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Article ; Online: Roadmap for the clinical integration of radiomics in neuro-oncology.

Hu, Leland S / Swanson, Kristin R

2020 Volume 22, Issue 6, Page(s) 743–745

MeSH term(s)	Brain Neoplasms ; Humans ; Image Processing, Computer-Assisted ; Radiosurgery
Language	English
Publishing date	2020-03-30
Publishing country	England
Document type	Editorial ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Comment
ZDB-ID	2028601-6
ISSN	1523-5866 ; 1522-8517
ISSN (online)	1523-5866
ISSN	1522-8517
DOI	10.1093/neuonc/noaa078
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Article: An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients.

Bond, Kamila M / Curtin, Lee / Ranjbar, Sara / Afshari, Ariana E / Hu, Leland S / Rubin, Joshua B / Swanson, Kristin R

Frontiers in oncology

2023 Volume 13, Page(s) 1185738

Abstract: Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image- ... ...

Abstract	Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.
Language	English
Publishing date	2023-10-02
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2023.1185738
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Book ; Online: Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis

Mao, Lingchao / Wang, Hairong / Hu, Leland S. / Tran, Nhan L / Canoll, Peter D / Swanson, Kristin R / Li, Jing

A review

2024

Abstract: Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine ... ...

Abstract	Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine learning. Comment: 41 pages, 4 figures, 2 tables
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; 92B99
Subject code	006
Publishing date	2024-01-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Quantifying Uncertainty and Robustness in a Biomathematical Model-Based Patient-Specific Response Metric for Glioblastoma.

Hawkins-Daarud, Andrea / Johnston, Sandra K / Swanson, Kristin R

JCO clinical cancer informatics

2019 Volume 3, Page(s) 1–8

Abstract: Purpose: Glioblastomas, lethal primary brain tumors, are known for their heterogeneity and invasiveness. A growing body of literature has been developed demonstrating the clinical relevance of a biomathematical model, the proliferation-invasion model, ... ...

Abstract	Purpose: Glioblastomas, lethal primary brain tumors, are known for their heterogeneity and invasiveness. A growing body of literature has been developed demonstrating the clinical relevance of a biomathematical model, the proliferation-invasion model, of glioblastoma growth. Of interest here is the development of a treatment response metric, days gained (DG). This metric is based on individual tumor kinetics estimated through segmented volumes of hyperintense regions on T1-weighted gadolinium-enhanced and T2-weighted magnetic resonance images. This metric was shown to be prognostic of time to progression. Furthermore, it was shown to be more prognostic of outcome than standard response metrics. Although promising, the original article did not account for uncertainty in the calculation of the DG metric, leaving the robustness of this cutoff in question. Methods: We harnessed the Bayesian framework to consider the impact of two sources of uncertainty: (1) image acquisition and (2) interobserver error in image segmentation. We first used synthetic data to characterize what nonerror variants are influencing the final uncertainty in the DG metric. We then considered the original patient cohort to investigate clinical patterns of uncertainty and to determine how robust this metric is for predicting time to progression and overall survival. Results: Our results indicate that the key clinical variants are the time between pretreatment images and the underlying tumor growth kinetics, matching our observations in the clinical cohort. Finally, we demonstrated that for this cohort, there was a continuous range of cutoffs between 94 and 105 for which the prediction of the time to progression was over 80% reliable. Conclusion: Although additional validation must be performed, this work represents a key step in ascertaining the clinical utility of this metric.
MeSH term(s)	Adolescent ; Adult ; Aged ; Aged, 80 and over ; Bayes Theorem ; Cohort Studies ; Data Accuracy ; Disease Progression ; Glioblastoma/diagnostic imaging ; Glioblastoma/pathology ; Glioblastoma/therapy ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Middle Aged ; Patient-Specific Modeling ; Precision Medicine/methods ; Prognosis ; Uncertainty ; Young Adult
Language	English
Publishing date	2019-02-12
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2473-4276
ISSN (online)	2473-4276
DOI	10.1200/CCI.18.00066
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Article: Knowledge-infused Global-Local Data Fusion for Spatial Predictive Modeling in Precision Medicine.

Wang, Lujia / Hawkins-Daarud, Andrea / Swanson, Kristin R / Hu, Leland S / Li, Jing

IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society

2021 Volume 19, Issue 3, Page(s) 2203–2215

Abstract: The automated capability of generating spatial prediction for a variable of interest is desirable in various science and engineering domains. Take Precision Medicine of cancer as an example, in which the goal is to match patients with treatments based on ...

