Shixiong Liang

The integration of immunotherapy with neoadjuvant therapy for proficient mismatch repair (pMMR) locally advanced rectal cancer (LARC) is a promising strate

This aim of the study was to compare the outcomes of radiation-induced hepatic toxicity (RIHT) of hepatocellular carcinoma (HCC) patients treated with radi

Purpose: This study aimed to investigate whether early dynamic variation in circulating neutrophil-to-lymphocyte ratio(NLR) during intensity-modulated radiotherapy (IMRT) can predict radiation-induced hepatic toxicity (RIHT) in patients with hepatocellular carcinoma(HCC). Patients and methods: Neutrophil and lymphocyte counts of 103 HCC patients were measured every 2 weeks before, during and after completion of IMRT.Generalized estimating equations analyses was used to analyze the dynamic changes of neutrophil and lymphocyte counts. The prognostic significant factors were assessed through logistic regression analyses. Statistical power was assessed using power analysis, and the model was adjusted for multiple comparisons via the Bonferroni correction. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the prediction accuracy.The predictive model was internally validated using 5-fold cross-validation. Results: Radiation-induced liver disease(RILD) is a type of RIHT and is a relatively severe phenomenon of hepatic toxicity.Overall, 23 patients (22%) developed RILD. In RILD group, NLR were significantly changed in the first 3 to 4 weeks during IMRT (p=0.006).In multivariate analysis, NLR in first 3– 4 weeks(NLR 4), the ratio of NLR in first 3– 4 weeks to 1– 2 weeks(NLR 4/2),and normal liver volume(NLV) were independent predictive factors for RILD. The area under the curve(AUC) was 0.860 (95% CI, 0.783– 0.937).Larger NLV correlated with a lower likelihood of developing RILD(p = 0.041).The mean AUC values was 0.86 in the training set and 0.81 in the test sets across 5-fold cross-validation (p=0.41). Conclusion: Circulating NLR in first 3 to 4 weeks and its relatively change during IMRT were significantly associated with RIHT.The model based on early dynamic variation of NLR and dosimetric factors NLV can predict RIHT with high accuracy in HCC patients.It can timely assist clinician to take preventive measures and adjusting treatment plans.

Objective: To develop an interpretable machine learning (ML) model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic data, dosimetric parameters, and clinical data for predicting radiation-induced hepatic toxicity (RIHT) in patients with hepatocellular carcinoma (HCC) following intensity-modulated radiation therapy (IMRT). Methods: A retrospective analysis of 150 HCC patients was performed, with a 7:3 ratio used to divide the data into training and validation cohorts. Radiomic features from the original MRI sequences and Delta-radiomic features were extracted. Seven ML models based on radiomics were developed: logistic regression (LR), random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), adaptive boosting (AdaBoost), decision tree (DT), and artificial neural network (ANN). The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis and calibration curves. Shapley additive explanations (SHAP) were employed to interpret the contribution of each variable and its risk threshold. Results: Original radiomic features and Delta-radiomic features were extracted from DCE-MRI images and filtered to generate Radiomics-scores and Delta-Radiomics-scores. These were then combined with independent risk factors (Body Mass Index (BMI), V5, and pre-Child-Pugh score(pre-CP)) identified through univariate and multivariate logistic regression and Spearman correlation analysis to construct the ML models. In the training cohort, the AUC values were 0.8651 for LR, 0.7004 for RF, 0.6349 for SVM, 0.6706 for XGBoost, 0.7341 for AdaBoost, 0.6806 for Decision Tree, and 0.6786 for ANN. The corresponding accuracies were 84.4%, 65.6%, 75.0%, 65.6%, 71.9%, 68.8%, and 71.9%, respectively. The validation cohort further confirmed the superiority of the LR model, which was selected as the optimal model. SHAP analysis revealed that Delta-radiomics made a substantial positive contribution to the model. Conclusion: The interpretable ML model based on radiomics provides a non-invasive tool for predicting RIHT in patients with HCC, demonstrating satisfactory discriminative performance.

Dihydroartemisinin (DHA) is a derivative of artemisinin, which affects inflammation, oxidative stress, and immune regulation. However, the mechanism underlying its effects remains largely unknown. This study aims to explore the mechanism by which DHA affects radiation‐induced lung injury (RILI), providing new insights for lung radiotherapy. To elucidate its mechanism of action, C57BL/6 J mice were irradiated with 15 Gy whole chest. RILI was evaluated by qRT‐PCR, ELISA, histology, Western blot analysis, immunohistochemistry, and RILI signaling cascade studies. In addition, small interfering RNAs were employed to knockdown cGAS proteins in the cGAS‐STING signaling pathway in the human bronchial epithelium cell line (BEAS‐2B). Both In Vivo and Vitro experiments were conducted to investigate the specific mechanism by which DHA alleviated RILI. We observed the activation of the cGAS‐STING pathway, along with the phosphorylation of the downstream target NF‐κB and an increase in inflammatory factor levels in the mouse model following radiation exposure. In the cell model, irradiation also triggered the activation of the cGAS‐STING signaling pathway and its downstream targets, leading to elevated levels of inflammatory factors. Notably, knocking down the cGAS using small interfering RNA in the BEAS‐2B cells significantly alleviated RILI in the cell model. Our study elucidated the mechanism of DHA reducing RILI through the cGAS/STING/NF‐κB signaling pathway, and revealed that the GAS/STING/NF‐κB axis may be a potential therapeutic target for RILI.

