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Article ; Online: The Future of Radioactive Medicine.

Sproull, M / Wilson, E / Miller, R W / Camphausen, K

2023 Volume 200, Issue 1, Page(s) 80–91

Abstract: The discovery of X rays in the late 19th century heralded the beginning of a new age in medicine, and the advent of channeling the power of radiation to diagnose and treat human disease. Radiation has been leveraged in medicine in a multitude of ways and ...

Abstract	The discovery of X rays in the late 19th century heralded the beginning of a new age in medicine, and the advent of channeling the power of radiation to diagnose and treat human disease. Radiation has been leveraged in medicine in a multitude of ways and is a critical element of cancer care including screening, diagnosis, surveillance, and interventional treatments. Modern radiotherapy techniques include a multitude of methodologies utilizing both externally and internally delivered radiation from a variety of approaches. This review provides a comprehensive overview of contemporary radiotherapy methodologies, the field of radiopharmaceuticals and theranostics, effects of low dose radiation and highlights the phenomena of fear of exposure to radiation and its impact in modern medicine.
MeSH term(s)	Humans ; Radiation Oncology ; X-Rays ; Radiography ; Precision Medicine
Language	English
Publishing date	2023-05-02
Publishing country	United States
Document type	Review ; Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Intramural
ZDB-ID	80322-4
ISSN	1938-5404 ; 0033-7587
ISSN (online)	1938-5404
ISSN	0033-7587
DOI	10.1667/RADE-23-00031.1
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Article: Identifying patients suitable for targeted adjuvant therapy: advances in the field of developing biomarkers for tumor recurrence following irradiation.

Jagasia, S / Tasci, E / Zhuge, Ying / Camphausen, K / Krauze, A V

Expert review of precision medicine and drug development

2023 Volume 8, Issue 1, Page(s) 33–42

Abstract: Introduction: Radiation therapy (RT) is commonly used to treat cancer in conjunction with chemotherapy, immunotherapy, and targeted therapies. Despite the effectiveness of RT, tumor recurrence due to treatment resistance still lead to treatment failure. ...

Abstract	Introduction: Radiation therapy (RT) is commonly used to treat cancer in conjunction with chemotherapy, immunotherapy, and targeted therapies. Despite the effectiveness of RT, tumor recurrence due to treatment resistance still lead to treatment failure. RT-specific biomarkers are currently lacking and remain challenging to investigate with existing data since, for many common malignancies, standard of care (SOC) paradigms involve the administration of RT in conjunction with other agents. Areas covered: Established clinically relevant biomarkers are used in surveillance, as prognostic indicators, and sometimes for treatment planning; however, the inability to intercept early recurrence or predict upfront resistance to treatment remains a significant challenge that limits the selection of patients for adjuvant therapy. We discuss attempts at intercepting early failure. We examine biomarkers that have made it into the clinic where they are used for treatment monitoring and management alteration, and novel biomarkers that lead the field with targeted adjuvant therapy seeking to harness these. Expert opinion: Given the growth of data correlating interventions with omic analysis toward identifying biomarkers of radiation resistance, more robust markers of recurrence that link to biology will increasingly be leveraged toward targeted adjuvant therapy to make a successful transition to the clinic in the coming years.
Language	English
Publishing date	2023-11-16
Publishing country	England
Document type	Journal Article
ISSN	2380-8993
ISSN	2380-8993
DOI	10.1080/23808993.2023.2276927
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Article ; Online: Prediction of Total-Body and Partial-Body Exposures to Radiation Using Plasma Proteomic Expression Profiles.

Sproull, M / Kawai, T / Krauze, A / Shankavaram, U / Camphausen, K

Radiation research

2022 Volume 198, Issue 6, Page(s) 573–581

Abstract: There is a need to identify new biomarkers of radiation exposure for not only systemic total-body irradiation (TBI) but also to characterize partial-body irradiation and organ specific radiation injury. In the current study, we sought to develop novel ... ...

