Prognostic prediction of histopathological images by local binary patterns and RUSBoost

Urdal, Jarle; Engan, Kjersti; Kvikstad, Vebjom; Janssen, E. A. M.

doi:10.23919/eusipco.2017.8081630

Cited by 12 publications

(9 citation statements)

References 19 publications

(26 reference statements)

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“…Of note, we opted for investigating these two higher-order feature groups due to the wavelet and LBP features ability to decipher textural information from different scales ( 68 , 69 ) and their power for texture analysis and classification ( 70 ). Moreover, these features have also been used for analyzing histopathological images ( 71 , 72 ).…”

Section: Discussionmentioning

confidence: 99%

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study

Brancato¹,

Cavaliere²,

Garbino³

et al. 2022

Front. Oncol.

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Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman’s correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as “virtual biopsy”, introducing new insights for omics integration toward a personalized medicine approach.

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Section: Discussionmentioning

confidence: 99%

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study

Brancato¹,

Cavaliere²,

Garbino³

et al. 2022

Front. Oncol.

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“…To increase the predictive performance of colorectal cancer with the imbalanced data, Zhao et al employed the RUSBoost algorithm for the colorectal cancer diagnosis [ 57 ]. A RUSBoost classifier was also used by Urdal et al to solve and alleviate the class imbalance problems for the prognosis prediction of superficial urothelial carcinomas [ 58 ].…”

Section: Overview Of the Existing Methods For The Class Imbalance Pro...mentioning

confidence: 99%

Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets

Taşçı

Zhuge

Camphausen

et al. 2022

Cancers

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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.

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“…Previous work from our group, on bladder cancer, included tissue segmentation [13] [27] [28], and prediction of recurrence in NMIBC patients [29]. In Wetteland et al [13], we experimented with three magnification scales and any combination of these.…”

Section: Previous Workmentioning

confidence: 99%

Automatic Diagnostic Tool for Predicting Cancer Grade in Bladder Cancer Patients Using Deep Learning

et al. 2021

Self Cite

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The most common type of bladder cancer is urothelial carcinoma, which is among the cancer types with the highest recurrence rate and lifetime treatment cost per patient. Diagnosed patients are stratified into risk groups, mainly based on grade and stage. However, it is well known that correct grading of bladder cancer suffers from intra-and interobserver variability and inconsistent reproducibility between pathologists, potentially leading to under-or overtreatment of the patients. The economic burden, unnecessary patient suffering, and additional load on the health care system illustrate the importance of developing new tools to aid pathologists. We propose a pipeline, called TRI grade , that will identify diagnostic relevant regions in the whole-slide image (WSI) and collectively predict the grade of the current WSI. The system consists of two main models, trained on weak slide-level grade labels. First, a WSI is segmented into the different tissue classes (urothelium, stroma, muscle, blood, damaged tissue, and background). Next, tiles are extracted from the diagnostic relevant urothelium tissue from three magnification levels (25x, 100x, and 400x) and processed sequentially by a convolutional neural network (CNN) based model. Ten models were trained for the slide-level grading experiment, where the best model achieved an F1-score of 0.90 on a test set consisting of 50 WSIs. The best model was further evaluated on a smaller segmentation test set, consisting of 14 WSIs where low-and high-grade regions were annotated by a pathologist. The TRI grade pipeline achieved an average F1-score of 0.91 for both the low-grade and high-grade classes.INDEX TERMS Automated cancer grading, bladder cancer, convolutional neural networks, multiscale classification, urothelial carcinoma, weakly labelled data, whole-slide image.

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Prognostic prediction of histopathological images by local binary patterns and RUSBoost

Cited by 12 publications

References 19 publications

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study

Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets

Automatic Diagnostic Tool for Predicting Cancer Grade in Bladder Cancer Patients Using Deep Learning

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