With the support by the National Natural Science Foundation of China,the research team jointly led by Porf.Liu Zaiyi(刘再毅)at Guangdong General Hospital and Prof.Tian Jie(田捷)at the Key Laboratory of Molecular Imagi...With the support by the National Natural Science Foundation of China,the research team jointly led by Porf.Liu Zaiyi(刘再毅)at Guangdong General Hospital and Prof.Tian Jie(田捷)at the Key Laboratory of Molecular Imaging,Chinese Academy of Sciences,developed a CT-based radiomics prediction model to preoperatively predict the lymph node metastasis in colorectal cancer(CRC),which was published展开更多
BACKGROUND Perineural invasion(PNI),as a key pathological feature of tumor spread,has emerged as an independent prognostic factor in patients with rectal cancer(RC).The preoperative stratification of RC patients accor...BACKGROUND Perineural invasion(PNI),as a key pathological feature of tumor spread,has emerged as an independent prognostic factor in patients with rectal cancer(RC).The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis.However,the preoperative evaluation of PNI status is still challenging.AIM To establish a radiomics model for evaluating PNI status preoperatively in RC patients.METHODS This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019.These patients were classified as the training cohort(n=242)and validation cohort(n=61)at a ratio of 8:2.A large number of intraand peritumoral radiomics features were extracted from portal venous phase images of computed tomography(CT).After deleting redundant features,we tested different feature selection(n=6)and machine-learning(n=14)methods to form 84 classifiers.The best performing classifier was then selected to establish Rad-score.Finally,the clinicoradiological model(combined model)was developed by combining Rad-score with clinical factors.These models for predicting PNI were compared using receiver operating characteristic curve(ROC)analysis and area under the ROC curve(AUC).RESULTS One hundred and forty-four of the 303 patients were eventually found to be PNIpositive.Clinical factors including CT-reported T stage(cT),N stage(cN),and carcinoembryonic antigen(CEA)level were independent risk factors for predicting PNI preoperatively.We established Rad-score by logistic regression analysis after selecting features with the L1-based method.The combined model was developed by combining Rad-score with cT,cN,and CEA.The combined model showed good performance to predict PNI status,with an AUC of 0.828[95%confidence interval(CI):0.774-0.873]in the training cohort and 0.801(95%CI:0.679-0.892)in the validation cohort.For comparison of the models,the combined model achieved a higher AUC than the clinical model(cT+cN+CEA)achieved(P<0.001 in the training cohort,and P=0.045 in the validation cohort).CONCLUSION The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.展开更多
Background:Infiltration is important for the surgical planning and prognosis of pituitary adenomas.Differences in preoperative diagnosis have been noted.The aim of this article is to assess the accuracy of machine lea...Background:Infiltration is important for the surgical planning and prognosis of pituitary adenomas.Differences in preoperative diagnosis have been noted.The aim of this article is to assess the accuracy of machine learning analysis of texture-derived parameters of pituitary adenoma obtained from preoperative MRI for the prediction of high infiltration.Methods:A total of 196 pituitary adenoma patients(training set:n=176;validation set:n=20)were enrolled in this retrospective study.In total,4120 quantitative imaging features were extracted from CE-T1 MR images.To select the most informative features,the least absolute shrinkage and selection operator(LASSO)and variance threshold method were performed.The linear support vector machine(SVM)was used to fit the predictive model based on infiltration features.Furthermore,the receiver operating characteristic curve(ROC)was generated,and the diagnostic performance of the model was evaluated by calculating the area under the curve(AUC),accuracy,precision,recall,and F1 value.Results:A variance threshold of 0.85 was used to exclude 16 features with small differences using the LASSO algorithm,and 19 optimal features were finally selected.The SVM models for predicting high infiltration yielded an AUC of 0.86(sensitivity:0.81,specificity 0.79)in the training set and 0.73(sensitivity:0.87,specificity:0.80)in the validation set.The four evaluation indicators of the predictive model achieved good diagnostic capabilities in the training set(accuracy:0.80,precision:0.82,recall:0.81,F1 score:0.81)and independent verification set(accuracy:0.85,precision:0.93,recall:0.87,F1 score:0.90).Conclusions:The radiomics model developed in this study demonstrates efficacy for the prediction of pituitary adenoma infiltration.This model could potentially aid neurosurgeons in the preoperative prediction of infiltration in PAs and contribute to the selection of ideal surgical strategies.展开更多
