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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: 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: 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: Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications.

Jiang, Will / Jones, Jennifer C / Shankavaram, Uma / Sproull, Mary / Camphausen, Kevin / Krauze, Andra V

Cancers

2022 Volume 14, Issue 9

Abstract: The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, ... ...

Abstract	The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.
Language	English
Publishing date	2022-04-29
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers14092227
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Article ; Online: Operational Ontology for Oncology: A Framework for Improved Communication and Understanding in Cancer Care.

Hong, David S / Caissie, Amanda / Hurkmans, Coen W / Krauze, Andra V / Kudner, Randi / Purdie, Thomas G / Xiao, Ying

International journal of radiation oncology, biology, physics

2023 Volume 117, Issue 3, Page(s) 551–553

MeSH term(s)	Humans ; Neoplasms/radiotherapy ; Medical Oncology
Language	English
Publishing date	2023-06-16
Publishing country	United States
Document type	Editorial ; Comment
ZDB-ID	197614-x
ISSN	1879-355X ; 0360-3016
ISSN (online)	1879-355X
ISSN	0360-3016
DOI	10.1016/j.ijrobp.2023.02.058
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Article ; Online: Revisiting Concurrent Radiation Therapy, Temozolomide, and the Histone Deacetylase Inhibitor Valproic Acid for Patients with Glioblastoma-Proteomic Alteration and Comparison Analysis with the Standard-of-Care Chemoirradiation.

Krauze, Andra V / Zhao, Yingdong / Li, Ming-Chung / Shih, Joanna / Jiang, Will / Tasci, Erdal / Cooley Zgela, Theresa / Sproull, Mary / Mackey, Megan / Shankavaram, Uma / Tofilon, Philip / Camphausen, Kevin

Biomolecules

2023 Volume 13, Issue 10

Abstract: Background: Glioblastoma (GBM) is the most common brain tumor with an overall survival (OS) of less than 30% at two years. Valproic acid (VPA) demonstrated survival benefits documented in retrospective and prospective trials, when used in combination ... ...

Abstract	Background: Glioblastoma (GBM) is the most common brain tumor with an overall survival (OS) of less than 30% at two years. Valproic acid (VPA) demonstrated survival benefits documented in retrospective and prospective trials, when used in combination with chemo-radiotherapy (CRT). Purpose: The primary goal of this study was to examine if the differential alteration in proteomic expression pre vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA as compared to standard-of-care CRT. The second goal was to explore the associations between the proteomic alterations in response to VPA/RT/TMZ correlated to patient outcomes. The third goal was to use the proteomic profile to determine the mechanism of action of VPA in this setting. Materials and methods: Serum obtained pre- and post-CRT was analyzed using an aptamer-based SOMAScan Results: A total of 124 proteins were identified pre- vs. post-CRT that were differentially expressed between the cohorts who received CRT plus VPA and those who received CRT alone. Clinical factors did not confound the results, and distinct proteomic clustering in the VPA-treated population was identified. Time-dependent ROC curves for OS and PFS for landmark times of 20 months and 6 months, respectively, revealed AUC of 0.531, 0.756, 0.774 for OS and 0.535, 0.723, 0.806 for PFS for protein expression, clinical factors, and the combination of protein expression and clinical factors, respectively, indicating that the proteome can provide additional survival risk discrimination to that already provided by the standard clinical factors with a greater impact on PFS. Several proteins of interest were identified. Alterations in GALNT14 (increased) and CCL17 (decreased) ( Conclusions: Differential alteration in proteomic expression pre- vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA. Using pre- vs. post-data, prognostic proteins emerged in the analysis. Using pre-CRT data, potentially predictive proteins were identified. The protein signals and hallmark gene sets associated with the alteration in the proteome identified between patients who received VPA and those who did not, align with known biological mechanisms of action of VPA and may allow for the identification of novel biomarkers associated with outcomes that can help advance the study of VPA in future prospective trials.
MeSH term(s)	Humans ; Temozolomide/therapeutic use ; Glioblastoma/drug therapy ; Glioblastoma/genetics ; Valproic Acid/pharmacology ; Valproic Acid/therapeutic use ; Histone Deacetylase Inhibitors/pharmacology ; Histone Deacetylase Inhibitors/therapeutic use ; Retrospective Studies ; Proteome ; Proteomics ; Antineoplastic Agents, Alkylating ; Hedgehog Proteins
Chemical Substances	Temozolomide (YF1K15M17Y) ; Valproic Acid (614OI1Z5WI) ; Histone Deacetylase Inhibitors ; Proteome ; Antineoplastic Agents, Alkylating ; Hedgehog Proteins
Language	English
Publishing date	2023-10-10
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	2701262-1
ISSN	2218-273X ; 2218-273X
ISSN (online)	2218-273X
ISSN	2218-273X
DOI	10.3390/biom13101499
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Article ; Online: Automated glioma grading on conventional MRI images using deep convolutional neural networks.

