Publications by authors named "Kristin Swanson"

92 Publications

Association of breast cancer risk, density, and stiffness: global tissue stiffness on breast MR elastography (MRE).

Breast Cancer Res Treat 2022 Jul 30;194(1):79-89. Epub 2022 Apr 30.

Diagnostic Radiology, Mayo Clinic, Rochester, MN, USA.

Purpose: Quantify in vivo biomechanical tissue properties in various breast densities and in average risk and high-risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association between breast biomechanical properties and cancer risk based on patient demographics and clinical data.

Methods: Patients with average risk or high-risk of breast cancer underwent 3.0 T breast MR imaging and elastography. Breast parenchymal enhancement (BPE), density (from most recent mammogram), stiffness, elasticity, and viscosity were recorded. Within each breast density group (non-dense versus dense), stiffness, elasticity, and viscosity were compared across risk groups (average versus high). Separately for stiffness, elasticity, and viscosity, a multivariable logistic regression model was used to evaluate whether the MRE parameter predicted risk status after controlling for clinical factors.

Results: 50 average risk and 86 high-risk patients were included. Risk groups were similar in age, density, and menopausal status. Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high-risk patients (N = 55) compared to average risk patients (N = 34; all p < 0.001). Stiffness remained a significant predictor of risk status (OR = 4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, BPE, age, and menopausal status. Similar results were seen for elasticity and viscosity.

Conclusion: A structurally based, quantitative biomarker of tissue stiffness obtained from MRE is associated with differences in breast cancer risk in dense breasts. Tissue stiffness could provide a novel prognostic marker to help identify high-risk women with dense breasts who would benefit from increased surveillance and/or risk reduction measures.
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http://dx.doi.org/10.1007/s10549-022-06607-2DOI Listing
July 2022

Shape matters: morphological metrics of glioblastoma imaging abnormalities as biomarkers of prognosis.

Sci Rep 2021 12 1;11(1):23202. Epub 2021 Dec 1.

Mathematical Neuro-Oncology Lab, Precision Neurotherapeutics Innovation Program, Department of Neurological Surgery, Mayo Clinic, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA.

Lacunarity, a quantitative morphological measure of how shapes fill space, and fractal dimension, a morphological measure of the complexity of pixel arrangement, have shown relationships with outcome across a variety of cancers. However, the application of these metrics to glioblastoma (GBM), a very aggressive primary brain tumor, has not been fully explored. In this project, we computed lacunarity and fractal dimension values for GBM-induced abnormalities on clinically standard magnetic resonance imaging (MRI). In our patient cohort (n = 402), we connect these morphological metrics calculated on pretreatment MRI with the survival of patients with GBM. We calculated lacunarity and fractal dimension on necrotic regions (n = 390), all abnormalities present on T1Gd MRI (n = 402), and abnormalities present on T2/FLAIR MRI (n = 257). We also explored the relationship between these metrics and age at diagnosis, as well as abnormality volume. We found statistically significant relationships to outcome for all three imaging regions that we tested, with the shape of T2/FLAIR abnormalities that are typically associated with edema showing the strongest relationship with overall survival. This link between morphological and survival metrics could be driven by underlying biological phenomena, tumor location or microenvironmental factors that should be further explored.
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http://dx.doi.org/10.1038/s41598-021-02495-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636508PMC
December 2021

IDH-wild-type glioblastoma cell density and infiltration distribution influence on supramarginal resection and its impact on overall survival: a mathematical model.

J Neurosurg 2021 Oct 29:1-9. Epub 2021 Oct 29.

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.

Objective: Recent studies have proposed resection of the T2 FLAIR hyperintensity beyond the T1 contrast enhancement (supramarginal resection [SMR]) for IDH-wild-type glioblastoma (GBM) to further improve patients' overall survival (OS). GBMs have significant variability in tumor cell density, distribution, and infiltration. Advanced mathematical models based on patient-specific radiographic features have provided new insights into GBM growth kinetics on two important parameters of tumor aggressiveness: proliferation rate (ρ) and diffusion rate (D). The aim of this study was to investigate OS of patients with IDH-wild-type GBM who underwent SMR based on a mathematical model of cell distribution and infiltration profile (tumor invasiveness profile).

Methods: Volumetric measurements were obtained from the selected regions of interest from pre- and postoperative MRI studies of included patients. The tumor invasiveness profile (proliferation/diffusion [ρ/D] ratio) was calculated using the following formula: ρ/D ratio = (4π/3)2/3 × (6.106/[VT21/1 - VT11/1])2, where VT2 and VT1 are the preoperative FLAIR and contrast-enhancing volumes, respectively. Patients were split into subgroups based on their tumor invasiveness profiles. In this analysis, tumors were classified as nodular, moderately diffuse, or highly diffuse.