Background:The preoperative prediction of muscular invasion status is important for adequately treating bladder cancer(BC)but nevertheless,there are some existing dilemmas in the current preoperative diagnostic accura...Background:The preoperative prediction of muscular invasion status is important for adequately treating bladder cancer(BC)but nevertheless,there are some existing dilemmas in the current preoperative diagnostic accuracy of BC with muscular invasion.Here,we investigated the potential association between the fluorescence in situ hybridization(FISH)assay and muscular invasion among patients with BC.A cytogenetic-clinical nomogram for the individualized preoperative differentiation of muscle-invasive BC(MIBC)from non-muscle-invasive BC(NMIBC)is also proposed.Methods:All eligible BC patients were preoperatively tested using a FISH assay,which included 4 sites(chromosome-specific centromeric probe[CSP]3,7,and 17,and gene locus-specific probe[GLP]-p16 locus).The correlation between the FISH assay and BC muscular invasion was evaluated using the Chi-square tests.In the training set,univariate and multivariate logistic regression analyses were used to develop a cytogenetic-clinical nomogram for preoperative muscular invasion prediction.Then,we assessed the performance of the nomogram in the training set with respect to its discriminatory accuracy and calibration for predicting muscular invasion,and clinica usefulness,which were then validated in the validation set.Moreover,model comparison was set to evaluate the discrimination and clinical usefulness between the nomogram and the individual variables incorporated in the nomogram.Results:Muscular invasion was more prevalent in BC patients with positive CSP3,CSP7 and CSP17 status(OR[95%CI],2.724[1.555 to 4.774],P<0.001;3.406[1.912 to 6.068],P<0.001 and 2.483[1.436 to 4.292],P=0.001,respectively).Radiologydetermined tumor size,radiology-determined clinical tumor stage and CSP7 status were identified as independent risk factors of BC muscular invasion by the multivariate regression analysis in the training set.Then,a cytogenetic-clinical nomogram incorporating these three independent risk factors was constructed and was observed to have satisfactory discrimination in the training(AUC 0.784;95%CI:0.715 to 0.853)and validation(AUC 0.743;95%CI:0.635 to 0.850)set.The decision curve analysis(DCA)indicated the clinical usefulness of our nomogram.In models comparison,using the receiver operator characteristic(ROC)analyses,the nomogram showed higher discriminatory accuracy than any variables incorporated in the nomogram alone and the DCAs also identified the nomogram as possessing the highest net benefits at wide range of threshold probabilities.Conclusion:CSP7 status was identified as an independent factor for predicting muscular invasion in BC patients and was successfully incorporated in a clinical nomogram combining the results of the FISH assay with clinical risk factors.展开更多
文摘With the support by the National Natural Science Foundation of China,the research team jointly led by Porf.Liu Zaiyi(刘再毅)at Guangdong General Hospital and Prof.Tian Jie(田捷)at the Key Laboratory of Molecular Imaging,Chinese Academy of Sciences,developed a CT-based radiomics prediction model to preoperatively predict the lymph node metastasis in colorectal cancer(CRC),which was published
基金This study was reviewed and approved by the Ethics Committee of West China Hospital of Sichuan University(Approved No.1159).
文摘BACKGROUND Perineural invasion(PNI),as a key pathological feature of tumor spread,has emerged as an independent prognostic factor in patients with rectal cancer(RC).The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis.However,the preoperative evaluation of PNI status is still challenging.AIM To establish a radiomics model for evaluating PNI status preoperatively in RC patients.METHODS This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019.These patients were classified as the training cohort(n=242)and validation cohort(n=61)at a ratio of 8:2.A large number of intraand peritumoral radiomics features were extracted from portal venous phase images of computed tomography(CT).After deleting redundant features,we tested different feature selection(n=6)and machine-learning(n=14)methods to form 84 classifiers.The best performing classifier was then selected to establish Rad-score.Finally,the clinicoradiological model(combined model)was developed by combining Rad-score with clinical factors.These models for predicting PNI were compared using receiver operating characteristic curve(ROC)analysis and area under the ROC curve(AUC).RESULTS One hundred and forty-four of the 303 patients were eventually found to be PNIpositive.Clinical factors including CT-reported T stage(cT),N stage(cN),and carcinoembryonic antigen(CEA)level were independent risk factors for predicting PNI preoperatively.We established Rad-score by logistic regression analysis after selecting features with the L1-based method.The combined model was developed by combining Rad-score with cT,cN,and CEA.The combined model showed good performance to predict PNI status,with an AUC of 0.828[95%confidence interval(CI):0.774-0.873]in the training cohort and 0.801(95%CI:0.679-0.892)in the validation cohort.For comparison of the models,the combined model achieved a higher AUC than the clinical model(cT+cN+CEA)achieved(P<0.001 in the training cohort,and P=0.045 in the validation cohort).CONCLUSION The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.
