Development of a Clinical Decision Support System With Artificial Intelligence for Cancer Care
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.
調査の概要
詳細な説明
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.
研究の種類
入学 (予想される)
連絡先と場所
研究場所
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Singapore、シンガポール、119228
- 募集
- National University Hospital
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コンタクト:
- Jimmy So, MBChB
- 電話番号:24236 +65 6772 5555
- メール:sursbyj@nus.edu.sg
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コンタクト:
- Guowei Kim, MBBS
- 電話番号:28830 +65 6772 5555
- メール:guo_wei_kim@nuhs.edu.sg
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参加基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
サンプリング方法
調査対象母集団
説明
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.
B. In prospective validation part:
- Patients with primary gastric adenocarcinoma including preinvasive carcinoma or
- Patients with esophageal or gastroesophageal junction adenocarcinoma
Exclusion Criteria:
A. In discovery and internal retrospective validation part:
- Patients with other primary cancers involving the stomach or oesophagus
- Patients with other cancer subtypes
- Patients with concomitant cancers of other organs
B. In prospective validation part:
- Patients with esophageal squamous cell carcinoma
- Patients who participate in clinical trials where the treatment modality is not standard of care
研究計画
研究はどのように設計されていますか?
デザインの詳細
この研究は何を測定していますか?
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
---|---|---|
Concordance Rate
時間枠:1 to 2 years
|
Comparative agreement in recommendations between the two study groups, as measured by concordance rate
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1 to 2 years
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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
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Reason for Discordance
時間枠:1 to 2 years
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To identify the reason for the discordance
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1 to 2 years
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協力者と研究者
出版物と役立つリンク
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (予想される)
研究の完了 (予想される)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。
胃癌の臨床試験
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