The AIDPRO-CRC Trial (AIDPRO-CRC)

AI-Driven Personalized Perioperative Management in Colorectal Cancer: A Randomized Controlled Clinical Trial - The AIDPRO-CRC Trial

The AIDPRO-CRC trial aims to improve outcomes for patients undergoing surgery for colorectal cancer by using artificial intelligence (AI) to assist surgeons in risk assessment. The trial will evaluate whether AI can help surgeons better predict the risk of complications and death, leading to improved care, fewer complications, and better use of healthcare resources.

In this nationwide, randomized clinical trial, participants will be divided into two groups. One group will have their risk assessed by a surgeon using standard clinical methods, while the other group will have their risk assessed by a surgeon using AI assistance. Based on the risk level, patients will receive varying levels of perioperative care. The AI-assisted risk assessment aims to tailor the treatment more precisely to each patient's individual needs, precisely allocating care to those who need it to more efficiently allocate heath system resources while having no deterioration in patient outcomes.

The primary hypothesis is that AI-assisted risk assessment will lead to more efficient and economic patient care without a deterioration in patient outcomes. The trial also aims to explore clinician satisfaction with the platform and its perceived effect. This is paired with a substudy exploring the variability of suggested treatment plans by clinicians with and without access to the MDT presentation platform.

The trial will include patients at seven hospitals across Denmark, involving patients diagnosed with colorectal cancer who are scheduled for curative surgery. All patients will receive standard treatment according to national guidelines, with the only difference being the modality of risk assessment. For the evaluation of the clinicians satisfactory with the device and the substudy of variability of suggested treatment plans, the trial will enroll clinicians using the device.

This study is a researcher-initiated, nationwide, randomized clinical trial involving patients diagnosed with colorectal cancer across eight hospitals in Denmark. Participants will be randomly assigned to one of two groups: AI-assisted risk assessment or standard surgeon-led assessment. The intervention focuses on optimizing perioperative care based on individual risk levels determined by either AI or the surgeon's clinical judgment.

The study builds on a successful pilot project (AID-SURG) that showed promising results in reducing complications, hospital stays, and readmissions.

Study Overview

• Status: Recruiting
• Conditions: Colo-rectal Cancer
• Intervention / Treatment: Device: AI augmented risk-stratification; Other: Expert-based Risk-stratification

Detailed Description

Introduction:

The AIDPRO-CRC trial is an investigator-initiated nationwide multicenter randomized controlled trial. The trial aims to investigate the clinical effects of an AI-augmented solution "AIDPRO manual CRC" for optimization of perioperative treatment by personalized risk stratification of patients undergoing CRC surgery. The protocol adheres to the SPIRIT Statement recommendations.

The AIDPRO-CRC trial is a pre-market, pivotal stage, confirmatory clinical investigation designed to evaluate the safety and effectiveness of the AIDPRO manual CRC algorithm. As a pivotal clinical investigation, this study is critical for generating the robust evidence required to support regulatory submissions for CE marking. The investigation involves an interventional approach, meaning that participants will undergo specific procedures or treatments as part of the study. This design has been specifically chosen to rigorously assess the performance of the AIDPRO manual CRC device in a real-world clinical setting, providing the necessary data to demonstrate its safety and effectiveness. The results from this trial will be used to seek CE marking, enabling the AIDPRO manual CRC to be brought to market in the future.

Objective of the Study:

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. Despite advances in standardized treatment protocols, significant challenges remain in reducing complications, readmissions, and mortality. Addressing these challenges necessitates a transition toward individualized, data-driven treatment strategies.

The AIDPRO-CRC trial aims to evaluate the effectiveness of using artificial intelligence (AI) to distinguish between high- and low-risk patients to offer a personalized and optimized perioperative care pathway for individuals undergoing surgery for colon and rectal cancer. This pathway is tailored based on each patient's specific risk factors and individual clinical profile.

The trial compares risk assessments made by a surgeon unaided to those made by a surgeon supported by an AI model.

For surgeons, achieving a comprehensive understanding of the numerous factors influencing a patient's postoperative risk can be both complex and time-consuming. This study therefore investigates whether AI-assisted risk assessment can improve the allocation of healthcare resources while simultaneously optimizing patient outcomes.

