Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis.
Yulia Lakhman,Harini Veeraraghavan,Joshua Chaim,Diana Feier,Diana Feier,Debra A. Goldman,Chaya S. Moskowitz,Stephanie Nougaret,Ramon E. Sosa,Hebert Alberto Vargas,Robert A. Soslow,Nadeem R. Abu-Rustum,Hedvig Hricak,Evis Sala +13 more
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TLDR
Four qualitative MR features demonstrated the strongest statistical association with LMS, and texture analysis was a feasible semi-automated approach for lesion categorization.Abstract:
To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA). This retrospective study included 41 women (ALM = 22, LMS = 19) imaged with MRI prior to surgery. Two readers (R1, R2) evaluated each lesion for qualitative MR features. Associations between MR features and LMS were evaluated with Fisher’s exact test. Accuracy measures were calculated for the four most significant features. TA was performed for 24 patients (ALM = 14, LMS = 10) with uniform imaging following lesion segmentation on axial T2-weighted images. Texture features were pre-selected using Wilcoxon signed-rank test with Bonferroni correction and analyzed with unsupervised clustering to separate LMS from ALM. Four qualitative MR features most strongly associated with LMS were nodular borders, haemorrhage, “T2 dark” area(s), and central unenhanced area(s) (p ≤ 0.0001 each feature/reader). The highest sensitivity [1.00 (95%CI:0.82-1.00)/0.95 (95%CI: 0.74-1.00)] and specificity [0.95 (95%CI:0.77-1.00)/1.00 (95%CI:0.85-1.00)] were achieved for R1/R2, respectively, when a lesion had ≥3 of these four features. Sixteen texture features differed significantly between LMS and ALM (p-values: <0.001-0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79). Combination of ≥3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible. • Four qualitative MR features demonstrated the strongest statistical association with LMS. • Combination of ≥3 these features could accurately differentiate LMS from ALM. • Texture analysis was a feasible semi-automated approach for lesion categorization.read more
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MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy.
Natally Horvat,Harini Veeraraghavan,Monika Khan,Ivana Blazic,Junting Zheng,Marinela Capanu,Evis Sala,Julio Garcia-Aguilar,Marc J. Gollub,Iva Petkovska +9 more
TL;DR: T2- Weighted-based radiomics showed better classification performance compared with qualitative assessment at T2-weighted and DW imaging for diagnosing pCR in patients with locally advanced rectal cancer after CRT.
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Soft Tissue and Uterine Leiomyosarcoma.
TL;DR: The current understanding of the epidemiology, diagnosis, genomics, and treatment options for uterine leiomyosarcoma is summarized.
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Uterine leiomyosarcoma: A review of the literature and update on management options.
TL;DR: A detailed review of uterine leiomyosarcoma including epidemiology, clinical presentation, diagnosis, and pathologic characteristics is provided, including detail management strategies, including options for adjuvant therapy, and highlight new and developing regimens in the field.
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Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets.
Hyemin Um,Florent Tixier,Dalton Bermudez,Joseph O. Deasy,Robert J. Young,Harini Veeraraghavan +5 more
TL;DR: Assessment of the impact of common image preprocessing methods on the scanner dependence of MRI radiomic features in multi-institutional glioblastoma multiforme (GBM) datasets demonstrates that histogram standardization contributes the most in reducing radiomic feature variability.
Journal ArticleDOI
European Society of Urogenital Radiology (ESUR) Guidelines: MR Imaging of Leiomyomas
Rahel A. Kubik-Huch,Michael Weston,Stephanie Nougaret,Henrik Leonhardt,Isabelle Thomassin-Naggara,Mariana Horta,Teresa Margarida Cunha,Cristina Maciel,Andrea Rockall,Andrea Rockall,Rosemarie Forstner +10 more
TL;DR: These imaging guidelines are based on the current practice among expert radiologists in the field of female pelvic imaging and also incorporate essentials of the current published MR literature of uterine leiomyomas.
References
More filters
Journal ArticleDOI
The measurement of observer agreement for categorical data
J. R. Landis,Gary G. Koch +1 more
TL;DR: A general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies is presented and tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interob server agreement are developed as generalized kappa-type statistics.
Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.
TL;DR: Evidence is presented that the 2D receptive-field profiles of simple cells in mammalian visual cortex are well described by members of this optimal 2D filter family, and thus such visual neurons could be said to optimize the general uncertainty relations for joint 2D-spatial-2D-spectral information resolution.
Proceedings Article
Self-Tuning Spectral Clustering
Lihi Zelnik-Manor,Pietro Perona +1 more
TL;DR: This work proposes that a 'local' scale should be used to compute the affinity between each pair of points and suggests exploiting the structure of the eigenvectors to infer automatically the number of groups.