Assessment of decision-making with locally run and web-based large language models versus human board recommendations in otorhinolaryngology, head and neck surgery.

Introduction: Tumor boards are a cornerstone of modern cancer treatment. Given their advanced capabilities, the role of Large Language Models (LLMs) in generating tumor board decisions for otorhinolaryngology (ORL) head and neck surgery is gaining increasing attention. However, concerns over data protection and the use of confidential patient information in web-based LLMs have restricted their widespread adoption and hindered the exploration of their full potential.

Spleen Volume Reduction Is a Reliable and Independent Biomarker for Long-Term Risk of Leukopenia Development in Peptide Receptor Radionuclide Therapy.

Lu-DOTATATE therapy is an effective treatment for advanced neuroendocrine tumors, despite its dose-limiting hematotoxicity. Herein, the significance of off-target splenic irradiation is unknown. Our study aims to identify predictive markers of peptide receptor radionuclide therapy-induced leukopenia.

Evaluation and mitigation of the limitations of large language models in clinical decision-making.

Clinical decision-making is one of the most impactful parts of a physician's responsibilities and stands to benefit greatly from artificial intelligence solutions and large language models (LLMs) in particular. However, while LLMs have achieved excellent performance on medical licensing exams, these tests fail to assess many skills necessary for deployment in a realistic clinical decision-making environment, including gathering information, adhering to guidelines, and integrating into clinical workflows. Here we have created a curated dataset based on the Medical Information Mart for Intensive Care database spanning 2,400 real patient cases and four common abdominal pathologies as well as a framework to simulate a realistic clinical setting.

Clinical Impact and Management of Incidental Renal Findings on Pre-TAVI CT Scan from the Urologist's Perspective.

Introduction: The aim of the study was to investigate prevalence and impact of incidental renal masses (IRMs) accompanying increasing computed tomography (CT) work-up for symptomatic aortic valve stenosis (sAVS) of the elderly with regard to the relevance of urological consultation for overall survival (OS).

Methods: A retrospective analysis of pre-transcatheter aortic-valve implantations (TAVIs) CT scans of patients with sAVS (N = 1,253) harboring IRM was performed for 2014-2019. According to the clinical management, groups 1 (urologic consultation) and 2 (findings ignored) were formed and analyzed in terms of OS.

Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study.

Background: Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC).

Patients And Methods: We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers.

Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans.

Purpose: Kidney volume is important in the management of renal diseases. Unfortunately, the currently available, semi-automated kidney volume determination is time-consuming and prone to errors. Recent advances in its automation are promising but mostly require contrast-enhanced computed tomography (CT) scans.

[Imaging of posttraumatic shoulder instability : Current concepts].

Posttraumatic instability accounts for more than 95% of all shoulder instabilities with the highest incidence in patients between 20 and 30 years of age. In this age group, lesions of the capsulolabral complex are the most common sequelae after the first shoulder dislocation. Typical acute findings are the Bankart and Perthes lesions and humeral avulsion of the glenohumeral ligament (HAGL).

Implementation of structured reporting in clinical routine: a review of 7 years of institutional experience.

Background: To evaluate the implementation process of structured reporting (SR) in a tertiary care institution over a period of 7 years.

Methods: We analysed the content of our image database from January 2016 to December 2022 and compared the numbers of structured reports and free-text reports. For the ten most common SR templates, usage proportions were calculated on a quarterly basis.

Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing.

Background: Structured reporting (SR) is recommended in radiology, due to its advantages over free-text reporting (FTR). However, SR use is hindered by insufficient integration of speech recognition, which is well accepted among radiologists and commonly used for unstructured FTR. SR templates must be laboriously completed using a mouse and keyboard, which may explain why SR use remains limited in clinical routine, despite its advantages.

ABER Position in Direct MR Arthrography of the Shoulder: Useful Adjunct or Waste of Imaging Time?

Purpose: The usefulness of direct MR arthrography of the shoulder with additional ABER position (ABER-MRA) has always been discussed. The goals of the following review are to analyze the usefulness of this technique according to the available literature and present recommendations with respect to indications and benefits in diagnostic imaging of shoulder abnormalities in the clinical routine.

Method: For this review we assessed the current literature databases of the Cochrane Library, Embase, and PubMed with regard to MRA in the ABER position up to the February 28, 2022.

Algorithmic transparency and interpretability measures improve radiologists' performance in BI-RADS 4 classification.

Objective: To evaluate the perception of different types of AI-based assistance and the interaction of radiologists with the algorithm's predictions and certainty measures.