Results: A total of 101 patients were included. Tumors were classified as nodular (n = 34), moderately diffuse (n = 34), and highly diffuse (n = 33). On multivariate analysis, increasing SMR had a significant positive correlation with OS for moderately and highly diffuse tumors (HR 0.99, 95% CI 0.98-0.99; p = 0.02; and HR 0.98, 95% CI 0.96-0.99; p = 0.04, respectively). On threshold analysis, OS benefit was seen with SMR from 10% to 29%, 10% to 59%, and 30% to 90%, for nodular, moderately diffuse, and highly diffuse, respectively.

Conclusions: The impact of SMR on OS for patients with IDH-wild-type GBM is influenced by the degree of tumor invasiveness. The authors' results show that increasing SMR is associated with increased OS in patients with moderate and highly diffuse IDH-wild-type GBMs. When grouping SMR into 10% intervals, this benefit was seen for all tumor subgroups, although for nodular tumors, the maximum beneficial SMR percentage was considerably lower than in moderate and highly diffuse tumors.
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http://dx.doi.org/10.3171/2021.6.JNS21925DOI Listing
October 2021

Molecular omics resources should require sex annotation: a call for action.

Nat Methods 2021 06;18(6):585-588

Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, USA.

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http://dx.doi.org/10.1038/s41592-021-01168-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8764747PMC
June 2021

Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma.

Sci Rep 2021 02 16;11(1):3932. Epub 2021 Feb 16.

Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.

Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor-a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.
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http://dx.doi.org/10.1038/s41598-021-83141-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886858PMC
February 2021

Assessment of Prognostic Value of Cystic Features in Glioblastoma Relative to Sex and Treatment With Standard-of-Care.

Front Oncol 2020 16;10:580750. Epub 2020 Nov 16.

Department of Radiology, Mayo Clinic, Arizona, AZ, United States.

Glioblastoma (GBM) is the most aggressive primary brain tumor and can have cystic components, identifiable through magnetic resonance imaging (MRI). Previous studies suggest that cysts occur in 7-23% of GBMs and report mixed results regarding their prognostic impact. Using our retrospective cohort of 493 patients with first-diagnosis GBM, we carried out an exploratory analysis on this potential link between cystic GBM and survival. Using pretreatment MRIs, we manually identified 88 patients with GBM that had a significant cystic component at presentation and 405 patients that did not. Patients with cystic GBM had significantly longer overall survival and were significantly younger at presentation. Within patients who received the current standard of care (SOC) ( = 184, 40 cystic), we did not observe a survival benefit of cystic GBM. Unexpectedly, we did not observe a significant survival benefit between this SOC cystic cohort and patients with cystic GBM diagnosed before the standard was established ( = 40 with SOC, = 19 without SOC); this significant SOC benefit was clearly observed in patients with noncystic GBM ( = 144 with SOC, = 111 without SOC). When stratified by sex, the survival benefit of cystic GBM was only preserved in male patients ( = 303, 47 cystic). We report differences in the absolute and relative sizes of imaging abnormalities on MRI and the prognostic implication of cysts based on sex. We discuss hypotheses for these differences, including the possibility that the presence of a cyst could indicate a less aggressive tumor.
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http://dx.doi.org/10.3389/fonc.2020.580750DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7705378PMC
November 2020

Education and Outreach in Physical Sciences in Oncology.

Trends Cancer 2021 01 7;7(1):3-9. Epub 2020 Nov 7.

Department of Biochemistry and Molecular Biology, Mayo Clinic, Jacksonville, FL, USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Jacksonville, FL, USA; Department of Transplantation, Mayo Clinic, Jacksonville, FL, USA; Center for Immunotherapeutic Transport Oncophysics, Houston Methodist Research Institute, Houston, TX, USA. Electronic address:

Physical sciences are often overlooked in the field of cancer research. The Physical Sciences in Oncology Initiative was launched to integrate physics, mathematics, chemistry, and engineering with cancer research and clinical oncology through education, outreach, and collaboration. Here, we provide a framework for education and outreach in emerging transdisciplinary fields.
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http://dx.doi.org/10.1016/j.trecan.2020.10.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895467PMC
January 2021

A Mechanistic Investigation into Ischemia-Driven Distal Recurrence of Glioblastoma.

Bull Math Biol 2020 11 7;82(11):143. Epub 2020 Nov 7.

Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, 85054, USA.

