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The aim of this study was to assess the relative sensitivities and specificities of fluorescence in situ hybridization (FISH) and routine cytology for the detection of malignancy in biliary tract strictures. Bile duct brushing and aspirate specimens were collected from 131 patients being evaluated for possible malignant bile duct strictures. Both specimen types were assessed by FISH but only brushing specimens were assessed by cytology. The FISH assay used a mixture of fluorescently-labeled probes to the centromeres of chromosomes 3, 7, and 17 and chromosomal band 9p21 (Vysis UroVysion) to identify cells having chromosomal abnormalities. A case was considered positive for malignancy if five or more cells exhibited polysomy. Sixty-six of the 131 patients had surgical pathologic and/or clinical evidence of malignancy. Thirty-nine patients had cholangiocarcinoma, 19 had pancreatic carcinoma, and 8 had other types of malignancy. The sensitivity of cytology and FISH for the detection of malignancy in bile duct brushing specimens in these patients was 15% and 34% (p < 0.01), respectively. The sensitivity of FISH for the bile aspirate specimens was 23%, and the combined sensitivity of FISH for aspirate and brushing specimens was 35%. The specificity of FISH and cytology brushings were 91% and 98% (p= 0.06), respectively. FISH is significantly more sensitive than and nearly as specific as conventional cytology for the detection of malignant biliary strictures in biliary brushing specimens. FISH may improve the clinical management of patients who are being evaluated for malignancy in bile duct strictures.

Abstract Objective Develop a consensus for the nomenclature and definition of adrenal histopathologic features in unilateral primary aldosteronism (PA). Context Unilateral PA is the most common surgically treated form of hypertension. Morphologic examination combined with CYP11B2 (aldosterone synthase) immunostaining reveals diverse histopathologic features of lesions in the resected adrenals. Patients and Methods Surgically removed adrenals (n = 37) from 90 patients operated from 2015 to 2018 in Munich, Germany, were selected to represent the broad histologic spectrum of unilateral PA. Five pathologists (Group 1 from Germany, Italy, and Japan) evaluated the histopathology of hematoxylin-eosin (HE) and CYP11B2 immunostained sections, and a consensus was established to define the identifiable features. The consensus was subsequently used by 6 additional pathologists (Group 2 from Australia, Brazil, Canada, Japan, United Kingdom, United States) for the assessment of all adrenals with disagreement for histopathologic diagnoses among group 1 pathologists. Results Consensus was achieved to define histopathologic features associated with PA. Use of CYP11B2 immunostaining resulted in a change of the original HE morphology-driven diagnosis in 5 (14%) of 37 cases. Using the consensus criteria, group 2 pathologists agreed for the evaluation of 11 of the 12 cases of disagreement among group 1 pathologists. Conclusion The HISTALDO (histopathology of primary aldosteronism) consensus is useful to standardize nomenclature and achieve consistency among pathologists for the histopathologic diagnosis of unilateral PA. CYP11B2 immunohistochemistry should be incorporated into the routine clinical diagnostic workup to localize the likely source of aldosterone production.

- Malignant mesothelioma (MM) is an uncommon tumor that can be difficult to diagnose. - To provide updated, practical guidelines for the pathologic diagnosis of MM. - Pathologists involved in the International Mesothelioma Interest Group and others with an interest and expertise in the field contributed to this update. Reference material included up-to-date, peer-reviewed publications and textbooks. - There was discussion and consensus opinion regarding guidelines for (1) distinguishing benign from malignant mesothelial proliferations (both epithelioid and spindle cell lesions), (2) cytologic diagnosis of MM, (3) recognition of the key histologic features of pleural and peritoneal MM, (4) use of histochemical and immunohistochemical stains in the diagnosis and differential diagnosis of MM, (5) differentiating epithelioid MM from various carcinomas (lung, breast, ovarian, and colonic adenocarcinomas, and squamous cell and renal cell carcinomas), (6) diagnosis of sarcomatoid MM, (7) use of molecular markers in the diagnosis of MM, (8) electron microscopy in the diagnosis of MM, and (9) some caveats and pitfalls in the diagnosis of MM. Immunohistochemical panels are integral to the diagnosis of MM, but the exact makeup of panels employed is dependent on the differential diagnosis and on the antibodies available in a given laboratory. Depending on the morphology, immunohistochemical panels should contain both positive and negative markers for mesothelial differentiation and for lesions considered in the differential diagnosis. Immunohistochemical markers should have either sensitivity or specificity greater than 80% for the lesions in question. Interpretation of positivity generally should take into account the localization of the stain (eg, nuclear versus cytoplasmic) and the percentage of cells staining (>10% is suggested for cytoplasmic and membranous markers). Selected molecular markers are now being used to distinguish benign from malignant mesothelial proliferations. These guidelines are meant to be a practical diagnostic reference for the pathologist; however, some new pathologic predictors of prognosis and response to therapy are also included.

