Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning

Baid, Ujjwal; Rane, Swapnil; Talbar, Sanjay N.; Gupta, Sudeep; Thakur, Meenakshi; Moiyadi, Aliasgar; Mahajan, Abhishek

doi:10.3389/fncom.2020.00061

Cited by 93 publications

(76 citation statements)

References 29 publications

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“…Wijethilake et al (37) evaluated the influence of radiomics with different ML algorithms, and their results varied between 40% and 53%. Similarly, Baid et al (38) used multi-layer perceptron (MLP) on radiomic features and the accuracy and p value of the algorithm were 0.571 and 0,427 respectively. Such results show that radiomic features are not promising enough in patients' OS prediction.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…Furthermore, radiomics and the application of machine learning/artificial intelligence to diagnostic MRI scans has the potential to identify early tumor recurrence/progression, distinguish pseudoprogression from progression (42,43) as well as to identify imaging signatures that are relatively specific to molecular subgroups of the more common diagnoses in adults (GBM, oligodendroglial tumors, low grade gliomas) (44,45) and children (low grade gliomas, medulloblastoma, ependymoma, diffuse midline gliomas) (46,47). While several techniques have been described, none have achieved widespread clinical acceptance for routine use.…”

Section: Neuroimaging and Neurosurgerymentioning

confidence: 99%

World Cancer Day 2021 - Perspectives in Pediatric and Adult Neuro-Oncology

Sulman

Eisenstat

2021

Front. Oncol.

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Significant advances in our understanding of the molecular genetics of pediatric and adult brain tumors and the resulting rapid expansion of clinical molecular neuropathology have led to improvements in diagnostic accuracy and identified new targets for therapy. Moreover, there have been major improvements in all facets of clinical care, including imaging, surgery, radiation and supportive care. In selected cohorts of patients, targeted and immunotherapies have resulted in improved patient outcomes. Furthermore, adaptations to clinical trial design have facilitated our study of new agents and other therapeutic innovations. However, considerable work remains to be done towards extending survival for all patients with primary brain tumors, especially children and adults with diffuse midline gliomas harboring Histone H3 K27 mutations and adults with isocitrate dehydrogenase (IDH) wild-type, O6 guanine DNA-methyltransferase gene (MGMT) promoter unmethylated high grade gliomas. In addition to improvements in therapy and care, access to the advances in technology, such as particle radiation or biologic therapy, neuroimaging and molecular diagnostics in both developing and developed countries is needed to improve the outcome of patients with brain tumors.

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“…There have been multiple attempts to provide individualized predictions for survival (Baid, Rane, et al, 2020), progression (Kumar, Verma, Arora, et al, 2017), and response prediction (Johannet et al, 2021; Kim et al, 2018) many of which cross boundaries set by traditional risk stratification (Lu et al, 2017) using deep learning based approaches on radiology and pathology images. Coupled with the ability to reflect, predict and model genomic mutations (Coudray et al, 2017; Mahajan et al, 2020; Wang et al, 2019; Zhao et al, 2019), protein expression (Anand et al, 2020), tumor markers (Scagliotti et al, 2012) as well as intratumoral heterogeneity (Jaber et al, 2020; Kumar, Zhao, et al, 2019; Truong, Sharmanska, Limbӓck‐Stanic, & Grech‐Sollars, 2020) in tumors, image‐based DL methods have made it feasible to converge these divergent multimodal approaches into a single workflow (Romo‐Bucheli et al, 2017).…”

Section: Successes Of Deep Learning In Cancer Image Analysismentioning

confidence: 99%

A 2021 update on cancer image analytics with deep learning

Kurian

Sethi

Konduru

et al. 2021

WIREs Data Min & Knowl

Self Cite

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Deep learning (DL)‐based interpretation of medical images has reached a critical juncture of expanding outside research projects into translational ones, and is ready to make its way to the clinics. Advances over the last decade in data availability, DL techniques, as well as computing capabilities have accelerated this journey. Through this journey, today we have a better understanding of the challenges to and pitfalls of wider adoption of DL into clinical care, which, according to us, should and will drive the advances in this field in the next few years. The most important among these challenges are the lack of an appropriately digitized environment within healthcare institutions, the lack of adequate open and representative datasets on which DL algorithms can be trained and tested, and the lack of robustness of widely used DL training algorithms to certain pervasive pathological characteristics of medical images and repositories. In this review, we provide an overview of the role of imaging in oncology, the different techniques that are shaping the way DL algorithms are being made ready for clinical use, and also the problems that DL techniques still need to address before DL can find a home in clinics. Finally, we also provide a summary of how DL can potentially drive the adoption of digital pathology, vendor neutral archives, and picture archival and communication systems. We caution that the respective researchers may find the coverage of their own fields to be at a high‐level. This is so by design as this format is meant to only introduce those looking in from outside of deep learning and medical research, respectively, to gain an appreciation for the main concerns and limitations of these two fields instead of telling them something new about their own. This article is categorized under: Technologies > Artificial Intelligence Algorithmic Development > Biological Data Mining

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Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning

Cited by 93 publications

References 29 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

World Cancer Day 2021 - Perspectives in Pediatric and Adult Neuro-Oncology

A 2021 update on cancer image analytics with deep learning

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