Abstract	The automated capability of generating spatial prediction for a variable of interest is desirable in various science and engineering domains. Take Precision Medicine of cancer as an example, in which the goal is to match patients with treatments based on molecular markers identified in each patient's tumor. A substantial challenge, however, is that the molecular markers can vary significantly at different spatial locations of a tumor. If this spatial distribution could be predicted, the precision of cancer treatment could be greatly improved by adapting treatment to the spatial molecular heterogeneity. This is a challenging task because no technology is available to measure the molecular markers at each spatial location within a tumor. Biopsy samples provide direct measurement, but they are scarce/local. Imaging, such as MRI, is global, but it only provides proxy/indirect measurement. Also available are mechanistic models or domain knowledge, which are often approximate or incomplete. This paper proposes a novel machine learning framework to fuse the three sources of data/information to generate spatial prediction, namely the knowledge-infused global-local data fusion (KGL) model. A novel mathematical formulation is proposed and solved with theoretical study. We present a real-data application of predicting the spatial distribution of Tumor Cell Density (TCD)-an important molecular marker for brain cancer. A total of 82 biopsy samples were acquired from 18 patients with glioblastoma, together with 6 MRI contrast images from each patient and biological knowledge encoded by a PDE simulator-based mechanistic model called Proliferation-Invasion (PI). KGL achieved the highest prediction accuracy and minimum prediction uncertainty compared with a variety of competing methods. The result has important implications for providing individualized, spatially-optimized treatment for each patient.
Language	English
Publishing date	2021-05-13
Publishing country	United States
Document type	Journal Article
ISSN	1545-5955
ISSN	1545-5955
DOI	10.1109/tase.2021.3076117
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Article ; Online: Molecular omics resources should require sex annotation: a call for action.

Bond, Kamila M / McCarthy, Margaret M / Rubin, Joshua B / Swanson, Kristin R

Nature methods

2021 Volume 18, Issue 6, Page(s) 585–588

MeSH term(s)	Computational Biology/methods ; Database Management Systems ; Female ; Genomics ; Humans ; Male ; Metabolomics ; Proteomics ; Sex Factors
Language	English
Publishing date	2021-06-07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2169522-2
ISSN	1548-7105 ; 1548-7091
ISSN (online)	1548-7105
ISSN	1548-7091
DOI	10.1038/s41592-021-01168-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients.

Ranjbar, Sara / Singleton, Kyle W / Curtin, Lee / Rickertsen, Cassandra R / Paulson, Lisa E / Hu, Leland S / Mitchell, Joseph Ross / Swanson, Kristin R

Frontiers in neuroimaging

2022 Volume 1, Page(s) 832512

Abstract: Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an ... ...

Abstract	Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an abundance of previous literature on learning-based methodologies for MRI segmentation, few works have focused on tackling MRI skull stripping of brain tumor patient data. This gap in literature can be associated with the lack of publicly available data (due to concerns about patient identification) and the labor-intensive nature of generating ground truth labels for model training. In this retrospective study, we assessed the performance of Dense-Vnet in skull stripping brain tumor patient MRI trained on our large multi-institutional brain tumor patient dataset. Our data included pretreatment MRI of 668 patients from our in-house institutional review board-approved multi-institutional brain tumor repository. Because of the absence of ground truth, we used imperfect automatically generated training labels using SPM12 software. We trained the network using common MRI sequences in oncology: T1-weighted with gadolinium contrast, T2-weighted fluid-attenuated inversion recovery, or both. We measured model performance against 30 independent brain tumor test cases with available manual brain masks. All images were harmonized for voxel spacing and volumetric dimensions before model training. Model training was performed using the modularly structured deep learning platform NiftyNet that is tailored toward simplifying medical image analysis. Our proposed approach showed the success of a weakly supervised deep learning approach in MRI brain extraction even in the presence of pathology. Our best model achieved an average Dice score, sensitivity, and specificity of, respectively, 94.5, 96.4, and 98.5% on the multi-institutional independent brain tumor test set. To further contextualize our results within existing literature on healthy brain segmentation, we tested the model against healthy subjects from the benchmark LBPA40 dataset. For this dataset, the model achieved an average Dice score, sensitivity, and specificity of 96.2, 96.6, and 99.2%, which are, although comparable to other publications, slightly lower than the performance of models trained on healthy patients. We associate this drop in performance with the use of brain tumor data for model training and its influence on brain appearance.
Language	English
Publishing date	2022-04-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	3123824-5
ISSN	2813-1193 ; 2813-1193
ISSN (online)	2813-1193
ISSN	2813-1193
DOI	10.3389/fnimg.2022.832512
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Article: Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma.