Abstract	There is a need to identify new biomarkers of radiation exposure for not only systemic total-body irradiation (TBI) but also to characterize partial-body irradiation and organ specific radiation injury. In the current study, we sought to develop novel biodosimetry models of radiation exposure using TBI and organ specific partial-body irradiation to only the brain, lung or gut using a multivariate proteomics approach. Subset panels of significantly altered proteins were selected to build predictive models of radiation exposure in a variety of sample cohort configurations relevant to practical field application of biodosimetry diagnostics during future radiological or nuclear event scenarios. Female C57BL/6 mice, 8-15 weeks old, received a single total-body or partial-body dose of 2 or 8 Gy TBI or 2 or 8 Gy to only the lung or gut, or 2, 8 or 16 Gy to only the brain using a Pantak X-ray source. Plasma was collected by cardiac puncture at days 1, 3 and 7 postirradiation for total-body exposures and only the lung and brain exposures, and at days 3, 7 and 14 postirradiation for gut exposures. Plasma was then screened using the aptamer-based SOMAscan proteomic assay technology, for changes in expression of 1,310 protein analytes. A subset panel of protein biomarkers which demonstrated significant changes (P < 0.01) in expression after irradiation were used to build predictive models of radiation exposure using different sample cohorts. Model 1 compared controls vs. all pooled irradiated samples, which included TBI and all organ specific partial irradiation. Model 2 compared controls vs. TBI vs. partial irradiation (with all organ specific partial exposure pooled within the partial-irradiated group), and model 3 compared controls vs. each individual organ specific partial-body exposure separately (brain, gut and lung). Detectable values were obtained for all 1,310 proteins included in the SOMAscan assay for all samples. Each model algorithm built using a unique sample cohort was validated with a training set of samples and tested with a separate new sample series. Overall predictive accuracies of 89%, 78% and 55% resulted for models 1-3, respectively, representing novel predictive panels of radiation responsive proteomic biomarkers. Though relatively high overall predictive accuracies were achieved for models 1 and 2, all three models showed limited accuracy at differentiating between the controls and partial-irradiated body samples. In our study we were able to identify novel panels of radiation responsive proteins useful for predicting radiation exposure and to create predictive models of partial-body exposure including organ specific radiation exposures. This proof-of-concept study also illustrates the inherent physiological limitations of distinguishing between small-body exposures and the unirradiated using proteomic biomarkers of radiation exposure. As use of biodosimetry diagnostics in future mass casualty settings will be complicated by the heterogeneity of partial-body exposure received in the field, further work remains in adapting these diagnostic tools for practical use.
MeSH term(s)	Female ; Mice ; Animals ; Mice, Inbred C57BL ; Proteomics
Language	English
Publishing date	2022-08-24
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	80322-4
ISSN	1938-5404 ; 0033-7587
ISSN (online)	1938-5404
ISSN	0033-7587
DOI	10.1667/RADE-22-00074.1
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Article ; Online: Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition.

Krauze, Andra V / Camphausen, Kevin

International journal of molecular sciences

2021 Volume 22, Issue 24

Abstract: Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the ...

Abstract	Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.
MeSH term(s)	Biomarkers, Tumor ; Central Nervous System Neoplasms/diagnostic imaging ; Central Nervous System Neoplasms/genetics ; Central Nervous System Neoplasms/metabolism ; Central Nervous System Neoplasms/radiotherapy ; Computational Biology ; Glioma/diagnostic imaging ; Glioma/genetics ; Glioma/metabolism ; Glioma/radiotherapy ; Humans ; Machine Learning ; Radiation Oncology/methods
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2021-12-10
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms222413278
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Article ; Online: AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models.

Krauze, A V / Zhuge, Y / Zhao, R / Tasci, E / Camphausen, K

Journal of biotechnology and biomedicine

2022 Volume 5, Issue 1, Page(s) 1–19

Abstract: The interpretation of imaging in medicine in general and in oncology specifically remains problematic due to several limitations which include the need to incorporate detailed clinical history, patient and disease-specific history, clinical exam features, ...

