Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas

Pei, Dongling; Guan, Fangzhan; Hong, Xuanke; Liu, Zhen; Wang, Weiwei; Qiu, Yuning; Duan, Wenchao; Wang, Minkai; Sun, Chen; Wang, Wenqing; Wang, Xiangxiang; Guo, Yu; Wang, Zilong; Liu, Zhongyi; Xing, Aoqi; Guo, Zhixuan; Luo, Lin; Liu, Xianzhi; Cheng, Jingliang; Zhang, Bin; Zhang, Zhenyu; Yan, Jing

doi:10.1007/s00330-023-09459-6

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…Some alternative investigations leveraged radiomic features derived from DSC-PWI, yielding promising results with innovative methodology [ 55 ]. In contrast, our study concentrates on the specific differentiation between astrocytoma and oligodendroglioma, and employs well-established and clinically accessible metrics, which facilitates ease of understanding and practical application while forges a direct connection with the foundational histological and biological processes, potentially opening new research avenues.…”

Section: Discussionmentioning

confidence: 99%

Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles

Pons-Escoda,

Garcia-Ruiz,

Naval-Baudin

et al. 2024

Eur Radiol

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Objective Presurgical differentiation between astrocytomas and oligodendrogliomas remains an unresolved challenge in neuro-oncology. This research aims to provide a comprehensive understanding of each tumor’s DSC-PWI signatures, evaluate the discriminative capacity of cerebral blood volume (CBV) and percentage of signal recovery (PSR) percentile values, and explore the synergy of CBV and PSR combination for pre-surgical differentiation. Methods Patients diagnosed with grade 2 and 3 IDH-mutant astrocytomas and IDH-mutant 1p19q-codeleted oligodendrogliomas were retrospectively retrieved (2010–2022). 3D segmentations of each tumor were conducted, and voxel-level CBV and PSR were extracted to compute mean, minimum, maximum, and percentile values. Statistical comparisons were performed using the Mann-Whitney U test and the area under the receiver operating characteristic curve (AUC-ROC). Lastly, the five most discriminative variables were combined for classification with internal cross-validation. Results The study enrolled 52 patients (mean age 45-year-old, 28 men): 28 astrocytomas and 24 oligodendrogliomas. Oligodendrogliomas exhibited higher CBV and lower PSR than astrocytomas across all metrics (e.g., mean CBV = 2.05 and 1.55, PSR = 0.68 and 0.81 respectively). The highest AUC-ROCs and the smallest p values originated from CBV and PSR percentiles (e.g., PSRp70 AUC-ROC = 0.84 and p value = 0.0005, CBVp75 AUC-ROC = 0.8 and p value = 0.0006). The mean, minimum, and maximum values yielded lower results. Combining the best five variables (PSRp65, CBVp70, PSRp60, CBVp75, and PSRp40) achieved a mean AUC-ROC of 0.87 for differentiation. Conclusions Oligodendrogliomas exhibit higher CBV and lower PSR than astrocytomas, traits that are emphasized when considering percentiles rather than mean or extreme values. The combination of CBV and PSR percentiles results in promising classification outcomes. Clinical relevance statement The combination of histogram-derived percentile values of cerebral blood volume and percentage of signal recovery from DSC-PWI enhances the presurgical differentiation between astrocytomas and oligodendrogliomas, suggesting that incorporating these metrics into clinical practice could be beneficial. Key Points • The unsupervised selection of percentile values for cerebral blood volume and percentage of signal recovery enhances presurgical differentiation of astrocytomas and oligodendrogliomas. • Oligodendrogliomas exhibit higher cerebral blood volume and lower percentage of signal recovery than astrocytomas. • Cerebral blood volume and percentage of signal recovery combined provide a broader perspective on tumor vasculature and yield promising results for this preoperative classification.

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Section: Discussionmentioning

confidence: 99%

Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles

Pons-Escoda,

Garcia-Ruiz,

Naval-Baudin

et al. 2024

Eur Radiol

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“…Another limitation is the exclusion of functional MRI techniques such as diffusion-and perfusion-weighted imaging from our analysis. These techniques can provide valuable insights into tumor cellularity and vascularity, complementing conventional imaging modalities [38,46,47]. However, their application is not widespread in clinical practice, being more commonly employed for research purposes.…”

