Preoperative metabolic classification of thyroid nodules using mass spectrometry imaging of fine-needle aspiration biopsies
Menée à l'aide de 178 échantillons d'adénome folliculaire bénin, de carcinome folliculaire malin ou de carcinome papillaire de la thyroïde et à l'aide de 114 125 spectres de masse, cette étude évalue la performance de deux modèles de classification, utilisant des données métaboliques obtenues au moyen de l'imagerie par spectrométrie de masse avec désorption-ionisation par électronébulisation, pour distinguer un nodule malin d'un nodule bénin à partir d'échantillons tissulaires prélevés par ponction à l'aiguille fine
Fine-needle aspiration (FNA) biopsy is a well-established technique for diagnosis of suspicious thyroid lesions. However, histologic discrimination between malignant and benign thyroid nodules from FNA can be challenging. Patients with an indeterminate FNA diagnosis often require diagnostic surgery, with the majority ultimately receiving a benign diagnosis. Here, we employ desorption electrospray ionization mass spectrometry (DESI-MS) imaging to diagnose thyroid lesions based on the molecular profiles obtained from FNA biopsy samples. Based on the molecular profiles obtained from malignant thyroid carcinomas and benign thyroid tissues, classification models were generated and used to predict on DESI-MSI data from FNA material with high performance. Our results demonstrate the potential for DESI-MSI to reduce the number of unnecessary diagnostic thyroid surgeries.Thyroid neoplasia is common and requires appropriate clinical workup with imaging and fine-needle aspiration (FNA) biopsy to evaluate for cancer. Yet, up to 20% of thyroid nodule FNA biopsies will be indeterminate in diagnosis based on cytological evaluation. Genomic approaches to characterize the malignant potential of nodules showed initial promise but have provided only modest improvement in diagnosis. Here, we describe a method using metabolic analysis by desorption electrospray ionization mass spectrometry (DESI-MS) imaging for direct analysis and diagnosis of follicular cell-derived neoplasia tissues and FNA biopsies. DESI-MS was used to analyze 178 tissue samples to determine the molecular signatures of normal, benign follicular adenoma (FTA), and malignant follicular carcinoma (FTC) and papillary carcinoma (PTC) thyroid tissues. Statistical classifiers, including benign thyroid versus PTC and benign thyroid versus FTC, were built and validated with 114,125 mass spectra, with accuracy assessed in correlation with clinical pathology. Clinical FNA smears were prospectively collected and analyzed using DESI-MS imaging, and the performance of the statistical classifiers was tested with 69 prospectively collected clinical FNA smears. High performance was achieved for both models when predicting on the FNA test set, which included 24 nodules with indeterminate preoperative cytology, with accuracies of 93% and 89%. Our results strongly suggest that DESI-MS imaging is a valuable technology for identification of malignant potential of thyroid nodules.
Proceedings of the National Academy of Sciences , résumé, 2018