Ultrasound (US) risk stratification systems (RSSs) have been developed to reduce the number of unnecessary fine-needle aspiration procedures (FNA) in patients with thyroid nodules. We conducted a systematic review and meta-analysis evaluating the ability of the 5 most common US RSSs for the appropriate selection of thyroid nodules for FNA. This systematic review and meta-analysis was registered on PROSPERO (CRD42019131771). PubMed, CENTRAL, Scopus, and Web of Science were searched until March 2019. Original articles reporting data on the performance of AACE/ACE/AME, ACR TI-RADS, ATA, EU-TIRADS, and K-TIRADS were included. The number of nodules classified as true negative, true positive, false negative, and false positive was extracted. Summary operating points were estimated using a random-effects model. Interobserver agreement was also assessed. Twelve studies evaluating 18 750 thyroid nodules were included. Participants were adult outpatients with thyroid nodules submitted to either FNA or core-needle biopsy or surgery and with available US images. The final diagnosis for malignant nodules was generally based on histology, while cytology was used for benign nodules. Diagnostic odds ratio (DOR) ranged from 2.2 to 4.9. A head-to-head comparison showed a higher relative DOR for ACR-TIRADS versus ATA (P = .002) or K-TIRADS (P = .002), due to a higher relative likelihood ratio for positive results. The present meta-analysis found a higher performance of ACR TI-RADS in selecting thyroid nodules for FNA. However, the comparison across the most common US RSSs was limited by the data available. Further studies are needed to confirm this finding.

Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.

In recent years cellular pathology has become more closely involved in the direct management of patients with the introduction of molecular technologies and targeted therapies. Advanced Techniques in Diagnostic Cellular Pathology introduces students and professionals to these concepts and the key technologies that are influencing clinical practice today. Each chapter is carefully structured to introduce the very latest techniques and describe their clinical purpose, principle, method and application in cellular pathology. The advantages of various methods for preparing, observing and demonstrating cells and tissues employed to assist in diagnosis are explored, in addition to the use of quantitative methods in the detection and diagnosis of disease. Supplementary web-based material including annotated virtual microscope slides is available with the book. This is provided courtesy of i-Path Diagnostics Ltd and can be accessed online from their website www.pathxl.com Describes the very latest, emerging and established molecular aspects of diagnostic pathology. A clear, focused approach with each chapter containing a summary, a review of basic principles and clinical applications. Includes web-based annotated virtual microscope slides. Contributions from experienced practitioners contain numerous real-world examples illustrating the use of different diagnostic techniques, and their clinical relevance Written by a team of experienced practitioners this book will prove invaluable both to postgraduate biomedical science students who are training to be cellular pathologists and to professionals working in diagnostic and research laboratories as part of their continuing professional development.

