
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-24-02699
00032
10.1097/MD.0000000000039512
3
6800
Research Article
Diagnostic Accuracy Study
Prediction of Glioma enhancement pattern using a MRI radiomics-based model
Wang Wen MD wyzth0118@163.com
ab
Wang Yu MD wyzth0118@163.com
ab
Meng WenYi MD 437176127@qq.com
a
Guo ErJia MD guoerjiawn@163.com
a
He HuiShan MD happyle2019@163.com
a
Huang GuangLong MD hgl1020@163.com
c
He WenLe MD happyle2019@163.com
d
https://orcid.org/0000-0002-5140-4860
Wu YuanKui MD ab*
a Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
b The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
c Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
d Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
* Correspondence: YuanKui Wu, Department of Medical Imaging, Nanfang Hospital, Southern Medical University, No. 1838 Guangzhou Avenue North, Guangzhou, Guangdong 510515, China (e-mail: ripleyor@126.com).
06 9 2024
06 9 2024
103 36 e3951218 3 2024
24 4 2024
09 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Contrast-MRI scans carry risks associated with the chemical contrast agents. Accurate prediction of enhancement pattern of gliomas has potential in avoiding contrast agent administration to patients. This study aimed to develop a machine learning radiomics model that can accurately predict enhancement pattern of gliomas based on T2 fluid attenuated inversion recovery images. A total of 385 cases of pathologically-proven glioma were retrospectively collected with preoperative magnetic resonance T2 fluid attenuated inversion recovery images, which were divided into enhancing and non-enhancing groups. Predictive radiomics models based on machine learning with 6 different classifiers were established in the training cohort (n = 201), and tested both in the internal validation cohort (n = 85) and the external validation cohort (n = 99). Receiver-operator characteristic curve was used to assess the predictive performance of these radiomics models. This study demonstrated that the radiomics model comprising of 15 features using the Gaussian process as a classifier had the highest predictive performance in both the training cohort and the internal validation cohort, with the area under the curve being 0.88 and 0.80, respectively. This model showed an area under the curve, sensitivity, specificity, positive predictive value and negative predictive value of 0.81, 0.98, 0.61, 0.82, 0.76 and 0.96, respectively, in the external validation cohort. This study suggests that the T2-FLAIR-based machine learning radiomics model can accurately predict enhancement pattern of glioma.

enhancement pattern
Glioma
MRI
radiomics
T2-FLAIR
the Southern Medical University student innovation and entrepreneurship training programS202112121081 Yuankui Wuthe Clinical Research Program of Nanfang Hospital, Southern Medical University2020CR004 Yuankui Wuthe Medical Research Foundation of Guangdong ProvinceA2021048 Yuankui WuOPEN-ACCESSTRUE
SDCT
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pmc 1. Introduction

Glioma is the most common type of primary brain tumor in humans. For newly-diagnosed gliomas, surgical resection is the cornerstone of all treatment options.[1,2] Magnetic resonance (MR) scans reveal the anatomical location and boundary of gliomas, measures that are crucial for planning surgical procedures.[3] On contrast-enhanced T1-weighted (CET1-W) images, about 70% of gliomas show avid contrast enhancement (enhancing pattern), allowing a clear delineation of the boundary of a tumor.[4] However, the remaining 30% of gliomas show minimal or no contrast enhancement (non-enhancing pattern) and are usually not well depicted on CET1-W images. Clinically, surgical planning is mainly based on CET1-W images for contrast-enhancing gliomas[5] while on T2 fluid-attenuated inversion recovery (T2-FLAIR) images for non-enhancing gliomas.[6–9] Therefore, CET1-W scan is not a necessary preoperative sequence for management of naïve patients with glioma if the enhancement pattern could be accurately predicted.

