Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective:To explore the predictive value of models based on clinical parameters, deep learning radiomics (DLR) from CT images, and traditional handcrafted radiomics (HCR) in assessing pathological complete response (pCR) after neoadjuvant radiotherapy combined with medical therapy in patients with esophageal cancer.Methods:A retrospective study was conducted on 130 patients with locally advanced esophageal cancer who underwent neoadjuvant radiotherapy combined with medical therapy followed by surgery at the First Affiliated Hospital of the University of Science and Technology of China from August 1, 2018, to August 31, 2024. Patients were randomly divided into a training set ( n=91) and a validation set ( n=39) at a ratio of 7:3. Logistic regression analysis was performed to identify clinical independent risk factors associated with pCR. DLR and HCR features were extracted from the tumor and the 5 mm peritumoral region on planning CT images. Features for modeling were selected using t-test, Mann-Whitney U test or Fisher exact probability method, least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (Rad-score). A nomogram was then constructed by integrating the clinical risk factors. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) to assess clinical benefits. Results:Multivariate logistic regression analysis identified body weight ( OR=1.101, 95% CI: 1.029-1.177, P=0.005) and lymph node positivity ( OR=0.100, 95% CI: 0.014-0.727, P=0.023) as independent predictors of pCR. The peritumoral DLR-HCR model showed superior predictive performance, with AUCs of 0.870 (95% CI: 0.799-0.942) in the training set and 0.866 (95% CI: 0.750-0.982) in the validation set. The combined model incorporating clinical parameters achieved the best performance, with AUCs of 0.903 (95% CI: 0.845-0.962) and 0.888 (95% CI: 0.782-0.994) in the training and validation sets, respectively. Conclusions:The combined model integrating peritumoral DLR-HCR features with clinical parameters provides excellent predictive value for pCR after neoadjuvant radiotherapy combined with medical therapy in esophageal cancer and offers valuable guidance for personalized treatment strategies.

Objective To investigate the value of multimodal model integrating digital mammography(MG)and MRI radiomics features for preoperative prediction of lymphovascular invasion(LVI)status in breast cancer.Methods The clinical and imaging data from 336 patients with pathologically confirmed invasive breast cancer were retrospectively analyzed and randomly divided into a training group(235 cases)and a test group(101 cases)according to the ratio of 7∶3.Feature dimensionality reduction was carried out by Pearson correlation analysis followed by least absolute shrinkage and selection operator(LASSO)regression.Radiomics models were constructed based on MG craniocaudal(CC),dynamic contrast enhancement(DCE),T2 WI,and integrated MRI sequences;a multimodal model was further developed by incorporating clinical high-risk factors.The predictive efficiency of each model was evaluated by plotting receiver operating characteristic(ROC)curve.Results The ROC curve analysis showed that the multimodal model performed the best predictive efficiency,with area under the curve(AUC)of 0.989 and 0.861,accuracy of 0.949 and 0.782,sensitivity of 0.923 and 0.828,and specificity of 0.962 and 0.764 in the training group and test group respectively.Conclusion The multimodal model,integrating MG and MRI radiomics features,show optimal performance and can be served as a preoperative prediction of LVI status in breast cancer.

Objective To investigate the value of multimodal model integrating digital mammography(MG)and MRI radiomics features for preoperative prediction of lymphovascular invasion(LVI)status in breast cancer.Methods The clinical and imaging data from 336 patients with pathologically confirmed invasive breast cancer were retrospectively analyzed and randomly divided into a training group(235 cases)and a test group(101 cases)according to the ratio of 7∶3.Feature dimensionality reduction was carried out by Pearson correlation analysis followed by least absolute shrinkage and selection operator(LASSO)regression.Radiomics models were constructed based on MG craniocaudal(CC),dynamic contrast enhancement(DCE),T2 WI,and integrated MRI sequences;a multimodal model was further developed by incorporating clinical high-risk factors.The predictive efficiency of each model was evaluated by plotting receiver operating characteristic(ROC)curve.Results The ROC curve analysis showed that the multimodal model performed the best predictive efficiency,with area under the curve(AUC)of 0.989 and 0.861,accuracy of 0.949 and 0.782,sensitivity of 0.923 and 0.828,and specificity of 0.962 and 0.764 in the training group and test group respectively.Conclusion The multimodal model,integrating MG and MRI radiomics features,show optimal performance and can be served as a preoperative prediction of LVI status in breast cancer.

