CT pulmonary angiography-based scoring system to predict the prognosis of acute pulmonary embolism

Kumamaru, Kanako K.; Saboo, Sachin S.; Aghayev, Ayaz; Cai, Phoebe; Quesada, Carlos Gonzalez; George, Elizabeth; Hussain, Zoha; Cai, Tianrun; Rybicki, Frank J.

doi:10.1016/j.jcct.2016.08.007

Cited by 33 publications

(25 citation statements)

References 22 publications

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“…For the binary logistic regression model, the residual sum of squares is replaced by the negative log-likelihood. If the l is large, there is no effect on the estimated regression parameters, but as the l gets smaller, some coefficients may be shrunk towards zero (29,30). We then selected the l for which the cross-validation error is the smallest.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

Zhang

Tian

Dong

et al. 2017

Clinical Cancer Research

430

351

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Purpose: To identify MRI-based radiomics as prognostic factors in patients with advanced nasopharyngeal carcinoma (NPC).Experimental Design: One-hundred and eighteen patients (training cohort: n ¼ 88; validation cohort: n ¼ 30) with advanced NPC were enrolled. A total of 970 radiomics features were extracted from T2-weighted (T2-w) and contrast-enhanced T1-weighted (CET1-w) MRI. Least absolute shrinkage and selection operator (LASSO) regression was applied to select features for progression-free survival (PFS) nomograms. Nomogram discrimination and calibration were evaluated. Associations between radiomics features and clinical data were investigated using heatmaps.Results: The radiomics signatures were significantly associated with PFS. A radiomics signature derived from joint CET1-w and T2-w images showed better prognostic performance than signatures derived from CET1-w or T2-w images alone. One radiomics nomogram combined a radiomics signature from joint CET1-w and T2-w images with the TNM staging system. This nomogram showed a significant improvement over the TNM staging system in terms of evaluating PFS in the training cohort (C-index, 0.761 vs. 0.514; P < 2.68 Â 10 À9 ). Another radiomics nomogram integrated the radiomics signature with all clinical data, and thereby outperformed a nomogram based on clinical data alone (C-index, 0.776 vs. 0.649; P < 1.60 Â 10 À7 ). Calibration curves showed good agreement. Findings were confirmed in the validation cohort. Heatmaps revealed associations between radiomics features and tumor stages. Conclusions: Multiparametric MRI-based radiomics nomograms provided improved prognostic ability in advanced NPC. These results provide an illustrative example of precision medicine and may affect treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

Zhang

Tian

Dong

et al. 2017

Clinical Cancer Research

430

351

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“…Therefore, we used lasso, which was shown to be an effective machine-learning algorithm to avoid over-fitting and select features that most significantly associated with the outcome. The radiomics features obtained from lasso are generally accurate, and the regression coefficients of most features are shrunk towards zero during model fitting, making the model easier to interpret [ 16 , 17 ]. In this present study, to develop the best radiomics signature, a total of 970 candidate features were reduced to a set of only five potential predictors by using a lasso logistic regression model.…”

Section: Discussionmentioning

confidence: 99%

Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma

et al. 2017

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There is no consensus on specific prognostic biomarkers potentially improving survival of nasopharyngeal carcinoma (NPC), especially in advanced-stage disease. The prognostic value of MRI-based radiomics signature is unclear. A total of 970 quantitative features were extracted from the tumor of 100 untreated NPC patients (stage III-IVb) (discovery set: n = 70, validation set: n = 30). We then applied least absolute shrinkage and selection operator (lasso) regression to select features that were most associated with progression-free survival (PFS). Candidate prognostic biomarkers included age, gender, overall stage, hemoglobin, platelet counts and radiomics signature. We developed model 1 (without radiomics signature) and model 2 (with radiomics signature) in the discovery set and then tested in the validation set. Multivariable Cox regression analysis was used to yield hazard ratio (HR) of each potential biomarker. We found the radiomics signature stratified patients in the discovery set into a low or high risk group for PFS (HR = 5.14, p < 0.001) and was successfully validated for patients in the validation set (HR = 7.28, p = 0.015). However, the other risk factors showed no significantly prognostic value (all p-values for HR, > 0.05). Accordingly, pretreatment MRI-based radiomics signature is a non-invasive and cost-effective prognostic biomarker in advanced NPC patients, which would improve decision-support in cancer care.

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“…The binomial deviance in the logistic regression model fitting method was used as the criterion to select the best value of l (18,19). The iterative selection process was undertaken by conducting 10-fold cross-validation method 100 times.…”

Section: Radiomics Features Selection and Radiomics Signature Buildingmentioning

confidence: 99%

A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors

Liang

Yang

Huang

et al. 2019

Clinical Cancer Research

154

110

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Purpose: The purpose of this study is to develop and validate a nomogram model combing radiomics features and clinical characteristics to preoperatively differentiate grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (pNET). Experimental Design: A total of 137 patients who underwent contrast-enhanced CT from two hospitals were included in this study. The patients from the second hospital (n ¼ 51) were selected as an independent validation set. The arterial phase in contrast-enhanced CT was selected for radiomics feature extraction. The Mann-Whitney U test and least absolute shrinkage and selection operator regression were applied for feature selection and radiomics signature construction. A combined nomogram model was developed by incorporating the radiomics signature with clinical factors. The association between the nomogram model and the Ki-67 index and rate of nuclear mitosis were also investigated respectively. The utility of the proposed model was evaluated using the ROC, area under ROC curve (AUC), calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was used for survival analysis. Results: An eight-feature-combined radiomics signature was constructed as a tumor grade predictor. The nomogram model combining the radiomics signature with clinical stage showed the best performance (training set: AUC ¼ 0.907; validation set: AUC ¼ 0.891). The calibration curve and DCA demonstrated the clinical usefulness of the proposed nomogram. A significant correlation was observed between the developed nomogram and Ki-67 index and rate of nuclear mitosis, respectively. The KM analysis showed a significant difference between the survival of predicted grade 1 and grade 2/3 groups (P ¼ 0.002). Conclusions: The combined nomogram model developed could be useful in differentiating grade 1 and grade 2/3 tumor in patients with pNETs.

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CT pulmonary angiography-based scoring system to predict the prognosis of acute pulmonary embolism

Cited by 33 publications

References 22 publications

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma

A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors

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