Smarter biopsy decisions in thyroid nodules via dual-modal photoacoustic and ultrasound imaging.

Abstract

Thyroid nodules are primarily diagnosed using ultrasound imaging (USI), but its low specificity leads to unnecessary fine-needle aspiration biopsies (FNABs). In particular, USI's limited ability to differentiate follicular neoplasms from benign nodules contributes to suboptimal biopsy decision-making. We propose a dual-modal imaging approach that combines multiparametric photoacoustic imaging (PAI) and USI to support smarter biopsy decisions. In 106 patients with 29 benign nodules, 45 papillary thyroid carcinomas, and 32 follicular neoplasms, three PAI-derived parameters-the photoacoustic spectral gradient, oxygen saturation, and skewness of the oxygen saturation distribution-were combined using a support vector machine. Following USI-based American Thyroid Association (ATA) guidelines, they were used to develop the ATA-Photoacoustic (ATAP) scoring system. The ATAP score achieved 97% sensitivity and 38% specificity in distinguishing nodules requiring FNAB. Our approach enabled better identification of benign nodules, reducing unnecessary FNAB in 11 of the 29 benign cases. This dual-modal strategy can assess thyroid nodules, effectively reducing unnecessary biopsies while maintaining high diagnostic accuracy.