The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview

Pati, Sarthak; Singh, Ashish; Rathore, Saima; Gastounioti, Aimilia; Bergman, Mark; Ngo, Phuc; Ha, Sung Min; Bounias, Dimitrios; Minock, James; Murphy, G. R.; Li, Hongming; Bhattarai, Aseem; Wolf, Alfred A.; Sridaran, Patmaa; Kalarot, Ratheesh; Akbari, Hamed; Sotiras, Aristeidis; Thakur, Siddhesh; Verma, Ragini; Shinohara, Russell T.; Yushkevich, Paul A.; Fan, Yong; Kontos, Despina; Davatzikos, Christos; Bakas, Spyridon

doi:10.1007/978-3-030-46643-5_38

Cited by 48 publications

(44 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 9 indicates the improvements in the rTRE before applying any registration, after applying only affine registration, and after the proposed approach. Notably, when compared to the other participating methods, the proposed method’s ( HistoReg ) score of 0.00279 was the highest score achieved using the public data during the 2019 ANHIR challenge [ 38 ] (as indicated in the official challenge webpage: anhir.grand-challenge.org/Workshop-ISBI19/ , last accessed: 13 May 2020). It is further noted that the median robustness of the proposed method, as defined by the challenge, was equal to 1 and the average robustness was 0.9898.…”

Section: Resultsmentioning

confidence: 99%

“…It is further noted that the median robustness of the proposed method, as defined by the challenge, was equal to 1 and the average robustness was 0.9898. As shown in Table III of the ANHIR article [ 38 ], HistoReg ’s overall rank during the challenge was 2, on the basis of the median of median rTRE values (our score was 0.0019). However, it was the best ranked method when the average or the median of average rTRE (score of 0.0029) values and average robustness (0.9898) were the evaluation criteria.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

et al. 2021

Self Cite

View full text Add to dashboard Cite

Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (n = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (<1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Diagnosis and treatment monitoring of gliomas currently utilizes conventional MRI which is readily available and changing standardized routine clinical protocols to include advanced imaging and analysis will require an implementation effort. An example of such an effort is the existing software platform CaPTk (Cancer Imaging Phenomics Toolkit), that integrates advanced tools for analysis of radiographic cancer images, thereby alleviating the translation of artificial intelligence into medical image analysis in daily practice [ 45 , 46 , 47 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature

2021

View full text Add to dashboard Cite

The aim of this study was to systematically review the literature concerning the integration of multimodality imaging with artificial intelligence methods for visualization of tumor cell infiltration in glioma patients. The review was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. The literature search was conducted in PubMed, Embase, The Cochrane Library and Web of Science and yielded 1304 results. 14 studies were included in the qualitative analysis. The reference standard for tumor infiltration was either histopathology or recurrence on image follow-up. Critical assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS2). All studies concluded their findings to be of significant value for future clinical practice. Diagnostic test accuracy reached an area under the curve of 0.74–0.91 reported in six studies. There was no consensus with regard to included image modalities, models or training and test strategies. The integration of artificial intelligence with multiparametric imaging shows promise for visualizing tumor cell infiltration in glioma patients. This approach can possibly optimize surgical resection margins and help provide personalized radiotherapy planning.

show abstract

“…Finally, the images were resampled to 3 mm × 3 mm × 3 mm voxels using a sitkBSpline interpolator. Preprocessing was performed using the ITK-SNAP software (http://www.itksnap.org), Cancer Imaging Phenomics Toolkit (17,18), and Pyradiomics (http://www.radiomics.io/pyradiomics.html).…”

Section: Image Preprocessing and Segmentationmentioning

confidence: 99%

Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics

Dong

Jiang

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

ObjectivesTo explore the magnetic resonance imaging (MRI) characteristics of brain diffuse midline gliomas with the H3 K27M mutation (DMG-M) using radiomics.Materials and MethodsThirty patients with diffuse midline gliomas, including 16 with the H3 K27M mutant and 14 with wild type tumors, were retrospectively included in this study. A total of 272 radiomic features were initially extracted from MR images of each tumor. Principal component analysis, univariate analysis, and three other feature selection methods, including variance thresholding, recursive feature elimination, and the elastic net, were used to analyze the radiomic features. Based on the results, related visually accessible features of the tumors were further evaluated.ResultsPatients with DMG-M were younger than those with diffuse midline gliomas with H3 K27M wild (DMG-W) (median, 25.5 and 48 years old, respectively; p=0.005). Principal component analysis showed that there were obvious overlaps in the first two principal components for both DMG-M and DMG-W tumors. The feature selection results showed that few features from T2-weighted images (T2WI) were useful for differentiating DMG-M and DMG-W tumors. Thereafter, four visually accessible features related to T2WI were further extracted and analyzed. Among these features, only cystic formation showed a significant difference between the two types of tumors (OR=7.800, 95% CI 1.476–41.214, p=0.024).ConclusionsDMGs with and without the H3 K27M mutation shared similar MRI characteristics. T2W sequences may be valuable, and cystic formation a useful MRI biomarker, for diagnosing brain DMG-M.

show abstract

The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview

Cited by 48 publications

References 32 publications

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature

Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics

Contact Info

Product

Resources

About