AIMC Topic: Lung Neoplasms
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Exploring machine learning tools in a retrospective case-study of patients with metastatic non-small cell lung cancer treated with first-line immunotherapy: A feasibility single-centre experience.
Lung cancer (Amsterdam, Netherlands)
Dec 31, 2024
BACKGROUND: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records. Their application in clinical routine is still challenging, due to technical limits and to explaina...
Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection.
Computers in biology and medicine
Dec 30, 2024
Artificial Intelligence (AI) models may fail or suffer from reduced performance when applied to unseen data that differs from the training data distribution, referred to as dataset shift. Automatic detection of out-of-distribution (OOD) data contribu...
Development and validation of a web-based calculator for determining the risk of psychological distress based on machine learning algorithms: A cross-sectional study of 342 lung cancer patients.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Dec 30, 2024
PURPOSE: Early and accurate identification of the risk of psychological distress allows for timely intervention and improved prognosis. Current methods for predicting psychological distress among lung cancer patients using readily available data are ...
Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT.
Scientific reports
Dec 30, 2024
Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significant management implications. We assessed the performance of radiomics-based models in distinguishing ...
Feasibility of AI as first reader in the 4-IN-THE-LUNG-RUN lung cancer screening trial: impact on negative-misclassifications and clinical referral rate.
European journal of cancer (Oxford, England : 1990)
Dec 28, 2024
BACKGROUND: Lung cancer screening (LCS) with low-dose CT (LDCT) reduces lung-cancer-related mortality in high-risk individuals. AI can potentially reduce radiologist workload as first-read-filter by ruling-out negative cases. The feasibility of AI as...
Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis.
Academic radiology
Dec 27, 2024
RATIONALE AND OBJECTIVES: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma.
Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach.
Computational biology and chemistry
Dec 27, 2024
Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenoca...
Pulmonologists-level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach.
Scientific reports
Dec 24, 2024
Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated consi...
Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network.
BMC medical informatics and decision making
Dec 24, 2024
BACKGROUND: The detection and classification of lung nodules are crucial in medical imaging, as they significantly impact patient outcomes related to lung cancer diagnosis and treatment. However, existing models often suffer from mode collapse and po...
Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study.
Cancer immunology, immunotherapy : CII
Dec 21, 2024
BACKGROUND: Existing biomarkers and models for predicting response to programmed cell death protein 1 monoclonal antibody in advanced squamous-cell non-small cell lung cancer (sqNSCLC) did not have enough accuracy. We used data from the ORIENT-3 stud...
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