We aim to evaluate the following three primary hypotheses:

• AI-based decision support will enable a more efficient allocation of healthcare resources, potentially improving the cost-effectiveness of care delivery.
• Surgeons and other healthcare professionals will find the AI-enabled software acceptable and user-friendly, and will perceive it as adding value compared to conventional decision-making processes.
• Surgeons using AI assistance will recommend more consistent treatment pathways than when relying solely on their usual clinical judgment.

Additionally, the study aims to assess the impact of AI-based risk stratification on the incidence of complicated postoperative courses in cancer patients.

The concept of AI-assisted risk assessment for colorectal cancer patients - enabling individualized perioperative optimization based on risk profiles - has already been pilot-tested at the Department of Surgery, Zealand University Hospital, Køge, under the name AID-SURG. This pilot has been fully implemented for approximately two years. Preliminary results suggest improvements in patient outcomes, including reductions in complications, readmissions, and length of hospital stay. A scientific manuscript describing these findings is currently under peer review for publication in an international journal.

Study Methodology, Design, and Procedures:

The AIDPRO-CRC trial is a nationwide, multicenter randomized clinical trial conducted across hospitals in all five Danish regions. The primary objective is to evaluate whether an artificial intelligence (AI)-driven solution can improve the treatment of patients undergoing surgery for colorectal cancer by tailoring perioperative care to the individual patient's risk profile. In addition to the randomized trial, the study comprises a questionnaire-based survey among healthcare professionals and a simulated substudy. The study has several aims:

• To determine the cost and clinical impact of using a digital decision-support platform powered by AI for risk assessment in surgical oncology. (Randomized controlled trial with patients)
• To explore how clinicians and other healthcare professionals perceive the AI tool in terms of usability and relevance in clinical practice. (Questionnaire survey)
• To examine whether treatment recommendations differ depending on whether clinicians use the AI-enabled decision-support platform. (Simulated substudy) Seven hospitals across Denmark are participating in the patient inclusion phase, while additional healthcare staff at these sites may participate in the questionnaire and simulated substudy. One hospital acts as the coordinating center but does not include patients.

Randomized Controlled Trial (RCT) with Patients:

All patients with suspected or confirmed colon and/or rectal cancer at the participating centers will be screened for eligibility. Only patients with a confirmed diagnosis and an indication for curative-intent surgery are eligible for inclusion. All patients will continue to receive care in accordance with national clinical guidelines and the colorectal cancer fast-track program.

Participants will be randomized to one of two risk-assessment arms:

• An AI-supported risk assessment arm
• A standard surgeon-assessed risk assessment arm The goal is to optimize perioperative treatment (pre-, intra-, and postoperative) to reduce postoperative complications. The intensity of the optimization offered is scaled based on the assigned risk level, whether determined by the surgeon or by the AI model. Patients are allocated to one of four predefined perioperative care packages (A, B, C, or D), which are already part of standard care across participating sites. Therefore, patients who decline participation will still receive personalized care based on these four packages.

The only difference between the study arms is the method of risk assessment used to guide the treatment plan: either an AI-based model or the surgeon's clinical judgment. All other treatment components follow national guidelines and are identical across all sites.

The aim of the intervention is to determine whether AI-assisted risk stratification can improve the allocation of patients into the four risk-based treatment groups, ensuring that only patients with a high risk of complications receive more intensive optimization, thus improving the efficiency of healthcare resource use.

Study Arms in the RCT AI-based Risk Assessment (Intervention Group)

• Description: An advanced AI model functions as a decision-support tool to estimate each patient's perioperative risk.
• Purpose: The AI model uses various patient-specific data inputs to predict risk and assign a tailored care pathway, based on a large historical dataset.
• Expected Outcome: The use of AI is expected to improve the precision of risk stratification, thereby optimizing resource utilization.

Surgeon-based Risk Assessment (Control Group)

• Description: Experienced colorectal surgeons assess patient risk based on clinical judgment and national guidelines.
• Purpose: Traditional clinical assessment is used to assign patients to the appropriate care pathway.
• Expected Outcome: This arm serves as the clinical standard-of-care comparator against which the effectiveness of AI-guided decision-making is evaluated.

Perioperative Care Packages (A-D)

These four standardized treatment packages are part of routine clinical practice and are not unique to the AIDPRO-CRC trial. The package assigned depends on the risk assessment method and stratifies patients as follows:

• Package A: Low-risk patients
• Package B: Moderate-risk patients
• Package C: High-risk patients
• Package D: Very high-risk patients

Each package includes:

• Preoperative optimization: Multidisciplinary interventions (e.g., surgeons, anesthesiologists, physiotherapists, geriatricians, and dieticians) to enhance patients' physical readiness for surgery.
• Intraoperative care: Standardized surgical techniques and anesthetic protocols designed to minimize physiological stress and reduce complications.
• Postoperative care: Enhanced recovery protocols emphasizing early mobilization, pain management, and nutritional support to promote faster recovery.

