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

Официальное название

A Multi-Reader Multi-Case Controlled Clinical Trial to Evaluate the Performance Improvement From Computer-aided Tool for the Prognostic Prediction of Colorectal Liver Metastases

Обзор

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.

Подробное описание

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.

Первичные конечные точки

• AUCs: Area Under the Receiver Operating Characteristic Curve (AUC-ROC) [Срок оценки: Up to approximately 120 months]

Вторичные конечные точки (4)

• AUCs: Area Under the Receiver Operating Characteristic Curve (AUC-ROC) [Срок оценки: Up to approximately 120 months]
• Sensitivity: the ratio of true positives to total (actual) positives. [Срок оценки: Up to approximately 120 months]
• Specificity: the ratio of true negatives to total (actual) negatives. [Срок оценки: Up to approximately 120 months]
• Inter-rater reliability: the consistency of ratings made by readers on the cases [Срок оценки: Up to approximately 120 months]

Критерии участия

Критерии включения

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

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Китай · 1 центр

• No. 17, South Panjiayuan, Chaoyang District, Beijing, Cancer Hospital, Chinese Academy of — Пекин

Публикации

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• 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
• 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
• 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
• 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
• 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

Идентификаторы

NCT: NCT07027605 · NCC-017834

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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