Zhuge, Ying / Ning, Holly / Mathen, Peter / Cheng, Jason Y / Krauze, Andra V / Camphausen, Kevin / Miller, Robert W

Medical physics

2020 Volume 47, Issue 7, Page(s) 3044–3053

Abstract: Purpose: Gliomas are the most common primary tumor of the brain and are classified into grades I-IV of the World Health Organization (WHO), based on their invasively histological appearance. Gliomas grading plays an important role to determine the ... ...

Abstract	Purpose: Gliomas are the most common primary tumor of the brain and are classified into grades I-IV of the World Health Organization (WHO), based on their invasively histological appearance. Gliomas grading plays an important role to determine the treatment plan and prognosis prediction. In this study we propose two novel methods for automatic, non-invasively distinguishing low-grade (Grades II and III) glioma (LGG) and high-grade (grade IV) glioma (HGG) on conventional MRI images by using deep convolutional neural networks (CNNs). Methods: All MRI images have been preprocessed first by rigid image registration and intensity inhomogeneity correction. Both proposed methods consist of two steps: (a) three-dimensional (3D) brain tumor segmentation based on a modification of the popular U-Net model; (b) tumor classification on segmented brain tumor. In the first method, the slice with largest area of tumor is determined and the state-of-the-art mask R-CNN model is employed for tumor grading. To improve the performance of the grading model, a two-dimensional (2D) data augmentation has been implemented to increase both the amount and the diversity of the training images. In the second method, denoted as 3DConvNet, a 3D volumetric CNNs is applied directly on bounding image regions of segmented tumor for classification, which can fully leverage the 3D spatial contextual information of volumetric image data. Results: The proposed schemes were evaluated on The Cancer Imaging Archive (TCIA) low grade glioma (LGG) data, and the Multimodal Brain Tumor Image Segmentation (BraTS) Benchmark 2018 training datasets with fivefold cross validation. All data are divided into training, validation, and test sets. Based on biopsy-proven ground truth, the performance metrics of sensitivity, specificity, and accuracy are measured on the test sets. The results are 0.935 (sensitivity), 0.972 (specificity), and 0.963 (accuracy) for the 2D Mask R-CNN based method, and 0.947 (sensitivity), 0.968 (specificity), and 0.971 (accuracy) for the 3DConvNet method, respectively. In regard to efficiency, for 3D brain tumor segmentation, the program takes around ten and a half hours for training with 300 epochs on BraTS 2018 dataset and takes only around 50 s for testing of a typical image with a size of 160 × 216 × 176. For 2D Mask R-CNN based tumor grading, the program takes around 4 h for training with around 60 000 iterations, and around 1 s for testing of a 2D slice image with size of 128 × 128. For 3DConvNet based tumor grading, the program takes around 2 h for training with 10 000 iterations, and 0.25 s for testing of a 3D cropped image with size of 64 × 64 × 64, using a DELL PRECISION Tower T7910, with two NVIDIA Titan Xp GPUs. Conclusions: Two effective glioma grading methods on conventional MRI images using deep convolutional neural networks have been developed. Our methods are fully automated without manual specification of region-of-interests and selection of slices for model training, which are common in traditional machine learning based brain tumor grading methods. This methodology may play a crucial role in selecting effective treatment options and survival predictions without the need for surgical biopsy.
MeSH term(s)	Brain Neoplasms/diagnostic imaging ; Glioma/diagnostic imaging ; Humans ; Image Processing, Computer-Assisted ; Machine Learning ; Magnetic Resonance Imaging ; Neural Networks, Computer
Language	English
Publishing date	2020-05-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	188780-4
ISSN	2473-4209 ; 0094-2405
ISSN (online)	2473-4209
ISSN	0094-2405
DOI	10.1002/mp.14168
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Article: Optimizing the Benefit of CNS Radiation Therapy in the Pediatric Population-PART 1: Understanding and Managing Acute and Late Toxicities.

Rowe, Lindsay S / Krauze, Andra V / Ning, Holly / Camphausen, Kevin A / Kaushal, Aradhana

Oncology (Williston Park, N.Y.)

2017 Volume 31, Issue 3, Page(s) 182–188

Abstract: Radiation therapy continues to be a key component in the management of pediatric malignancies. Increasing the likelihood of cure while minimizing late treatment toxicity in these young patients remains the primary goal. Within the realm of central ... ...

Abstract	Radiation therapy continues to be a key component in the management of pediatric malignancies. Increasing the likelihood of cure while minimizing late treatment toxicity in these young patients remains the primary goal. Within the realm of central nervous system neoplasms, efforts to further improve the efficacy of radiation therapy continue, while balancing risks of damage to uninvolved tissue. Radiation therapy can result in second malignancies, as well as cerebrovascular, neurotoxic, neurocognitive, endocrine, psychosocial, and quality-of-life effects. In this article we describe these acute and late effects and their implications, and we highlight strategies that have emerged to reduce both the volume of tissue that is irradiated and the radiation dose delivered. The feasibility, efficacy, and risks of these newer approaches to radiation therapy continue to be evaluated and monitored; robust outcome data are needed.
MeSH term(s)	Adult ; Age Factors ; Central Nervous System Neoplasms/diagnosis ; Central Nervous System Neoplasms/radiotherapy ; Child ; Cranial Irradiation/adverse effects ; Humans ; Quality of Life ; Radiation Dosage ; Radiation Injuries/diagnosis ; Radiation Injuries/etiology ; Radiation Injuries/psychology ; Radiation Injuries/therapy ; Risk Factors ; Survivors/psychology ; Time Factors ; Treatment Outcome
Language	English
Publishing date	2017-03-15
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1067950-9
ISSN	0890-9091
ISSN	0890-9091
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Zs.A 3168: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article: Optimizing the Benefit of CNS Radiation Therapy in the Pediatric Population-PART 2: Novel Methods of Radiation Delivery.