Healthcare Professional Involvement - Usability and Simulation Studies:

To investigate the final two hypotheses, healthcare professionals involved in using the AI model during the study period will participate in both a usability survey and a simulated decision-making substudy.

Usability Survey Two months after patient inclusion begins at each site, all relevant users will receive an email invitation to complete a questionnaire regarding their experience with the MDTPlatform. Prior to participation, informed consent and eligibility confirmation are required. The survey is repeated after one year and again at study completion.

Simulated Substudy Eligible physicians are contacted by a designated researcher and receive information about the simulated decision-making exercise. Upon consent, participants complete a simulation involving 20 patient cases: 10 cases in a standard format (e.g., Word documents) and 10 via the MDTPlatform with AI support. Each case is presented only once to each physician-either in the standard format or via the platform. Physicians indicate their treatment recommendation for each case. If necessary, multiple sessions may be arranged. Physicians may assess multiple cases until all 75 cases have been reviewed by at least one participant.

Patients

All patients referred to a surgical department with a confirmed first-time diagnosis of colon and/or rectal cancer and deemed eligible for potentially curative surgery will be offered participation in the study. Participants will be randomly assigned to one of two groups:

1. Intervention group: Risk assessment is performed using an artificial intelligence (AI)-based prediction model to estimate the risk of 1-year postoperative mortality.
2. Control group: Risk assessment is performed by an experienced colorectal surgeon based on clinical judgment and standard risk evaluation practices.

Based on the estimated risk, patients are assigned to one of four predefined perioperative care pathways (Packages A-D). Patients who decline consent for risk estimation will not be enrolled in the trial but will still be offered optimized perioperative care as per local standard clinical protocols.

Sample size estimation - patients Sample size was calculated based on data from the AID-SURG pilot project to detect a cost-saving effect of the AI-based risk stratification. A simulation-based power analysis determined that 600 participants per group (1,200 total) are needed to achieve 93% statistical power with a 5% significance level, to detect a cost reduction of $94.9 USD per patient in the AI group compared to the control group, assuming a tendency of surgeons to allocate more patients to high-risk groups.

No research biobank will be established, and no biological samples will be collected or used from existing biobanks in this study.

Healthcare Professionals Surgeons and other healthcare professionals involved in the use of the AI-based platform will be invited to participate in two sub-studies: a user satisfaction survey and a simulation-based study. Eligible personnel will be automatically invited upon registration as platform users.

Sample size estimation - user survey User experience will be considered positive if more than 50% of respondents agree that the AI platform provides relevant and actionable information. A minimum of 28 participants is required to detect a statistically significant result with high confidence.

Sample size estimation - simulation study The simulation study aims to assess whether AI support increases inter-rater agreement in treatment decisions. Agreement will be quantified using entropy measures. Based on prior pilot data and 1 million Monte Carlo simulations, a total of 75 unique patient cases (600 assessments in total) are needed, corresponding to participation from approximately 30 physicians.

Adverse Effects, Risks, and Disadvantages

Individual Risk Stratification Using Artificial Intelligence (AI):

The primary difference between the AIDPRO-CRC trial and standard clinical practice lies in the use of an AI-based decision-support tool for pre- and postoperative risk assessment, instead of traditional clinician-based evaluation. The study design ensures that the intervention does not influence decisions regarding overall cancer treatment, such as chemotherapy, radiotherapy, or the determination of eligibility for curative surgery. These treatment decisions are made independently of trial participation and irrespective of group allocation. Both arms in the AIDPRO-CRC trial aim to optimize patient outcomes using four predefined treatment pathways based on the latest ERAS (Enhanced Recovery After Surgery) protocols, which are already implemented at all participating centers.

However, the use of AI introduces specific risks and disadvantages:

• AI-Based Risk Assessment: Patients in the intervention group will receive an optimization plan based on the AI-supported risk stratification model. Although the AI model is designed to improve outcomes by accurately predicting risk, potential issues include:

• Algorithmic Accuracy: The effectiveness of the AI model depends on its accuracy and predictive capability. Inaccurate predictions could lead to suboptimal clinical decisions.
• Data Entry Errors: Incorrect or outdated data inputs from clinicians may result in misleading risk estimates.