It is well known that gadolinium-based contrast agents (GBCA) used in CET1-W scanning are associated with a number of health risks for patients.[10] First, the use of GBCA may lead to complications such as allergic reactions, deposition of gadolinium in the brain, and nephrogenic systemic fibrosis.[11] Second, intravenous injection of GBCA might induce movement artifacts and result in compromised image quality and even the failure of a scan. Consequently, any potential risk of administering contrast agents to an individual should be weighed against the diagnostic benefit of information that can be obtained.[12] In fact, the European Medicines Agency limits the use of several GBCAs and has suspended authorization of others.[13] Research studies have begun to investigate alternative imaging methods that reduce or bypass the need of GBCAs. This includes advanced MRI analysis[14–16] and assessments of cell metabolism, such as chemical exchange saturation transfer to measure mobile proteins and peptides in the cytoplasm within a given area of tissue.[17] Moreover, neuroimaging studies generated deep-learning algorithms to synthesize CET1-W images from non-contrasted MRI data.[18,19] Different from previous studies, this analysis demonstrated a novel way to address safety concerns related to GBCA administration. Continuing to develop ways to accurately predict tissue enhancement patterns may save patients with non-enhancing gliomas from undergoing unnecessary CET1-W scans.

The rapid development and refinement of artificial intelligence is quickly advancing medical diagnostics.[20] Radiomics has become an important tool for clinical cancer research,[21,22] and machine learning combined with advanced radiomics is an attractive prospect for patient care in the near future.[23–25] However, to our knowledge, there are no published studies on the application of machine learning to predict glioma enhancement pattern. Therefore, we hypothesized that machine learning-based MRI models can accurately predict contrast-based enhancement pattern of glioma in humans.

In this study, we attempted to develop a machine learning radiomics model based on T2-FLAIR images and evaluate its accuracy in predicting glioma enhancement pattern, aiming to provide a potential tool to triage naïve patients with non-enhancing glioma for post-contrast MR scans.

2. Materials and methods

2.1. Study participants

Data were collected from medical centers A and B. This retrospective study was approved by the institutional review committee of the 2 centers; written informed consent was waived. The chart in Figure 1 illustrates the workflow of this study. Between January 2012 and December 2021, a total of 565 and 178 patients with surgically- and pathologically-confirmed gliomas were selected from each medical center, respectively. The exclusion criteria were as follows: absence of preoperative MRI sequence: axial T1-weighted (T1-W), T2-FLAIR or CET1-W; poor MRI image quality; history of any major interventions prior to MR scan, such as biopsy and radiotherapy; recurrent glioma cases. After accounting for these criteria, 385 patients were included in the study. Demographic and clinical characteristics, including age, sex, and pathology, were collected from each hospital’s archive and communication system.

Figure 1. Flowchart of the study population.

Patients from medical center A were randomly divided into the training (n = 201) and internal validation cohorts (n = 85) with a ratio of 7:3 by using stratified sampling on the Feature Explorer Pro (FAE) research platform (magnetic resonance key laboratory of Shanghai, East China Normal University, https://github.com/salan668/FAE).[26] Patients from medical center B were used as an external validation cohort (n = 99).

2.2. MRI data acquisition and analysis

In medical center A, MR examination was performed with two 3T scanners (GE Signa Excite and Philips Achieva) and four 1.5T clinical scanners (GE Optima MR 360, GE Signa HDxt, Siemens Magnetom Vision Plus, and Siemens Avanto) with head or head-neck coil.

MR scans included T1-W (repetition time [TR]/echo time [TE] = 550.0–2175.0/10.0–24.0 ms, voxel = 0.55 × 0.88 × 5–0.70 × 0.92 × 6 mm3); T2-FLAIR (TR/TE = 8600.0–11000.0/110.0–160.0 ms, voxel = 0.65 × 1.06 × 5–0.7 × 0.7 × 6 mm3); CET1-W (TR/TE = 550.0–260.0/4.6–19.0 ms, voxel = 0.65 × 0.80 × 5–0.75 × 0.70 × 6 mm3). CET1-W scan was performed after intravenous injection of 0.1to 0.2 mmol/kg gadolinium-based contrast agents (Omniscan TM, GE Healthcare, Ireland; Magnevist, Schering, Berlin, Germany; Gadopenteate Dimeglumine, Consun, Guangzhou, China) at a rate of 2.0 to 2.5 mL/s, followed by a 20 mL sterile saline flush.

