Background The International System for Reporting Serous Fluid Cytopathology (TIS) was recently proposed. We retrospectively applied TIS recommendations for reporting the cytological diagnosis of serous effusions and reported our experience. Methods All the serous effusions from January 2018 to September 2021 were retrieved from the database. Recategorization was performed using the TIS classification, the risk of malignancy (ROM) was calculated for each TIS category. In addition, on the basis of the original TIS classification, we further subdivided the TIS category IV (suspicious for malignancy, SFM) into 2 groups (IVa and IVb) according to cytological characteristics (quality and quantity) to explore the necessity of SFM subclassification. The performance evaluation was carried out using different samples (pleural, peritoneal and pericardial effusions) and preparation methods (conventional smears, liquid-based preparations and cell blocks). Results A total of 3633 cases were studied: 17 (0.5%) were diagnosed as ‘nondiagnostic’ (I, ND), 1100 (30.3%) as ‘negative for malignancy’ (II, NFM), 101 (2.8%) as ‘atypia of undetermined significance’ (III, AUS), 677 (18.6%) as ‘suspicious for malignancy’ (IV, SFM), and 1738 (47.8%) as ‘malignant’ (V, MAL). The ROMs for the categories were 38.5%, 28.6%, 52.1%, 99.4% and 100%, respectively. The ROM for SFM was significantly higher than that for AUS (P < 0.001), while the difference between the ROMs for IVa and IVb was insignificant. The sensitivity, negative predictive value (NPV) and diagnostic accuracy of liquid-based preparations were all superior to those of conventional smears and cell blocks in detecting abnormalities. Using the three preparation methods simultaneously had the highest sensitivity, NPV and diagnostic accuracy. Conclusion Serous effusion cytology has a high specificity and positive predictive value (PPV), and TIS is a user-friendly reporting system. Liquid-based preparations could improve the sensitivity of diagnosis, and it is best to use three different preparation methods simultaneously for serous effusion cytologic examination.
Rationale:Neonatal appendicitis is extremely rare, and preoperative diagnosis is challenging. This study aimed to investigate the utility of ultrasound for the diagnosis of neonatal appendicitis.Patient concerns:Four cases of neonatal appendicitis were included in this case series. One was a female infant and the other 3 were male infants; they were aged from 10 to 17 days.Diagnoses:Neonatal appendicitis.Interventions:Four newborns in our hospital were diagnosed with neonatal appendicitis by abdominal ultrasound. Their sonographic features were summarized and compared with surgical and pathological findings.Outcomes:In these infants, abdominal ultrasound demonstrated ileocecal bowel dilatation, intestinal and bowel wall thickening, and localized encapsulated effusion in the right lower quadrant and the abscess area, which was assumed to surround the appendix.Lessons:Ultrasound is helpful for the diagnosis of neonatal appendicitis.
Cytopathological examination is one of the main examinations for pleural effusion, and especially for many patients with advanced cancer, pleural effusion is the only accessible specimen for establishing a pathological diagnosis. The lack of cytopathologists and the high cost of gene detection present opportunities for the application of deep learning. In this retrospective analysis, data representing 1321 consecutive cases of pleural effusion were collected. We trained and evaluated our deep learning model based on several tasks, including the diagnosis of benign and malignant pleural effusion, the identification of the primary location of common metastatic cancer from pleural effusion, and the prediction of genetic alterations associated with targeted therapy. We achieved good results in identifying benign and malignant pleural effusions (0.932 AUC (area under the ROC curve)) and the primary location of common metastatic cancer (0.910 AUC). In addition, we analyzed ten genes related to targeted therapy in specimens and used them to train the model regarding four alteration statuses, which also yielded reasonable results (0.869 AUC for ALK fusion, 0.804 AUC for KRAS mutation, 0.644 AUC for EGFR mutation and 0.774 AUC for NONE alteration). Our research shows the feasibility and benefits of deep learning to assist in cytopathological diagnosis in clinical settings.
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