
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12632-1
10.1016/j.heliyon.2024.e36601
e36601
Research Article
Diagnostic performance of MRI-based radiomics models using machine learning approaches for the triple classification of parotid tumors
Guo Junjie a
Feng Jiajun a
Huang Yuqian b
Li Xianqing c
Hu Zhenbin hu_zhenbin1127@163.com
d∗∗∗
Zhou Quan zhouquan3777@smu.edu.cn
d∗∗
Xu Honggang eyxuhonggang@scut.edu.cn
a∗
a Department of Medical Imaging, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510030, Guangdong, China
b Department of Medical Imaging Center, Baiyun Branch, Nanfang Hospital, Southern Medical University, Guangzhou, 510600, Guangdong, China
c Department of Otolaryngology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, Guangdong, China
d Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, China
∗ Corresponding author. eyxuhonggang@scut.edu.cn
∗∗ Corresponding author. zhouquan3777@smu.edu.cn
∗∗∗ Corresponding author. hu_zhenbin1127@163.com
22 8 2024
15 9 2024
22 8 2024
10 17 e3660112 2 2024
26 5 2024
19 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Rationale and objectives

Preoperative differentiation of malignant tumors (MT), pleomorphic adenomas (PA), and other benign tumors of the parotid gland is critical to clinical strategy, this study aimed to develop and validate a T2-weighted image (T2WI) based radiomics model through machine learning approaches for the triple classification of parotid gland tumors.

Materials and methods

We retrospectively enrolled 147 patients from January 2010 to July 2022. T2WIs were used to extract radiomics features. Max-Relevance and Min-Redundancy (mRMR) and Extreme Gradient Boosting (XGBoost) algorithms were used to select features. Using a 5-fold cross-validation strategy, radiomics models were constructed using a Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (KNN) for the triple classification of parotid tumors. The three models were evaluated and compared using the receiver operator characteristic (ROC) curve, sensitivity, specificity, and accuracy.

Results

A total of 1057 radiomics features were extracted, and 8 features were selected to developed the radiomics model, including First-order Median, First-order Skewness, First-order Minimum, Original_shape_Flatness, Glcm Inverse Variance, Glcm Inverse Variance, Glszm Low Gray Level Zone Emphasis, and Glszm Small Area Low Gray Level Emphasis. The mean area under the curves (AUCs) for the radiomics models in training and validation sets through LR, SVM and KNN were 0.85 and 0.80, 0.85 and 0.80 and 0.83 and 0.79, respectively.

Conclusion

The T2WI-based radiomics models through LR, SVM and KNN demonstrated good performance in the triple classification of parotid tumors.

Keywords

Parotid neoplasms
Machine learning
Magnetic resonance imaging
Classification
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pmcAbbreviations:

mRMR Max-Relevance and Min-Redundancy

XGBoost Extreme Gradient Boosting

ICC interclass correlation coefficient

SVM Support Vector Machine;

LR Logistic Regression

KNN k-Nearest neighbor

AUC area under the curve

ROC, receiver operator characteristic curve

T2WI T2-weighted image

PA pleomorphic adenoma

MT malignant tumors

FNAB Fine-needle aspiration biopsy

CT computed tomography

MRI magnetic resonance imaging

ROIs regions of interest

GLCM gray level co-occurrence matrix

GLSZM gray level size zone matrix

1 Introduction

Approximately 79.4 % of parotid tumors are benign, while 20.6 % are malignant; Pleomorphic adenoma (PA) account for 47.8 % of these tumors. Despite being benign, a 4.2 % recurrence rate after superficial parotidectomy has been reported by Johns Hopkins University, with a malignant transformation rate of about 1 %. For most other benign tumors, such as Warthin tumor(WT), myoepithelioma and lipoma, complete surgical excision is typically sufficient. In contrast, malignancies such as squamous cell carcinoma, adenoid cystic carcinoma, and high-grade mucoepidermoid carcinoma require more aggressive treatments, including total and/or radical parotid excision and neck dissection [1]. Therefore, accurate differentiation between PA, malignant tumors (MT), and other benign tumors of the parotid gland is essential.

Fine-needle aspiration biopsy (FNAB) can provide pathological diagnoses of parotid gland tumors; however, it may lead to cell transplantation or tumor capsule rupture [2], and limited sampling can result in inconclusive diagnoses [3]. Preoperative imaging for diagnosing parotid gland tumors includes computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound. Ultrasound, however, is highly dependent on the operator's experience and knowledge. CT, while radiative, has inferior soft tissue resolution compared to MRI. Nonetheless, MRI findings can sometimes be uncertain due to the overlapping image features of parotid gland tumors [4].

