Dr. Cheng Lu Profile

Dr. Cheng Lu

at Case Western Reserve Univ

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Publications (6)

Proceedings Article | 4 April 2022 Presentation + Paper

Identifying the origination of liver metastasis using a hand-crafted computational pathology approach

Chuheng Chen, Cheng Lu, Joseph Willis, Anant Madabhushi

Proceedings Volume 12039, 1203904 (2022) https://doi.org/10.1117/12.2613387

KEYWORDS: Tumors, Pathology

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The liver is a common site for metastases for multiple cancer types – with adenocarcinoma being the most common subtype. Standard pathology assessments fail to identify a site of origin for three to five percent of these patients, who are then treated in an empirical manner.¹ Computational pathology is an ideal technology to improve diagnostic accuracy for metastatic cancers with a significant reduction in costs and increase in tissue preservation for future testing. Recently, Deep Learning (DL)-based algorithms have been developed to automatically identify the origination of metastases using only H&E-stained whole slide images as inputs.² However, immunohistochemical based analysis is time- and resource-intensive and tissue destructive, while the “black box” nature of the DL-based approach makes it difficult if not impossible to explain the biology behind the classifier’s decision. This pilot study demonstrates that an analysis of computer extracted quantitative cellular features from tumor epithelial nuclei, including a tumor nuclei morphological feature and graph feature as well as cytoplasm texture from H&E stained histology slides, can be used to identify the origination of liver metastasis. Since the primary source of liver metastasis is typically the colon, breast, pancreas, or esophagus,³ a data set consisting of 120 patients with primary adenocarcinoma and matched liver metastases from the breast, colon, esophagus, and pancreas was constructed. A combination of supervised classification and unsupervised clustering was applied in order to identify the tumors’ origination using the above mentioned histomorphometric features from the data set. We evaluated the utility of histomorphometric features to identify the origination of the metastatic tumors in the liver using both supervised machine classifiers and unsupervised clustering. A Random Forest classifier achieved AUCs of 0.83, 0.63, 0.82, and 0.64 in classifying the primary or metastatic tumor from colon, esophagus, breast, and pancreas. The top performing features included local cellular diversity as well as cytoplasmic texture feature families. We further verified selected features using unsupervised methods such as UMAP, hierarchical clustering, and content-based image retrieving. Based off similarity measures calculated between image tiles in the primary and metastatic tissue, a heatmap corresponding to the WSIs for the primary tumor was constructed to highlight the sites representing potential points of origination of the metastases. Our preliminary results suggest that the site of origination for metastases in the context of primary breast tumors appears to be within the stroma.

Proceedings Article | 16 March 2020 Presentation + Paper

Three-dimensional histo-morphometric features from light sheet microscopy images result in improved discrimination of benign from malignant glands in prostate cancer

Can Koyuncu, Andrew Janowczyk, Cheng Lu, Patrick Leo, Mehdi Alilou, Adam Glaser, Nicholas Reder, Jonathan T. Liu, Anant Madabhushi

Proceedings Volume 11320, 113200G (2020) https://doi.org/10.1117/12.2549726

KEYWORDS: Image segmentation, 3D image processing, Prostate cancer, Biopsy, Cancer, Microscopy, Pathology, Feature extraction, Tissues, Natural surfaces

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Proceedings Article | 16 March 2020 Presentation + Paper

Compactness measures of tumor infiltrating lymphocytes in lung adenocarcinoma are associated with overall patient survival and immune scores

Ruiwen Ding, Prateek Prasanna, Germán Corredor, Cheng Lu, Priya Velu, Khoi Le, Patrick Leo, Niha Beig, Vamsidhar Velcheti, David Rimm, Kurt Schalper, Anant Madabhushi

Proceedings Volume 11320, 1132003 (2020) https://doi.org/10.1117/12.2549588

KEYWORDS: Tumors, Tissues, Feature extraction, Lung, Lung cancer, Biological research, Cancer, Data modeling, Principal component analysis, Statistical analysis

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Proceedings Article | 15 March 2019 Paper

Spatial arrangement of leakage patterns in diabetic macular edema is associated with tolerance of aflibercept treatment interval length: preliminary findings

Prateek Prasanna, Justis Ehlers, Vishal Bobba, Natalia Figueredo, Cheng Lu, Sumit Sharma, Sunil Srivastava, Anant Madabhushi

Proceedings Volume 10953, 1095311 (2019) https://doi.org/10.1117/12.2513654

KEYWORDS: Visualization, Angiography, Tolerancing, Eye, Macula, Pathophysiology, Statistical analysis, Image segmentation, Medical research, Lung cancer

