Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer

Yue, Xiaoning; He, XiaoYu; He, Shuaijie; Wu, Jingjing; Wang, Fan; Zhang, Haijun; Wang, Chengwei

doi:10.3389/fonc.2023.1081134

Cited by 8 publications

(1 citation statement)

References 33 publications

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“…According to dynamic contrast enhanced-MRI and apparent diffusion coe cient maps, radiomics features can be used as imaging biomarkers to con rm Ki-67 status in patients with breast cancer [40]. Based on multi-parametric MRI, a radiomic nomogram can predict the tumor grade to guide the dilation and surgery model in endometrial cancer [41]. The multi-layer perceptron model, including clinical features, mutation status of the epidermal growth factor receptor, and radiomics, effectively predicted the risk of death, which can promote the individualized management of patients with lung cancer and brain metastases [42].…”

Section: Discussionmentioning

confidence: 99%

Magnetic resonance imaging-based machine learning radiomics predicts CCND1 expression level and survival in low-grade gliomas

Zhao

Zhang

Lin

et al. 2023

Preprint

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Low-grade glioma (LGG) is associated with increased mortality owing to the recrudescence and tendency for malignant transformation. Therefore, novel prognostic biomarkers must be identified as the current traditional prognostic biomarkers of glioma, including clinicopathological features and imaging examinations, are unable to meet the clinical demand for precision medicine. Accordingly, we aimed to evaluate the prognostic value of cyclin D1 (CCND1) expression levels and construct radiomic models to predict these levels in patients with LGG. A total of 412 LGG cases from The Cancer Genome Atlas (TCGA) were used for gene-based prognostic analysis. Using magnetic resonance imaging (MRI) images stored in The Cancer Imaging Archive with genomic data from TCGA, 149 cases were selected for radiomics feature extraction and model construction. After feature extraction, the radiomic signature was constructed using logistic regression (LR) and support vector machine (SVM) analyses. Involved in the regulation of the cell cycle and immune response, CCND1 was identified as a differentially expressed prognosis-related gene in tumor and normal samples. Landmark analysis revealed that high expression levels of CCND1 were beneficial for survival (P < 0.05) in advanced LGG. Four optimal radiomics features were selected to construct radiomics models. The performance of LR and SVM achieved areas under the curve of 0.703, 0.705, as well as 0.724 and 0.726 in the training and validation sets, respectively. CCND1 expression levels could affect the prognosis of patients with LGG. MRI-based radiomics can serve as a novel tool for predicting the prognosis.

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Section: Discussionmentioning

confidence: 99%

Magnetic resonance imaging-based machine learning radiomics predicts CCND1 expression level and survival in low-grade gliomas

Zhao

Zhang

Lin

et al. 2023

Preprint

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Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools

Russo,

Bottazzi,

Kocak

et al. 2024

Eur Radiol

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Objective To assess the methodological quality of radiomics-based models in endometrial cancer using the radiomics quality score (RQS) and METhodological radiomICs score (METRICS). Methods We systematically reviewed studies published by October 30th, 2023. Inclusion criteria were original radiomics studies on endometrial cancer using CT, MRI, PET, or ultrasound. Articles underwent a quality assessment by novice and expert radiologists using RQS and METRICS. The inter-rater reliability for RQS and METRICS among radiologists with varying expertise was determined. Subgroup analyses were performed to assess whether scores varied according to study topic, imaging technique, publication year, and journal quartile. Results Sixty-eight studies were analysed, with a median RQS of 11 (IQR, 9–14) and METRICS score of 67.6% (IQR, 58.8–76.0); two different articles reached maximum RQS of 19 and METRICS of 90.7%, respectively. Most studies utilised MRI (82.3%) and machine learning methods (88.2%). Characterisation and recurrence risk stratification were the most explored outcomes, featured in 35.3% and 19.1% of articles, respectively. High inter-rater reliability was observed for both RQS (ICC: 0.897; 95% CI: 0.821, 0.946) and METRICS (ICC: 0.959; 95% CI: 0.928, 0.979). Methodological limitations such as lack of external validation suggest areas for improvement. At subgroup analyses, no statistically significant difference was noted. Conclusions Whilst using RQS, the quality of endometrial cancer radiomics research was apparently unsatisfactory, METRICS depicts a good overall quality. Our study highlights the need for strict compliance with quality metrics. Adhering to these quality measures can increase the consistency of radiomics towards clinical application in the pre-operative management of endometrial cancer. Clinical relevance statement Both the RQS and METRICS can function as instrumental tools for identifying different methodological deficiencies in endometrial cancer radiomics research. However, METRICS also reflected a focus on the practical applicability and clarity of documentation. Key Points The topic of radiomics currently lacks standardisation, limiting clinical implementation. METRICS scores were generally higher than the RQS, reflecting differences in the development process and methodological content. A positive trend in METRICS score may suggest growing attention to methodological aspects in radiomics research.

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An ultrasound-based radiomics model for survival prediction in patients with endometrial cancer

et al. 2023

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Purpose To establish a nomogram integrating radiomics features based on ultrasound images and clinical parameters for predicting the prognosis of patients with endometrial cancer (EC). Materials and methods A total of 175 eligible patients with ECs were enrolled in our study between January 2011 and April 2018. They were divided into a training cohort (n = 122) and a validation cohort (n = 53). Least absolute shrinkage and selection operator (LASSO) regression were applied for selection of key features, and a radiomics score (rad-score) was calculated. Patients were stratified into high risk and low-risk groups according to the rad-score. Univariate and multivariable COX regression analysis was used to select independent clinical parameters for disease-free survival (DFS). A combined model based on radiomics features and clinical parameters was ultimately established, and the performance was quantified with respect to discrimination and calibration. Results Nine features were selected from 1130 features using LASSO regression in the training cohort, which yielded an area under the curve (AUC) of 0.823 and 0.792 to predict DFS in the training and validation cohorts, respectively. Patients with a higher rad-score were significantly associated with worse DFS. The combined nomogram, which was composed of clinically significant variables and radiomics features, showed a calibration and favorable performance for DFS prediction (AUC 0.893 and 0.885 in the training and validation cohorts, respectively). Conclusion The combined nomogram could be used as a tool in predicting DFS and may assist individualized decision making and clinical treatment.

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Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer

Cited by 8 publications

References 33 publications

Magnetic resonance imaging-based machine learning radiomics predicts CCND1 expression level and survival in low-grade gliomas

Magnetic resonance imaging-based machine learning radiomics predicts CCND1 expression level and survival in low-grade gliomas

Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools

An ultrasound-based radiomics model for survival prediction in patients with endometrial cancer

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