Papers by Sahirzeeshan Ali
Abstract 4349: Cancer histologic and cell nucleus architecture differentiate prostate cancer Gleason patterns 3 from 4
Cancer Research, 2015
International Journal of Radiation Oncology*Biology*Physics, 2009
Method and Apparatus for Shape Based Deformable Segmentation of Multiple Overlapping Objects
The Journal of Urology, 2015
Plasma sMet levels accurately distinguished patients with PCa (n¼83) from those without (n¼80, Pe... more Plasma sMet levels accurately distinguished patients with PCa (n¼83) from those without (n¼80, Pearson r¼0.440, p < 0.0001; area under the curve AUC¼0.9385, threshold value of 146 ng/ml) with good sensitivity (87%) and excellent specificity (94%), and patients with pathologic stage 2A and higher from normals (Pearson r¼0.468, p<0.0001). Correlations between plasma sMet and Gleason score or PSA value were not statistically significant.
Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology
IEEE transactions on medical imaging, Jan 14, 2015
Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appe... more Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appearance on digital pathology images. This procedure typically involves extraction of hundreds of features, which may be used to predict disease presence, aggressiveness, or outcome, from digitized images of tissue slides. Due to the "curse of dimensionality", constructing a robust and interpretable classifier is very challenging when the dimensionality of the feature space is high. Dimensionality reduction (DR) is one approach for reducing the dimensionality of the feature space to facilitate classifier construction. When DR is performed, however, it can be challenging to quantify the contribution of each of the original features to the final classification or prediction result. In QH it is often important not only to create an accurate classifier of disease presence and aggressiveness, but also to identify the features that contribute most substantially to class separability. T...
Co-occurring gland angularity in localized subgraphs: predicting biochemical recurrence in intermediate-risk prostate cancer patients
PloS one, 2014
Quantitative histomorphometry (QH) refers to the application of advanced computational image anal... more Quantitative histomorphometry (QH) refers to the application of advanced computational image analysis to reproducibly describe disease appearance on digitized histopathology images. QH thus could serve as an important complementary tool for pathologists in interrogating and interpreting cancer morphology and malignancy. In the US, annually, over 60,000 prostate cancer patients undergo radical prostatectomy treatment. Around 10,000 of these men experience biochemical recurrence within 5 years of surgery, a marker for local or distant disease recurrence. The ability to predict the risk of biochemical recurrence soon after surgery could allow for adjuvant therapies to be prescribed as necessary to improve long term treatment outcomes. The underlying hypothesis with our approach, co-occurring gland angularity (CGA), is that in benign or less aggressive prostate cancer, gland orientations within local neighborhoods are similar to each other but are more chaotically arranged in aggressive...
Variable importance in nonlinear kernels (VINK): classification of digitized histopathology
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013
Quantitative histomorphometry is the process of modeling appearance of disease morphology on digi... more Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of...
Variable importance in nonlinear kernels (VINK): classification of digitized histopathology
Quantitative histomorphometry is the process of modeling appearance of disease morphology on digi... more Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of...
Frontiers of Medical Imaging, 2014
This review utilizes prostate cancer (CaP) as a case study to assess the application of quantitat... more This review utilizes prostate cancer (CaP) as a case study to assess the application of quantitative histomorphometry to characterize the aggressive phenotype and also to make predictions of outcome like recurrence, metastasis and survival. Dr. Robert Veltri describes the use of a microspectrophotometry microscope and novel software to capture nuclear morphometry of Feulgen (DNA) stained features and successfully identify indolent and aggressive CaP as well as predict outcomes such as biochemical recurrence, metastasis and survival. His research also indicates that quantitative nuclear morphometry by this method indicates a field effect nearby the can that also has predictive value. However, the original technology was bottle-necked by the fact that it could not be extended to whole slide images. Subsequently, the initiation of a collaboration with Dr. Anant Madabhushi who has developed high throughput quantitative image and histomorphometric tools that are amenable to work on whole slide digitized images, has allowed for confirmation of Dr. Veltri's prior and published observations that nuclear size, shape and texture as well as the glandular structure or architecture of prostate cancer are critical in predicting disease aggressiveness. Dr. Madabhushi's algorithms take advantage of advanced machine vision imaging images for diagnosis and prognosis. Additionally Dr. Madabhushi's group has also been developing machine learning tools to combine image based and molecular measurements for creating unified predictors of disease aggressiveness and patient outcome. It is hoped that the additional development and validation of these tools will set the stage for creation of decision tools to aid the pathologist to predict severe outcomes early so that appropriate interventions can be made by the urologist and patient.
