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

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ClinicalTrials.gov Identifier: NCT04675138
Recruitment Status : Recruiting
First Posted : December 19, 2020
Last Update Posted : December 19, 2020
Sponsor:
Information provided by (Responsible Party):
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.


Condition or disease Intervention/treatment
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.

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Study Type : Observational
Estimated 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 : August 20, 2020
Estimated Primary Completion Date : December 31, 2022
Estimated Study Completion Date : December 31, 2022

Resource links provided by the National Library of Medicine



Intervention Details:
  • Other: No intervention will be provided to the subject
    No intervention will be provided to the subject


Primary Outcome Measures :
  1. Concordance Rate [ Time Frame: 1 to 2 years ]
    Comparative agreement in recommendations between the two study groups, as measured by concordance rate


Secondary Outcome Measures :
  1. Impact of CDSS in decision making [ Time Frame: 1 to 2 years ]
    Percentage of cases in which the MTB recommendations change due to suggestions from CDSS



Information from the National Library of Medicine

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Ages Eligible for Study:   21 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
The population will be selected from hospitals that conduct tumour board for these cancers
Criteria

Inclusion Criteria:

  • Patients with primary gastric adenocarcinoma including preinvasive carcinoma.
  • Patients with gastric gastrointestinal stroma tumors.
  • Patients with gastroesophageal junction cancers
  • Patients with gastric neuroendocrine tumors.
  • Patients with esophageal cancer including adenocarcinoma, squamous cell carcinoma and preinvasive carcinoma subtypes

Exclusion Criteria:

  • Patients with other primary cancers involving the stomach or esophagus.
  • Patients with other cancer subtypes.
  • Patients with concomitant cancers of other organs.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04675138


Contacts
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Contact: Bok Yan, Jimmy So +65 6772 5555 ext 24236 sursbyj@nus.edu.sg

Locations
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Singapore
National University Hospital Recruiting
Singapore, Singapore, 119228
Contact: Jimmy So, MBChB    +65 6772 5555 ext 24236    sursbyj@nus.edu.sg   
Contact: Guowei Kim, MBBS    +65 6772 5555 ext 28830    guo_wei_kim@nuhs.edu.sg   
Sponsors and Collaborators
National University Hospital, Singapore
Publications of Results:
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Responsible Party: National University Hospital, Singapore
ClinicalTrials.gov Identifier: NCT04675138    
Other Study ID Numbers: 2020/00493
First Posted: December 19, 2020    Key Record Dates
Last Update Posted: December 19, 2020
Last Verified: December 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by National University Hospital, Singapore:
Gastric Cancer
Esophageal Cancer
Artificial Intelligence
Multidisciplinary Tumor Board
Clinical Decision Support System
Concordance
Additional relevant MeSH terms:
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Stomach Neoplasms
Esophageal Neoplasms
Gastrointestinal Neoplasms
Digestive System Neoplasms
Neoplasms by Site
Neoplasms
Digestive System Diseases
Gastrointestinal Diseases
Stomach Diseases
Head and Neck Neoplasms
Esophageal Diseases