Dataset Information

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma.

ABSTRACT:

Objective

To develop a combined nomogram based on preoperative multimodal magnetic resonance imaging (mMRI) and clinical information for predicting recurrence in patients with high-grade serous ovarian carcinoma (HGSOC).

Methods

This retrospective study enrolled 141 patients with clinicopathologically confirmed HGSOC, including 65 patients with recurrence and 76 without recurrence. Radiomics features were extracted from the mMRI images (FS-T2WI, DWI, and T1WI+C). L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select radiomics features. A multivariate logistic regression analysis was used to build the classification models. A nomogram was established by incorporating clinical risk factors and radiomics Radscores. The area under the curve (AUC) of receiver operating characteristics, accuracy, and calibration curves were assessed to evaluate the performance of classification models and nomograms in discriminating recurrence. Kaplan-Meier survival analysis was used to evaluate the associations between the Radscore or clinical factors and disease-free survival (DFS).

Results

One clinical factor and seven radiomics signatures were ultimately selected to establish the predictive model for this study. The AUCs for identifying recurrence in the training and validation cohorts were 0.76 (0.68, 0.84) and 0.67 (0.53, 0.81) with the clinical model, 0.78 (0.71, 0.86) and 0.74 (0.61, 0.86) with the multiradiomics model, and 0.83 (0.77, 0.90) and 0.78 (0.65, 0.90) with the combined nomogram, respectively. The DFS was significantly shorter in the high-risk group than in the low-risk group.

Conclusion

By incorporating radiomics Radscores and clinical factors, we created a radiomics nomogram to preoperatively identify patients with HGSOC who have a high risk of recurrence, which may serve as a potential tool to guide personalized treatment.

SUBMITTER: Li C

PROVIDER: S-EPMC9095390 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma.

Li Cuiping C Wang Hongfei H Chen Yulan Y Fang Mengshi M Zhu Chao C Gao Yankun Y Li Jianying J Dong Jiangning J Wu Xingwang X

Journal of oncology 20220504

<h4>Objective</h4>To develop a combined nomogram based on preoperative multimodal magnetic resonance imaging (mMRI) and clinical information for predicting recurrence in patients with high-grade serous ovarian carcinoma (HGSOC).<h4>Methods</h4>This retrospective study enrolled 141 patients with clinicopathologically confirmed HGSOC, including 65 patients with recurrence and 76 without recurrence. Radiomics features were extracted from the mMRI images (FS-T2WI, DWI, and T1WI+C). L1 regularization ...[more]

PMID: 35571486

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Project description:The purpose of this study was to explore the effectiveness of radiomics based on multisequence MRI in predicting the expression of PD-1/PD-L1 in hepatocellular carcinoma (HCC). One hundred and eight patients with HCC who underwent contrast-enhanced MRI 2 weeks before surgical resection were enrolled in this retrospective study. Corresponding paraffin sections were collected for immunohistochemistry to detect the expression of PD-1 and PD-L1. All patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Univariate and multivariate analyses were used to select potential clinical characteristics related to PD-1 and PD-L1 expression. Radiomics features were extracted from the axial fat-suppression T2-weighted imaging (FS-T2WI) images and the arterial phase and portal venous phase images from the axial dynamic contrast-enhanced MRI, and the corresponding feature sets were generated. The least absolute shrinkage and selection operator (LASSO) was used to select the optimal radiomics features for analysis. Logistic regression analysis was performed to construct single-sequence and multisequence radiomics and radiomic-clinical models. The predictive performance was judged by the area under the receiver operating characteristic curve (AUC) in the training and validation cohorts. In the whole cohort, PD-1 expression was positive in 43 patients, and PD-L1 expression was positive in 34 patients. The presence of satellite nodules served as an independent predictor of PD-L1 expression. The AUC values of the FS-T2WI, arterial phase, portal venous phase and multisequence models in predicting the expression of PD-1 were 0.696, 0.843, 0.863, and 0.946 in the training group and 0.669, 0.792, 0.800 and 0.815 in the validation group, respectively. The AUC values of the FS-T2WI, arterial phase, portal venous phase, multisequence and radiomic-clinical models in predicting PD-L1 expression were 0.731, 0.800, 0.800, 0.831 and 0.898 in the training group and 0.621, 0.743, 0.771, 0.810 and 0.779 in the validation group, respectively. The combined models showed better predictive performance. The results of this study suggest that a radiomics model based on multisequence MRI has the potential to predict the preoperative expression of PD-1 and PD-L1 in HCC, which could become an imaging biomarker for immune checkpoint inhibitor (ICI)-based treatment.

Project description:ObjectiveTo develop and validate a radiomics nomogram for predicting stroke recurrence in symptomatic intracranial atherosclerotic stenosis (SICAS).MethodsThe data of 156 patients with SICAS were obtained from the hospital database. Those with and without stroke recurrence were identified. The 156 patients were separated into a training cohort (n = 110) and a validation cohort (n = 46). Baseline clinical data were collected from our medical records, and plaque radiological features were extracted from vascular wall high-resolution imaging (VW-HRMRI). The imaging sequences included 3D-T1WI-VISTA, T2WI, and 3D-T1WI-VISTA-enhanced imaging. Least absolute shrinkage and selection operator (LASSO) analysis were used to select the radiomics features associated with stroke recurrence. Then, multiple logistic regression analysis of clinical risk factors, radiological features, and radiomics signatures were performed, and a predictive nomogram was constructed to predict the probability of stroke recurrence in SICAS. The performance of the nomogram was evaluated.ResultsDiabetes mellitus, plaque burden, and enhancement ratio were independent risk factors for stroke recurrence [odds ratio (OR) = 1.24, 95% confidence interval (CI): 1.04-3.79, p = 0.018; OR = 1.76, per 10% increase, 95% CI, 1.28-2.41, p < 0.001; and OR = 1.94, 95% CI: 1.27-3.09, p < 0.001]. Five features of 3D-T1WI-VISTA, six features of T2WI, and nine features of 3D-T1WI-VISTA-enhanced images were associated with stroke recurrence. The radiomics signature in 3D-T1WI-VISTA-enhanced images was superior to the radiomics signature of the other two sequences for predicting stroke recurrence in both the training cohort [area under the curve (AUC), 0.790, 95% CI: 0.669-0.894] and the validation cohort (AUC, 0.779, 95% CI: 0.620-0.853). The combination of clinical risk factors, radiological features, and radiomics signature had the best predictive value (AUC, 0.899, 95% CI: 0.844-0.936 in the training cohort; AUC, 0.803, 95% CI: 0.761-0.897 in the validation cohort). The C-index of the nomogram was 0.880 (95% CI: 0.805-0.934) and 0.817 (95% CI: 0.795-0.948), respectively, in the training and validation cohorts. The decision curve analysis further confirmed that the radiomics nomogram had good clinical applicability with a net benefit of 0.458.ConclusionThe radiomics features were helpful to predict stroke recurrence in patients with SICAS. The nomogram constructed by combining clinical high-risk factors, plaque radiological features, and radiomics features is a reliable tool for the individualized risk assessment of predicting the recurrence of SICAS stroke.

Dataset Information

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma.

Objective

Methods

Results

Conclusion

Publications

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma.

Similar Datasets

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