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 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.

Development and Validation of Nomogram to Predict Very Early Recurrence of Combined Hepatocellular-Cholangiocarcinoma After Hepatic Resection: A Multi-Institutional Study

2021 ◽  
Author(s):  
Yijun Wu ◽  
Hongzhi Liu ◽  
Jianxing Zeng ◽  
Yifan Chen ◽  
Guoxu Fang ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Risk Groups ◽  
Early Recurrence ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Good Discrimination ◽  
Decision Curve Analysis ◽  
High Incidence ◽  
Combined Hepatocellular Cholangiocarcinoma ◽  
Development And Validation

Abstract Background and Objectives Combined hepatocellular cholangiocarcinoma (cHCC) has a high incidence of early recurrence. The objective of this study is to construct a model predicting very early recurrence (VER)(ie, recurrence within 6 months after surgery) of cHCC. Methods 131 consecutive patients from Eastern Hepatobiliary Surgery Hospital served as a development cohort to construct a nomogram predicting VER by using multivariable logistic regression analysis. The model was internally and externally validated in an validation cohort of 90 patients from Mengchao Hepatobiliary Hospital using the C concordance statistic, calibration analysis and decision curve analysis (DCA). Results The VER nomogram contains microvascular invasion(MiVI), macrovascular invasion(MaVI) and CA19-9>25mAU/mL. The model shows good discrimination with C-indexes of 0.77 (95%CI: 0.69 - 0.85 ) and 0.76 (95%CI:0.66 - 0.86) in the development cohort and validation cohort respectively. Decision curve analysis demonstrated that the model are clinically useful and the calibration of our model was favorable. Our model stratified patients into two different risk groups, which exhibited significantly different VER. Conclusions Our model demonstrated favorable performance in predicting VER in cHCC patients.

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.

Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

2016 ◽  
Vol 34 (18) ◽  
pp. 2157-2164 ◽  
Author(s):  
Yan-qi Huang ◽  
Chang-hong Liang ◽  
Lan He ◽  
Jie Tian ◽  
Cui-shan Liang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Lymph Node ◽  
Validation Cohort ◽  
Good Discrimination ◽  
Internal Validation ◽  
Preoperative Prediction ◽  
Individualized Prediction ◽  
Radiomics Signature ◽  
Good Calibration

Purpose To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC). Patients and Methods The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous–phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011. Results The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Conclusion This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.

scholarly journals Predicting Neovascular Glaucoma risk in a Chinese Proliferative Diabetic Retinopathy population: development and assessment of a new predictive nomogram

2020 ◽  
Author(s):  
Xinyue Zhang ◽  
li xu ◽  
Xiaolong Chen
Keyword(s):  
Diabetic Nephropathy ◽  
Regression Model ◽  
Clinical Data ◽  
Risk Model ◽  
Curve Analysis ◽  
Calibration Plot ◽  
Multivariable Logistic Regression Analysis ◽  
Decision Curve Analysis ◽  
Anti Vegf ◽  
Selection Operator

Abstract Background: The aim of this study was to develop and evaluate a postoperative NVG risk nomogram based on the clinical data of a Chinese population of patients with PDR.Methods: A prediction model has been established based on the clinical data of 107 PDR patients who underwent vitrectomy from March,2017 to March,2018 in Shenyang Fourth People’s Hospital, and they were followed up for at least 12 months.The presence or absence of NVG were observed.The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the postoperative NVG risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. C-index, calibration plot, and decision curve analysis were also introduced to evaluate the model. The bootstrapping validation was also utilized to accomplish internal validation.Results: Risk factors screened out by the model included HbAlc level, presence of diabetic nephropathy and anti-VEGF therapy. The model was testified with a satisfying C-index of 0.852 (95% CI: 0.740–0.964).Decision curve analysis showed that the NVG nomogram was clinically useful when intervention was adopted with the NVG possibility threshold of 2%.Conclusion: This novel nomogram revealed that a good control of HbAlc level, absence of diabetic nephropathy and anti-VEGF therapy are prophylactic factors of postoperative NVG in PDR patients.

