Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features

OBJECTIVES: To analyze the distant metastasis possibility based on computed tomography (CT) radiomic features in patients with lung cancer. METHODS: This was a retrospective analysis of 348 patients with lung cancer enrolled between 2014 and February 2015. A feature set containing clinical features and 485 radiomic features was extracted from the pretherapy CT images. Feature selection via concave minimization (FSV) was used to select effective features. A support vector machine (SVM) was used to evaluate the predictive ability of each feature. RESULTS: Four radiomic features and three clinical features were obtained by FSV feature selection. Classification accuracy by the proposed SVM with SGD method was 71.02%, and the area under the curve was 72.84% with only the radiomic features extracted from CT. After the addition of clinical features, 89.09% can be achieved. CONCLUSION: The radiomic features of the pretherapy CT images may be used as predictors of distant metastasis. And it also can be used in combination with the patient's gender and tumor T and N phase information to diagnose the possibility of distant metastasis in lung cancer.     

Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction

PURPOSE: To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS: A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS: Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell’s Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION: The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.     

Clinical Features of Brain Metastases in Small Cell Lung Cancer: an Implication for Hippocampal Sparing Whole Brain Radiation Therapy

PURPOSE: To assess the clinical features and distribution of brain metastases (BMs) of small cell lung cancer (SCLC) in the hippocampal and perihippocampal region, with the purpose of exploring the viability of hippocampal-sparing whole-brain radiation therapy (HS-WBRT) on reducing neurocognitive deficits. METHODS: This was a retrospective analysis of the clinical characteristics and patterns of BMs in patients with SCLC. Associations between the clinical characteristics and hippocampal metastases (HMs)/perihippocampal metastases (PHMs) were evaluated in univariate and multivariate regression analyses. RESULTS: A total of 1594 brain metastatic lesions were identified in 180 patients. Thirty-two (17.8%) patients were diagnosed with BMs at the time of primary SCLC diagnosis. The median interval between diagnosis of primary SCLC and BMs was 9.3 months. There were 9 (5.0%) and 22 (12.2%) patients with HMs and PHMs (patients with BMs located in or within 5 mm around the hippocampus), respectively. In the univariate and multivariate analysis, the number of BMs was the risk factor for HMs and PHMs. Patients with BMs ≥ 5 had significantly higher risk of HMs (odds ratio [OR] 7.892, 95% confidence interval [CI] 1.469-42.404, P = .016), and patients with BMs ≥ 7 had significantly higher risk of PHMs (OR 5.162, 95% CI 2.017-13.213, P = .001). Patients with extracranial metastases are also associated with HMs. CONCLUSIONS: Our results indicate that patients with nonoligometastatic disease are significantly associated with HMs and PHMs. The incidence of PHMs may be acceptably low enough to perform HS-WBRT for SCLC. Our findings provide valuable clinical data to assess the benefit of HS-WBRT in SCLC patients with BMs.