Clinical Fine-needle Aspiration Research Articles

Evaluating the Generalizability of Predictive Classifiers Built from DESI Imaging Lipid Data across Mass Spectrometry Platforms.

In this study, we evaluate the generalizability of predictive classifiers built from DESI lipid data for thyroid fine needle aspiration (FNA) biopsy analysis and classification using two high-performance mass spectrometers (time-of-flight and orbitrap) suited with different DESI imaging sources operated by different users. The molecular profiles obtained from thyroid samples with the different platforms presented similar trends, although specific differences in ion abundances were observed. When using a previously published statistical model built to discriminate thyroid cancer from benign thyroid tissues to predict on a new independent data set obtained, agreement for 24 of the 30 samples across the imaging platforms was achieved. We also tested the classifier on six clinical FNAs and obtained agreement between the predictive results and clinical diagnosis for the different conditions. Altogether, our results provide evidence that statistical classifiers generated from DESI lipid data are applicable across different high-resolution mass spectrometry platforms for thyroid FNA classification.

Distinguishing Non-Small Cell Lung Cancer Subtypes in Fine Needle Aspiration Biopsies by Desorption Electrospray Ionization Mass Spectrometry Imaging.

Distinguishing adenocarcinoma and squamous cell carcinoma subtypes of non-small cell lung cancers is critical to patient care. Preoperative minimally-invasive biopsy techniques, such as fine needle aspiration (FNA), are increasingly used for lung cancer diagnosis and subtyping. Yet, histologic distinction of lung cancer subtypes in FNA material can be challenging. Here, we evaluated the usefulness of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to diagnose and differentiate lung cancer subtypes in tissues and FNA samples. DESI-MSI was used to analyze 22 normal, 26 adenocarcinoma, and 25 squamous cell carcinoma lung tissues. Mass spectra obtained from the tissue sections were used to generate and validate statistical classifiers for lung cancer diagnosis and subtyping. Classifiers were then tested on DESI-MSI data collected from 16 clinical FNA samples prospectively collected from 8 patients undergoing interventional radiology guided FNA. Various metabolites and lipid species were detected in the mass spectra obtained from lung tissues. The classifiers generated from tissue sections yielded 100% accuracy, 100% sensitivity, and 100% specificity for lung cancer diagnosis, and 73.5% accuracy for lung cancer subtyping for the training set of tissues, per-patient. On the validation set of tissues, 100% accuracy for lung cancer diagnosis and 94.1% accuracy for lung cancer subtyping were achieved. When tested on the FNA samples, 100% diagnostic accuracy and 87.5% accuracy on subtyping were achieved per-slide. DESI-MSI can be useful as an ancillary technique to conventional cytopathology for diagnosis and subtyping of non-small cell lung cancers.

Preoperative metabolic classification of thyroid nodules using mass spectrometry imaging of fine-needle aspiration biopsies

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.

Preoperative metabolic classification of thyroid nodules using mass spectrometry imaging of fine-needle aspiration biopsies

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.

Clinical Fine Needle Aspiration cytology and core biopsy in symptomatic patients with normal breast radiological imaging

Clinical Fine Needle Aspiration cytology and core biopsy in symptomatic patients with normal breast radiological imaging

