NCT04273477

Brief Summary

In this study, investigators utilize a radiomics prediction model to predict the tumor response to neoadjuvant chemoradiotherapy (nCRT) before the nCRT is administered for patients with locally advanced rectal cancer (LARC). Previously, the radiomics prediction model has been constructed based on the radiomics features extracted from pretreatment Magnetic Resonance Imaging (MRI) in the training set, and optimized in the external validation set. The predictive power of this radiomics prediction model to discriminate the pathologic complete response (pCR) patients from non-pCR individuals, will be further verified in this prospective, multicenter clinical study.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
100

participants targeted

Target at P50-P75 for all trials

Timeline
Completed

Started Jan 2020

Shorter than P25 for all trials

Geographic Reach
1 country

3 active sites

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

January 10, 2020

Completed
1 month until next milestone

First Submitted

Initial submission to the registry

February 15, 2020

Completed
3 days until next milestone

First Posted

Study publicly available on registry

February 18, 2020

Completed
4 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

July 1, 2020

Completed
5 months until next milestone

Study Completion

Last participant's last visit for all outcomes

December 1, 2020

Completed
Last Updated

February 18, 2020

Status Verified

February 1, 2020

Enrollment Period

6 months

First QC Date

February 15, 2020

Last Update Submit

February 15, 2020

Conditions

Rectal Cancer

Keywords

MRI RadiomicsArtificial intelligenceLocally advanced rectal cancerPathologic complete responseNeoadjuvant chemoradiotherapy

Outcome Measures

Primary Outcomes (1)

  • The prediction accuracy of the radiomics prediction model

    The prediction accuracy of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.

    baseline

Secondary Outcomes (3)

  • The specificity of the radiomics prediction model

    baseline

  • The sensitivity of the radiomics prediction model

    baseline

  • The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiomics prediction model

    baseline

Eligibility Criteria

Age18 Years - 75 Years
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

The population in the study are the patients with LARC, who are intended to receive or undergoing standard, concurrent neoadjuvant chemoradiotherapy with tumor response unknown.

You may qualify if:

  • pathologically diagnosed as rectal adenocarcinoma
  • defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis by enhanced Magnetic Resonance Imaging (MRI)
  • intending to receive or undergoing neoadjuvant concurrent chemoradiotherapy (5-fluorouracil based chemotherapy, given orally or intravenously; Intensity-Modulated Radiotherapy or Volume-Modulated Radiotherapy delivered at 50 gray (Gy) in gross tumor volume (GTV) and 45 Gy in clinical target volume (CTV) by 25 fractions)
  • intending to receive total mesorectum excision (TME) surgery after neoadjuvant therapy (not completed at the enrollment), and adjuvant chemotherapy
  • MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed before the neoadjuvant chemoradiotherapy

You may not qualify if:

  • with history of other cancer
  • insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts)
  • incomplete neoadjuvant chemoradiotherapy
  • no surgery after neoadjuvant chemoradiotherapy resulting in lack of pathologic assessment of tumor response
  • tumor recurrence or distant metastasis during neoadjuvant chemoradiotherapy

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (3)

the Sixth Affiliated Hospital of Sun Yat-sen University

Guangzhou, Guangdong, 510655, China

RECRUITING

The Third Affiliated Hospital of Kunming Medical College

Kunming, Yunnan, 650000, China

RECRUITING

Sir Run Run Shaw Hospital

Hangzhou, Zhejiang, 310000, China

RECRUITING

MeSH Terms

Conditions

Rectal NeoplasmsPathologic Complete Response

Condition Hierarchy (Ancestors)

Colorectal NeoplasmsIntestinal NeoplasmsGastrointestinal NeoplasmsDigestive System NeoplasmsNeoplasms by SiteNeoplasmsDigestive System DiseasesGastrointestinal DiseasesIntestinal DiseasesRectal DiseasesDisease ProgressionDisease AttributesPathologic ProcessesPathological Conditions, Signs and Symptoms

Study Officials

  • Xiangbo Wan, MD, PhD

    Sixth Affiliated Hospital, Sun Yat-sen University

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Xiangbo Wan, MD, PhD

CONTACT

+86 13826017157wanxbo@mail.sysu.edu.cn

Study Design

Study Type
observational
Observational Model
OTHER
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Associate Professor of Radiation Oncology, Vice Director, Department of Radiation Oncology

Study Record Dates

First Submitted

February 15, 2020

First Posted

February 18, 2020

Study Start

January 10, 2020

Primary Completion

July 1, 2020

Study Completion

December 1, 2020

Last Updated

February 18, 2020

Record last verified: 2020-02

Data Sharing

IPD Sharing
Will not share

Locations

CN(3)