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Edited by: Timothy Carroll, The University of Chicago, United States

Reviewed by: Xiaojing Wang, Shanghai Jiao Tong University, China; Parmede Vakil, Northwestern University, United States

†These authors share first authorship

‡These authors share last authorship

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2–4) from 3 different centers.

To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space.

The Mean slope of increase (MSI) and the K_{1} parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively.

The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation.

Gliomas are the most frequent type of brain tumors, which are classified with a grading system outlined by the WHO, ranging from Grade I to IV (

The identification of IDH mutations is therefore instrumental in predicting patient prognosis, glioma surveillance (

Dynamic Susceptibility Contrast (DSC) perfusion imaging, specifically rCBV maps, can identify defined angiogenesis transcriptome signatures, which could be indicative of IDH mutant gliomas through the evident perfusion phenotypes (

Artificial Intelligence is quickly becoming a field of research which could potentially inform clinical decision making processes. It uses radiological images as minable databases that utilize quantitative data that can, once learned, predict clinically relevant information (

Therefore, and to the best of our knowledge, the use of DSC perfusion imaging with machine learning to predict IDH mutation status has only been investigated in four studies thus far (

The heterogeneity in MRI scanner characteristics, such as software variations, MRI equipment (receiver coils), scan protocols, and reconstruction algorithms, can lead to inter- and intra-site variations. These variations affect the signal intensity of the MR image, which may conceal the region of interest in terms of signal to noise ratio (SNR) and lead to the failure of (ML) analysis (

This radiogenomics study aims to explore the value of using machine learning (ML) directly on features from DSC parametric maps, derived from mathematical signal modeling, to predict IDH mutation status in gliomas. This methodology is applied on a previously studied dataset (

The patient cohort consisted of 160 patients [age: 58.4 ± 15.9 (mean ± SD), 70 female] from three different imaging centers. Each patient underwent histopathological diagnosis of primary glioma (WHO grades 2–4), molecular characterization of IDH mutation status (IDH-mutant = 41, IDH-wildtype = 119) and DSC–MRI prior to any treatment. The first cohort consisted of 92 patients (66 out of 92 IDH-mutant) from Stanford Medicine Imaging Center, Stanford CA, USA. The second cohort included 50 patients (39 out of 50 IDH-mutant) from Health Lancaster Imaging Center, South Carolina, USA. The third cohort contained 14 out of 18 patients with an IDH-mutant status from Ljubljana University Medical Center, Ljubljana, Slovenia. Patients without a histologically confirmed diagnosis of glioma, incomplete molecular characterization of IDH status, non-enhancing grade III gliomas, or patients having received any treatment prior to image acquisition were excluded.

The imaging parameters for each DSC acquisition for each cohort are summarized in

MR imaging parameters per cohort used.

Acquisition type | 2D Echo-Planar Imaging (EPI) with fat suppression (FS) | |||

Magnetic field strength | 3T | 3T | 1.5T | 1.5T |

Repetition time (ms) | 1800 | 1870 | 1850 | 1525 |

Echo time (ms) | 40 | 30 | 30 | 40 |

Echo train length | 1 | 63 | 1 | 47 |

Flip angle (deg) | 60 | 90 | 90 | 75 |

In-plane resolution (mm^{2}) |
1.718 × 1.718 | 1.719 × 1.719 | 1.796 × 1.796 | 1.75 × 1.75 |

Number of averages | 1 | 1 | 1 | 1 |

Image slice thickness (mm) | 5 | 5 | 5 | 5 |

Image slice spacing (mm) | 5 | 5 | 5 | 5 |

Temporal resolution | 60 × 1.87 s | 60 × 2.07 s | 40 × 1.53s | 60 × 1.8s |

Matrix size | 128 × 128 | 128 × 128 | 128 × 128 | 128 × 128 |

Bratumia software (

DSC MRI and Computed Tomography perfusion (CTP) are the most widely used tracer kinetic techniques to measure brain perfusion. They both examine how the injected contrast agent is distributed and diluted inside the vascular system (

Given a probability density function or transport function _{t}) with the concentration of CA in the tissue _{t}(t) is given by the formula below:

where,

where, _{1}, σ_{1} and _{1} are related with the mean transit time and the dispersion of

In order to obtain [_{t}, _{1}, σ_{1}, _{1}], the (scipy.optimize.least_squares) (

In addition, the TMAX and the mean slope of increase (MSI) were also calculated. TMAX represents the time taken for the DSC curve to reach its maximum. Assuming _{t}(_{0} the last time of the baseline, MSI was calculated as:

It is also important to note that, the concentration of CA over time [_{t}(

where TE is the echo time and S(0) is the baseline signal prior to the CA's arrival. Thus, prior to fitting every DSC intensity curve was converted to concentration of CA using Equation 7 (