Methods: In this retrospective observer study, four radiologists were asked to classify Breast Imaging-Reporting and Data System 4 (BI-RADS4) lesions (n = 101 benign, n = 99 malignant). The effect of different types of AI-based assistance (occlusion-based interpretability map, classification, and certainty) on the radiologists' performance (sensitivity, specificity, questionnaire) were measured.

Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and reveal gemcitabine-induced hypo-vascularization.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a molecularly heterogeneous tumor entity with no clinically established imaging biomarkers. We hypothesize that tumor morphology and physiology, including vascularity and perfusion, show variations that can be detected by differences in contrast agent (CA) accumulation measured non-invasively. This work seeks to establish imaging biomarkers for tumor stratification and therapy response monitoring in PDAC, based on this hypothesis.

Smartglass augmented reality-assisted targeted prostate biopsy using cognitive point-of-care fusion technology.

Introduction: MRI-guided targeted biopsy has become standard of care for diagnosis of prostate cancer, with establishment of several biopsy techniques and platforms. Augmented reality smart glasses have emerged as novel technology to support image-guided interventions. We aimed to investigate its usage while prostate biopsy.

Quantitative washout in patients with hepatocellular carcinoma undergoing TACE: an imaging biomarker for predicting prognosis?

Background: The delayed percentage attenuation ratio (DPAR) was recently identified as a novel predictor of an early complete response in patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE). In this study, we aimed to validate the role of DPAR as a predictive biomarker for short-, mid-, and long-term outcomes after TACE.

Methods: We retrospectively reviewed laboratory and imaging data for 103 treatment-naïve patients undergoing initial TACE treatment at our tertiary care center between January 2016 and November 2020.

Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation?

Objectives: In response to the COVID-19 pandemic, many researchers have developed artificial intelligence (AI) tools to differentiate COVID-19 pneumonia from other conditions in chest CT. However, in many cases, performance has not been clinically validated. The aim of this study was to evaluate the performance of commercial AI solutions in differentiating COVID-19 pneumonia from other lung conditions.

Multimodal Deep Learning for Prognosis Prediction in Renal Cancer.

Background: Clear-cell renal cell carcinoma (ccRCC) is common and associated with substantial mortality. TNM stage and histopathological grading have been the sole determinants of a patient's prognosis for decades and there are few prognostic biomarkers used in clinical routine. Management of ccRCC involves multiple disciplines such as urology, radiology, oncology, and pathology and each of these specialties generates highly complex medical data.

Structured reporting of CT scans of patients with trauma leads to faster, more detailed diagnoses: An experimental study.

Purpose: This study aimed to determine whether structured reports (SRs) reduce reporting time and/or increase the level of detail for trauma CT scans compared to free-text reports (FTRs).

Method: Eight radiology residents used SRs and FTRs to describe 14 whole-body CT scans of patients with polytrauma in a simulated emergency room setting. Each resident created both a brief report and a detailed report for each case using one of the two formats.

Violation of triboelectric charge conservation on colliding particles.

In microgravity experiments, we quantified the net charge on systems of two identical, 434-μm-diameter glass spheres before and after a collision. We find that charge conservation is significantly violated. Independent of the sign of the total charge, the systems regularly lose some of their net charges, that is, they slightly discharge.

[F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma.

Purpose: In this prospective exploratory study, we evaluated the feasibility of [F]fluorodeoxyglucose ([F]FDG) PET/MRI-based chemotherapy response prediction in pancreatic ductal adenocarcinoma at two weeks upon therapy onset.

Material And Methods: In a mixed cohort, seventeen patients treated with chemotherapy in neoadjuvant or palliative intent were enrolled. All patients were imaged by [F]FDG PET/MRI before and two weeks after onset of chemotherapy.

Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging.

Background: PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task.

Natural language processing of radiology reports to investigate the effects of the COVID-19 pandemic on the incidence and age distribution of fractures.

Objective: During the COVID-19 pandemic, the number of patients presenting in hospitals because of emergency conditions decreased. Radiology is thus confronted with the effects of the pandemic. The aim of this study was to use natural language processing (NLP) to automatically analyze the number and distribution of fractures during the pandemic and in the 5 years before the pandemic.

Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.

Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study.

Effect of fluid resuscitation on cerebral integrity: A prospective randomised porcine study of haemorrhagic shock.

Background: The treatment of haemorrhagic shock is a challenging task. Colloids have been regarded as standard treatment, but their safety and benefit have been the subject of controversial debates. Negative effects, including renal failure and increased mortality, have resulted in restrictions on their administration.