Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma

Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Akbari, Hamed; Kalarot, Ratheesh; Sridharan, Patmaa; Rozycki, Martin; Bergman, Mark; Tunç, Birkan; Verma, Ragini; Bilello, Michel; Davatzikos, Christos

doi:10.1007/978-3-319-75238-9_12

Cited by 43 publications

(41 citation statements)

References 39 publications

(52 reference statements)

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“…Second, another notable difference between the two studies include consideration of only FLAIR and T1Gd scans by Tixier et al, in contrast to the present study that considers all four structural MRI modalities, that is, T1, T1Gd, T2, and FLAIR. Third, a major difference was in terms of the radiomic features considered across the two analyses, where Tixier et al evaluated a total of 108 features (extracted using the open-source CERR package 54 ), whereas we extracted a total of 11 700 radiomic descriptors from various different feature families (Table I) (extracted using opensource packages, COLLAGE 15 and CaPTk 29,38 ). Finally, we performed our statistical analysis based on Spearman's correlation coefficient.…”

Section: Discussionmentioning

confidence: 99%

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Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset

Pati

Verma²,

Akbari

et al. 2020

Medical Physics

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Purpose The availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of radiomic markers for prognostic/predictive applications in glioblastoma (GBM). In this work, we address two critical challenges with regard to developing robust radiomic approaches: (a) the lack of availability of reliable segmentation labels for glioblastoma tumor sub‐compartments (i.e., enhancing tumor, non‐enhancing tumor core, peritumoral edematous/infiltrated tissue) and (b) identifying “reproducible” radiomic features that are robust to segmentation variability across readers/sites. Acquisition and validation methods From TCIA’s Ivy GAP cohort, we obtained a paired set (n = 31) of expert annotations approved by two board‐certified neuroradiologists at the Hospital of the University of Pennsylvania (UPenn) and at Case Western Reserve University (CWRU). For these studies, we performed a reproducibility study that assessed the variability in (a) segmentation labels and (b) radiomic features, between these paired annotations. The radiomic variability was assessed on a comprehensive panel of 11 700 radiomic features including intensity, volumetric, morphologic, histogram‐based, and textural parameters, extracted for each of the paired sets of annotations. Our results demonstrated (a) a high level of inter‐rater agreement (median value of DICE ≥0.8 for all sub‐compartments), and (b) ≈24% of the extracted radiomic features being highly correlated (based on Spearman’s rank correlation coefficient) to annotation variations. These robust features largely belonged to morphology (describing shape characteristics), intensity (capturing intensity profile statistics), and COLLAGE (capturing heterogeneity in gradient orientations) feature families. Data format and usage notes We make publicly available on TCIA’s Analysis Results Directory (https://doi.org/10.7937/9j41‐7d44), the complete set of (a) multi‐institutional expert annotations for the tumor sub‐compartments, (b) 11 700 radiomic features, and (c) the associated reproducibility meta‐analysis. Potential applications The annotations and the associated meta‐data for Ivy GAP are released with the purpose of enabling researchers toward developing image‐based biomarkers for prognostic/predictive applications in GBM.

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Section: Discussionmentioning

confidence: 99%

“…2), was obtained using the last approach on skullstripped images using Greedy 6 with normalized mutual information called from CaPTk. 29,38…”

Section: Each Mni-registered T1gd Scan To the Correspondingmentioning

confidence: 99%

Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset

Pati

Verma²,

Akbari

et al. 2020

Medical Physics

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“…In fact, most publications do not provide enough information to re-create their method independently. A few examples of open source tools that may facilitate sharing of different methods are Modelhub (http://modelhub.ai/), Pyradiomics (http://www.Radiomics.io/) (108), and the Cancer Imaging Phenomics Toolkit (https://med.upenn.edu/cbica/captk/), which was developed to facilitate clinical translation of these tools (109,110). Alternatively, optimal solutions for integration into routine clinical workflow may ultimately be provided through emerging commercial ventures.…”

Section: Promises and Challenges Of Ai In Neuro-oncologic Imagingmentioning

confidence: 99%

Emerging Applications of Artificial Intelligence in Neuro-Oncology

et al. 2019

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Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.

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“… 63 Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk) is developed as a type of cancer imaging phenomics toolkit for quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. 64 , 65 Even with the development of these software tools, there may still be significant variations in features among software packages and indicates the need for standardization. 66 Recently, the IBSI standardized a set of 169 radiomic features, which could verify and calibrate different radiomics software.…”

Section: Current Efforts To Improve Standardizationmentioning

confidence: 99%

Standardization of imaging methods for machine learning in neuro-oncology

Huang

2020

Neuro-Oncology Advances

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Radiomics is a novel technique in which quantitative phenotypes or features are extracted from medical images. Machine learning enables analysis of large quantities of medical imaging data generated by radiomic feature extraction. A growing number of studies based on these methods have developed tools for neuro-oncology applications. Despite the initial promises, many of these imaging tools remain far from clinical implementation. One major limitation hindering the use of these models is their lack of reproducibility when applied across different institutions and clinical settings. In this article, we discuss the importance of standardization of methodology and reporting in our effort to improve reproducibility. Ongoing efforts of standardization for neuro-oncological imaging are reviewed. Challenges related to standardization and potential disadvantages in over-standardization are also described. Ultimately, greater multi-institutional collaborative effort is needed to provide and implement standards for data acquisition and analysis methods to facilitate research results to be interoperable and reliable for integration into different practice environments.

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Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma

Cited by 43 publications

References 39 publications

Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset

Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset

Emerging Applications of Artificial Intelligence in Neuro-Oncology

Standardization of imaging methods for machine learning in neuro-oncology

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