Abstract	The interpretation of imaging in medicine in general and in oncology specifically remains problematic due to several limitations which include the need to incorporate detailed clinical history, patient and disease-specific history, clinical exam features, previous and ongoing treatment, and account for the dependency on reproducible human interpretation of multiple factors with incomplete data linkage. To standardize reporting, minimize bias, expedite management, and improve outcomes, the use of Artificial Intelligence (AI) has gained significant prominence in imaging analysis. In oncology, AI methods have as a result been explored in most cancer types with ongoing progress in employing AI towards imaging for oncology treatment, assessing treatment response, and understanding and communicating prognosis. Challenges remain with limited available data sets, variability in imaging changes over time augmented by a growing heterogeneity in analysis approaches. We review the imaging analysis workflow and examine how hand-crafted features also referred to as traditional Machine Learning (ML), Deep Learning (DL) approaches, and hybrid analyses, are being employed in AI-driven imaging analysis in central nervous system tumors. ML, DL, and hybrid approaches coexist, and their combination may produce superior results although data in this space is as yet novel, and conclusions and pitfalls have yet to be fully explored. We note the growing technical complexities that may become increasingly separated from the clinic and enforce the acute need for clinician engagement to guide progress and ensure that conclusions derived from AI-driven imaging analysis reflect that same level of scrutiny lent to other avenues of clinical research.
Language	English
Publishing date	2022-01-10
Publishing country	United States
Document type	Journal Article
ISSN	2642-9128
ISSN (online)	2642-9128
DOI	10.26502/jbb.2642-91280046
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Article: Novel Murine Biomarkers of Radiation Exposure Using An Aptamer-Based Proteomic Technology.

Sproull, Mary / Shankavaram, Uma / Camphausen, Kevin

Frontiers in pharmacology

2021 Volume 12, Page(s) 633131

Abstract: Purpose: ...

Abstract	Purpose:
Language	English
Publishing date	2021-04-26
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587355-6
ISSN	1663-9812
ISSN	1663-9812
DOI	10.3389/fphar.2021.633131
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Article: Bias and Class Imbalance in Oncologic Data-Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets.

Tasci, Erdal / Zhuge, Ying / Camphausen, Kevin / Krauze, Andra V

Cancers

2022 Volume 14, Issue 12

Abstract: Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to ... ...

Abstract	Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.g., oncologic) data sets, due to the limitations in patient numbers, cost, privacy, and security of data sharing, and the complexity of generated data. Depending on the data set and the research question, the methods applied to address class imbalance problems can provide more effective, successful, and meaningful results. This review discusses the essential strategies for addressing and mitigating the class imbalance problems for different medical data types in the oncologic domain.
Language	English
Publishing date	2022-06-12
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers14122897
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Article ; Online: Bench to bedside radiosensitizer development strategy for newly diagnosed glioblastoma.

Degorre, Charlotte / Tofilon, Philip / Camphausen, Kevin / Mathen, Peter

Radiation oncology (London, England)

2021 Volume 16, Issue 1, Page(s) 191

Abstract: Glioblastoma is the most common primary brain malignancy and carries with it a poor prognosis. New agents are urgently needed, however nearly all Phase III trials of GBM patients of the past 25 years have failed to demonstrate improvement in outcomes. In ...

Abstract	Glioblastoma is the most common primary brain malignancy and carries with it a poor prognosis. New agents are urgently needed, however nearly all Phase III trials of GBM patients of the past 25 years have failed to demonstrate improvement in outcomes. In 2019, the National Cancer Institute Clinical Trials and Translational Research Advisory Committee (CTAC) Glioblastoma Working Group (GBM WG) identified 5 broad areas of research thought to be important in the development of new herapeutics for GBM. Among those was optimizing radioresponse for GBM in situ. One such strategy to increase radiation efficacy is the addition of a radiosensitizer to improve the therapeutic ratio by enhancing tumor sensitivity while ideally having minimal to no effect on normal tissue. Historically the majority of trials using radiosensitizers have been unsuccessful, but they provide important guidance in what is required to develop agents more efficiently. Improved target selection is essential for a drug to provide maximal benefit, and once that target is identified it must be validated through pre-clinical studies. Careful selection of appropriate in vitro and in vivo models to demonstrate increased radiosensitivity and suitable bioavailability are then necessary to prove that a drug warrants advancement to clinical investigation. Once investigational agents are validated pre-clinically, patient trials require consistency both in terms of planning study design as well as reporting efficacy and toxicity in order to assess the potential benefit of the drug. Through this paper we hope to outline strategies for developing effective radiosensitizers against GBM using as models the examples of XPO1 inhibitors and HDAC inhibitors developed from our own lab.
MeSH term(s)	Brain Neoplasms/radiotherapy ; Clinical Trials as Topic ; Glioblastoma/radiotherapy ; Humans ; Karyopherins/antagonists & inhibitors ; Neoplastic Stem Cells/drug effects ; Radiation-Sensitizing Agents/therapeutic use ; Receptors, Cytoplasmic and Nuclear/antagonists & inhibitors ; Valproic Acid/therapeutic use ; Exportin 1 Protein
Chemical Substances	Karyopherins ; Radiation-Sensitizing Agents ; Receptors, Cytoplasmic and Nuclear ; Valproic Acid (614OI1Z5WI)
Language	English
Publishing date	2021-09-28
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2224965-5
ISSN	1748-717X ; 1748-717X
ISSN (online)	1748-717X
ISSN	1748-717X
DOI	10.1186/s13014-021-01918-y
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Article ; Online: Machine learning based survival prediction in Glioma using large-scale registry data.

