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

Sponsor

National University Hospital, Singapore (Other)

Overall Status

Recruiting

CT.gov ID

NCT04675138

Collaborator

(none)

1,000

Enrollment

Location

28.4

Anticipated Duration (Months)

35.3

Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Gastric Cancer Esophageal Cancer Esophagogastric Junction Cancer	Other: No intervention will be provided to the subject

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 Design

Study Type:

Observational

Anticipated Enrollment :

1000 participants

Observational Model:

Case-Only

Time Perspective:

Other

Official Title:

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

Actual Study Start Date :

Aug 20, 2020

Anticipated Primary Completion Date :

Dec 31, 2022

Anticipated Study Completion Date :

Dec 31, 2022

Outcome Measures

Primary Outcome Measures

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

Secondary Outcome Measures

Reason for Discordance [1 to 2 years]
To identify the reason for the discordance

Eligibility Criteria

Criteria

Ages Eligible for Study:

21 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

A. In discovery and internal retrospective validation part:

Patients with primary gastric adenocarcinoma including preinvasive carcinoma or
Patients with gastroesophageal junction cancers or
Patients with oesophageal cancer including adenocarcinoma, squamous cell carcinoma and preinvasive carcinoma subtypes.