Supervised Regularized Canonical Correlation Analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery

Golugula, Abhishek; Lee, George; Master, Stephen R.; Feldman, Michael D.; Tomaszewski, John E.; Speicher, David W.; Madabhushi, Anant

doi:10.1186/1471-2105-12-483

Cited by 41 publications

(36 citation statements)

References 66 publications

(88 reference statements)

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“…performed dimensionality reduction via supervised canonical correlation analysis to learn a low dimensional space in which patient classification was performed. 24 Naik et. al.…”

Section: Previous Work 31 Automated Gleason Gradingmentioning

confidence: 98%

“…used proteomic data in conjunction with histology derived image features to distinguish between prostate cancer patients who following radical prostatectomy had biochemical recurrence within 5 years from those who did not. 24 Most automated Gleason grading systems are described by a high dimensional feature space. 11,12,15,17,18,24 To perform accurate classification the high dimensional feature space must be reduced to a lower dimensional space.…”

Section: Previous Work 31 Automated Gleason Gradingmentioning

confidence: 99%

“…24 Most automated Gleason grading systems are described by a high dimensional feature space. 11,12,15,17,18,24 To perform accurate classification the high dimensional feature space must be reduced to a lower dimensional space. 20 One approach to reduce the high dimensional feature space is to perform feature selection, thereby determining a small subset of the original feature space in which accurate classification can be performed.…”

Section: Previous Work 31 Automated Gleason Gradingmentioning

confidence: 99%

“…[21][22][23] Previous applications of ML to histopathology datasets have demonstrated that the low dimensional embedding space is better suited for classification compared to the original high dimensional space. 11,17,24 ML finds a low dimensional manifold representation M from a dataset O which preserves relationships between samples in O. Most ML methods assume that O is contained in a high dimensional space R D .…”

Section: Introductionmentioning

confidence: 99%

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Gleason grading of prostate histology utilizing manifold regularization via statistical shape model of manifolds

Sparks

Madabhushi

2012

SPIE Proceedings

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Gleason patterns of prostate cancer histopathology, characterized primarily by morphological and architectural attributes of histological structures (glands and nuclei), have been found to be highly correlated with disease aggressiveness and patient outcome. Gleason patterns 4 and 5 are highly correlated with more aggressive disease and poorer patient outcome, while Gleason patterns 1-3 tend to reflect more favorable patient outcome. Because Gleason grading is done manually by a pathologist visually examining glass (or digital) slides, subtle morphologic and architectural differences of histological attributes may result in grading errors and hence cause high inter-observer variability. Recently some researchers have proposed computerized decision support systems to automatically grade Gleason patterns by using features pertaining to nuclear architecture, gland morphology, as well as tissue texture. Automated characterization of gland morphology has been shown to distinguish between intermediate Gleason patterns 3 and 4 with high accuracy. Manifold learning (ML) schemes attempt to generate a low dimensional manifold representation of a higher dimensional feature space while simultaneously preserving nonlinear relationships between object instances. Classification can then be performed in the low dimensional space with high accuracy. However ML is sensitive to the samples contained in the dataset; changes in the dataset may alter the manifold structure. In this paper we present a manifold regularization technique to constrain the low dimensional manifold to a specific range of possible manifold shapes, the range being determined via a statistical shape model of manifolds (SSMM). In this work we demonstrate applications of the SSMM in (1) identifying samples on the manifold which contain noise, defined as those samples which deviate from the SSMM, and (2) accurate out-of-sample extrapolation (OSE) of newly acquired samples onto a manifold constrained by the SSMM. We demonstrate these applications of the SSMM in the context of distinguishing between Gleason patterns 3 and 4 using glandular morphologic features in a prostate histopathology dataset of 58 patient studies. Identifying and eliminating noisy samples from the manifold via the SSMM results in a statistically significant improvement in classification accuracy (CA), 93.0 ± 1.0% with removal of noisy samples compared to a CA of 90.9 ± 1.1% without removal of samples. The use of the SSMM for OSE of new independent test instances also shows statistically significant improvement in CA, 87.1 ± 0.8% with the SSMM compared to 85.6 ± 0.1% without the SSMM. Similar improvements were observed for the synthetic Swiss Roll and Helix datasets.

