Knowledge graph · Trial
Deep Learning Model to Predict the Recurrence of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection
COMPLETED
- Identifier
- CI-TRIAL-00081975
- Entity page
- /trial/NCT06659601 ↗
- Edges
- 1
- Neighbours
- 1 cancers
- Drawn
- 1 of 1 neighbours (cap 60); table lists every fetched edge
Accepted: cancer:<slug>, gene:<symbol>, variant:<slug>, drug:<slug>, trial:<NCT id>, or a bare CI id. Find slugs with search.
Most connectedLung Non-Small Cell Carcinoma 3,147Malignant Colorectal Neoplasm 1,866ABL1 298EGFR 276Imatinib 450Dasatinib 321
Neighbourhood
Radial view
Focus at the centre; neighbours grouped by entity type. Click a node to re-centre the graph on it; ↗ opens the entity page. Hover a spoke for relationship, evidence level, cancer context and source.
Edges
Every edge, grouped by relationship
Source-native edges first (aggregated per neighbour, direction, evidence level and source), then derived registry links. Up to 15 per relationship (10 trials); “show all” raises one group to 200.
| Relationship | Neighbour | Direction | Evidence level | Cancer context | Support | Claim | Source | Expand |
|---|---|---|---|---|---|---|---|---|
| Deep Learning Model to Predict the Recurrence of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection condition of …CONDITION_OFderived1 of 1 | ||||||||
| ← condition of | Lung Neoplasm registry condition “Focus on Developing a Deep Learning Model to Predict the Recurrence Risk of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection” mapped ONTOLOGY_EXACT | — | — | Lung Neoplasm | 1 | Observed | clinicaltrials | graph → |
An edge is a relationship stated by a source (CIViC evidence item, ChEMBL indication or mechanism, openFDA approval) and kept with its native evidence level, direction and cancer context — CancerIndex never infers, merges or re-scales it. Rows marked are counts and measurements read from registries (ClinicalTrials.gov conditions and interventions, GDC/cBioPortal cohort frequencies ≥ 5 % with ≥ 20 cases affected, regulatory approval records): they say how often two entities co-occur in a registry, not that a source asserted a biological or clinical link. Trials are rolled up over the cancer and its descendants; approvals list authority, jurisdiction and date as published.