Personalized dose selection platform for patients with solid tumors in the PRECISE CURATE.AI feasibility trial
Abstract In oncology, the conventional reliance on the maximum tolerated dose (MTD) strategy for chemotherapy may not optimize treatment outcomes for individual patients. CURATE.AI is an AI-derived platform that utilizes a patient’s own, small dataset to dynamically personalize only their own dose r...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-00835-7 |
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| author | Agata Blasiak Anh T. L. Truong Nigel Foo Lester W. J. Tan Kirthika S. Kumar Shi-Bei Tan Chong Boon Teo Benjamin K. J. Tan Xavier Tadeo Hon Lyn Tan Cheng Ean Chee Wei Peng Yong Dean Ho Raghav Sundar |
| author_facet | Agata Blasiak Anh T. L. Truong Nigel Foo Lester W. J. Tan Kirthika S. Kumar Shi-Bei Tan Chong Boon Teo Benjamin K. J. Tan Xavier Tadeo Hon Lyn Tan Cheng Ean Chee Wei Peng Yong Dean Ho Raghav Sundar |
| author_sort | Agata Blasiak |
| collection | DOAJ |
| description | Abstract In oncology, the conventional reliance on the maximum tolerated dose (MTD) strategy for chemotherapy may not optimize treatment outcomes for individual patients. CURATE.AI is an AI-derived platform that utilizes a patient’s own, small dataset to dynamically personalize only their own dose recommendations. The primary objective of this feasibility trial was to assess the logistical and scientific feasibility of providing dynamically personalized AI-derived chemotherapy dose recommendations for patients with advanced solid tumors at/for treatment with single-agent capecitabine, capecitabine in combination with oxaliplatin (XELOX), or capecitabine in combination with irinotecan (XELIRI). CURATE.AI demonstrated adaptability to clinically relevant situations encountered by patients often treated with palliative intent of care. High rates of user adherence were demonstrated, which could be in part due to the high engagement of the physicians in selecting data and boundaries for CURATE.AI operations. |
| format | Article |
| id | doaj-art-72fd84f04f5d413ab8313f843daa6652 |
| institution | OA Journals |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-72fd84f04f5d413ab8313f843daa66522025-08-20T02:15:15ZengNature Portfolionpj Precision Oncology2397-768X2025-02-01911710.1038/s41698-025-00835-7Personalized dose selection platform for patients with solid tumors in the PRECISE CURATE.AI feasibility trialAgata Blasiak0Anh T. L. Truong1Nigel Foo2Lester W. J. Tan3Kirthika S. Kumar4Shi-Bei Tan5Chong Boon Teo6Benjamin K. J. Tan7Xavier Tadeo8Hon Lyn Tan9Cheng Ean Chee10Wei Peng Yong11Dean Ho12Raghav Sundar13The Institute for Digital Medicine (WisDM), National University of SingaporeThe Institute for Digital Medicine (WisDM), National University of SingaporeThe Institute for Digital Medicine (WisDM), National University of SingaporeThe Institute for Digital Medicine (WisDM), National University of SingaporeThe Institute for Digital Medicine (WisDM), National University of SingaporeThe Institute for Digital Medicine (WisDM), National University of SingaporeYong Loo Lin School of Medicine, National University of SingaporeYong Loo Lin School of Medicine, National University of SingaporeThe Institute for Digital Medicine (WisDM), National University of SingaporeDepartment of Haematology-Oncology, National University Cancer Institute, Singapore (NCIS), National University Health SystemYong Loo Lin School of Medicine, National University of SingaporeDepartment of Haematology-Oncology, National University Cancer Institute, Singapore (NCIS), National University Health SystemThe Institute for Digital Medicine (WisDM), National University of SingaporeThe N.1 Institute for Health (N.1), National University of SingaporeAbstract In oncology, the conventional reliance on the maximum tolerated dose (MTD) strategy for chemotherapy may not optimize treatment outcomes for individual patients. CURATE.AI is an AI-derived platform that utilizes a patient’s own, small dataset to dynamically personalize only their own dose recommendations. The primary objective of this feasibility trial was to assess the logistical and scientific feasibility of providing dynamically personalized AI-derived chemotherapy dose recommendations for patients with advanced solid tumors at/for treatment with single-agent capecitabine, capecitabine in combination with oxaliplatin (XELOX), or capecitabine in combination with irinotecan (XELIRI). CURATE.AI demonstrated adaptability to clinically relevant situations encountered by patients often treated with palliative intent of care. High rates of user adherence were demonstrated, which could be in part due to the high engagement of the physicians in selecting data and boundaries for CURATE.AI operations.https://doi.org/10.1038/s41698-025-00835-7 |
| spellingShingle | Agata Blasiak Anh T. L. Truong Nigel Foo Lester W. J. Tan Kirthika S. Kumar Shi-Bei Tan Chong Boon Teo Benjamin K. J. Tan Xavier Tadeo Hon Lyn Tan Cheng Ean Chee Wei Peng Yong Dean Ho Raghav Sundar Personalized dose selection platform for patients with solid tumors in the PRECISE CURATE.AI feasibility trial npj Precision Oncology |
| title | Personalized dose selection platform for patients with solid tumors in the PRECISE CURATE.AI feasibility trial |
| title_full | Personalized dose selection platform for patients with solid tumors in the PRECISE CURATE.AI feasibility trial |
| title_fullStr | Personalized dose selection platform for patients with solid tumors in the PRECISE CURATE.AI feasibility trial |
| title_full_unstemmed | Personalized dose selection platform for patients with solid tumors in the PRECISE CURATE.AI feasibility trial |
| title_short | Personalized dose selection platform for patients with solid tumors in the PRECISE CURATE.AI feasibility trial |
| title_sort | personalized dose selection platform for patients with solid tumors in the precise curate ai feasibility trial |
| url | https://doi.org/10.1038/s41698-025-00835-7 |
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