Periodic Reporting for period 1 - GLIOHAB (Multiparametric imaging of glioblastoma tumour heterogeneity for supporting treatment decisions and accurate prognostic estimation)
Reporting period: 2019-06-01 to 2021-05-31
Glioblastoma is the most frequent primary brain tumor. Glioblastoma has a poor prognosis, with an average survival of 14.6 months for patients undergoing Stupp standard treatment. Due to the extremely complex and heterogeneous molecular biology of glioblastoma “the same treatment for all” approach does not work well in this disease, and standard of care is not always the best option. Their intra-patient heterogeneity has been identified as one of the factors associated with its high aggressiveness, representing a key factor to understand tumor resistance against effective therapies. In this context, additional efforts are needed for generate novel strategies able to define more tailored personalized treatments that facilitate higher safety and efficacy for relevant sub-populations.
GLIOHAB project has contributed to the characterization of the intra-patient heterogeneity of glioblastoma by developing new multi-parametric image analysis techniques and by applying them to improve prognosis estimation and patient follow-up.
GLIOHAB project has contributed to the characterization of the intra-patient heterogeneity of glioblastoma by developing new multi-parametric image analysis techniques and by applying them to improve prognosis estimation and patient follow-up.
In the GLIOHAB project, a fully automated pipeline for processing MRI data was implemented to generate functional maps depicting vascular, diffusivity, as well as biomechanical characteristics of the lesion and surrounding tissue at the pixel level. This pipeline was used to process the project data, but also to analyze TCGA-GBM and Ivy Gap public datasets. The results of these analyses were published in open repositories for their re-use and enrichment of the original datasets [1,2]. In addition, this pipeline is currently being used within the NCT03951142 clinical trial at the Oslo University Hospital. Advances in the state-of-the-art and innovations related to the development of this pipeline were published in leading scientific journals and congresses [3–7] .
At GLIOHAB we have gone a step further in the characterization of intratumoral heterogeneity. From a more theoretical point of view, we developed a novel method of structural unsupervised segmentation that allows an improvement in the robustness of the heterogeneity characterization [8]. In addition, we developed a new concept of habitat linked to the characterization of tumor progression, which is currently under consideration for patenting.
Throughout the GLIOHAB project, we discovered relevant associations between vascular and mechanical habitats and the survival of glioblastoma patients. Moreover, in the case of vascular habitats we validated this relationship in an international multicenter setting [9]. We discovered of an association between vascularity in the peripheral infiltrated glioblastoma and different molecular subtypes of glioblastoma [10]. We assessed local microvascular proliferation in glioblastoma using relative Cerebral Blood Volume [11]. Finally, we discovered that the prognostic ability of vascularity in glioblastoma patients is higher in long-surviving patients [12].
Finally, in GLIOHAB we studied the clinical relevance of the characterization of tumor heterogeneity for treatment response. One of the most relevant findings is that combining MGMT methylation status together with vascularity estimated in the high angiogenic part of the tumor, obtained automatically from standard-of-care MRI studies, can provide a more reliable prognostic indicator for designing patient stratification strategies [13]. Another relevant finding is the association found between small peritumoral displacements due to tumor growth and survival throughout patient follow-up. The methodology of quantification of these displacements proposed by GLIOHAB could contribute significantly to the differentiation of pseudoprogression and tumor progression phenomena as well as, to estimate the effect of tumor growth in eloquent areas.
Main results of GLIOHAB project:
1. ONCOhabitats results for The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM): Segmentation and Hemodynamic Tissue Signature. (2021) doi:10.5281/zenodo.4704090.
2. ONCOhabitats results for Ivy Glioblastoma Atlas Project (Ivy Gap): Segmentation and Hemodynamic Tissue Signature. (2021) doi:10.5281/zenodo.4704106.
3. Efficiency of deep learning on segmenting longitudinal postoperative glioblastomas. ECR 2020 EPOS 2020(opens in new window).
4. CBV BRAIN ATLAS v1. (Zenodo, 2021). doi:10.5281/zenodo.4757126.
5. JOncohabitats glioma segmentation model. Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma. 11992 LNCS, 295–303 (2020).
6. The use of a DSC-MRI perfusion atlas for cerebral blood volume normalization and its impact in improving prognostic estimation (Ref. 1087). in (2021).
7. The impact of EPI-based distortion correction of dynamic susceptibility contrast MRI on cerebral blood volume estimation in patients with glioblastoma. Eur. J. Radiol. 132, 109278 (2020).
8. Non-local spatially varying finite mixture models for image segmentation. Stat. Comput. 31, (2021).
9. Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study. J. Magn. Reson. Imaging 51, 1478–1486 (2020).
10. CHigher vascularity at infiltrated peripheral edema differentiates proneural glioblastoma subtype. PLOS ONE 15, e0232500 (2020).
11. Detection of local microvascular proliferation in IDH wild-type Glioblastoma using relative Cerebral Blood Volume. medRxiv 2021.04.19.21255589 (2021) doi:10.1101/2021.04.19.21255589.
12. Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma. NMR Biomed. (2021)
13. MGMT methylation may benefit overall survival in patients with moderately vascularized glioblastomas. Eur. Radiol. (2020)
At GLIOHAB we have gone a step further in the characterization of intratumoral heterogeneity. From a more theoretical point of view, we developed a novel method of structural unsupervised segmentation that allows an improvement in the robustness of the heterogeneity characterization [8]. In addition, we developed a new concept of habitat linked to the characterization of tumor progression, which is currently under consideration for patenting.
Throughout the GLIOHAB project, we discovered relevant associations between vascular and mechanical habitats and the survival of glioblastoma patients. Moreover, in the case of vascular habitats we validated this relationship in an international multicenter setting [9]. We discovered of an association between vascularity in the peripheral infiltrated glioblastoma and different molecular subtypes of glioblastoma [10]. We assessed local microvascular proliferation in glioblastoma using relative Cerebral Blood Volume [11]. Finally, we discovered that the prognostic ability of vascularity in glioblastoma patients is higher in long-surviving patients [12].
Finally, in GLIOHAB we studied the clinical relevance of the characterization of tumor heterogeneity for treatment response. One of the most relevant findings is that combining MGMT methylation status together with vascularity estimated in the high angiogenic part of the tumor, obtained automatically from standard-of-care MRI studies, can provide a more reliable prognostic indicator for designing patient stratification strategies [13]. Another relevant finding is the association found between small peritumoral displacements due to tumor growth and survival throughout patient follow-up. The methodology of quantification of these displacements proposed by GLIOHAB could contribute significantly to the differentiation of pseudoprogression and tumor progression phenomena as well as, to estimate the effect of tumor growth in eloquent areas.
Main results of GLIOHAB project:
1. ONCOhabitats results for The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM): Segmentation and Hemodynamic Tissue Signature. (2021) doi:10.5281/zenodo.4704090.
2. ONCOhabitats results for Ivy Glioblastoma Atlas Project (Ivy Gap): Segmentation and Hemodynamic Tissue Signature. (2021) doi:10.5281/zenodo.4704106.
3. Efficiency of deep learning on segmenting longitudinal postoperative glioblastomas. ECR 2020 EPOS 2020(opens in new window).
4. CBV BRAIN ATLAS v1. (Zenodo, 2021). doi:10.5281/zenodo.4757126.
5. JOncohabitats glioma segmentation model. Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma. 11992 LNCS, 295–303 (2020).
6. The use of a DSC-MRI perfusion atlas for cerebral blood volume normalization and its impact in improving prognostic estimation (Ref. 1087). in (2021).
7. The impact of EPI-based distortion correction of dynamic susceptibility contrast MRI on cerebral blood volume estimation in patients with glioblastoma. Eur. J. Radiol. 132, 109278 (2020).
8. Non-local spatially varying finite mixture models for image segmentation. Stat. Comput. 31, (2021).
9. Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study. J. Magn. Reson. Imaging 51, 1478–1486 (2020).
10. CHigher vascularity at infiltrated peripheral edema differentiates proneural glioblastoma subtype. PLOS ONE 15, e0232500 (2020).
11. Detection of local microvascular proliferation in IDH wild-type Glioblastoma using relative Cerebral Blood Volume. medRxiv 2021.04.19.21255589 (2021) doi:10.1101/2021.04.19.21255589.
12. Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma. NMR Biomed. (2021)
13. MGMT methylation may benefit overall survival in patients with moderately vascularized glioblastomas. Eur. Radiol. (2020)
GLIOHAB focuses on detecting complex hidden patterns within solid tumors with relevant underlying tumor microenvironment information. This information is not currently reflected by conventional supervised morphological tissue delineation approaches, which only consider a limited number of visible tissues and thus cannot sufficiently cope with this biological diversity. GLIOHAB technology relies on biomarkers of images related to complex processes in cancer, allowing to delimit tumor microenvironments associated with response to different therapies. Hence, GLIOHAB constitutes a radically new approach to design image-based tests for supporting therapeutic decisions in oncological treatments.
GLIOHAB project placed glioma patients into the center of the care process due to the development of tools that support a higher personalization of care, an earlier diagnosis, and more accurate monitoring of tumors evolution. Specifically, the results obtained during the validation of the clinical implication of the technology developed in GLIOHAB will contribute to provide more accurate diagnoses and prognoses with less incidence of errors that favor implementing quicker and better clinical decisions, and timely delivery of effective personalized treatments. GLIOHAB developments are currently being applied in Clinical Trials (ImPRESS, NCT03951142), facilitating the processing and quantification of functional sequences in the experimental design of novel drugs for glioblastoma patients.
GLIOHAB project placed glioma patients into the center of the care process due to the development of tools that support a higher personalization of care, an earlier diagnosis, and more accurate monitoring of tumors evolution. Specifically, the results obtained during the validation of the clinical implication of the technology developed in GLIOHAB will contribute to provide more accurate diagnoses and prognoses with less incidence of errors that favor implementing quicker and better clinical decisions, and timely delivery of effective personalized treatments. GLIOHAB developments are currently being applied in Clinical Trials (ImPRESS, NCT03951142), facilitating the processing and quantification of functional sequences in the experimental design of novel drugs for glioblastoma patients.