Radiomics is an emerging and promising method that systematically quantifies tumors by extracting and mining extensive texture information through high-throughput processes [5]. With the rapid development of algorithms, an increasing number of studies have demonstrated that radiomics aids in the diagnosis, treatment, and prognosis of tumors [[6], [7], [8]]. The development, validation, and comparison of different machine learning approaches are crucial to the success of radiomics [9]. A recent article explored the value of DWI-based radiomics for classifying PA, WT, and malignancies, showing good performance [10]. However, the study was limited to 2D images for feature extraction, which may not fully represent the entire tumor volume in comparison to three-dimensional (3D) analysis. Federica Vernuccio et al. found that T2-weighted imaging (T2WI)-based radiomics outperformed other conventional sequences in differentiating PA from WT [11]. However, the study had a small sample size. Michela Gabelloni et al. conducted a radiomic analysis of parotid tumors using T2WI and found that PA could be distinguished from WT and malignancies with high sensitivity, specificity, and diagnostic accuracy [12]. However, the data were not normalized or preprocessed. To date, little research has investigated the importance of different MRI-based radiomics models in differentiating malignant tumors (MT), PA, and other benign tumors of the parotid gland, which could be more clinically useful.

In the present research, we select T2-weighted images (T2WI) they can reveal the inner structure of tumors in greater detail than T1WI. T2WI can be obtained through MRI scanning without the routine use of contrast agents [13,14]. The radiomics model based on T2WI has demonstrated the highest diagnostic efficiency for diagnosing PA [11].

This study aims to develop and validate MRI-based radiomics models using various machine-learning methods for the triple classification of parotid gland tumors. This may assist in providing precise clinical strategies for the management of parotid tumors.

2 Materials and methods

The retrospective nature of this investigation received approval from the Ethics Committees of two academic centers, and informed consent from participants was waived due to the study's non-interventional design.

2.1 Patients

The inclusion criteria were as follows: (1) patients with pathologically confirmed parotid tumors and complete clinical information; (2) patients who underwent MRI, including axial T2WI, within seven days prior to surgery. The exclusion criteria were: (1) tumors with a shortest diameter of less than 5 mm; (2) images with artifacts. To minimize partial volume effects [15], tumors with a shortest diameter of less than 5 mm were excluded.

Between January 2010 and July 2022, 147 patients (101 males, 46 females, age range 9–91, median age 53) from two centers were included. The study population comprised 20 MTs, which included the following specific types: three adenoid cystic carcinoma, three mucoepidermoid carcinomas, three acinar cell carcinomas, two myoepithelial carcinomas, two ductal adenocarcinomas, two lymphoepithelioma-like carcinomas, one secretory carcinoma, one adenocarcinoma, one squamous cell carcinoma, one lymphoma, and one metastatic carcinoma, Additionally, there were 56 PA and 71 other benign tumors, categorized as follows: 63 Warthin tumors, one myoepithelioma, one cystadenoma, one basal cell adenoma, one dermatofibroma, one cavernous hemangioma, one cyst, one lipoma, and one neurofibroma).

2.2 MRI protocol

All subjects underwent examination using one 1.5T and two 3.0T MRI scanners: Achieva 1.5T; Ingenia 3.0T (Philips Healthcare); and Skyra 3.0T (Siemens). Patients were scanned in the supine position. Axial T2WI was obtained with the following parameters: excitation times, 2; echo time: 89, 80, 100 ms; repetition time: 2818 ms, 2500 ms, 3000 ms; The layer spacing was 0.6, 1.2, or 0.5 mm; slice thickness, 5 or 4 mm; scanning matrix 368 × 283, 220 × 154, or 384 × 384; and fields of view 220 × 220, 220 × 200, or 240 × 240 mm.

2.3 ROI segmentation and feature extraction

Radiologist 1 with 9 years of experience in head and neck imaging diagnosis, manually delineated regions of interest (ROIs) on T2WIs using ITK-SNAP (Version 3.8) [16]. The tumor was contoured layer by layer, ensuring coverage of the maximum tumor range without exceeding the boundary. Fig. 1 illustrates the development and validation of the radiomics model. Feature extraction was performed using Pyradiomics software, which can be found at http://pyradiomics.readthedocs.io [17]. To ensure the reproducibility of the results, we employed image resampling and z-score normalization for data preprocessing [18].Fig. 1 Workflow of this research. mRMR = Max-Relevance and Min-Redundancy; ICC = interclass correlation coefficient; SVM=Support Vector Machine; KNN = k-Nearest neighbor; AUC = area under the curve; ROC = receiver operator characteristic curve.

