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

19. august 2026 oppdatert av: 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.

Studieoversikt

Status

Rekruttering

Forhold

Intervensjon / Behandling

Studietype

Intervensjonell

Registrering (Antatt)

225

Fase

  • Ikke aktuelt

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiekontakt

Studer Kontakt Backup

Studiesteder

  • Kina
    • Shanghai Municipality
      • Shanghai, Shanghai Municipality, Kina, 200032
        • Rekruttering
        • Fudan University Shanghai Cancer Center
        • Ta kontakt med:

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

  • Voksen
  • Eldre voksen

Tar imot friske frivillige

Nei

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

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

  • Primært formål: Annen
  • Tildeling: Ikke-randomisert
  • Intervensjonsmodell: Sekvensiell tildeling
  • Masking: Ingen (Open Label)

Våpen og intervensjoner

Deltakergruppe / Arm
Intervensjon / Behandling
Annen: 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.
Annen: 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.
Annen: 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.
Annen: 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.

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
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
Tiltaksbeskrivelse
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

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

Fudan University

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Faktiske)

27. august 2021

Primær fullføring (Antatt)

27. august 2028

Studiet fullført (Antatt)

27. november 2028

Datoer for studieregistrering

Først innsendt

1. juli 2026

Først innsendt som oppfylte QC-kriteriene

19. august 2026

Først lagt ut (Faktiske)

20. august 2026

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

20. august 2026

Siste oppdatering sendt inn som oppfylte QC-kriteriene

19. august 2026

Sist bekreftet

1. juni 2026

Mer informasjon

Begreper knyttet til denne studien

Ytterligere relevante MeSH-vilkår

Andre studie-ID-numre

  • FDRT-BC029

Plan for individuelle deltakerdata (IPD)

Planlegger du å dele individuelle deltakerdata (IPD)?

JA

IPD-deling Støtteinformasjonstype

  • STUDY_PROTOCOL
  • ICF

Legemiddel- og utstyrsinformasjon, studiedokumenter

Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

Studerer et amerikansk FDA-regulert enhetsprodukt

Nei

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