PubMed
open access
Explainable AI-based breath metabolite profiling for early lung cancer detection.
Lung cancer (LC) is a major cause of cancer-related mortality and current screening methods are invasive, costly or radiation-intensive. This study aims to develop a machine-learning-framework using electronic nose (E-nose) sensor data and demographic variables to classify LC in a cohort of 118 part
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
- Advances in AI for detecting pulmonary inflammation and perioperative medicine: a mini-review.#446102
- Evaluation of a Genetic Risk Score for Lung Metastasis and Prognosis in Breast Cancer#1112028
- A Prospective Registry for Patients at High-Risk for Pancreatic Cancer#915712
- Development and validation of venous thrombosis risk prediction model and scale for lung cancer patients in intensive care unit based on interpretable machine learning.#685580
- Clinical usefulness of polygenic risk scores in risk prediction models for lung cancer screening and lung nodule management.#122805
Record · ID 1300210
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