Glioblastoma (GBM) is the most aggressive primary brain tumor with a short median survival. Tumor recurrence is a clinical expectation of this disease and usually occurs along the resection cavity wall. However, previous clinical observations have suggested that in cases of ischemia following surgery, tumors are more likely to recur distally. Through the use of a previously established mechanistic model of GBM, the Proliferation Invasion Hypoxia Necrosis Angiogenesis (PIHNA) model, we explore the phenotypic drivers of this observed behavior. We have extended the PIHNA model to include a new nutrient-based vascular efficiency term that encodes the ability of local vasculature to provide nutrients to the simulated tumor. The extended model suggests sensitivity to a hypoxic microenvironment and the inherent migration and proliferation rates of the tumor cells are key factors that drive distal recurrence.
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http://dx.doi.org/10.1007/s11538-020-00814-yDOI Listing
November 2020

Sex differences in health and disease: A review of biological sex differences relevant to cancer with a spotlight on glioma.

Cancer Lett 2021 02 29;498:178-187. Epub 2020 Oct 29.

Mathematical NeuroOncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, USA; Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, USA; School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA. Electronic address:

The influence of biological sex differences on human health and disease, while being increasingly recognized, has long been underappreciated and underexplored. While humans of all sexes are more alike than different, there is evidence for sex differences in the most basic aspects of human biology and these differences have consequences for the etiology and pathophysiology of many diseases. In a disease like cancer, these consequences manifest in the sex biases in incidence and outcome of many cancer types. The ability to deliver precise, targeted therapies to complex cancer cases is limited by our current understanding of the underlying sex differences. Gaining a better understanding of the implications and interplay of sex differences in diseases like cancer will thus be informative for clinical practice and biological research. Here we review the evidence for a broad array of biological sex differences in humans and discuss how these differences may relate to observed sex differences in various diseases, including many cancers and specifically glioblastoma. We focus on areas of human biology that play vital roles in healthy and disease states, including metabolism, development, hormones, and the immune system, and emphasize that the intersection of sex differences in these areas should not go overlooked. We further propose that mathematical approaches can be useful for exploring the extent to which sex differences affect disease outcomes and accounting for those in the development of therapeutic strategies.
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http://dx.doi.org/10.1016/j.canlet.2020.07.030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409250PMC
February 2021

Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations.

Math Biosci Eng 2020 07;17(5):4905-4941

School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK.

Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another in a competitive or cooperative manner; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies. We combine genetic information from biopsies with a mechanistic model of interacting GBM sub-populations to characterise the nature of interactions between two commonly occurring GBM sub-populations, those with EGFR and PDGFRA genes amplified. We study population levels found across image-localized biopsy data from a cohort of 25 patients and compare this to model outputs under competitive, cooperative and neutral interaction assumptions. We explore other factors affecting the observed simulated sub-populations, such as selection advantages and phylogenetic ordering of mutations, which may also contribute to the levels of EGFR and PDGFRA amplified populations observed in biopsy data.
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http://dx.doi.org/10.3934/mbe.2020267DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8382158PMC
July 2020

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data.

J Med Imaging (Bellingham) 2020 Sep 16;7(5):055501. Epub 2020 Oct 16.

Rhode Island Hospital and Alpert Medical School of Brown University, Department of Diagnostic Imaging, Providence, Rhode Island, United States.

Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations. We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap). The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively ( ). The DL method achieved a mean Dice coefficient of 0.87 on the test cases. This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its "human teachers" and produced output that was better, on average, than its training data.
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http://dx.doi.org/10.1117/1.JMI.7.5.055501DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567400PMC
September 2020

Quantifying Glioblastoma Drug Response Dynamics Incorporating Treatment Sensitivity and Blood Brain Barrier Penetrance From Experimental Data.

Front Physiol 2020 21;11:830. Epub 2020 Aug 21.

Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States.

Many drugs investigated for the treatment of glioblastoma (GBM) have had disappointing clinical trial results. Efficacy of these agents is dependent on adequate delivery to sensitive tumor cell populations, which is limited by the blood-brain barrier (BBB). Additionally, tumor heterogeneity can lead to subpopulations of cells with different sensitivities to anti-cancer drugs, further impacting therapeutic efficacy. Thus, it may be important to evaluate the extent to which BBB limitations and heterogeneous sensitivity each contribute to a drug's failure. To address this challenge, we developed a minimal mathematical model to characterize these elements of overall drug response, informed by time-series bioluminescence imaging data from a treated patient-derived xenograft (PDX) experimental model. By fitting this mathematical model to a preliminary dataset in a series of nonlinear regression steps, we estimated parameter values for individual PDX subjects that correspond to the dynamics seen in experimental data. Using these estimates as a guide for parameter ranges, we ran model simulations and performed a parameter sensitivity analysis using Latin hypercube sampling and partial rank correlation coefficients. Results from this analysis combined with simulations suggest that BBB permeability may play a slightly greater role in therapeutic efficacy than relative drug sensitivity. Additionally, we discuss recommendations for future experiments based on insights gained from this model. Further research in this area will be vital for improving the development of effective new therapies for glioblastoma patients.
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http://dx.doi.org/10.3389/fphys.2020.00830DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472531PMC
August 2020

Does location matter? Characterisation of the anatomic locations, molecular profiles, and clinical features of gliomas.

