COLORS-Validate Study of Decision-Support Software for Surgical Sequencing in Colorectal Liver Metastases

NCT07760558 | Not Yet Recruiting

• 1 other identifier: NCC-023039
• Study Type: observational
• Target: 12
• Locations: 0 countries
• Sites: N/A

Brief Summary

This study evaluates a surgical decision-support software designed to assist physicians in selecting the optimal surgical sequence for patients with colorectal cancer liver metastases. In this multicenter prospective simulation study, participating surgeons from multiple centers will review standardized clinical case scenarios. For each case, physicians will first make a surgical sequencing decision based on their own clinical judgment. They will then review the recommendation provided by the decision-support software and make a second decision if they choose to revise their initial plan. The study will assess whether the software influences clinical decision-making, including changes in surgical strategy, decision confidence, decision time, and agreement with software recommendations. Physician user experience will also be evaluated using standardized questionnaires, including system usability and cognitive workload scales. The goal of this study is to determine the clinical utility and usability of the decision-support software in improving surgical decision-making consistency and supporting clinical reasoning in colorectal liver metastases cases.

Conditions

Colorectal Liver Metastases

Outcome Measures

Primary Outcomes (1)

• Change in Surgical Decision-Making Between Software-Assisted and Non-Assisted Conditions

  The primary outcome is the proportion of cases in which physicians change their surgical sequencing decision after reviewing decision-support software recommendations compared with their initial unaided decision.

  During each simulated case evaluation session

Secondary Outcomes (3)

• Software utilization outcomes

  During each simulated case evaluation session

• Decision performance outcomes

  During each simulated case evaluation session

• Human factors outcomes

  During each simulated case evaluation session

Study Arms (2)

Unaided Clinical Decision-Making

Physicians make surgical sequencing decisions based on routine clinical judgment without decision-support software.

Other: Unaided Clinical Decision-Making

Decision-Support Software-Assisted Decision-Making

Physicians make surgical sequencing decisions after reviewing recommendations from the decision-support software.

Other: Software-Assisted Clinical Decision-Making

Interventions

Unaided Clinical Decision-Making OTHER

Usual Clinical Decision-Making (Without Decision Support Software)

Unaided Clinical Decision-Making

Software-Assisted Clinical Decision-Making OTHER

Physicians make surgical sequencing decisions after reviewing recommendations generated by the decision-support software, including weighted scores and predicted outcomes.

Decision-Support Software-Assisted Decision-Making

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This study will enroll physicians from three tertiary academic medical centers in China, including hepatobiliary surgeons and colorectal surgeons with varying levels of clinical experience. Participants will be required to complete standardized simulation-based case evaluations involving surgical decision-making for colorectal cancer liver metastases. The study aims to assess changes in decision-making behavior, confidence, and usability of a decision-support software system.

You may qualify if:

• Licensed physicians specializing in hepatobiliary surgery or colorectal surgery
• At least 1 year of clinical experience after graduation
• Willing to participate in the simulation-based decision-making study
• Practicing at one of the participating tertiary academic hospitals

You may not qualify if:

• Not actively involved in clinical surgical decision-making
• Unable to complete all required simulation sessions
• Prior involvement in the development of the decision-support software

Sponsors & Collaborators

• Cancer Institute and Hospital, Chinese Academy of Medical Sciences: lead

Related Publications (3)

• Chen Q, Chen J, Deng Y, Bi X, Zhao J, Zhou J, Huang Z, Cai J, Xing B, Li Y, Li K, Zhao H. Personalized prediction of postoperative complication and survival among Colorectal Liver Metastases Patients Receiving Simultaneous Resection using machine learning approaches: A multi-center study. Cancer Lett. 2024 Jul 1;593:216967. doi: 10.1016/j.canlet.2024.216967. Epub 2024 May 18.

  PMID: 38768679 BACKGROUND

• Chen Q, Tong J, Deng Y, Bi X, Li Y, Li K, Zhao H. Impact of an AI prognostic tool on clinician performance in colorectal liver metastases. NPJ Digit Med. 2026 Apr 8;9(1):432. doi: 10.1038/s41746-026-02606-5.

  PMID: 41951838 BACKGROUND

• Chen Q, Deng Y, Wang K, Li Y, Bi X, Li K, Zhao H. Dynamic Prognostic Models for Colorectal Cancer With Liver Metastases. JAMA Netw Open. 2025 Aug 1;8(8):e2529093. doi: 10.1001/jamanetworkopen.2025.29093.

  PMID: 40864468 BACKGROUND

Study Officials

• Hong Zhao, MD

  Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

  STUDY CHAIR

Central Study Contacts

Hong Zhao, MD

CONTACT

+86 01087787100; tongjinliang0202@126.com

QICHEN CHEN, MD

CONTACT

+86101881055; chenqichen0822@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 23, 2026
• First Posted: August 12, 2026
• Study Start (Estimated): September 1, 2026
• Primary Completion (Estimated): September 30, 2026
• Study Completion (Estimated): October 30, 2026
• Last Updated: August 12, 2026

Record last verified: 2026-08

Data Sharing

• IPD Sharing: Will not share