基金Postdoctoral Innovation Program of Shandong Province(NO.202103064)Linyi People’s Hospital Doctoral Research Foundation(NO.2021LYBS05)
文摘Background:Infiltration is important for the surgical planning and prognosis of pituitary adenomas.Differences in preoperative diagnosis have been noted.The aim of this article is to assess the accuracy of machine learning analysis of texture-derived parameters of pituitary adenoma obtained from preoperative MRI for the prediction of high infiltration.Methods:A total of 196 pituitary adenoma patients(training set:n=176;validation set:n=20)were enrolled in this retrospective study.In total,4120 quantitative imaging features were extracted from CE-T1 MR images.To select the most informative features,the least absolute shrinkage and selection operator(LASSO)and variance threshold method were performed.The linear support vector machine(SVM)was used to fit the predictive model based on infiltration features.Furthermore,the receiver operating characteristic curve(ROC)was generated,and the diagnostic performance of the model was evaluated by calculating the area under the curve(AUC),accuracy,precision,recall,and F1 value.Results:A variance threshold of 0.85 was used to exclude 16 features with small differences using the LASSO algorithm,and 19 optimal features were finally selected.The SVM models for predicting high infiltration yielded an AUC of 0.86(sensitivity:0.81,specificity 0.79)in the training set and 0.73(sensitivity:0.87,specificity:0.80)in the validation set.The four evaluation indicators of the predictive model achieved good diagnostic capabilities in the training set(accuracy:0.80,precision:0.82,recall:0.81,F1 score:0.81)and independent verification set(accuracy:0.85,precision:0.93,recall:0.87,F1 score:0.90).Conclusions:The radiomics model developed in this study demonstrates efficacy for the prediction of pituitary adenoma infiltration.This model could potentially aid neurosurgeons in the preoperative prediction of infiltration in PAs and contribute to the selection of ideal surgical strategies.
基金National Key Research and Development Program of China,Grant/Award Number:2018YFA0902803National Natural Science Foundation of China,Grant/Award Numbers:81825016,81961128027,81772719,81772728+6 种基金Key Areas Research and Development Program of Guangdong,Grant/Award Number:2018B010109006Science and Technology Planning Project of Guang dong Province,Grant/Award Number:2017B020227007Guangdong Special Support Program,Grant/Award Number:2017TX04R246special fund for basic scientific research operating expenses of Sun Yat-sen university,Grant/Award Number:19ykyjs29Key Laboratory of Malignant Tumor Molecular Mechanism and Translational Medicine of Guangzhou Bureau of Science and Information Technology,Grant/Award Number:[2013]163Key Laboratory of Malignant Tumor Gene Regulation and Target Therapy of Guangdong Higher Education Institutes,Grant/Award Number:KLB09001Guangdong Science and Technology Department,Grant/Award Number:2017B030314026。
文摘Background:The preoperative prediction of muscular invasion status is important for adequately treating bladder cancer(BC)but nevertheless,there are some existing dilemmas in the current preoperative diagnostic accuracy of BC with muscular invasion.Here,we investigated the potential association between the fluorescence in situ hybridization(FISH)assay and muscular invasion among patients with BC.A cytogenetic-clinical nomogram for the individualized preoperative differentiation of muscle-invasive BC(MIBC)from non-muscle-invasive BC(NMIBC)is also proposed.Methods:All eligible BC patients were preoperatively tested using a FISH assay,which included 4 sites(chromosome-specific centromeric probe[CSP]3,7,and 17,and gene locus-specific probe[GLP]-p16 locus).The correlation between the FISH assay and BC muscular invasion was evaluated using the Chi-square tests.In the training set,univariate and multivariate logistic regression analyses were used to develop a cytogenetic-clinical nomogram for preoperative muscular invasion prediction.Then,we assessed the performance of the nomogram in the training set with respect to its discriminatory accuracy and calibration for predicting muscular invasion,and clinica usefulness,which were then validated in the validation set.Moreover,model comparison was set to evaluate the discrimination and clinical usefulness between the nomogram and the individual variables incorporated in the nomogram.Results:Muscular invasion was more prevalent in BC patients with positive CSP3,CSP7 and CSP17 status(OR[95%CI],2.724[1.555 to 4.774],P<0.001;3.406[1.912 to 6.068],P<0.001 and 2.483[1.436 to 4.292],P=0.001,respectively).Radiologydetermined tumor size,radiology-determined clinical tumor stage and CSP7 status were identified as independent risk factors of BC muscular invasion by the multivariate regression analysis in the training set.Then,a cytogenetic-clinical nomogram incorporating these three independent risk factors was constructed and was observed to have satisfactory discrimination in the training(AUC 0.784;95%CI:0.715 to 0.853)and validation(AUC 0.743;95%CI:0.635 to 0.850)set.The decision curve analysis(DCA)indicated the clinical usefulness of our nomogram.In models comparison,using the receiver operator characteristic(ROC)analyses,the nomogram showed higher discriminatory accuracy than any variables incorporated in the nomogram alone and the DCAs also identified the nomogram as possessing the highest net benefits at wide range of threshold probabilities.Conclusion:CSP7 status was identified as an independent factor for predicting muscular invasion in BC patients and was successfully incorporated in a clinical nomogram combining the results of the FISH assay with clinical risk factors.
