Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI

Hou, Yi; Xie, Xiaoyang; Chen, Jixin; Lv, Peng; Jiang, Shijie; He, Xiaowei; Yang, Lijuan; Zhao, Fengjun

doi:10.1007/s00330-020-07110-2

Cited by 21 publications

(25 citation statements)

References 39 publications

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“…Of the 33 studies that applied ML techniques to diagnose a hematological malignancy or to differentiate it from another disease state or malignancy ( Table 1 ), 18 were designed to establish and train ML models to discriminate gliomas [predominantly GBM from PCNSL ( 29 – 31 , 34 – 37 , 40 – 42 , 44 , 45 , 49 , 50 , 53 – 56 )] using features extracted from FDG-PET [one study ( 29 ),] or MRI ( 30 , 31 , 34 – 37 , 40 – 42 , 44 , 45 , 49 , 50 , 53 – 56 ) images. The remaining studies belonged to two major categories: those developing models to discriminate solid hematological malignancies from other benign and malignant lesions at other sites [nasopharyngeal carcinomas from nasopharyngeal lymphoma ( 46 , 48 ), idiopathic orbital inflammation from ocular adnexal lymphoma ( 33 ), thymic neoplasm from thymic lymphoma ( 14 ), breast carcinoma from breast lymphoma ( 15 ), lymphoma from normal nodes ( 43 ), or multiple myeloma from bone metastases ( 51 )] and those that detect the location of hematological malignancies either at diagnosis or during the disease course [location of ( 18 ) or evolving/residual lymphoma ( 32 ) or leukemia ( 17 ) or bone marrow involvement with multiple myeloma ( 16 , 38 , 47 , 52 ) or mantle cell lymphoma ( 39 )].…”

Section: Resultsmentioning

confidence: 99%

“…Others developed models using a single approach or a combination of approaches in an end-to-end manner ( 31 , 32 , 55 , 56 ). The following diverse ML approaches were used to discriminate lymphomas from other benign or malignant lesions: support vector machines (SVMs ( 29 – 31 , 33 – 37 , 46 , 48 , 50 , 51 , 53 – 55 );), linear discriminant analysis (LDA ( 14 , 15 , 30 , 34 , 37 );), logistic regression (LR ( 30 );), artificial/convolutional neural networks (A/CNNs ( 31 , 40 , 45 , 49 , 51 , 55 , 56 );), k -nearest neighbors (K-NNs ( 34 , 51 );), naïve Bayes classification (NB ( 34 , 50 , 51 );), decision trees (DTs ( 34 );), random forests (RFs ( 34 , 35 , 43 , 44 , 50 , 51 , 55 );), adaptive boosting ( 34 ), and gradient boosting ( 41 , 43 ). The ML approaches used to detect the location of hematological malignancies either at diagnosis or during the course of disease were similarly diverse: A/CNNs ( 18 , 32 , 48 , 77 ), SVMs ( 32 , 38 ), K-NN ( 32 , 38 ), RF ( 16 , 17 , 32 ).…”

Section: Resultsmentioning

confidence: 99%

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The application of machine learning to imaging in hematological oncology: A scoping review

Kotsyfakis¹,

Iliaki-Giannakoudaki²,

Anagnostopoulos³

et al. 2022

Front. Oncol.

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BackgroundHere, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps.MethodsThe review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (population); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (concept); and (iii) original research articles conducted in any setting globally (context). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle–Ottawa scale was used to assess the quality of observational studies.ResultsOf 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case–control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation.ConclusionTo deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The application of machine learning to imaging in hematological oncology: A scoping review

Kotsyfakis¹,

Iliaki-Giannakoudaki²,

Anagnostopoulos³

et al. 2022

Front. Oncol.

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“…Moreover, dynamic enhancement sequence plays a great role in the diagnosis and differential diagnosis of OCVM. 36 Establishment an AI model based on this dynamic sequence of human brain-eye recognition needs further research.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Based Non-Enhanced CT Radiomics for the Identification of Orbital Cavernous Venous Malformations: An Innovative Tool

Han

Yan

et al. 2022

Journal of Craniofacial Surgery

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Purpose: To evaluate the capability of non-enhanced computed tomography (CT) images for distinguishing between orbital cavernous venous malformations (OCVM) and non-OCVM, and to identify the optimal model from radiomics-based machine learning (ML) algorithms. Methods: A total of 215 cases of OCVM and 120 cases of non-OCVM were retrospectively analyzed in this study. A stratified random sample of 268 patients (80%) was used as the training set (172 OCVM and 96 non-OCVM); the remaining data were used as the testing set. Six feature selection techniques and thirteen ML models were evaluated to construct an optimal classification model. Results: There were statistically significant differences between the OCVM and non-OCVM groups in the density and tumor location (P < 0.05), whereas other indicators were comparable (age, gender, sharp, P > 0.05). Linear regression (area under the curve [AUC] ¼ 0.9351; accuracy ¼ 0.8657) and Stochastic Gradient Descent (AUC ¼ 0.9448; accuracy ¼ 0.8806) classifiers, both of which coupled with the f test and L1-based feature selection method, achieved optimal performance. The support vector machine (AUC ¼ 0.9186; accuracy ¼ 0.8806), Random Forest (AUC ¼ 0.9288; accuracy ¼ 0.8507) and eXtreme Gradient Boosting (AUC ¼ 0.9147; accuracy ¼ 0.8507) classifier combined with f test method showed excellent average performance among our study, respectively. Conclusions:The effect of non-enhanced CT images in OCVM not only can help ophthalmologist to find and locate lesion, but also bring great help for the qualitative diagnosis value using radiomicbased ML algorithms.

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“…The diagnosing results yielded an AUC of 0.953, indicating that the DL-based analysis may successfully help distinguish between OAL and IOI. Hou et al (2021) used an SVM classifier and the bag-of-features (BOF) technique to distinguish OAL from IOI based on orbital MRI images. During an independent verification test, the proposed method with augmentation achieved an AUC of 0.803, indicating that BOF-based radiomics might be a new tool for the differentiation between OAL and IOI.…”

Section: Sabatesmentioning

confidence: 99%

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Bao

Sun

Zhan

et al. 2022

Front. Cell Dev. Biol.

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Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.

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Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI

Cited by 21 publications

References 39 publications

The application of machine learning to imaging in hematological oncology: A scoping review

The application of machine learning to imaging in hematological oncology: A scoping review

Machine Learning Based Non-Enhanced CT Radiomics for the Identification of Orbital Cavernous Venous Malformations: An Innovative Tool

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

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