PDF(Pubmed)
Abstract：
UNASSIGNED: The growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective diagnostic imaging tools to predict the non-iodine-avid status of lung metastasis (LMs) in differentiated thyroid cancer (DTC) patients is underscored to prevent unnecessary radioactive iodine treatment (RAI).
UNASSIGNED: Primary cohort consisted 1962 pretreated LMs of 496 consecutive DTC patients with pretreated initially diagnosed LMs who underwent chest CT and subsequent post-treatment radioiodine SPECT. After automatic lesion segmentation by SE V-Net, SE Net deep learning was trained to predict non-iodine-avid status of LMs. External validation cohort contained 123 pretreated LMs of 24 consecutive patients from other two hospitals. Stepwise validation was further performed according to the nodule\'s largest diameter.
UNASSIGNED: The SE-Net deep learning network yielded area under the receiver operating characteristic curve (AUC) values of 0.879 (95% confidence interval: 0.852-0.906) and 0.713 (95% confidence interval: 0.613-0.813) for internal and external validation. With the LM diameter decreasing from ≥10mm to ≤4mm, the AUCs remained relatively stable, for smallest nodules (≤4mm), the model yielded an AUC of 0.783. Decision curve analysis showed that most patients benefited using deep learning to decide radioactive I131 treatment.
UNASSIGNED: This study presents a noninvasive, less radioactive and fully automatic approach that can facilitate suitable DTC patient selection for RAI therapy of LMs. Further prospective multicenter studies with larger study cohorts and related metabolic factors should address the possibility of comprehensive clinical transformation.
UNASSIGNED: Primary cohort consisted 1962 pretreated LMs of 496 consecutive DTC patients with pretreated initially diagnosed LMs who underwent chest CT and subsequent post-treatment radioiodine SPECT. After automatic lesion segmentation by SE V-Net, SE Net deep learning was trained to predict non-iodine-avid status of LMs. External validation cohort contained 123 pretreated LMs of 24 consecutive patients from other two hospitals. Stepwise validation was further performed according to the nodule\'s largest diameter.
UNASSIGNED: The SE-Net deep learning network yielded area under the receiver operating characteristic curve (AUC) values of 0.879 (95% confidence interval: 0.852-0.906) and 0.713 (95% confidence interval: 0.613-0.813) for internal and external validation. With the LM diameter decreasing from ≥10mm to ≤4mm, the AUCs remained relatively stable, for smallest nodules (≤4mm), the model yielded an AUC of 0.783. Decision curve analysis showed that most patients benefited using deep learning to decide radioactive I131 treatment.
UNASSIGNED: This study presents a noninvasive, less radioactive and fully automatic approach that can facilitate suitable DTC patient selection for RAI therapy of LMs. Further prospective multicenter studies with larger study cohorts and related metabolic factors should address the possibility of comprehensive clinical transformation.
摘要：
■分化型甲状腺癌（DTC）的发病率增加与胰岛素抵抗和代谢综合征有关。强调迫切需要开发有效的诊断成像工具来预测分化型甲状腺癌（DTC）患者的肺转移（LM）的非碘活跃状态，以防止不必要的放射性碘治疗（RAI）。
■主要队列包括1962年的496例连续DTC患者的预处理LMs，这些患者接受了胸部CT和随后的治疗后放射性碘SPECT。通过SEV-Net进行自动病变分割后，对SENet深度学习进行了训练，以预测LMs的非碘活跃状态。外部验证队列包含来自其他两家医院的24名连续患者的123名经过预处理的LMs。根据结节的最大直径进一步进行逐步验证。
■SE-Net深度学习网络获得了用于内部和外部验证的受试者工作特征曲线（AUC）下面积值0.879（95％置信区间：0.852-0.906）和0.713（95％置信区间：0.613-0.813）。随着LM直径从≥10mm减小到≤4mm，AUC保持相对稳定，对于最小的结节（≤4mm），该模型的AUC为0.783。决策曲线分析表明，大多数患者受益于使用深度学习来决定放射性I131治疗。
■这项研究提出了一种非侵入性的，放射性较低且全自动的方法可以帮助选择适合的DTC患者进行LMs的RAI治疗。进一步的前瞻性多中心研究以及更大的研究队列和相关代谢因素应解决全面临床转化的可能性。
■主要队列包括1962年的496例连续DTC患者的预处理LMs，这些患者接受了胸部CT和随后的治疗后放射性碘SPECT。通过SEV-Net进行自动病变分割后，对SENet深度学习进行了训练，以预测LMs的非碘活跃状态。外部验证队列包含来自其他两家医院的24名连续患者的123名经过预处理的LMs。根据结节的最大直径进一步进行逐步验证。
■SE-Net深度学习网络获得了用于内部和外部验证的受试者工作特征曲线（AUC）下面积值0.879（95％置信区间：0.852-0.906）和0.713（95％置信区间：0.613-0.813）。随着LM直径从≥10mm减小到≤4mm，AUC保持相对稳定，对于最小的结节（≤4mm），该模型的AUC为0.783。决策曲线分析表明，大多数患者受益于使用深度学习来决定放射性I131治疗。
■这项研究提出了一种非侵入性的，放射性较低且全自动的方法可以帮助选择适合的DTC患者进行LMs的RAI治疗。进一步的前瞻性多中心研究以及更大的研究队列和相关代谢因素应解决全面临床转化的可能性。