To mitigate these risks and safeguard patient safety, the AIDPRO-CRC study incorporates the following safety measures:

• Risk Mitigation Strategies: These include comprehensive training for clinicians in the use of the AIDPRO algorithm and the MDT platform, clear patient selection criteria, informed consent procedures, and transparent communication about risks and benefits associated with both AI- and clinician-based approaches.
• Restricted Access: The AIDPRO algorithm is only accessible to experienced colorectal surgeons capable of making independent clinical decisions to ensure optimal patient care regardless of the algorithm's output.
• Monitoring: The algorithm will continuously monitor outcomes related to severe postoperative complications for up to 90 days postoperatively.
• Safety Plan: A predefined safety protocol will allow for adjustments or discontinuation of the intervention in the event of significant safety concerns, thereby ensuring patient welfare remains paramount.
• Radiation and Sample Collection: No additional radiation (e.g., X-rays) will be administered beyond standard care. No extra blood or tissue samples will be collected apart from those already part of routine care.

Recruitment of Study Participants:

Recruitment may only begin once all necessary approvals from regulatory authorities and ethics committees have been obtained. Each potential participant must receive both oral and written information and provide written informed consent before inclusion. Screening will be conducted using information from medical records, including age, radiological findings, indication for curative surgery, blood test results, and overall health status. Screening of medical records will be performed in compliance with the Danish Health Act, Section 46.

The first contact between the patient and a physician will occur during the initial outpatient consultation at one of the participating centers. The consent conversation will take place in a private setting to avoid disruptions. If the patient is eligible and agrees to participate, they will be enrolled by a physician from the surgical department, based on the inclusion and exclusion criteria defined in the protocol. Only patients who meet all inclusion criteria and no exclusion criteria may be included. Any uncertainties must be clarified directly with the coordinating investigator.

In addition to the oral and written explanation, patients will receive written study information and a copy of the national leaflet "Your rights as a research subject in medical device trials" published by the National Committee on Health Research Ethics. Patients will be informed that they may bring a companion to the consent meeting. They may also be contacted by phone within 24-48 hours for a final decision, if needed.

There may be unforeseen risks associated with participation in a clinical trial. If new information arises during the study regarding treatment efficacy, risks, or complications, participants will be informed.

Participants will be withdrawn from the study if:

• They voluntarily withdraw their informed consent.
• They fail to follow study procedures.
• The treating physician or investigator deems withdrawal necessary (e.g., due to alternative treatment needs).
• The study is terminated prematurely. The reason and date of withdrawal will be recorded in the electronic case report form (eCRF) and the patient's medical record. Withdrawn patients will continue to be followed and evaluated unless they explicitly decline.

Publication of Study Results:

The results of this study will be published in international peer-reviewed journals, regardless of whether the findings are positive, negative, or inconclusive. The trial will be registered on www.clinicaltrials.gov and continuously updated. Subsequent publications will cover safety, efficacy, and clinical outcomes. Additionally, results will be published on www.clinicaltrialsregister.eu within one year after the study's completion. Any deviations from the pre-specified statistical analysis plan will be described and justified. Future publications will include long-term survival analyses and any exploratory analyses not outlined in the original plan will be identified as post hoc, with rationale provided.

Study results will be submitted to the Clinical Trial Information System (CTIS) within one year of trial completion.

Ethical Justification:

The central ethical difference between the AIDPRO-CRC trial and standard care is the use of an AI-based risk assessment model instead of traditional clinical judgment for planning perioperative treatment. Importantly, this intervention does not impact decisions related to chemotherapy, radiotherapy, or eligibility for curative surgery-all of which are determined independently of trial participation and before group allocation.

Both arms in the AIDPRO-CRC trial aim to optimize patient outcomes via four predefined perioperative treatment packages, based on current ERAS protocols, already implemented at participating hospitals.

There is a potential risk that the AIDPRO algorithm may misclassify individual patients, shifting their risk category from low (A/B) to high (C/D) or vice versa. Patients classified into a lower-risk group by the algorithm will receive standard ERAS treatment without additional prehabilitation and will undergo surgery sooner, consistent with current Danish practice. Conversely, high-risk classifications may result in a 2-4 week postponement of surgery to allow for intensified pre- and postoperative optimization. Research suggests this does not negatively affect outcomes.

