Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
March 1, 2024Journal of Thoracic DiseaseOpen Access

A comparison of machine learning methods for radiomics modeling in prediction of occult lymph node metastasis in clinical stage IA lung adenocarcinoma patients

View Full Paper
Ask AI
Bookmark
Share

Authors

MLMengwen LiuXZXue ZhangYWYanmei Wang

Discussion

Loading...

Member takes

Overview

Key Points

Key points are not available for this paper at this time.

Cite This Study

Liu et al. (2024) studied this question.

synapsesocial.com/papers/68e761d7b6db6435876d82cdhttps://doi.org/10.21037/jtd-23-1578
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine Learning based Radiomics from Multi-parametric Magnetic Resonance Imaging for Predicting Lymph Node Metastasis in Cervical Cancer2025
  2. 2Machine Learning based Radiomics from Multiparametric Magnetic Resonance Imaging for Predicting Lymph Node Metastasis in Cervical Cancer2024
  3. 3An advanced nomogram model using deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma2024 · 12 citations
  4. 4Combining computed tomography radiomics and clinical features to predict lymph node metastasis in patients with lung cancer2026
  5. 5A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma2024 · 7 citations