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Application of CT-Linac-Based "All-in-One" One-Stop Radiotherapy in Breast Cancer

19. august 2026 opdateret af: Xiaoli Yu, Fudan University

Application of CT-Linac-Based "All-in-One" One-Stop Radiotherapy in All-Scenario Breast Cancer Radiotherapy: A Prospective Clinical Study

This study aims to evaluate and report the clinical adverse events and dosimetric parameters in breast cancer patients undergoing an "all-in-one (AIO)" one-stop, fully automated radiotherapy workflow. By systematically tracking these clinical and physical metrics, we seek to establish a standardized clinical protocol for AIO radiotherapy in breast cancer management.

Studieoversigt

Status

Rekruttering

Betingelser

Intervention / Behandling

Undersøgelsestype

Interventionel

Tilmelding (Anslået)

225

Fase

  • Ikke anvendelig

Kontakter og lokationer

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

Studiekontakt

Undersøgelse Kontakt Backup

Studiesteder

  • Kina
    • Shanghai Municipality
      • Shanghai, Shanghai Municipality, Kina, 200032
        • Rekruttering
        • Fudan University Shanghai Cancer Center
        • Kontakt:

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

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ingen

Beskrivelse

Inclusion Criteria:

  • Histologically or pathologically confirmed breast cancer with definitive indications for radiotherapy (preoperative, postoperative, or radical)
  • ECOG performance status of 0-2
  • Able to remain still and supine on the treatment couch for up to 30 minutes
  • Provision of signed, written informed consent
  • Able to comply with daily follow-ups and blood sample collections

Exclusion Criteria:

  • Palliative radiotherapy for concurrent distant metastasis
  • Incomplete or ongoing chemotherapy
  • Synchronous multiple primary tumors
  • Current pregnancy or lactation
  • Prior history of radiotherapy to the ipsilateral breast, chest wall, thorax, or regional lymph nodes
  • Severe non-malignant comorbidities (e.g., cardiovascular or pulmonary diseases, systemic lupus erythematosus, scleroderma) resulting in a short life expectancy or inability to tolerate radical radiotherapy
  • Inability or unlikelihood to comply with study follow-up
  • Inability or unwillingness to provide written informed consent

Studieplan

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

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

  • Primært formål: Andet
  • Tildeling: Ikke-randomiseret
  • Interventionel model: Sekventiel tildeling
  • Maskning: Ingen (Åben etiket)

Våben og indgreb

Deltagergruppe / Arm
Intervention / Behandling
Andet: ARM1: AI-empowered AIO WBI
Evaluates the feasibility, safety, and patient experiences of the AI-empowered AIO workflow in breast cancer patients undergoing whole-breast irradiation (WBI) without regional nodal involvement.
Andet: Radiotherapy procedure
The workflow relies on specialized convolutional neural networks for automated segmentation and dose-prediction auto-planning. These breast cancer models were trained on 285 historical institutional cases spanning radical mastectomy and breast-conserving surgery over five years. Auto-delineated structures include the clinical target volume, regional lymph nodes (if involved), tumor bed (identified by surgical clips), heart, bilateral lungs, unaffected breast, spinal cord, esophagus, thyroid, and affected humeral head. These contours guide dose prediction to generate deliverable tangential arc plans via clinical-goal-guided automated optimization in the treatment planning system. To adapt to the on-couch treatment scenario, models were validated on retrospective data and offline routines to maximize target delineation accuracy and the first-approval rate of auto-plans.
Andet: ARM2: Expanded-Scenario AIO RT
Evaluates the feasibility, safety, and patient experiences of the AI-empowered AIO workflow in breast cancer patients with broader radiotherapy indications, including breast/chest wall irradiation with or without regional nodal radiotherapy.
Andet: Radiotherapy procedure
The workflow relies on specialized convolutional neural networks for automated segmentation and dose-prediction auto-planning. These breast cancer models were trained on 285 historical institutional cases spanning radical mastectomy and breast-conserving surgery over five years. Auto-delineated structures include the clinical target volume, regional lymph nodes (if involved), tumor bed (identified by surgical clips), heart, bilateral lungs, unaffected breast, spinal cord, esophagus, thyroid, and affected humeral head. These contours guide dose prediction to generate deliverable tangential arc plans via clinical-goal-guided automated optimization in the treatment planning system. To adapt to the on-couch treatment scenario, models were validated on retrospective data and offline routines to maximize target delineation accuracy and the first-approval rate of auto-plans.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Acute adverse events
Tidsramme: 6 months
The incidence and severity of acute adverse event include radiation dermatitis, pruritus, skin pain, radiation esophagitis, and radiation pneumonitis.
6 months

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Accuracy
Tidsramme: 2 months
Auto-segmentation accuracy was assessed by comparing automatically generated contours against the final physician-approved contours
2 months
Success rate
Tidsramme: 2 months
Record AIO workflow success rate: online planning one-pass optimization success rate.
2 months
Quality of life (QoL)
Tidsramme: 6 months
Quality of life will be evaluated via standardized QoL scales.
6 months
Time efficiency
Tidsramme: 2 months
The time efficiency of the workflow was automatically recorded by the system
2 months
Full-Workflow Patient Intrafraction Motion
Tidsramme: 2 months
Evaluated based on geometric deviations between pretreatment image-guided radiotherapy (IGRT), posttreatment imaging, and the baseline simulation CT
2 months
Correlation of Patient Metrology with Setup Error and Dosimetric Performance
Tidsramme: 2 months
Evaluation of how Body Mass Index (BMI) and weight fluctuations correlate with geometric setup errors and in vivo gamma pass rates
2 months
Correlation of Anatomical Scale with Setup Error and Dosimetric Performance
Tidsramme: 2 months
Evaluation of how anatomical scale/breast size correlates with geometric setup errors and in vivo gamma pass rates
2 months

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)

27. august 2021

Primær færdiggørelse (Anslået)

27. august 2028

Studieafslutning (Anslået)

27. november 2028

Datoer for studieregistrering

Først indsendt

1. juli 2026

Først indsendt, der opfyldte QC-kriterier

19. august 2026

Først opslået (Faktiske)

20. august 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

20. august 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

19. august 2026

Sidst verificeret

1. juni 2026

Mere information

Begreber relateret til denne undersøgelse

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • FDRT-BC029

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

JA

IPD-deling Understøttende informationstype

  • STUDY_PROTOCOL
  • ICF

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