In medical center B, two 3T MRI scanners with head or head-neck coil (Philips Achieva and Philips Ingenia) were used. MR scans included T1-W (TR/TE = 1750.0/24.0 ms, voxel = 0.75 × 0.80 × 5 mm3); T2-FLAIR (TR/TE = 11000.0/125.0 ms, voxel = 0.7 × 0.7 × 6 mm3); CET1-W (TR/TE = 340.0/9.8 ms, voxel = 0.75 × 1.25 × 5 mm3). Contrast agent at a dose of 0.2 mmol/kg was injected intravenously (Omniscan TM, GE Healthcare, Ireland; Gadopentetate Dimeglumine, Consun, Guangzhou, China) at a rate of 2.0 to 2.5 mL/s, followed by a 20 mL sterile saline flush.

MR images were reviewed independently by 2 investigators (21 years of experience in neuroradiology, and 16 years of experience in neurosurgery, respectively). In cases of discrepancies, MR images were reevaluated by both investigators until a consensus was reached. The following contrast enhancement characteristics were evaluated by visual assessment: minimal or no, nodule-like, patch and avid, or ring-like (Fig. 2). Those showing minimal or no enhancement were included into the non-enhancing group and those showing nodule-like, patch and avid, or ring-like enhancement were included into the enhancing group. Intratumoral blood vessels with linear remarkable enhancement were not considered as tumor enhancement. Histopathological diagnosis was made in a blind manner by an experienced neuropathologist according to WHO criteria for glioma classification.[1]

Figure 2. Examples of enhancement patterns of gliomas. (A) Minimal or no enhancement. The mass in the right frontal and island lobe shows minimal or no enhancement. (B) Nodule-like enhancement. The mass in the right frontal lobe shows an enhancing nodule at the periphery (arrow). (C) Patch and avid enhancement. The mass in the right hypothalamus shows avid and uneven enhancement (arrow). (D) Ring-like enhancement. The mass in the right cerebellar hemisphere shows an avidly enhancing ring at the periphery (arrow). Note the decreased delineation of tumor borders on the post-contrast image (A, arrowheads) compared with the pre-contrast T1 image (A, arrowheads) and T2-FLAIR image (A, arrowheads). CET1 = contrast-enhanced T1-weighted image, T1 = T1-weighted image, T2F = T2 fluid attenuated inversion recovery image.

2.3. Image preprocessing

To ensure uniformity for radiomics analysis, all MR images of each cohort were resampled to a common pixel spacing of 0.7 × 0.7 × 6 mm3 and image intensities were normalized to a dynamic range of 0 to 255.

2.4. Tumor segmentation

Volumes of interest (VOIs) covering the entire tumor were drawn manually along with the tumor contours on axial T2-FLAIR sequence images by 1 neuroradiologist (X.X. with 5 years of experience) using ITK - SNAP software (https://www.itksnap.org, version 3.6.0). All contours were examined by a senior neuroradiologist (X.X.). Only the VOIs drawn by the senior neuroradiologist were used for further model training and validation. The intraclass correlation coefficient (ICC) was used to assess the reproducibility of the texture features extracted from two different VOI groups.[27] The features that had good to excellent reliability (ICC ≥ 0.80) and significant difference between the enhancing and non-enhancing groups were included for developing the predictive model.

