MRI radiomics and potential applications to glioblastoma

Hooper, Grayson W.; Ginat, Daniel Thomas

doi:10.3389/fonc.2023.1134109

Cited by 5 publications

(5 citation statements)

References 56 publications

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“…However, GB and BM share very similar radiological features. As a result, conventional brain MRI is often unable to distinguish between GB and BM, which makes it challenging to differentiate between the two in a clinical setting [7]. Moreover, although advanced MRI characteristics have been increasingly effective in outlining the distinction of GB from BM, no single feature has proven to hold sufficient precision in order to guide clinical decision-making.…”

Section: Ai In the Differential Diagnostic Of Gbmentioning

confidence: 99%

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One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging

Chirica,

Haba,

Cojocaru

et al. 2023

Life

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Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma (GB) remains the most common primary malignant brain tumor, with a median survival of 15 months. This paper presents literature data on GB imaging and the contribution of AI to the characterization and tracking of GB, as well as recurrence. Furthermore, from an imaging point of view, the differential diagnosis of these tumors can be problematic. How can an AI algorithm help with differential diagnosis? The integration of clinical, radiomics and molecular markers via AI holds great potential as a tool for enhancing patient outcomes by distinguishing brain tumors from mimicking lesions, classifying and grading tumors, and evaluating them before and after treatment. Additionally, AI can aid in differentiating between tumor recurrence and post-treatment alterations, which can be challenging with conventional imaging methods. Overall, the integration of AI into GB imaging has the potential to significantly improve patient outcomes by enabling more accurate diagnosis, precise treatment planning and better monitoring of treatment response.

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Section: Ai In the Differential Diagnostic Of Gbmentioning

confidence: 99%

“…GB is characterized by its extremely invasive nature and rapid progression, leading to poor outcomes for patients. The high recurrence rate also contributes to the increasing mortality of this entity [7][8][9]. Taking this into consideration, early diagnosis plays a crucial role in the therapeutic management of GB.…”

Section: Introductionmentioning

confidence: 99%

One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging

Chirica,

Haba,

Cojocaru

et al. 2023

Life

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show abstract

“…In contemporary clinical practice, gliomas are graded based on surgical or puncture histopathologic investigation ( 10 ). This diagnostic method is intrusive and slow, though.…”

Section: Introductionmentioning

confidence: 99%

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study

Tan,

Sui,

Wang

et al. 2024

Front. Oncol.

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BackgroundAccurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade.MethodsA total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice.ResultsThe intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort.ConclusionThis study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

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“…Hooper [15] reviewed various MRI radiomic features of glioblastoma, providing an overview of the potential applications of radiomics in this context. Zhu [16] developed a non-invasive prediction model for overall survival time in glioblastoma patients based on multimodal MRI radiomics, highlighting the potential of radiomic features in predicting patient outcomes.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Maximum Diameters of Tumor Sub-regions for Survival Prediction in Glioblastoma Patients via Machine Learning, Considering Resection Status

Babaei,

Bonakdar,

Shakourifar

et al. 2024

j. of electron. & inf. syst.

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In recent decades, there have been significant advancements in medical diagnosis and treatment techniques. However, there is still much progress to be made in effectively managing a wide range of diseases, particularly cancer. Timely diagnosis of cancer remains a critical step towards successful treatment, as it significantly impacts patients’ chances of survival. Among various types of cancer, glioma stands out as the most common primary brain tumor, exhibiting different levels of aggressiveness. One of the monitoring techniques is magnetic resonance imaging (MRI) that provides a precise visual representation of the tumor and its sub-regions (edema (ED), enhancing tumor (ET), and non-enhancing necrotic tumor core (NEC)), enabling monitoring of its location, shape, and sub- regional characteristics. In this study, we aim to investigate the underlying relationship between the maximumdiameters of tumor sub-regions and patients’ overall survival (OS) in glioblastoma cases. Using an MRI dataset of glioblastoma patients, we categorized them based on resection status: gross total resection (GTR) and unknown (NA). By employing the Euclidean distance algorithm, we estimated sub-regions’ maximum diameters. Machine learning algorithms were used to explore the correlation between sub-regions’ maximum diameters and survival outcomes. The results of the univariate prediction models showed that tumor sub-regions’ maximum diameters have a noticeable correlation with the survival rates among patients with unknown resection status with the average spearman correlation of -0.254. Also, addition of the sub-regions’ maximum diameter feature to the radiomics increased the accuracy of ML algorithms in predicting the survival rates with an average of 4.58%.

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MRI radiomics and potential applications to glioblastoma

Cited by 5 publications

References 56 publications

One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging

One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study

Evaluating Maximum Diameters of Tumor Sub-regions for Survival Prediction in Glioblastoma Patients via Machine Learning, Considering Resection Status

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