BACKGROUND: Among other structures, nuclear grooves are vastly found in papillary thyroid carcinoma (PTC). Considering that the application of artificial intelligence in thyroid cytology has potential for diagnostic routine, our goal was to develop a new supervised convolutional neural network capable of identifying nuclear grooves in Diff-Quik stained whole-slide images (WSI) obtained from thyroid fineneedle aspiration. METHODS: We selected 22 Diff-Quik stained cytological slides with cytological diagnosis of PTC and concordant histological diagnosis. Each of the slides was scanned, forming a WSI. Images that contained the region of interest were obtained, followed by pre-formatting, annotation of the nuclear grooves and data augmentation techniques. The final dataset was divided into training and validation groups in a 7:3 ratio. RESULTS: This is the first artificial intelligence model based on object detection applied to nuclear structures in thyroid cytopathology. A total of 7,255 images were obtained from 22 WSI, totaling 7,242 annotated nuclear grooves. The best model was obtained after it was submitted 15 times with the train dataset (14th epoch), with 67% true positives, 49.8% for sensitivity and 43.1% for predictive positive value. CONCLUSIONS: The model was able to develop a structure predictor rule, indicating that the application of an artificial intelligence model based on object detection in the identification of nuclear grooves is feasible. Associated with a reduction in interobserver variability and in time per slide, this demonstrates that nuclear evaluation constitutes one of the possibilities for refining the diagnosis through computational models.
Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.
The estimation of tumor cellular fraction (TCF) is a crucial step in predictive molecular pathology, representing an entry adequacy criterion also in the next-generation sequencing (NGS) era. However, heterogeneity of quantification practices and inter-pathologist variability hamper the robustness of its evaluation, stressing the need for more reliable results. Here, 121 routine histological samples from non-small cell lung cancer (NSCLC) cases with complete NGS profiling were used to evaluate TCF interobserver variability among three different pathologists (pTCF), developing a computational tool (cTCF) and assessing its reliability vs ground truth (GT) tumor cellularity and potential impact on the final molecular results. Inter-pathologist reproducibility was fair to good, with overall Wk ranging between 0.46 and 0.83 (avg. 0.59). The obtained cTCF was comparable to the GT (p = 0.129, 0.502, and 0.130 for surgical, biopsies, and cell block, respectively) and demonstrated good reliability if elaborated by different pathologists (Wk = 0.9). Overall cTCF was lower as compared to pTCF (30 ± 10 vs 52 ± 19, p < 0.001), with more cases < 20% (17, 14%, p = 0.690), but none containing < 100 cells for the algorithm. Similarities were noted between tumor area estimation and pTCF (36 ± 29, p < 0.001), partly explaining variability in the human assessment of tumor cellularity. Finally, the cTCF allowed a reduction of the copy number variations (CNVs) called (27 vs 29, - 6.9%) with an increase of effective CNVs detection (13 vs 7, + 85.7%), some with potential clinical impact previously undetected with pTCF. An automated computational pipeline (Qupath Analysis of Nuclei from Tumor to Uniform Molecular tests, QuANTUM) has been created and is freely available as a QuPath extension. The computational method used in this study has the potential to improve efficacy and reliability of TCF estimation in NSCLC, with demonstrated impact on the final molecular results.
Transition from optical to digital observation requires an additional procedure in the pathology laboratory, the scanning of glass slides, leading to increased time and digital archive consumption. Thyroid surgical samples often carry the need to collect several tissue fragments that generate many slides to be scanned. This study evaluated the impact of using different inking colours for the surgical margin, section thickness, and glass slide type, in the consumption of time and archive. The series comprehended 40 nodules from 30 patients, including 34 benign nodules in follicular nodular disease, 1 NIFTP, and 5 papillary carcinomas. In 12 nodules, the dominant pattern was microfollicular/solid and in 28 it was macrofollicular. Scanning times/mm2 were longer in red-inked fragments in comparison to green (p = 0.04) and black ones (p = 0.024), and in blue-inked in comparison to green ones (p = 0.043). File sizes/mm2 were larger in red-inked fragments in comparison to green (p = 0.008) and black ones (p = 0.002). The dominant pattern microfollicular/solid was associated with bigger file size/mm2 in comparison with the macrofollicular one (p < 0.001). All scanner outputs increase significantly with the thickness of the section. All scanning outputs increase with the usage of adhesive glass slides in comparison to non-adhesive ones. Small interventions in thyroid sample management that can help optimizing the digital workflow include to prefer black and green inking colours for the surgical margins and 2 µm section in non-adhesive glass slides for increased efficiency.
