RESUMEN
BACKGROUND: Mantle cell lymphoma (MCL) is a subtype of Non-Hodgkin's lymphoma (NHL). MCL frequently affects extranodal sites while endobronchial involvement is uncommon. Only 5 cases of MCL with endobronchial involvement have been previously reported. CASE SUMMARY: A 56-year-old male patient arrived at the hospital complaining of a dry cough. A mass in the right upper lobe of the lung was revealed in Chest computed tomography (CT). Right lung hilar and mediastinal lymphadenopathies were also found by CT scan. The patient was diagnosed with central-type lung cancer with multiple lymph node metastases after positron emission tomography (PET) CT scan examination. The fiber optic bronchoscope examination revealed diffuse neoplasm infiltration in the inlet of the right up lobar bronchus. The patient was finally diagnosed with MCL based on the bronchoscopy and mediastinoscopy biopsy results. CONCLUSION: MCL could masquerade as central type lung cancer. An endobronchial biopsy examination is necessary for the early diagnosis of MCL.
RESUMEN
AIM: Noninvasive evaluation of hypoxia in rabbit VX2 lung transplant tumors using spectral CT parameters and texture analysis. MATERIALS AND METHODS: Twenty-five VX2 lung transplant tumors of twenty-two rabbits were included in the study. Contrast-enhanced spectral CT scanning in the arterial phase (AP) and venous phase (VP) was performed. Tumors were divided into strong and weak hypoxic groups by hypoxic probe staining results. Spectral CT image-related parameters [70 keV CT value, normalized iodine concentration (NIC), slope of spectral HU curve (λHU)] were measured and the texture analysis on the monochromatic images was performed. Imaging parameters and texture features between tumors with different hypoxic degrees were compared and their diagnostic efficacies for predicting hypoxia in lung cancers were analyzed using receiver operating characteristic (ROC) curve. RESULTS: NIC in VP and λHU in VP of the strong hypoxic group were significantly higher than those in the weak hypoxic group (p < 0.05). For the texture features, entropy in VP and kurtosis in AP were significantly different between the two hypoxic groups. According to ROC analysis, λHU in VP had a better diagnostic ability for predicting hypoxia in tumors [Area Under Curve (AUC): 0.883, sensitivity: 85.7%, specificity: 100%]. The combination of four features improved AUC to 0.955. CONCLUSION: NIC in VP, λHU in VP, entropy in VP and kurtosis in AP have certain values in predicting tumor hypoxia and a combination of image parameters and texture features improves diagnostic efficiency.