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“…performed dimensionality reduction via supervised canonical correlation analysis to learn a low dimensional space in which patient classification was performed. 24 Naik et. al.…”

Section: Previous Work 31 Automated Gleason Gradingmentioning

confidence: 98%

Section: Previous Work 31 Automated Gleason Gradingmentioning

confidence: 99%

Section: Previous Work 31 Automated Gleason Gradingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gleason grading of prostate histology utilizing manifold regularization via statistical shape model of manifolds

Sparks

Madabhushi

2012

SPIE Proceedings

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“…Recently several researchers have attempted to develop automated, computerized Gleason grading algorithms for prostate pathology [2][3][4][5][6]. Graph tesselations of cell nuclei using Voronoi or Delaunay graphs have been found to be predictive of Gleason grade [2,3].…”

Section: Introductionmentioning

confidence: 99%

Co-occurring gland tensors in localized cluster graphs: Quantitative histomorphometry for predicting biochemical recurrence for intermediate grade prostate cancer

Lee

Sparks

Ali

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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Quantitative histomorphometry (QH), computational tools to analyze digitized tissue histology, has become increasingly important for aiding pathologists in assessing cancer severity. In this study, we introduce a novel set of QH features utilizing co-occurring gland tensors (CGT) in localized cluster graphs to quantitatively evaluate prostate cancer (CaP) histology. CGTs offer three main advantages over previous QH features: 1) gland tensors represent a novel measurement that has been anecdotally described as one of interest, but never quantitatively modeled, 2) CGTs extract measurements based on local rather than global glandular networks, constructed using cluster graphs, and 3) second order statistical features (energy, homogeneity, energy, and correlation) obtained from a co-occurrence matrix capture the spatial interactions of gland tensors in the image. We extract 4 CGT features from 56 regions across 40 intermediate grade CaP patients and evaluated the ability of CGT features to predict biochemical recurrence (BCR) within 5 years of radical prostatectomy. Intermediate Gleason score 7 cancers represent the predictive borderline for BCR cases, where 50% of cases develop BCR. We found that CGT features outperformed 5 different sets of QH features, previously shown to be effective in CaP grading, when evaluated via a Random Forest classifier (66% accuracy for CGT features versus 55% for the next closest QH feature set), all comparisons being statistically significant.

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Advances in the computational and molecular understanding of the prostate cancer cell nucleus

Carleton

Lee

Madabhushi

et al. 2018

J of Cellular Biochemistry

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Nuclear alterations are a hallmark of many types of cancers, including prostate cancer (PCa). Recent evidence shows that subvisual changes, ones that may not be visually perceptible to a pathologist, to the nucleus and its ultrastructural components can precede visual histopathological recognition of cancer. Alterations to nuclear features, such as nuclear size and shape, texture, and spatial architecture, reflect the complex molecular-level changes that occur during oncogenesis. Quantitative nuclear morphometry, a field that uses computational approaches to identify and quantify malignancy-induced nuclear changes, can enable a detailed and objective analysis of the PCa cell nucleus. Recent advances in machine learning-based approaches can now automatically mine data related to these changes to aid in the diagnosis, decision making, and prediction of PCa prognoses. In this review, we use PCa as a case study to connect the molecular-level mechanisms that underlie these nuclear changes to the machine learning computational approaches, bridging the gap between the clinical and computational understanding of PCa. First, we will discuss recent developments to our understanding of the molecular events that drive nuclear alterations in the context of PCa: the role of the nuclear matrix and lamina in size and shape changes, the role of 3-dimensional chromatin organization and epigenetic modifications in textural changes, and the role of the tumor microenvironment in altering nuclear spatial topology. We will then discuss the advances in the applications of machine learning algorithms to automatically segment nuclei in prostate histopathological images, extract nuclear features to aid in diagnostic decision making, and predict potential outcomes, such as biochemical recurrence and survival. Finally, we will discuss the challenges and opportunities associated with translation of the quantitative nuclear morphometry methodology into the clinical space. Ultimately, accurate identification and quantification of nuclear alterations can contribute to the field of nucleomics and has applications for computationally driven precision oncologic patient care.

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Supervised Regularized Canonical Correlation Analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery

Cited by 41 publications

References 66 publications

Gleason grading of prostate histology utilizing manifold regularization via statistical shape model of manifolds

Gleason grading of prostate histology utilizing manifold regularization via statistical shape model of manifolds

Co-occurring gland tensors in localized cluster graphs: Quantitative histomorphometry for predicting biochemical recurrence for intermediate grade prostate cancer

Advances in the computational and molecular understanding of the prostate cancer cell nucleus

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