Purpose Cytopathology detecting for endometrial cancer is becoming accepted, and Tao Brush is the most widely used sampler for endometrial cells. This study aims to compare the effectiveness between Li brushes and Tao brushes for the diagnosis of endometrial lesions and to evaluate the diagnostic accuracy of endometrial cytology compared with histology. Methods There were 109 patients needing dilation and curettage (D&C) and 21 patients needing hysterectomies included from November 2017 to April 2018. Every patient was sampled by Tao brush and Li brush before D&C or hysterectomy performed. The cytological results were compared based on the gold standard histological results of D&C or hysterectomy. Results The sensitivity of Li brush cytology for detecting endometrial cancer and atypical hyperplasia was estimated at 83.33%, specificity at 100%, positive predictive value (PPV) at 100%, and negative predictive value (NPV) at 98.02%, respectively. While for the Tao brush, it was 91.67% of sensitivity, 96.04% of specificity, 73.33% of PPV, and 98.98% of NPV, respectively. The kappa value was 0.767, which indicated a substantial agreement. Cytology by both two brushes had a lower insufficient sample rate (2.75% of Tao brush, 4.59% of Li brush) than did D&C (11.93%). Discussion Endometrial cytology is a reliable approach for evaluating endometrium with a lower insufficient sample rate. Cytology sampled by both Li brushes and Tao brushes has a high accuracy with histological diagnosis in detecting endometrial cancer and atypical hyperplasia. Combining social and economic benefits, the Li brush may be a better endometrial cell collector.

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ObjectivesSpread through air spaces (STAS), a new invasive pattern in lung adenocarcinoma (LUAD), is a risk factor for poor outcome in early-stage LUAD. This study aimed to develop and validate a CT-based radiomics model for predicting STAS in stage IA LUAD.MethodsA total of 395 patients (169 STAS positive and 226 STAS negative cases, including 316 and 79 patients in the training and test sets, respectively) with stage IA LUAD before surgery were retrospectively included. On all CT images, tumor size, types of nodules (solid, mix ground-glass opacities [mGGO] and pure GGO [pGGO]), and GGO percentage were recorded. Region of interest (ROI) segmentation was performed semi-automatically, and 1,037 radiomics features were extracted from every segmented lesion. Intraclass correlation coefficients (ICCs), Pearson’s correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were used to filter unstable (ICC < 0.75) and redundant features (r > 0.8). A temporary model was established by multivariable logistic regression (LR) analysis based on selected radiomics features. Then, seven radiomics features contributing the most were selected for establishing the radiomics model. We then built two predictive models (clinical-CT model and MixModel) based on clinical and CT features only, and the combination of clinical-CT and Rad-score, respectively. The performances of these three models were assessed.ResultsThe radiomics model achieved good performance with an area under of curve (AUC) of 0.812 in the training set, versus 0.850 in the test set. Furthermore, compared with the clinical-CT model, both radiomics model and MixModel showed higher AUC and better net benefit to patients in the training and test cohorts.ConclusionThe CT-based radiomics model showed satisfying diagnostic performance in early-stage LUAD for preoperatively predicting STAS, with superiority over the clinical-CT model.