Survival Prediction in Gliomas: Current State and Novel Approaches

Zhao, Rachel; Krauze, Andra

doi:10.36255/exonpublications.gliomas.2021.chapter9

Cited by 11 publications

(13 citation statements)

References 84 publications

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“…Like SVM, RF is also increasingly utilised for survival prediction. 26,30,31 The random forest consists of 1000 decision trees trained using the training dataset. Risk score prediction was the average across all trees in the forest.…”

Section: Methodsmentioning

confidence: 99%

“…23 We built a CPH model using clinical features in the training data as covariates to allow for comparison of performance accuracy between the most commonly used model and other increasingly employed models. 14,21,[24][25][26][27] Support vector machine (SVM) model. Support vector machine is an increasingly utilised ML algorithm that functions by separating observations into classes 26,28 and its use has been described in the context of glioma survival.…”

Section: Techniques Employed In the Analysismentioning

confidence: 99%

“…14,21,[24][25][26][27] Support vector machine (SVM) model. Support vector machine is an increasingly utilised ML algorithm that functions by separating observations into classes 26,28 and its use has been described in the context of glioma survival. 29 We optimized a linear SVM classifier through a hyperparameter search to find the best regularization hyperparameter which was used to train the classifier using the entire training set.…”

Section: Techniques Employed In the Analysismentioning

confidence: 99%

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Machine learning based survival prediction in Glioma using large-scale registry data

Zhao

Zhuge

Camphausen

et al. 2022

Health Informatics J

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Gliomas are the most common central nervous system tumors exhibiting poor clinical outcomes. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Machine learning (ML) allows for sophisticated approaches to survival prediction using real world clinical parameters needed to achieve superior predictive accuracy. We employed Cox Proportional hazards (CPH) model, Support Vector Machine (SVM) model, Random Forest (RF) model in a large glioma dataset (3462 patients, diagnosed 2000–2018) to explore the most optimal approach to survival prediction. Features employed were age, sex, surgical resection status, tumor histology and tumor site, administration of radiation therapy (RT) and chemotherapy status. Concordance index (c-index) was employed to assess the accuracy of survival time prediction. All three models performed well with prediction accuracy (CI 0.767, 0.771, 0.57 for CPH, SVM, RF models respectively) with the best performance achieved when incorporating RT and chemotherapy administration status which emerged as key predictive features. Within the subset of glioblastoma patients, similar prediction accuracy was achieved. These findings should prompt stricter clinician oversight over registry data accuracy through quality assurance as we move towards meaningful predictive ability using ML approaches in glioma.

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Section: Methodsmentioning

confidence: 99%

Section: Techniques Employed In the Analysismentioning

confidence: 99%

Section: Techniques Employed In the Analysismentioning

confidence: 99%

See 1 more Smart Citation

Machine learning based survival prediction in Glioma using large-scale registry data

Zhao

Zhuge

Camphausen

et al. 2022

Health Informatics J

Self Cite

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“…Basic MRI indicators, including maximum size and enhanced volume, are more effective in predicting prognosis than clinical models. 71,72 The presence of PsP after treatment is also an imaging feature of better prognosis. 73 Other advanced MRI indicators, such as DWI and PWI parameters, ADC, and CBV, are also reliable prognostic markers for HGG patients.…”

Section: Prognosis/survival Predictionmentioning

confidence: 99%

“…There are also many studies that associate image characteristics with survival prediction. Basic MRI indicators, including maximum size and enhanced volume, are more effective in predicting prognosis than clinical models 71,72 . The presence of PsP after treatment is also an imaging feature of better prognosis 73 .…”

Section: Prognosis/survival Predictionmentioning

confidence: 99%

Treatment Response and Prognosis Evaluation in High‐Grade Glioma: An Imaging Review Based on MRI

Qin

Xue

et al. 2022

Magnetic Resonance Imaging

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In recent years, the development of advanced magnetic resonance imaging (MRI) technology and machine learning (ML) have created new tools for evaluating treatment response and prognosis of patients with high-grade gliomas (HGG); however, patient prognosis has not improved significantly. This is mainly due to the heterogeneity between and within HGG tumors, resulting in standard treatment methods not benefitting all patients. Moreover, the survival of patients with HGG is not only related to tumor cells, but also to noncancer cells in the tumor microenvironment (TME). Therefore, during preoperative diagnosis and follow-up treatment of patients with HGG, noninvasive imaging markers are needed to characterize intratumoral heterogeneity, and then to evaluate treatment response and predict prognosis, timeously adjust treatment strategies, and achieve individualized diagnosis and treatment. In this review, we summarize the research progress of conventional MRI, advanced MRI technology, and ML in evaluation of treatment response and prognosis of patients with HGG. We further discuss the significance of the TME in the prognosis of HGG patients, associate imaging features with the TME, indirectly reflecting the heterogeneity within the tumor, and shifting treatment strategies from tumor cells alone to systemic therapy of the TME, which may be a major development direction in the future.

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New insights into glioma frequency maps: From genetic and transcriptomic correlate to survival prediction

Bao

Ren

et al. 2022

Intl Journal of Cancer

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Increasing evidence indicates that glioma topographic location is linked to the cellular origin, molecular alterations and genetic profile. This research aims to (a) reveal the underlying mechanisms of tumor location predilection in glioblastoma multiforme (GBM) and lower-grade glioma (LGG) and (b) leverage glioma location features to predict prognosis. MRI images from 396 GBM and 190 LGG (115 astrocytoma and 75 oligodendroglioma) patients were standardized to construct frequency maps and analyzed by voxel-based lesion-symptom mapping. We then investigated the spatial correlation between glioma distribution with gene expression in healthy brains. We also evaluated transcriptomic differences in tumor tissue from predilection and nonpredilection sites. Furthermore, we quantitively characterized tumor anatomical localization and explored whether it was significantly related to overall survival. Finally, we employed a support vector machine to build a survival prediction model for GBM patients. GBMs exhibited a distinct location predilection from LGGs. GBMs were nearer to the subventricular zone and more likely to be localized to regions enriched with synaptic signaling, whereas astrocytoma and oligodendroglioma tended to occur in areas associated with the immune response. Synapse, neurotransmitters and calcium ion channel-related genes were all activated in GBM tissues coming from predilection regions. Furthermore, we characterized tumor location features in terms of a series of tumor-to-predilection distance metrics, which were able to predict GBM 1-year survival status with an accuracy of 0.71. These findings provide new perspectives on our understanding of tumor anatomic localization. The spatial features of glioma are of great value in individual therapy and prognosis prediction.

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Survival Prediction in Gliomas: Current State and Novel Approaches

Cited by 11 publications

References 84 publications

Machine learning based survival prediction in Glioma using large-scale registry data

Machine learning based survival prediction in Glioma using large-scale registry data

Treatment Response and Prognosis Evaluation in High‐Grade Glioma: An Imaging Review Based on MRI

New insights into glioma frequency maps: From genetic and transcriptomic correlate to survival prediction

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