Robustness of Radiomics for Survival Prediction of Brain Tumor Patients Depending on Resection Status

Weninger, Leon; Haarburger, Christoph; Merhof, Dorit

doi:10.3389/fncom.2019.00073

Cited by 24 publications

(24 citation statements)

References 32 publications

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“…Prateek Prasanna (7) used computerized texture (i.e., radiomic) analysis to evaluate the efficacy of the peritumoral brain zone (PBZ) features from pre-operative MRI in predicting long-(>18 months) versus short-term (<7 months) survival in GBM, whereas Cho (8) applied radiomics to distinguish between high-grade and low-grade glioma and the efficacy of PBZ features from pre-operative MRI. Similarly, the research done by Weninger et al (9) worked on "age-only regression model" and with the accuracy of 56% showing that adding radiomics to the age parameter does not necessarily improve the prediction accuracy for different resection statuses. Shboul et al (10) used random forest regression (RFR) with radiomic features and Feng et al (11) used linear models with geometrics.…”

Section: Introductionmentioning

confidence: 94%

Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status

et al. 2021

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Cancer stands out as one of the fatal diseases people are facing all the time. Each year, a countless number of people die because of the late diagnosis of cancer or wrong treatments. Glioma, one of the most common primary brain tumors, has different aggressiveness and sub-regions, which can affect the risk of disease. Although prediction of overall survival based on multimodal magnetic resonance imaging (MRI) is challenging, in this study, we assess if and how location-based features of tumors can affect overall survival prediction. This approach is evaluated independently and in combination with radiomic features. The process is carried out on a data set entailing MRI images of patients with glioblastoma. To assess the impact of resection status, the data set is divided into two groups, patients were reported as gross total resection and unknown resection status. Then, different machine learning algorithms were used to evaluate how location features are linked with overall survival. Results from regression models indicate that location-based features have considerable effects on the patients’ overall survival independently. Additionally, classifier models show an improvement in prediction accuracy by the addition of location-based features to radiomic features.

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Section: Introductionmentioning

confidence: 94%

Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status

et al. 2021

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“…The other regressors performed poorly compared to RFR, and even the finetuning of the parameters did not improve the performance. The possible reasons are the redundant nature of radiomics [20], over complexity due to too many features and fewer training samples. Radiomics features are shallow and low-order image features, and unable to fully describe distinct image characteristics [22].…”

Section: Radiomics Feature-based Predictionmentioning

confidence: 99%

“…Radiomics features are shallow and low-order image features, and unable to fully describe distinct image characteristics [22]. Also, when the number of observations is less for large extracted features, survival prediction is an ill-posed problem [20]. 2 that the large feature set is unable to yield stateof-the-art accuracy results.…”

Section: Radiomics Feature-based Predictionmentioning

confidence: 99%

Glioblastoma Multiforme Patient Survival Prediction

Rajput¹,

Agravat²,

Roy³

et al. 2021

Preprint

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Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location and the extent of the cancer tissue, it is difficult to detect and dissect the tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that the radiomics shape features have the highest correlation with survival prediction. The proposed approaches were assessed on the Brain Tumor Segmentation (BraTS-2020) challenge dataset. The highest accuracy of image features with random forest regressor approach was 51.5\% for the training and 51.7\% for the validation dataset. The gradient boosting regressor with shape features gave an accuracy of 91.5\% and 62.1\% on training and validation datasets respectively. It is better than the BraTS 2020 survival prediction challenge winners on the training and validation datasets. Our work shows that handcrafted features exhibit a strong correlation with survival prediction. The consensus based regressor with gradient boosting and radiomics shape features is the best combination for survival prediction.

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“…Risk parameters yielding negative regression coefficients (ie, low feature values correlated with long-term survival) produce a HR between 0 and 1; features yielding positive regression coefficients (ie, low feature values correlated with short-term survival) produce a HR between 1 and infinity. 40 , 84 , 85 …”

Section: Overview Of Radiomic and Radiogenomics Pipelinementioning

confidence: 99%

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

Beig

Bera

Tiwari

2020

Neuro-Oncology Advances

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Neuro-oncology largely consists of malignancies of the brain and central nervous system including both primary as well as metastatic tumors. Currently, a significant clinical challenge in neuro-oncology is to tailor therapies for patients based on a priori knowledge of their survival outcome or treatment response to conventional or experimental therapies. Radiomics or the quantitative extraction of subvisual data from conventional radiographic imaging has recently emerged as a powerful data-driven approach to offer insights into clinically relevant questions related to diagnosis, prediction, prognosis, as well as assessing treatment response. Furthermore, radiogenomic approaches provide a mechanism to establish statistical correlations of radiomic features with point mutations and next-generation sequencing data to further leverage the potential of routine MRI scans to serve as “virtual biopsy” maps. In this review, we provide an introduction to radiomic and radiogenomic approaches in neuro-oncology, including a brief description of the workflow involving preprocessing, tumor segmentation, and extraction of “hand-crafted” features from the segmented region of interest, as well as identifying radiogenomic associations that could ultimately lead to the development of reliable prognostic and predictive models in neuro-oncology applications. Lastly, we discuss the promise of radiomics and radiogenomic approaches in personalizing treatment decisions in neuro-oncology, as well as the challenges with clinical adoption, which will rely heavily on their demonstrated resilience to nonstandardization in imaging protocols across sites and scanners, as well as in their ability to demonstrate reproducibility across large multi-institutional cohorts.

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Robustness of Radiomics for Survival Prediction of Brain Tumor Patients Depending on Resection Status

Cited by 24 publications

References 32 publications

Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status

Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status

Glioblastoma Multiforme Patient Survival Prediction

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

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