Incorporating multiple magnetic resonance diffusion models to differentiate low- and high-grade adult gliomas: a machine learning approach

Xu, Jincheng; Ren, Yan; Zhao, Xueying; Wang, Xiaoqing; Yu, Xuchen; Yao, Zhenwei; Zhou, Yan; Feng, Xiaoyuan; Zhou, Xiaohong Joe; Wang, He

doi:10.21037/qims-22-145

Cited by 8 publications

(1 citation statement)

References 40 publications

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“…The radiogenomics-based classification model is an alternative that noninvasively and preoperatively distinguishes the histological and molecular factors of gliomas, and researchers have made progress on this model ( 9 , 10 ). Compared to computed tomography (CT) and positron emission tomography (PET), magnetic resonance imaging (MRI) achieves high tissue contrast without radiation.…”

Section: Introductionmentioning

confidence: 99%

Predicting isocitrate dehydrogenase genotype, histological phenotype, and Ki-67 expression level in diffuse gliomas with an advanced contrast analysis of magnetic resonance imaging sequences

Cui,

Dang,

Zhang

et al. 2023

Quant Imaging Med Surg

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Background The present study aimed to establish a robust predictive model based on a machine learning (ML) algorithm providing preoperative noninvasive diagnosis and to further explore the contribution of each magnetic resonance imaging (MRI) sequence to the classification to help select images for future model development. Methods This was a retrospective cross-sectional study, and consecutive patients with histologically confirmed diffuse gliomas in our hospital from November 2015 to October 2019 were recruited. The participants were grouped into a training and testing set based on a ratio of 8:2. Five MRI sequences were employed to develop the support vector machine (SVM) classification model. An advanced contrast analysis of single-sequence-based classifiers was performed, according to which different sequence combinations were tested, and the best one was selected to form an ultimate classifier. Patients whose MRIs were acquired with other types of scanners formed an additional, independent validation set. Results A total of 150 patients with gliomas were used in the present study. Contrast analysis revealed that the contribution of the apparent diffusion coefficient (ADC) was the most significant [accuracies were as follows: histological phenotype, 0.640; isocitrate dehydrogenase (IDH) status, 0.656; and Ki-67 expression, 0.699] and that of T1 weighted imaging was limited (accuracies were as follows: histological phenotype, 0.521; IDH status, 0.492; and Ki-67 expression, 0.556). The ultimate classifiers for IDH status, histological phenotype, and Ki-67 expression achieved promising performances with area under the curve (AUC) values of 0.88, 0.93, and 0.93, respectively. The classifiers for the histological phenotype, IDH status, and Ki-67 expression correctly predicted 3 of 5 subjects, 6 of 7 subjects, and 9 of 13 subjects in the additional validation set, respectively. Conclusions The present study showed satisfactory performance in predicting the IDH genotype, histological phenotype, and Ki-67 expression level. The contrast analysis revealed the contribution of different MRI sequences and suggested that the combination of all the acquired sequences was not the optimal strategy to build the radiogenomics-based classifier.

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Section: Introductionmentioning

confidence: 99%

Predicting isocitrate dehydrogenase genotype, histological phenotype, and Ki-67 expression level in diffuse gliomas with an advanced contrast analysis of magnetic resonance imaging sequences

Cui,

Dang,

Zhang

et al. 2023

Quant Imaging Med Surg

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A data‐driven intravoxel mean diffusivities distribution approach for molecular classifications and MIB‐1 prediction of gliomas

Xu,

Sheng,

et al. 2024

Medical Physics

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BackgroundMeasuring non‐parametric intravoxel mean diffusivity distributions (MDDs) using magnetic resonance imaging (MRI) is a sensitive method for detecting intracellular diffusivity changes during physiological alterations. Histological and molecular glioma classifications are essential for prognosis and treatment, with distinct water diffusion dynamics among subtypes.PurposeWe developed a data‐driven approach using a fully connected network (FCN) to enhance the speed and stability of calculating MDDs across varying SNRs, enable tumor microstructural mapping, and test its reliability in identifying MIB‐1 labeling index (LI) levels and molecular status of gliomas.MethodsAn FCN was trained to learn the mapping between the simulated diffusion decay curves and the ground truth MDDs. We performed 5 000 000 simulation curves with various diffusivity components and random SNR . Eighty percent of simulation curves were used for the FCN training, 10% for validation, and the others were external tests for the FCN performance evaluation. In vivo data were collected to evaluate its clinical reliability. One hundred one patients (44 years 14, 67 men) with gliomas and six healthy controls underwent a 3.0 T MRI examination with a spin echo–echo planar imaging (SE‐EPI) diffusion‐weighted imaging (DWI) sequence. The trained FCN was employed to calculate MDDs of each brain voxel by voxel. We used the Fuzzy C‐means algorithm to cluster the MDDs of tumor voxels, facilitating the characterization of distinct glioma tissues. Quantitative assessments were conducted through sectional integrals of the MDDs, demarcated by six bands to derive signal fractions () and diffusivities of the maximum peaks (). Cosine similarity scores (CSS) were used for MDD similarity. ANOVA and Mann–Whitney U test were used for difference analysis. Logistic regression and area under the receiver operator characteristic curve (AUC) were used for classification evaluation.ResultsThe simulation results showed that the FCN‐based MDD approach (FCN‐MDD) achieved higher CSS than non‐negative least squares‐based MDD (NNLS‐MDD). For in vivo data, the spectra of ET and NET obtained by FCN‐MDD are more distinguishable than NNLS‐MDD. Fraction maps delineate the characteristics of different tumor tissues (enhancing and non‐enhancing tumor, edema, and necrosis). showed a positive and negative correlation with MIB‐1 respectively (, all ). The AUC of for predicting MIB‐1 LI levels was 0.900 (95% CI, 0.826–0.974), versus 0.781 (0.677–0.886) of ADC. The highest AUC of isocitrate dehydrogenase (IDH) mutation status, assessed by a logistic regression model () was 0.873 (95% CI, 0.802–0.944).ConclusionThe proposed FCN‐MDD method was more robust to variations in SNR and less reliant on empirically set regularization values than the NNLS‐MDD method. FCN‐MDD also enabled qualitative and quantitative evaluation of the composition of gliomas.

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Whole-tumor histogram analysis of multiple non-Gaussian diffusion models at high b values for assessing cervical cancer

Yang,

Hu,

Yang

et al. 2024

Abdom Radiol

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Incorporating multiple magnetic resonance diffusion models to differentiate low- and high-grade adult gliomas: a machine learning approach

Cited by 8 publications

References 40 publications

Predicting isocitrate dehydrogenase genotype, histological phenotype, and Ki-67 expression level in diffuse gliomas with an advanced contrast analysis of magnetic resonance imaging sequences

Predicting isocitrate dehydrogenase genotype, histological phenotype, and Ki-67 expression level in diffuse gliomas with an advanced contrast analysis of magnetic resonance imaging sequences

A data‐driven intravoxel mean diffusivities distribution approach for molecular classifications and MIB‐1 prediction of gliomas

Whole-tumor histogram analysis of multiple non-Gaussian diffusion models at high b values for assessing cervical cancer

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