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Diabetic macular edema (DME) is a leading cause of vision loss in diabetic patients. The underlying cause for the onset of DME is the degradation of the blood-retinal barrier, whose primary function is maintaining the extracellular fluid at an optimal range. Vascular endothelial growth factor (VEGF) has proven to be a catalyst in altering the permeability of the blood-retinal barrier, thereby initiating a cascade of events that ultimately results in a loss of visual acuity.1 The primary imaging techniques to recognize and diagnose DME are fluorescein angiography (FA) and spectral-domain optical coherence tomography (SD-OCT). Taking a multimodal approach of FA in combination with SD-OCT provides images of vasculature and other eye structures to help better identify key features such as level, location, and amount of leakage.2 First-line treatments for DME have now evolved to using anti-VEGF to inhibit the effects VEGF has on increasing the permeability of the blood-retinal barrier.3 Because VEGF also increases the chance of leakage, we can also expect anti-VEGF treatments to decrease the amount of leakage DME patients suffer from. Anti-VEGF treatments also have a peripheral effect of modifying the disease burden and allowing for extended time in between treatments.4 Although current conventional treatment parameters exist to determine the efficacy of such VEGF treatments, many of these markers rely on clinicians to make a judgment call based on a minor qualitative difference of retinal scans or involve clinicians taking a fluid assessment, an option deemed too invasive to demand from all patients. In this work, we seek to find new imaging features that derive from a sub-visual feature analysis, and ideally provide a prognostic metric for clinicians to help streamline the diagnostic process. The rationale for these new biomarkers derives from leakage properties and their activity in the retina once edema develops. A decrease in leakage within certain structures in the eye would also lead to a change in the densities of leakage patterns, correlating with better clinical outcomes. In this work, we use morphological and graph-based attributes to model the global properties and spatial distribution of leakage areas on baseline FA scans of patients subsequently treated with intravitreal anti-VEGF therapy (i.e. aibercept). The features were then used in conjunction with a classifier to distinguish between eyes tolerating extended dosing intervals (N=15) and those eyes requiring more frequent dosing (N=12), based on initial response following treatment interval extension. The cross-validated area under the receiver operating characteristic curve (AUC) was found to be 0.74±0.11% using the computed imaging attributes. Edge length disorder of minimum spanning tree showed a statistically significant difference (p=0.007) between the two groups. Clinical parameters such as central subfield thickness and macular volume were not statistically significantly different. Our results indicate that there may be differences in spatial distribution of leakage areas between eyes that will exhibit favorable response to extended interval aibercept dosing and eyes that require more frequent dosing.

SPIE Journal Paper | 8 February 2019

Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images

Jun Xu, Lei Gong, Guanhao Wang, Cheng Lu, Hannah Gilmore, Shaoting Zhang, Anant Madabhushi

JMI, Vol. 6, Issue 01, 017501, (February 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.1.017501

KEYWORDS: Image segmentation, Convolutional neural networks, Performance modeling, Data modeling, Breast, Breast cancer, Sensors, Cancer, Tissues, Computing systems

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Automated detection and segmentation of nuclei from high-resolution histopathological images is a challenging problem owing to the size and complexity of digitized histopathologic images. In the context of breast cancer, the modified Bloom–Richardson Grading system is highly correlated with the morphological and topological nuclear features are highly correlated with Modified Bloom–Richardson grading. Therefore, to develop a computer-aided prognosis system, automated detection and segmentation of nuclei are critical prerequisite steps. We present a method for automated detection and segmentation of breast cancer nuclei named a convolutional neural network initialized active contour model with adaptive ellipse fitting (CoNNACaeF). The CoNNACaeF model is able to detect and segment nuclei simultaneously, which consist of three different modules: convolutional neural network (CNN) for accurate nuclei detection, (2) region-based active contour (RAC) model for subsequent nuclear segmentation based on the initial CNN-based detection of nuclear patches, and (3) adaptive ellipse fitting for overlapping solution of clumped nuclear regions. The performance of the CoNNACaeF model is evaluated on three different breast histological data sets, comprising a total of 257 H&E-stained images. The model is shown to have improved detection accuracy of F-measure 80.18%, 85.71%, and 80.36% and average area under precision-recall curves (AveP) 77%, 82%, and 74% on a total of 3 million nuclei from 204 whole slide images from three different datasets. Additionally, CoNNACaeF yielded an F-measure at 74.01% and 85.36%, respectively, for two different breast cancer datasets. The CoNNACaeF model also outperformed the three other state-of-the-art nuclear detection and segmentation approaches, which are blue ratio initialized local region active contour, iterative radial voting initialized local region active contour, and maximally stable extremal region initialized local region active contour models.

Proceedings Article | 6 March 2018 Paper

A watershed and feature-based approach for automated detection of lymphocytes on lung cancer images

Germán Corredor, Xiangxue Wang, Cheng Lu, Vamsidhar Velcheti, Eduardo Romero, Anant Madabhushi

Proceedings Volume 10581, 105810R (2018) https://doi.org/10.1117/12.2293147

KEYWORDS: Lung cancer, Image segmentation, Visualization, Data modeling, Feature extraction, Tumors, Image processing, Cancer, Visual process modeling, Pathology

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