Europe PMC
open access
CC BY 4.0
How many slices are required for accurate tumour pathological response assessment? A computational modelling approach on real-world solid tumours
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
- Graphical machine learning for precision medicine: radiomic analysis of hepatocellular carcinoma. (Machine learning graphique pour la médecine de précision: analyse radiomique du carcinome hépatocellulaire).#489482
- Application of Radiomics-based AI Models in Predicting Clinical Outcome of Patients With Renal Cell Carcinoma After Surgical Treatment#897480
- Volumetric Analysis in the Assessment of Therapy Response by CT#913963
- A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer.#222942
- A Predictive Modeling Framework for Studying Disparities in Colorectal Cancer Incidence#939360
Record · ID 475673
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