Thyroid cancer is one of the most common cancers, with an increasing global incidence rate in men and women [63,64,65]. The gold standard for diagnosing thyroid nodules is fine-needle aspiration biopsy (FNAB) [66]. The triaging for FNAB of the nodule is determined by the characteristics of nodules in USI [67,68,69]. Although the sensitivity of US-guided triaging is greater than 90%, the lack of functional metabolomics results in a low specificity of 20–50% [70]. The high false-positive rate leads to unnecessary FNAB, which results in the over-diagnosing of the tumor. Thus, clinical trials have been conducted to enhance the accuracy of triaging thyroid nodules using PAI due to its molecular and functional imaging capability.

Dogra et al. analyzed 88 resected tissues (13 malignant nodules, 30 benign nodules, 13 colloid accumulations, and 32 normal tissues) from 50 patients (11 malignant and 39 benign) [71]. Four different wavelengths (760, 850, 930, and 970 nm) were used for the spectral unmixing of HbO, HbR, lipid, and water components from multispectral PA data. Statistically significant differences were found in HbO and HbR between malignant and other types of tissues. In particular, HbR components were significantly different between malignant and normal tissue, with a p-value of 0.003 in the student t-test. The results showed the promising feasibility of PA-guided classification with a sensitivity of 69.2% and a specificity of 96.9%, but this study was limited to ex vivo environments only. Thus, for clinical translation, further in vivo validation is needed.

Dima et al. demonstrated the in vivo imaging capability of their PA and US system for the human thyroid [72]. They recruited two healthy volunteers to acquire PA images with a single excitation wavelength of 800 nm. US Doppler images were also acquired in the same region to verify the blood vessel’s position. By comparing the PA images with the US Doppler images, surrounding blood vessels extending from the isthmus and carotid artery to the anterior of the thyroid gland were identified. The results showed the feasibility of in vivo PAI using the arc array US transducer by confirming the matched positions of blood vessels (white arrows in. However, the spectral analysis of cancerous nodules was not available in this study. Yang et al. compared in vivo PA images between papillary thyroid cancer (PTC) patients and healthy volunteers [73]. Although they achieved PA responses from cancerous nodules, the number of patients (10 PTC and 3 normal) included in the study was insufficient for statistical analysis. In addition, multispectral analysis was also not available in this study because they used a single excitation wavelength of 1064 nm.

Roll et al. presented multispectral PA analyses for differentiating tissue disorders in the thyroid gland [74]. The composition of HbO, HbR, fat, and water were spectrally unmixed from the in vivo PA images of the enrolled patients (6 Graves’ disease, 3 malignant, 13 benign, and 8 healthy), obtained using eight excitation wavelengths (700, 730, 760, 800, 850, 900, 920, and 950 nm). The sO₂ levels of the thyroid were also visualized and investigated. The contours of the thyroid glands were determined by the corresponding US images. Statistical analyses demonstrated significant differences between diseased and normal thyroid tissues.

Recently, Kim et al. presented a multispectral PA analysis with a statistically sufficient number of samples (23 PTC and 29 benign), the largest number of patients in a clinical thyroid study to date [75]. They achieved multispectral PAI using five wavelengths (700, 756, 796, 866, and 900 nm). The corresponding US data were also acquired simultaneously for delineating the boundary of nodules. Similar to the previous studies, the sO₂ levels in nodules were acquired through the spectral unmixing of HbO and HbR. Three parameters were quantified and used for training the decision function: (i) PA spectral gradient: the slope of a first-order polynomial fitted line to the average value of the top 50% of PA signals within the nodule boundary at each wavelength; (ii) relative sO₂ level: the average value of the top 50% sO₂ values within the nodule; (iii) skewed angle of sO₂ distribution: the skewed angle of the Gaussian-fitted distribution for the top 50% of sO₂ values within the nodule. With the values of the three parameters scattered in a 3D plane, a support vector machine was trained to determine the 3D decision boundary, which showed a good classification accuracy with a sensitivity of 78% and a specificity of 93%. The classification accuracy was further enhanced using a novel scoring method (ATAP score), which combined a conventional USI-based scoring method (i.e., ATA guideline score) and the photoacoustic probability of malignancy. The novel scoring method improved the sensitivity to 83% and the specificity to 93%. Thus, the results showed a great potential for enhancing the triaging accuracy of thyroid nodules using a multiparametric analysis of multispectral PA data as a complementary method to the conventional triaging method.

While PA analyses of thyroid nodules have been conducted by various groups worldwide, the validation of multispectral PA analysis is still at the initial stage of evaluation. Further studies are required to address the following issues for successful clinical translation. (i) Larger number of patients are needed to enhance the reliability of this technique. (ii) In addition to PTC, the classification of other types of thyroid cancers such as follicular, medullary, and anaplastic thyroid cancers would expand the application area. (iii) Quantitative analyses of PA responses in skin color are needed. (iv) System improvement with a deeper imaging depth, faster frame rate, and smaller size would enhance the image quality for multispectral analyses.