Prediction of Lymph Node Metastasis of Mixed Ground-glass Nodules Based on Clinical Imaging Information / 中国肺癌杂志

BACKGROUND@#Previous studies have shown that lymph node metastasis only occurs in some mixed ground-glass nodules (mGGNs) which the pathological results were invasive adenocarcinoma (IAC). However, the presence of lymph node metastasis leads to the upgrading of tumor-node-metastasis (TNM) stage and worse prognosis of the patients, so it is important to perform the necessary evaluation before surgery to guide the operation method of lymph node. The aim of this study was to find suitable clinical and radiological indicators to distinguish whether mGGNs with pathology as IAC is accompanied by lymph node metastasis, and to construct a prediction model for lymph node metastasis.@*METHODS@#From January 2014 to October 2019, the patients with resected IAC appearing as mGGNs in computed tomography (CT) scan were reviewed. All the lesions were divided into two groups (with lymph node metastasis or not) according to their lymph node status. Lasso regression model analysis by applying R software was used to evaluate the relationship between clinical and radiological parameters and lymph node metastasis of mGGNs.@*RESULTS@#A total of 883 mGGNs patients were enroled in this study, among which, 12 (1.36%) showed lymph node metastasis. Lasso regression model analysis of clinical imaging information in mGGNs with lymph node metastasis showed that previous history of malignancy, mean density, mean density of solid components, burr sign and percentage of solid components were informative. Prediction model for lymph node metastasis in mGGNs was developed based on the results of Lasso regression model with area under curve=0.899.@*CONCLUSIONS@#Clinical information combined with CT imaging information can predict lymph node metastasis in mGGNs.

Research Progress in 3D-reconstruction Based Imaging Analysis in Partial Solid Pulmonary Nodule / 中国肺癌杂志

The incidence and mortality of lung cancer rank first among all malignant tumors in China. With the popularization of high resolution computed tomography (CT) in clinic, chest CT has become an important means of clinical screening for early lung cancer and reducing the mortality of lung cancer. Imaging findings of early lung adenocarcinoma often show partial solid nodules with ground glass components. With the development of imaging, the relationship between the imaging features of some solid nodules and their prognosis has attracted more and more attention. At the same time, with the development of 3D-reconstruction technology, clinicians can improve the accuracy of diagnosis and treatment of such nodules.This article focuses on the traditional imaging analysis of partial solid nodules and the imaging analysis based on 3D reconstruction, and systematically expounds the advantages and disadvantages of both.
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Predictive value of quantitative CT features of pulmonary subsolid nodules in three different dimensions for the pathologic grade / 实用放射学杂志

Objective To investigate the predictive value of the whole nodule size and solid component size of lung adenocarcinoma manifesting as subsolid nodule(SSN) in three different dimensions for pathologic grade.Methods We evaluated retrospectively preoperative chest HRCT data of 125 patients with 127 SSNs surgically resected and pathologically conformed lung adenocarcinomas.All specimens were divided into two groups: a total of 69 SSNs in group A, including 22 AIS and 47 MIA;a total of 58 SSNs in group B, only including IAC.Computer aided diagnosis software were used to measure the one dimension maximum diameter of solid component with lung window setting(1D-SCLW),two dimension maximum diameter of solid component with lung window setting(2D-SCLW),one dimension maximum diameter of solid component with mediastinal window setting(1D-SCMW),two dimension maximum diameter of solid component with mediastinal window setting(2D-SCMW),one dimension maximum diameter of whole nodule with lung window setting (1D-WNLW), two dimension maximum diameter of whole nodule with lung window setting (2D-WNLW), and volume of solid component with threshold of-300 HU (SCT) of all SSNs.Results 1D-SCLW, 2D-SCLW,1D-SCMW,2D-SCMW,1D-WNLW,2D-WNLW and SCT of the group B were significantly larger than those of the group A(P=0.000).ROC analyses indicated that the diagnostic efficiency of SCT for the pathologic grade was the highest among 7 CT features(AUC=0.887, sensitivity:81%,specificity:93%);The cut-off values of 1D-SCLW,2D-SCLW,1D-SCMW,2D-SCMW,1D-WNLW, 2D-WNLW and SCT were 17.50 mm,14.75 mm,9.50 mm,7.75 mm,0.50 mm,1.25 mm and 139.00 mm3.Multiple Logistic regression analysis revealed that SCT was the independent predictor of pathologic grade(OR=4.978,95%CI=1.430-17.331,P=0.012).SCT of 139.00 mm3 or greater was a significant indicator of IAC.Conclusion Among the whole nodule size and solid component size of SSN in three different dimensions on preoperative HRCT, SCT is found to be the independent predictor of pathologic grade, which may provide reference for surgery.