Neurol Neurochir Pol 2020 11;54(5):456-465. Epub 2020 Sep 11.

Mayo Clinic Arizona Department of Neurology.

Background: Neuroanatomic locations of gliomas may influence clinical presentations, molecular profiles, and patients' prognoses.

Methods: We investigated our institutional cancer registry to include patients with glioma over a 10-year period. Statistical tests were used to compare demographic, genetic, and clinical characteristics among patients with gliomas in different locations. Survival analysis methods were then used to assess associations between location and overall survival in the full cohort, as well as in relevant subgroups.

Results: 182 gliomas were identified. Of the tumours confined to a single lobe, there were 51 frontal (28.0%), 50 temporal (27.5%), 22 parietal (12.1%), and seven occipital tumours (3.8%) identified. Tumours affecting the temporal lobe were associated with reduced overall survival when compared to all other tumours (11 months vs. 13 months, log-rank p = 0.0068). In subgroup analyses, this result was significant for males [HR (95%CI) 2.05 (1.30, 3.24), p = 0.002], but not for females [HR (95%CI) 1.12 (0.65, 1.93), p = 0.691]. Out of 82 cases tested for IDH-1, 10 were mutated (5.5%). IDH-1 mutation was present in six frontal, two temporal, one thalamic, and one multifocal tumour. Out of 21 cases tested for 1p19q deletions, 12 were co-deleted, nine of which were frontal lobe tumours. MGMT methylation was assessed in 45 cases; 7/14 frontal tumours and 6/13 temporal tumours were methylated.

Conclusion: Our results support the hypothesis that the anatomical locations of gliomas influence patients' clinical courses. Temporal lobe tumours were associated with poorer survival, though this association appeared to be driven by these patients' more aggressive tumour profiles and higher risk baseline demographics. Independently, female patients who had temporal lobe tumours fared better than males. Molecular analysis was limited by the low prevalence of genetic testing in the study sample, highlighting the importance of capturing this information for all gliomas.

Importance Of This Study: The specific neuroanatomic location of tumours in the brain is thought to be predictive of treatment options and overall prognosis. Despite evidence for the clinical significance of this information, there is relatively little information available regarding the incidence and prevalence of tumours in the different anatomical regions of the brain. This study has more fully characterised tumour prevalence in different regions of the brain. Additionally, we have analysed how this information may affect tumours' molecular characteristics, treatment options offered to patients, and patients' overall survival. This information will be informative both in the clinical setting and in directing future research.
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http://dx.doi.org/10.5603/PJNNS.a2020.0067DOI Listing
November 2020

Learning Equations from Biological Data with Limited Time Samples.

Bull Math Biol 2020 09 9;82(9):119. Epub 2020 Sep 9.

Department of Mathematics, North Carolina State University, Raleigh, NC, USA.

Equation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have demonstrated that these methods can infer models from rich datasets; however, the performance of these methods in the presence of common challenges from biological data has not been thoroughly explored. We present an equation learning methodology comprised of data denoising, equation learning, model selection and post-processing steps that infers a dynamical systems model from noisy spatiotemporal data. The performance of this methodology is thoroughly investigated in the face of several common challenges presented by biological data, namely, sparse data sampling, large noise levels, and heterogeneity between datasets. We find that this methodology can accurately infer the correct underlying equation and predict unobserved system dynamics from a small number of time samples when the data are sampled over a time interval exhibiting both linear and nonlinear dynamics. Our findings suggest that equation learning methods can be used for model discovery and selection in many areas of biology when an informative dataset is used. We focus on glioblastoma multiforme modeling as a case study in this work to highlight how these results are informative for data-driven modeling-based tumor invasion predictions.
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http://dx.doi.org/10.1007/s11538-020-00794-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409251PMC
September 2020

Days gained response discriminates treatment response in patients with recurrent glioblastoma receiving bevacizumab-based therapies.

Neurooncol Adv 2020 Jan-Dec;2(1):vdaa085. Epub 2020 Jul 9.

Mathematical NeuroOncology Lab, Precision NeuroTherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona.

Background: Accurate assessments of patient response to therapy are a critical component of personalized medicine. In glioblastoma (GBM), the most aggressive form of brain cancer, tumor growth dynamics are heterogenous across patients, complicating assessment of treatment response. This study aimed to analyze days gained (DG), a burgeoning model-based dynamic metric, for response assessment in patients with recurrent GBM who received bevacizumab-based therapies.

