ClinicalTrials.gov
public full text
Public Domain
Improving the Intraoperative Diagnosis Accuracy of Invasiveness for Small-sized Lung Adenocarcinoma
The goal of this observational study is to improve the intraoperative diagnosis accuracy of invasiveness for small-sized lung adenocarcinoma by combining multi-modal information. The main question it aims to answer is whether multi-modal information have great value of prediction on the invasiveness for small-sized lung adenocarcinoma. Since a promising limited resection is largely based on intraoperative frozen section diagnosis, there is a growing demand on the high-accuracy of timely pathology diagnosis. The multi-modal information of participants will be collected retrospectively.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ctDNA Mutations and Methylation Status for Early Detection of Lung Cancer in Patients With Suspicious Lung Nodules#832488
- Pleural Ultrasonography in Lung Cancer#913539
- The Efficacy of Shear-wave Elastography-guided Aspiration and Biopsy for Diagnosis in Lung Tumor#1075679
- Using ctDNA to Detect Minimal Residual Disease After Lung Cancer Resection#1072137
- GRIPNet: Gaussian Radial Intensity Prior Guided Architecture for Pulmonary Nodule Detection in CT#998849
Record · ID 835053
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive