scholarly journals Cervical Spine Osteoradionecrosis or Bone Metastasis after Radiotherapy for Nasopharyngeal Carcinoma? The MRI-Based Radiomics for Characterization

2020 ◽  
Author(s):  
Xi Zhong ◽  
Li Li ◽  
Jinxue Yin ◽  
Bingui Lu ◽  
Wen Han ◽  
...  
Keyword(s):  
Cervical Spine ◽  
Nasopharyngeal Carcinoma ◽  
Clinical Utility ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Contrast Enhanced ◽  
Validation Set ◽  
Selection Operator ◽  
Radiomics Signature ◽  
Good Calibration

Abstract Background To develop and validate a MRI-based radiomics nomogram for differentiation of cervical spine ORN from metastasis after radiotherapy (RT) in nasopharyngeal carcinoma (NPC). Methods A radiomics nomogram was developed in a training set that comprised 46 NPC patients after RT with 95 cervical spine lesions (ORN, n = 51; metastasis, n = 44), and data was gathered from January 2008 to December 2012. 279 radiomics features were extracted from axial contrast-enhanced T1-weighted image (CE-T1WI), a radiomics signature was created by using the least absolute shrinkage and selection operator (LASSO) algorithmin, and a nomogram was developed based on the radiomics scores. The performance of the nomogram was determined in terms of its discrimination, calibration, and clinical utility. An independent validation set contained 25 consecutive patients with 47 lesions (ORN, n = 25; metastasis, n = 22) from January 2013 to December 2015. Results The radiomics signature that comprised eight selected features was significantly associated with the differentiation of cervical spine ORN and metastasis. The nomogram model demonstrated good calibration and discrimination in the training set [AUC, 0.725; 95% confidence interval (CI), 0.622–0.828] and the validation set (AUC, 0.720; 95% CI, 0.573–0.867). Decision curve analysis indicated that the radiomics nomogram was clinically useful. Conclusions MRI-based radiomics nomogram shows potential value to differentiate cervical spine ORN from metastasis after RT in NPC.

scholarly journals Cervical Spine Osteoradionecrosis or Bone Metastasis after Radiotherapy for Nasopharyngeal Carcinoma? The MRI-Based Radiomics for Characterization

2020 ◽  
Author(s):  
Xi Zhong ◽  
Li Li ◽  
Huali Jiang ◽  
Jinxue Yin ◽  
Bingui Lu ◽  
...  
Keyword(s):  
Cervical Spine ◽  
Nasopharyngeal Carcinoma ◽  
Clinical Utility ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Contrast Enhanced ◽  
Validation Set ◽  
Selection Operator ◽  
Radiomics Signature ◽  
Good Calibration

Abstract Background: To develop and validate an MRI-based radiomics nomogram for differentiation of cervical spine ORN from metastasis after radiotherapy (RT) in nasopharyngeal carcinoma (NPC).Methods: A radiomics nomogram was developed in a training set that comprised 46 NPC patients after RT with 95 cervical spine lesions (ORN, n = 51; metastasis, n = 44), and data were gathered from January 2008 to December 2012. 279 radiomics features were extracted from the axial contrast-enhanced T1-weighted image (CE-T1WI). A radiomics signature was created by using the least absolute shrinkage and selection operator (LASSO) algorithm. A nomogram model was developed based on the radiomics scores. The performance of the nomogram was determined in terms of its discrimination, calibration, and clinical utility. An independent validation set contained 25 consecutive patients with 47 lesions (ORN, n = 25; metastasis, n = 22) from January 2013 to December 2015. Results: The radiomics signature that comprised eight selected features was significantly associated with the differentiation of cervical spine ORN and metastasis. The nomogram model demonstrated good calibration and discrimination in the training set [AUC, 0.725; 95% confidence interval (CI), 0.622–0.828] and the validation set (AUC, 0.720; 95% CI, 0.573–0.867). The decision curve analysis indicated that the radiomics nomogram was clinically useful.Conclusions: MRI-based radiomics nomogram shows potential value to differentiate cervical spine ORN from metastasis after RT in NPC.

scholarly journals Cervical Spine Osteoradionecrosis or Bone Metastasis after Radiotherapy for Nasopharyngeal Carcinoma? The MRI-Based Radiomics for Characterization

2020 ◽  
Author(s):  
Xi Zhong ◽  
Li Li ◽  
Huali Jiang ◽  
Jinxue Yin ◽  
Bingui Lu ◽  
...  
Keyword(s):  
Cervical Spine ◽  
Nasopharyngeal Carcinoma ◽  
Clinical Utility ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Contrast Enhanced ◽  
Validation Set ◽  
Selection Operator ◽  
Radiomics Signature ◽  
Good Calibration

Abstract Background: To develop and validate a MRI-based radiomics nomogram for differentiation of cervical spine ORN from metastasis after radiotherapy (RT) in nasopharyngeal carcinoma (NPC).Methods: A radiomics nomogram was developed in a training set that comprised 46 NPC patients after RT with 95 cervical spine lesions (ORN, n = 51; metastasis, n = 44), and data was gathered from January 2008 to December 2012. 279 radiomics features were extracted from axial contrast-enhanced T1-weighted image (CE-T1WI), a radiomics signature was created by using the least absolute shrinkage and selection operator (LASSO) algorithmin, and a nomogram was developed based on the radiomics scores. The performance of the nomogram was determined in terms of its discrimination, calibration, and clinical utility. An independent validation set contained 25 consecutive patients with 47 lesions (ORN, n = 25; metastasis, n = 22) from January 2013 to December 2015. Results: The radiomics signature that comprised eight selected features was significantly associated with the differentiation of cervical spine ORN and metastasis. The nomogram model demonstrated good calibration and discrimination in the training set [AUC, 0.725; 95% confidence interval (CI), 0.622–0.828] and the validation set (AUC, 0.720; 95% CI, 0.573–0.867). Decision curve analysis indicated that the radiomics nomogram was clinically useful.Conclusions: MRI-based radiomics nomogram shows potential value to differentiate cervical spine ORN from metastasis after RT in NPC.

scholarly journals Cervical Spine Osteoradionecrosis or Bone Metastasis after Radiotherapy for Nasopharyngeal Carcinoma? The MRI-Based Radiomics for Characterization

2020 ◽  
Author(s):  
Xi Zhong ◽  
Li Li ◽  
Huali Jiang ◽  
Jinxue Yin ◽  
Bingui Lu ◽  
...  
Keyword(s):  
Cervical Spine ◽  
Nasopharyngeal Carcinoma ◽  
Clinical Utility ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Contrast Enhanced ◽  
Validation Set ◽  
Selection Operator ◽  
Radiomics Signature ◽  
Good Calibration

Abstract Background: To develop and validate an MRI-based radiomics nomogram for differentiation of cervical spine ORN from metastasis after radiotherapy (RT) in nasopharyngeal carcinoma (NPC).Methods: A radiomics nomogram was developed in a training set that comprised 46 NPC patients after RT with 95 cervical spine lesions (ORN, n = 51; metastasis, n = 44), and data were gathered from January 2008 to December 2012. 279 radiomics features were extracted from the axial contrast-enhanced T1-weighted image (CE-T1WI). A radiomics signature was created by using the least absolute shrinkage and selection operator (LASSO) algorithm. A nomogram model was developed based on the radiomics scores. The performance of the nomogram was determined in terms of its discrimination, calibration, and clinical utility. An independent validation set contained 25 consecutive patients with 47 lesions (ORN, n = 25; metastasis, n = 22) from January 2013 to December 2015.Results: The radiomics signature that comprised eight selected features was significantly associated with the differentiation of cervical spine ORN and metastasis. The nomogram model demonstrated good calibration and discrimination in the training set [AUC, 0.725; 95% confidence interval (CI), 0.622–0.828] and the validation set (AUC, 0.720; 95% CI, 0.573–0.867). The decision curve analysis indicated that the radiomics nomogram was clinically useful.Conclusions: MRI-based radiomics nomogram shows potential value to differentiate cervical spine ORN from metastasis after RT in NPC.

scholarly journals A Radiomics nomogram for preoperative predicting recurrence of low grade gliomas based on multiparametric MRI

2021 ◽  
Author(s):  
Zhenhua Wang ◽  
Xinlan Xiao ◽  
Zhaotao Zhang ◽  
Keng He ◽  
Peipei Pang ◽  
...  
Keyword(s):  
Multivariate Logistic Regression Analysis ◽  
Low Grade ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Logistic Analysis ◽  
Contrast Enhanced ◽  
Fluid Attenuated Inversion Recovery ◽  
Validation Set ◽  
The Individual ◽  
Good Agreement

Abstract Objective To develop a radiomics nomogram to predict the recurrence of Low grade glioma(LGG) after their first surgery; Methods A retrospective analysis of pathological, clinical and Magnetic resonance image(MRI) of LGG patients who underwent surgery and had a recurrence between 2017 and 2020 in our hospital was performed. After a rigorous selection,64 patients were eligible and enrolled in the study(22 cases were with recurrent gliomas),which was randomly assigned in a 7:3 ratio to either the training set and validation set; T1WI,T2WI fluid-attenuated-inversion-recovery(T2WI-FLAIR) and contrast-enhanced T1-weighted(T1CE) sequences, 396 radiomics features were extracted from each image sequence, minimum-redundancy maximum-relevancy(mRMR) alone or combining with univariate logistic analysis were used for features screening, the screened features were performed by multivariate logistic regression analysis and developed a predictive model both in training set and validation set; Receiver operating characteristic(ROC) curve, calibration curve, and decision curve analysis(DCA) were used to assess the performance of each model. Results The radiomics nomogram derived from three MRI sequence yielded an ideal performance than the individual ones, the AUC in the training set and validation set were 0.966 and 0.93 respectively, 95% confidence interval(95%CI) were 0.949-0.99 and 0.905-0.973 respectively; the calibration curves indicated good agreement between the predictive and the actual probability. The DCA demonstrated that a combination of three sequences had more favorable clinical predictive value than single sequence imaging. Conclusion Our multiparametric radiomics nomogram could be an efficient and accurate tool for predicting the recurrence of LGG after its first resection.

scholarly journals Establishment and Validation of a Clinically Predictive Nomogram Model for Thyroid Carcinoma Patients

2020 ◽  
Author(s):  
Ruyi Zhang ◽  
Mei Xu ◽  
Xiangxiang Liu ◽  
Miao Wang ◽  
Qiang Jia ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
External Validation ◽  
Cancer Staging ◽  
Curve Analysis ◽  
Calibration Plot ◽  
Net Benefit ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Validation Set ◽  
Good Calibration

Abstract Objectives To develop a clinically predictive nomogram model which can maximize patients’ net benefit in terms of predicting the prognosis of patients with thyroid carcinoma based on the 8th edition of the AJCC Cancer Staging method. MethodsWe selected 134,962 thyroid carcinoma patients diagnosed between 2004 and 2015 from SEER database with details of the 8th edition of the AJCC Cancer Staging Manual and separated those patients into two datasets randomly. The first dataset, training set, was used to build the nomogram model accounting for 80% (94,474 cases) and the second dataset, validation set, was used for external validation accounting for 20% (40,488 cases). Then we evaluated its clinical availability by analyzing DCA (Decision Curve Analysis) performance and evaluated its accuracy by calculating AUC, C-index as well as calibration plot.ResultsDecision curve analysis showed the final prediction model could maximize patients’ net benefit. In training set and validation set, Harrell’s Concordance Indexes were 0.9450 and 0.9421 respectively. Both sensitivity and specificity of three predicted time points (12 Months,36 Months and 60 Months) of two datasets were all above 0.80 except sensitivity of 60-month time point of validation set was 0.7662. AUCs of three predicted timepoints were 0.9562, 0.9273 and 0.9009 respectively for training set. Similarly, those numbers were 0.9645, 0.9329, and 0.8894 respectively for validation set. Calibration plot also showed that the nomogram model had a good calibration.ConclusionThe final nomogram model provided with both excellent accuracy and clinical availability and should be able to predict patients’ survival probability visually and accurately.

scholarly journals Development and validation of a radiomics nomogram for identification of severity of patients with COVID-19

2020 ◽  
Author(s):  
Jian Wang ◽  
Zhihua Xu ◽  
Guohua Cheng ◽  
Qiuxiang Hu ◽  
Linyang He ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Chest Ct ◽  
Multivariable Logistic Regression Analysis ◽  
Good Discrimination ◽  
Decision Curve Analysis ◽  
Selection Operator ◽  
Lasso Method ◽  
Development And Validation ◽  
Radiomics Signature ◽  
Good Calibration

Abstract Background The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severe COVID-19 determines the management and treatment, even prognosis. Thus, we aim to develop and validate a radiomics nomogram for identifying severe patients with COVID-19.Methods There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness.Results The radiomics signature consisting of 4 selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P < 0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts, showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19.Conclusions We present an easy-to-use radiomics nomogram to identify the severe patients with COVID-19 for better guiding a prompt management and treatment.

scholarly journals CT-based Radiomic Nomogram for Predicting the Severity of Patients With COVID-19

2021 ◽  
Author(s):  
Hengfeng Shi ◽  
Zhihua Xu ◽  
Guohua Cheng ◽  
Hongli Ji ◽  
Linyang He ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Chest Ct ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Good Discrimination ◽  
Decision Curve Analysis ◽  
Selection Operator ◽  
Lasso Method ◽  
Radiomics Signature ◽  
Good Calibration