Methods: DG response scores were calculated using volumetric tumor segmentations for patients receiving bevacizumab with and without concurrent cytotoxic therapy ( = 62). Kaplan-Meier and Cox proportional hazards analyses were implemented to examine DG prognostic relationship to overall (OS) and progression-free survival (PFS) from the onset of treatment for recurrent GBM.

Results: In patients receiving concurrent bevacizumab and cytotoxic therapy, Kaplan-Meier analysis showed significant differences in OS and PFS at DG cutoffs consistent with previously identified values from newly diagnosed GBM using T1-weighted gadolinium-enhanced magnetic resonance imaging (T1Gd). DG scores for bevacizumab monotherapy patients only approached significance for PFS. Cox regression showed that increases of 25 DG on T1Gd imaging were significantly associated with a 12.5% reduction in OS hazard for concurrent therapy patients and a 4.4% reduction in PFS hazard for bevacizumab monotherapy patients.

Conclusion: DG has significant meaning in recurrent therapy as a metric of treatment response, even in the context of anti-angiogenic therapies. This provides further evidence supporting the use of DG as an adjunct response metric that quantitatively connects treatment response and clinical outcomes.
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http://dx.doi.org/10.1093/noajnl/vdaa085DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447137PMC
July 2020

Sex-specific impact of patterns of imageable tumor growth on survival of primary glioblastoma patients.

BMC Cancer 2020 May 19;20(1):447. Epub 2020 May 19.

Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA.

Background: Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences.

Methods: Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females).

Results: Among males, tumor (T1Gd) radius was a predictor of overall survival (HR = 1.027, p = 0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR = 1.011, p < 0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p = 0.010 t-test), but tumor size was not correlated with female overall survival (p = 0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p = 0.004, F p = 0.001, t-test).

Conclusion: Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.
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http://dx.doi.org/10.1186/s12885-020-06816-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7238585PMC
May 2020

Implementation of an antibiotic stewardship quality improvement initiative in a community hospital for infants born at ≥35 weeks.

Proc (Bayl Univ Med Cent) 2020 Apr 23;33(2):188-190. Epub 2019 Dec 23.

Department of Anesthesiology, Baylor Scott & White Medical Center - FriscoFriscoTexas.

We present our experience in the implementation of an antibiotic stewardship quality improvement initiative directed toward infants born at ≥35 weeks using as a primary tool the Kaiser Permanente early onset sepsis calculator (KP-EOS-C) at the Baylor Scott & White Medical Center - Frisco. After the approval and support of the medical staff and administration, we proceeded to launch an extensive educational program for all women's services nursing staff on how to utilize this calculator to communicate results to the pediatricians on staff. After implementation, we saw a 54% reduction in the number of infants undergoing sepsis workup evaluations and a 51% reduction in the number of infants receiving antibiotics ( < 0.001). We conclude that the implementation of this type of initiative may be feasible and worthwhile in other similar community hospitals, provided there is buy-in by physicians and administration as well as an extensive educational program to the hospital medical and nursing staff.
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http://dx.doi.org/10.1080/08998280.2019.1700725DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7155974PMC
April 2020

Roadmap for the clinical integration of radiomics in neuro-oncology.

Neuro Oncol 2020 06;22(6):743-745

Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, Phoenix, Arizona.

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http://dx.doi.org/10.1093/neuonc/noaa078DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7283033PMC
June 2020

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

PLoS One 2020 27;15(3):e0230492. Epub 2020 Mar 27.

Mathematical NeuroOncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona, United States of America.

Background: Temozolomide (TMZ) has been the standard-of-care chemotherapy for glioblastoma (GBM) patients for more than a decade. Despite this long time in use, significant questions remain regarding how best to optimize TMZ therapy for individual patients. Understanding the relationship between TMZ response and factors such as number of adjuvant TMZ cycles, patient age, patient sex, and image-based tumor features, might help predict which GBM patients would benefit most from TMZ, particularly for those whose tumors lack O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation.

Methods And Findings: Using a cohort of 90 newly-diagnosed GBM patients treated according to the standard of care, we examined the relationships between several patient and tumor characteristics and volumetric and survival outcomes during adjuvant chemotherapy. Volumetric changes in MR imaging abnormalities during adjuvant therapy were used to assess TMZ response. T1Gd volumetric response is associated with younger patient age, increased number of TMZ cycles, longer time to nadir volume, and decreased tumor invasiveness. Moreover, increased adjuvant TMZ cycles corresponded with improved volumetric response only among more nodular tumors, and this volumetric response was associated with improved survival outcomes. Finally, in a subcohort of patients with known MGMT methylation status, methylated tumors were more diffusely invasive than unmethylated tumors, suggesting the improved response in nodular tumors is not driven by a preponderance of MGMT methylated tumors.