MicroRNA expression profiling is a potential diagnostic tool for thyroid cancer

Approximately 30% of fine-needle aspiration (FNA) biopsies of thyroid nodules are indeterminate or nondiagnostic. Recent studies suggest microRNA (miRNA, miR) is differentially expressed in malignant tumors and may have a role in carcinogenesis, including thyroid cancer. The authors therefore tested the hypothesis that miRNA expression analysis would identify putative markers that could distinguish benign from malignant thyroid neoplasms that are often indeterminate on FNA biopsy. A miRNA array was used to identify differentially expressed genes (5-fold higher or lower) in pooled normal, malignant, and benign thyroid tissue samples. Real-time quantitative polymerase chain reaction was used to confirm miRNA array expression data in 104 tissue samples (7 normal thyroid, 14 hyperplastic nodule, 12 follicular variant of papillary thyroid cancer, 8 papillary thyroid cancer, 15 follicular adenoma, 12 follicular carcinoma, 12 Hurthle cell adenoma, 20 Hurthle cell carcinoma, and 4 anaplastic carcinoma cases), and 125 indeterminate clinical FNA samples. The diagnostic accuracy of differentially expressed genes was determined by analyzing receiver operating characteristics. Ten miRNAs showed >5-fold expression difference between benign and malignant thyroid neoplasms on miRNA array analysis. Four of the 10 miRNAs were validated to be significantly differentially expressed between benign and malignant thyroid neoplasms by quantitative polymerase chain reaction (P < .002): miR-100, miR-125b, miR-138, and miR-768-3p were overexpressed in malignant samples of follicular origin (P < .001), and in Hurthle cell carcinoma samples alone (P < .01). Only miR-125b was significantly overexpressed in follicular carcinoma samples (P < .05). The accuracy for distinguishing benign from malignant thyroid neoplasms was 79% overall, 98% for Hurthle cell neoplasms, and 71% for follicular neoplasms. The miR-138 was overexpressed in the FNA samples (P = .04) that were malignant on final pathology with an accuracy of 75%. MicroRNA expression differs for normal, benign, and malignant thyroid tissue. Expression analysis of differentially expressed miRNA could help distinguish benign from malignant thyroid neoplasms that are indeterminate on thyroid FNA biopsy.

Molecular Diagnostics and Predictors in Thyroid Cancer

The accuracy of cancer detection in thyroid nodules by fine-needle aspiration (FNA) cytology and prognostication of thyroid cancer needs further improvement and can benefit from testing for molecular alterations known to occur in thyroid tumors. Recent studies have demonstrated the feasibility of mutation detection in clinical FNA samples from thyroid nodules and their contribution to improving the diagnostic accuracy of FNA cytology. It appears that molecular testing is most beneficial for thyroid FNA samples with indeterminate cytology, where it can resolve the diagnosis in a significant number of cases. In addition to BRAF mutation, which has been studied most extensively, detection of RAS, RET/PTC, and PAX8/PPARgamma mutations also contribute substantially to cancer diagnosis. Some of these molecular markers, particularly BRAF, can also be used for tumor prognostication. In clinical setting, molecular testing of thyroid FNA samples and surgically removed tumors should utilize a restricted number of techniques that provide high accuracy and specificity of mutation detection. Testing for cancer-specific mutations in thyroid FNA samples and surgically removed tumor tissues increases diagnostic accuracy of FNA cytology and offers better prognostication of thyroid cancer.

Principles and techniques of biopsy with special reference to fine-needle aspiration cytology

Fine-needle aspiration (FNA) cytology is an inexpensive, effective and (generally) safe procedure for diagnostic sampling of superficial masses. In the UK, it has attained central importance in the management of palpable lesions of the breast and thyroid gland. The use of image guidance means that aspiration of impalpable and deep-seated lesions is routine in radiological practice in the UK. The aim of the FNA procedure is to collect sufficient representative material from the lesion to enable a cytological diagnosis ‘i.e. based on the appearance of dispersed individual cells and cell groups’ to be made. As well as its role in the primary diagnosis of tumours, FNA cytology in very useful in the staging of malignant tumours. For example, endoscopic ultrasound-guided FNA of a paraoesophageal lymph node can detect metastatic oesophageal carcinoma. Another useful role for FNA is in diagnosing recurrence of malignancy, particularly lymphoma. Clinical FNA procedures are described and potential causes of an inadequate ‘i.e. non-diagnostic’ aspirate are discussed. Various modalities of image guidance are discussed and methods of slide preparation compared. The role of special techniques such as immunocytochemistry and fluorescence in situ hybridization is illustrated by means of diagnostic scenarios. The uses and limitations of FNA cytology of the breast, thyroid, lymph nodes and salivary glands are discussed.