Foundational Segmentation Models and Clinical Data Mining Enable Accurate Computer Vision for Lung Cancer.

This study aims to assess the effectiveness of integrating Segment Anything Model (SAM) and its variant MedSAM into the automated mining, object detection, and segmentation (MODS) methodology for developing robust lung cancer detection and segmentation models without post hoc labeling of training images. In a retrospective analysis, 10,000 chest computed tomography scans from patients with lung cancer were mined. Line measurement annotations were converted to bounding boxes, excluding boxes < 1 cm or > 7 cm.

Extraction and classification of structured data from unstructured hepatobiliary pathology reports using large language models: a feasibility study compared with rules-based natural language processing.

Aims: Structured reporting in pathology is not universally adopted and extracting elements essential to research often requires expensive and time-intensive manual curation. The accuracy and feasibility of using large language models (LLMs) to extract essential pathology elements, for cancer research is examined here.

Methods: Retrospective study of patients who underwent pathology sampling for suspected hepatocellular carcinoma and underwent Ytrrium-90 embolisation.

Virtual and Augmented Reality in Interventional Radiology: Current Applications, Challenges, and Future Directions.

Virtual reality (VR) and augmented Reality (AR) are emerging technologies with the potential to revolutionize Interventional radiology (IR). These innovations offer advantages in patient care, interventional planning, and educational training by improving the visualization and navigation of medical images. Despite progress, several challenges hinder their widespread adoption, including limitations in navigation systems, cost, clinical acceptance, and technical constraints of AR/VR equipment.

Automatically tracking brain metastases after stereotactic radiosurgery.

Background And Purpose: Patients with brain metastases (BMs) are surviving longer and returning for multiple courses of stereotactic radiosurgery. BMs are monitored after radiation with follow-up magnetic resonance (MR) imaging every 2-3 months. This study investigated whether it is possible to automatically track BMs on longitudinal imaging and quantify the tumor response after radiotherapy.

Automated full body tumor segmentation in DOTATATE PET/CT for neuroendocrine cancer patients.

Purpose: Neuroendocrine tumors (NETs) are a rare form of cancer that can occur anywhere in the body and commonly metastasizes. The large variance in location and aggressiveness of the tumors makes it a difficult cancer to treat. Assessments of the whole-body tumor burden in a patient image allow for better tracking of disease progression and inform better treatment decisions.

Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI.

Objectives: While fully supervised learning can yield high-performing segmentation models, the effort required to manually segment large training sets limits practical utility. We investigate whether data mined line annotations can facilitate brain MRI tumor segmentation model development without requiring manually segmented training data.

Methods: In this retrospective study, a tumor detection model trained using clinical line annotations mined from PACS was leveraged with unsupervised segmentation to generate pseudo-masks of enhancing tumors on T1-weighted post-contrast images (9911 image slices; 3449 adult patients).

Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment.

In large clinical centers a small subset of patients present with hydrocephalus that requires surgical treatment. We aimed to develop a screening tool to detect such cases from the head MRI with performance comparable to neuroradiologists. We leveraged 496 clinical MRI exams collected retrospectively at a single clinical site from patients referred for any reason.

Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations.

Background Artificial intelligence (AI) applications for cancer imaging conceptually begin with automated tumor detection, which can provide the foundation for downstream AI tasks. However, supervised training requires many image annotations, and performing dedicated post hoc image labeling is burdensome and costly. Purpose To investigate whether clinically generated image annotations can be data mined from the picture archiving and communication system (PACS), automatically curated, and used for semisupervised training of a brain MRI tumor detection model.

Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle.

Purpose: To generate and assess an algorithm combining eye tracking and speech recognition to extract brain lesion location labels automatically for deep learning (DL).

Materials And Methods: In this retrospective study, 700 two-dimensional brain tumor MRI scans from the Brain Tumor Segmentation database were clinically interpreted. For each image, a single radiologist dictated a standard phrase describing the lesion into a microphone, simulating clinical interpretation.

Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging.

Background: Differentiating glioblastoma, brain metastasis, and central nervous system lymphoma (CNSL) on conventional magnetic resonance imaging (MRI) can present a diagnostic dilemma due to the potential for overlapping imaging features. We investigate whether machine learning evaluation of multimodal MRI can reliably differentiate these entities.

Methods: Preoperative brain MRI including diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE), and dynamic susceptibility contrast (DSC) perfusion in patients with glioblastoma, lymphoma, or metastasis were retrospectively reviewed.

Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup.

Sharing radiologic image annotations among multiple institutions is important in many clinical scenarios; however, interoperability is prevented because different vendors' PACS store annotations in non-standardized formats that lack semantic interoperability. Our goal was to develop software to automate the conversion of image annotations in a commercial PACS to the Annotation and Image Markup (AIM) standardized format and demonstrate the utility of this conversion for automated matching of lesion measurements across time points for cancer lesion tracking. We created a software module in Java to parse the DICOM presentation state (DICOM-PS) objects (that contain the image annotations) for imaging studies exported from a commercial PACS (GE Centricity v3.

Automated deep-neural-network surveillance of cranial images for acute neurologic events.

Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcomes and preserving neurologic function-'time is brain'. Although these disorders are often recognizable by their symptoms, the critical means of their diagnosis is rapid imaging. Computer-aided surveillance of acute neurologic events in cranial imaging has the potential to triage radiology workflow, thus decreasing time to treatment and improving outcomes.

Radioembolization for Unresectable Intrahepatic Cholangiocarcinoma: Review of Safety, Response Evaluation Criteria in Solid Tumors 1.1 Imaging Response and Survival.

The optimal palliative treatment for unresectable intrahepatic cholangiocarcinoma (ICC) remains controversial. While selective internal radiation therapy (SIRT) using yttrium-90 microspheres is a well-accepted treatment for hepatocellular carcinoma, data related to its use for locally advanced ICC remain relatively scarce. Twenty-nine patients (mean age 66 ± 11 years; 15 female) with unresectable biopsy-proven ICC treated with SIRT between June 2008 and April 2015 were retrospectively evaluated for post-treatment toxicity, overall survival, and imaging response using response evaluation criteria in solid tumors (RECIST) 1.

Neuroimaging in Central Nervous System Infections.

Radiologic imaging is often employed to supplement clinical evaluation in cases of suspected central nervous system (CNS) infection. While computed tomography (CT) is superior for evaluating osseous integrity, demineralization, and erosive changes and may be more readily available at many institutions, magnetic resonance imaging (MRI) has significantly greater sensitivity for evaluating the cerebral parenchyma, cord, and marrow for early changes that have not yet reached the threshold for CT detection. For these reasons, MRI is generally superior to CT for characterizing bacterial, viral, fungal, and parasitic infections of the CNS.

A Technical Guide Describing the Use of Transradial Access Technique for Endovascular Interventions.

Transradial arterial access (TRA) has been employed for transcatheter coronary procedures for more than 25 years, with numerous studies demonstrating improved patient safety as compared with transfemoral arterial access. However, TRA remains underused by the interventional radiology and vascular surgery communities. Advantages of TRA over transfemoral arterial access include easier accomplishment of postprocedure hemostasis, decreased risk of hemorrhagic complications, shorter patient recovery leading to immediate ambulation and decreased procedure-related costs, and increased patient satisfaction.