04 · 21 proprietary layers
Layer 13 of 21 · Clinical integration
Staging & Prognostic Models Stage, risk models and outcome estimates.
Why it's needed
What staging or prognosis-related information is available and applicable to the case?
What it draws on
3 of 8 evidence domains.
What it produces
Staging and risk or prognosis context with method and source attribution.
Why it's essential
Layer 13 of 21.
1 · What it does
What Staging & Prognostic Models does
Staging and risk information shapes almost every treatment conversation. This layer is intended to summarise the staging and prognosis-related information applicable to the case, with the method and its source stated.
2 · Scientific question
The question it addresses
What staging or prognosis-related information is available and applicable to the case?
Intended function
Describe the staging and prognostic methods that are actually implemented and their evidence base.
3 · Data inputs and required context
What it draws on
- 03 Clinical context
- 01 Molecular & genomic
- 04 Imaging integration
Clinical stage, relevant molecular risk markers and imaging findings.
4 · Scientific basis
The science behind it
Scientific basis
Staging systems and disease-specific risk scores are developed and validated in particular populations, and some risk scores incorporate molecular findings, particularly in blood cancers. Applying a model outside the population in which it was validated reduces its reliability.
5 · Output and evidence sources
What it produces
Main output
Staging and risk or prognosis context with method and source attribution.
Output type
- Evidence-linked interpretation. Connects a finding to published or curated evidence, which has a source and a date.
- Clinical-context integration. Depends on clinical information supplied by the treating team.
6 · Why it\u2019s essential
How it powers the Twin
Frames the executive summary and the context in which treatment options are considered.
Report section
7 · Why it\u2019s needed
What it changes — for doctors, patients and researchers
Patients & caregivers
Prognosis is a conversation to have with the oncologist; population estimates are not personal predictions.
Patient & Caregiver8 · Dependencies and limitations
Limitations
9 · Review and uncertainty
How the output is handled
Output status
An AI-assisted analytical output presented as structured findings for clinician review. It becomes part of a report only after the treating oncologist has reviewed and signed it.
How uncertainty is communicated
Each estimate names its model, the population it was validated in, and whether the case falls within that population.
10 · Claim boundary
What this layer does not establish
It does not predict an individual patient's outcome or survival.
The treating oncologist owns every decision. Every Twin report is reviewed and signed by the treating oncologist, then taken to KPCIRC's Molecular Tumour Board.
KPCIRC sign-off process