Diagnostic value of maximum signal intensity on T1-weighted MRI images for differentiating parotid gland tumours along with pathological correlation

Wei, Peiying; Shao, Changming; Tao, Hongbing; Wang, H.B.; Ding, Zhongxiang; Han, Zhijiang

doi:10.1016/j.crad.2021.02.011

Cited by 6 publications

(6 citation statements)

References 34 publications

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“…However, it was unable to distinguish PAs from MTs. 24 Based on our previous studies, we further explored the value of SIR in differentiating between the three parotid tumor types simultaneously. In this study, SIR P , SIR C , and SIR F were obtained by conventional T2-weighted imaging with parotid gland, spinal cord, and buccal subcutaneous fat as references, and the corresponding efficacies in the differential diagnosis of PAs, WTs, and MTs were evaluated.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Analysis and Pathological Basis of Signal Intensity on T2-Weighted MR Images in Benign and Malignant Parotid Tumors

Wei

Shao

Tian

et al. 2021

CMAR

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To investigate the value of the signal intensity on T2-weighted magnetic resonance (MR) imaging using quantitative analysis in the differentiation of parotid tumors. Materials and Methods: MR data of 80 pleomorphic adenomas (PAs), 68 Warthin tumors (WTs), and 34 malignant tumors (MTs) confirmed by surgery and histology were retrospectively analyzed. The signal intensities of tumor, normal parotid gland, spinal cord, and buccal subcutaneous fat were measured, and the signal intensity ratios (SIRs) between the tumor and the three references were calculated. Receiver operating characteristic curve was used to determine the optimal threshold and diagnostic efficiency of SIR for differentiating PAs, WTs, and MTs. Results: The area under the curve (AUC) of tumor to parotid gland SIR (SIR P ), tumor to spinal cord SIR (SIR C ), and tumor to buccal subcutaneous fat SIR (SIR F ) for differentiating PAs and WTs was 0.922, 0.918, and 0.934, respectively. The sensitivity and specificity at an optimal SIR threshold were 86.3% and 91.2%, 80.0% and 97.1%, and 85.0% and 94.1%, respectively. The AUC of SIR P , SIR C , and SIR F for distinguishing PAs from MTs was 0.793, 0.802, and 0.774, respectively. The sensitivity and specificity at an optimal SIR threshold was 86.3% and 61.8%, 80.0% and 73.5%, and 82.5% and 73.5%, respectively. The AUC of SIR P , SIR C , and SIR F for distinguishing WTs from MTs was 0.716, 0.709, and 0.759, respectively. The sensitivity and specificity at an optimal SIR threshold were 61.8% and 82.4%, 55.9% and 82.4%, and 64.7% and 86.8%, respectively. Conclusion: SIR P , SIR C , and SIR F on T2-weighted MR images had high diagnostic efficiency for differentiating between PAs and WTs, while SIR P and SIR C for differentiating between PAs and MTs, and SIR F for differentiating between WTs and MTs had relatively high diagnostic efficiency.

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Section: Discussionmentioning

confidence: 99%

Quantitative Analysis and Pathological Basis of Signal Intensity on T2-Weighted MR Images in Benign and Malignant Parotid Tumors

Wei

Shao

Tian

et al. 2021

CMAR

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“…The features of MRI sequences are critical in the diagnosis of parotid neoplasms 27–30 . The present study is innovative in terms of utilizing a newly designed CNN to classify parotid gland tumors.…”

Section: Discussionmentioning

confidence: 99%

“…The features of MRI sequences are critical in the diagnosis of parotid neoplasms. 27 , 28 , 29 , 30 The present study is innovative in terms of utilizing a newly designed CNN to classify parotid gland tumors. Briefly, the first two layers of the pre‐trained Resnet‐18 were selected to extract the overall image features based on the concept of learning transfer.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences

et al. 2022

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Objective: To design a deep learning model based on multimodal magnetic resonance image (MRI) sequences for automatic parotid neoplasm classification, and to improve the diagnostic decision-making in clinical settings.Methods: First, multimodal MRI sequences were collected from 266 patients with parotid neoplasms, and an artificial intelligence (AI)-based deep learning model was designed from scratch, combining the image classification network of Resnet and the Transformer network of Natural language processing. Second, the effectiveness of the deep learning model was improved through the multi-modality fusion of MRI sequences, and the fusion strategy of various MRI sequences was optimized. In addition, we compared the effectiveness of the model in the parotid neoplasm classification with experienced radiologists.Results: The deep learning model delivered reliable outcomes in differentiating benign and malignant parotid neoplasms. The model, which was trained by the fusion of T2-weighted, postcontrast T1-weighted, and diffusion-weighted imaging (b = 1000 s/mm 2 ), produced the best result, with an accuracy score of 0.85, an area under the receiver operator characteristic (ROC) curve of 0.96, a sensitivity score of 0.90, and a specificity score of 0.84. In addition, the multi-modal paradigm exhibited reliable outcomes in diagnosing the pleomorphic adenoma and the Warthin tumor, but not in the identification of the basal cell adenoma.Conclusion: An accurate and efficient AI based classification model was produced to classify parotid neoplasms, resulting from the fusion of multimodal MRI sequences. The effectiveness certainly outperformed the model with single MRI images or single MRI sequences as input, and potentially, experienced radiologists.

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“…These tend to be unifocal and are hypermetabolic on FDG PET-CT ( Figure 27 ). Although MRI and ultrasound features may be suggestive of Warthin tumor, diagnosis typically requires biopsy [ 64 , 65 ].…”

Section: Head and Neck Cancermentioning

confidence: 99%

PET-CT in Clinical Adult Oncology—V. Head and Neck and Neuro Oncology

Wiggins

Hoffman

Fine

et al. 2022

Cancers

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PET-CT is an advanced imaging modality with many oncologic applications, including staging, assessment of response to therapy, restaging, and longitudinal surveillance for recurrence. The goal of this series of six review articles is to provide practical information to providers and imaging professionals regarding the best use of PET-CT for specific oncologic indications, and the potential pitfalls and nuances that characterize these applications. In addition, key tumor-specific clinical information and representative PET-CT images are provided to outline the role that PET-CT plays in the management of oncology patients. Hundreds of different types of tumors exist, both pediatric and adult. A discussion of the role of FDG PET for all of these is beyond the scope of this review. Rather, this series of articles focuses on the most common adult malignancies that may be encountered in clinical practice. It also focuses on FDA-approved and clinically available radiopharmaceuticals, rather than research tracers or those requiring a local cyclotron. The fifth review article in this series focuses on PET-CT imaging in head and neck tumors, as well as brain tumors. Common normal variants, key anatomic features, and benign mimics of these tumors are reviewed. The goal of this review article is to provide the imaging professional with guidance in the interpretation of PET-CT for the more common head and neck malignancies and neuro oncology, and to inform the referring providers so that they can have realistic expectations of the value and limitations of PET-CT for the specific type of tumor being addressed.

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Diagnostic value of maximum signal intensity on T1-weighted MRI images for differentiating parotid gland tumours along with pathological correlation

Cited by 6 publications

References 34 publications

Quantitative Analysis and Pathological Basis of Signal Intensity on T2-Weighted MR Images in Benign and Malignant Parotid Tumors

Quantitative Analysis and Pathological Basis of Signal Intensity on T2-Weighted MR Images in Benign and Malignant Parotid Tumors

A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences

PET-CT in Clinical Adult Oncology—V. Head and Neck and Neuro Oncology

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