2.5. Radiomics feature extraction

After segmentation, 3 groups of radiomic features were extracted: shape feature, first-order feature and texture feature using the Philips Radiomics tool (Philips Medical Systems, Shanghai, China) based on pyRadiomics.[26] In short, first-order features describe the distribution of voxel intensity throughout the tumor, and texture features describe spatial interdependence among voxels, including gray-level co-occurrence matrix (GLCM), gray-level running length matrix (GLRLM), gray-level size zone matrix (GLSZM), neighboring gray tone difference matrix (NGTDM) and gray-level dependence matrix (GLDM). Furthermore, radiomic features also included the first order feature and texture feature of the image processed by 5 different filters, namely, exponential, square, square root, logarithm and wavelet. Wavelet filter was used to extract features from 8 wavelet decomposition images. For each patient, a total of 107 original features and 1116 filter-based features were collected in each sequence (Table S1, Supplemental Digital Content, http://links.lww.com/MD/N501). The 107 original features included 14 shape features, 18 first-order features, and 75 texture features (24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM). Definitions and formulas of these radiomic features can be found at this website: https://pyradiomics.readthedocs.io/en/latest/features.html. Normalization was applied to all radiomic features to remove different feature magnitudes by scaling values to [−1, 1].

2.6. Feature selection and radiomics model development

In this study, 2 methods, including Pearson correlation coefficient (PCC) and analysis of variance (ANOVA), were used to reduce the dimensions of the features and select those most appropriate for radiomics model-building in the training cohort. Firstly, we used PCC method to reduce the dimension of the feature space. Due to the high dimension of the feature space, we compared the similarity of each feature pair. If the PCC value of feature pairs was >0.99, one feature was removed. Then, we used ANOVA to select distinct features. ANOVA is a common method to explore salient features corresponding to labels. We calculated F values to evaluate the relationship between features and tags, sorted features according to the corresponding F-value, and selected a specific number of features to build the model. Gaussian process (GP), linear regression (LR), LR-least absolute shrinkage and selection operator, Support Vector Machine, linear discriminant analysis (LDA) and naïve Bayes (NB) were used as classifiers. Combined with these features, a joint distribution was established to estimate the probability of classes, and the optimal model was obtained according to AUC values. After this, machine learning models were built based on the selected radiomic features with these 6 classifiers. In order to determine the hyperparameters of the model (such as the number of features), we used 5-fold cross-validation on the training data set. The hyperparameters were set according to the performance of the model on the validation data set. The flowchart for the data processing is displayed in Figure 3.

Figure 3. Radiomics pipeline of the study. ANOVA = analysis of variance, FAE = feature explorer pro, PCC = Pearson correlation coefficient, ROC = receiver operating characteristic curve.

2.7. Statistical analysis

All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corporation, Armonk). P < .05 was considered significant. The receiver operating characteristic (ROC) curve was used to analyze the predictive performance of the model, and the areas under the ROC curve (AUC) were calculated. Accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated, with cutoff values being established by maximizing the Youden index. In addition, 95% confidence intervals (CIs) were estimated using the bootstrap method.

3. Results

3.1. Patient demographics

Table 1 shows the clinical and pathological characteristics and enhancement pattern of the population in the 3 cohorts. There were no significant differences in gender (P = .23), age (P = .35), location (P = .68), WHO grade (P = .59) or enhancement pattern (P = .88).

Table 1 Basic characteristics of the study population.

Characteristic		Training cohort
(n = 201)	Internal validation cohort
(n = 85)	External validation cohort
(n = 99)	P value	
Sex	Male	124	47	67	.23	
Female	77	38	32	
Age (yr)	≥18	169	77	85	.35	
<18	32	8	14	
Location	Cerebrum	170	73	87	.75	
Cerebellum	18	5	8	
Brainstem	9	3	3	
Multiple*	4	4	1	
WHO grade	I	22	10	13	.59	
II	67	30	34	
III	23	15	17	
IV	89	30	35	
Enhancement patterns†	Enhancing	137	52	68	.88	
Non-enhancing	64	33	31	
* Invading >1 part of brain.

† Enhancement patterns are determined jointly by a neuroradiologist and a neurosurgeon.

