CT radiomics analysis discriminates pulmonary lesions in patients with pulmonary MALT lymphoma and non-pulmonary MALT lymphoma.

PURPOSE: The aim of this study is to create and validate a radiomics model based on CT scans, enabling the distinction between pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma and other pulmonary lesion causes. METHODS: Patients diagnosed with primary pulmonary MALT lymphoma and lung infections at Fuzhou Pulmonary Hospital were randomly assigned to either a training group or a validation group. Meanwhile, individuals diagnosed with primary pulmonary MALT lymphoma and lung infections at Fujian Provincial Cancer Hospital were chosen as the external test group. We employed ITK-SNAP software for delineating the Region of Interest (ROI) within the images. Subsequently, we extracted radiomics features and convolutional neural networks using PyRadiomics, a component of the Onekey AI software suite. Relevant radiomic features were selected to build an intelligent diagnostic prediction model utilizing CT images, and the model's efficacy was assessed in both the validation group and the external test group. RESULTS: Leveraging radiomics, ten distinct features were carefully chosen for analysis. Subsequently, this study employed the machine learning techniques of Logistic Regression (LR), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) to construct models using these ten selected radiomics features within the training groups. Among these, SVM exhibited the highest performance, achieving an accuracy of 0.868, 0.870, and 0.90 on the training, validation, and external testing groups, respectively. For LR, the accuracy was 0.837, 0.863, and 0.90 on the training, validation, and external testing groups, respectively. For KNN, the accuracy was 0.884, 0.859, and 0.790 on the training, validation, and external testing groups, respectively. CONCLUSION: We established a noninvasive radiomics model utilizing CT imaging to diagnose pulmonary MALT lymphoma associated with pulmonary lesions. This model presents a promising adjunct tool to enhance diagnostic specificity for pulmonary MALT lymphoma, particularly in populations where pulmonary lesion changes may be attributed to other causes.

X-rays induced IL-8 production in lung cancer cells via p38/MAPK and NF-κB pathway.

PURPOSE: It is reported inflammatory cytokine interleukin-8 (IL-8) could predict radiation-induced lung toxicity (RILT). RILT is believed to be a consequence of a cascade of cytokine production. It is considered that vascular endothelial cell and macrophages are the mainly source of cytokines. This study was investigated the production of IL-8 from cancer cells induced by X-rays may involve in the radiation-induced inflammation. MATERIALS AND METHODS: We analyzed IL-8 in human lung cancer cell lines after expose to X-rays, and we also detect IL-8 in HUVEC cells and THP1 cells as endothelial cell and macrophage model to identify the change in normal cells after expose. Furthermore, we added the inhibitors to the culture with or without radiation to identify the role of MAPK and NF-κB pathways on the radiation-induced secretion of IL-8. RESULTS: Radiation could induce IL-8 production both in non-lung cancer cells (HUVECs and THP1 cells) and in lung cancer cells (A549 cells, H446 cells, PC-9 cells). Simultaneously, radiation activated p38/MAPK and NF-κB signal pathways in lung cancer cells. Moreover, p38/MAPK inhibitor SB203580 and NF-κB inhibitor BAY11-7082 could block the IL-8 up-regulated by X-rays but JNK inhibitor SP600125, ERK inhibitor U0126, ROS Scavenger NAC could not inhibit this phenomenon. CONCLUSIONS: X-rays could induce IL-8 production in lung cancer cells, which may be related to the activation of p38/MAPK and NF-κB signaling pathway, providing a new point for elucidating the mechanism of radiation pneumonitis.