Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma

Venianaki, Maria; Salvetti, Ovidio; Bree, Eelco de; Maris, Thomas G.; Karantanas, Apostolos H.; Kontopodis, Eleftherios; Nikiforaki, Katerina; Marias, Kostas

doi:10.1007/s11042-017-5046-6

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…The approach also allows detection of habitats at a subpixel resolution [5], leading to more accurate determination of habitat locations and allowing the analysis of both habitat-specific pharmacokinetic and anatomical parameters. Specifically, in sarcoma, a similar pattern recognition approach on DCE-MRI data has been applied with good agreement between determined habitats and histopathology [31].…”

Section: Discussionmentioning

confidence: 99%

https://researchopenworld.com/quantitative-imaging-features-of-habitats-in-soft-tissue-sarcomas-are-associated-with-treatment-response-a-pilot-study/#

2019

Cancer Stud Ther J

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Objective: To determine if tumor habitat perfusion and diffusion characteristics are related to treatment response in soft tissue sarcoma patients. Methods: Eight patients (58.6 ± 10.2 y/o) with soft tissue sarcomas underwent pre-treatment Dynamic Contrast Enhanced-(DCE-) and Diffusion Weighted-MRI and post-treatment resection allowing pathology to determine treatment response. Tumors were manually segmented from T1-weighted post-contrast images. Tumor habitats, classified as well-perfused, hypoxic and necrotic, were determined from DCE-MRI using a pattern recognition technique. Each habitat was characterized with pharmacokinetic parameters, k ep and K trans , calculated from the DCE-MRI sequence and the apparent diffusion coefficients from the co-registered Diffusion Weighted-MRI. The quantitative imaging features were examined for associations with treatment response. Patients were classified as responders and nonresponders based on histopathology. Results: Using unsupervised clustering on the imaging features, the patients were divided accurately between responders and nonresponders. Out of all determined features, k ep (p = 0.04), K trans (p < 0.01), and percent volume (p = 0.02) for well-perfused habitats were significantly lower in nonresponders, whereas volume (p = 0.04) and percent volume (p < 0.01) for necrotic habitats were significantly higher in responders. Conclusion: Prediction of treatment response in soft tissue sarcoma patients yielded promising results when utilizing differences in Dynamic Contrast Enhanced-and Diffusion Weighted-MRI features between unique tumor habitats.

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Section: Discussionmentioning

confidence: 99%

https://researchopenworld.com/quantitative-imaging-features-of-habitats-in-soft-tissue-sarcomas-are-associated-with-treatment-response-a-pilot-study/#

2019

Cancer Stud Ther J

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“…• NMF: standard nonnegative matrix factorization was utilized to learn the representation of a tumor, which is an additive combination of three basis patterns (Venianaki et al 2018). Then, the tumor was decomposed into three components associated with the patterns.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

A weakly supervised NMF method to decipher molecular subtype-related dynamic patterns in breast DCE-MR images

Guan,

Fan,

2023

Phys. Med. Biol.

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Objective. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an important imaging modality for breast cancer diagnosis. Intratumoral heterogeneity causes a major challenge in the interpretation of breast DCE-MRI. Previous studies have introduced decomposition methods on DCE-MRI to reveal intratumoral heterogeneity by analyzing distinct dynamic patterns within each tumor. However, these methods estimated the dynamic patterns and their corresponding component coefficients in an unsupervised manner, without considering any clinically relevant information. Approach. To decipher molecular subtype-related dynamic patterns, we propose a weakly supervised nonnegative matrix factorization method (WSNMF), which is able to decompose the pixel kinetics of DCE-MRI with image-level subtype labels. The WSNMF is developed based on a discriminant NMF to utilize coarse-grained subtype information, in which between- and within-class scatters are defined on the mean vector of component coefficients over all pixels in each tumor, rather than directly on the vector of component coefficients of each pixel. Main results. Experiments demonstrated that the dynamic patterns identified by WSNMF had superior performance in distinguishing between luminal A and the other subtype tumors. The classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). WSNMF yielded better classification performance (AUC = 0.822) than other heterogeneity analysis methods, including two partitioning based methods (KPC with AUC = 0.697 and TTP with AUC =0.760) and two unsupervised decomposition-based methods (PCA with AUC = 0.774 and NMF with AUC = 0.797). Significance. Our method adds a valuable new perspective into DCEMRI decomposition-based heterogeneity analysis by taking advantage of intrinsic tumor characteristics to improve the diagnosis of breast cancer.

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“…This analysis has been widely used to reduce feature dimensions in various pattern recognition tasks, especially for object recognition. In the field of pattern recognition for medical data, PCA is used to reduce the dimension of large size features in dynamic contrast enhanced MR imaging (DCE-MRI) data of hypoxia tumors [15]. In the context of the study, the use of PCA is intended to find the number of components that can distinguish the overall variability of data.…”

Section: Introductionmentioning

confidence: 99%

Writer Identification of Lampung Handwritten Documents Based on Selected Characters

Junaidi

Trianingsih

Iqbal

2020

khif

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Writer identification is a sub-field in handwriting recognition which its objective is to determine the identity of the writer based on handwriting input. The goal is usually for forensic purposes such as finding the perpetrators of crimes that leave traces of evidence in the form of written messages. In addition, writer identification can also be used to determine the identity of a historical actor if he or she leaves a valuable written artefact. The object of this research is the traditional character of the Lampung region which is so-called Had Lampung by the local community. The traditional character of Lampung consists of 20 main characters and 12 diacritics. Based on selected characters, the writer will be recognized using the Principal Component Analysis (PCA) feature. PCA is one linear feature extraction method of an object in pattern recognition. The PCA algorithm consists of several stages, namely the calculation of the average dataset, the subtraction of the vector dataset with averages, the calculation of covariance, the calculation of eigenvectors and eigenvalues, eigenvector reduction, and the projection of the dataset against reduced eigenvector space. PCA in this paper is used as a feature in image recognition. The dataset utilized in this study is the Lampung Dataset which is a handwritten character recognition (HWCR) dataset. Lampung Dataset consists of 82 Lampung handwritten documents. All Lampung character images in the dataset were extracted from these documents using the connected component extraction algorithm and eventually generated 32,140 images. Furthermore, these images are converted into grayscale images. In this research, as many as 12,500 grayscale images of Lampung handwriting characters were chosen to represent 82 different writers. This data is employed as training and testing data on the proposed method. The highest accuracy of the identification of the writer using this PCA feature is 82.92%, while the lowest accuracy is 28.29%.

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Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma

Cited by 6 publications

References 30 publications

https://researchopenworld.com/quantitative-imaging-features-of-habitats-in-soft-tissue-sarcomas-are-associated-with-treatment-response-a-pilot-study/#

https://researchopenworld.com/quantitative-imaging-features-of-habitats-in-soft-tissue-sarcomas-are-associated-with-treatment-response-a-pilot-study/#

A weakly supervised NMF method to decipher molecular subtype-related dynamic patterns in breast DCE-MR images

Writer Identification of Lampung Handwritten Documents Based on Selected Characters

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