TY - GEN
T1 - Radiographic-deformation and textural heterogeneity (r-DepTH)
T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
AU - Prasanna, Prateek
AU - Mitra, Jhimli
AU - Beig, Niha
AU - Partovi, Sasan
AU - Singh, Gagandeep
AU - Pinho, Marco
AU - Madabhushi, Anant
AU - Tiwari, Pallavi
N1 - Funding Information:
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 Research was supported by 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R21CA179327-01, R21CA195152-01, R01DK098503-02, 1C06-RR12463-01, PC120857, LC130463, the DOD Prostate Cancer Idea Development Award, W81XWH-16-1-0329, the Case Comprehensive Cancer Center Pilot Grant, VelaSano Grant from the Cleveland Clinic, I-Corps program, Ohio Third Frontier Program, and the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-319-66185-8_52
DO - 10.1007/978-3-319-66185-8_52
M3 - Conference contribution
AN - SCOPUS:85029480738
SN - 9783319661841
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 459
EP - 467
BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
A2 - Jannin, Pierre
A2 - Duchesne, Simon
A2 - Descoteaux, Maxime
A2 - Franz, Alfred
A2 - Collins, D. Louis
A2 - Maier-Hein, Lena
PB - Springer Verlag
Y2 - 11 September 2017 through 13 September 2017
ER -