| Autor: |
Lütfiye Özlem Atay, Leyla Memiş, Mahsun Özçelik, Abdullah Irfan Tastepe, Uğuray Aydos, Emel Rodoplu Unal, Ümit Özgür Akdemir, Özgür Ekinci, Deniz Akdemir |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Revista Española de Medicina Nuclear e Imagen Molecular (English Edition). 40:343-350 |
| ISSN: |
2253-8089 |
| Popis: |
Purpose The aims of this study were to evaluate the relationships between textural features of the primary tumor on FDG PET images and clinical-histopathological parameters which are useful in predicting prognosis in newly diagnosed non-small cell lung cancer (NSCLC) patients. Methods PET/CT images of ninety (90) patients with NSCLC prior to surgery were analyzed retrospectively. All patients had resectable tumors. From the images we acquired data related to metabolism (SUVmax, MTV, TLG) and texture features of primary tumors. Histopathological tumor types and subgroups, degree of Ki-67 expression and necrosis rates of the primary tumor, mediastinal lymph node (MLN) status and nodal stages were recorded. Results Among the two histologic tumor types (adenocarcinoma and squamous cell carcinoma) significant differences were present regarding metabolic parameters, Ki-67 index with higher values and kurtosis with lower values in the latter group. Textural heterogeneity was found to be higher in poorly differentiated tumors compared to moderately differentiated tumors in patients with adenocarcinoma. While Ki-67 index had significant correlations with metabolic parameters and kurtosis, tumor necrosis rate was only significantly correlated with textural features. By univariate and multivariate analyses of the imaging and histopathological factors examined, only gradient variance was significant predictive factor for the presence of MLN metastasis. Conclusions Textural features had significant associations with histologic tumor types, degree of pathological differentiation, tumor proliferation and necrosis rates. Texture analysis has potential to differentiate tumor types and subtypes and to predict MLN metastasis in patients with NSCLC. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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