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Computed tomography-based delta-radiomics enabling early prediction ofshort-term responses to concurrent chemoradiotherapy for patients withnon-small cell lung cancer

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Radiation Medicine and Protection 2023年第4期4卷 227-235页

作者： Fengqin Zhou Jianping Bi Shen Wu Yi Ding Jun Chen Mengting Yuan Yaoyao He Guang Han Zilong Yuan Department of Radiology Hubei Cancer HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430079China Department of Radiation Oncology Hubei Cancer HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430079China GE Healthiness Shanghai 200126China

Objective:To explore the potential of computed tomography(CT)-based delta-radiomics in predicting early shortterm responses to concurrent chemoradiotherapy for patients with non-small cell lung cancer(NSCLC),in order to determine the optimal time point for the ***:A total of 20 patients with pathologically confirmed NSCLC were prospectively enrolled in this study,who did not receive surgical treatment between February 2021 and February *** each case,a total of 1,210 radiomic features(RFs)were extracted from both planning CT(pCT)images along with each of the subsequent three weeks of CT ***ΔRFs were selected using intra-class correlation coefficient(ICC)analysis,Pearson's correlation,ANOVA test(or Mann-Whitney U-test),and univariate logistic *** area under the curve(AUC)of the receiver operating characteristic(ROC)curve was used to evaluate the potential to predict short-term responses of different time ***:Among the 1,210ΔRFs for 1-3 weeks,121 common features were retained after processing using ICC analysis and Pearson's *** retained features included 54 and 58 of all time points that differed significantly between the response and non-response groups for the first and third months,respectively(Pdelta-radiomics has the potential to provide reasonable biomarkers of short-term responses to concurrent chemoradiotherapy for NSCLC patients,and it can help improve clinical decisions for early treatment adaptation.

关键词： Non-small cell lung cancer delta-radiomics Short-term response Computed tomography Concurrent chemoradiotherapy

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Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis

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World Journal of Gastroenterology 2024年第15期30卷 2128-2142页

作者： Gui-Xiang Qian Zi-Ling Xu Yong-Hai Li Jian-Lin Lu Xiang-Yi Bu Ming-Tong Wei Wei-Dong Jia Department of Hepatic Surgery Anhui Provincial HospitalThe First Affiliated Hospital of USTCDivision of Life Science and MedicineUniversity of Science and Technology of ChinaHefei 230001Anhui ProvinceChina Department of Hepatic Surgery Anhui Provincial Hospital Affiliated to Anhui Medical UniversityAnhui Medical UniversityHefei 230001Anhui ProvinceChina Department of Anorectal Surgery the First People’s Hospital of HefeiHefei 230001Anhui ProvinceChina

BACKGROUND The prognosis for hepatocellular carcinoma(HCC)in the presence of cirrhosis is unfavourable,primarily attributable to the high incidence of *** To develop a machine learning model for predicting early recurrence(ER)of posthepatectomy HCC in patients with cirrhosis and to stratify patients’overall survival(OS)based on the predicted risk of *** In this retrospective study,214 HCC patients with cirrhosis who underwent curative hepatectomy were *** feature selection was conducted using the least absolute shrinkage and selection operator and recursive feature elimination ***-radiologic features were selected through univariate and multivariate logistic regression *** machine learning methods were used for model comparison,aiming to identify the optimal *** model’s performance was evaluated using the receiver operating characteristic curve[area under the curve(AUC)],calibration,and decision curve ***,the Kaplan-Meier(K-M)curve was used to evaluate the stratification effect of the model on patient *** Within this study,the most effective predictive performance for ER of post-hepatectomy HCC in the background of cirrhosis was demonstrated by a model that integrated radiomics features and clinical-radiologic *** the training cohort,this model attained an AUC of 0.844,while in the validation cohort,it achieved a value of *** K-M curves illustrated that the combined model not only facilitated risk stratification but also exhibited significant discriminatory ability concerning patients’*** The combined model,integrating both radiomics and clinical-radiologic characteristics,exhibited excellent performance in HCC with *** K-M curves assessing OS revealed statistically significant differences.

关键词： Machine learning radiomics Hepatocellular carcinoma Cirrhosis Early recurrence Overall survival Computed tomography Prognosis Risk factor delta-radiomics

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