Although international concerns exist regarding delays in cancer surgery, national data (under publication) and a systematic review show that surgical delays of up to 7 weeks in stage I-III colorectal cancer do not reduce long-term survival. Furthermore, a 4-week optimization period aligns with the latest guidelines developed by the study team. Importantly, delays in categories C and D are accompanied by active interventions (e.g., physical training, nutritional optimization, and anemia correction), which reduce complications and enhance recovery.

Preliminary data from the AID-SURG pilot study showed that implementation of AI-guided risk assessment and individualized care plans halved the rate of postoperative medical complications-benefiting both the individual and the healthcare system, where such complications account for up to 30% of healthcare spending (manuscript in preparation).

This clinical investigation will be conducted in compliance with applicable regional and national laws, ISO 14155:2020 for medical device trials, the ethical principles of the Declaration of Helsinki (1964 and subsequent revisions), and relevant ICH/GCP and GDPR regulations. It will also conform to the EU Medical Device Regulation (EU) 2017/745 (EU MDR).

• Study Type: Interventional
• Enrollment (Estimated): 1200
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Ismail Gögenur, Professor; Phone Number: +4526336426; Email: igo@regionsjaelland.dk
• Study Contact Backup: Name: Magnus N Jung, MD; Phone Number: +4524825249; Email: majuj@regionsjaelland.dk

Study Locations

• Denmark
  • Aalborg, Denmark, 9000
    • Recruiting
    • Aalborg University Hospital, Department of Gastrointestinal Surgery
    • Contact: Michael Bødker Lauritzen, MD; Phone Number: +4597 66 12 05; Email: m.lauritzen@rn.dk
  • Herning, Denmark, 7400
    • Recruiting
    • Regional Hospital Gødstrup, Department of Surgery
    • Contact: Claudia Jaensch, MD, PhD; Phone Number: +45 78 43 08 15; Email: claudia.jaensch@goedstrup.rm.dk
  • Hillerød, Denmark, 3400
    • Not yet recruiting
    • Copenhagen University Hospital - North Zealand, Hillerød, Department of Surgery
    • Contact: Anders Bertelsen, MD, PhD, Associate Prof.; Phone Number: +45 51 90 63 03; Email: claus.anders.bertelsen@regionh.dk
  • Hvidovre, Denmark, 2650
    • Recruiting
    • Copenhagen University Hospital Hvidovre, Gastro Unit, Surgical Division
    • Contact: Monica L Kjær, Associate Professor, PhD; Phone Number: +45 38 62 67 43; Email: monica.linda.kjaer@regionh.dk
  • Randers, Denmark, 8930
    • Recruiting
    • Regional Hospital Randers, Department of Surgery
    • Contact: Katrine Emmertsen, MD, PhD, Professor; Phone Number: +45 48 42 37 86; Email: katremme@rm.dk
  • Svendborg, Denmark, 5700
    • Not yet recruiting
    • Odense University Hospital, Svendborg, Department of Colorectal Surgery
    • Contact: Issam Al-Najami, MD, PhD; Phone Number: +45 61 31 03 98; Email: issam.al-najami@rsyd.dk
  • Viborg, Denmark, 8800
    • Recruiting
    • Viborg Regional Hospital, Hospitalunit Midt, Department of Surgery
    • Contact: Casper Nielsen, MD, PhD; Phone Number: +45 78 44 64 41; Email: casper.nielsen@viborg.rm.dk

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion criteria - patients

To be eligible for study participation, the following criteria must be met:

• Histologically confirmed diagnosis or strong clinical suspicion of first-time colon or rectal cancer, clinical stage I-IV.
• Signed written informed consent obtained prior to any study-specific procedures.
• Age ≥18 years at the time of consent.
• Scheduled for potentially curative surgery as determined by a multidisciplinary team (MDT) conference.
• Availability of all required input variables for the AI model not directly assessed by the surgeon (e.g., ASA score, WHO performance status).

Exclusion criteria - patients

A patient will be excluded from the study if:

• Surgery with curative intent is no longer planned despite previous eligibility.

Healthcare Professionals Surgeons and other healthcare professionals involved in the use of the AI-based platform will be invited to participate in two sub-studies: a user satisfaction survey and a simulation-based study. Eligible personnel will be automatically invited upon registration as platform users.

