Georgiou Michalis F, Nielsen Joshua A, Chiriboga Rommel, Kuker Russ A
Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Department of Radiology, Jackson Memorial Hospital, Miami, FL 33136, USA.
J Clin Med. 2023 Dec 25;13(1):117. doi: 10.3390/jcm13010117.
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 -test ( = 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.
甲状腺癌，特别是分化型甲状腺癌（DTC），是全球最常见的内分泌恶性肿瘤之一。使用I-131的放射性碘治疗（RAIT）一直是DTC的标准治疗方法，因为它能够在手术后消融残留的甲状腺疾病，从而降低复发风险。它也用于治疗摄碘转移灶。RAIT剂量测定可用于确定I-131的最佳治疗剂量，以有效治疗癌细胞，同时防止不良辐射效应，如骨髓毒性或放射性肺炎。然而，传统的RAIT剂量测定方案复杂且耗时，需要进行多天的成像和血液采样。本研究探讨了人工智能（AI）在简化和优化RAIT中的应用。对1996年至2023年期间在本机构接受RAIT剂量测定的83例成年DTC患者进行了回顾性分析。基于传统MIRD的剂量测定方案包括在给予I-131示踪剂活性后4、24、48、72和96小时进行成像和血液采样。开发了一种基于深度学习神经网络的AI系统，仅使用从最初4、24和48小时时间点获得的数据来预测RAIT的最大允许活性（MPA）。该AI系统预测的MPA值具有很高的准确性，与使用配对检验的传统基于MIRD的分析结果相比，无显著差异（P = 0.351，95%CI）。开发的AI系统有可能简化剂量测定过程，减少成像和血液采样次数，同时优化资源分配。此外，AI方法可以揭示以前未知的数据潜在关系。我们的研究结果表明，基于AI的剂量测定可能是分化型甲状腺癌患者个体化治疗计划的一种有前景的方法，代表了朝着将精准医学应用于甲状腺癌迈出的一步。需要进一步的验证和实施研究来评估该AI系统的临床适用性。