Section: Discussionmentioning

confidence: 99%

Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients

Zhang,

Wu,

Zhang

et al. 2024

Preprint

Self Cite

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Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneously predict molecular alterations and histological grade (auxiliary tasks), as well as prognosis (primary task) in gliomas. Further, we aimed to provide the biological mechanisms underlying the model's predictions. We collected multiscale data including baseline MRI images from 2,776 glioma patients across two private (FAHZU and HPPH, n=1,931) and three public datasets (TCGA, n=213; UCSF, n=410; and EGD, n=222). We trained and internally validated the MDL model using our private datasets, and externally validated it using the three public datasets. We used the model-predicted deep prognosis score (DPS) to stratify patients into low-DPS and high-DPS subtypes. Additionally, a radio-multiomics analysis was conducted to elucidate the biological basis of the DPS. In the external validation cohorts, the MDL model achieved average areas under the curve of 0.892-0.903, 0.710-0.894, and 0.850-0.879 for predicting IDH mutation status, 1p/19q co-deletion status, and tumor grade, respectively. Moreover, the MDL model yielded a C-index of 0.723 in the TCGA and 0.671 in the UCSF for the prediction of overall survival. The DPS exhibits significant correlations with activated oncogenic pathways, immune infiltration patterns, specific protein expression, DNA methylation, tumor mutation burden, and tumor-stroma ratio. Accordingly, our work presents an accurate and biologically meaningful tool for predicting molecular subtypes, tumor grade, and survival outcomes in gliomas, which provides personalized clinical decision-making in a global and non-invasive manner.

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“…Pei et al. ( 41 ) have also investigated the integration of multimodal MRIs to improve the accuracy of glioma subclassification. Adding DSC-PWI to conventional MRIs can improve glioma subtype prediction in patients with diffuse gliomas (AUC: 0.864, 0.787, and 0.816 in IDH wildtype, IDH mutant and 1p/19q -noncodeleted, and IDH mutant and 1p/19q -codeleted, respectively).…”

Section: Discussionmentioning

confidence: 99%

“…The image fusion model (multivariate logistic regression) incorporating radiomic features from T1CE and DWI-ADC achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. Pei et al (41) have also investigated the integration of multimodal MRIs to improve the accuracy of glioma subclassification. Adding DSC-PWI to conventional MRIs can improve glioma subtype prediction in patients with diffuse gliomas (AUC: 0.864, 0.787, and 0.816 in IDH wildtype, IDH mutant and 1p/19q-noncodeleted, and IDH mutant and 1p/19q-codeleted, respectively).…”

Section: Discussionmentioning

confidence: 99%

Deep-learning and conventional radiomics to predict IDH genotyping status based on magnetic resonance imaging data in adult diffuse glioma

Zhang,

Fan,

Zhang

et al. 2023

Front. Oncol.

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ObjectivesIn adult diffuse glioma, preoperative detection of isocitrate dehydrogenase (IDH) status helps clinicians develop surgical strategies and evaluate patient prognosis. Here, we aim to identify an optimal machine-learning model for prediction of IDH genotyping by combining deep-learning (DL) signatures and conventional radiomics (CR) features as model predictors.MethodsIn this study, a total of 486 patients with adult diffuse gliomas were retrospectively collected from our medical center (n=268) and the public database (TCGA, n=218). All included patients were randomly divided into the training and validation sets by using nested 10-fold cross-validation. A total of 6,736 CR features were extracted from four MRI modalities in each patient, namely T1WI, T1CE, T2WI, and FLAIR. The LASSO algorithm was performed for CR feature selection. In each MRI modality, we applied a CNN+LSTM–based neural network to extract DL features and integrate these features into a DL signature after the fully connected layer with sigmoid activation. Eight classic machine-learning models were analyzed and compared in terms of their prediction performance and stability in IDH genotyping by combining the LASSO–selected CR features and integrated DL signatures as model predictors. In the validation sets, the prediction performance was evaluated by using accuracy and the area under the curve (AUC) of the receiver operating characteristics, while the model stability was analyzed by using the relative standard deviation of the AUC (RSDAUC). Subgroup analyses of DL signatures and CR features were also individually conducted to explore their independent prediction values.ResultsLogistic regression (LR) achieved favorable prediction performance (AUC: 0.920 ± 0.043, accuracy: 0.843 ± 0.044), whereas support vector machine with the linear kernel (l-SVM) displayed low prediction performance (AUC: 0.812 ± 0.052, accuracy: 0.821 ± 0.050). With regard to stability, LR also showed high robustness against data perturbation (RSDAUC: 4.7%). Subgroup analyses showed that DL signatures outperformed CR features (DL, AUC: 0.915 ± 0.054, accuracy: 0.835 ± 0.061, RSDAUC: 5.9%; CR, AUC: 0.830 ± 0.066, accuracy: 0.771 ± 0.051, RSDAUC: 8.0%), while DL and DL+CR achieved similar prediction results.ConclusionIn IDH genotyping, LR is a promising machine-learning classification model. Compared with CR features, DL signatures exhibit markedly superior prediction values and discriminative capability.

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Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas

Cited by 7 publications

References 35 publications

Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles

Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles

Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients

Deep-learning and conventional radiomics to predict IDH genotyping status based on magnetic resonance imaging data in adult diffuse glioma

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