Conclusions: Our finding that less diffusely invasive tumors are associated with greater volumetric response to TMZ suggests patients with these tumors may benefit from additional adjuvant TMZ cycles, even for those without MGMT methylation.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230492PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7100932PMC
June 2020

Speed Switch in Glioblastoma Growth Rate due to Enhanced Hypoxia-Induced Migration.

Bull Math Biol 2020 03 16;82(3):43. Epub 2020 Mar 16.

School of Mathematical Sciences, University of Nottingham, Nottingham, UK.

We analyze the wave speed of the Proliferation Invasion Hypoxia Necrosis Angiogenesis (PIHNA) model that was previously created and applied to simulate the growth and spread of glioblastoma (GBM), a particularly aggressive primary brain tumor. We extend the PIHNA model by allowing for different hypoxic and normoxic cell migration rates and study the impact of these differences on the wave-speed dynamics. Through this analysis, we find key variables that drive the outward growth of the simulated GBM. We find a minimum tumor wave-speed for the model; this depends on the migration and proliferation rates of the normoxic cells and is achieved under certain conditions on the migration rates of the normoxic and hypoxic cells. If the hypoxic cell migration rate is greater than the normoxic cell migration rate above a threshold, the wave speed increases above the predicted minimum. This increase in wave speed is explored through an eigenvalue and eigenvector analysis of the linearized PIHNA model, which yields an expression for this threshold. The PIHNA model suggests that an inherently faster-diffusing hypoxic cell population can drive the outward growth of a GBM as a whole, and that this effect is more prominent for faster-proliferating tumors that recover relatively slowly from a hypoxic phenotype. The findings presented here act as a first step in enabling patient-specific calibration of the PIHNA model.
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http://dx.doi.org/10.1007/s11538-020-00718-xDOI Listing
March 2020

Imaging of intratumoral heterogeneity in high-grade glioma.

Cancer Lett 2020 05 27;477:97-106. Epub 2020 Feb 27.

Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd, Support, Services Building Suite 2-700, Phoenix, AZ, 85054, USA. Electronic address:

High-grade glioma (HGG), and particularly Glioblastoma (GBM), can exhibit pronounced intratumoral heterogeneity that confounds clinical diagnosis and management. While conventional contrast-enhanced MRI lacks the capability to resolve this heterogeneity, advanced MRI techniques and PET imaging offer a spectrum of physiologic and biophysical image features to improve the specificity of imaging diagnoses. Published studies have shown how integrating these advanced techniques can help better define histologically distinct targets for surgical and radiation treatment planning, and help evaluate the regional heterogeneity of tumor recurrence and response assessment following standard adjuvant therapy. Application of texture analysis and machine learning (ML) algorithms has also enabled the emerging field of radiogenomics, which can spatially resolve the regional and genetically distinct subpopulations that coexist within a single GBM tumor. This review focuses on the latest advances in neuro-oncologic imaging and their clinical applications for the assessment of intratumoral heterogeneity.
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http://dx.doi.org/10.1016/j.canlet.2020.02.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7108976PMC
May 2020

From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response.

PLoS Comput Biol 2020 02 26;16(2):e1007672. Epub 2020 Feb 26.

Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America.

Glioblastomas are aggressive primary brain tumors known for their inter- and intratumor heterogeneity. This disease is uniformly fatal, with intratumor heterogeneity the major reason for treatment failure and recurrence. Just like the nature vs nurture debate, heterogeneity can arise from intrinsic or environmental influences. Whilst it is impossible to clinically separate observed behavior of cells from their environmental context, using a mathematical framework combined with multiscale data gives us insight into the relative roles of variation from different sources. To better understand the implications of intratumor heterogeneity on therapeutic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor kinetics and the individual cellular behavior. We track single cells as agents, cell density on a coarser scale, and growth factor diffusion and dynamics on a finer scale over time and space. Our model parameters were fit utilizing serial MRI imaging and cell tracking data from ex vivo tissue slices acquired from a growth-factor driven glioblastoma murine model. When fitting our model to serial imaging only, there was a spectrum of equally-good parameter fits corresponding to a wide range of phenotypic behaviors. When fitting our model using imaging and cell scale data, we determined that environmental heterogeneity alone is insufficient to match the single cell data, and intrinsic heterogeneity is required to fully capture the migration behavior. The wide spectrum of in silico tumors also had a wide variety of responses to an application of an anti-proliferative treatment. Recurrent tumors were generally less proliferative than pre-treatment tumors as measured via the model simulations and validated from human GBM patient histology. Further, we found that all tumors continued to grow with an anti-migratory treatment alone, but the anti-proliferative/anti-migratory combination generally showed improvement over an anti-proliferative treatment alone. Together our results emphasize the need to better understand the underlying phenotypes and tumor heterogeneity present in a tumor when designing therapeutic regimens.
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http://dx.doi.org/10.1371/journal.pcbi.1007672DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062288PMC
February 2020

Technical Note: A digital reference object representing Hoffman's 3D brain phantom for PET scanner simulations.

