Diagnosis of thyroid micronodules on ultrasound using a deep convolutional neural network – Scientific Reports

The institutional review board of Severance Hospital (Seoul, South Korea) approved this retrospective study, with a waiver for informed consent (IRB number: 2020-3659-001). Signed informed consent for biopsy or surgical procedures was obtained preoperatively from all patients. All methods were performed in accordance with relevant guidelines and regulations.

Patients

This study was performed at a single tertiary referral center from March 2016 to February 2018, during which 4110 nodules in 3716 consecutive patients were consulted for US-guided FNA. The initial FNA was performed in 3323 nodules in 3240 patients, of which 698 nodules were < 10 mm in 683 patients. Our study included nodules < 10 mm if they (a) were cytologically confirmed as benign or malignant (Bethesda category II or VI) or (b) were confirmed as malignant on postsurgical histology. We excluded nodules that were not confirmed or lost to follow-up. Finally, a total of 370 thyroid nodules in 362 patients were included and analyzed (Fig. 1). Two thyroid nodules were included for 8 patients, among which 6 patients had both malignant nodules and 2 patients had one benign and one malignant nodule.

US imaging

US examinations of both thyroid glands and neck areas were performed using a 5–12 MHz linear array transducer (iU22, Philips Healthcare, Amsterdam, Netherlands). Real-time US scans and subsequent US-FNA were performed by 12 radiologists with 1–20 years of experience in thyroid imaging.

Each radiologist who performed the US and US-FNA/core biopsy procedures interpreted each US scan of the thyroid nodules and recorded US features prospectively in our institutional database^26,27. US features including composition, echogenicity, margin, calcifications, and shape were recorded using descriptors that have been used from June 2012 to the present in our institution²⁸. Each thyroid nodule was categorized according to the Thyroid Imaging Reporting and Data System suggested by the Korean Society of Thyroid Radiology (KSThR TIRADS) using pre-recorded US features⁷.

Image acquisition and CNN evaluation

An experienced radiologist with 20 years of experience dedicated to thyroid imaging who was blinded to clinical information and pathological results selected and retrieved a representative US image for each thyroid nodule from the PACS and stored it in JPEG format. For each image, a square ROI enclosing the entire targeted thyroid nodule was manually labeled using the Paint program of Windows 10 by the same radiologist who retrieved the images.

We used a computer-aided diagnosis (CAD) program to assess the malignancy risk of 370 thyroid nodules on US images. The performance of a CNN algorithm differs by data set, that is, it highly depends on the data used to train its network. There are many pre-trained models and a few of their test results (accuracy, sensitivity, and specificity of 370 test data sets) are reported in Supplemental Table S1. As ResNet101 shows one of the best performances with current US images, this paper focuses on analyzing the results from transfer learning using ResNet101. The pretrained CNN model ResNet101^29,30 was fine-tuned with 13,560 US images of thyroid nodules ≥ 10 mm in size (further details on the CAD program are provided in the Supplemental Material)²¹. ResNet101 is a deep neural network that was originally trained with 1000 object classes, 1,281,167 training images, and 50,000 validation images. The basic algorithm of the residual net family (ResNet-18,34,50,101, and 152) has been previously introduced²⁹ and the paper achieved state-of-the-art results in image classification by taking a standard feed-forward ConvNet and adding skip-connections that bypassed a few convolution layers at a time. Each bypass/shortcut produced a residual block from which the convolution layers predicted a residual further used in the block’s input tensor. ResNet101 consists of 347 layers capable of learning rich feature representations of images with an image input size of 224-by-224. For transfer learning, 13,560 US images composed of 7160 malignant and 6400 benign nodule images were used. To balance the number of data sets, we used the left–right mirroring augmentation of 760 randomly selected benign images so that a final total of 14,320 images were used in training. Since the fully connected layer and classification layer at the end of the original pretrained network were configured for 1000 classes, they were replaced with new layers adapted to the new data set (benign and malignant) with learning rates for weights and biases set to 10 each. In the fine-tuning process, the stochastic gradient descent with a momentum optimizer was used to train the network, the initial learning rate was set to 10-4, 10 epochs were conducted, and the mini-batch size was set to 50. The momentum of the stochastic gradient descent optimizer was set to 0.9 and the learning rate dropped by a factor of 0.5 every 4 epochs. The model was validated with internal data (95 benign, 539 malignant) and external data from three different hospitals (429 benign, 761 malignant).

Using the CAD program, we calculated the risks of malignancy as continuous values ranging from 0 to 100% (CAD value). We also categorized nodules by designating categories based on the CAD value (CNN TIRADS) according to the predicted probability from KSThR TIRADS. CNN TIRADS category 2 was assigned to nodules with a malignancy probability < 3%, category 3 for a probability < 15%, category 4 for a probability < 60% and category 5 for a probability ≥ 60%⁷.

Statistical analysis

For the reference standard, histopathologic results from FNA or surgery were used to confirm the final diagnosis of each thyroid nodule. If there was a discrepancy between the two results, the reference standard was the histopathologic result from the surgical specimen.

Baseline patient characteristics and nodal US features were compared between malignant and benign nodules with the Student’s t-test and Pearson’s χ²-test at the patient level and the logistic regression analysis with the generalized estimating equation method for clustered data in a nodule-level comparison. Areas under the receiver operating characteristics curve (AUCs) with 95% CIs were obtained and the TIRADS category and CAD value of each thyroid nodule were divided as either positive or negative according to the Youden index. We compared the diagnostic performances of the TIRADS category and CNN by analyzing the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value using logistic regression with the generalized estimating equation method. AUC values were compared with the Obuchowski algorithm for clustered data³¹. The same statistical analysis was performed for the subgroup analysis separately according to nodule size with a cut-off value of 5 mm.

We assessed the categorization performances of CNN TIRADS and KSThR TIRADS using the likelihood ratio χ²-test and the linear trend χ²-test for each categorization system to determine heterogeneity (small differences in risk of malignancy among nodules in the same category) and monotonicity of gradients (whether the risk of malignancy of nodules increases as the category increases), respectively^32,33. We also used the Akaike information criterion, which is a widely used estimator for model selection. Smaller Akaike information criterion values indicate a more informative model in terms of goodness of fit³⁴.

Statistical analysis was performed using statistical software (SAS version 9.4, SAS Institute, Cary, NC, USA) and the R Statistical Package (Version 4.0.2, Institute for Statistics and Mathematics, Vienna, Austria). Two-sided P values < 0.05 were considered to indicate statistical significance.