Objective:To explore the predictive value of models based on clinical parameters, deep learning radiomics (DLR) from CT images, and traditional handcrafted radiomics (HCR) in assessing pathological complete response (pCR) after neoadjuvant radiotherapy combined with medical therapy in patients with esophageal cancer.Methods:A retrospective study was conducted on 130 patients with locally advanced esophageal cancer who underwent neoadjuvant radiotherapy combined with medical therapy followed by surgery at the First Affiliated Hospital of the University of Science and Technology of China from August 1, 2018, to August 31, 2024. Patients were randomly divided into a training set ( n=91) and a validation set ( n=39) at a ratio of 7:3. Logistic regression analysis was performed to identify clinical independent risk factors associated with pCR. DLR and HCR features were extracted from the tumor and the 5 mm peritumoral region on planning CT images. Features for modeling were selected using t-test, Mann-Whitney U test or Fisher exact probability method, least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (Rad-score). A nomogram was then constructed by integrating the clinical risk factors. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) to assess clinical benefits. Results:Multivariate logistic regression analysis identified body weight ( OR=1.101, 95% CI: 1.029-1.177, P=0.005) and lymph node positivity ( OR=0.100, 95% CI: 0.014-0.727, P=0.023) as independent predictors of pCR. The peritumoral DLR-HCR model showed superior predictive performance, with AUCs of 0.870 (95% CI: 0.799-0.942) in the training set and 0.866 (95% CI: 0.750-0.982) in the validation set. The combined model incorporating clinical parameters achieved the best performance, with AUCs of 0.903 (95% CI: 0.845-0.962) and 0.888 (95% CI: 0.782-0.994) in the training and validation sets, respectively. Conclusions:The combined model integrating peritumoral DLR-HCR features with clinical parameters provides excellent predictive value for pCR after neoadjuvant radiotherapy combined with medical therapy in esophageal cancer and offers valuable guidance for personalized treatment strategies.

Purpose To investigate the effect of autophagy intervention on ferroptosis and drug resistance of colorectal canc-er cells and its molecular mechanism.Methods The human colorectal cancer cell lines HCT-8,COLO205,HCT-116,SW620,and SW480 were cultured.HCT-116 cells with moder-ate expression of LC3 were screened,and the expression differ-ences of LC3,p62,Keap1,Nrf2,GPX4 proteins,Fe2+,GSH,and MDA between them and OXA-resistant HCT-116/OXA cell lines were detected.The expression levels of LC3,p62,Keap1,Nrf2,GPX4,Fe2+,GSH and MDA were assessed in HCT-116/OXA cells through the intervention of autophagy and ferroptosis intervention agent combined with oxaliplatin.The proliferative activity and sensitivity to oxaliplatin in each group were detected by CCK-8 assay.Cell growth and invasion ability of each group were detected by plate cloning and Trans well assay.Results LC3,p62 and GPX4 expression levels of HCT-116 cells in the 5 groups were moderate.Compared with HCT-116 cells,HCT-116/OXA was less sensitive to oxaliplatin,and the proteins of p62,Nrf2 and GPX4 were highly expressed,LC3 and Keap1 were lowly expressed,and the expression of Fe2+,GSH and MDA were increased(P＜0.05).The levels of LC3,Keap1 protein,Fe2+and MDA in Rapa and Rapa+Fer-1 groups were higher than those in Fer-1 and control groups,while p62,Nrf2,GPX4 and GSH levels were lower.The expressions of GPX4 pro-tein and GSH in Rapa+Fer-1 group were lower than those in Rapa group(P＜0.05).In the autophagy inhibitor group,LC3,p62,Nrf2,GPX4 and GSH were highly expressed in the CQ and CQ+Erastin groups compared with the control and Eras-tin groups,while Keap1 protein,Fe2+and MDA were low.The levels of GPX4 protein and GSH in Erastin group were lower than those in the other three groups,and the levels of Fe2+and MDA were higher than those in the other three groups(P＜0.05).The combination of autophagy activator OXA showed that Rapa intervention group had higher chemical sensitivity to OXA,less number of migrating cells and lower cell proliferation activity than the other three groups.The sensitivity of Rapa+Fer-1 group to oxaliplatin was lower than that of Rapa group,but higher than that of Fer-1 group and control group(P＜0.05).There was no significant difference between Fer-1 group and con-trol group(P＜0.05).Compared with the control group,the cell activity,migration capacity and clonogenesis capacity of Erastin,CQ+Erastin and CQ groups were decreased when auto-phagy inhibitor was combined with OXA,and the Erastin group was the lowest,while the CQ+Erastin group was higher than the Erastin group,and lower than the CQ group(P＜0.05).Con-clusion In colorectal cancer,autophagy is involved in the regu-lation of ferroptosis,and intervention in autophagy can regulate ferroptosis in colorectal cancer cells through the p62-Keap1/Nrf2-GPX4 pathway,thereby reversing oxaliplatin resistance.