Non-malignant pathological results from CT-guided biopsy for pulmonary nodules: a predictive model for identifying false-negative results.

BACKGROUND: Computed tomography (CT)-guided biopsy (CTB) procedures are commonly used to aid in the diagnosis of pulmonary nodules (PNs). When CTB findings indicate a non-malignant lesion, it is critical to correctly determine false-negative results. Therefore, the current study was designed to construct a predictive model for predicting false-negative cases among patients receiving CTB for PNs who receive non-malignant results. MATERIALS AND METHODS: From January 2016 to December 2020, consecutive patients from two centers who received CTB-based non-malignant pathology results while undergoing evaluation for PNs were examined retrospectively. A training cohort was used to discover characteristics that predicted false negative results, allowing the development of a predictive model. The remaining patients were used to establish a testing cohort that served to validate predictive model accuracy. RESULTS: The training cohort included 102 patients with PNs who showed non-malignant pathology results based on CTB. Each patient underwent CTB for a single nodule. Among these patients, 85 and 17 patients, respectively, showed true negative and false negative PNs. Through univariate and multivariate analyses, higher standardized maximum uptake values (SUVmax, P = 0.001) and CTB-based findings of suspected malignant cells (P = 0.043) were identified as being predictive of false negative results. Following that, these two predictors were combined to produce a predictive model. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.945. Furthermore, it demonstrated sensitivity and specificity values of 88.2% and 87.1% respectively. The testing cohort included 62 patients, each of whom had a single PN. When the developed model was used to evaluate this testing cohort, this yielded an AUC value of 0.851. CONCLUSIONS: In patients with PNs, the predictive model developed herein demonstrated good diagnostic effectiveness for identifying false-negative CTB-based non-malignant pathology data.

Clinical and magnetic resonance imaging features predict microvascular invasion in intrahepatic cholangiocarcinoma.

Introduction: Clinical features and magnetic resonance imaging (MRI)-related data are commonly employed in clinical settings and can be used to predict the microvascular invasion (MVI) status of intrahepatic cholangiocarcinoma (ICC) patients. Aim: To generate a clinical and MRI-based model capable of predicting the MVI status of ICC patients. Material and methods: Consecutive ICC patients evaluated from June 2015 to December 2018 were retrospectively enrolled in a training group to establish a predictive clinical MRI model. Consecutive ICC patients evaluated from January 2019 to June 2019 were prospectively enrolled in a validation group to test the reliability of this model. Results: In total, 143 patients were enrolled in the training group, of whom 46 (32.2%) and 96 (67.8%) were MVI-positive and MVI-negative, respectively. Logistics analyses revealed larger tumour size (p = 0.008) and intrahepatic duct dilatation (p = 0.01) to be predictive of MVI positivity, enabling the establishment of the following predictive model: -2.468 + 0.024 × tumour size + 1.094 × intrahepatic duct dilatation. The area under the receiver operating characteristic (ROC) curve (AUC) for this model was 0.738 (p < 0.001). An optimal cut-off value of -1.0184 was selected to maximize sensitivity (71.7%) and specificity (61.9%). When the data from the validation group were incorporated into the predictive model, the AUC value was 0.716 (p = 0.009). Conclusions: Both larger tumour size and intrahepatic duct dilatation were predictive of MVI positivity in patients diagnosed with ICC, and the predictive model developed based on these variables can offer quantitative guidance for assessing the risk of MVI.