Aim: To build and externally validate machine learning-based models for radiation pneumonitis (RP) prediction in patients with locally advanced non-small cell lung cancer (LA-NSCLC) treated with intensity-modulated radiation therapy (IMRT) in the era of precision radiotherapy. Patients and Methods: In this two-center retrospective study, a total of 218 patients (131 in the training cohort, and 87 in the external validation cohort) with LA-NSCLC. All patients underwent primary IMRT with strict lung dose constraints. Pretreatment CT radiomics features were extracted and then generated radiomics score (Rad-score). The study factors included Rad-score, dose-volume parameters and clinical features. Based on the independent risk factors, three machine learning models (random forest, logistic regression and decision tree) were developed and validated for predicting RP. The predictive performances of the models were evaluated using area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Results: Within both cohorts, the overwhelming majority of patients were safely treated with radiotherapy within known lungs dose constraints. PE,ILD,N2-N3, ipsilateral lung Rad-score and contralateral lung Rad-score were independent risk factors for RP (P＜0.05). The AUC of random forest model, logistic regression model and decision tree model were 0.938, 0.859 and 0.632 in the training cohort, and 0.885, 0.911 and 0.721 in the external validation cohort, respectively. The calibration curve and DCA demonstrated goodness-of-ft and improved benefits in random forest model. Conclusion: PE, ILD, N2-N3 and CT radiomics features of lungs were independent predictors of RP in the LA-NSCLC patients treated with IMRT. The model using random forest algorithm exhibited the best predictive accuracy, outperforming logistic regression and decision tree.

Radiation-induced lung injury (RILI) is one of the side effects of thoracic malignancy radiotherapy. Once RILI occurs, delayed or untreated cases with Grade IV lung damage can lead to respiratory failure or even death. Therefore, identifying potential biomarkers and treatment targets to improve the prognosis of thoracic radiotherapy patients is of crucial clinical significance. Cell death is a genetically determined process. Active and orderly cell death is ubiquitous in the processes of living organisms and plays a critical role in regulating life homeostasis. Based on the speed of occurrence and whether influenced by drugs or genes, cell death is classified into two categories: accidental cell death (ACD) and regulated cell death (RCD). Non-apoptotic RCD can be subdivided into ferroptosis, autophagy, pyroptosis and necroptosis. Different PCDs have mutual and intricate connections to promote immune dysregulation and contribute to the development of RILI. Studies have shown that ferroptosis, autophagy, pyroptosis and necroptosis are involved in the distinct and progression of a variety of diseases, including tumors. In this review, we summarized the recent advances in ferroptosis, autophagy, pyroptosis and necroptosis in the occurrence, development, and therapeutic potential of RILI.

Background: Radiation‐induced lung injury (RILI) is a common consequence of thoracic radiation therapy that lacks effective preventative and treatment strategies. Dihydroartemisinin (DHA), a derivative of artemisinin, affects oxidative stress, immunomodulation, and inflammation. It is uncertain whether DHA reduces RILI. In this work, we investigated the specific mechanisms of action of DHA in RILI.Methods: Twenty‐four C57BL/6J mice were randomly divided into four groups of six mice each: Control group, irradiation (IR) group, IR + DHA group, and IR + DHA + Brusatol group. The IR group received no interventions along with radiation treatment. Mice were killed 30 days after the irradiation. Morphologic and pathologic changes in lung tissue were observed with hematoxylin and eosin staining. Detection of hydroxyproline levels for assessing the extent of pulmonary fibrosis. Tumor necrosis factor α (TNF‐α), transforming growth factor‐β (TGF‐β), glutathione peroxidase (GPX4), Nuclear factor erythroid 2‐related factor 2 (Nrf2), and heme oxygenase‐1 (HO‐1) expression in lung tissues were detected. In addition, mitochondrial ultrastructural changes in lung tissues were also observed, and the glutathione (GSH) content in lung tissues was assessed.Results: DHA attenuated radiation‐induced pathological lung injury and hydroxyproline levels. Additionally, it decreased TNF‐α and TGF‐β after irradiation. DHA may additionally stimulate the Nrf2/HO‐1 pathway. DHA upregulated GPX4 and GSH levels and inhibited cellular ferroptosis. Brusatol reversed the inhibitory effect of DHA on ferroptosis and its protective effect on RILI.Conclusion: DHA modulated the Nrf2/HO‐1 pathway to prevent cellular ferroptosis, which reduced RILI. Therefore, DHA could be a potential drug for the treatment of RILI.

Background: Radiation-induced lung injury (RILI) is a common consequence of thoracic radiation therapy that lacks effective preventative and treatment strategies. Dihydroartemisinin (DHA), a derivative of artemisinin, affects oxidative stress, immunomodulation, and inflammation. It’s uncertain, nevertheless, whether DHA reduces RILI. In this work, we looked into the specific mechanisms of action of DHA in RILI. Methods: The mice were irradiated with 20 Gy 6 MV irradiation dose followed by administration of DHA and Brusatol for 30 days. Pathologic changes in the lungs were observed thereafter. TNF-α, TGF-β, glutathione peroxidase (GPX4), Nuclear factor erythroid 2-related factor 2 (NRF2), and heme oxygenase-1 (HO-1) expression in lung tissues were detected. In addition, mitochondrial ultrastructural changes in lung tissues were also observed, and the GSH content in lung tissues was assessed. Results: DHA attenuated radiation-induced pathological lung injury and inflammatory cell infiltration. Additionally, it decreased the synthesis of inflammatory substances like TNF-α and TGF-β. DHA additionally stimulated the Nrf2/HO-1 pathway. DHA inhibited cellular ferroptosis and upregulated GPX4 and GSH levels. The Nrf2 inhibitor Brusatol reduced DHA’s inhibitory effect on ferroptosis. Conclusion: DHA modulated the Nrf2/HO-1 pathway to prevent cellular ferroptosis, hence reducing RILI. Therefore, DHA could be a potential drug for the treatment of RILI.