Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer

Kazerouni, Anum S.; Hormuth, David A.; Davis, Tessa; Bloom, Meghan J.; Mounho, Sarah; Rahman, Gibraan; Virostko, John; Yankeelov, Thomas E.; Sorace, Anna G.

doi:10.3390/cancers14071837

Cited by 23 publications

(12 citation statements)

References 51 publications

(65 reference statements)

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“…While the proposed pipeline for modelling tumor habitats confers some advances over existing modelling efforts for high grade glioma, there are opportunities for further investigation. First, while the identified habitats agree with previous work 7 , 8 in which the habitats were histologically verified, there is a need to repeat histological analysis in this study of murine glioma. This added analysis would validate the image-based tumor habitats, making it feasible for future work to perform habitat imaging in independent cohorts of animals with greater certainty.…”

Section: Discussionsupporting

confidence: 85%

“…In addition to heterogeneity arising from genotypic and phenotypic variables, heterogeneity in tumor vasculature also contributes to the mixed tumor landscape and substantially informs treatment outcomes. Previous work has described tumor heterogeneity through the investigation of tumor subregions, termed habitats 6 , with unique physiological features stemming from properties of the tumor vasculature and cellularity 7 , 8 . These habitats can be non-invasively identified through multiparametric imaging for three-dimensional (3D) analysis of tumor heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Slavkova

Patel

Cacini

et al. 2023

Sci Rep

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Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or “habitats” in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupled ordinary differential equations (ODEs) in the presence and absence of radiotherapy. Female Wistar rats (N = 21) were inoculated intracranially with 106 C6 glioma cells, a subset of which received 20 Gy (N = 5) or 40 Gy (N = 8) of radiation. All rats underwent diffusion-weighted and dynamic contrast-enhanced MRI at up to seven time points. All MRI data at each visit were subsequently clustered using k-means to identify physiological tumor habitats. A family of four models consisting of three coupled ODEs were developed and calibrated to the habitat time series of control and treated rats and evaluated for predictive capability. The Akaike Information Criterion was used for model selection, and the normalized sum-of-square-error (SSE) was used to evaluate goodness-of-fit in model calibration and prediction. Three tumor habitats with significantly different imaging data characteristics (p < 0.05) were identified: high-vascularity high-cellularity, low-vascularity high-cellularity, and low-vascularity low-cellularity. Model selection resulted in a five-parameter model whose predictions of habitat dynamics yielded SSEs that were similar to the SSEs from the calibrated model. It is thus feasible to mathematically describe habitat dynamics in a preclinical model of glioma using biology-based ODEs, showing promise for forecasting heterogeneous tumor behavior.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Slavkova

Patel

Cacini

et al. 2023

Sci Rep

Self Cite

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“…First, while the identified habitats agree with previous work 7,8 in which the habitats were histologically verified, there is a need to repeat histological analysis in this study of murine glioma.…”

Section: Discussionsupporting

confidence: 84%

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Slavkova

Patel

Cacini

et al. 2022

Preprint

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Tumors are highly heterogeneous with unique sub-regions termed "habitats". We evaluate the ability of a mathematical model built on coupled ordinary differential equations (ODEs) to describe and predict tumor habitat dynamics in a murine model of glioma. Female Wistar rats (N=21) were inoculated intracranially with 10 6 C6 glioma cells, a subset of which received 20 (N=5) or 40 Gy (N=8) of radiation. All rats underwent diffusion-weighted (DW) and dynamic contrast-enhanced magnetic (DCE) resonance imaging (MRI) at up to seven time points. All MRI data at each visit were subsequently clustered using k-means to identify physiological tumor habitats. A family of four models consisting of three coupled ODEs were developed and calibrated to the habitat time series of eight control rats and eight treated rats and evaluated for predictive capability. The Akaike Information Criterion (AIC) was used for model selection, and the normalized sum-of-square-error (SSE) was used to evaluate goodness-of-fit in model calibration and prediction. Three tumor habitats with significantly different imaging data characteristics (p < 0.05) were identified: high-vascularity high-cellularity, low-vascularity high-cellularity, and lowvascularity low-cellularity. Model selection yielded a five-parameter model whose predictions of habitat dynamics yielded SSEs that were similar to the SSEs from the calibrated model. It is thus feasible to mathematically describe habitat dynamics in a preclinical model of glioma using biology-based ODEs, showing promise for forecasting heterogeneous tumor behavior.

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“…While fully considering the internal differences of their subclassifications, clustering parameters are designed to evaluate the correlation or eccentricity of different subgroups (spatial subregions). For different parameters, they represent different perspectives to evaluate the heterogeneity components within tumors, which is undoubtedly consistent with previous studies [36][37][38][39]. In addition to the absolute characterization of tumors, we pay attention to the direct internal relations of different subregions, and their ability to respond to internal relations is stronger than direct indicators.…”

Section: Discussionsupporting

confidence: 77%

A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas

Wang

Sun

et al. 2022

BioMed Research International

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Purpose. We aim to develop and validate a machine learning model by enhanced MRI to determine the pathological grading of meningiomas with unsupervised clustering image analysis method, which are multihabitat to reflect the inherent heterogeneity of tumors. Materials and Methods. A total of 120 patients with meningiomas confirmed by postoperative pathology were included in the study, including 60 patients with low-grade meningiomas (WHO grade I) and 60 patients with high-grade meningiomas (WHO grade II and WHO grade III). All patients underwent complete head enhanced magnetic resonance scans before surgery or any anti-tumor treatment. Enrolled patients in the group received surgical resection and obtained postoperative pathological data. The patients in the training group (84 people) and the test group (36 people) were randomly divided into two groups according to the ratio of 7 to 3. Multi-habitat features were extracted from MRI images based on enhanced T1. Machine learning method was used to model, which was used to distinguish high-grade meningioma from low-grade meningioma. At the same time, the obtained machine learning model was calibrated and evaluated. Results. In patients with low-grade meningioma and high-grade meningioma, we found significant differences in Silhouette coefficient (P<0.05). In the machine learning model, the area under the curve was 0.838 in the training group (sensitivity, 67.65%; specificity, 88.82%) and 0.73 in the test group (sensitivity, 69.05%; specificity, 71.43%). After the analysis of calibration curve and decision curve analysis, the model had shown the potential of great application value. Conclusions. Multi-habitat analysis based on enhanced MRI (T1) could accurately predict the pathological grading of meningiomas. This unsupervised image-based method could reflect the direct heterogeneity between high-grade meningiomas and low-grade meningiomas, which is of great significance for patients’ treatment and prevention of recurrence.

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Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer

Cited by 23 publications

References 51 publications

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas

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