OBJECTIVE: Interventional pathologists have expanded their expertise by acquiring proficiency in ultrasound-guided thyroid fine-needle aspiration biopsy (FNAB) and are now required to optimize punction procedures due to low resources and digital workflows. The aim of this study is to compare FNAB sample adequacy in two series with one versus two slides available for cytopathological analysis and its influence on diagnosis categorization, time taken to reach a final diagnosis, scanning time and size of the digital files produced. METHODS: Patients were retrospectively selected based on the sampling of thyroid nodules using either two glass slides (two-slide group) or one slide only (one-slide group) and cytological diagnosis was performed using the second edition of the Bethesda system. For each group, the initial 15 cases were sorted to be scanned. RESULTS: From a total of 713 procedures, 328 were sampled into two slides and 385 on one slide only. No significant differences were found regarding nodule size, location or EU-TIRADS classification between the two groups. The one-slide group did not exhibit a higher prevalence of non-diagnostic or atypia of undetermined significance (AUS) categories. As expected, the mean time taken to finalize diagnoses in cases where only one slide was prepared was 1.2 days faster. Scanning time and total file size were also significantly smaller in the one-slide group. CONCLUSIONS: Adopting the 'one nodule-one puncture-one slide' strategy for thyroid FNAB optimization enhances procedural efficiency in digital workflows, leading to cost savings without compromising diagnostic accuracy.
The past 70 years have been characterized by rapid advancements in computer technology, and the health care system has not been immune to this trend. However, anatomical pathology has remained largely an analog discipline. In recent years, this has been changing with the growing adoption of digital pathology, partly driven by the potential of computer-aided diagnosis. As part of an international collaboration, we conducted a comprehensive survey to gain a deeper understanding of the status of digital pathology implementation in Europe and Asia. A total of 127 anatomical pathology laboratories participated in the survey, including 75 from Europe and 52 from Asia, with 72 laboratories having established digital pathology workflow and 55 without digital pathology. Laboratories using digital pathology for diagnostic (n = 29) and nondiagnostic (n = 43) purposes were thoroughly questioned about their implementation strategies and institutional experiences, including details on equipment, storage, integration with laboratory information system, computer-aided diagnosis, and the costs of going digital. The impact of the digital pathology workflow was also evaluated, focusing on turnaround time, specimen traceability, quality control, and overall satisfaction. Laboratories without access to digital pathology were asked to provide insights into their perceptions of the technology, expectations, barriers to adoption, and potential facilitators. Our findings indicate that although digital pathology is still the future for many, it is already the present for some. This decade may be a time when anatomical pathology finally embraces digital revolution on a larger scale.
