Thyroid scintigraphy in the era of fine-needle aspiration cytology

Nationwide trends and the impact of an oncology hospital network on reducing the burden of thyroid cytology procedures

The diagnostic care pathway of thyroid nodules spans multiple institutions. Collaborative networks are important to deal with such pathways that result from centralization and differentiation of care. Despite the high prevalence of thyroid nodules and the increase in cancer diagnoses, most nodules are benign and attributable to overdiagnosis, leading to an increase in fine-needle aspirations (FNAs). This study assessed the effectiveness of a multi-hospital network that implemented a unified thyroid care pathway in reducing the number of FNAs without compromising malignancy detection. In this nationwide population-based cohort study, Bethesda scores were extracted from all thyroid FNA reports from 2010 to 2021 in the Netherlands using text mining. Trends in the number of FNAs and Bethesda scores were visualized for the network and the rest of the country. Joinpoint analyses with the Davies test determined the statistical significance of observed trend changes. Nationwide, FNAs increased by an average of 5.7% annually from 2010 to 2018. In the network, FNAs started to decrease in 2016-2017, coinciding with the care pathway implementation (p < 0.001). In contrast, in the rest of the Netherlands, a decline was observed in 2020, potentially attributable to the COVID-19 pandemic. In both cases, the reduction mainly involved Bethesda categories 1 and 2, without compromising malignancy detection. High-volume surgical centers seemed to initiate a decline more rapidly compared to non-high-volume surgical centers. This study indicates that a unified care pathway within a multi-hospital network can reduce the number of FNAs without compromising malignancy detection, which could alleviate the burden on both patients and the healthcare system.

Diagnosis of thyroid disease using deep convolutional neural network models applied to thyroid scintigraphy images: a multicenter study

Objectives

The aim of this study was to improve the diagnostic performance of nuclear medicine physicians using a deep convolutional neural network (DCNN) model and validate the results with two multicenter datasets for thyroid disease by analyzing clinical single-photon emission computed tomography (SPECT) image data.

Methods

In this multicenter retrospective study, 3194 SPECT thyroid images were collected for model training (n=2067), internal validation (n=514) and external validation (n=613). First, four pretrained DCNN models (AlexNet, ShuffleNetV2, MobileNetV3 and ResNet-34) for were tested multiple medical image classification of thyroid disease types (i.e., Graves' disease, subacute thyroiditis, thyroid tumor and normal thyroid). The best performing model was then subjected to fivefold cross-validation to further assess its performance, and the diagnostic performance of this model was compared with that of junior and senior nuclear medicine physicians. Finally, class-specific attentional regions were visualized with attention heatmaps using gradient-weighted class activation mapping.

Results

Each of the four pretrained neural networks attained an overall accuracy of more than 0.85 for the classification of SPECT thyroid images. The improved ResNet-34 model performed best, with an accuracy of 0.944. For the internal validation set, the ResNet-34 model showed higher accuracy (p < 0.001) when compared to that of the senior nuclear medicine physician, with an improvement of nearly 10%. Our model achieved an overall accuracy of 0.931 for the external dataset, a significantly higher accuracy than that of the senior physician (0.931 vs. 0.868, p < 0.001).

Conclusion

The DCNN-based model performed well in terms of diagnosing thyroid scintillation images. The DCNN model showed higher sensitivity and greater specificity in identifying Graves' disease, subacute thyroiditis, and thyroid tumors compared to those of nuclear medicine physicians, illustrating the feasibility of deep learning models to improve the diagnostic efficiency for assisting clinicians.