Diagnostic Prediction Model for De Novo Metastatic Breast Cancer
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Breast Cancer, Metastatic Invasive Breast Cancer. Basic parameters: from 18 years · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Development of a Diagnostic Prediction Model for de Novo Metastatic Breast Cancer Using Routinely Available Baseline Parameters
Overview
The goal of this observational study is to create a tool to estimate the risk of macroscopic distant tumor spread in women with newly diagnosed breast cancer. The main question it aims to answer is: • Can a model, which combines routine medical information collected at diagnosis, accurately predict if a patient has metastasis large enough to be found during systemic imaging? Researchers will review the past medical records of participants who were treated at a university hospital. Because this study looks at past data, participants will not be asked to do any new tasks, take new tests, or change their medical care.
Detailed description
BACKGROUND AND RATIONALE
Baseline risk assessment traditionally relies on anatomical extent, while intrinsic tumour aggressiveness is established as a key determinant of distant spread. In addition, the systemic inflammatory response is increasingly recognized as a driver of tumour progression. The evaluation of these diverse and often conflicting factors complicates early clinical decision-making.
OBJECTIVE
The objective of this study is to develop and internally validate a multivariable diagnostic prediction model using routinely available baseline parameters to estimate the individualised probability of macroscopic distant metastasis among patients with newly diagnosed invasive breast cancer.
STUDY DESIGN AND SETTING
This is an investigator-initiated, single-centre, retrospective cohort study adhering to the TRIPOD+AI statement. The study is conducted at a university teaching hospital, where comprehensive baseline systemic staging is the institutional standard for all newly diagnosed breast cancer patients.
PREDICTORS
The model integrates routinely available baseline parameters: clinical tumor size, clinical lymphnode status, the Ki-67 proliferation marker, and a composite systemic inflammatory marker (Pan-immune-inflammation value).
DATA COLLECTION AND QUALITY ASSURANCE
Data extraction is performed independently by two multidisciplinary teams. Any discrepancies are resolved through a formal adjudication process by an expert panel not involved in data collection (senior oncologist, radiologist, pathologist, and surgeon).
SAMPLE SIZE AND MISSING DATA Based on the criteria proposed by Riley et al., the study requires a minimum of 925 participants and 93 events to accommodate maximum model complexity. Missing data will be handled using complete-case analysis if the rate is \<5%, or multiple imputation if \>5%.
STATISTICAL ANALYSIS AND MODEL DEVELOPMENT
For the multivariable logistic regression model, all pre-specified predictors are entered simultaneously. Continuous variables are retained in their continuous form. Non-linear relationships modeled using restricted cubic splines. Model performance is evaluated via discrimination and calibration metrics, while clinical utility is assessed through decision curve analysis. Internal validation is conducted using bootstrapping. To correct for model optimism, a global shrinkage factor based on the bootstrap calibration slope is applied. Instability plots are used to illustrate the stability of predictions, calibration, and net benefit.
PATIENT AND PUBLIC INVOLVEMENT (PPI)
To initiate Patient and Public Involvement, an inaugural patient engagement event will be held to discuss the clinical acceptability of a diagnostic prediction model and its potential role in shared decision-making. This event will serve as the foundation to establish a voluntary Patient Advisory Group (PAG). In subsequent project stages, the newly formed PAG will collaborate to co-produce a Plain Language Summary and explore potential pathways for the tool's future clinical application.
Primary outcome measures
- De novo metastatic breast cancer [Time frame: From the initial diagnostic mammogram up to 12 months of follow-up.]
Secondary outcome measures (3)
- Incidence of rapid metastatic recurrence [Time frame: Up to 12 months from the initial diagnostic mammogram.]
- Systemic staging interval [Time frame: Up to 12 months from the initial diagnostic mammogram.]
- Diagnostic interval [Time frame: Up to 12 months from the initial diagnostic mammogram.]
Eligibility criteria
Inclusion criteria
- Female sex, aged 18 years or older.
- Newly diagnosed invasive breast cancer.
- Case reviewed by the Multidisciplinary Tumour Board at the study site during the inclusion period (between October 1, 2018, and July 31, 2025).
- No history of prior or synchronous invasive malignancies, except for synchronous bilateral breast cancer.
- Intent for primary treatment and clinical management at the study site.
- Availability of longitudinal clinical records throughout the 12-month follow-up period or until death.
Exclusion criteria
-Absence of baseline systemic staging.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT07752238 · VAS-RKEB/BNJ-6/2026