Objective To explore the prognostic determinants and clinical treatment strategy in 142 patients with severe trauma brain injury(STBI).Methods Retrospective analysis of clinical data of 142 patients with STBI in our department from April 2006 to April 2012.All the patients were divided into good prognosis group(Ⅲ~V grade)and poor prognosis group(I~Ⅱgrade)ac-cording to the GOS classification standard.Age,gender,GCS,encephalocele,morphotogy of the basal cisterns on CT scanning,asso-ciated inj ury,shock,hyoxemia,underlying disease and hyperglycemia were chosen as the observation index.Statistical analysis was performed with Pearson Chi-square Test.Results 52.11% of patients with good prognosis,47.89% of patients with poor progno-sis and 3 1 .6 9% of patients were dead.Age,GCS,encephalocele,morphotogy of the basal cisterns on CT scanning,associated inj ury, shock,underlying disease were the prognostic determinants of STBI(all results P<0.05).Conclusion Age,GCS,encephalocele, morphotogy of the basal cisterns on CT scanning,associated inj ury,shock,underlying disease can determine the prognosis of STBI. Multidisciplinary cooperation treatment depending on the patient′s conditions is the key of improving the outcomes of STBI.

OBJECTIVEThe aim of this study was to retrospectively investigate the multi-detector computed tomography (MDCT) features of preinvasive lesions and minimally invasive adenocarcinoma (MIA) appearing as ground-glass nodules (GGNs), and to analyze their significance in differential diagnosis.

METHODSThe pathological data and MDCT images of 111 GGNs in 93 patients were reviewed and analyzed retrospectively, to identify the differentiating CT features between preinvasive lesions and MIA and to evaluate their differentiating accuracy.

RESULTSIn the 93 patients included in the study, there were 27 cases with preinvasive lesions (38 GGNs) and 66 cases with MIA (73 GGNs). No statistically significant difference was observed in terms of the gender, age and number of lesions between the two groups. There were significant differences (P<0.05) in the size of lesion, size of solid portion, content of solid portion, and morphological characteristics of the lesion edge between preinvasive lesions and MIA. ROC curve analysis showed that the optimal cut-off value of lesion size for differentiating preinvasive lesions from MIA was 13.0 mm (sensitivity, 83.0%; specificity, 80.0%), and that of solid portion size was 2.0 mm (sensitivity, 90.0%; specificity, 97.0%) and that of solid proportion was 12.0% (sensitivity, 88.0%; specificity, 97.0%). The analysis of CT morphological features showed that there were significant differences in the terms of lesion nature (pGGO, mGGO), presence or absence of lobulated sign and spiculated sign (P<0.05) between preinvasive lesions and MIA, but there were no significant differences in terms of the lesion edge, the presence or absence of vacuole sign, bubble lucency and pleural retraction (P>0.05).

CONCLUSIONSPreinvasive lesions can be accurately distinguished from MIA by the size of lesion, size of solid portion,solid proportion and morphological characteristics of the lesion edge. The size of lesion, size of solid portion, content of solid proportion and morphological characteristics of the lesion edge are of significance in the differential diagnosis of preinvasive lesions and minimally invasive adenocarcinoma of the lung.

Adenocarcinoma ; diagnostic imaging ; pathology ; Diagnosis, Differential ; Humans ; Lung Neoplasms ; diagnostic imaging ; pathology ; Multidetector Computed Tomography ; Neoplasm Invasiveness ; ROC Curve ; Retrospective Studies ; Sensitivity and Specificity