Radiographic-deformation and textural heterogeneity (r-DepTH): An integrated descriptor for brain tumor prognosis

Prateek Prasanna, Jhimli Mitra, Niha Beig, Sasan Partovi, Gagandeep Singh, Marco Pinho, Anant Madabhushi, Pallavi Tiwari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Most aggressive tumors are systemic, implying that their impact is not localized to the tumor itself but extends well beyond the visible tumor borders. Solid tumors (e.g. Glioblastoma) typically exert pressure on the surrounding normal parenchyma due to active proliferation, impacting neighboring structures and worsening survival. Existing approaches have focused on capturing tumor heterogeneity via shape, intensity, and texture radiomic statistics within the visible surgical margins on pre-treatment scans, with the clinical purpose of improving treatment management. However, a poorly understood aspect of heterogeneity is the impact of active proliferation and tumor burden, leading to subtle deformations in the surrounding normal parenchyma distal to the tumor. We introduce radiographic-Deformation and Textural Heterogeneity (r-DepTH), a new descriptor that attempts to capture both intra-, as well as extra-tumoral heterogeneity. r-DepTH combines radiomic measurements of (a) subtle tissue deformation measures throughout the extraneous surrounding normal parenchyma, and (b) the gradient-based textural patterns in tumor and adjacent peri-tumoral regions. We demonstrate that r-DepTH enables improved prediction of disease outcome compared to descriptors extracted from within the visible tumor alone. The efficacy of r-DepTH is demonstrated in the context of distinguishing long-term (LTS) versus short-term (STS) survivors of Glioblastoma, a highly malignant brain tumor. Using a training set (N=68) of treatment-naive Gadolinium T1w MRI scans, r-DepTH achieved an AUC of 0.83 in distinguishing STS versus LTS. Kaplan Meier survival analysis on an independent cohort (N = 11) using the r-DepTH descriptor resulted in p = 0.038 (log-rank test), a significant improvement over employing deformation descriptors from normal parenchyma (p = 0.17), or textural descriptors from visible tumor (p = 0.81) alone.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
EditorsPierre Jannin, Simon Duchesne, Maxime Descoteaux, Alfred Franz, D. Louis Collins, Lena Maier-Hein
PublisherSpringer Verlag
Pages459-467
Number of pages9
ISBN (Print)9783319661841
DOIs
StatePublished - Jan 1 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: Sep 11 2017Sep 13 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10434 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period9/11/179/13/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Prasanna, P., Mitra, J., Beig, N., Partovi, S., Singh, G., Pinho, M., Madabhushi, A., & Tiwari, P. (2017). Radiographic-deformation and textural heterogeneity (r-DepTH): An integrated descriptor for brain tumor prognosis. In P. Jannin, S. Duchesne, M. Descoteaux, A. Franz, D. L. Collins, & L. Maier-Hein (Eds.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (pp. 459-467). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10434 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66185-8_52