Development of a Clinical Decision Support System With Artificial Intelligence for Cancer Care

Recruiting

• Conditions: Gastric Cancer; Esophageal Cancer; Esophagogastric Junction Cancer

• Registration Number: NCT04675138
• Lead Sponsor: National University Hospital, Singapore

Brief Summary

Clinical Decision Support Systems (CDSSs) to augment clinical care and decision making. These are platforms which aim to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information, and other health information.

In view of the benefit of developing a CDSS, we sought to develop an alternative CDSS for oncologic therapy selection through a partnership with Ping An Technology (Shenzhen, China), beginning with gastric and oesophagal cancer. This would be done in a piecemeal fashion, with the prototype platform utilizing only international guidelines and high-quality published evidence from journals to arrive at case-specific treatment recommendations. This platform would then be evaluated by comparing its recommendations with that from the multidisciplinary tumour boards of several tertiary care institutions to determine the concordance rate.

Detailed Description

Management of cancer is a complex process which involves numerous stakeholders. In view of this, institutions worldwide have adopted the use of Multidisciplinary Tumor Boards (MTBs) for delivery of cancer care. By tapping on the collective specialized knowledge and experience of various specialties, MTBs have been shown in some studies to result in more appropriate recommendations and improved patient outcomes. At our institution, cancer cases are similarly discussed at regular MTBs which comprises surgeons, oncologists, pathologists and radiologists who review and recommend treatments.

However, in smaller centres or centres with limited resources and minimal multi-disciplinary expertise, delivery of timely and appropriate cancer care could be a challenge. Additionally, clinicians, with their busy schedule, may not be able to keep abreast of new developments in cancer research. With rapid advances in scientific research, this pool of knowledge is expected to continue to burgeon, making keeping up-to-date increasingly onerous.

To address this need, clinicians have adopted the use of Clinical Decision Support Systems (CDSSs) to augment clinical care and decision-making. These are platforms which aim to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information, and other health information. Various studies have shown CDSSs to be beneficial in selected settings such as patient safety and diagnosis \[4\], and to even increase adherence to clinical guidelines. In recent years, advancements in artificial intelligence have also seen its use expand to include oncologic therapy selection, with IBM's Watson for Oncology (WFO) being the most prominent and only platform in use to-date. In a 2018 study, WFO's ability to provide treatment advice for breast cancer was compared against recommendations from a multidisciplinary board, where it showed a high degree of concordance. Since then, several other studies have sought to examine WFO's ability to provide treatment recommendations for cancer such as ovarian, gastric, lung, cervical and colorectal cancers, with mixed results. In particular, both studies which examined the recommendations for gastric cancers showed a much lower concordance rate compared to other cancers.

In view of the above, we sought to develop an alternative CDSS for oncologic therapy selection through partnership with Ping An Technology (Shenzhen, China), beginning with gastric and esophageal cancer. This would be done in a piecemeal fashion, with the prototype platform utilizing only international guidelines and high-quality published evidence from journals to arrive at case-specific treatment recommendations. This platform would then be evaluated retrospectively and prospectively by comparing its recommendations with that from the multidisciplinary tumor boards of several tertiary care institutions to determine the concordance rate.

Study Timeline

• Study Start: 2020-08-20
• Study First Submitted: 2020-12-15
• Study First Submitted QC: 2020-12-15
• Study First Posted: 2020-12-19
• Status Verified: 2024-05
• Last Update Submitted: 2024-10-08
• Last Update Posted: 2024-10-10
• Primary Completion: 2024-12-31
• Study Completion: 2024-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000

Inclusion Criteria

Not provided

Exclusion Criteria

A. In discovery and internal retrospective validation part:

1. Patients with other primary cancers involving the stomach or oesophagus
2. Patients with other cancer subtypes
3. Patients with concomitant cancers of other organs

B. In prospective validation part:

1. Patients with esophageal squamous cell carcinoma
2. Patients who participate in clinical trials where the treatment modality is not standard of care

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Concordance Rate	1 to 2 years	Comparative agreement in recommendations between the two study groups, as measured by concordance rate

Secondary Outcome Measures

Name	Time	Method
Reason for Discordance	1 to 2 years	To identify the reason for the discordance

Trial Locations

Locations (9)

University Hospital Leipzig; 🇩🇪 Leipzig, Germany

National Cancer Centre Hospital East; 🇯🇵 Kashiwa, Japan

Seoul National University Hospital; 🇰🇷 Seoul, Korea, Republic of

National Cancer Centre Singapore; 🇸🇬 Singapore, Singapore

Ng Teng Feng General Hospital; 🇸🇬 Singapore, Singapore

Tan Tock Seng Hospital; 🇸🇬 Singapore, Singapore

Karolinska Institute Hospital; 🇸🇪 Stockholm, Sweden

The University of Edinburgh; 🇬🇧 Edinburgh, United Kingdom

National University Hospital; 🇸🇬 Singapore, Singapore