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 < 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 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 nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer

2020 ◽  
Author(s):  
Hesan Luo ◽  
Shao-Fu Huang ◽  
Hong-Yao Xu ◽  
Xu-Yuan Li ◽  
Sheng-Xi Wu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Squamous Cell ◽  
Cell Cancer ◽  
Complete Response ◽  
Clinical Staging ◽  
Training Set ◽  
Simple Method ◽  
Validation Set ◽  
The Impact ◽  
Radiomics Signature

Abstract Purpose: To develop and validate a nomogram model to predict complete response (CR) after concurrent chemoradiotherapy (CCRT) in esophageal squamous cell carcinoma (ESCC) patients using pretreatment CT radiomic features. Methods: Data of patients diagnosed as ESCC and treated with CCRT in Shantou Central Hospital during the period from January 2013 to December 2015 were retrospectively collected. Eligible patients were included in this study and randomize divided into a training set and a validation set after successive screening. The least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomics features calculating Rad-score in the training set. The logistic regression analysis was performed to identify the predictive clinical factors for developing a nomogram model. The area under the receiver operating characteristic curves (AUC) was used to assess the performance of the predictive nomogram model and decision curve was used to analyze the impact of the nomogram model on clinical treatment decisions. Results: A total of 226 patients were included and randomly divided into two groups, 160 patients in training set and 66 patients in validation set. After LASSO analysis, seven radiomics features were screened out to develop a radiomics signature Rad-score. The AUC of Rad-score was 0.812 (95%CI: 0.742-0.869, p<0.001) in the training set and 0.744 (95%CI: 0.632-0.851, p=0.003) in the validation set. Multivariate analysis showed that Rad-score and clinical staging were independent predictors of CR status, with P values of 0.035 and 0.023, respectively. A nomogram model incorporating Rad-socre and clinical staging was developed and validated, with an AUC of 0.844 (95％CI: 0.779-0.897) in the training set and 0.807 (95％CI: 0.691-0.894) in the validation set．Delong test showed that the nomogram model was significantly superior to the clinical staging, with P<0.001 in the training set and P=0.026 in the validation set. The decision curve showed that the nomogram model was superior to the clinical staging when the risk threshold was greater than 25%. Conclusion: We developed and validated a nomogram model for predicting CR status of ESCC patients after CCRT. The nomogram model was combined radiomics signature Rad-score and clinical staging. This model provided us with an economical and simple method for evaluating the response of chemoradiotherapy for patients with ESCC.

scholarly journals A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer

2020 ◽  
Author(s):  
Hesan Luo ◽  
Shao-Fu Huang ◽  
Hong-Yao Xu ◽  
Xu-Yuan Li ◽  
Sheng-Xi Wu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Squamous Cell ◽  
Cell Cancer ◽  
Complete Response ◽  
Clinical Staging ◽  
Training Set ◽  
Simple Method ◽  
Validation Set ◽  
The Impact ◽  
Radiomics Signature

Abstract Purpose To develop and validate a nomogram model to predict complete response (CR) after concurrent chemoradiotherapy (CCRT) in esophageal squamous cell carcinoma (ESCC) patients using pretreatment CT radiomic features. Methods Data of patients diagnosed as ESCC and treated with CCRT in Shantou Central Hospital during the period from January 2013 to December 2015 were retrospectively collected. Eligible patients were included in this study and randomize divided into a training set and a validation set after successive screening. The least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomics features calculating Rad-score in the training set. The logistic regression analysis was performed to identify the predictive clinical factors for developing a nomogram model. The area under the receiver operating characteristic curves (AUC) was used to assess the performance of the predictive nomogram model and decision curve was used to analyze the impact of the nomogram model on clinical treatment decisions. Results A total of 226 patients were included and randomly divided into two groups, 160 patients in training set and 66 patients in validation set. After LASSO analysis, seven radiomics features were screened out to develop a radiomics signature Rad-score. The AUCs of Rad-score was 0.812 (95%CI: 0.742–0.869) in the training set and 0.744 (95%CI: 0.632–0.851) in the validation set. Multivariate analysis showed that Rad-score and clinical staging were independent predictors of CR status, with P values of 0.035 and 0.023, respectively. A nomogram model incorporating Rad-socre and clinical staging was developed and validated, with an AUC of 0.844 (95%CI: 0.779–0.897) in the training set and 0.807 (95༅CI: 0.691–0.894) in the validation set༎Delong test showed that the nomogram model was significantly superior to the clinical staging, with P < 0.001 in the training set and P = 0.026 in the validation set. The decision curve showed that the nomogram model was superior to the clinical staging when the risk threshold was greater than 25%. Conclusion We developed and validated a nomogram model for predicting CR status of ESCC patients after CCRT. The nomogram model was combined radiomics signature Rad-score and clinical staging. This model provided us with an economical and simple method for evaluating the response of chemoradiotherapy for patients with ESCC.