Diagnosis, Computer-Assisted , Image Interpretation, Computer-Assisted , Image Interpretation, Computer-Assisted/methods , Laboratories , Workflow , Surveys and Questionnaires
Cell blocks may be hard to be totally automatically detected by the scanner (ADS), generating incomplete whole slide images (WSIs), with areas that are not scanned, leading to possible false negative diagnosis. The aim of this study is to test if inking the cell blocks helps increasing ADS. Test 1: 15 cell blocks were sectioned, one half inked black (1HB) and the other inked green (1HG). Each of the halves was individually processed to generate a WSI stained by the H&E. 1HBs and 1HGs had similar scanning time (median 59 s vs. 65 s, p = .126) and file sizes (median 382 Mb vs. 381 Mb, p = .567). The black ink interfered less in the observation (2.2% vs. 44.4%; p < .001) than in the green one. Test 2: 15 cell blocks were sectioned, one half inked black (2HB) and the other left unstained/null (2HN). Each of the halves was individually processed to generate three WSIs-one HE, one periodic-acid Schiff (PAS), and one immunostained by cytokeratin AE1&AE3 (CKAE1AE3). HE and PAS WSIs from both 2HN and 2HB groups were all totally ADS and had similar scanning times and file sizes. Concerning immunostaining with CKAE1AE3: ADS (46.7% vs. 93.3%; p = .014), median time for scanning (57 s vs. 83 s; p < .001) and file size (178 Mb vs. 338 Mb; p < .001) were reduced significantly in the 2HN group in comparison with the 2HB. Although increasing scanning time and file size, inking the cell blocks helps increasing ADS after immunostaining, improving the safety and efficiency of the digital pathology workflow.
Ink , Microscopy , Humans , Microscopy/methods
BACKGROUND: After a series of standardized reporting systems in cytopathology, the Sydney system was recently introduced to address the need for reproducibility and standardization in lymph node cytopathology. Since then, the risk of malignancy for the categories of the Sydney system has been explored by several studies, but no studies have yet examined the interobserver reproducibility of the Sydney system. METHODS: The authors assessed interobserver reproducibility of the Sydney system on 85 lymph node fine-needle aspiration cytology cases reviewed by 15 cytopathologists from 12 institutions in eight different countries, resulting in 1275 diagnoses. In total, 186 slides stained with Diff-Quik, Papanicolaou, and immunocytochemistry were scanned. A subset of the cases included clinical data and results from ultrasound examinations, flow cytometry immunophenotyping, and fluorescence in situ hybridization analysis. The study participants assessed the cases digitally using whole-slide images. RESULTS: Overall, the authors observed an almost perfect agreement of cytopathologists with the ground truth (median weighted Cohen κ = 0.887; interquartile range, κ = 0.210) and moderate overall interobserver concordance (Fleiss κ = 0.476). There was substantial agreement for the inadequate and malignant categories (κ = 0.794 and κ = 0.729, respectively), moderate agreement for the benign category (κ = 0.490), and very slight agreement for the suspicious (κ = 0.104) and atypical (κ = 0.075) categories. CONCLUSIONS: The Sydney system for reporting lymph node cytopathology shows adequate interobserver concordance. Digital microscopy is an adequate means to assess lymph node cytopathology specimens.
Neoplasms , Humans , Reproducibility of Results , In Situ Hybridization, Fluorescence , Neoplasms/pathology , Cytodiagnosis/methods , Lymph Nodes/diagnostic imaging , Lymph Nodes/pathology
The immunohistochemical (IHC) expression of PD-L1 in cancer models is used as a predictive biomarker of response to immunotherapy. We aimed to evaluate the impact of the usage of 3 different tissue processors in the IHC expression of PD-L1 antibody clones: 22C3 and SP142. Three different topographies of samples (n = 73) were selected at the macroscopy room: 39 uterine leiomyomas, 17 placentas and 17 palatine tonsils. Three fragments were collected from each sample and were inked with a specific color that represented their separate processing in a different tissue processor (A, B or C). During embedding, the 3 fragments with distinct processing were ensemble in the same cassette for sectioning of 3 slides/each: hematoxylin-eosin, 22C3 PDL1 IHC staining and SP142 PD-L1 IHC staining, that were blindly observed by 2 pathologists under digital environment. All but one set of 3 fragments were considered adequate for observation even in the presence of artifacts associated with processing issues that were recorded as high as 50.7 % for processor C. The occurrence of background non-specific staining and the presence of false positive results appear to be unrelated with the PD-L1 clone or the type of tissue processing. 22C3 PD-L1 was more frequently considered adequate for evaluation than SP142 PD-L1 that, in 29.2 % of WSIs (after tissue processor C) were considered not adequate for observation due to lack of the typical pattern of expression. Similarly, the intensity of PD-L1 staining was significantly decreased in fragments processed by C (both PD-L1 clones) in tonsil and placenta specimens, and by A (both clones) in comparison with those processed by B. This study demonstrates the need to standardize the tissue processing in pathology to cope with the growing needs of precision medicine quantifications and the production of high-quality material necessary for computational pathology usage.
