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MRI-Driven Precision Typing and Response Prediction in Luminal Breast Cancer

8. juli 2026 opdateret af: Yajia Gu, MD, Fudan University

MRI-driven Multiomics Research on Precise Typing and Response Prediction of Luminal Breast Cancer

Luminal breast cancer is characterized by marked heterogeneity, resulting in diverse treatment responses and long-term outcomes. This project aims to integrate MRI and multiomics data to achieve non-invasive molecular typing and precise response prediction. By linking imaging phenotypes with underlying molecular and pathological characteristics, the investigators will develop predictive models for treatment resistance, recurrence, and metastasis, ultimately supporting personalized treatment strategies and precision oncology.

Studieoversigt

Status

Aktiv, ikke rekrutterende

Betingelser

Detaljeret beskrivelse

Luminal breast cancer represents the most common type of breast cancer, characterized by its intricate tumor heterogeneity that poses a significant challenge in clinical management due to resistance to endocrine therapy and high risk of long-term recurrence. It is significant for the accurate prediction of molecular subtypes and treatment response for luminal breast cancer. Our team has previously identified four molecular subtypes and seven pivotal molecules associated with luminal breast cancer utilizing multiomics techniques. The investigators posit that the integration of MRI-driven multiomics studies holds promise in achieving precise typing and response prediction for luminal breast cancer. This project intends to use multiomics molecular subtypes and key molecules as the gold standard to extract comprehensive quantitative features from diverse regions and levels utilizing MRI, thus facilitating non-invasive diagnosis. Additionally, our approach involves correlating MRI data with multiomics information to unveil the biological significance of imaging models at both pathological and molecular levels. Finally, the investigators aim to construct response prediction models through the fusion of multi-temporal MRI features and multiomics data across various scales, enabling precise forecasts of treatment resistance, recurrence, and metastasis. This initiative aims to enhance treatment decision-making and promote application transformation. This study will include a large-scale real-world retrospective and prospective population to validate and improve the effectiveness of model.

Undersøgelsestype

Observationel

Tilmelding (Anslået)

2000

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiesteder

  • Kina
    • Shanghai Municipality
      • Shanghai, Shanghai Municipality, Kina, 200032
        • Fudan University Shanghai Cancer Center

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Barn
  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ingen

Prøveudtagningsmetode

Sandsynlighedsprøve

Studiebefolkning

Patients with invasive luminal breast cancer (HR+/HER2-)

Beskrivelse

Inclusion Criteria:

  1. Histopathologically confirmed invasive luminal breast cancer (HR+/HER2-);
  2. Patients who underwent breast MRI examination.

Exclusion Criteria:

  1. Pathological biopsy performed prior to the baseline MRI examination;
  2. Patients have received any form of prior treatment for the breast cancer;
  3. History of other malignancies;
  4. Incomplete or poor-quality MRI and/or pathological images;
  5. Missing clinical data.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Diagnostic performance of breast MRI for molecular subtyping of luminal breast cancer, with comparison to multiomics
Tidsramme: 1 year
The primary outcome is the diagnostic performance of AI-assisted analysis for molecular subtyping of luminal breast cancer on contrast-enhanced breast MRI. Quantitative radiomic features and deep learning features are extracted from DCE-MRI, followed by classification into multiomics-defined molecular subtypes. Performance metrics include sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve (AUC). Participants must have undergone both breast MRI and multiomics profiling of tumor tissue. Performance metrics will be compared with those obtained from multiomics classification within the same participants to evaluate the relative diagnostic performance.
1 year

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Predictive Performance of Multiomics Model for Pathological Complete Response (pCR) in Luminal Breast Cancer
Tidsramme: 1 years
The model integrates multiomics data, including breast MRI, pathological features, and other relevant molecular and clinical variables, to predict pathological complete response (ypT0/is ypN0) following neoadjuvant therapy in patients with luminal breast cancer. Performance metrics include sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the receiver operating characteristic curve (AUC), C-index, and time-dependent AUC. Participants must have undergone neoadjuvant therapy with available pathological response assessment.
1 years

Andre resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Predictive Performance of Multiomics Model for Disease-Free Survival (DFS) in Luminal Breast Cancer
Tidsramme: 5 years

The model integrates multiomics data, including breast MRI, pathological features, and other relevant molecular and clinical variables, to predict disease-free survival in luminal breast cancer, defined as time from surgery to first documented disease recurrence, distant metastasis, or death from any cause.

Performance metrics include sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the receiver operating characteristic curve (AUC), C-index, and time-dependent AUC. Participants must have undergone surgery and completed 5 years follow-up.
5 years

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Fudan University

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

6. januar 2026

Primær færdiggørelse (Anslået)

31. december 2027

Studieafslutning (Anslået)

31. december 2029

Datoer for studieregistrering

Først indsendt

26. juni 2026

Først indsendt, der opfyldte QC-kriterier

2. juli 2026

Først opslået (Faktiske)

9. juli 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

10. juli 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

8. juli 2026

Sidst verificeret

1. juli 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Andre undersøgelses-id-numre

  • 2410-Exp103-KY
  • 82430061 (Andet bevillings-/finansieringsnummer: Key Project of National Natural Science Foundation of China)

Plan for individuelle deltagerdata (IPD)

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UBESLUTET

Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter

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Disse oplysninger blev hentet direkte fra webstedet clinicaltrials.gov uden ændringer. Hvis du har nogen anmodninger om at ændre, fjerne eller opdatere dine undersøgelsesoplysninger, bedes du kontakte register@clinicaltrials.gov. Så snart en ændring er implementeret på clinicaltrials.gov, vil denne også blive opdateret automatisk på vores hjemmeside .

Kliniske forsøg med HR Positiv/HER-2 negativ brystkræft

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Sponsorer og samarbejdspartnere

Medicinske tilstande

Narkotikainterventioner

CROs by country

CROs in Moldova

Betingelser

Sjældne sygdomme

Narkotikainterventioner

Kosttilskud

Sponsor / samarbejdspartnere

Placeringer

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