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Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma

The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcom...

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Autores principales: Ismail, Marwa, Prasanna, Prateek, Bera, Kaustav, Statsevych, Volodymyr, Hill, Virginia, Singh, Gagandeep, Partovi, Sasan, Beig, Niha, McGarry, Sean, Laviolette, Peter, Ahluwalia, Manmeet, Madabhushi, Anant, Tiwari, Pallavi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575333/
https://www.ncbi.nlm.nih.gov/pubmed/35108202
http://dx.doi.org/10.1109/TMI.2022.3148780
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author Ismail, Marwa
Prasanna, Prateek
Bera, Kaustav
Statsevych, Volodymyr
Hill, Virginia
Singh, Gagandeep
Partovi, Sasan
Beig, Niha
McGarry, Sean
Laviolette, Peter
Ahluwalia, Manmeet
Madabhushi, Anant
Tiwari, Pallavi
author_facet Ismail, Marwa
Prasanna, Prateek
Bera, Kaustav
Statsevych, Volodymyr
Hill, Virginia
Singh, Gagandeep
Partovi, Sasan
Beig, Niha
McGarry, Sean
Laviolette, Peter
Ahluwalia, Manmeet
Madabhushi, Anant
Tiwari, Pallavi
author_sort Ismail, Marwa
collection PubMed
description The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients’ MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity and infiltration within the tumor confines, on MRI scans. In this work, we extensively evaluated r-DepTH for survival risk-stratification on a total of 207 GBM cases from 3 different cohorts (Cohort 1 (n1 = 53), Cohort 2 (n2 = 75), and Cohort 3 (n3 = 79)), where each of these three cohorts was used as a training set for our model separately, and the other two cohorts were used for testing, independently, for each training experiment. When employing Cohort 1 for training, r-DepTH yielded Concordance indices (C-indices) of 0.7 and 0.65, hazard ratios (HR) and Confidence Intervals (CI) of 10 (6 – 19) and 5 (3 – 8) on Cohorts 2 and 3, respectively. Similarly, training on Cohort 2 yielded C-indices of 0.6 and 0.7, HR and CI of 1 (0.7 – 2) and 3 (2 – 5) on Cohorts 1 and 3, respectively. Finally, training on Cohort 3 yielded C-indices of 0.75 and 0.63, HR and CI of 24 (10 – 57) and 12 (6 – 21) on Cohorts 1 and 2, respectively. Our results show that r-DepTH descriptor may serve as a comprehensive and a robust MRI-based prognostic marker of disease aggressiveness and survival in solid tumors.
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spelling pubmed-95753332022-10-17 Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma Ismail, Marwa Prasanna, Prateek Bera, Kaustav Statsevych, Volodymyr Hill, Virginia Singh, Gagandeep Partovi, Sasan Beig, Niha McGarry, Sean Laviolette, Peter Ahluwalia, Manmeet Madabhushi, Anant Tiwari, Pallavi IEEE Trans Med Imaging Article The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients’ MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity and infiltration within the tumor confines, on MRI scans. In this work, we extensively evaluated r-DepTH for survival risk-stratification on a total of 207 GBM cases from 3 different cohorts (Cohort 1 (n1 = 53), Cohort 2 (n2 = 75), and Cohort 3 (n3 = 79)), where each of these three cohorts was used as a training set for our model separately, and the other two cohorts were used for testing, independently, for each training experiment. When employing Cohort 1 for training, r-DepTH yielded Concordance indices (C-indices) of 0.7 and 0.65, hazard ratios (HR) and Confidence Intervals (CI) of 10 (6 – 19) and 5 (3 – 8) on Cohorts 2 and 3, respectively. Similarly, training on Cohort 2 yielded C-indices of 0.6 and 0.7, HR and CI of 1 (0.7 – 2) and 3 (2 – 5) on Cohorts 1 and 3, respectively. Finally, training on Cohort 3 yielded C-indices of 0.75 and 0.63, HR and CI of 24 (10 – 57) and 12 (6 – 21) on Cohorts 1 and 2, respectively. Our results show that r-DepTH descriptor may serve as a comprehensive and a robust MRI-based prognostic marker of disease aggressiveness and survival in solid tumors. 2022-07 2022-06-30 /pmc/articles/PMC9575333/ /pubmed/35108202 http://dx.doi.org/10.1109/TMI.2022.3148780 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Ismail, Marwa
Prasanna, Prateek
Bera, Kaustav
Statsevych, Volodymyr
Hill, Virginia
Singh, Gagandeep
Partovi, Sasan
Beig, Niha
McGarry, Sean
Laviolette, Peter
Ahluwalia, Manmeet
Madabhushi, Anant
Tiwari, Pallavi
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma
title Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma
title_full Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma
title_fullStr Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma
title_full_unstemmed Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma
title_short Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma
title_sort radiomic deformation and textural heterogeneity (r-depth) descriptor to characterize tumor field effect: application to survival prediction in glioblastoma
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575333/
https://www.ncbi.nlm.nih.gov/pubmed/35108202
http://dx.doi.org/10.1109/TMI.2022.3148780
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