shaping long-term outcomes in HNC patients.

Outcomes of I-125 Low-Dose-Rate Brachytherapy in Patients with Localized Prostate Cancer: A Comprehensive Analysis from a Specialized Tertiary Referral Center.

Low-dose-rate (LDR) brachytherapy with I-125 seeds is one of the most common primary tumor treatments for low-risk and low-intermediate-risk prostate cancer. This report aimed to present an analysis of single-institution long-term results. We analyzed the treatment outcomes of 119 patients with low- and intermediate-risk prostate cancer treated with LDR brachytherapy at our institution between 2014 and 2020.

Comprehensive multimodal deep learning survival prediction enabled by a transformer architecture: A multicenter study in glioblastoma.

Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical, and molecular-pathologic data in a transformer-based deep learning model, addressing data heterogeneity and performance generalizability.

Methods: We propose and evaluate a transformer-based nonlinear and nonproportional survival prediction model. The model employs self-supervised learning techniques to effectively encode the high-dimensional MRI input for integration with nonimaging data using cross-attention.

Principles of artificial intelligence in radiooncology.

Purpose: In the rapidly expanding field of artificial intelligence (AI) there is a wealth of literature detailing the myriad applications of AI, particularly in the realm of deep learning. However, a review that elucidates the technical principles of deep learning as relevant to radiation oncology in an easily understandable manner is still notably lacking. This paper aims to fill this gap by providing a comprehensive guide to the principles of deep learning that is specifically tailored toward radiation oncology.

The course of tumor-related epilepsy in glioblastoma patients: A retrospective analysis.

Purpose: Many patients with glioblastoma suffer from tumor-related seizures. However, there is limited data on the characteristics of tumor-related epilepsy achieving seizure freedom. The aim of this study was to characterize the course of epilepsy in patients with glioblastoma and the factors that influence it.

Treatment with (chemo)-radiation in old patients (≥76 years of age) with newly diagnosed non-metastatic squamous cell cancer of the head and neck region: real-world data from a tertiary referral center.

Purpose: Treatment of patients with cancer of the head and neck region is in focus in a multitude of studies. Of these patients, one patient group, those aged 76 and more, is mostly underrepresented despite requiring thorough and well-reasoned treatment decisions to offer curative treatment. This study investigates real-world data on curative treatment of old (≥76 years) patients with newly diagnosed squamous cell carcinoma of the head and neck region (HNSCC).

"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.

Background: Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process.

Quality requirements for MRI simulation in cranial stereotactic radiotherapy: a guideline from the German Taskforce "Imaging in Stereotactic Radiotherapy".

Accurate Magnetic Resonance Imaging (MRI) simulation is fundamental for high-precision stereotactic radiosurgery and fractionated stereotactic radiotherapy, collectively referred to as stereotactic radiotherapy (SRT), to deliver doses of high biological effectiveness to well-defined cranial targets. Multiple MRI hardware related factors as well as scanner configuration and sequence protocol parameters can affect the imaging accuracy and need to be optimized for the special purpose of radiotherapy treatment planning. MRI simulation for SRT is possible for different organizational environments including patient referral for imaging as well as dedicated MRI simulation in the radiotherapy department but require radiotherapy-optimized MRI protocols and defined quality standards to ensure geometrically accurate images that form an impeccable foundation for treatment planning.

Toward a deep learning-based magnetic resonance imaging only workflow for postimplant dosimetry in I-125 seed brachytherapy for prostate cancer.

Background And Purpose: The current standard imaging-technique for creating postplans in seed prostate brachytherapy is computed tomography (CT), that is associated with additional radiation exposure and poor soft tissue contrast. To establish a magnetic resonance imaging (MRI) only workflow combining improved tissue contrast and high seed detectability, a deep learning-approach for automatic seed segmentation on MRI-scans was developed.

Material And Methods: Patients treated with I-125 seed brachytherapy received a postplan-CT and a 1.

Weight loss induced alleviation of sleep-disordered breathing is associated with improvement of non-alcoholic fatty liver disease.

Introduction: Sleep-disordered breathing (SDB) and non-alcoholic fatty liver disease (NAFLD) are both common comorbidities in obese patients. Structured weight loss programs are effective and can reduce the incidence and severity of obesity-related comorbidities. The objective of the present analysis is to test whether weight loss induced alleviation of SDB is a predictor for improvement of NAFLD.

Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology.

Purpose: The potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology.

Methods: The 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4.

Deep Learning and Registration-Based Mapping for Analyzing the Distribution of Nodal Metastases in Head and Neck Cancer Cohorts: Informing Optimal Radiotherapy Target Volume Design.

We introduce a deep-learning- and a registration-based method for automatically analyzing the spatial distribution of nodal metastases (LNs) in head and neck (H/N) cancer cohorts to inform radiotherapy (RT) target volume design. The two methods are evaluated in a cohort of 193 H/N patients/planning CTs with a total of 449 LNs. In the deep learning method, a previously developed nnU-Net 3D/2D ensemble model is used to autosegment 20 H/N levels, with each LN subsequently being algorithmically assigned to the closest-level autosegmentation.

Status epilepticus in patients with glioblastoma: Clinical characteristics, risk factors, and epileptological outcome.

Purpose: Epilepsy is a common comorbidity in patients with glioblastoma, however, clinical data on status epilepticus (SE) in these patients is sparse. We aimed to investigate the risk factors associated with the occurrence and adverse outcomes of SE in glioblastoma patients.

Methods: We retrospectively analysed electronic medical records of patients with de-novo glioblastoma treated at our institution between 01/2006 and 01/2020 and collected data on patient, tumour, and SE characteristics.

Non-Professional Phagocytosis Increases in Melanoma Cells and Tissues with Increasing E-Cadherin Expression.

Non-professional phagocytosis in cancer has been increasingly studied in recent decades. In malignant melanoma metastasis, cell-in-cell structures have been described as a sign of cell cannibalism. To date, only low rates of cell-in-cell structures have been described in patients with malignant melanoma.

Climbing plants beat trees to soil nutrient patches.

Charles Darwin reasoned that because climbing plants are freed from the need to be mechanically self-supporting, their stems can remain thin, elongate quickly, and efficiently colonize and display leaves in well-illuminated areas where trellises are available. Herein, I report that this tremendous exploratory capacity also applies below-ground - roots of woody climbers (i.e.

Synthetic CTs for MRI-only brain RT treatment: integration of immobilization systems.

Purpose: Auxiliary devices such as immobilization systems should be considered in synthetic CT (sCT)-based treatment planning (TP) for MRI-only brain radiotherapy (RT). A method for auxiliary device definition in the sCT is introduced, and its dosimetric impact on the sCT-based TP is addressed.

Methods: T1-VIBE DIXON was acquired in an RT setup.

Time-dependent risk factors for epileptic seizures in glioblastoma patients: A retrospective analysis of 520 cases.

Objective: Epilepsy is a common comorbidity of glioblastoma. Seizures may occur in various phases of the disease. We aimed to assess potential risk factors for seizures in accordance with the point in time at which they occurred.