To determine the relative cerebral blood volume (nrCBV) is a challenging task in brain tumors since a leaky blood-brain barrier (BBB) can affect measurements (^{*} time further. Thus, a more complex model must be taken into account to allow the uni- or bi-directional exchange of CA between EES and intravascular space (IVS). Thus, multi compartmental modeling is presented below for the calculation of the corrected nrCBV (

In general, nrCBV is the integral of _{t}(_{0} and the replenish _{1}time points as shown in Equation 8 below:

In the unidirectional leakage correction algorithm only the transfer from IVS to EES is assumed and _{t}(

where, _{1} and _{2} are obtained by fitting equation 9 to the concentration _{t}(_{t unidir}(_{unidir} can be computed for each voxel from the next two equations:

In the case of the bidirectional corrected algorithm the bidirectional transfer between EES and IVS is assumed and _{t}(

where _{ep} is the transfer constant for extra- to intravascular compartments and ⊛ is the convolution operator. Again, _{1}, _{2} and _{ep} are obtained by fitting Equation 12 to the concentration _{t}(_{t bidir}(_{bidir} can be computed for each voxel from the next two equations:

For the leakage corrected algorithms the search space for the unknown fitted parameters _{1}, _{2} and _{ep} was the real numbers without any constrains with the Levenberg-Marquardt algorithm (

Parametric maps calculated from the gamma fitting and the leakage correction algorithms superimposed to the corresponding anatomical image.

For each of the aforementioned parametric maps (rCBF, rCBV, rMTT, MSI, TMAX and nrCBV, K1unidir, K2unidir, nrCBVunidir, K1bidir, K2bidir, Kepbidir, nrCBVbidir) described in section 2.4 the pyradiomics library (

The feature selection process used in this study consisted of 3 steps. The first step was to apply a variance threshold to remove features with zero variance (constant features with variance lower than 0.5). Secondly, a univariate method (ANOVA, analysis of variance) was used to remove noisy information in a feature by feature basis. The last step was to apply a multivariate method (linear logistic regression) using the l1 norm (l1 penalty) that produces the feature importance weights by solving a minimization problem using as penalty parameter C = 0.3 (

A common problem in classification analyses is the imbalanced number of samples in each class. In our study, we had 41 IDH-mutant and 119 IDH-wildtype cases which can lead to a biased machine learning classifier with reduced sensitivity. To overcome this limitation, the synthetic minority oversampling technique (SMOTE) was applied on the training phase of the classification and the trained models were evaluated exclusively on the unseen testing sets (

In order to differentiate the IDH mutation status, the support vector machine (SVM) classifier with the radial basis function kernel (RBF) from the scikit-learn library (

The overall data analysis process with the proposed MRI parametric maps for IDH prediction.

To evaluate and compare the performance of radiomic analysis in the produced biomarkers with other studies in bibliography a variety of metrics were used. More specifically, for every fold, sensitivity, specificity, F1-score, accuracy (ACC) and area under the receiver operating characteristic curve (AUC) with their standard deviations were calculated on the unseen testing sets. The performance metrics are defined as: sensitivity

To assess the explainability of the model, the SHAP method (Shapley Additive Explanations) was used to explain individual predictions. SHAP values are not model dependent, meaning they can be used to interpret any machine learning model. Their background relies on a game theoretic approach that measures the contribution of each player to the final outcome. In ML, each feature is assigned an importance value representing its contribution to the model's output. The Shapley value is the average marginal contribution of a feature value across all possible combinations in the feature space (

The performance evaluation metrics for the prediction of IDH mutation are summarized for radiomics features obtained with the Gamma fitting method and with the leakage correction algorithms in

Classification metrics ± standard deviation per parametric map from the gamma fitting algorithm.

rCBF | 46.1 ± 10.7 | 69.8 ± 22.8 | 41.7 ± 11.7 | 63.7 ± 17.8 | 62.9 ± 15.5 |

rCBV | 68.6 ± 11.3 | 58.1 ± 18.9 | 48.8 ± 12.3 | 60.6 ± 15.6 | 62.8 ± 13.1 |

rMTT | 31.3 ± 11.3 | 80.5 ± 12.4 | 33.2 ± 9.4 | 68.1 ± 8.0 | 55.9 ± 8.2 |

MSI | 51.3 ± 16.8 | 82.4 ± 12.9 | 50.0 ± 10.5 | 74.3 ± 7.7 | 74.2 ± 7.4 |

TMAX | 41.3 ± 20.0 | 78.1 ± 11.5 | 37.8 ± 11.7 | 68.7 ± 5.2 | 61.2 ± 6.6 |

Classification metrics ± standard deviation per parametric map from the leakage correction algorithm.

nrCBV | 52.5 ± 28.9 | 62.2 ± 15 | 37.8 ± 21.1 | 59.3 ± 11.5 | 61.5 ± 15.3 |

K1_{unidir} |
21.6 ± 13.7 | 85.7 ± 9.3 | 24.7 ± 15.4 | 69.3 ± 5.7 | 59.2 ± 7.76 |

