Radiomics-based malignancy prediction of parotid gland tumor

Kamezawa, Hidemi; Arimura, Hidetaka; Yasumatsu, Ryuji; Ninomiya, Kenta; Haseai, Shu

doi:10.1117/12.2521362

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Although the cohort was very small and only limited features were examined, this study was one of the first to implement radiomics for this exact purpose. The same group reports also of another approach, namely six conventional machine-learning and five deep learning (DL) algorithms 35 . Despite the small sample size both studies provided promising results regarding performance, with higher AUC, ACC and sensitivity as in the present study.…”

Section: Discussionmentioning

confidence: 99%

MRI-based radiomics for predicting histology in malignant salivary gland tumors: methodology and “proof of principle”

Khodabakhshi,

Motisi,

Bink

et al. 2024

Sci Rep

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Defining the exact histological features of salivary gland malignancies before treatment remains an unsolved problem that compromises the ability to tailor further therapeutic steps individually. Radiomics, a new methodology to extract quantitative information from medical images, could contribute to characterizing the individual cancer phenotype already before treatment in a fast and non-invasive way. Consequently, the standardization and implementation of radiomic analysis in the clinical routine work to predict histology of salivary gland cancer (SGC) could also provide improvements in clinical decision-making. In this study, we aimed to investigate the potential of radiomic features as imaging biomarker to distinguish between high grade and low-grade salivary gland malignancies. We have also investigated the effect of image and feature level harmonization on the performance of radiomic models. For this study, our dual center cohort consisted of 126 patients, with histologically proven SGC, who underwent curative-intent treatment in two tertiary oncology centers. We extracted and analyzed the radiomics features of 120 pre-therapeutic MRI images with gadolinium (T1 sequences), and correlated those with the definitive post-operative histology. In our study the best radiomic model achieved average AUC of 0.66 and balanced accuracy of 0.63. According to the results, there is significant difference between the performance of models based on MRI intensity normalized images + harmonized features and other models (p value < 0.05) which indicates that in case of dealing with heterogeneous dataset, applying the harmonization methods is beneficial. Among radiomic features minimum intensity from first order, and gray level-variance from texture category were frequently selected during multivariate analysis which indicate the potential of these features as being used as imaging biomarker. The present bicentric study presents for the first time the feasibility of implementing MR-based, handcrafted radiomics, based on T1 contrast-enhanced sequences and the ComBat harmonization method in an effort to predict the formal grading of salivary gland carcinoma with satisfactory performance.

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Section: Discussionmentioning

confidence: 99%

MRI-based radiomics for predicting histology in malignant salivary gland tumors: methodology and “proof of principle”

Khodabakhshi,

Motisi,

Bink

et al. 2024

Sci Rep

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“…Deep convolution models have been highly successful in biomedical image segmentation and have been introduced to the head and neck anatomy segmentation field [16]. In recent years there are many excellent works that have attempted to implement segmentation of the parotid region using several different deep learning methods [17]- [24]. Recently, Transformer has made a breakthrough in computer vision (CV), and many new Transformer-based methods for CV tasks have been proposed [25].…”

Section: Introductionmentioning

confidence: 99%

Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images

Yin¹,

Zi'an²,

Liu³

et al. 2022

Preprint

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Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Preoperative tumor localization, differential diagnosis, and subsequent selection of appropriate treatment for parotid gland tumors is critical. However, the relative rarity of these tumors and the highly dispersed tissue types have left an unmet need for a subtle differential diagnosis of such neoplastic lesions based on preoperative radiomics. Recently, deep learning methods have developed rapidly, especially Transformer beats the traditional convolutional neural network in computer vision. Many new Transformer-based networks have been proposed for computer vision tasks. In this study, multicenter multimodal parotid gland MRI images were collected. The Swin-Unet which was based on Transformer was used. MRI images of STIR, T1 and T2 modalities were combined into a threechannel data to train the network. We achieved segmentation of the region of interest for parotid gland and tumor. The DSC of the model on the test set was 88.63%, MPA was 99.31%, MIoU was 83.99%, and HD was 3.04. Then a series of comparison experiments were designed in this paper to further validate the segmentation performance of the algorithm.

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Radiomics-based malignancy prediction of parotid gland tumor

Cited by 2 publications

References 7 publications

MRI-based radiomics for predicting histology in malignant salivary gland tumors: methodology and “proof of principle”

MRI-based radiomics for predicting histology in malignant salivary gland tumors: methodology and “proof of principle”

Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images

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