Gastric cancer(GC)remains a major global health challenge,because of its poor prognosis and limited treatment options in advanced stages1,2.Recent advancements in immunotherapy,highlighted by the findings of the CHECK...Gastric cancer(GC)remains a major global health challenge,because of its poor prognosis and limited treatment options in advanced stages1,2.Recent advancements in immunotherapy,highlighted by the findings of the CHECKMATE-649,ORIENT-16,and KEYNOTE-859 trials,have markedly transformed the treatment paradigm for advanced gastric cancer(AGC)3-5.展开更多
Background:Pneumonia-like primary pulmonary lymphoma(PPL)was commonly misdiagnosed as infectious pneumonia,leading to delayed treatment.The purpose of this study was to establish a computed tomography(CT)-based radiom...Background:Pneumonia-like primary pulmonary lymphoma(PPL)was commonly misdiagnosed as infectious pneumonia,leading to delayed treatment.The purpose of this study was to establish a computed tomography(CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia.Methods:In this retrospective study,79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled.Patients from center 1 to center 7 were assigned to the training or validation cohort,and the remaining patients from other centers were used as the external test cohort.Radiomics features were extracted from CT images.A three-step procedure was applied for radiomics feature selection and radiomics signature building,including the inter-and intra-class correlation coefficients(ICCs),a one-way analysis of variance(ANOVA),and least absolute shrinkage and selection operator(LASSO).Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model.Two radiologists reviewed the CT images for the external test set.Performance of the radiomics model,clinical factor model,and each radiologist were assessed by receiver operating characteristic,and area under the curve(AUC)was compared.Results:A total of 144 patients(44 with pneumonia-like PPL and 100 infectious pneumonia)were in the training cohort,38 patients(12 with pneumonia-like PPL and 26 infectious pneumonia)were in the validation cohort,and 73 patients(23 with pneumonia-like PPL and 50 infectious pneumonia)were in the external test cohort.Twenty-three radiomics features were selected to build the radiomics model,which yielded AUCs of 0.95(95%confidence interval[CI]:0.94-0.99),0.93(95%CI:0.85-0.98),and 0.94(95%CI:0.87-0.99)in the training,validation,and external test cohort,respectively.The AUCs for the two readers and clinical factor model were 0.74(95%CI:0.63-0.83),0.72(95%CI:0.62-0.82),and 0.73(95%CI:0.62-0.84)in the external test cohort,respectively.The radiomics model outperformed both the readers’interpretation and clinical factor model(P<0.05).Conclusions:The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia,which might provide assistance for clinicians in tailoring precise therapy.展开更多
基金supported by The National Key Research and Development Program of China(Grant no.2021YFA0910100)Healthy Zhejiang One Million People Cohort(Grant no.K-20230085)+5 种基金Post-doctoral Innovative Talent Support Program(Grant no.BX2023375)Lingyan Project of Zhejiang Provincial Department of Science and Technology(Grant no.2025C02059)the National Natural Science Foundation of China(Grant nos.82304946,82473489,and 82403546)Natural Science Foundation of Zhejiang Province(Grant nos.LR21H280001,LGF22H160056,ZCLQN25H1602,and LMS25H160006)Medicine and Health Science Fund of Zhejiang Province Health Commission(Grant nos.2025KY047 and 2022KY658)Traditional Chinese Medicine Science and Technology Project of Zhejiang Provincial Health Commission(Grant no.2022ZA023).
文摘Gastric cancer(GC)remains a major global health challenge,because of its poor prognosis and limited treatment options in advanced stages1,2.Recent advancements in immunotherapy,highlighted by the findings of the CHECKMATE-649,ORIENT-16,and KEYNOTE-859 trials,have markedly transformed the treatment paradigm for advanced gastric cancer(AGC)3-5.
基金National Natural Science Foundation of China(Nos.81871354 and 81571672)Academic Promotion Program of Shandong First Medical University(No.2019QL023)
文摘Background:Pneumonia-like primary pulmonary lymphoma(PPL)was commonly misdiagnosed as infectious pneumonia,leading to delayed treatment.The purpose of this study was to establish a computed tomography(CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia.Methods:In this retrospective study,79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled.Patients from center 1 to center 7 were assigned to the training or validation cohort,and the remaining patients from other centers were used as the external test cohort.Radiomics features were extracted from CT images.A three-step procedure was applied for radiomics feature selection and radiomics signature building,including the inter-and intra-class correlation coefficients(ICCs),a one-way analysis of variance(ANOVA),and least absolute shrinkage and selection operator(LASSO).Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model.Two radiologists reviewed the CT images for the external test set.Performance of the radiomics model,clinical factor model,and each radiologist were assessed by receiver operating characteristic,and area under the curve(AUC)was compared.Results:A total of 144 patients(44 with pneumonia-like PPL and 100 infectious pneumonia)were in the training cohort,38 patients(12 with pneumonia-like PPL and 26 infectious pneumonia)were in the validation cohort,and 73 patients(23 with pneumonia-like PPL and 50 infectious pneumonia)were in the external test cohort.Twenty-three radiomics features were selected to build the radiomics model,which yielded AUCs of 0.95(95%confidence interval[CI]:0.94-0.99),0.93(95%CI:0.85-0.98),and 0.94(95%CI:0.87-0.99)in the training,validation,and external test cohort,respectively.The AUCs for the two readers and clinical factor model were 0.74(95%CI:0.63-0.83),0.72(95%CI:0.62-0.82),and 0.73(95%CI:0.62-0.84)in the external test cohort,respectively.The radiomics model outperformed both the readers’interpretation and clinical factor model(P<0.05).Conclusions:The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia,which might provide assistance for clinicians in tailoring precise therapy.
