Literature DB >> 29531967

Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning.

Saima Rathore1,2, Hamed Akbari1,2, Jimit Doshi1,2, Gaurav Shukla1,3, Martin Rozycki1,2, Michel Bilello1,2, Robert Lustig4, Christos Davatzikos1,2.   

Abstract

Standard surgical resection of glioblastoma, mainly guided by the enhancement on postcontrast T1-weighted magnetic resonance imaging (MRI), disregards infiltrating tumor within the peritumoral edema region (ED). Subsequent radiotherapy typically delivers uniform radiation to peritumoral FLAIR-hyperintense regions, without attempting to target areas likely to be infiltrated more heavily. Noninvasive in vivo delineation of the areas of tumor infiltration and prediction of early recurrence in peritumoral ED could assist in targeted intensification of local therapies, thereby potentially delaying recurrence and prolonging survival. This paper presents a method for estimating peritumoral edema infiltration using radiomic signatures determined via machine learning methods, and tests it on 90 patients with de novo glioblastoma. The generalizability of the proposed predictive model was evaluated via cross-validation in a discovery cohort ([Formula: see text]) and was subsequently evaluated in a replication cohort ([Formula: see text]). Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. The cross-validated accuracy of our predictive infiltration model on the discovery and replication cohorts was 87.51% (odds ratio = 10.22, sensitivity = 80.65, and specificity = 87.63) and 89.54% (odds ratio = 13.66, sensitivity = 97.06, and specificity = 76.73), respectively. The radiomic signature of the recurrent tumor region revealed higher vascularity and cellularity when compared with the nonrecurrent region. The proposed model shows evidence that multiparametric pattern analysis from clinical MRI sequences can assist in in vivo estimation of the spatial extent and pattern of tumor recurrence in peritumoral edema, which may guide supratotal resection and/or intensification of postoperative radiation therapy.

Entities:  

Keywords:  glioblastoma; machine learning; radiomics; tumor infiltration; tumor recurrence

Year:  2018        PMID: 29531967      PMCID: PMC5831697          DOI: 10.1117/1.JMI.5.2.021219

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  35 in total

1.  Morphological and flow cytometric analysis of cell infiltration in glioblastoma: a comparison of autopsy brain and neuroimaging.

Authors:  Takahiro Yamahara; Yoshihiro Numa; Tetsuya Oishi; Takuya Kawaguchi; Toshitaka Seno; Akio Asai; Keiji Kawamoto
Journal:  Brain Tumor Pathol       Date:  2010-11-03       Impact factor: 3.298

2.  The relationship between Cho/NAA and glioma metabolism: implementation for margin delineation of cerebral gliomas.

Authors:  Jun Guo; Chengjun Yao; Hong Chen; Dongxiao Zhuang; Weijun Tang; Guang Ren; Yin Wang; Jinsong Wu; Fengping Huang; Liangfu Zhou
Journal:  Acta Neurochir (Wien)       Date:  2012-06-23       Impact factor: 2.216

3.  Evaluation of peritumoral edema in the delineation of radiotherapy clinical target volumes for glioblastoma.

Authors:  Eric L Chang; Serap Akyurek; Tedde Avalos; Neal Rebueno; Chris Spicer; John Garcia; Robin Famiglietti; Pamela K Allen; K S Clifford Chao; Anita Mahajan; Shiao Y Woo; Moshe H Maor
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-02-15       Impact factor: 7.038

4.  Combining generative models for multifocal glioma segmentation and registration.

Authors:  Dongjin Kwon; Russell T Shinohara; Hamed Akbari; Christos Davatzikos
Journal:  Med Image Comput Comput Assist Interv       Date:  2014

5.  Survival and failure patterns of high-grade gliomas after three-dimensional conformal radiotherapy.

Authors:  June L Chan; Susan W Lee; Benedick A Fraass; Daniel P Normolle; Harry S Greenberg; Larry R Junck; Stephen S Gebarski; Howard M Sandler
Journal:  J Clin Oncol       Date:  2002-03-15       Impact factor: 44.544

6.  Prospective analysis of parametric response map-derived MRI biomarkers: identification of early and distinct glioma response patterns not predicted by standard radiographic assessment.

Authors:  Craig J Galbán; Thomas L Chenevert; Charles R Meyer; Christina Tsien; Theodore S Lawrence; Daniel A Hamstra; Larry Junck; Pia C Sundgren; Timothy D Johnson; Stefanie Galbán; Judith S Sebolt-Leopold; Alnawaz Rehemtulla; Brian D Ross
Journal:  Clin Cancer Res       Date:  2011-04-28       Impact factor: 12.531

7.  Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors.

Authors:  Stanley Lu; Daniel Ahn; Glyn Johnson; Soonmee Cha
Journal:  AJNR Am J Neuroradiol       Date:  2003-05       Impact factor: 3.825

8.  Combined modality approach to treatment of malignant gliomas--re-evaluation of RTOG 7401/ECOG 1374 with long-term follow-up: a joint study of the Radiation Therapy Oncology Group and the Eastern Cooperative Oncology Group.