3.2. Development and performance of radiomics model

The predictive performance of the radiomics model with different classifiers is shown in Table 2. The radiomics model using GP as a classifier showed the highest AUC value in both the training cohort (0.88; 95% CI: 0.81–0.94) and the internal validation cohort (0.80; 95% CI: 0.71–0.88). The predictive performance of this model was further evaluated in the external validation cohort, with an AUC value of 0.81 (95% CI: 0.71–0.90). This model showed a high sensitivity (0.98, 54/55) and NPV (0.96, 27/28) but a relatively low specificity (0.61, 27/44) and PPV (0.76, 54/71). Figure 4 shows the ROC curve of the radiomics model in predicting tumor enhancement pattern in 3 cohorts. The features selected in the radiomics model are summarized in Table 3. Details regarding the radiomic feature selection of this model are shown in Figure S1, Supplemental Digital Content, http://links.lww.com/MD/N502. Four representative cases showing the predictive performance of the radiomics model are shown in Figure S2, Supplemental Digital Content, http://links.lww.com/MD/N503.

Table 2 Model performance of enhancement pattern prediction for patients with cerebral Glioma in the training and internal validation cohorts.

NO	NF	Classifier	Training cohort	Internal validation cohort	
AUC [95% CI]	Sen	Spe	PPV	NPV	AUC [95% CI]	Sen	Spe	PPV	NPV	
1	15	GP	0.88 [0.81–0.94]	133/137 (97.0)	47/64 (73.4)	133/150 (88.7)	47/51 (92.2)	0.80 [0.71–0.88]	64/65 (98.5)	12/20 (60.0)	64/72 (88.9)	12/13 (92.3)	
2	15	LDA	0.71 [0.63–0.79]	117/137 (85.6)	32/64 (50.0)	117/149 (78.5)	32/52 (61.5)	0.70 [0.61–0.80]	31/65 (47.7)	19/20 (95.0)	31/32 96.9	19/53 (35.8)	
3	15	LR	0.72 [0.64–0.79]	84/137 (60.7)	48/64 (75.0)	83/99 (83.8)	48/102 (47.1)	0.74 [0.65–0.83]	34/65 (52.3)	18/20 (90.0)	34/36 (94.4)	18/49 (36.7)	
4	15	LRLasso	0.72 [0.64–0.79]	89/137 (65.2)	44/64 (68.8)	89/109 (81.7)	44/92 (47.8)	0.74 [0.65–0.83]	34/65 (52.3)	18/20 (90.0)	34/36 (94.4)	18/49 (36.7)	
5	15	NB	0.70 [0.62–0.77]	86/137 (62.7)	44/64 (68.8)	86/106 (81.1)	44/95 (46.3)	0.64 [0.58–0.71]	30/65 (46.2)	17/20 (85.0)	30/33 (90.9)	17/52 (32.7)	
6	11	SVM	0.67 [0.58–0.76]	109/137 (79.6)	35/64 (54.7)	109/138 (79.0)	35/63 (55.6)	0.69 [0.60–0.78]	50/65 (76.9)	13/20 (65.0)	50/57 (87.7)	13/28 (46.4)	
AUC = area under the curve, CI = confidence interval, GP = Gaussian process, Lasso = least absolute shrinkage and selection operator, LDA = linear discriminant analysis, LR = linear regression, NB = naïve Bayes, NF = number of features, NPV = negative predictive value, PPV = positive predictive value, Sen = sensitivity, Spe = specificity, SVM = support vector machine.

Table 3 Rank of importance of selected features of the radiomics model.