Abstract Background: The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severe COVID-19 determines the management and treatment, even prognosis. We aim to develop and validate a radiomics nomogram for identifying severe patients with COVID-19. To develop and validate a radiomics nomogram for identifying severe patients with COVID-19.Methods: There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness.Results: The radiomics signature consisting of 4 selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P<0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts, showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19.Conclusion: We present an easy-to-use radiomics nomogram to identify the severe patients with COVID-19 for better guiding a prompt management and treatment.

scholarly journals A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Lei Bi ◽  
Yubo Liu ◽  
Jingxu Xu ◽  
Ximing Wang ◽  
Tong Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Multivariate Logistic Regression Model ◽  
Training Set ◽  
Preoperative Prediction ◽  
Validation Set ◽  
Periampullary Carcinomas ◽  
Radiomics Signature

PurposeTo establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.Materials and MethodsA total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation.ResultsThe radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p &lt; 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram.ConclusionOur CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.

scholarly journals Scoring system development and validation for prediction choledocholithiasis before open cholecystectomy

2015 ◽  
Vol 143 (11-12) ◽  
pp. 681-687 ◽  
Author(s):  
Tomislav Pejovic ◽  
Miroslav Stojadinovic
Keyword(s):  
Bile Duct ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Open Cholecystectomy ◽  
Univariate Analysis ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Scoring Model ◽  
Validation Set

Introduction. Accurate precholecystectomy detection of concurrent asymptomatic common bile duct stones (CBDS) is key in the clinical decision-making process. The standard preoperative methods used to diagnose these patients are often not accurate enough. Objective. The aim of the study was to develop a scoring model that would predict CBDS before open cholecystectomy. Methods. We retrospectively collected preoperative (demographic, biochemical, ultrasonographic) and intraoperative (intraoperative cholangiography) data for 313 patients at the department of General Surgery at Gornji Milanovac from 2004 to 2007. The patients were divided into a derivation (213) and a validation set (100). Univariate and multivariate regression analysis was used to determine independent predictors of CBDS. These predictors were used to develop scoring model. Various measures for the assessment of risk prediction models were determined, such as predictive ability, accuracy, the area under the receiver operating characteristic curve (AUC), calibration and clinical utility using decision curve analysis. Results. In a univariate analysis, seven risk factors displayed significant correlation with CBDS. Total bilirubin, alkaline phosphatase and bile duct dilation were identified as independent predictors of choledocholithiasis. The resultant total possible score in the derivation set ranged from 7.6 to 27.9. Scoring model shows good discriminatory ability in the derivation and validation set (AUC 94.3 and 89.9%, respectively), excellent accuracy (95.5%), satisfactory calibration in the derivation set, similar Brier scores and clinical utility in decision curve analysis. Conclusion. Developed scoring model might successfully estimate the presence of choledocholithiasis in patients planned for elective open cholecystectomy.

Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer

2021 ◽  
pp. 20210191
Author(s):  
Liuhui Zhang ◽  
Donggen Jiang ◽  
Chujie Chen ◽  
Xiangwei Yang ◽  
Hanqi Lei ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Multiparametric Mri ◽  
Curve Analysis ◽  
Training Set ◽  
Threshold Probability ◽  
Roc Curve Analysis ◽  
Aggressive Prostate Cancer ◽  
Therapeutic Decisions ◽  
Validation Set ◽  
Radiomics Signature

Objectives: To develop and validate a noninvasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy. Methods: In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (n = 93) and a validation set (n = 46). A total of 1576 radiomics features were extracted from the T2WI (n = 788) and DWI (n = 788) for each patient. The Select K Best and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to construct a radiomics signature in the training set. The predictive performance of the radiomics signature was assessed in the training set and then validated in the validation set by receiver operating characteristic (ROC) curve analysis. We computed the calibration curve and the decision curve to evaluate the calibration and clinical usefulness of the signature. Results: nine radiomics features were identified to form the radiomics signature. The radiomics score (Rad-score) was significantly different between indolent and aggressive PCa (p < 0.001). The radiomics signature exhibited favorable discrimination between the indolent and aggressive PCa groups in the training set (AUC: 0.853, 95% CI: 0.766 to 0.941) and validation set (AUC: 0.901, 95% CI: 0.793 to 1.000). The decision curve analysis showed that a greater net benefit would be obtained when the threshold probability ranged from 20 to 90%. Conclusions: The multiparametric MRI-based radiomics signature can potentially serve as a noninvasive tool for distinguishing between indolent and aggressive PCa prior to therapy. Advances in knowledge: The multiparametric MRI-based radiomics signature has the potential to noninvasively distinguish between the indolent and aggressive PCa, which might aid clinicians in making personalized therapeutic decisions.

Sign in / Sign up

Export Citation Format

Share Document