Authors:  D F Nelson; M Diener-West; J Horton; C H Chang; D Schoenfeld; J S Nelson
Journal:  NCI Monogr       Date:  1988

9.  Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

Authors:  Christos Davatzikos; Saima Rathore; Spyridon Bakas; Sarthak Pati; Mark Bergman; Ratheesh Kalarot; Patmaa Sridharan; Aimilia Gastounioti; Nariman Jahani; Eric Cohen; Hamed Akbari; Birkan Tunc; Jimit Doshi; Drew Parker; Michael Hsieh; Aristeidis Sotiras; Hongming Li; Yangming Ou; Robert K Doot; Michel Bilello; Yong Fan; Russell T Shinohara; Paul Yushkevich; Ragini Verma; Despina Kontos
Journal:  J Med Imaging (Bellingham)       Date:  2018-01-11

Review 10.  FSL.

Authors:  Mark Jenkinson; Christian F Beckmann; Timothy E J Behrens; Mark W Woolrich; Stephen M Smith
Journal:  Neuroimage       Date:  2011-09-16       Impact factor: 6.556

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  26 in total

1.  Erratum.

Authors: 
Journal:  AJNR Am J Neuroradiol       Date:  2019-05-09       Impact factor: 3.825

2.  A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery.

Authors:  Yan Tan; Shuai-Tong Zhang; Jing-Wei Wei; Di Dong; Xiao-Chun Wang; Guo-Qiang Yang; Jie Tian; Hui Zhang
Journal:  Eur Radiol       Date:  2019-04-10       Impact factor: 5.315

3.  AI-based prognostic imaging biomarkers for precision neuro-oncology: the ReSPOND consortium.

Authors:  Christos Davatzikos; Jill S Barnholtz-Sloan; Spyridon Bakas; Rivka Colen; Abhishek Mahajan; Carmen Balaña Quintero; Jaume Capellades Font; Josep Puig; Rajan Jain; Andrew E Sloan; Chaitra Badve; Daniel S Marcus; Yoon Seong Choi; Seung-Koo Lee; Jong Hee Chang; Laila M Poisson; Brent Griffith; Adam P Dicker; Adam E Flanders; Thomas C Booth; Saima Rathore; Hamed Akbari; Chiharu Sako; Michel Bilello; Gaurav Shukla; Anahita Fathi Kazerooni; Steven Brem; Robert Lustig; Suyash Mohan; Stephen Bagley; MacLean Nasrallah; Donald M O'Rourke
Journal:  Neuro Oncol       Date:  2020-06-09       Impact factor: 12.300

4.  Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.

Authors:  Andreas Mang; Spyridon Bakas; Shashank Subramanian; Christos Davatzikos; George Biros
Journal:  Annu Rev Biomed Eng       Date:  2020-06-04       Impact factor: 9.590

5.  WHERE DID THE TUMOR START? AN INVERSE SOLVER WITH SPARSE LOCALIZATION FOR TUMOR GROWTH MODELS.

Authors:  Shashank Subramanian; Klaudius Scheufele; Miriam Mehl; George Biros
Journal:  Inverse Probl       Date:  2020-02-26       Impact factor: 2.407

Review 6.  Precision diagnostics based on machine learning-derived imaging signatures.

Authors:  Christos Davatzikos; Aristeidis Sotiras; Yong Fan; Mohamad Habes; Guray Erus; Saima Rathore; Spyridon Bakas; Rhea Chitalia; Aimilia Gastounioti; Despina Kontos
Journal:  Magn Reson Imaging       Date:  2019-05-06       Impact factor: 2.546

7.  The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview.

Authors:  Sarthak Pati; Ashish Singh; Saima Rathore; Aimilia Gastounioti; Mark Bergman; Phuc Ngo; Sung Min Ha; Dimitrios Bounias; James Minock; Grayson Murphy; Hongming Li; Amit Bhattarai; Adam Wolf; Patmaa Sridaran; Ratheesh Kalarot; Hamed Akbari; Aristeidis Sotiras; Siddhesh P Thakur; Ragini Verma; Russell T Shinohara; Paul Yushkevich; Yong Fan; Despina Kontos; Christos Davatzikos; Spyridon Bakas
Journal:  Brainlesion       Date:  2020-05-19

Review 8.  Emerging Applications of Artificial Intelligence in Neuro-Oncology.

Authors:  Jeffrey D Rudie; Andreas M Rauschecker; R Nick Bryan; Christos Davatzikos; Suyash Mohan
Journal:  Radiology       Date:  2019-01-22       Impact factor: 11.105

9.  Quantitative mapping of individual voxels in the peritumoral region of IDH-wildtype glioblastoma to distinguish between tumor infiltration and edema.

Authors:  Archya Dasgupta; Benjamin Geraghty; Arjun Sahgal; Gregory J Czarnota; Pejman Jabehdar Maralani; Nauman Malik; Michael Sandhu; Jay Detsky; Chia-Lin Tseng; Hany Soliman; Sten Myrehaug; Zain Husain; James Perry; Angus Lau
Journal:  J Neurooncol       Date:  2021-04-27       Impact factor: 4.130

10.  A Multiparametric MRI-Based Radiomics Analysis to Efficiently Classify Tumor Subregions of Glioblastoma: A Pilot Study in Machine Learning.

Authors:  Fang-Ying Chiu; Nguyen Quoc Khanh Le; Cheng-Yu Chen
Journal:  J Clin Med       Date:  2021-05-10       Impact factor: 4.241

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