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Endocrine Abstracts (2023) 98 T12 | DOI: 10.1530/endoabs.98.T12

NANETS2023 Trials In Progress (12 abstracts)

Neuroendocrine tumors AI-based clinical trial search tool eases clinical trial discovery for patients and health care professionals

Josh Mailman 1 , Danielle Ralic 2 , Jaydira del Rivero MD 3 , Germo Gericke MD 4 , Thorvardur R. Halfdanarson MD 5 , Ken Herrmann MD 6 , Ronald Hollander 7 & George Albert Fisher Jr. MD 8

1Northern California CarciNET Community, Oakland, CA; AG, Zurich, Switzerland; 3Developmental Therapeutics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD; 4Ariceum International AG, Basel, Switzerland; 5Mayo Clinic Comprehensive Cancer Center, Rochester, MN; 6Department of Nuclear Medicine, University of Duisburg-Essen, and German Cancer Consortium-University Hospital Essen, Essen, Germany; 7Research Committee International Neuroendocrine Cancer Alliance, Boston, MA; 8Stanford Cancer Center, Stanford University, Palo Alto, CA.
Corresponding Author is Danielle Ralic, AG, Zurich, Switzerland

Background: Finding relevant neuroendocrine tumor (NET) trials remains a challenge for patients and healthcare professionals (HCPs).’s taxonomy associates many conditions with NETs, complicating the discovery process. As of 6/30/23 listed 700 recruiting, phase 1-3 interventional trials using the terms Neuroendocrine Tumors, Pheochromocytoma, and Paraganglioma. Many of these trials are not relevant for patients with NETs. Even with several redesigns of, it can still be challenging to navigate the user interface to find trials for a patient’s specific situation, especially with uncommon cancers. Here and NorCal CarciNET Community present a joint project to leverage technology, specifically Artificial Intelligence (AI), to improve the discoverability of trials, and thereby help patients and HCPs more easily find relevant NET trials.

Methods: The project assembled an expert panel consisting of physicians, scientific experts, patients, and a healthcare technology partner ( to assess which clinical trials should be brought in and what eligibility criteria should be considered.’s proprietary AI model automatically searches trials to recognize eligibility criteria and restructures them to enable patient-trial matching using a digital questionnaire. The trial extraction model was refined based on NET-specific criteria proposed by the expert panel. After testing the tool with scientific experts and patients, the beta trial finder tool was released at NANETS’ annual symposium in 2022. At NANETs, the project team collected feedback from both HCPs and patient advocates. Once the feedback had been incorporated, the tool was officially launched with NorCal CarciNETs in January 2023. In April 2023, the tool was additionally deployed to patient advocacy partners including INCA, NETRF, LACNETs, and Para Pheo Alliance.

Results: With the trial extraction model, only relevant and validated trials for NETs are included in the digital search tool, reducing the number of trial options from 700 to 155. The questionnaire-based interface allows HCPs and patients to quickly navigate the trials database without prior knowledge of the taxonomy. Since its beta launch on November 1, 2022 to June 30, 2023, the tool has seen over 1,250 searches from across 44 countries. Current usage trends show 73% of users can complete the search and find personalized trial options in under 5 minutes.

Conclusion: By using AI technology and a curated questionnaire, we have been able to improve patients’ and HCPs’ ability to efficiently and effectively discover relevant clinical trials.

Acknowledgments: This project was funded by NorCal CarciNETs.

Abstract ID 23789

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