Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment

Liu, Tiffany Ting; Achrol, Achal S.; Mitchell, Lex A.; Rodriguez, Scott; Feroze, Abdullah H.; Michael, Cassia; Kim, Christine; Chaudhary, Navjot; Gevaert, Olivier; Stuart, Joshua M.; Harsh, Griffith R.; Chang, Daniel T.; Rubin, Daniel L.

doi:10.1093/neuonc/now270

Cited by 36 publications

(37 citation statements)

References 33 publications

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“…Subtype classification problems are not unique to adult gliomas, however. Conceptually similar work has been done on other brain tumors, in which it has been demonstrated that classification algorithms trained on radiomics features extracted from conventional MRI can generate predictive models for pituitary adenoma subtypes and pediatric brain tumors (Table ) …”

Section: Cns Tumor Imagingmentioning

confidence: 93%

“…Imaging plays an important role in the initial diagnosis of brain tumors and is a routine part of both initial and subsequent evaluation. The complex imaging features of brain tumors, as well as the frequent genetic heterogeneity within tumor types and the invasive nature of the procedures needed to obtain a tissue diagnosis, give rise to diagnostic dilemmas in this field (Table ) …”

Section: Cns Tumor Imagingmentioning

confidence: 99%

“…AI can improve the utility of current standard diagnostic imaging techniques by refining the preoperative classification of gliomas beyond what human experts can provide. For example, AI has been applied in the research setting to preoperative MRI to distinguish between low‐grade and high‐grade tumors as well as individual WHO grades by training machine‐learning classifiers using image texture features obtained from spatially coregistered, multimodal MRIs (Table ) . Furthermore, clinically relevant molecular subtypes of gliomas, such as the presence of an isocitrate dehydrogenase (IDH) mutation, can be identified using machine‐learning methods, including deep CNNs trained on conventional MR images …”

Section: Cns Tumor Imagingmentioning

confidence: 99%

“…In a retrospective evaluation of single‐institution and multi‐institutional, single‐arm data sets, radiomic models were constructed using conventional and diffusion MRI features to differentiate long‐term from short‐term survivors . Unsupervised clusters of radiomic features based on nonparametric parameters of preoperative perfusion MRI were first extracted independently from 2 data sets of patients with glioblastoma, and the feature clusters subsequently were combined and evaluated for their association with patient survival outcome . The radiomic cluster that was associated with poor survival (HR, >3.0) was associated with mutations in the angiogenesis and hypoxia pathways.…”

Section: Cns Tumor Imagingmentioning

confidence: 99%

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Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

1,151

820

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Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

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Section: Cns Tumor Imagingmentioning

confidence: 93%

Section: Cns Tumor Imagingmentioning

confidence: 99%

Section: Cns Tumor Imagingmentioning

confidence: 99%

Section: Cns Tumor Imagingmentioning

confidence: 99%

See 2 more Smart Citations

Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

1,151

820

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“…Cerebral blood volume (CBV) and cerebral blood flow (CBF) maps were obtained through the standard algorithm proposed by Liu et al 32 Only concentration-time curves with signal-to-noise ratio greater than 5 and a goodness of fit R 2 > 0.95 to the bivariate gamma function were quantified. A post-processing technique based on the leakage correction method of Boxerman et al 33 was used to correct for over-and under-estimations of CBV and CBF markers.…”

Section: Dsc Quantification and Hts Analysismentioning

confidence: 99%

Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures

et al. 2018

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Advanced MRI and molecular markers have been raised as crucial to improve prognostic models for patients having glioblastoma (GBM) lesions. In particular, different MR perfusion based markers describing vascular intrapatient heterogeneity have been correlated with tumor aggressiveness, and represent key information to understand tumor resistance against effective therapies of these neoplasms. Recently, hemodynamic tissue signature (HTS) markers based on MR perfusion images have been demonstrated to be useful for describing the heterogeneity of GBM at the voxel level, as well as demonstrating significant correlations with the patient's overall survival. In this work, we analyze the abilities of these markers to improve the conventional prognostic models based on clinical, morphological, and demographic features. Our results, in both the regression and classification tests, show that inclusion of the HTS markers improves the reliability of prognostic models. The HTS method is fully automatic and it is available for research use at http://www.oncohabitats.upv.es.

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Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study

Álvarez‐Torres

Juan–Albarracín

Fuster–Garcia

et al. 2019

Magnetic Resonance Imaging

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Background Glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by a heterogeneous and abnormal vascularity. Subtypes of vascular habitats within the tumor and edema can be distinguished: high angiogenic tumor (HAT), low angiogenic tumor (LAT), infiltrated peripheral edema (IPE), and vasogenic peripheral edema (VPE). Purpose To validate the association between hemodynamic markers from vascular habitats and overall survival (OS) in glioblastoma patients, considering the intercenter variability of acquisition protocols. Study Type Multicenter retrospective study. Population In all, 184 glioblastoma patients from seven European centers participating in the NCT03439332 clinical study. Field Strength/Sequence 1.5T (for 54 patients) or 3.0T (for 130 patients). Pregadolinium and postgadolinium‐based contrast agent‐enhanced T1‐weighted MRI, T2‐ and FLAIR T2‐weighted, and dynamic susceptibility contrast (DSC) T2* perfusion. Assessment We analyzed preoperative MRIs to establish the association between the maximum relative cerebral blood volume (rCBVmax) at each habitat with OS. Moreover, the stratification capabilities of the markers to divide patients into "vascular" groups were tested. The variability in the markers between individual centers was also assessed. Statistical Tests Uniparametric Cox regression; Kaplan–Meier test; Mann–Whitney test. Results The rCBVmax derived from the HAT, LAT, and IPE habitats were significantly associated with patient OS (P < 0.05; hazard ratio [HR]: 1.05, 1.11, 1.28, respectively). Moreover, these markers can stratify patients into "moderate‐" and "high‐vascular" groups (P < 0.05). The Mann–Whitney test did not find significant differences among most of the centers in markers (HAT: P = 0.02–0.685; LAT: P = 0.010–0.769; IPE: P = 0.093–0.939; VPE: P = 0.016–1.000). Data Conclusion The rCBVmax calculated in HAT, LAT, and IPE habitats have been validated as clinically relevant prognostic biomarkers for glioblastoma patients in the pretreatment stage. This study demonstrates the robustness of the hemodynamic tissue signature (HTS) habitats to assess the GBM vascular heterogeneity and their association with patient prognosis independently of intercenter variability. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1478–1486.

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Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment

Abstract: Our findings suggest that an angiogenic subtype of GBM patients may benefit from antiangiogenic therapy with improved overall survival.

Cited by 36 publications

References 33 publications

Artificial intelligence in cancer imaging: Clinical challenges and applications

Artificial intelligence in cancer imaging: Clinical challenges and applications

Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures

Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study

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