scholarly journals Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Yun Bian ◽  
Shiwei Guo ◽  
Hui Jiang ◽  
Suizhi Gao ◽  
Chengwei Shao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Validation Cohort ◽  
Ductal Adenocarcinoma ◽  
Arterial Phase ◽  
Multivariable Logistic Regression Analysis ◽  
Preoperative Prediction ◽  
Multivariable Logistic Regression ◽  
Radiomics Signature

Abstract Purpose To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC). Materials and methods In this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA). Results The radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68–0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69–0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful. Conclusions The presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC.

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 A wavelet features derived radiomics nomogram for prediction of malignant and benign early-stage lung nodules

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rui Jing ◽  
Jingtao Wang ◽  
Jiangbing Li ◽  
Xiaojuan Wang ◽  
Baijie Li ◽  
...  
Keyword(s):  
Mayo Clinic ◽  
Predictive Accuracy ◽  
Early Stage ◽  
Multivariate Logistic Regression Model ◽  
Lung Nodules ◽  
Solitary Pulmonary Nodules ◽  
Training Set ◽  
Wavelet Features ◽  
Validation Set ◽  
Radiomics Signature

AbstractThis study was to develop a radiomics nomogram mainly using wavelet features for identifying malignant and benign early-stage lung nodules for high-risk screening. A total of 116 patients with early-stage solitary pulmonary nodules (SPNs) (≤ 3 cm) were divided into a training set (N = 70) and a validation set (N = 46). Radiomics features were extracted from plain LDCT images of each patient. A radiomics signature was then constructed with the LASSO with the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. This radiomics signature, consisting of one original and nine wavelet features, achieved favorable predictive efficacy than Mayo Clinic Model. The radiomics nomogram with radiomics signature and age also showed good calibration and discrimination in the training set (AUC 0.9406; 95% CI 0.8831–0.9982) and the validation set (AUC 0.8454; 95% CI 0.7196–0.9712). The decision curve indicated the clinical usefulness of our nomogram. The presented radiomics nomogram shows favorable predictive accuracy for identifying malignant and benign lung nodules in early-stage patients and is much better than the Mayo Clinic Model.

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 Radiomics Nomogram for Identifying Sub-1 cm Benign and Malignant Thyroid Lesions

2021 ◽  
Vol 11 ◽  
Author(s):  
Xinxin Wu ◽  
Jingjing Li ◽  
Yakui Mou ◽  
Yao Yao ◽  
Jingjing Cui ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Predictive Factors ◽  
Multivariate Logistic Regression Model ◽  
Preoperative Prediction ◽  
Computed Tomography Images ◽  
Contrast Enhanced ◽  
Selection Operator ◽  
Enhanced Computed Tomography ◽  
Radiomics Signature ◽  
Thyroid Lesions

