Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma

Massey, Susan Christine; White, Haylye; Whitmire, Paula; Doyle, Tatum E; Johnston, Sandra K.; Singleton, Kyle; Jackson, Pamela; Hawkins-Daarud, Andrea; Bendok, Bernard R.; Porter, Alyx; Vora, Sujay A.; Sarkaria, Jann N.; Hu, Leland; Mrugala, Maciej M.; Swanson, Kristin R.

doi:10.1371/journal.pone.0230492

Cited by 12 publications

(8 citation statements)

References 35 publications

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“…Most glioma models are based on the Fisher-Kolmogorov equation, which describes tumour proliferation and infiltration into the surrounding tissue [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Such models have been used to simulate spatio-temporal disease progression, response to treatment or transition from LGGs to HGGs [14,23,[25][26][27][28][29][30]. The models can be further calibrated to patient-specific conditions to provide estimates about tumour infiltration pathways beyond the lesion outlines visible on medical scans [17,24,[31][32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

Lipková

Menze

Wiestler

et al. 2022

J. R. Soc. Interface.

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Increased intracranial pressure is the source of most critical symptoms in patients with glioma, and often the main cause of death. Clinical interventions could benefit from non-invasive estimates of the pressure distribution in the patient's parenchyma provided by computational models. However, existing glioma models do not simulate the pressure distribution and they rely on a large number of model parameters, which complicates their calibration from available patient data. Here we present a novel model for glioma growth, pressure distribution and corresponding brain deformation. The distinct feature of our approach is that the pressure is directly derived from tumour dynamics and patient-specific anatomy, providing non-invasive insights into the patient's state. The model predictions allow estimation of critical conditions such as intracranial hypertension, brain midline shift or neurological and cognitive impairments. A diffuse-domain formalism is employed to allow for efficient numerical implementation of the model in the patient-specific brain anatomy. The model is tested on synthetic and clinical cases. To facilitate clinical deployment, a high-performance computing implementation of the model has been publicly released.

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Section: Introductionmentioning

confidence: 99%

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

Lipková

Menze

Wiestler

et al. 2022

J. R. Soc. Interface.

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“…In silico models based on evolutionary dynamics may capture relevant aspects of tumor growth and have proved helpful in understanding tumor clonal heterogeneity, one of the main hallmarks of cancer ( 49 ). Mechanistic mathematical models of different levels of complexity have been shown to provide biomarkers of clinical significance ( 24 , 50 – 57 ). This type of approach provides a rational alternative to radiomic and deep-learning studies, where a mechanistic explanation is often missing.…”

Section: Discussionmentioning

confidence: 99%

Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers

Jiménez-Sánchez

Bosque

Londoño

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatiotemporal dynamics during tumor growth. We studied the reshaping of metabolic activity in human cancers by means of continuous and discrete mathematical models and matched the results to positron emission tomography (PET) imaging data. Our models revealed that the location of increasingly active proliferative cellular spots progressively drifted from the center of the tumor to the periphery, as a result of the competition between gradually more aggressive phenotypes. This computational finding led to the development of a metric, normalized distance from 18F-fluorodeoxyglucose (18F-FDG) hotspot to centroid (NHOC), based on the separation from the location of the activity (proliferation) hotspot to the tumor centroid. The NHOC metric can be computed for patients using 18F-FDG PET–computed tomography (PET/CT) images where the voxel of maximum uptake (standardized uptake value [SUV]max) is taken as the activity hotspot. Two datasets of 18F-FDG PET/CT images were collected, one from 61 breast cancer patients and another from 161 non–small-cell lung cancer patients. In both cohorts, survival analyses were carried out for the NHOC and for other classical PET/CT-based biomarkers, finding that the former had a high prognostic value, outperforming the latter. In summary, our work offers additional insights into the evolutionary mechanisms behind tumor progression, provides a different PET/CT-based biomarker, and reveals that an activity hotspot closer to the tumor periphery is associated to a worst patient outcome.

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“…Mathematical models of cancer define a forward problem, whose solution provides state variables (e.g., tumor cell density). In general, these models are parameterized by unknown biophysical parameters (and possibly initial conditions) that typically manifest substantial variability across subjects [68,75,98]. The estimation of these unknown variables (also called inversion variables) should be patient-specific and can be mathematically posed as an inverse problem, which aims at optimizing an objective function constrained by the model.…”

Section: Calibrating Image-based Mathematical Oncology Modelsmentioning

confidence: 99%

Quantitative in vivo imaging to enable tumor forecasting and treatment optimization

Lorenzo,

Hormuth,

Jarrett

et al. 2021

Preprint

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Current clinical decision-making in oncology relies on averages of large patient populations to both assess tumor status and treatment outcomes. However, cancers exhibit an inherent evolving heterogeneity that requires an individual approach based on rigorous and precise predictions of cancer growth and treatment response. To this end, we advocate the use of quantitative in vivo imaging data to calibrate mathematical models for the personalized forecasting of tumor development. In this chapter, we summarize the main data types available from both common and emerging in vivo medical imaging technologies, and how these data can be used to obtain patient-specific parameters for common mathematical models of cancer. We then outline computational methods designed to solve these models, thereby enabling their use for producing personalized tumor forecasts in silico, which, ultimately, can be used to not only predict response, but also optimize treatment. Finally, we discuss the main barriers to making the above paradigm a clinical reality.

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Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma

Cited by 12 publications

References 35 publications

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers

Quantitative in vivo imaging to enable tumor forecasting and treatment optimization

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