Fig. 1

2.4 Feature selection and the radiomics models through three machine-learning approaches

A total of 45 patients, comprising 15 MT, 15 PA, and 15 other benign tumors, were randomly selected for ROI segmentation. This process was conducted to evaluate intra-observer and inter-observer agreement through intraclass correlation coefficients (ICCs) [19], An ICC value greater than 0.75 indicated good consistency in the reproducibility of the extracted features. Thirty days after the initial segmentation, the 45 cases were re-evaluated by Radiologist 1 to assess intra-observer consistency. Additionally, Radiologist 2, who possesses 6 years of experience in head and neck imaging diagnosis, segmented these cases to evaluate inter-observer agreement.

Features with ICCs greater than 0.75 were retained. The Max-Relevance Min-Redundancy (mRMR) algorithm was utilized to reduce the dimensionality of the features. The Extreme Gradient Boosting (XGBoost) algorithm was employed to rank the significance of the features, thereby enhancing the prediction accuracy of the models. The radiomics models were constructed using Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbor (KNN) algorithms. These models were validated through a 5-fold cross-validation strategy.

2.5 Performance of three models

The performance of the three models for the triple classification of parotid gland tumors were evaluated by calculating the mean area under the curves (AUCs), sensitivity, specificity, and accuracy of the training and validation sets.

2.6 Statistical analysis

Statistical analyses were conducted using (version 3.8.0, https://www.python.org/) and R (version 4.2, https://www.R-project.org/). Quantitative data were analyzed using analysis of variance or nonparametric test, as appropriate. Three machine learning algorithms—KNN, LR, and SVM—were employed for feature classification. The original sample was divided into five subsamples through 5-fold cross-validation; each subsample's data was retained for model validation, while the data from the remaining four subsamples were used for training. This process was repeated for each subsample, and the average performance across the 5-fold cross-validation was calculated. The receiver operating characteristic (ROC) curves were analyzed using the 'pROC' package. Intra-observer and inter-observer agreements were assessed using ICCs. A p-value of less than 0.05 was considered to indicate statistical significance.

3 Results

3.1 Feature extraction and selection

In total, 1057 features were extracted from the radiomic analysis. Of these, 1024 features with intra- and inter-observer ICCs greater than 0.75 were retained, as detailed in Supplementary Material A1, A2, and A3. Utilizing the mRMR algorithm, the feature set was further reduced to 100 features, which were then ranked for importance by the XGBoost model. Ultimately, eight features were selected, including: Firstorder Minimum, Firstorder Median, Firstorder Skewness, Original_shape_Flatness, Glcm InverseVariance, Glcm InverseVariance, Glszm LowGrayLevelZoneEmphasis, Glszm SmallAreaLowGrayLevelEmphasis. Wavelet filters were employed for image denoising to enhance the texture analysis. Table 1 presents the selected features along with their corresponding filters. Fig. 2 illustrates the distribution of these features among PA, WT, and other benign tumors of the parotid gland.Table 1 Radiomics feature selection results and the filters used.

Table 1Radiomics feature name	Feature classes	Filter	
Minimum	First Order Features	wavelet-LLL	
Median	First Order Features	wavelet-HLH	
Skewness	First Order Features	wavelet-HLH	
Original shape Flatness	Shape Features (3D)	NA	
Inverse Variance	Gray Level Co-occurrence Matrix Features	wavelet-HLL	
Inverse Variance	Gray Level Co-occurrence Matrix Features	wavelet-LLL	
Low Gray Level Zone Emphasis	Gray Level Size Zone Matrix Features	wavelet-HLH	
Small Area Low Gray Level Emphasis	Gray Level Size Zone Matrix Features	wavelet-LHL	
Abbreviations: NA: not applicable.

Fig. 2 The distribution and comparison of 8 selected features in WT, PA, and other benign tumors of parotid gland. An asterisk indicated Significant differences at p ≤ 0.05; two asterisk indicated Significant differences at p ≤ 0.01; three asterisk indicated Significant differences at p ≤ 0.001; ns indicated no Significant differences. PA = pleomorphic adenomas; MT = malignant tumors; Other = other benign tumors.

Fig. 2

3.2 Performance of three models

The AUCs, sensitivity, specificity, and accuracy for the training and validation sets of the SVM, LR, and KNN models are presented in Table 2. In both training and validation sets, the T2WI-based radiomics models through LR, SVM and KNN showed good performance. The ROC curves for these three models are depicted in Fig. 3.Table 2 Classification and predictive performances of the machine learning models in training and validation sets.