Features	Rank	
Logarithm_GLSZM_LowGrayLevelZoneEmphasis	1	
Llogarithm_GLSZM_SmallAreaLowGrayLevelEmphasis	2	
Original_Shape_MinorAxisLength	3	
Squareroot_GLRLM_ShortRunLowGrayLevelEmphasis	4	
Squareroot_GLSZM_LowGrayLevelZoneEmphasis	5	
Squareroot_GLSZM_SmallAreaLowGrayLevelEmphasis	6	
Wavelet-HLH_GLSZM_ZoneEntropy	7	
Wavelet-LHH_GLCM_Inverse difference moment normalized	8	
Wavelet-LHH_GLCM_Inverse difference normalized	9	
Wavelet-LHH_NGTDM_Contrast	10	
Wavelet-LHL_GLDM_LowGrayLevelEmphasis	11	
Wavelet-LHL_GLDM_SmallDependenceLowGrayLevelEmphasis	12	
Wavelet-LHL_GLSZM_ZoneEntropy	13	
Wavelet-LLL_GLDM_SmallDependenceLowGrayLevelEmphasis	14	
Wavelet-LLL_GLSZM_SmallAreaLowGrayLevelEmphasis	15	
GLCM = gray-level co-occurrence matrix, GLRLM = gray-level running length matrix, GLSZM = gray-level size zone matrix, NGTDM = neighboring gray tone difference.

Figure 4. Performance of the radiomics model in predicting enhancement patterns of glioma. (A) training cohort; (B) internal validation cohort; and (C) external validation cohort. The model shows steady performance and has a high negative predictive value in all 3 cohorts.

4. Discussion

The objective of the study is to develop a machine learning-based radiomics model using T2-FLAIR alone for the prediction of glioma enhancement patterns, and assessed its potential value in triaging glioma patients for post-contrast MRI scans before surgery. The developed GP radiomics model was effective in the training cohort (AUC, 0.88; 95% CI: 0.81–0.94), internal validation cohort (AUC, 0.80; 95% CI: 0.71–0.88) and independent external validation cohort (AUC, 0.81; 95% CI: 0.71–0.90) in predicting the enhancement pattern of gliomas. In particular, this model had an excellent negative predictive value in all 3 cohorts (all > 0.92).

Development of optimal and consistent machine learning methods is essential for clinical application.[26] Therefore, 6 machine learning methods were evaluated in order to attain the best performance in this study. The developed GP radiomics model correctly predicted tumor enhancement patterns in the external validation cohort, with an NPV value as high as 0.964. If utilized in a clinical setting, 27.3% of patients would obviate the need for universal contrast-enhanced MR scans, avoiding potential risks associated with contrast agent administration. Not only is this beneficial to patients with other health problems, it can save labor and costs associated with patient preparation and running the scanners. Nevertheless, the model did misclassify a small proportion of patients that showed obvious enhancement (1.8%, 1/55) as non-enhancing in the external validation cohort. This would unintentionally exclude patients from CET1-W scans that might be useful in measuring tumor borders and determining the extent of surgical resection needed. However, for suspected glioma patients, multimodal MRI including functional MRI sequences such as diffusion- and perfusion-weighted MRI without the need of exogenous contrast agents, as well as amide proton transfer imaging, are increasingly being used in daily practice, and may serve as an alternative method for evaluation of gliomas.[28–30]

In this study, the GP radiomics model showed a low specificity and suboptimal PPV, which means that some of the non-enhancing glioma cases did not benefit from the predictive model. Of note, the LDA, LR and LRLasso models have high specificity and PPV, and the NB model has a high PPV. However, all of these models have a very low NPV. Considering the objective of our study, a model with a high NPV is preferable. The low specificity of the GP model may be related to the fact that only the T2-FLAIR sequence is used for model training. Since the enhancement patterns were evaluated according to signal intensity differences between T1-W images with and without contrast agent administration, additional T1-W images might help develop a more robust predictive model. Also, advanced MRI sequences such as diffusion-weighted MRI might provide valuable information to combine with the present analyses.[18] The reasons the model was developed based on T2-FLAIR data alone are 3-fold. First, T2-FLAIR is the best sequence for showing the borders of glioma among all pre-contrast MRI sequences,[3,31] and can also provide valuable information related to contrast enhancement.[12] Second, a model based on a single sequence might have potential for wider application and easier generalization. Finally, this strategy is more flexible in clinical practice, since MRI sequences can be obtained at the same time the predictive analysis with the radiomics model is being performed. This will make the entire workflow of MRI examination more fluent and less time consuming.

