The Ki-67 antigen, a marker of cell proliferation, serves as a biomarker for assessing tumor malignancy. However, measuring Ki-67 levels through immunohistochemistry is often challenging due to difficulties in specimen collection and individual health issues. Radiological analysis has emerged as a potential alternative for predicting Ki-67 levels, although its accuracy has been limited. This study aims to enhance the prediction of Ki-67 levels using chest X-rays by employing a refined approach that combines detailed, manually delineated radiological features with conventional imaging characteristics. This study collected X-ray images and Ki-67 expression data from 109 patients diagnosed with Non-Small Cell Lung Cancer (NSCLC). Seven radiological features related to tumor progression were annotated on each image by clinical professionals. Tumor areas were delineated using Python, resulting in the generation of 5 types of data from these regions. Data integration facilitated the development of predictive models utilizing Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN), with feature selection processes applied. Using the RF, 8 predictive features were selected from the datasets, of which 7 exhibited a linear correlation with Ki-67 levels (Mantel-Haenszel test, P < .05). The model demonstrated robust performance metrics: Accuracy: 0.818, Precision: 0.823, Recall: 0.849, and F1 Score: 0.783. This research underscores the effectiveness of integrating specific radiological features, manually delineated regions of interest (ROIs), with traditional imaging characteristics and machine learning techniques. This approach significantly enhances the predictive accuracy of chest X-rays for Ki-67 levels, offering a non-invasive method for Ki-67 estimation.
Advanced Non-Small Cell Lung Carcinoma (NSCLC) patients with ROS1 gene rearrangement have shown significant therapeutic responses to tyrosine kinase inhibitors approved by the US Food and Drug Administration, with approximately 40 fusion partners documented in the existing literature. Our report highlights a novel fusion partner of ROS1 that has demonstrated a conclusive response to the current standard of treatment.
Advanced Non-Small Cell Lung Carcinoma (NSCLC) patients with ROS1 gene rearrangement have shown significant therapeutic responses to tyrosine kinase inhibitors approved by the US Food and Drug Administration, with approximately 40 fusion partners documented in the existing literature. Our report highlights a novel fusion partner of ROS1 that has demonstrated a conclusive response to the current standard of treatment.
Aim/objective: Mucosal neuroma (MN) is a benign peripheral nerve tumour that usually occurs as a symptom of multiple endocrine neoplasia type 2B (MEN2B) (MEN2B-associated MN). MN that is not associated with MEN2B is termed solitary mucosal neuroma (SMN). We present a case of SMN in the upper gingiva and examine previously reported cases. Methods: The study included nine patients (eight from seven articles and one from this study) with SMN. Age of onset, sex, disease duration, tumour site, clinical findings including subjective symptoms, diagnostic method, treatment, prognosis and relationship with MEN2B were investigated in these nine patients. Results and conclusion: SMN is thought to be caused by the overproduction of nerve growth factors due to trauma or chronic continuous stimulation, whereas MEN2B-associated MN is caused by GNDF-induced hyperplasia. Considering the pathogenesis of their development, both diseases exhibit hyperplasia of the nerve fibre bundles and have the same histological type of hypertrophy. MNs (SMN and MEN2B-induced MN) must be differentiated from benign peripheral neurogenic tumours. Morphological recognition and immunohistopathological analyses make this differentiation possible.
Objectives: Mid-regional pro-adrenomedullin (MR-proADM) and monocyte CD169 (CD169) are valuable prognostic indicators of severe COVID-19. Methods: We assessed the predictive ability of a single measurement of MR-proADM and CD169 at emergency department (ED) admission to forecast in-hospital and 60-day mortality in adult COVID-19 patients. We analyzed clinical and laboratory data, with in-hospital mortality as the primary endpoint and 60-day mortality as the secondary endpoint. We examined associations with clinical and laboratory variables through univariate and multivariate analyses. Results: Data from 382 patients over 14 months were analyzed. Significant predictors of in-hospital mortality included age ⩾ 70 years (hazard ratio [HR] 8.1; 95% confidence interval [CI] 2.2-29.5), CD169 ratio ⩾ 20 (HR: 2.4; 95%CI: 1.6-5.6), MR-proADM ⩾ 1.1 mmol/L (HR: 5.1; 95%CI: 1.7-15.6), the need for invasive mechanical ventilation (HR: 6.8; 95%CI: 2.4-19.1), and active cancer (HR: 5.2; 95%CI: 1.8-15.2). For 60-day mortality, only elevated MR-proADM levels showed predictive value (HR: 6.7; 95%CI: 1.7-25.0), while high serologic titer was protective (HR: 0.4; 95%CI: 0.1-0.9). Conclusion: A single MR-proADM and CD169 measurement upon ED admission has prognostic value for in-hospital mortality, with MR-proADM also predicting 60-day mortality.