Wang, Hairong / Argenziano, Michael G / Yoon, Hyunsoo / Boyett, Deborah / Save, Akshay / Petridis, Petros / Savage, William / Jackson, Pamela / Hawkins-Daarud, Andrea / Tran, Nhan / Hu, Leland / Al Dalahmah, Osama / Bruce, Jeffrey N / Grinband, Jack / Swanson, Kristin R / Canoll, Peter / Li, Jing

Research square

2024

Abstract: Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral ... ...

Abstract	Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse Magnetic Resonance Imaging (MRI) with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically-informed neural network model, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet offers valuable insights into the integration of multiple implicit and qualitative biological domain knowledge, which are challenging to describe in mathematical formulations. BioNet performs significantly better than a range of existing methods on cross-validation and blind test datasets. Voxel-level prediction maps of the gene modules by BioNet help reveal intratumoral heterogeneity, which can improve surgical targeting of confirmatory biopsies and evaluation of neuro-oncological treatment effectiveness. The non-invasive nature of the approach can potentially facilitate regular monitoring of the gene modules over time, and making timely therapeutic adjustment. These results also highlight the emerging role of ML in precision medicine.
Language	English
Publishing date	2024-03-27
Publishing country	United States
Document type	Preprint
DOI	10.21203/rs.3.rs-3891425/v1
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Article: Androgen loss weakens anti-tumor immunity and accelerates brain tumor growth.

Lee, Juyeun / Chung, Yoon-Mi / Curtin, Lee / Silver, Daniel J / Hao, Yue / Li, Cathy / Volovetz, Josephine / Hong, Ellen S / Jarmula, Jakub / Wang, Sabrina Z / Kay, Kristen E / Berens, Michael / Nicosia, Michael / Swanson, Kristin R / Sharifi, Nima / Lathia, Justin D

Research square

2024

Abstract: Many cancers, including glioblastoma (GBM), have a male-biased sex difference in incidence and outcome. The underlying reasons for this sex bias are unclear but likely involve differences in tumor cell state and immune response. This effect is further ... ...

Abstract	Many cancers, including glioblastoma (GBM), have a male-biased sex difference in incidence and outcome. The underlying reasons for this sex bias are unclear but likely involve differences in tumor cell state and immune response. This effect is further amplified by sex hormones, including androgens, which have been shown to inhibit anti-tumor T cell immunity. Here, we show that androgens drive anti-tumor immunity in brain tumors, in contrast to its effect in other tumor types. Upon castration, tumor growth was accelerated with attenuated T cell function in GBM and brain tumor models, but the opposite was observed when tumors were located outside the brain. Activity of the hypothalamus-pituitary-adrenal gland (HPA) axis was increased in castrated mice, particularly in those with brain tumors. Blockade of glucocorticoid receptors reversed the accelerated tumor growth in castrated mice, indicating that the effect of castration was mediated by elevated glucocorticoid signaling. Furthermore, this mechanism was not GBM specific, but brain specific, as hyperactivation of the HPA axis was observed with intracranial implantation of non-GBM tumors in the brain. Together, our findings establish that brain tumors drive distinct endocrine-mediated mechanisms in the androgen-deprived setting and highlight the importance of organ-specific effects on anti-tumor immunity.
Language	English
Publishing date	2024-03-29
Publishing country	United States
Document type	Preprint
DOI	10.21203/rs.3.rs-4014556/v1
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Article: Quantifying Glioblastoma Drug Response Dynamics Incorporating Treatment Sensitivity and Blood Brain Barrier Penetrance From Experimental Data.

Massey, Susan Christine / Urcuyo, Javier C / Marin, Bianca Maria / Sarkaria, Jann N / Swanson, Kristin R

Frontiers in physiology

2020 Volume 11, Page(s) 830

Abstract: 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 ( ... ...

Abstract	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.
Language	English
Publishing date	2020-08-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564217-0
ISSN	1664-042X
ISSN	1664-042X
DOI	10.3389/fphys.2020.00830
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