Proposed Ninth Edition TNM Staging System for Lung Cancer: Guide for Radiologists.

Lung cancer continues to be the primary cause of cancer-related deaths globally. Precise staging is imperative for the development of successful treatment approaches and improvement of patient outcomes. Traditionally, lung cancer staging has depended on the TNM staging system, and the International Association for the Study of Lung Cancer (IASLC) has recently recommended modifications.

Update: The Ninth Edition TNM Staging System for Thymic Epithelial Tumors.

- Update articles supplement or update information found in full-length articles previously published in . These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

The Ninth Edition TNM Staging Classification for Thymic Epithelial Tumors.

Staging classification is essential in cancer management and is based on three components: tumor extent (T), lymph node involvement (N), and distant metastatic disease (M). For thymic epithelial malignancies, clinical Tumour, Node, Metastasis (cTNM) staging is primarily determined by imaging, making radiologists integral to clinical practice, treatment decisions, and maintaining the quality of staging databases. The ninth edition of the TNM classification for thymic epithelial tumors will be implemented in January 2025.

Thymic Imaging Pitfalls and Strategies for Optimized Diagnosis.

Thymic imaging is challenging because the imaging appearance of a variety of benign and malignant thymic conditions are similar. CT is the most commonly used modality for mediastinal imaging, while MRI and fluorine 18 fluorodeoxyglucose (FDG) PET/CT are helpful when they are tailored to the correct indication. Each of these imaging modalities has limitations and technical pitfalls that may lead to an incorrect diagnosis and mismanagement.

Retrospectively assessing evaluation and management of artificial-intelligence detected nodules on uninterpreted chest radiographs in the era of radiologists shortage.

Purpose: High volumes of chest radiographs (CXR) remain uninterpreted due to severe shortage of radiologists. These CXRs may be informally reported by non-radiologist physicians, or not reviewed at all. Artificial intelligence (AI) software can aid lung nodule detection.

Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know.

Ongoing discoveries in cancer genomics and epigenomics have revolutionized clinical oncology and precision health care. This knowledge provides unprecedented insights into tumor biology and heterogeneity within a single tumor, among primary and metastatic lesions, and among patients with the same histologic type of cancer. Large-scale genomic sequencing studies also sparked the development of new tumor classifications, biomarkers, and targeted therapies.

The impact on clinical outcomes after 1 year of implementation of an artificial intelligence solution for the detection of intracranial hemorrhage.

Background: To assess the effect of a commercial artificial intelligence (AI) solution implementation in the emergency department on clinical outcomes in a single level 1 trauma center.

Methods: A retrospective cohort study for two time periods-pre-AI (1.1.

Magnetic resonance imaging for prospective assessment of local recurrence of non-small cell lung cancer after stereotactic body radiation therapy.

Objectives: To evaluate multi-parametric MRI for distinguishing stereotactic body radiation therapy (SBRT) induced pulmonary fibrosis from local recurrence (LR).

Materials And Methods: SBRT treated non-small cell lung cancer (NSCLC) patients suspected of LR by conventional imaging underwent MRI: T2 weighted, diffusion weighted imaging, dynamic contrast enhancement (DCE) with a 5-minute delayed sequence. MRI was reported as high or low suspicion of LR.

Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel.

Background: AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty.

Purpose: To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness.

Outcomes of Stable Lung Colorectal Metastases on Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy.

Background: Cytoreductive surgery (CRS) and heated intraperitoneal chemotherapy (HIPEC) have demonstrated benefit in patients with colorectal peritoneal metastases (CRPM). Traditionally, extraperitoneal disease is considered a contraindication to CRS/HIPEC. Stable lung metastases in patients with colorectal cancer often have an indolent course, while the presence of untreated peritoneal metastases poorly affects short-term survival.

Liver Attenuation Assessment in Reduced Radiation Chest Computed Tomography.

Objective: This study aimed to evaluate the reliability of liver and spleen Hounsfield units (HU) measurements in reduced radiation computed tomography (RRCT) of the chest within the sub-millisievert range.

Methods: We performed a prospective, institutional review board-approved study of accrued patients who underwent unenhanced normal-dose chest CT (NDCT) and with an average radiation dose of less than 5% of NDCT. In-house artificial intelligence-based denoising methods produced 2 denoised RRCT (dRRCT) series.

Diagnostic and Prognostic Value of Chest Radiographs for COVID-19 at Presentation.

Introduction: Pulmonary opacities in COVID-19 increase throughout the illness and peak after ten days. The radiological literature mainly focuses on CT findings. The purpose of this study was to assess the diagnostic and prognostic value of chest radiographs (CXR) for coronavirus disease 2019 (COVID-19) at presentation.

A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score.

Background: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications.

Promoting head CT exams in the emergency department triage using a machine learning model.

Purpose: In this study, we aimed to develop a novel prediction model to identify patients in need of a non-contrast head CT exam during emergency department (ED) triage.

Methods: We collected data of all adult ED visits in our institution for five consecutive years (1/2013-12/2017). Retrieved variables included the following: demographics, mode of arrival to the ED, comorbidities, home medications, structured and unstructured chief complaints, vital signs, pain scale score, emergency severity index, ED wing assignment, documentation of previous ED visits, hospitalizations and CTs, and current visit non-contrast head CT usage.

Monosodium glutamate intake affect the function of the kidney through NMDA receptor.

Aims: We investigated whether the chronic intake of monosodium glutamate (MSG) with food affects kidney function, and renal response to glycine. We also established if the NMDA receptors are involved in the changes observed.

Main Methods: Male Wistar rats (5weeks old) were fed a diet supplemented with MSG (3g/kg b.