Multi-Reader Multi-Case Trial Evaluating Computer-Aided Tool for Prognostic Prediction of Colorectal Liver Metastases

NCT ID: NCT07027605

Last Updated: 2025-08-19

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 166 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-01-01
• Study Completion Date: 2025-09-25

Brief Summary

This study evaluates the impact of a novel computer-aided prognostic prediction tool for colorectal liver metastases (CRLM) on clinician performance. Colorectal cancer is a leading cause of cancer-related mortality worldwide, with 20-30% of patients presenting synchronous liver metastases, which are associated with poor prognosis and high postoperative recurrence rates. Simultaneous resection of primary tumor and liver metastases is a preferred treatment for selected patients but outcomes vary significantly. The latest web-based tool uses Random Forest models integrating demographic, clinical, laboratory, and genetic data to predict postoperative recurrence and mortality specifically for CRLM patients undergoing simultaneous resection. This multiple-reader, multiple-case (MRMC) study will assess 12 physicians who will predict 1-, 3-, and 5-year recurrence and mortality risks in 166 retrospective cases, with and without the tool's aid, separated by a washout period. The primary focus is to determine whether the tool improves prediction accuracy for 3-year postoperative mortality, measured by AUC-ROC. Secondary and exploratory endpoints include other time points, sensitivity, specificity, inter-rater reliability, decision-making confidence, and evaluation time. By enabling individualized risk assessment, this tool aims to support optimized clinical decision-making and tailored treatment strategies for CRLM patients undergoing simultaneous resection.

Detailed Description

This study aims to evaluate the impact of a novel computer-aided prognostic prediction tool on clinician performance in managing patients with colorectal liver metastases (CRLM). Colorectal cancer remains one of the leading causes of cancer-related mortality worldwide, with approximately 20-30% of patients presenting synchronous liver metastases at diagnosis. These metastases are associated with poor prognosis and a high rate of postoperative recurrence.

For selected patients, simultaneous resection of the primary colorectal tumor and liver metastases is the preferred treatment approach, though clinical outcomes vary widely. To address this variability, the latest web-based prediction tool employs Random Forest machine learning models that integrate comprehensive demographic, clinical, laboratory, and genetic data. This tool is specifically designed to predict postoperative recurrence and mortality for CRLM patients undergoing simultaneous resection, enabling individualized risk assessment.

In this multiple-reader, multiple-case (MRMC) study, 12 physicians will independently evaluate 166 retrospective patient cases. Each physician will estimate the risk of disease recurrence and mortality at 1-, 3-, and 5-year time points, both with and without access to the prediction tool. These two assessment phases will be separated by a washout period to minimize bias.

The primary objective is to determine whether use of the tool improves the accuracy of predicting 3-year postoperative mortality, quantified by the area under the receiver operating characteristic curve (AUC-ROC). Secondary and exploratory endpoints include prediction accuracy at other time points, sensitivity, specificity, inter-rater reliability, clinician confidence in decision-making, and time required for evaluation.

By providing specific, data-driven risk estimates, this computer-aided prognostic tool aims to enhance clinical decision-making and support personalized treatment planning for CRLM patients undergoing simultaneous resection, ultimately striving to improve patient outcomes.

Conditions

Colorectal Liver Metastasis (CRLM)

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Reader Group A: Interprets Dataset A in unaided scenario and Dataset B in aided scenario

A reader study with 12 readers (4 Junior Physician, 4 mid-level Physician and 4 Senior Physician) from the Department of Surgical Oncology of the Digestive Tract will be conducted. The readers are equally and randomly split between Group A and Group B. The study will target 166 CRLM patient cases receiving simultaneous resection.Patient cases will be equally and randomly split between Dataset A and Dataset B.

No interventions assigned to this group

Reader Group B: Interprets Dataset A in aided scenario and Dataset B in unaided scenario

A reader study with 12 readers (4 Junior Physician, 4 mid-level Physician and 4 Senior Physician) from the Department of Surgical Oncology of the Digestive Tract will be conducted. The readers are equally and randomly split between Group A and Group B. The study will target 166 CRLM patient cases receiving simultaneous resection.Patient cases will be equally and randomly split between Dataset A and Dataset B.

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• ≥ 18 years old
• confirmation of histologically diagnosed liver metastases of colorectal adenocarcinoma
• receiving colorectal resection with simultaneous liver resection.

Exclusion Criteria

• presence of other malignancies
• absence of follow-up data
• patients who were followed up postoperatively for less than 5 years and had no occurrences of death.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Hong Zhao, MD

Role: PRINCIPAL_INVESTIGATOR

Cancer Hospital Chinese Academy of Medical Science

Locations

No. 17, South Panjiayuan, Chaoyang District, Beijing, Cancer Hospital, Chinese Academy of Medical Sciences, China

Beijing, Beijing Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

HONG ZHAO, MD

Role: CONTACT

zhaohong@cicams.ac.cn

+8613381106850

Qichen Chen, MD

Role: CONTACT

chenqichen0822@126.com

+8618810550822

Facility Contacts

HONG ZHAO, MD

Role: primary

zhaohong@cicams.ac.cn

01087788224

References

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Reference Type: RESULT

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Provided Documents

Document Type: Study Protocol

View Document

Other Identifiers

NCC-017834

Identifier Type: -

Identifier Source: org_study_id