eScholarship
metadata only
Deep learning AI and Restriction Spectrum Imaging for patient-level detection of clinically significant prostate cancer on MRI
Abstract Background The Prostate Imaging Reporting & Data System (PI-RADS), based on multiparametric MRI (mpMRI), is widely used for the detection of clinically significant prostate cancer (csPCa, Gleason Grade Group (GG≥2)). However, its diagnostic accuracy can be impacted by variability in in
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
- Deep learning applied to cancer prostate histology slides : diagnosis, prediction of bio-chemical recurrence and genetic mutations#179409
- Detection of ISUP≥2 Prostate Cancers Using Multiparametric MRI: Prospective Multicenter Comparison of the PI-RADS Score and an Artificial Intelligence System#902630
- Multi-regional Multiparametric Deep Learning Radiomics for Diagnosis of Clinically Significant Prostate Cancer.#258390
- Prostate Assessment with Restriction Spectrum Imaging (RSI) MRI#891706
- Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients#1232008
Record · ID 1358088
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