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

Conditions: Colorectal Liver Metastasis (CRLM)

Summary: a multiple reader, multiple case (mrmc) clinical study including a concurrent reading design will be conducted to determine the impact of the crlm tool on physicians' performance in predicting disease progression and death, as characterized by the designated endpoints, when the physician's evaluation is compared with the ground truth. a total of 12 readers will evaluate 238 patients' medical records, then made prediction of the risk of post-operative 1, 3, 5-years recurrence and death, and provide follow-up timelines and treatment recommendations with or without the aid of crlm prediction tool, by a memory washout period of approximately 5 weeks. the study will utilize retrospective data that is independent from the training dataset of the prediction tool. the terms 'physicians' and 'reader' are used interchangeably in this protocol. the same applies to 'patients' and 'case'.colorectal cancer is the third most commonly diagnosed cancer globally and the second leading cause of cancer-related deaths\[2\]. at the time of initial diagnosis, around 20% to 30% of colorectal cancer patients present with synchronous liver metastases\[3\]. this condition often results in more aggressive disease progression and poorer survival outcomes for those affected \[4\]. therefore, improving the prognosis for individuals with synchronous crlm has become a critical challenge and priority in clinical practice.the simultaneous resection of colorectal cancer along with liver metastases is the preferred strategy for selected patients with synchronous crlm. this approach offers numerous advantages, such as reduced surgical burden, shorter anesthesia time, and improved survival rates compared to staged resections\[5, 6\]. however, it is important to note that not all patients benefit equally from this method. research indicates that approximately 62.8% of crlm patients experience postoperative recurrence, with relapse rates exceeding 50% within the first two years. furthermore, the median progression-free survival is only 11.7 months, which correlates strongly with worse overall survival rates \[5, 6\]. consequently, early identification of crlm patients undergoing simultaneous resection who are at high risk for postoperative recurrence and mortality is critical for clinicians. this enables the development of tailored follow-up and treatment strategies. there is a pressing need for effective outcome prediction tools for crlm patients undergoing simultaneous resection in specific clinical contexts, as this could significantly enhance clinical decision-making processes. accurate prognostic assessments are essential for devising appropriate treatment and follow-up plans.high-performing predictive models are vital tools that can aid clinicians in developing treatment strategies and creating effective follow-up plans to improve the prognosis of patients with crlm. although several institutions have created prognostic nomograms to predict outcomes for crlm patients\[7-10\], there is a lack of studies specifically addressing those undergoing simultaneous resection. in previous research, chen et al. \[1\]firstly establish rf models that incorporate a wide range of variables, including demographic, clinical, laboratory, and genetic data, and to develop a web-based prediction tool designed to forecast long-term outcomes for crlm patients undergoing simultaneous resection. the rf models showed significant enhancements in predictive performance during both internal and external validation compared to traditional crs scores. the auc values for the survival model at 1 year, 3 years, and 5 years outperformed those of other models, such as cerr score, game score, and modified clinical score, reported in previous studies \[11-13\]. furthermore, chen et al. developed a user-friendly web-based prediction tool based on patients' baseline characteristics for clinical application. the practical implications of this prediction tool are considerable, as it provides clinicians with accurate risk assessments tailored to a patient's tumor characteristics, ultimately facilitating personalized treatment interventions and follow-up plans. 1. q. chen, j. chen, y. deng, x. bi, j. zhao, j. zhou, z. huang, j. cai, b. xing, y. li, k. li, h. zhao, personalized prediction of postoperative complication and survival among colorectal liver metastases patients receiving simultaneous resection using machine learning approaches: a multi-center study, cancer letters, 593 (2024) 216967. 2. f. bray, m. laversanne, h. sung, j. ferlay, r.l. siegel, i. soerjomataram, a. jemal, global cancer statistics 2022: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries, ca: a cancer journal for clinicians, 74 (2024) 229-263.


Eligibility criteria:
inclusion 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.

Keywords: a multi-reader multi-case control for the prognostic prediction of colorectal liver metastases