AIMC Topic: Lung Neoplasms

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C-UQ: Conflict-based uncertainty quantification-A case study in lung cancer classification.

Computers in biology and medicine
Uncertainty quantification is crucial in deep learning, especially in medical diagnostics, to measure model prediction confidence and ensure reliable clinical decisions. This study introduces a novel conflict-based uncertainty quantification approach...

Dose prediction via deep learning to enhance treatment planning of lung radiotherapy including simultaneous integrated boost techniques.

Medical physics
BACKGROUND: Recent studies have shown deep learning techniques are able to predict three-dimensional (3D) dose distributions of radiotherapy treatment plans. However, their use in dose prediction for treatments with varied prescription doses includin...

Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point F-FDG PET/CT.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point F-FDG PET/CT to predict the malignant ris...

Multi-omics and single-cell analysis reveals machine learning-based pyrimidine metabolism-related signature in the prognosis of patients with lung adenocarcinoma.

International journal of medical sciences
Pyrimidine metabolism is a hallmark of tumor metabolic reprogramming, while its significance in the prognostic and therapeutic implications of patients with lung adenocarcinoma (LUAD) still remains unclear. In this study, an integrated framework of...

Comparative performance of PD-L1 scoring by pathologists and AI algorithms.

Histopathology
AIM: This study evaluates the comparative effectiveness of pathologists versus artificial intelligence (AI) algorithms in scoring PD-L1 expression in non-small cell lung carcinoma (NSCLC). Immune-checkpoint inhibitors have revolutionized NSCLC treatm...

Application of Artificial Intelligence Software to Identify Emotions of Lung Cancer Patients in Preoperative Health Education: A Cross-Sectional Study.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
AIM(S): To determine the correlation between preoperative health education and the emotions of lung cancer patients, artificial intelligence software was used.

Recurrence patterns and prediction of survival after recurrence for gallbladder cancer.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
BACKGROUND: Gallbladder cancer (GBC) is associated with a poor prognosis. Recurrence patterns and their effect on survival remain ill-defined. This study aimed to analyze recurrence patterns and develop a machine learning (ML) model to predict surviv...

A series of natural language processing for predicting tumor response evaluation and survival curve from electronic health records.

BMC medical informatics and decision making
BACKGROUND: The clinical information housed within unstructured electronic health records (EHRs) has the potential to promote cancer research. The National Cancer Center Hospital (NCCH) is widely recognized as a leading institution for the treatment ...

Prior knowledge-based multi-task learning network for pulmonary nodule classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for classification of benign and malignant nodules. In clinical, radiologists usually conduct a comprehensive analysis of correlations between different ...