Background: Limited data exist regarding the correlation between MRI tumour regression grade (mrTRG) and pathological TRG (pTRG) in rectal cancer. Methods: mrTRG and pTRG were compared in rectal cancer patients from two phase II trials (EXPERT and EXPERT-C). The agreement between radiologist and pathologist was assessed with the weighted κ test while the Kaplan–Meier method was used to estimate survival outcomes. Results: One hundred ninety-one patients were included. Median time from completion of neoadjuvant treatment to pre-operative MRI and surgery was 4.1 weeks (interquartile range (IQR): 3.7–4.7) and 6.6 weeks (IQR: 5.9–7.6), respectively. Fair agreement was found between mrTRG and pTRG when regression was classified according to standard five-tier systems ( κ =0.24) or modified three-tier systems ( κ =0.25). Sensitivity and specificity of mrTRG 1–2 (complete/good radiological regression) for the prediction of pathological complete response was 74.4% (95% CI: 58.8–86.5) and 62.8% (95% CI: 54.5–70.6), respectively. Survival outcomes of patients with intermediate pathological regression (pTRG 2) were numerically better if complete/good regression was also observed on imaging (mrTRG 1–2) compared to poor regression (mrTRG 3–5) (5-year recurrence-free survival 76.9% vs 65.9%, P =0.18; 5-year overall survival 80.6% vs 68.8%, P =0.22). Conclusions: The agreement between mrTRG and pTRG is low and mrTRG cannot be used as a surrogate of pTRG. Further studies are warranted to assess the ability of mrTRG to identify pathological complete responders for the adoption of non-operative management strategies and to provide complementary prognostic information to pTRG for better risk-stratification after surgery.

Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal ( n  = 12,799) and two external ( n  = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P  < 0.001), enhancing junior pathologists’ diagnostic scores significantly (1.326 versus 1.101, P  < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P  = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted. Developed on cytology images of hydrothorax and ascites from 57,220 cases at four hospitals, a deep-learning model shows high accuracy in tumor origin prediction and presents prognostic value when patient treatment is consistent with the cancer origin predicted by the model.

Abstract Context Molecular tests have improved the accuracy of preoperative diagnosis of indeterminate thyroid nodules. The Afirma Gene Sequencing Classifier (GSC) was developed to improve the specificity of the Gene Expression Classifier (GEC). Independent studies are needed to assess the performance of GSC. Objective The aim was to compare the performance of GEC and GSC in the assessment of indeterminate nodules. Design, Settings, and Participants Retrospective analysis of Bethesda III and IV nodules tested with GEC or GSC in an academic center between December 2011 and September 2018. Benign call rates (BCRs) and surgical outcomes were compared. Histopathologic data were collected on nodules that were surgically resected to calculate measures of test performance. Results The BCR was 41% (73/178) for GEC and 67.8% (82/121) for GSC (P < .001). Among specimens with dominant Hürthle cell cytology, the BCR was 22% (6/27) for GEC and 63.2% (12/19) for GSC (P = .005). The overall surgery rate decreased from 47.8% in the GEC group to 34.7% in the GSC group (P = .025). One GEC-benign and 3 GSC-benign nodules proved to be malignant on surgical excision. GSC had a statistically significant higher specificity (94% vs 60%, P < .001) and positive predictive value (PPV) (85.3% vs 40%, P < .001) than GEC. While sensitivity and negative predictive value (NPV) dropped with GSC (97.0% vs 90.6% and 98.6% vs 96.3%, respectively), these differences were not significant. Conclusions GSC reclassified more indeterminate nodules as benign and improved the specificity and PPV of the test. These enhancements appear to be resulting in fewer diagnostic surgeries.

Compared with histological examination of lung cancer, cytology is less invasive and provides better preservation of complete morphology and detail. However, traditional cytological diagnosis requires an experienced pathologist to evaluate all sections individually under a microscope, which is a time-consuming process with low interobserver consistency. With the development of deep neural networks, the You Only Look Once (YOLO) object-detection model has been recognized for its impressive speed and accuracy. Thus, in this study, we developed a model for intraoperative cytological segmentation of pulmonary lesions based on the YOLOv8 algorithm, which labels each instance by segmenting the image at the pixel level. The model achieved a mean pixel accuracy and mean intersection over union of 0.80 and 0.70, respectively, on the test set. At the image level, the accuracy and area under the receiver operating characteristic curve values for malignant and benign (or normal) lesions were 91.0% and 0.90, respectively. In addition, the model was deemed suitable for diagnosing pleural fluid cytology and bronchoalveolar lavage fluid cytology images. The model predictions were strongly correlated with pathologist diagnoses and the gold standard, indicating the model’s ability to make clinical-level decisions during initial diagnosis. Thus, the proposed method is useful for rapidly localizing lung cancer cells based on microscopic images and outputting image interpretation results.