A novel clinical model for predicting malignancy of solitary pulmonary nodules: a multicenter study in chinese population

Abstract

Background This study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs). Methods Records from 295 patients with SPNs in Sun Yat-sen University Cancer Center were retrospectively reviewed. The novel prediction model was established using LASSO logistic regression analysis by integrating clinical features, radiologic characteristics and laboratory test data, the calibration of model was analyzed using the Hosmer-Lemeshow test (HL test). Subsequently, the model was compared with PKUPH, Shanghai and Mayo models using receiver-operating characteristics curve (ROC), decision curve analysis (DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI) with the same data. Other 101 SPNs patients in Henan Tumor Hospital were used for external validation cohort. Results A total of 11 variables were screened out and then aggregated to generate new prediction model. The model showed good calibration with the HL test (P = 0.964). The AUC for our model was 0.768, which was higher than other three reported models. DCA also showed our model was superior to the other three reported models. In our model, sensitivity = 78.84%, specificity = 61.32%. Compared with the PKUPH, Shanghai and Mayo models, the NRI of our model increased by 0.177, 0.127, and 0.396 respectively, and the IDI changed − 0.019, -0.076, and 0.112, respectively. Furthermore, the model was significant positive correlation with PKUPH, Shanghai and Mayo models. Conclusions The novel model in our study had a high clinical value in diagnose of MSPNs.

Development and validation of a risk prediction score for patients with nasopharyngeal carcinoma

“…We collected information regarding gender, age, family history, smoking, body mass index(BMI), TNM stage, treatment, white blood cells (WBC), neutrophils, lymphocyte, monocyte, platelets, HGB, NLR, neutrophils/(WBC-neutrophils) ratio (dNLR), lymphocyte/monocyte ratio (LMR), platelet/lymphocyte ratio (PLR), systemic immune-inflammation index (SII), total protein (TP), albumin (ALB), GLOB, ALB/GLOB ratio (AGR), C-reactive protein (CRP), CRP/ALB ratio (CAR), apolipoprotein B (APOA), apolipoprotein B (APOB), APOA/APOB ratio (ABR), LDH, high density lipoprotein(HDL), EBV DNA, virus capsid antigen specific immunoglobulin A (VCA-IgA), early antigen immunoglobulin A (EA-IgA), prognostic nutritional index (PNI) and prognostic index (PI). The PNI was calculated based on the following formula: Alb (g/L) + 5 × lymphocyte count × 10 9 /L [ 17 ]. The SII was calculated based on this formula: PLR × Neutrophil × 10 9 /L [ 18 ].…”

Development and validation of a prediction model for malignant pulmonary nodules

“…Some invasive pathological examination such as surgical resection, fine needle biopsy, and bronchoscopy can identify the benign and malignant SPNs, but there are risks of respiratory impairment, pneumothorax, and bleeding, it should be avoided especially in the case of benign tumors. Currently, a number of studies on the risk factors related to MPN have been carried out: The risk factors include clinical features, serum tumor markers, imaging features, and gene tags [6–9] . Traditional biomarkers such as carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1), neuron-specific enolase (NSE) are widely used in the establishment of prediction model.…”

“…Currently, a number of studies on the risk factors related to MPN have been carried out: The risk factors include clinical features, serum tumor markers, imaging features, and gene tags. [6][7][8][9] Traditional biomarkers such as carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1), neuron-specific enolase (NSE) are widely used in the establishment of prediction model. However, the predictive efficacy of these markers is not ideal, false-positive results often occur as a result of infection, benign tumors, pregnancy, and other factors.…”