Application of Enhanced T1WI of MRI Radiomics in Glioma Grading

Zhou, Hong‐Zhang; Xu, Rong; Mei, Haitao; Zhang, Ling; Yu, Qiyun; Liu, Rong; Fan, Bing

doi:10.1155/2022/3252574

Cited by 10 publications

(6 citation statements)

References 21 publications

(29 reference statements)

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“…A recent systematic review of the current status and quality of radiomics for glioma differential diagnosis in 2022 showed that the radiomic quality score (RQS) of 42 studies was only 24.21%, which meant that current radiomic studies for glioma differential diagnosis still lack the quality required to allow its introduction into clinical practice [ 26 , 27 ]. We identified several research trends based on radiomics and gliomas (not only DMG), including construction using multiparametric magnetic resonance radiomics (several MRI sequences combined with genotype status and clinical features), [ 25 , 28 , 29 ]; PET-extracted radiomics [ 30 , 31 , 32 , 33 ]; radiomics-based machine learning [ 34 , 35 , 36 ]; predictive models of recurrence [ 37 , 38 ]; survival and classification in gliomas [ 39 , 40 , 41 ]; and differential diagnosis [ 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation

Chilaca-Rosas,

Contreras-Aguilar,

Garcia-Lezama

et al. 2023

Diagnostics

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Background: Radiomics refers to the acquisition of traces of quantitative features that are usually non-perceptible to human vision and are obtained from different imaging techniques and subsequently transformed into high-dimensional data. Diffuse midline gliomas (DMG) represent approximately 20% of pediatric CNS tumors, with a median survival of less than one year after diagnosis. We aimed to identify which radiomics can discriminate DMG tumor regions (viable tumor and peritumoral edema) from equivalent midline normal tissue (EMNT) in patients with the positive H3.F3K27M mutation, which is associated with a worse prognosis. Patients and methods: This was a retrospective study. From a database of 126 DMG patients (children, adolescents, and young adults), only 12 had H3.3K27M mutation and available brain magnetic resonance DICOM file. The MRI T1 post-gadolinium and T2 sequences were uploaded to LIFEx software to post-process and extract radiomic features. Statistical analysis included normal distribution tests and the Mann–Whitney U test performed using IBM SPSS® (Version 27.0.0.1, International Business Machines Corp., Armonk, NY, USA), considering a significant statistical p-value ≤ 0.05. Results: EMNT vs. Tumor: From the T1 sequence 10 radiomics were identified, and 14 radiomics from the T2 sequence, but only one radiomic identified viable tumors in both sequences (p < 0.05) (DISCRETIZED_Q1). Peritumoral edema vs. EMNT: From the T1 sequence, five radiomics were identified, and four radiomics from the T2 sequence. However, four radiomics could discriminate peritumoral edema in both sequences (p < 0.05) (CONVENTIONAL_Kurtosis, CONVENTIONAL_ExcessKurtosis, DISCRETIZED_Kurtosis, and DISCRETIZED_ExcessKurtosis). There were no radiomics useful for distinguishing tumor tissue from peritumoral edema in both sequences. Conclusions: Less than 5% of the radiomic characteristics identified tumor regions of medical–clinical interest in T1 and T2 sequences of conventional magnetic resonance imaging. The first-order and second-order radiomic features suggest support to investigators and clinicians for careful evaluation for diagnosis, patient classification, and multimodality cancer treatment planning.

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Section: Discussionmentioning

confidence: 99%

Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation

Chilaca-Rosas,

Contreras-Aguilar,

Garcia-Lezama

et al. 2023

Diagnostics

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“…Pathologists with more than 10 years of experience graded the postoperative specimens, based on the 2021 WHO classification of CNS tumors, classifying gliomas into grades I-IV, with grades I-II being LGG and grades III-IV being HGG ( 19 ).…”

Section: Methodsmentioning

confidence: 99%

“…Over the past decades, magnetic resonance imaging (MRI) has emerged as a crucial non-invasive diagnostic and assistant therapeutic technique for brain tumors, which is used to aid in differential diagnosis, guide treatment planning, and monitor therapy response (14)(15)(16)(17). Nevertheless, competent radiologists may easily spot tumors from MRI sequences with the naked eye, gliomas are difficult to discriminate based on grade because of the variability and diversity of the tumors, which is undoubtedly a great challenge for imaging technology (18)(19)(20).…”

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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“…The line was drawn carefully to maintain an approximate distance of 1–2 mm from ALNs margin on CECT images. 326 quantitative radiomics features were extracted for each ROI using AK software 39,40 . These radiomics features were further divided into six categories, including histogram, formfactor, haralick, gray‐level co‐occurrence matrix (GLCM), run length matrix (RLM), and gray level size zone matrix (GLSZM).…”

Section: Methodsmentioning

confidence: 99%

Radiomics model based on features of axillary lymphatic nodes to predict axillary lymphatic node metastasis in breast cancer

et al. 2022

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Background Breast cancer (BC) is among the most common cancers worldwide. Machine learning–based radiomics model could predict axillary lymph node metastasis (ALNM) of BC accurately. Purpose The purpose is to develop a machine learning model to predict ALNM of BC by focusing on the radiomics features of axillary lymphatic node (ALN). Methods A group of 398 BC patients with 800 ALNs were retrospectively collected. A set of patient characteristics were obtained to form clinical factors. Three hundred and twenty‐six radiomics features were extracted from each region of interest for ALN in contrast‐enhanced computed tomography (CECT) image. A framework composed of four feature selection methods and 14 machine learning classification algorithms was systematically applied. A clinical model, a radiomics model, and a combined model were developed using a cross‐validation approach and compared. Metrics of the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the performance of these models in the prediction of ALNM in BC. Results Among the 800 cases of ALNs, there were 388 cases of positive metastasis (48.50%) and 412 cases of negative metastasis (51.50%). The baseline clinical model achieved the performance with an AUC = 0.8998 (95% CI [0.8540, 0.9457]). The radiomics model achieved an AUC = 0.9081 (95% CI [0.8640, 0.9523]). The combined model using the clinical factors and radiomics features achieved the best results with an AUC = 0.9305 (95% CI [0.8928, 0.9682]). Conclusions Combinations of feature selection methods and machine learning‐based classification algorithms can develop promising predictive models to predict ALNM in BC using CECT features. The combined model of clinical factors and radiomics features outperforms both the clinical model and the radiomic model.

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Application of Enhanced T1WI of MRI Radiomics in Glioma Grading

Cited by 10 publications

References 21 publications

Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation

Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation

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

Radiomics model based on features of axillary lymphatic nodes to predict axillary lymphatic node metastasis in breast cancer

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