B7-H1 Antigen , Lung Neoplasms , Humans , Immunohistochemistry , B7-H1 Antigen/metabolism , Antibodies , Biomarkers, Tumor , Pathologists , Lung Neoplasms/pathology
Paige Prostate is a clinical-grade artificial intelligence tool designed to assist the pathologist in detecting, grading, and quantifying prostate cancer. In this work, a cohort of 105 prostate core needle biopsies (CNBs) was evaluated through digital pathology. Then, we compared the diagnostic performance of four pathologists diagnosing prostatic CNB unaided and, in a second phase, assisted by Paige Prostate. In phase 1, pathologists had a diagnostic accuracy for prostate cancer of 95.00%, maintaining their performance in phase 2 (93.81%), with an intraobserver concordance rate between phases of 98.81%. In phase 2, pathologists reported atypical small acinar proliferation (ASAP) less often (about 30% less). Additionally, they requested significantly fewer immunohistochemistry (IHC) studies (about 20% less) and second opinions (about 40% less). The median time required for reading and reporting each slide was about 20% lower in phase 2, in both negative and cancer cases. Lastly, the average total agreement with the software performance was observed in about 70% of the cases, being significantly higher in negative cases (about 90%) than in cancer cases (about 30%). Most of the diagnostic discordances occurred in distinguishing negative cases with ASAP from small foci of well-differentiated (less than 1.5 mm) acinar adenocarcinoma. In conclusion, the synergic usage of Paige Prostate contributes to a significant decrease in IHC studies, second opinion requests, and time for reporting while maintaining highly accurate diagnostic standards.
Prostate , Prostatic Neoplasms , Male , Humans , Prostate/pathology , Pathologists , Artificial Intelligence , Biopsy , Prostatic Neoplasms/diagnosis , Prostatic Neoplasms/pathology
BACKGROUND: Several ultrasound-based systems for classification of thyroid nodules are available. They allow for a better triage of the nodules that require cytological assessment, and lead to standardized recommendations. Our aim was to compare patients and nodules referred to fine-needle aspiration (FNA) before and after the introduction of these systems. METHODS: A retrospective study comparing two cohorts of patients referred for FNA was performed (386 patients and 463 nodules in 2015; 220 patients and 263 nodules in 2021). RESULTS: The sex distribution (89.1% vs 85.9% females, p=0.243), number of nodules referred to FNA per patient (median of 1), and the distribution of the Bethesda categories (p=0.082) was similar in both years. In 2021, patients were older (53.4±14.5 years vs 57.8±13.2 years, p<0.001) and nodules over one centimetre were larger (median 17.0mm vs 19.0mm, p=0.002), especially the ones categorized as Bethesda III (median size 11mm vs 23mm, p=0.043). In 2021, at least 23.1% of the nodules referred to FNA did not have any criteria, and 38.8% of the nodules were not categorized by any system. CONCLUSION: This analysis draws attention to the importance of systematically applying ultrasound-based classification systems. It seems that, by not being focused mainly on size thresholds, they allow for longer surveillance periods, without aggravating the cytology results when FNA becomes indicated. Nevertheless, greater efforts are needed to ensure more standardized reports, and to increase adherence to the resulting recommendations to reduce clinical uncertainty, unnecessary FNA, and overtreatment.
