{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T21:25:50Z","timestamp":1787865950293,"version":"build-2784847793"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T00:00:00Z","timestamp":1703462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Thyroid cancer, specifically differentiated thyroid carcinoma (DTC), is one of the most prevalent endocrine malignancies worldwide. Radioactive iodine therapy (RAIT) using I-131 has been a standard-of-care approach for DTC due to its ability to ablate remnant thyroid disease following surgery, thus reducing the risk of recurrence. It is also used for the treatment of iodine-avid metastases. RAIT dosimetry can be employed to determine the optimal treatment dose of I-131 to effectively treat cancer cells while safeguarding against undesirable radiation effects such as bone marrow toxicity or radiation pneumonitis. Conventional dosimetry protocols for RAIT, however, are complex and time-consuming, involving multiple days of imaging and blood sampling. This study explores the use of Artificial Intelligence (AI) in simplifying and optimizing RAIT. A retrospective analysis was conducted on 83 adult patients with DTC who underwent RAIT dosimetry at our institution between 1996 and 2023. The conventional MIRD-based dosimetry protocol involved imaging and blood sampling at 4, 24, 48, 72, and 96 h post-administration of a tracer activity of I-131. An AI system based on a deep-learning neural network was developed to predict the maximum permissible activity (MPA) for RAIT using only the data obtained from the initial 4, 24, and 48 h time points. The AI system predicted the MPA values with high accuracy, showing no significant difference compared to the results obtained from conventional MIRD-based analysis utilizing a paired t-test (p = 0.351, 95% CI). The developed AI system offers the potential to streamline the dosimetry process, reducing the number of imaging and blood sampling sessions while also optimizing resource allocation. Additionally, the AI approach can uncover underlying relationships in data that were previously unknown. Our findings suggest that AI-based dosimetry may be a promising method for patient-specific treatment planning in differentiated thyroid carcinoma, representing a step towards applying precision medicine for thyroid cancer. Further validation and implementation studies are warranted to assess the clinical applicability of the AI system.<\/jats:p>","DOI":"10.3390\/jcm13010117","type":"journal-article","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T22:59:09Z","timestamp":1703545149000},"page":"117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Artificial Intelligence System for Optimizing Radioactive Iodine Therapy Dosimetry"],"prefix":"10.3390","volume":"13","author":[{"given":"Michalis","family":"Georgiou","sequence":"first","affiliation":[{"name":"Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua","family":"Nielsen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Jackson Memorial Hospital, Miami, FL 33136, USA"},{"name":"Nuclear Medicine, Brooke Army Medical Center, San Antonio, TX 78234, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rommel","family":"Chiriboga","sequence":"additional","affiliation":[{"name":"Department of Radiology, Jackson Memorial Hospital, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0074-6596","authenticated-orcid":false,"given":"Russ","family":"Kuker","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3322\/caac.21166","article-title":"Cancer statistics, 2013","volume":"63","author":"Siegel","year":"2013","journal-title":"CA Cancer J. 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