Med Phys 2020 Mar 5;47(3):1174-1180. Epub 2020 Feb 5.

Department of Radiology, University of Washington Medical Center, Box 357987, Seattle, WA, 98195-7987, USA.

Purpose: Physical and digital phantoms play a key role in the development and testing of nuclear medicine instrumentation and processing algorithms for clinical and research applications, including neuroimaging using positron emission tomography (PET). We have developed and tested a digital reference object (DRO) version of the original segmented magnetic resonance imaging (MRI) data used for the three-dimensional (3D) PET brain phantom developed by Hoffman et al., which is used as the basis of a commercially available physical test phantom.

Methods: The DRO was constructed by subdividing the MRI image planes the original phantom was based on to create equal-thickness slices and re-labeling voxels. The digital data was then embedded in a PET Digital Imaging and Communications in Medicine format and tested for compliance.

Results: We then tested the DRO by comparing it to computed tomography (CT) images of the physical phantom summed to form composite slices with axial extent similar to the DRO, but with a factor of two better in-slice resolution. For composite slices, 91% of voxels were labeled in full agreement, 5% of the voxels were 50-75% accurate, and the remaining 4% of voxels had 25% or less agreement.

Conclusions: This DRO can be used as an input for PET scanner simulation studies or for comparing simulations to measured Hoffman phantom images.
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http://dx.doi.org/10.1002/mp.14012DOI Listing
March 2020

Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models.

Clin Cancer Res 2020 04 23;26(8):1787-1795. Epub 2019 Dec 23.

Genentech-Roche, South San Francisco, California.

There is a need for new approaches and endpoints in oncology drug development, particularly with the advent of immunotherapies and the multiple drug combinations under investigation. Tumor dynamics modeling, a key component to oncology "model-informed drug development," has shown a growing number of applications and a broader adoption by drug developers and regulatory agencies in the past years to support drug development and approval in a variety of ways. Tumor dynamics modeling is also being investigated in personalized cancer therapy approaches. These models and applications are reviewed and discussed, as well as the limitations and issues open for further investigations. A close collaboration between stakeholders like clinical investigators, statisticians, and pharmacometricians is warranted to advance clinical cancer therapeutics.
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http://dx.doi.org/10.1158/1078-0432.CCR-19-0287DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415106PMC
April 2020

A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type.

J Digit Imaging 2020 04;33(2):439-446

Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA.

The explosion of medical imaging data along with the advent of big data analytics has launched an exciting era for clinical research. One factor affecting the ability to aggregate large medical image collections for research is the lack of infrastructure for automated data annotation. Among all imaging modalities, annotation of magnetic resonance (MR) images is particularly challenging due to the non-standard labeling of MR image types. In this work, we aimed to train a deep neural network to annotate MR image sequence type for scans of brain tumor patients. We focused on the four most common MR sequence types within neuroimaging: T1-weighted (T1W), T1-weighted post-gadolinium contrast (T1Gd), T2-weighted (T2W), and T2-weighted fluid-attenuated inversion recovery (FLAIR). Our repository contains images acquired using a variety of pulse sequences, sequence parameters, field strengths, and scanner manufacturers. Image selection was agnostic to patient demographics, diagnosis, and the presence of tumor in the imaging field of view. We used a total of 14,400 two-dimensional images, each visualizing a different part of the brain. Data was split into train, validation, and test sets (9600, 2400, and 2400 images, respectively) and sets consisted of equal-sized groups of image types. Overall, the model reached an accuracy of 99% on the test set. Our results showed excellent performance of deep learning techniques in predicting sequence types for brain tumor MR images. We conclude deep learning models can serve as tools to support clinical research and facilitate efficient database management.
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http://dx.doi.org/10.1007/s10278-019-00282-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7165226PMC
April 2020

ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma: A Multidisciplinary Team-based, Multifactorial Analytical Approach.

Am J Clin Oncol 2019 08;42(8):655-661

Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program.