Table 2Model	AUC (95 % CI)	Sensitivity	Specificity	Accuracy	
Train	
SVM	0.85 (0.82–0.87)	60.61 %	81.74 %	80.23 %	
LR	0.85 (0.83–0.86)	62.23 %	82.98 %	81.30 %	
KNN	0.83 (0.82–0.84)	48.03 %	77.68 %	75.96 %	
Validation	
SVM	0.80 (0.75–0.85)	57.50 %	80.13 %	78.25 %	
LR	0.80 (0.74–0.86)	57.06 %	79.89 %	77.39 %	
KNN	0.79 (0.75–0.83)	46.38 %	76.41 %	74.61 %	
Abbreviations: SVM: Support Vector Machine; LR: Logistic Regression; KNN: k-Nearest Neighbor; CI: confidence interval.

Fig. 3 ROC curves of three models in training and validation sets: KNN(a), LR (b) and SVM(c). SVM=Support Vector Machine; LR = Logistic Regression; KNN = k-Nearest neighbor; AUC = area under the curve; ROC = receiver operator characteristic curve.

Fig. 3

4 Discussion

In this investigation, we developed T2WI-based radiomics models through LR, SVM and KNN to triple classify parotid tumors using the 5-fold cross-validation strategy. AUCs of the models through LR, SVM and KNN were 0.85, 0.85 and 0.83 and in the training and 0.80 0.80 and 0.79 in the validation sets, respectively, suggesting that they could differentiate PA from MT and other benign tumors of the parotid gland.

Previous studies have reported MRI scoring algorithms for diagnosing PA with specificities of 95.1 % and 98.8 % [20]. Previous studies have reported MRI scoring algorithms for diagnosing PA with specificities of 95.1 % and 98.8 % [21]; Additionally, these methods require contrast agent injection, which may pose a risk of contrast allergy in patients [22]. The AUC for DCE-MRI in differentiating benign from malignant tumors was 0.846 [23]. The AUC for DWI in differentiating PA from other tumors was 0.945 [24]. However, these studies did not investigate areas of cystic and necrosis within the tumors.

In the present study, our analysis encompassed the entire tumor area. A previous article constructed a DWI-based radiomics model to classify WT, PA, and malignant salivary gland tumors [10]. However, that study performed ROI segmentation solely on 2D DWI images, which can be limited by the noise inherent in DWI that may affect data interpretation. Michela Gabelloni et al. utilized T2WI-based radiomics to differentiate PA from WT or MT [12]. Nevertheless, applying MRI-based radiomics models through various machine-learning approaches to differentiate MT, PA, and other benign tumors of the parotid gland could be more meaningful for devising clinical strategies.

Radiomics can more effectively extract profound medical image information, reflecting intratumoral heterogeneity [25]. In this study, we selected higher-order features, including the Gray Level Co-occurrence Matrix (GLCM) and Gray Level Size Zone Matrix (GLSZM) features, which illustrate the spatial distribution of pixels and reflect intratumoral heterogeneity [26]. The GLCM Inverse Variance and GLSZM Low Gray Level Zone Emphasis were found to be higher in other benign tumors and MT compared to PA, indicating greater intratumoral heterogeneity in the former [27]. We employed wavelet transform to enhance texture, edge, or intensity for image denoising. Previous radiomic studies on parotid tumors have focused on differentiating PA, WT, and MTs [[10], [11], [12]]. However, few studies have explored the importance of differentiating MT, PA, and other benign parotid tumors, which could be more clinically relevant and aligned with actual clinical needs.

This study has several limitations. Firstly, the uneven distribution of various tumor types, attributed to differing incidence rates and clinical strategies, may introduce potential bias. To mitigate this, a larger and more representative sample size is required. Secondly, while not explored in this study, multi-parameter MRI-based radiomics warrant investigation in future research. Employing multiple sequences could provide richer feature extraction data than a single sequence alone. Lastly, the segmentation of ROIs was dependent on manual image delineation, which is labor-intensive and time-consuming. This aspect could be significantly enhanced through advancements in automatic feature extraction methodologies.

5 Conclusion

This research indicates that the T2WI-based radiomics models through LR, SVM and KNN for the triple classification of parotid gland tumors have good performance; this may aid in diagnosing parotid gland tumors for radiologists and clinicians.

Availability of data and materials

The data generated or analyzed during this study are available by contacting the corresponding author on reasonable request.

CRediT authorship contribution statement

Junjie Guo: Writing – original draft, Conceptualization. Jiajun Feng: Software, Data curation. Yuqian Huang: Software, Data curation. Xianqing Li: Data curation. Zhenbin Hu: Supervision. Quan Zhou: Supervision. Honggang Xu: Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Multimedia component 2

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Multimedia component 3

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Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36601.
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