Deep learning model developed by multiparametric MRI in differential diagnosis of parotid gland tumors

Gündüz, Emrah; Alçın, Ömer Faruk; Kızılay, Ahmet; Yıldırım, İsmail Okan

doi:10.1007/s00405-022-07455-y

Cited by 14 publications

(11 citation statements)

References 24 publications

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“…In radiomics, the discrimination of SGTs have mostly reported results from T1WI, T2WI and DWI [ 18 , 19 , 20 , 21 ]. Only three machine learning study [ 58 , 59 , 60 ], using deep learning instead of radiomics, evaluated CE-T1WI images of the parotid gland. In agreement with our study, one of them found that CE-T1WI did not improve the classification of SGTs [ 59 ].…”

Section: Discussionmentioning

confidence: 99%

Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used?

Zhang

Wong

et al. 2022

Cancers

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The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and MRI sequence for characterizing SGTs, which could serve as a step towards obtaining a consensus on a radiomics signature. Preliminary radiomics models were built to discriminate malignant SGTs (n = 34) from benign SGTs (n = 57) on T1-weighted (T1WI), fat-suppressed (FS)-T2WI and contrast-enhanced (CE)-T1WI images using six feature categories. The discrimination performances of these preliminary models were evaluated using 5-fold-cross-validation with 100 repetitions and the area under the receiver operating characteristic curve (AUC). The differences between models’ performances were identified using one-way ANOVA. Results show that the best feature categories were logarithm for T1WI and CE-T1WI and exponential for FS-T2WI, with AUCs of 0.828, 0.754 and 0.819, respectively. These AUCs were higher than the AUCs obtained using all feature categories combined, which were 0.750, 0.707 and 0.774, respectively (p < 0.001). The highest AUC (0.846) was obtained using a combination of T1WI + logarithm and FS-T2WI + exponential features, which reduced the initial features by 94.0% (from 1015 × 3 to 91 × 2). CE-T1WI did not improve performance. Using one feature category rather than all feature categories combined reduced the number of initial features without compromising radiomic performance.

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Section: Discussionmentioning

confidence: 99%

Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used?

Zhang

Wong

et al. 2022

Cancers

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“…Multiparametric MRI has also been used for the classification or diagnosis of diseases using deep learning. 135,[150][151][152][153][154][155][156][157][158] Hagiwara et al 135 applied CNN to T1, T2, and proton density maps of the brain to extract features common to multiple sclerosis and neuromyelitis optica spectrum disorder, another demyelinating disease, and used this information to differentiate these 2 disorders, achieving an accuracy of 80%. Some studies showed the superiority of the diagnostic ability of CNN over radiomics based on multiparametric MRI.…”

Section: Deep Learningmentioning

confidence: 99%

Multiparametric MRI

et al. 2023

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With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.

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“…Specialized DL algorithms have been developed to assist in differential diagnosis between benign and malignant parotid gland tumors in contrast-enhanced CT images [30], and ultrasonography [31]. MRI remains the gold standard in the diagnosis of salivary gland diseases, where DL models intend to automatically classify salivary gland tumors with very high accuracy [32,33].…”

Section: Head and Neck Imagingmentioning

confidence: 99%

Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review

Tsilivigkos,

Athanasopoulos,

Micco

et al. 2023

JCM

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Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative trend. As the medical landscape evolves, the integration of these technologies becomes imperative in augmenting patient care, fostering innovation, and actively participating in the ever-evolving synergy between computer vision techniques in otorhinolaryngology and AI. To that end, we conducted a thorough search on MEDLINE for papers published until June 2023, utilizing the keywords ‘otorhinolaryngology’, ‘imaging’, ‘computer vision’, ‘artificial intelligence’, and ‘deep learning’, and at the same time conducted manual searching in the references section of the articles included in our manuscript. Our search culminated in the retrieval of 121 related articles, which were subsequently subdivided into the following categories: imaging in head and neck, otology, and rhinology. Our objective is to provide a comprehensive introduction to this burgeoning field, tailored for both experienced specialists and aspiring residents in the domain of deep learning algorithms in imaging techniques in otorhinolaryngology.

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Deep learning model developed by multiparametric MRI in differential diagnosis of parotid gland tumors

Cited by 14 publications

References 24 publications

Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used?

Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used?

Multiparametric MRI

Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review

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