Inclusion criteria - healthcare professionals

To be eligible to participate in the survey and simulation study, individuals must:

• Be licensed medical doctors.
• Be either board-certified specialists in surgical oncology or currently in training to become one.

Exclusion criteria - healthcare professionals There are no exclusion criteria for participation in the survey or simulation study.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Supportive Care
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: AI-augmented risk-stratification; Description: An advanced AI model functions as a decision-support tool to estimate each patient's perioperative risk.; Purpose: The AI model uses various patient-specific data inputs to predict risk and assign a tailored care pathway, based on a large historical dataset.; Expected Outcome: The use of AI is expected to improve the precision of risk stratification, thereby optimizing resource utilization.	Device: AI augmented risk-stratification; A state-of-the-art artificial intelligence (AI) model called AIDPRO manual CRC is used as a decision support tool to estimate the 1-year mortality risk for each patient
Active Comparator: Expert-based Risk-stratification; Description: Experienced colorectal surgeons assess patient risk based on clinical judgment and national guidelines.; Purpose: Traditional clinical assessment is used to assign patients to the appropriate care pathway.; Expected Outcome: This arm serves as the clinical standard-of-care comparator against which the effectiveness of AI-guided decision-making is evaluated.	Other: Expert-based Risk-stratification; Experienced colorectal surgeons assess patient risk based on clinical judgment and national guidelines.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Cost Effectiveness	This primary endpoint is the saved marginal cost of perioperative intervention bundles achieved by integrating AI-augmented decision support. This will be assessed by comparing the overall marginal cost per patient between the AI-assisted arm (Intervention-arm) and the standard clinician-based stratification arm (control-arm). This cost-calculation factors in the following: Distribution of patients across risk strata (A, B, C, D); Cost per intervention bundle; Total perioperative expenditure per patient pr. bundle	Baseline
Perceived Effect of Clinical Support Tool & User Feedback	This primary endpoint domain evaluates the user-perceived satisfaction with and clinical relevance of the AI-driven MDTPlatform medical device which contains the risk prediction algorithm.; Perceived relevance of the MDTPlatform provided information (i.e., the risk stratification and other displayed data) in support of making clinical decisions regarding perioperative treatment for a patient?; Perceived relevance of the displayed information provided by the MDTPlatform to clinical decision-making?; Comparison of the ability of MDTPlatform to provide a better overview of the current patient's treatment course in the context of multidisciplinary decision-making compared to usual practice? Each measured using a 7 point Likert scale where responses of 5, 6 or 7 are considered relevant All perceived clinician satisfaction with the use of the MDTPlatform will be assessed by questionnaire sent to users	After 8 weeks of use again at 24-52 weeks of use and at after inclusion of last patient
Variability of Suggested Treatment With and Without MDTPlatform	This primary endpoint relates to the simulation substudy which will be carried out by letting users a subset of a set of patients. Over 1-2 sessions clinicians will evaluate a subset of a predetermined set of realistic patient cases. The total set will include 75 patients with and without MDTPlatform, yielding a total of 150 (2x75) cases. Clinicians will be asked to evaluate a minimum of 20 cases. Clinicians will score risk class based on the given data and will suggest a treatment plan, which will be recorded. The data given will be the same for cases with and without MDTPlatform, except for the risk stratification which will only be included in the cases presented via the MDTPlatform. The cases that are not presented via the MDTPlatform will be presented in the standard manner of the site where the clinician works.	Baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The rate of complicated postoperative course 90 days after surgery	Defined by a Clavien-Dindo (CD) score >2 or a Comprehensive Complication Index (CCI) >20 within 90 days postoperatively	90 days post-operative
Postoperative Complications	Total number of surgical complications (30, 90 days postoperatively) Both total and stratified by ≥ Clavien-Dindo grade 2. Medical complications at 90 days post-surgery Both total and stratified by ≥ Clavien-Dindo grade 2	90 days Post-Operative
Length of Hospital stay (LOS) > 4 days	Defined by total postoperative duration of initial hospital stays measured in days. Analysis of LOS within each study arm and subsequent intervention group. Proportion of patients with LOS >4 days	90 days post-operative
Readmission Rates	Hospital readmissions within 30 and 90 days related to postoperative complications	90 days postoperatively
Days Alive and Out of Hospital 30 days and 90 days	Defined by total postoperative duration of initial and any subsequent hospital stays, over the defined period (30, 90 postoperative days), was subtracted from the total period length to obtain the number of days spent out of hospital. If a patient dies within that period, DAOH will be defines as 0 days. A longer DAOH is a more favorable outcome DAOH90 <85 days as a marker of prolonged morbidity	90 Days Postoperatively
Composite outcomes:	Any medical complication within 90 days OR days alive and out of hospital within the first 90 postoperative days (DAOH90) less than 85 (85 NOT included); LOS >4 days + any registered medical complication; LOS >4 days + readmission within 90 days; Any medical complication + readmission within 90 days	90 days post-operative
Time from MDT to Surgery	Measured as days passed between MDT date and the date of surgery	Preoperative