Although glioblastoma (GBM) is a fatal primary brain cancer with short median survival of 15 months, a small number of patients survive >5 years after diagnosis; they are known as extreme survivors (ES). Because of their rarity, very little is known about what differentiates these outliers from other patients with GBM. For the purpose of identifying unknown drivers of extreme survivorship in GBM, the ENDURES consortium (ENvironmental Dynamics Underlying Responsive Extreme Survivors of GBM) was developed. This consortium is a multicenter collaborative network of investigators focused on the integration of multiple types of clinical data and the creation of patient-specific models of tumor growth informed by radiographic and histologic parameters. Leveraging our combined resources, the goals of the ENDURES consortium are 2-fold: (1) to build a curated, searchable, multilayered repository housing clinical and outcome data on a large cohort of ES patients with GBM; and (2) to leverage the ENDURES repository for new insights into tumor behavior and novel targets for prolonging survival for all patients with GBM. In this article, the authors review the available literature and discuss what is already known about ES. The authors then describe the creation of their consortium and some preliminary results.
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http://dx.doi.org/10.1097/COC.0000000000000564DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416695PMC
August 2019

Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI.

Sci Rep 2019 07 11;9(1):10063. Epub 2019 Jul 11.

School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA.

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.
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http://dx.doi.org/10.1038/s41598-019-46296-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6624304PMC
July 2019

Improved model prediction of glioma growth utilizing tissue-specific boundary effects.

Math Biosci 2019 06 19;312:59-66. Epub 2019 Apr 19.

Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, USA.

Kinetic parameter estimates for mathematical models of glioblastoma multiforme (GBM), derived from clinical scans, have been used to predict the occurrence of hypoxia, necrosis, response to radiation therapy, and overall survival. Modeling GBM growth in a cerebral model encounters anatomical boundaries that interfere with model calibration from clinical measurements.

Methods: The effect of boundaries is examined on both spherically symmetric and anatomical models of tumor growth. This effect is incorporated into a method that updates kinetic parameters. The efficacy of this method in reproducing clinical image-derived subject data is evaluated.

Results: Spherically symmetric simulations of tumor growth with simple boundaries behave predictably when in a linear phase of growth. Anatomic simulations of eleven out of twenty subjects demonstrated improved fit to subject data with the new method. When only subjects exhibiting linear growth are considered, eight out of nine subject demonstrate improved fit to the data.

Conclusion: Anatomical boundaries to tumor growth measurably deflect progression and affect estimates of kinetic parameters. The presented method reliably updates kinetic parameters to fit anatomic computational models to clinically derived subject data when those data are in a linear regime.
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http://dx.doi.org/10.1016/j.mbs.2019.04.004DOI Listing
June 2019

The 2019 mathematical oncology roadmap.

Phys Biol 2019 06 19;16(4):041005. Epub 2019 Jun 19.

Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA 91010, United States of America. Author to whom any correspondence should be addressed.

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.
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http://dx.doi.org/10.1088/1478-3975/ab1a09DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6655440PMC
June 2019

Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data.

Anal Chem 2019 05 22;91(9):6206-6216. Epub 2019 Apr 22.

Department of Neurosurgery, Brigham and Women's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , United States.

Multimodal integration between mass spectrometry imaging (MSI) and radiology-established modalities such as magnetic resonance imaging (MRI) would allow the investigations of key questions in complex biological systems such as the central nervous system. Such integration would provide complementary multiscale data to bridge the gap between molecular and anatomical phenotypes, potentially revealing new insights into molecular mechanisms underlying anatomical pathologies presented on MRI. Automatic coregistration between 3D MSI/MRI is a computationally challenging process due to dimensional complexity, MSI data sparsity, lack of direct spatial-correspondences, and nonlinear tissue deformation. Here, we present a new computational approach based on stochastic neighbor embedding to nonlinearly align 3D MSI to MRI data, identify and reconstruct biologically relevant molecular patterns in 3D, and fuse the MSI datacube to the MRI space. We demonstrate our method using multimodal high-spectral resolution matrix-assisted laser desorption ionization (MALDI) 9.4 T MSI and 7 T in vivo MRI data, acquired from a patient-derived, xenograft mouse brain model of glioblastoma following administration of the EGFR inhibitor drug of Erlotinib. Results show the distribution of some identified molecular ions of the EGFR inhibitor erlotinib, a phosphatidylcholine lipid, and cholesterol, which were reconstructed in 3D and mapped to the MRI space. The registration quality was evaluated on two normal mouse brains using the Dice coefficient for the regions of brainstem, hippocampus, and cortex. The method is generic and can therefore be applied to hyperspectral images from different mass spectrometers and integrated with other established in vivo imaging modalities such as computed tomography (CT) and positron emission tomography (PET).
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http://dx.doi.org/10.1021/acs.analchem.9b00854DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6691508PMC
May 2019
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