Rowe, Lindsay S / Krauze, Andra V / Ning, Holly / Camphausen, Kevin A / Kaushal, Aradhana

Oncology (Williston Park, N.Y.)

2017 Volume 31, Issue 3, Page(s) 224–6, 228

Abstract: Newer approaches in the field of radiation therapy have raised the bar in the treatment of central nervous system (CNS) malignancies, with recognized advances that have aimed to increase the therapeutic index by improving conformality of the radiation ... ...

Abstract	Newer approaches in the field of radiation therapy have raised the bar in the treatment of central nervous system (CNS) malignancies, with recognized advances that have aimed to increase the therapeutic index by improving conformality of the radiation dose to the planned target volume. Beyond these advances, the continued evolution of more effective systems for delivery of radiation to the CNS may offer further benefit not only to adults but also to pediatric patients, a cohort of the population that may be more sensitive to the long-term effects of radiation. This article describes several novel irradiation techniques under investigation that hold promise in the pediatric population. These include newer approaches to intensity-modulated radiation therapy; stereotactic radiosurgery and radiation therapy; particle therapy, most notably proton therapy, which may be of particular benefit in enabling young patients to avoid radiation-related adverse effects; and radioimmunotherapy strategies that spare healthy tissue from radiotoxicity by delivering therapy directly to tumor tissue. Although emerging strategies for the delivery of radiation therapy hold promise for improved outcomes in pediatric patients, there must be rigorous long-term evaluation of consequences associated with the various techniques employed, to weigh risks, benefits, and impact on quality of life.
MeSH term(s)	Adult ; Age Factors ; Central Nervous System Neoplasms/diagnosis ; Central Nervous System Neoplasms/radiotherapy ; Child ; Cranial Irradiation/adverse effects ; Cranial Irradiation/methods ; Humans ; Quality of Life ; Radiation Dosage ; Radiation Injuries/etiology ; Radiation Injuries/prevention & control ; Radioimmunotherapy ; Radiosurgery/adverse effects ; Radiotherapy Planning, Computer-Assisted/methods ; Radiotherapy, Intensity-Modulated/adverse effects ; Risk Factors ; Survivors ; Time Factors ; Treatment Outcome
Language	English
Publishing date	2017-03-15
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1067950-9
ISSN	0890-9091
ISSN	0890-9091
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Zs.A 3168: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article: Late toxicity in long-term survivors from a phase 2 study of concurrent radiation therapy, temozolomide and valproic acid for newly diagnosed glioblastoma.

Krauze, Andra V / Mackey, Megan / Rowe, Lindsay / Chang, Michal G / Holdford, Diane J / Cooley, Theresa / Shih, Joanna / Tofilon, Philip J / Camphausen, Kevin

Neuro-oncology practice

2018 Volume 5, Issue 4, Page(s) 246–250

Abstract: Background: Valproic acid (VPA) is an antiepileptic agent with histone deacetylase inhibitor activity shown to enhance overall survival and progression free survival in patients with newly diagnosed glioblastoma (GBM). This reports on the late toxicity ... ...

Abstract	Background: Valproic acid (VPA) is an antiepileptic agent with histone deacetylase inhibitor activity shown to enhance overall survival and progression free survival in patients with newly diagnosed glioblastoma (GBM). This reports on the late toxicity of the VPA/radiotherapy (RT)/temozolomide (TMZ) combination in the long-term survivors of a phase 2 study evaluating this regimen. Methods: 37 patients with newly diagnosed GBM were initially enrolled on this trial and received combination therapy. VPA/RT/TMZ related late toxicities were evaluated in the 6 patients that lived greater than 3 years using the Cancer Therapy and Evaluation Program Common Toxicity Criteria (CTC) Version 4.0 for toxicity and adverse event reporting as well as the RTOG/EORTC Radiation Morbidity Scoring Scheme. Results: The median duration of follow-up for these 6 patients was 69.5m. In this cohort, the median OS was 73.8m (60.8-103.8m) and median PFS was 53.1m (37.3 - 103.8m). The most common late toxicity of VPA in conjunction with RT/TMZ were the CTC classifications of Conclusions: The addition of VPA to concurrent RT/TMZ in patients with newly diagnosed GBM was well tolerated with little late toxicity. Additionally, VPA may result in improved outcomes as compared to historical data and merits further study.
Language	English
Publishing date	2018-04-16
Publishing country	England
Document type	Journal Article
ZDB-ID	2768945-1
ISSN	2054-2585 ; 2054-2577
ISSN (online)	2054-2585
ISSN	2054-2577
DOI	10.1093/nop/npy009
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