Thyroid Neoplasms , Thyroid Nodule , Female , Humans , Male , Thyroid Nodule/diagnostic imaging , Thyroid Neoplasms/diagnostic imaging , Retrospective Studies , Clinical Decision-Making , Uncertainty
BACKGROUND: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. METHODS: Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. FINDINGS: Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI's role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. INTERPRETATION: This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI's successful clinical implementation. FUNDING: No specific funding was provided for this study.
Algorithms , Artificial Intelligence , Humans , Delphi Technique , Surveys and Questionnaires , Forecasting
AIMS: We investigated the trend in case reports (CRs) publication in a sample of pathology journals. Furthermore, we proposed an alternative publishing route through new digital communication platforms, represented by the 'social media case report'. METHODS: 28 pathology journals were selected from SCImago database and searched in PubMed to identify the number of published CRs. Four reference decades (1981-2020) were selected. The 5-year impact factor (IF) was retrieved from the Academic Accelerator database. RESULTS: CRs increased during the first three decades (6752, 8698 and 11148, respectively; mean values: 355, 27.3%; 334, 26.4%; 398, 28.8%) as the number of CR-publishing journals (19, 26 and 28, respectively). In the last decade, CRs significantly decreased (9341; mean 334, 23.6%) without variation in the number of CR-publishing journals (28). Half of the journals reduced CRs (from -1.1% to -37.9%; mean decreasing percentage -14.7%), especially if active since the first decade (11/14, 79%); the other half increased CRs (from +0.5% to +34.2%; mean increasing percentage +11.8%), with 8/14 (57%) starting publishing in the first decade. The 5-year IF ranged from 0.504 to 5.722. Most of the journals with IF ≥2 (10/14, 71%) reduced the CRs number, while 71% of journals with IF <2 increased CRs publication (especially journals with IF <1, +15.1%). CONCLUSIONS: CRs publication decreased during the last decade, especially for journals which are older or have higher IF. Social media CRs may represent a valid alternative and by using standardised templates to enter all relevant data may be organised in digital databases and/or transformed in traditional CRs.
Databases, Factual , Humans
Digital pathology workflow aims to create whole slide images (WSIs) for diagnosis. The quality of the WSIs depends primarily on the quality of the glass slides produced by the pathology laboratory, where the coverslipping method plays an important role. In this study we compare the glass, the film, and the liquid coverslipping methods to evaluate which ones are suitable to create WSIs for diagnosis. The study included 18 formalin-fixed paraffin-embedded tissue blocks. Of each block, 3 consecutive sections were covered using 1 of the 3 methods. The slides were scanned and evaluated for quality criteria by 2 pathologists experienced in digital pathology. The coverslipping method interferes with the quality of the WSIs, as well as with the scanning time and the file size of the WSIs. All coverslipping methods were found suitable for diagnosis. The glass and liquid methods were manual and had similar results concerning the presence of air bubbles/polymer accumulation, air drying artefacts, tissue exposed, and staining alterations. The glass method was the one with more air bubbles. The liquid method was associated with more alterations on the WSIs, but with the lowest file sizes. Automation of coverslipping and calibration of the scanner for the coverslipping method chosen by the pathology laboratory are relevant for the final quality of the WSIs.
Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal growth factor receptor 2 (HER2) transmembrane protein, through specialized techniques, such as immunohistochemistry or in situ hybridization. In this work, we present the HER2 on hematoxylin and eosin (HEROHE) challenge, a parallel event of the 16th European Congress on Digital Pathology, which aimed to predict the HER2 status in breast cancer based only on hematoxylin-eosin-stained tissue samples, thus avoiding specialized techniques. The challenge consisted of a large, annotated, whole-slide images dataset (509), specifically collected for the challenge. Models for predicting HER2 status were presented by 21 teams worldwide. The best-performing models are presented by detailing the network architectures and key parameters. Methods are compared and approaches, core methodologies, and software choices contrasted. Different evaluation metrics are discussed, as well as the performance of the presented models for each of these metrics. Potential differences in ranking that would result from different choices of evaluation metrics highlight the need for careful consideration at the time of their selection, as the results show that some metrics may misrepresent the true potential of a model to solve the problem for which it was developed. The HEROHE dataset remains publicly available to promote advances in the field of computational pathology.
Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.
INTRODUCTION: The subjective evaluation of nuclear features in follicular-patterned lesions of the thyroid is a reason for diagnosis discordance. The assessment of nuclear features also varies whether the observation is performed optically or digitally. Our objective was to study the concordance among pathologists regarding the nuclear score (NS) evaluation in a series of follicular-patterned lesions, using optical versus three digital scanning protocols. METHODS: Three pathologists evaluated the NS in a 3mm2 area randomly selected from 20 hematoxylin-eosin slides representative of the respective 20 follicular-patterned thyroid lesions. The NS evaluation was performed using optical and three different scanning protocols in two scanners: P1000_20x, P1000_40x and DP200_20x. Kappa statistic (κ) and intraclass correlation coefficient (ICC) were obtained for intra- and interpathologist concordance. RESULTS: We recorded a good agreement among pathologists in the optical evaluation of the NS (ICC of 0.73). The concordance between optical versus digital observation had an almost perfect agreement for P1000_20x [κ = 0.85 (0.67-1.02); p < 0.0001] and a substantial agreement for both P1000_40x [κ = 0.69 (0.43-0.95) p = 0.002] and DP200_20x [κ = 0.77 (0.57-0.97); p = 0.001]. The P1000_20x protocol had the best intrapathologist concordance with the optical method, classified as almost perfect agreement for pathologists A (80%) and B (85%), and substantial agreement for pathologist C (70%). CONCLUSION: Digital observation of the WSI is valid for the NS evaluation in follicular-patterned thyroid lesions, with good agreement among pathologists and between optical and scanning protocols. Performance studies and validation procedures cannot be avoided in this setting to prevent diagnostic discordance due to the scanning process.
Cell Nucleus , Thyroid Gland , Cell Nucleus/pathology , Humans , Observer Variation , Thyroid Gland/diagnostic imaging , Thyroid Gland/pathology
BACKGROUND: Thyroid nodules are common in the general population. The current diagnostic method for nodules is the ultrasound guided fine needle aspiration (US-FNA). The aim of the study was to evaluate the usefulness of cellblock preparation in addition to routine US-FNA in the diagnosis of thyroid nodules. METHODS: A retrospective study of patients with thyroid nodules submitted to US-FNA, with collection of material using both smears and cellblock preparation. Two air-dried smears were prepared for each nodule. After centrifugation, the residual aspirate in the syringe and needle was processed as a standard histology specimen (cellblock). Then a pathologist reviewed the smears and cellblock slides of each case. RESULTS: A total of 12.360 thyroid nodules were submitted to US-FNA. Cellblock preparation was performed in 153 (1.2%) in addition to smears. Among the satisfactory cellblocks (80.5%, 120), 31.7% (38) provided additional morphological information in comparison with smears alone. No significant differences were found between the smear and the combined smear and cellblock evaluation concerning the number of unsatisfactory (12.1% vs. 11.4%, p = .85) and indeterminate (27.5% vs. 24.2%, p = .52) results. Overall, 10 samples (6.7%) had their diagnosis changed after cellblock evaluation, nine of them due to immunohistochemical studies. Immunohistochemistry confirmed parathyroid origin of the nodule in six cases. CONCLUSION: Cellblocks did not contribute to increase cellularity of the samples or to reduce indetermined results of FNA of thyroid nodules. Immunohistochemistry was essential to characterize rare cases without follicular histogenesis. Cellblock must only be prepared when considering performing immunohistochemistry.
Thyroid Neoplasms , Thyroid Nodule , Biopsy, Fine-Needle/methods , Humans , Image-Guided Biopsy , Retrospective Studies , Thyroid Neoplasms/pathology , Thyroid Nodule/diagnosis , Thyroid Nodule/pathology , Ultrasonography
