AIMC Topic: Lung Neoplasms
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A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma.
Scientific reports
Mar 16, 2024
This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological data and whole slide images from 164 LUAD cases were collected and us...
Predicting overall survival and prophylactic cranial irradiation benefit in small-cell lung cancer with CT-based deep learning: A retrospective multicenter study.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Mar 12, 2024
BACKGROUND AND PURPOSE: To develop a computed tomography (CT)-based deep learning model to predict overall survival (OS) among small-cell lung cancer (SCLC) patients and identify patients who could benefit from prophylactic cranial irradiation (PCI) ...
Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.
PloS one
Mar 12, 2024
Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (...
Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models.
Journal of the American College of Radiology : JACR
Mar 8, 2024
OBJECTIVE: To quantify the relative importance of demographic, contextual, socio-economic, and nodule-related factors that influence patient adherence to incidental pulmonary nodule (IPN) follow-up visits and evaluate the predictive performance of ma...
Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.
Journal of applied clinical medical physics
Mar 4, 2024
PURPOSE: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strateg...
Neoplasm Recurrence, Local
Radiotherapy Planning, Computer-Assisted
Radiotherapy Dosage
Radiotherapy, Intensity-Modulated
Radiosurgery
Lung Neoplasms
Carcinoma, Non-Small-Cell Lung
Positron Emission Tomography Computed Tomography
Male
Middle Aged
Image Processing, Computer-Assisted
Algorithms
Prognosis
Retrospective Studies
Aged, 80 and over
Radiomics
Female
Aged
Humans
Machine Learning
Advanced hybrid attention-based deep learning network with heuristic algorithm for adaptive CT and PET image fusion in lung cancer detection.
Medical engineering & physics
Mar 4, 2024
Lung cancer is one of the most deadly diseases in the world. Lung cancer detection can save the patient's life. Despite being the best imaging tool in the medical sector, clinicians find it challenging to interpret and detect cancer from Computed Tom...
LGDNet: local feature coupling global representations network for pulmonary nodules detection.
Medical & biological engineering & computing
Mar 2, 2024
Detection of suspicious pulmonary nodules from lung CT scans is a crucial task in computer-aided diagnosis (CAD) systems. In recent years, various deep learning-based approaches have been proposed and demonstrated significant potential for addressing...
Transformaer-based model for lung adenocarcinoma subtypes.
Medical physics
Mar 1, 2024
BACKGROUND: Lung cancer has the highest morbidity and mortality rate among all types of cancer. Histological subtypes serve as crucial markers for the development of lung cancer and possess significant clinical values for cancer diagnosis, prognosis,...
Telemedicine-Enhanced Lung Cancer Screening Using Mobile Computed Tomography Unit with Remote Artificial Intelligence Assistance in Underserved Communities: Initial Results of a Population Cohort Study in Western China.
Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Mar 1, 2024
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Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: a multi-center study.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 1, 2024
OBJECTIVES: To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting.
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