AIMC Topic: Carcinoma, Non-Small-Cell Lung

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Dual-Region Computed Tomography Radiomics-Based Machine Learning Predicts Subcarinal Lymph Node Metastasis in Patients with Non-small Cell Lung Cancer.

Annals of surgical oncology Mar 23, 2024
BACKGROUND: Noninvasively and accurately predicting subcarinal lymph node metastasis (SLNM) for patients with non-small cell lung cancer (NSCLC) remains challenging. This study was designed to develop and validate a tumor and subcarinal lymph nodes (...
Lymphatic Metastasis Lymph Node Excision Pneumonectomy Radiomics Male Middle Aged Lung Neoplasms Lymph Nodes Carcinoma, Non-Small-Cell Lung Follow-Up Studies Female Aged Aged, 80 and over Humans Adult Machine Learning Prognosis Retrospective Studies Tomography, X-Ray Computed
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A matching-based machine learning approach to estimating optimal dynamic treatment regimes with time-to-event outcomes.

Statistical methods in medical research Mar 19, 2024
Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history....
Humans Machine Learning Electronic Health Records Lung Neoplasms Treatment Outcome Carcinoma, Non-Small-Cell Lung Models, Statistical
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Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] Mar 18, 2024
OBJECTIVE: This study aims to examine the ability of deep learning (DL)-derived imaging features for the prediction of radiation pneumonitis (RP) in locally advanced non-small-cell lung cancer (LA-NSCLC) patients.
Tomography, X-Ray Computed Female Aged Adult Humans Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiation Pneumonitis Cohort Studies Male Imaging, Three-Dimensional Middle Aged Lung Neoplasms Deep Learning Carcinoma, Non-Small-Cell Lung
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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...
Radiomics Male Middle Aged Image Processing, Computer-Assisted Female Aged Aged, 80 and over Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Lung Neoplasms Radiotherapy, Intensity-Modulated Carcinoma, Non-Small-Cell Lung Radiosurgery Humans Positron Emission Tomography Computed Tomography Machine Learning Neoplasm Recurrence, Local Algorithms Prognosis Retrospective Studies
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A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.

Journal of applied clinical medical physics Feb 19, 2024
PURPOSE: Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical ...
Image Processing, Computer-Assisted Humans Neural Networks, Computer Lung Neoplasms Algorithms Carcinoma, Non-Small-Cell Lung Tomography, X-Ray Computed Deep Learning
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Comparing Robotic, Thoracoscopic, and Open Segmentectomy: A National Cancer Database Analysis.

The Journal of surgical research Feb 15, 2024
INTRODUCTION: Minimally invasive approaches to lung resection have become widely acceptable and more recently, segmentectomy has demonstrated equivalent oncologic outcomes when compared to lobectomy for early-stage non-small cell lung cancer (NSCLC)....
Robotics Robotic Surgical Procedures Pneumonectomy Thoracic Surgery, Video-Assisted Retrospective Studies Lung Neoplasms Treatment Outcome Carcinoma, Non-Small-Cell Lung Humans
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METnet: A novel deep learning model predicting MET dysregulation in non-small-cell lung cancer on computed tomography images.

Computers in biology and medicine Feb 13, 2024
BACKGROUND: Mesenchymal epithelial transformation (MET) is a key molecular target for diagnosis and treatment of non-small cell lung cancer (NSCLC). The corresponding molecularly targeted therapeutics have been approved by Food and Drug Administratio...
Lung Neoplasms Carcinoma, Non-Small-Cell Lung Humans Algorithms Retrospective Studies Tomography, X-Ray Computed Deep Learning
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Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.

Journal of imaging informatics in medicine Feb 12, 2024
The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patient...
Humans Brain Retrospective Studies Female Lung Neoplasms Carcinoma, Non-Small-Cell Lung Magnetic Resonance Imaging Feasibility Studies Image Interpretation, Computer-Assisted Small Cell Lung Carcinoma Male Middle Aged Deep Learning ROC Curve Aged Adult Brain Neoplasms
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Explainable deep learning-based survival prediction for non-small cell lung cancer patients undergoing radical radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Jan 18, 2024
BACKGROUND AND PURPOSE: Survival is frequently assessed using Cox proportional hazards (CPH) regression; however, CPH may be too simplistic as it assumes a linear relationship between covariables and the outcome. Alternative, non-linear machine learn...
Carcinoma, Non-Small-Cell Lung Oxides Humans Survival Analysis Calcium Compounds Deep Learning Lung Neoplasms
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Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based radiomics and deep learning signatures.

La Radiologia medica Jan 12, 2024
BACKGROUND: This study aimed to develop and validate radiomics and deep learning (DL) signatures for predicting distal metastasis (DM) of non-small cell lung cancer (NSCLC) in low-dose computed tomography (LDCT).
Carcinoma, Non-Small-Cell Lung Early Detection of Cancer Humans Computers Tomography, X-Ray Computed Retrospective Studies Radiomics Deep Learning Lung Neoplasms
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