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 Distinguish Coronavirus Disease 2019 Patients in General Surgery Emergency by CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China

2020 ◽  
Author(s):  
Bangbo Zhao ◽  
Yingxin Wei ◽  
Wenwu Sun ◽  
Cheng Qin ◽  
Xingtong Zhou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Acute Abdomen ◽  
Logistic Regression Analysis ◽  
Validation Cohort ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Decision Curve Analysis ◽  
Multivariable Logistic Regression

ABATRACTIMPORTANCEIn the epidemic, surgeons cannot distinguish infectious acute abdomen patients suspected COVID-19 quickly and effectively.OBJECTIVETo develop and validate a predication model, presented as nomogram and scale, to distinguish infectious acute abdomen patients suspected coronavirus disease 2019 (COVID-19).DESIGNDiagnostic model based on retrospective case series.SETTINGTwo hospitals in Wuhan and Beijing, China.PTRTICIPANTS584 patients admitted to hospital with laboratory confirmed SARS-CoV-2 from 2 Jan 2020 to15 Feb 2020 and 238 infectious acute abdomen patients receiving emergency operation from 28 Feb 2019 to 3 Apr 2020.METHODSLASSO regression and multivariable logistic regression analysis were conducted to develop the prediction model in training cohort. The performance of the nomogram was evaluated by calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and clinical impact curves in training and validation cohort. A simplified screening scale and managing algorithm was generated according to the nomogram.RESULTSSix potential COVID-19 prediction variables were selected and the variable abdominal pain was excluded for overmuch weight. The five potential predictors, including fever, chest computed tomography (CT), leukocytes (white blood cells, WBC), C-reactive protein (CRP) and procalcitonin (PCT), were all independent predictors in multivariable logistic regression analysis (p ≤0.001) and the nomogram, named COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration (C-index of 0.981 (95% CI, 0.963 to 0.999) and AUC of 0.970 (95% CI, 0.961 to 0.982)), which was validated in the validation cohort (C-index of 0.966 (95% CI, 0.960 to 0.972) and AUC of 0.966 (95% CI, 0.957 to 0.975)). Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified into the CIAAD scale.CONCLUSIONSWe established an easy and effective screening model and scale for surgeons in emergency department to distinguish COVID-19 patients from infectious acute abdomen patients. The algorithm based on CIAAD scale will help surgeons manage infectious acute abdomen patients suspected COVID-19 more efficiently.

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 Predicting Neovascular Glaucoma Risk in a Chinese Proliferative Diabetic Retinopathy Population: Development and Assessment of a New Predictive Nomogram

2020 ◽  
Author(s):  
Xinyue Zhang ◽  
Xiaolong Chen ◽  
Li Xu
Keyword(s):  
Diabetic Nephropathy ◽  
Regression Model ◽  
Curve Analysis ◽  
Training Dataset ◽  
Calibration Plot ◽  
Multivariable Logistic Regression Analysis ◽  
Predicting Model ◽  
Decision Curve Analysis ◽  
Anti Vegf ◽  
Selection Operator

Abstract Background: The aim of this study was to develop and internally validate a postoperative NVG risk nomogram in a Chinese population of patients with PDR.Methods: We developed a prediction model based on a training dataset of 107 PDR patients who underwent vitrectomy from March,2017 to March,2018 in Shenyang Fourth People’s Hospital, and they were followed up for at least 12 months. The presence or absence of NVG were observed. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the postoperative NVG risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation.Results: Predictors contained in the prediction nomogram included HbAlc level, presence of diabetic nephropathy and anti-VEGF therapy. The model displayed good discrimination with a C-index of 0.852 (95% CI: 0.740–0.964) and good calibration. High C-index value of 0.849 could still be reached in the interval validation. Decision curve analysis showed that the NVG nomogram was clinically useful when intervention was decided at the NVG possibility threshold of 2%.Conclusion: This novel NVG nomogram incorporating HbAlc level, presence of diabetic nephropathy and anti-VEGF therapy could be conveniently used to facilitate the postoperative NVG risk prediction in PDR patients.

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