The present study has several limitations. First, this was a retrospective study with inherent shortcomings, such as selection bias. Second, the dataset used to train the model was limited to a single institution. We did, however, collect data that were acquired over a period of ten years with 6 different MRI vendors including 3T and 1.5T scanners, and the trained model was applicable to an external dataset. Third, all cases used for model training were patients with pathologically-confirmed gliomas, while other types of cerebral tumors and non-tumoral diseases, such as inflammation and infectious diseases, were not included. Therefore, prospective studies are needed to evaluate and test our model in a wider spectrum of brain diseases before full application in the clinic. Moreover, the evaluation of enhancement patterns was subject to inter-observer variability due to the lack of a gold standard for such analyses. Of note, a neurosurgeon who is the most experienced in glioma resection in our hospital participated the evaluation of enhancement pattern, which might indicate a great potential of our radiomics model. Finally, manual VOI segmentation in this study is time-consuming and might cause inter-observer inconsistency, and would require the development of an automated or semi-automated segmentation tool based on deep learning.

In conclusion, we developed a machine learning radiomics model based on a single MRI sequence with excellent performance in predicting enhancement pattern of glioma. This provides an alternate platform for the decision-making process of whether to perform post-contrast MRI scans in a patient. Strategies such as these can utilize radiologic machine learning to reduce the unnecessary use of contrast agents in diagnostic assessments.

Acknowledgments

This manuscript was funded by the College Students’ Innovative Entrepreneurial Training Plan Program, Southern Medical University [Grant Numbers: S202112121081].

Author contributions

Conceptualization: YuanKui Wu.

Data curation: Wen Wang, Yu Wang, WenYi Meng, ErJia Guo, HuiShan He, GuangLong Huang.

Formal analysis: Wen Wang, Yu Wang, YuanKui Wu.

Funding acquisition: YuanKui Wu.

Investigation: Wen Wang, Yu Wang, WenLe He.

Methodology: Wen Wang, Yu Wang.

Project administration: YuanKui Wu.

Resources: YuanKui Wu.

Software: Yu Wang, WenYi Meng, ErJia Guo, HuiShan He, WenLe He.

Supervision: WenLe He, YuanKui Wu.

Validation: Wen Wang, Yu Wang, WenYi Meng, ErJia Guo, HuiShan He, YuanKui Wu.

Visualization: Wen Wang, WenYi Meng, HuiShan He.

Writing – original draft: Wen Wang, Yu Wang.

Writing – review & editing: WenLe He, YuanKui Wu.

Supplementary Material

Abbreviations:

ANOVA analysis of variance

AUC areas under the ROC curve

CET1-W contrast-enhanced T1-weighted

CIs 95% confidence intervals

GBCA gadolinium-based contrast agents

GLCM gray-level co-occurrence matrix

GLDM gray-level dependence matrix

GLRLM gray-level running length matrix

GLSZM gray-level size zone matrix

GP Gaussian process

LDA linear discriminant analysis

LR linear regression

MR magnetic resonance

NB naïve Bayes

NGTDM neighboring gray tone difference matrix

NPV negative predictive value

PCC Pearson correlation coefficient

PPV positive predictive value

ROC the receiver operating characteristic

T1-W T1-weighted

T2-FLAIR T2 fluid-attenuated inversion recovery

VOIs volumes of interest.

This work was supported by the Southern Medical University student innovation and entrepreneurship training program (No. S202112121081), the Clinical Research Program of Nanfang Hospital, Southern Medical University (No. 2020CR004), and the Medical Research Foundation of Guangdong Province (No. A2021048).

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of Nanfang Hospital (medical centers A) and Zhujiang Hospital (medical centers B) and individual consent for this retrospective analysis was waived.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

Supplemental Digital Content is available for this article.

How to cite this article: Wang W, Wang Y, Meng W, Guo E, He H, Huang G, He W, Wu Y. Prediction of Glioma enhancement pattern using a MRI radiomics-based model. Medicine 2024;103:36(e39512).

WW, YW, WH, and YW contributed to this article equally.
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