Assessment of Emphysema on X-ray Equivalent Dose Photon-Counting Detector CT: Evaluation of Visual Scoring and Automated Quantification Algorithms.

Objectives: The aim of this study was to evaluate the feasibility and efficacy of visual scoring, low-attenuation volume (LAV), and deep learning methods for estimating emphysema extent in x-ray dose photon-counting detector computed tomography (PCD-CT), aiming to explore future dose reduction potentials.

Methods: One hundred one prospectively enrolled patients underwent noncontrast low- and chest x-ray dose CT scans in the same study using PCD-CT. Overall image quality, sharpness, and noise, as well as visual emphysema pattern (no, trace, mild, moderate, confluent, and advanced destructive emphysema; as defined by the Fleischner Society), were independently assessed by 2 experienced radiologists for low- and x-ray dose images, followed by an expert consensus read.

Potential of ChatGPT and GPT-4 for Data Mining of Free-Text CT Reports on Lung Cancer.

Background The latest large language models (LLMs) solve unseen problems via user-defined text prompts without the need for retraining, offering potentially more efficient information extraction from free-text medical records than manual annotation. Purpose To compare the performance of the LLMs ChatGPT and GPT-4 in data mining and labeling oncologic phenotypes from free-text CT reports on lung cancer by using user-defined prompts. Materials and Methods This retrospective study included patients who underwent lung cancer follow-up CT between September 2021 and March 2023.

Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.

Objectives: To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation.

Methods: A total of 251 subjects (median [IQR] age, 65 (57-73) years; 37% females) with pulmonary nodules on non-enhanced thin-section CT were retrospectively included. Twenty percent of the nodules were malignant, the remainder benign either histologically or at least 1-year follow-up.

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine.

Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow.

Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring.

Background: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths. The development of therapies targeting molecular alterations has significantly improved the treatment of NSCLC patients. To identify these targets, tumor phenotyping is required, with tissue biopsies and molecular pathology being the gold standard.

Cell Cycle-specific Measurement of γH2AX and Apoptosis After Genotoxic Stress by Flow Cytometry.

The presented method or slightly modified versions have been devised to study specific treatment responses and side effects of various anti-cancer treatments as used in clinical oncology. It enables a quantitative and longitudinal analysis of the DNA damage response after genotoxic stress, as induced by radiotherapy and a multitude of anti-cancer drugs. The method covers all stages of the DNA damage response, providing endpoints for induction and repair of DNA double-strand breaks (DSBs), cell cycle arrest and cell death by apoptosis in case of repair failure.