Update on MR Imaging of Soft Tissue Tumors of Head and Neck

Rodriguez, Justin D; Selleck, Anne Morgan; Razek, Ahmed Abdel Khalek Abdel; Huang, Benjamin

doi:10.1016/j.mric.2021.06.019

Cited by 3 publications

(1 citation statement)

References 277 publications

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“…In the present study, we investigated and developed the diagnostic value of radiomics models using image features extracted from conventional MRI in discriminating PCNSL from SBM. MRI can distinguish different soft tissues and determine the exact location of the neoplasm [13][14][15]. Conventional MRI is con ned in discriminating PCNSL from SBM by visual inspection on account of lacking image features [16].…”

Section: Discussionmentioning

confidence: 99%

Radiomics-Based Machine Learning in Differentiation Between Primary Central Nervous System Lymphoma And Solitary Brain Metastasis

Xia,

Du,

Qiu

et al. 2023

Preprint

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Purpose: Primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (SBM) are common malignant brain tumors. Due to distinct treatment approaches, accurately distinguishing between them preoperatively is crucial. However, conventional MRI techniques often face challenges in differentiating them due to their similar imaging appearances. This study aimed to develop and validate radiomics-derived nomogram models for distinguishing PCNSL from SBM based on conventional MRI. Methods: In this retrospective study, 324 patients diagnosed with either PCNSL (115 patients) or SBM (209 patients) from January 2011 to May 2021 were included. Conventional MRI data were processed to extract a comprehensive set of 1561 radiomics features. A two-step feature selection using LASSO regression narrowed down significant features. Twenty classification algorithms were applied to create predictive models, which were validated by receiver operating characteristic (ROC) curves. Results A total of 23 features were shortlisted by LASSO regression. Among the twenty classifiers evaluated, many displayed AUCs greater than 0.9 in both 10-fold cross-validation and independent testing. The SVMRBF classifier, in particular, showcased the highest AUC of 0.9310 in the independent test, followed closely by the RF, ANN, ELDA, and others. Conclusion Radiomics-based models, utilizing conventional MRI data, demonstrated promising potential in effectively distinguishing PCNSL from SBM. The SVMRBF classifier emerged as the most efficient in terms of diagnostic efficacy, suggesting its clinical utility in the differential diagnosis of these two entities.

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Section: Discussionmentioning

confidence: 99%

Radiomics-Based Machine Learning in Differentiation Between Primary Central Nervous System Lymphoma And Solitary Brain Metastasis

Xia,

Du,

Qiu

et al. 2023

Preprint

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Retrospective analysis of radiological investigation of surgically excised head and neck lipomas

Schranz,

Riordan,

Dolan

et al. 2024

Eur Arch Otorhinolaryngol

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Purpose Differentiating benign lipomas from malignant causes is challenging and preoperative investigative guidelines are not well-defined. The purpose of this study was to retrospectively identify cases of head and neck lipomas that were surgically resected over a 5-year period and to identify the radiological modality chosen and features discussed in the final report. Multidisciplinary outcomes and pathology reports were examined with a view to identifying high risk features of a lipoma to aid in future risk stratification. Methods Retrospective chart review of pathology characteristics, radiological features (modality, size, calcifications, septations, globular/nodular foci), multidisciplinary discussion and history of presenting complaint was performed. Results Two liposarcomas and 138 lipomas were identified. Twenty-two percent of all lipomas received radiological investigation. Twenty-two percent of imaging referrals were possibly inappropriate. Furthermore, radiological features suggestive of malignancy were not present in the final radiology report, X2 = 28.8, p < 0.0001. Conclusion As expected, the incidence of liposarcoma is low. There is limited awareness of radiology referral guidelines superimposed with a tendency to over-investigate lipomas. Furthermore, radiological features suggestive of malignancy were inconsistently reported on and not documented in multidisciplinary discussions. Therefore, we propose a multidisciplinary checklist for referring physicians and radiologists to aid in diagnostic work-up.

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