Zhao, Rachel / Zhuge, Ying / Camphausen, Kevin / Krauze, Andra V

Health informatics journal

2022 Volume 28, Issue 4, Page(s) 14604582221135427

Abstract: Gliomas are the most common central nervous system tumors exhibiting poor clinical outcomes. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Machine learning (ML) allows for ... ...

Abstract	Gliomas are the most common central nervous system tumors exhibiting poor clinical outcomes. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Machine learning (ML) allows for sophisticated approaches to survival prediction using real world clinical parameters needed to achieve superior predictive accuracy. We employed Cox Proportional hazards (CPH) model, Support Vector Machine (SVM) model, Random Forest (RF) model in a large glioma dataset (3462 patients, diagnosed 2000-2018) to explore the most optimal approach to survival prediction. Features employed were age, sex, surgical resection status, tumor histology and tumor site, administration of radiation therapy (RT) and chemotherapy status. Concordance index (c-index) was employed to assess the accuracy of survival time prediction. All three models performed well with prediction accuracy (CI 0.767, 0.771, 0.57 for CPH, SVM, RF models respectively) with the best performance achieved when incorporating RT and chemotherapy administration status which emerged as key predictive features. Within the subset of glioblastoma patients, similar prediction accuracy was achieved. These findings should prompt stricter clinician oversight over registry data accuracy through quality assurance as we move towards meaningful predictive ability using ML approaches in glioma.
MeSH term(s)	Humans ; Glioma/diagnosis ; Glioma/therapy ; Machine Learning ; Support Vector Machine ; Prognosis ; Registries
Language	English
Publishing date	2022-10-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Intramural
ZDB-ID	2213115-2
ISSN	1741-2811 ; 1460-4582
ISSN (online)	1741-2811
ISSN	1460-4582
DOI	10.1177/14604582221135427
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Article ; Online: Glioma-BioDP: database for visualization of molecular profiles to improve prognosis of brain cancer.

Deng, Xiang / Das, Shaoli / Kaur, Harpreet / Wilson, Evan / Camphausen, Kevin / Shankavaram, Uma

BMC medical genomics

2023 Volume 16, Issue 1, Page(s) 168

Abstract: Cancer researchers often seek user-friendly interactive tools for validation, exploration, analysis, and visualization of molecular profiles in cancer patient samples. To aid researchers working on the both low- and high-grade gliomas, we developed ... ...

Abstract	Cancer researchers often seek user-friendly interactive tools for validation, exploration, analysis, and visualization of molecular profiles in cancer patient samples. To aid researchers working on the both low- and high-grade gliomas, we developed Glioma-BioDP, a web tool for exploration and visualization of RNA and protein expression profiles of interest in these tumor types. Glioma-BioDP is user friendly application that include expression data from both the low- and high-grade glioma patient samples from The Cancer Genome Atlas and enabled querying by mRNA, microRNA, and protein level expression data from Illumina HiSeq and RPPA platforms respectively. Glioma-BioDP provides advance query interface and enables users to explore the association of genes, proteins, and miRNA expression with molecular and/or histological subtypes of gliomas, surgical resection status and survival. The prognostic significance and visualization of the selected expression profiles can be explored using interactive utilities provided. This tool may also enable validation and generation of new hypotheses of novel therapies impacting gliomas that aid in personalization of treatment for optimum outcomes.
MeSH term(s)	Humans ; Brain Neoplasms/metabolism ; Glioma/genetics ; Glioma/metabolism ; Prognosis ; MicroRNAs/genetics ; Databases, Factual
Chemical Substances	MicroRNAs
Language	English
Publishing date	2023-07-15
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Intramural ; Research Support, N.I.H., Extramural
ZDB-ID	2411865-5
ISSN	1755-8794 ; 1755-8794
ISSN (online)	1755-8794
ISSN	1755-8794
DOI	10.1186/s12920-023-01593-w
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