K2_{unidir} |
56.1 ± 5.5 | 74.7 ± 5.2 | 48.9 ± 2.7 | 70 ± 3.1 | 71.7 ± 5.89 |

nrCBV_{unidir} |
59.1 ± 25 | 54.7 ± 20.1 | 39.1 ± 5.5 | 55.6 ± 9.3 | 65.0 ± 5.30 |

K_{1bidir} |
38.9 ± 17.8 | 87.4 ± 7.8 | 42.8 ± 16.7 | 75 ± 6.5 | 70.5 ± 7.93 |

K_{2bidir} |
34.4 ± 33.1 | 74.8 ± 17 | 27.9 ± 13.8 | 64.3 ± 4.6 | 63.9 ± 17.4 |

K_{epbidir} |
44.1 ± 13.3 | 51.2 ± 12.4 | 30.5 ± 7.0 | 49.3 ± 8.2 | 50.7 ± 10.5 |

nrCBV_{bidir} |
58.8 ± 17.3 | 54.9 ± 27.7 | 41.5 ± 7.9 | 55.6 ± 16.9 | 57.9 ± 15.8 |

The calculated summary plot for the features stemming from the MSI and the K1bidir are presented in

Summary plot of the SHAP values for the best of MSI radiomic features.

Summary plot of the SHAP values for the best of K1bidir radiomic features.

In this work, DSC perfusion qualitative metrics, stemming from different mathematical modeling techniques, were used to quantify the perfusion signal into meaningful imaging markers with the goal to develop a machine learning model for the non-invasive phenotyping of the IDH mutation status. To achieve this, we used an in-house software to calculate the DSC qualitative metrics in order to avoid the variability in the computation of parametric maps, since different software packages can produce results with significant variability in multi-center studies (

Driven from the results of _{1bidir} with an ACC of 75% and an AUCROC of 70.5%. In addition, most biomarkers showed high specificity while the CBV-related (model dependent) parameters showed high sensitivity. This can be attributed to the imbalanced nature of the dataset, calling for further future research with additional data.

It is notable from the summary plots in

Specifically, the identification of IDH mutations is recommended for the classification of gliomas in accordance with the World Health Organization grading system (

A whole brain scan was achieved in acquisition time below 2 seconds while adjusting the rest of the parameters for optimal spatial resolution given the software and hardware constraints of each scanner. Taking that into account, the derived parametric maps are independent from imaging parameters This is an important contribution of this work since it is well-known that in multi-centric studies imaging data from different vendors and protocols introduce significant variability in MRI image quality, contrast and intensity range that affects radiomics analyses. While there are remedies for this problem, there is still no standardized harmonization pipeline hampering trustworthiness and clinical adoption in multisite studies (

The major limitation of this study is the high imbalance in IDH mutation status (41 IDH-mutant and 119 IDH-wildtype cases) which occurs due to the natural prevalence of the disease. Furthermore, another limitation of our work is that our radiomic model does not work with non-enhancing-anaplastic gliomas since perfusion curves are absent and cannot produce parametric maps. In future radiomics studies, it could be interesting to investigate a meta-model which combines the highly specific outputs such as K1 with the highly sensitive parameters such as nrCBVunidir and nrCBVbidir and rCBV from gamma fitting algorithm for an overall more robust model. However, a crucial step for future advancement of imaging biomarkers will be the correct and consistent use of internationally standardized and accepted quality criteria, terminology and definitions within the field of advanced neuroimaging and radiomics. In this study the AIF selection was performed manually by the experts from the anterior cerebral artery as the mean value of all voxels inside the AIF ROI. In a future relevant work we could include an automated AIF selection software to possibly achieve more stable fitting performance and therefore more accurate parametric maps avoiding user dependency (

In conclusion, we developed a fully automated procedure for the characterization of the IDH mutation status from the DSC imaging markers. Despite the variability of the multi-centric data, the analysis was focused directly on the imaging biomarkers, without the use of complex histogram oriented or other normalization techniques. This framework has the potential of becoming an objective diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status, which can aid in the diagnosis and management of gliomas.

The data analyzed in this study is subject to the following licenses/restrictions: some of the data might contain personal information. Of course we can share the DSC data upon a reasonable request. Requests to access these datasets should be directed to GI,

The studies involving humans were approved by University College London/University College London Hospitals Joint Research Office (reference number: 213920) and the assigned North West–Liverpool Central Research Ethics Committee (reference number: 18/NW/0395). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

GI: study concept and design, mathematical modeling of the DSC curves, ML classification, interpretation, and writing of the draft. LP: literature review, writing the clinical part of the draft, and final revision. MI, KS-P, and MW: full text review, visually inspection of the produced tumor ROIs, and final revision. SB and KM: supervision and writing—review and editing. All authors contributed to the article and approved the submitted version.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This publication was funded by the ICS Internal Grants of the Foundation for Research and Technology-Hellas under the program PCa RADical PATH.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.