Prognostic value of magnetic resonance imaging features in low-grade gliomas

Deng, Liang; Shen, Liangfang; Lin, Senjie; Zhao, Zhao; Peng, Yingpeng; Liu, Hongjiao; Liu, Haipeng; Zhang, Guangying; Li, Zhanzhan; Li, Kai; Shen, Erdong; Liu, Yuanyuan; Liu, Chao; Huang, Xinqiong

doi:10.1042/bsr20190544

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…identified immune-related subtypes to select optimally patients suffering from LGG responsive to immunotherapy (28). A retrospective study suggested MRI feature that cyst formation on preoperative MR images was used to predict a favorable prognosis in patients with LGG (29). In a study of more than 1, 0000 people with LGG, researchers noted radiotherapy improved survival outcomes (5).…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Radiosensitivity Prediction Model for Lower Grade Glioma Based on Spike-and-Slab Lasso

Cai

Yan

et al. 2021

Front. Oncol.

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Background and PurposeLower grade glioma (LGG) is one of the leading causes of death world worldwide. We attempted to develop and validate a radiosensitivity model for predicting the survival of lower grade glioma by using spike-and-slab lasso Cox model.MethodsIn this research, differentially expressed genes based on tumor microenvironment was obtained to further analysis. Log-rank test was used to identify genes in patients who received radiotherapy and patients who did not receive radiotherapy, respectively. Then, spike-and-slab lasso was performed to select genes in patients who received radiotherapy. Finally, three genes (INA, LEPREL1 and PTCRA) were included in the model. A radiosensitivity-related risk score model was established based on overall rate of TCGA dataset in patients who received radiotherapy. The model was validated in TCGA dataset that PFS as endpoint and two CGGA datasets that OS as endpoint. A novel nomogram integrated risk score with age and tumor grade was developed to predict the OS of LGG patients.ResultsWe developed and verified a radiosensitivity-related risk score model. The radiosensitivity-related risk score is served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, the nomogram integrated risk score with age and tumor grade was established to perform better for predicting 1, 3, 5-year survival rate.ConclusionsThis model can be used by clinicians and researchers to predict patient’s survival rates and achieve personalized treatment of LGG.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Radiosensitivity Prediction Model for Lower Grade Glioma Based on Spike-and-Slab Lasso

Cai

Yan

et al. 2021

Front. Oncol.

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“…Firstly, we did not consider the tumor size and intratumor features, such as necrosis size and texture features extracted from images. Previous studies showed no relationship between tumor size, necrosis size and patient survival [39][40][41]. It is challengeable for texture features used in predicting survival outcomes due to unsolved issued of reproducibility and interpretability before application in clinical setting.…”

Section: Discussionmentioning

confidence: 99%

Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery

et al. 2021

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Background: To develop machine-learning based models to predict the progression-free survival (PFS) and overall survival (OS) in patients with gliomas and explore the effect of different feature selection methods on the prediction. Methods: We included 505 patients (training cohort, n = 354; validation cohort, n = 151) with gliomas between January 1, 2011 and December 31, 2016. The clinical, neuroimaging, and molecular genetic data of patients were retrospectively collected. The multi-causes discovering with structure learning (McDSL) algorithm, least absolute shrinkage and selection operator regression (LASSO), and Cox proportional hazards regression model were employed to discover the predictors for 3-year PFS and OS, respectively. Eight machine learning classifiers with 5-fold cross-validation were developed to predict 3-year PFS and OS. The area under the curve (AUC) was used to evaluate the prognostic performance of classifiers. Results: McDSL identified four causal factors (tumor location, WHO grade, histologic type, and molecular genetic group) for 3-year PFS and OS, whereas LASSO and Cox identified wide-range number of factors associated with 3-year PFS and OS. The performance of each machine learning classifier based on McDSL, LASSO, and Cox was not significantly different. Logistic regression yielded the optimal performance in predicting 3-year PFS based on the McDSL (AUC, 0.872, 95% confidence interval [CI]: 0.828-0.916) and 3-year OS based on the LASSO (AUC, 0.901, 95% CI: 0.861-0.940). Conclusions: McDSL is more reproducible than LASSO and Cox model in the feature selection process. Logistic regression model may have the highest performance in predicting 3-year PFS and OS of gliomas.

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“…In order to explore the prognostic significance of MRI features, Deng et al (2019) analyzed clinical data of 80 patients with pathologically proved LGGs. MRI features were classified as contrast enhancement pattern (focal enhancement, diffuse enhancement and ring-like enhancement), necrosis and cysts based on the preoperative MR images.…”

Section: The Biomarker In Radiographic Imaging Omicsmentioning

confidence: 99%

Comprehensive analysis of potential prognostic biomarker in gliomas

2020

LHT

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PurposeGliomas are common intracranial tumors with the characteristic of diffuse and invasive growth. The prognosis is poor, and the recurrence rate and mortality are higher. With the development of big data technology, many methods such as natural language processing, computer vision and image processing have been deeply applied in the medical field. This can help clinicians to provide personalized and precise diagnosis and therapeutic schedule for patients with different type of gliomas to achieve the best therapeutic effect. The purpose of this paper is to summarize and extract useful information from published research results by conducting a secondary analysis of the literature.Design/methodology/approachThe PubMed and China National Knowledge Infrastructure (CNKI) literature database were used to retrieve published Chinese and English research papers about human gliomas. Comprehensive analysis was applied to conduct this research. The factors affecting survival and prognosis were screened and analyzed respectively in this paper, and different methods for multidimensional data of patients were discussed.FindingsThis paper identified biomarkers and therapeutic modalities associated with prognosis for different grade of gliomas. This paper investigated the relationship among these clinical prognostic factors and different histopathologic tying and grade of gliomas by comprehensive analysis. This paper summarizes the research progress of biomarker in medical imaging and genomics of gliomas to improve prognosis and the current status of treatment in China.Originality/valueCombined with multimodal data such as genomics data, medical image data and clinical information data, this paper comprehensively analyzed the prognostic factors of glioma and provided guidance and evidence for rational treatment planning and improvement of clinical treatment prognosis.

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Prognostic value of magnetic resonance imaging features in low-grade gliomas

Cited by 6 publications

References 31 publications

Development and Validation of a Radiosensitivity Prediction Model for Lower Grade Glioma Based on Spike-and-Slab Lasso

Development and Validation of a Radiosensitivity Prediction Model for Lower Grade Glioma Based on Spike-and-Slab Lasso

Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery

Comprehensive analysis of potential prognostic biomarker in gliomas

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