Back to search

Article

Clinical-Radiomics Model Enhancing Prediction of Occult Nodal Metastasis in cT1a-bN0M0-stage Lung Adenocarcinoma: A Multi-center Study

2024-10-22

Abstract excerpt

<title>Abstract</title> <p>Purpose To construct radiomics models for predicting occult nodal metastasis (ONM) in cT1a-bN0M0-stage lung adenocarcinoma (LUAD) and evaluate the multi-center diagnostic performance of models. Methods 1672 patients from six hospitals were collected including training set (n = 687), test set (n = 297) and external validation set (n = 688). Generalized linear model (GLM), support vecto...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
70ede152-339f-59a8-bb70-ca18be082f5e
DOI
10.21203/rs.3.rs-5025314/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Clinical-Radiomics Model Enhancing Prediction of Occult Nodal Metastasis in cT1a-bN0M0-stage Lung Adenocarcinoma: A Multi-center StudyDOI 10.21203/rs.3.rs-5025314/v1
Evaluation of the prognostic value of the new 9th edition Tumor-Node-Metastases (TNM) staging system for epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma patients with bone metastases.Semantic proximity
Incidence Rate of Occult Lymph Node Metastasis in Clinical T 1-2 N 0 M 0 Small Cell Lung Cancer Patients and Radiomic Prediction Based on Contrast-enhanced CT Imaging: A Multicentre StudySemantic proximity
Bone metastasis prediction in non-small-cell lung cancer: primary CT-based radiomics signature and clinical featureSemantic proximity
PET/CT radiomics and machine learning enable non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinomaSemantic proximity
Predicting pathological highly invasive lung cancer from preoperative 18F-FDG PET/CT with multiple machine learning modelsSemantic proximity
Predicting EGFR mutation status in lung adenocarcinoma presenting as ground-glass opacity: utilizing radiomics model in clinical translationSemantic proximity
Multimodal CT Model: Enhancing Lung Cancer STAS Prediction vs. Unimodal CTSemantic proximity
Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutationSemantic proximity
Identification of a High-Risk Group for Brain Metastases in Non-Small Cell Lung Cancer Patients.Semantic proximity
Machine Learning Model Based on Multidimensional Laboratory Indicators for Predicting Distant Metastasis in Nasopharyngeal Carcinoma: A Multicenter Retrospective Cohort StudySemantic proximity
Development and validation of a machine learning-based model incorporating systemic inflammatory, nutritional, and coagulation parameters for predicting metastasis in lung cancerSemantic proximity
Using contrast-enhanced CT and non-contrast-enhanced CT to predict EGFR mutation status in NSCLC patients-a radiomics nomogram analysis.Semantic proximity
Select a neighboring publication to make it the new centre.