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

NCT07027605 | Recruiting DMC

• 1 other identifier: NCC-017834
• Study Type: observational
• Target: 166
• Locations: 1 country
• Sites: 1

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.

Conditions

Colorectal Liver Metastasis (CRLM)

Keywords

Multi-Reader Multi-Case Controlled Clinical Trial; Prognostic Prediction; Colorectal Liver Metastases

Outcome Measures

Primary Outcomes (1)

• AUCs: Area Under the Receiver Operating Characteristic Curve (AUC-ROC)

  The comparative accuracy of model aided versus unaided risk prediction of post-operative 3-year mortality assessed by readers. Under model aided condition, prediction model will estimate the overall survival (OS) rate at Year 3 for each individual patient. OS was defined as the time from the date of simultaneous resection to death due to any cause. Patients without death were censored at their last known alive date. The mortality data were retrospectively collected and the event status were known. Patients were followed up to approximately 120 months after simultaneous resection.

  Up to approximately 120 months

Secondary Outcomes (4)

• AUCs: Area Under the Receiver Operating Characteristic Curve (AUC-ROC)

  Up to approximately 120 months

• Sensitivity: the ratio of true positives to total (actual) positives.

  Up to approximately 120 months

• Specificity: the ratio of true negatives to total (actual) negatives.

  Up to approximately 120 months

• Inter-rater reliability: the consistency of ratings made by readers on the cases

  Up to approximately 120 months

Other Outcomes (3)

• Inter-rater reliability: the consistency of ratings made by readers on the cases

  Up to approximately 120 months

• Decision-making confidence: confidence of a reader in completing the evaluation for a case

  Up to approximately 120 months

• Decision-making time: duration of a reader in completing the evaluation for a case

  Up to approximately 120 months

Study Arms (2)

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.

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This reader study involves 12 physicians (4 junior, 4 mid-level, 4 senior) from the Surgical Oncology Department of the Digestive Tract. All readers will receive standardized training on accessing patient data through the medical record system and completing the questionnaire. The study includes 166 colorectal liver metastases (CRLM) patients who underwent simultaneous colorectal and liver resection. Inclusion criteria: age ≥18 years, histologically confirmed colorectal adenocarcinoma liver metastases, and simultaneous resection. Exclusion criteria: presence of other malignancies, lack of follow-up data, or postoperative follow-up less than 5 years without death occurrence.

You may qualify if:

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

You may not qualify if:

• 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.

Sponsors & Collaborators

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

Study Sites (1)

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

Beijing, Beijing Municipality, 100021, China

RECRUITING

Related Publications (7)

• Imai K, Allard MA, Castro Benitez C, Vibert E, Sa Cunha A, Cherqui D, Castaing D, Bismuth H, Baba H, Adam R. Nomogram for prediction of prognosis in patients with initially unresectable colorectal liver metastases. Br J Surg. 2016 Apr;103(5):590-9. doi: 10.1002/bjs.10073. Epub 2016 Jan 18.

  PMID: 26780341 RESULT

• Chen Q, Deng Y, Li Y, Chen J, Zhang R, Yang L, Guo R, Xing B, Ding P, Cai J, Zhao H. Association of preoperative aspartate aminotransferase to platelet ratio index with outcomes and tumour microenvironment among colorectal cancer with liver metastases. Cancer Lett. 2024 Apr 28;588:216778. doi: 10.1016/j.canlet.2024.216778. Epub 2024 Mar 6.

  PMID: 38458593 RESULT

• Wu Y, Mao A, Wang H, Fang G, Zhou J, He X, Cai S, Wang L. Association of Simultaneous vs Delayed Resection of Liver Metastasis With Complications and Survival Among Adults With Colorectal Cancer. JAMA Netw Open. 2022 Sep 1;5(9):e2231956. doi: 10.1001/jamanetworkopen.2022.31956.

  PMID: 36121654 RESULT

• Kataoka K, Takahashi K, Takeuchi J, Ito K, Beppu N, Ceelen W, Kanemitsu Y, Ajioka Y, Endo I, Hasegawa K, Takahashi K, Ikeda M. Correlation between recurrence-free survival and overall survival after upfront surgery for resected colorectal liver metastases. Br J Surg. 2023 Jun 12;110(7):864-869. doi: 10.1093/bjs/znad127.

  PMID: 37196147 RESULT

• Machairas N, Di Martino M, Primavesi F, Underwood P, de Santibanes M, Ntanasis-Stathopoulos I, Urban I, Tsilimigras DI, Siriwardena AK, Frampton AE, Pawlik TM. Simultaneous resection for colorectal cancer with synchronous liver metastases: current state-of-the-art. J Gastrointest Surg. 2024 Apr;28(4):577-586. doi: 10.1016/j.gassur.2024.01.034. Epub 2024 Feb 9.

  PMID: 38583912 RESULT

• Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229-263. doi: 10.3322/caac.21834. Epub 2024 Apr 4.

  PMID: 38572751 RESULT

• 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 RESULT

Study Officials

• Hong Zhao, MD

  Cancer Hospital Chinese Academy of Medical Science

  PRINCIPAL INVESTIGATOR

Central Study Contacts

HONG ZHAO, MD

CONTACT

+8613381106850; zhaohong@cicams.ac.cn

Qichen Chen, MD

CONTACT

+8618810550822; 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: June 11, 2025
• First Posted: June 18, 2025
• Study Start: January 1, 2025
• Primary Completion: September 20, 2025
• Study Completion: September 25, 2025
• Last Updated: August 19, 2025

Record last verified: 2025-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)