Development of a Clinical Decision Support System With Artificial Intelligence for Cancer Care
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| ClinicalTrials.gov Identifier: NCT04675138 |
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Recruitment Status :
Recruiting
First Posted : December 19, 2020
Last Update Posted : December 19, 2020
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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 |
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 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 |
- Other: No intervention will be provided to the subject
No intervention will be provided to the subject
- Concordance Rate [ Time Frame: 1 to 2 years ]Comparative agreement in recommendations between the two study groups, as measured by concordance rate
- 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
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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 |
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.
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
| Contact: Bok Yan, Jimmy So | +65 6772 5555 ext 24236 | sursbyj@nus.edu.sg |
| 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 | |
| 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 |
| Studies a U.S. FDA-regulated Drug Product: | No |
| Studies a U.S. FDA-regulated Device Product: | No |
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Gastric Cancer Esophageal Cancer Artificial Intelligence |
Multidisciplinary Tumor Board Clinical Decision Support System Concordance |
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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 |