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Surgical & Medical Complications	Total number of surgical complications (30, 90 days postoperatively) Complication severity assessed by Comprehensive Complication Index (CCI)	90 Days Postoperatively
30, 90 and 365 day mortality	Is the patient is alive up to one year post-surgery?	Up to 1 year post-operatively
Return to intended oncological treatment (RIOT) for patients requiring further treatment in addition to surgery, including chemotherapy, radiotherapy, or a combination hereof.	The proportion of patients who are able to initiate their planned adjuvant oncological therapy (chemotherapy, radiotherapy, or both) within the expected timeframe following surgery. This measure reflects the patient's postoperative recovery and readiness for further cancer treatment.	30 days post-operative
Patient-reported Postoperative Recovery	Measured by The Quality of Recovery-15 (QoR-15) repeatedly at baseline, 1-14 days after surgery and 30 days after surgery	30 days post-operative
Geriatric G8 Score at Baseline	A screening tool used at baseline to assess frailty in older patients (typically ≥70 years) based on nutritional status, mobility, neuropsychological problems, medication use, and self-perceived health. The G8 score helps identify patients at risk of poor treatment outcomes and guides further geriatric assessment.	Baseline
Nutritional Status	As assessed by PG-SGA-SF score at baseline	Baseline
Activity Status	Assessed by DASI score at baseline	Baseline
Functional Assessments in High Risk Patients	In patients allocated to group C and group D Sit to stand test 6 Minute walk test Hand-grip strength test	Preoperative
Compliance with ERAS protocols across study centers	Assessment of how consistently the participating study centers adhere to the standardized Enhanced Recovery After Surgery (ERAS) protocols, including pre-, intra-, and postoperative elements. This measure evaluates protocol fidelity and ensures comparability of care across centers.	Perioperative
Learning effect of AI exposure on surgeons' risk estimation over time	Evaluation of how repeated exposure to AI-generated risk assessments influences surgeons' ability to independently estimate perioperative risk, measured longitudinally to assess alignment with AI predictions and potential improvement in clinical judgment.	Throughout the duration of the study
Variability in AI impact across different geographical centers in Denmark	Assessment of differences in clinical outcomes, adherence, and integration of the AI-based risk assessment tool across participating study centers, to evaluate regional variation in effectiveness, implementation, and healthcare delivery practices.	Throughout the duration of the study
Patient centric satisfaction	Satisfaction with AI-based risk assessment as part of CRC treatment and patient perception of AI's role in their surgical care	At 90 days post surgery
AI vs. clinician/surgeon prediction agreement	Evaluating concordance between AI-augmented risk stratification and surgeon-based classification	Perioperative

Collaborators and Investigators

• Sponsor: Zealand University Hospital
• Collaborators: Odense University Hospital; Hvidovre University Hospital; Hillerod Hospital, Denmark; Aalborg University Hospital; Gødstrup Hospital; Viborg Regional Hospital; Randers Regional Hospital

Publications and helpful links

Helpful Links

• Website hosting documents and information central to the study

Study record dates

Study Major Dates

• Study Start (Actual): October 24, 2025
• Primary Completion (Estimated): February 1, 2027
• Study Completion (Estimated): February 1, 2028

Study Registration Dates

• First Submitted: October 9, 2024
• First Submitted That Met QC Criteria: October 15, 2024
• First Posted (Actual): October 16, 2024

Study Record Updates

• Last Update Posted (Actual): June 2, 2026
• Last Update Submitted That Met QC Criteria: May 28, 2026
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Prehabilitation; AI; perioperative; Post-operative complications; Algorithm; Risk-stratification; Risk-estimation
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Intestinal Diseases; Gastrointestinal Neoplasms; Digestive System Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colorectal Neoplasms; Intestinal Neoplasms; Colonic Diseases; Colonic Neoplasms
• Other Study ID Numbers: p-2025-19466

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No