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013
Quantitative histomorphometry (QH), computational tools to analyze digitized tissue histology, ha... more 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.
We introduce a novel feature descriptor to describe cancer cells called Cell Orientation Entropy ... more We introduce a novel feature descriptor to describe cancer cells called Cell Orientation Entropy (COrE). The main objective of this work is to employ COrE to quantitatively model disorder of cell/nuclear orientation within local neighborhoods and evaluate whether these measurements of directional disorder are correlated with biochemical recurrence (BCR) in prostate cancer (CaP) patients. COrE has a number of novel attributes that are unique to digital pathology image analysis. Firstly, it is the first rigorous attempt to quantitatively model cell/nuclear orientation. Secondly, it provides for modeling of local cell networks via construction of subgraphs. Thirdly, it allows for quantifying the disorder in local cell orientation via second order statistical features. We evaluated the ability of 39 COrE features to capture the characteristics of cell orientation in CaP tissue microarray (TMA) images in order to predict 10 year BCR in men with CaP following radical prostatectomy. Randomized 3-fold cross-validation via a random forest classifier evaluated on a combination of COrE and other nuclear features achieved an accuracy of 82.7 ± 3.1% on a dataset of 19 BCR and 20 non-recurrence patients. Our results suggest that COrE features could be extended to characterize disease states in other histological cancer images in addition to prostate cancer.
Quantitatively Characterizing Disease Morphology With Cell Orientation Entropy
Variable importance in nonlinear kernels (VINK): classification of digitized histopathology
Quantitative histomorphometry is the process of modeling appearance of disease morphology on digi... more Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of...
Spatially aware cell cluster(spACC1) graphs: predicting outcome in oropharyngeal pl6+ tumors
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013
Quantitative measurements of spatial arrangement of nuclei in histopathology images for different... more Quantitative measurements of spatial arrangement of nuclei in histopathology images for different cancers has been shown to have prognostic value. Traditionally, graph algorithms (with cell/nuclei as node) have been used to characterize the spatial arrangement of these cells. However, these graphs inherently extract only global features of cell or nuclear architecture and, therefore, important information at the local level may be left unexploited. Additionally, since the graph construction does not draw a distinction between nuclei in the stroma or epithelium, the graph edges often traverse the stromal and epithelial regions. In this paper, we present a new spatially aware cell cluster (SpACC1) graph that can efficiently and accurately model local nuclear interactions, separately within the stromal and epithelial regions alone. SpACC1 is built locally on nodes that are defined on groups/clusters of nuclei rather than individual nuclei. Local nodes are connected with edges which hav...
Journal of Pathology Informatics
Computer-assisted Gleason grading of prostate cancer: Two novel approaches using nuclear shape and texture feature to classify pathologic Gleason grade patterns 3 and 4
Use of quantitative histomorphometrics to classify disease progression in HPV-positive squamous cell carcinoma
A Quantitative Histomorphometric Classifier Identifies Aggressive Versus Indolent p16 Positive Oropharyngeal Squamous Cell Carcinoma
Quantitatively Characterizing Disease Morphology With Cell Orientation Entropy
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, Jan 12, 2014
Shape based active contours have emerged as a natural solution to overlap resolution. However, mo... more Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all three terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particu...
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Papers by Sahirzeeshan Ali