Application of thermography in the diagnostic investigation of thyroid nodules

Thyroid nodules (TN) are common in the general population, and the clinical importance of diagnosing thyroid nodules is based on excluding the possibility of thyroid cancer, which occurs in 7-15% of cases. The thyroid gland, owing to its superficial location, is easily accessible via thermography, a noninvasive method of recording body temperature that measures infrared radiation emitted by the body surface. Therefore, this study aimed to evaluate the temperature differences between benign and malignant TN by using thermography. We conducted a cross-sectional study where 147 TN were divided into two groups: the first group included 120 benign nodules and the other included 27 malignant nodules. All the nodules were subjected to ultrasound, fine needle aspiration biopsy, and thermography. On analyzing the thermography results, the benign nodules had a higher temperature at the beginning of the thermography evaluation, and the malignant nodules showed a higher temperature in the middle and at the end (Ft). Using the relationships, it was observed that the temperature delta (ΔT), ΔT nodule/ΔT healthy, ΔT nodule minus ΔT healthy, and nodule Ft minus Ft of the healthy region were higher in malignant nodules. The ROC curve analysis of ΔT demonstrated a cutoff point of 2.38°C, with a sensitivity of 0.963 and specificity of 0.992. Malignant nodules have higher temperatures than benign nodules on thermographic evaluation. This finding suggests that thermography can be a useful tool in the diagnosis of thyroid nodules.

On the possibility of using temperature to aid in thyroid nodule investigation

Thyroid nodules are common, and their investigation is very important to exclude the possibility of cancer. The increase in blood vessels of malignant tumours may be related to local temperature augmentation detectable on the skin surface. The objective of this paper is to evaluate the feasibility of Infrared Thermography for cancer identification. For this purpose, two studies were performed. One used numerical modelling to simulate regional metabolic temperature propagation to evaluate whether a nodule is perceptible on the skin surface. A second study considered thyroid nodule identification by using convolutional neural networks (CNNs). First, variations in nodular size and fat thickness were investigated, showing that the fat layer has an important role in regional heat transfer. In the second study, the training process achieved accuracy of 96% for in-sample and 95% for validation. In the testing phase, 92% accuracy, 100% precision and 80% recall were achieved. Thus, the presented studies suggest the feasibility of using Infrared Thermography with the CNN Artificial Intelligence technique as additional information in the investigation of thyroid nodules for patients without a very thick subcutaneous fat layer.