PurposeTo develop and validate a radiomics nomogram for identifying sub-1 cm benign and malignant thyroid lesions.MethodA total of 171 eligible patients with sub-1 cm thyroid lesions (56 benign and 115 malignant) who were treated in Yantai Yuhuangding Hospital between January and September 2019 were retrospectively collected and randomly divided into training (n = 136) and validation sets (n = 35). The radiomics features were extracted from unenhanced and arterial contrast-enhanced computed tomography images of each patient. In the training set, one-way analysis of variance and least absolute shrinkage and selection operator (LASSO) logistic regression were used to select the features related to benign and malignant lesions, and the LASSO algorithm was used to construct the radiomics signature. Combined with clinical independent predictive factors, a radiomics nomogram was constructed with a multivariate logistic regression model. The performance of the radiomics nomogram was evaluated by using the receiver operating characteristic (ROC) and calibration curves in the training and validation sets. The clinical usefulness was evaluated by using decision curve analysis (DCA).ResultsThe radiomics signature consisting of 13 selected features achieved favorable prediction efficiency. The radiomics nomogram, which incorporated radiomics signature and clinical independent predictive factors including age and Thyroid Imaging Reporting and Data System category, showed good calibration and discrimination in the training (area under the ROC [AUC]: 0.853; 95% confidence interval [CI]: 0.797, 0.899) and validation sets (AUC: 0.851; 95% CI: 0.735, 0.931). DCA demonstrated that the nomogram was clinically useful.ConclusionAs a noninvasive preoperative prediction tool, the radiomics nomogram incorporating radiomics signature and clinical predictive factors shows favorable predictive efficiency for identifying sub-1 cm benign and malignant thyroid lesions.

scholarly journals A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
He-San Luo ◽  
Shao-Fu Huang ◽  
Hong-Yao Xu ◽  
Xu-Yuan Li ◽  
Sheng-Xi Wu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Squamous Cell ◽  
Cell Cancer ◽  
Complete Response ◽  
Clinical Staging ◽  
Training Set ◽  
Simple Method ◽  
Validation Set ◽  
The Impact ◽  
Radiomics Signature

Abstract Purpose To develop and validate a nomogram model to predict complete response (CR) after concurrent chemoradiotherapy (CCRT) in esophageal squamous cell carcinoma (ESCC) patients using pretreatment CT radiomic features. Methods Data of patients diagnosed as ESCC and treated with CCRT in Shantou Central Hospital during the period from January 2013 to December 2015 were retrospectively collected. Eligible patients were included in this study and randomize divided into a training set and a validation set after successive screening. The least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomics features calculating Rad-score in the training set. The logistic regression analysis was performed to identify the predictive clinical factors for developing a nomogram model. The area under the receiver operating characteristic curves (AUC) was used to assess the performance of the predictive nomogram model and decision curve was used to analyze the impact of the nomogram model on clinical treatment decisions. Results A total of 226 patients were included and randomly divided into two groups, 160 patients in training set and 66 patients in validation set. After LASSO analysis, seven radiomics features were screened out to develop a radiomics signature Rad-score. The AUC of Rad-score was 0.812 (95% CI 0.742–0.869, p < 0.001) in the training set and 0.744 (95% CI 0.632–0.851, p = 0.003) in the validation set. Multivariate analysis showed that Rad-score and clinical staging were independent predictors of CR status, with p values of 0.035 and 0.023, respectively. A nomogram model incorporating Rad-socre and clinical staging was developed and validated, with an AUC of 0.844 (95% CI 0.779–0.897) in the training set and 0.807 (95% CI 0.691–0.894) in the validation set. Delong test showed that the nomogram model was significantly superior to the clinical staging, with p < 0.001 in the training set and p = 0.026 in the validation set. The decision curve showed that the nomogram model was superior to the clinical staging when the risk threshold was greater than 25%. Conclusion We developed and validated a nomogram model for predicting CR status of ESCC patients after CCRT. The nomogram model was combined radiomics signature Rad-score and clinical staging. This model provided us with an economical and simple method for evaluating the response of chemoradiotherapy for patients with ESCC.

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.

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