Literature DB >> 30733252

Disorder in Pixel-Level Edge Directions on T1WI Is Associated with the Degree of Radiation Necrosis in Primary and Metastatic Brain Tumors: Preliminary Findings.

P Prasanna1, L Rogers2, T C Lam3, M Cohen2, A Siddalingappa2, L Wolansky2, M Pinho4, A Gupta2, K J Hatanpaa4, A Madabhushi5, P Tiwari5.   

Abstract

BACKGROUND AND
PURPOSE: Co-occurrence of local anisotropic gradient orientations (COLLAGE) is a recently developed radiomic (computer extracted) feature that captures entropy (measures the degree of disorder) in pixel-level edge directions and was previously shown to distinguish predominant cerebral radiation necrosis from recurrent tumor on gadolinium-contrast T1WI. In this work, we sought to investigate whether COLLAGE measurements from posttreatment gadolinium-contrast T1WI could distinguish varying extents of cerebral radiation necrosis and recurrent tumor classes in a lesion across primary and metastatic brain tumors.
MATERIALS AND METHODS: On a total of 75 gadolinium-contrast T1WI studies obtained from patients with primary and metastatic brain tumors and nasopharyngeal carcinoma, the extent of cerebral radiation necrosis and recurrent tumor in every brain lesion was histopathologically defined by an expert neuropathologist as the following: 1) "pure" cerebral radiation necrosis; 2) "mixed" pathology with coexistence of cerebral radiation necrosis and recurrent tumors; 3) "predominant" (>80%) cerebral radiation necrosis; 4) predominant (>80%) recurrent tumor; and 5) pure tumor. COLLAGE features were extracted from the expert-annotated ROIs on MR imaging. Statistical comparisons of COLLAGE measurements using first-order statistics were performed across pure, mixed, and predominant pathologies of cerebral radiation necrosis and recurrent tumor using the Wilcoxon rank sum test.
RESULTS: COLLAGE features exhibited decreased skewness for patients with pure (0.15 ± 0.12) and predominant cerebral radiation necrosis (0.25 ± 0.09) and were statistically significantly different (P < .05) from those in patients with predominant recurrent tumors, which had highly skewed (0.42 ± 0.21) COLLAGE values. COLLAGE values for the mixed pathology studies were found to lie between predominant cerebral radiation necrosis and recurrent tumor categories.
CONCLUSIONS: With additional independent multisite validation, COLLAGE measurements might enable noninvasive characterization of the degree of recurrent tumor or cerebral radiation necrosis in gadolinium-contrast T1WI of posttreatment lesions.
© 2019 by American Journal of Neuroradiology.

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Year:  2019        PMID: 30733252      PMCID: PMC6599398          DOI: 10.3174/ajnr.A5958

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  16 in total

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Authors:  Anant Madabhushi; Jayaram K Udupa
Journal:  Med Phys       Date:  2006-09       Impact factor: 4.071

2.  Delayed radiation necrosis of the brain.

Authors:  A N Martins; J S Johnston; J M Henry; T J Stoffel; G Di Chiro
Journal:  J Neurosurg       Date:  1977-09       Impact factor: 5.115

3.  Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study.

Authors:  P Tiwari; P Prasanna; L Wolansky; M Pinho; M Cohen; A P Nayate; A Gupta; G Singh; K J Hatanpaa; A Sloan; L Rogers; A Madabhushi
Journal:  AJNR Am J Neuroradiol       Date:  2016-09-15       Impact factor: 3.825

4.  Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI.

Authors:  Andrés Larroza; David Moratal; Alexandra Paredes-Sánchez; Emilio Soria-Olivas; María L Chust; Leoncio A Arribas; Estanislao Arana
Journal:  J Magn Reson Imaging       Date:  2015-04-10       Impact factor: 4.813

5.  Diffusion-weighted imaging of radiation-induced brain injury for differentiation from tumor recurrence.

Authors:  Chiaki Asao; Yukunori Korogi; Mika Kitajima; Toshinori Hirai; Yuji Baba; Keishi Makino; Masato Kochi; Shoji Morishita; Yasuyuki Yamashita
Journal:  AJNR Am J Neuroradiol       Date:  2005 Jun-Jul       Impact factor: 3.825

6.  Histologic factors of prognostic significance in the glioblastoma multiforme.

Authors:  P C Burger; R T Vollmer
Journal:  Cancer       Date:  1980-09-01       Impact factor: 6.860

7.  Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.

Authors:  Prateek Prasanna; Jay Patel; Sasan Partovi; Anant Madabhushi; Pallavi Tiwari
Journal:  Eur Radiol       Date:  2016-10-24       Impact factor: 5.315

8.  Clinical outcomes of 174 nasopharyngeal carcinoma patients with radiation-induced temporal lobe necrosis.

Authors:  Tai-Chung Lam; Frank C S Wong; To-Wai Leung; S H Ng; Stewart Y Tung
Journal:  Int J Radiat Oncol Biol Phys       Date:  2011-04-07       Impact factor: 7.038

9.  Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor.

Authors:  Prateek Prasanna; Pallavi Tiwari; Anant Madabhushi
Journal:  Sci Rep       Date:  2016-11-22       Impact factor: 4.379

10.  Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.

Authors:  Nathaniel M Braman; Maryam Etesami; Prateek Prasanna; Christina Dubchuk; Hannah Gilmore; Pallavi Tiwari; Donna Plecha; Anant Madabhushi
Journal:  Breast Cancer Res       Date:  2017-05-18       Impact factor: 6.466

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  5 in total

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Authors:  Timothy J Kaufmann; Marion Smits; Jerrold Boxerman; Raymond Huang; Daniel P Barboriak; Michael Weller; Caroline Chung; Christina Tsien; Paul D Brown; Lalitha Shankar; Evanthia Galanis; Elizabeth Gerstner; Martin J van den Bent; Terry C Burns; Ian F Parney; Gavin Dunn; Priscilla K Brastianos; Nancy U Lin; Patrick Y Wen; Benjamin M Ellingson
Journal:  Neuro Oncol       Date:  2020-06-09       Impact factor: 12.300

2.  Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging.

Authors:  Prateek Prasanna; Ayush Karnawat; Marwa Ismail; Anant Madabhushi; Pallavi Tiwari
Journal:  J Med Imaging (Bellingham)       Date:  2019-05-07

3.  Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges.

Authors:  Niha Beig; Kaustav Bera; Pallavi Tiwari
Journal:  Neurooncol Adv       Date:  2021-01-23

Review 4.  Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors.

Authors:  Darius Kalasauskas; Michael Kosterhon; Naureen Keric; Oliver Korczynski; Andrea Kronfeld; Florian Ringel; Ahmed Othman; Marc A Brockmann
Journal:  Cancers (Basel)       Date:  2022-02-07       Impact factor: 6.639

Review 5.  Radiomics and radiogenomics in gliomas: a contemporary update.

Authors:  Prateek Prasanna; Vadim Spektor; Gagandeep Singh; Sunil Manjila; Nicole Sakla; Alan True; Amr H Wardeh; Niha Beig; Anatoliy Vaysberg; John Matthews
Journal:  Br J Cancer       Date:  2021-05-06       Impact factor: 7.640

  5 in total

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