Diagnostic Prediction Model for De Novo Metastatic Breast Cancer
Development of a Diagnostic Prediction Model for de Novo Metastatic Breast Cancer Using Routinely Available Baseline Parameters
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.
Studieoversigt
Status
Status
Betingelser
Betingelser
Detaljeret beskrivelse
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.
Undersøgelsestype
Undersøgelsestype
Tilmelding (Anslået)
Tilmelding
Kontakter og lokationer
Studiekontakt
Studiekontakt
- Navn: Oliver L Stari, MD
- Telefonnummer: 011-36-94-311542
- E-mail: stari.oliver@gmail.com
Studiesteder
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Vas County
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Szombathely, Vas County, Ungarn, 9700
- Rekruttering
- Markusovszky University Teaching Hospital
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Kontakt:
- Oliver Laszlo Stari, MD
- Telefonnummer: 011-36-94-311542
- E-mail: stari.oliver@gmail.com
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Deltagelseskriterier
Berettigelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Prøveudtagningsmetode
Studiebefolkning
Beskrivelse
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.
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Antal grupper/kohorter
Kohorter og interventioner
Gruppe / kohorteGruppe / kohorte |
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Primary study cohort
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Hvad måler undersøgelsen?
Primære resultatmål
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
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De novo metastatic breast cancer
Tidsramme: From the initial diagnostic mammogram up to 12 months of follow-up.
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A newly diagnosed breast cancer with distant spread identified at baseline systemic staging.
Distant lesions are categorized as de novo metastasis if confirmed via biopsy, secondary imaging, follow-up imaging, or Multidisciplinary Tumor Board consensus based on definitive clinical evidence.
Equivocal cases are adjudicated through a review of the 12 month clinical follow-up.
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From the initial diagnostic mammogram up to 12 months of follow-up.
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Sekundære resultatmål
Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
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Incidence of rapid metastatic recurrence
Tidsramme: Up to 12 months from the initial diagnostic mammogram.
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Patients developing systemic spread within the 12-month follow-up period, subsequent to an initially negative baseline systemic staging.
Patients with rapid metastatic recurrence are excluded from the multivariable analysis and reported descriptively.
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Up to 12 months from the initial diagnostic mammogram.
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Systemic staging interval
Tidsramme: Up to 12 months from the initial diagnostic mammogram.
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The time elapsed from the initial diagnostic mammogram to the completion of baseline systemic staging.
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Up to 12 months from the initial diagnostic mammogram.
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Diagnostic interval
Tidsramme: Up to 12 months from the initial diagnostic mammogram.
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The time elapsed from the initial diagnostic mammogram to the confirmation of macroscopic distant metastasis.
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Up to 12 months from the initial diagnostic mammogram.
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Samarbejdspartnere og efterforskere
Sponsor
Sponsor
Datoer for undersøgelser
Studer store datoer
Studiestart (Faktiske)
Studiestart
Primær færdiggørelse (Anslået)
Primær færdiggørelse
Studieafslutning (Anslået)
Studieafslutning
Datoer for studieregistrering
Først indsendt
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Først opslået
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering sendt
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
Andre undersøgelses-id-numre
- VAS-RKEB/BNJ-6/2026
Plan for individuelle deltagerdata (IPD)
Planlægger du at dele individuelle deltagerdata (IPD)?
IPD-planbeskrivelse
IPD-delingstidsramme
IPD-delingsadgangskriterier
IPD-deling Understøttende informationstype
- STUDY_PROTOCOL
- SAP
- ANALYTIC_CODE
Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter
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