Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction

Qian, Zenghui; Li, Yiming; Sun, Zimin; Fan, Xing; Xu, Kaibin; Wang, Kai; Li, Shaowu; Zhang, Zhong; Jiang, Tao; Liu, Xing; Wang, Yinyan

doi:10.18632/aging.101594

Cited by 26 publications

(15 citation statements)

References 42 publications

(45 reference statements)

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“…Radiomics is an emerging non-invasive method that extracts high-dimensional sets of imaging features to build appropriate models for survival prediction ( 24 ), distant metastasis prediction ( 25 ), and molecular characteristics classification ( 26 ). However, dimensionality is a critical challenge in radiomics analysis and limits the potential of the radiomics model.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma

Qian

Zhang

et al. 2021

Front. Oncol.Has correction 2021-10-18 Has erratum 2021-10-18

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ObjectiveTo identify optimal machine-learning methods for the radiomics-based differentiation of gliosarcoma (GSM) from glioblastoma (GBM).Materials and MethodsThis retrospective study analyzed cerebral magnetic resonance imaging (MRI) data of 83 patients with pathologically diagnosed GSM (58 men, 25 women; mean age, 50.5 ± 12.9 years; range, 16-77 years) and 100 patients with GBM (58 men, 42 women; mean age, 53.4 ± 14.1 years; range, 12-77 years) and divided them into a training and validation set randomly. Radiomics features were extracted from the tumor mass and peritumoral edema. Three feature selection and classification methods were evaluated in terms of their performance in distinguishing GSM and GBM: the least absolute shrinkage and selection operator (LASSO), Relief, and Random Forest (RF); and adaboost classifier (Ada), support vector machine (SVM), and RF; respectively. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) of each method were analyzed.ResultsBased on tumor mass features, the selection method LASSO + classifier SVM was found to feature the highest AUC (0.85) and ACC (0.77) in the validation set, followed by Relief + RF (AUC = 0.84, ACC = 0.72) and LASSO + RF (AUC = 0.82, ACC = 0.75). Based on peritumoral edema features, Relief + SVM was found to have the highest AUC (0.78) and ACC (0.73) in the validation set. Regardless of the method, tumor mass features significantly outperformed peritumoral edema features in the differentiation of GSM from GBM (P < 0.05). Furthermore, the sensitivity, specificity, and accuracy of the best radiomics model were superior to those obtained by the neuroradiologists.ConclusionOur radiomics study identified the selection method LASSO combined with the classifier SVM as the optimal method for differentiating GSM from GBM based on tumor mass features.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma

Qian

Zhang

et al. 2021

Front. Oncol.Has correction 2021-10-18 Has erratum 2021-10-18

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“…Compared with a previously proposed long noncoding RNA-based (lncRNA-based) signature (21), the number of cases included in this study was larger and the predictive AUC was higher, indicating that our immune-related prognostic signature was more accurate. Also, several predictive nomograms were established in previous studies to predict the long-term OS of LGGs (22,23). In comparison, the more adequate validation with consistently high C-indices of our nomogram indicated the improvement of reliability.…”

Section: Discussionmentioning

confidence: 64%

Prognostic and predictive value of an immune infiltration signature in diffuse lower-grade gliomas

Song¹,

Weng²,

Li³

et al. 2020

JCI Insight

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BACKGROUND. Lower-grade gliomas (LGGs) vary widely in terms of the patient's overall survival (OS). There is no current, valid method that could exactly predict the survival. The effects of intratumoral immune infiltration on clinical outcome have been widely reported. Thus, we aim to develop an immune infiltration signature to predict the survival of LGG patients. METHODS. We analyzed 1216 LGGs from 5 public data sets, including 2 RNA sequencing data sets and 3 microarray data sets. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select an immune infiltration signature and build a risk score. The performance of the risk score was assessed in the training set (329 patients), internal validation set (140 patients), and 4 external validation sets (405, 118, 88, and 136 patients). RESULTS. An immune infiltration signature consisting of 20 immune metagenes was used to generate a risk score. The performance of the risk score was thoroughly verified in the training and validation sets. Additionally, we found that the risk score was positively correlated with the expression levels of TGF-β and PD-L1, which were important targets of combination immunotherapy. Furthermore, a nomogram incorporating the risk score, patient's age, and tumor grade was developed to predict the OS, and it performed well in all the training and validation sets (C-index: 0.873, 0.881, 0.781, 0.765, 0.721, and 0.753). CONCLUSION. The risk score based on the immune infiltration signature has reliable prognostic and predictive value for patients with LGGs and is a potential biomarker for the cotargeting immunotherapy.

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“…As a clinical application tool, our nomogram included only routine clinical examination items for glioma and did not use factors that may require statistical software or trained analysts such as tumor volume, the extent of resection, and epilepsy seizure types 25,26 . Although not perfect, this represents an encouraging level of predictive accuracy.…”

Section: Discussionmentioning

confidence: 99%

A novel DNA repair‐related nomogram predicts survival in low‐grade gliomas

Guan-zhang

Zeng

et al. 2020

CNS Neurosci Ther

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Aims We aimed to create a tumor recurrent‐based prediction model to predict recurrence and survival in patients with low‐grade glioma. Methods This study enrolled 291 patients (188 in the training group and 103 in the validation group) with clinicopathological information and transcriptome sequencing data. LASSO‐COX algorithm was applied to shrink predictive factor size and build a predictive recurrent signature. GO, KEGG, and GSVA analyses were performed for function annotations of the recurrent signature. The calibration curves and C‐Index were assessed to evaluate the nomogram's performance. Results This study found that DNA repair functions of tumor cells were significantly enriched in recurrent low‐grade gliomas. A predictive recurrent signature, built by the LASSO‐COX algorithm, was significantly associated with overall survival and progression‐free survival in low‐grade gliomas. Moreover, function annotations analysis of the predictive recurrent signature exhibited that the signature was associated with DNA repair functions. The nomogram, combining the predictive recurrent signature and clinical prognostic predictors, showed powerful prognostic ability in the training and validation groups. Conclusion An individualized prediction model was created to predict 1‐, 2‐, 3‐, 5‐, and 10‐year survival and recurrent rate of patients with low‐grade glioma, which may serve as a potential tool to guide postoperative individualized care.

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Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction

Cited by 26 publications

References 42 publications

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma

Prognostic and predictive value of an immune infiltration signature in diffuse lower-grade gliomas

A novel DNA repair‐related nomogram predicts survival in low‐grade gliomas

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