Dr. Sharyn I. Katz Profile

Dr. Sharyn I. Katz

at Penn Medicine

SPIE Involvement:

Author

Publications (9)

Proceedings Article | 3 April 2024 Paper

Exploring the prognostic power and biological significance of a robust radiomic biomarker of overall survival in advanced non-small cell lung cancer patients treated with first-line immunotherapy

Apurva Singh, Hannah Horng, Leonid Roshkovan, Charu Aggarwal, Erica Carpenter, Sharyn Katz, Despina Kontos

Proceedings Volume 12927, 1292733 (2024) https://doi.org/10.1117/12.3006810

KEYWORDS: Radiomics, Tumors, Tumor growth modeling, Brain-machine interfaces, Image segmentation, Feature extraction, Computed tomography, Lung cancer, Statistical analysis, Matrices

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Tumor expression of PDL1 is a suboptimal biomarker to predict response to anti-PD1/PDL1 therapy, including pembrolizumab (PEMBRO). We hypothesize that radiomic features, high-throughput descriptors of tumor heterogeneity, can improve performance of models built using established clinical biomarkers for prediction of overall survival and presence of relevant genetic mutations KRAS and STK11 (significant in patients selected for PEMBRO-based therapy). Our study includes baseline CT scans from 308 patients with stage 4 NSCLC, treated with first line PEMBRO, from a single-center retrospective study conducted at Hospital of the University of Pennsylvania (November 2016-December 2020). Region of interest (ROI) segmentation on 3D tumor CT volumes was performed using ITK-SNAP. Feature extraction on ROIs was performed using CaPTk. Scan parameter heterogeneity (presence/absence of contrast enhancement, reconstruction kernel and voxel spacing) was mitigated using a nested ComBat harmonization technique, while protecting the variable BMI to prevent removal of heterogeneity arising from biological variation during harmonization, noting BMI had statistically significant correlation with first radiomic principal component (PC). Dimensionality of resultant set was reduced using an unsupervised hierarchical clustering technique. These identified radiomic phenotypes, when combined with clinical variables (longest tumor diameter at baseline, tumor PDL1 expression, age, sex, race, smoking status, BMI), increased the five-fold cross-validated c-score in the prediction of overall survival from 0.60 [0.54,0.63] to 0.64 [0.57,0.66]. For a subset of 287 patients (further sub-divided based on histology: squamous vs. non-squamous), the radiomic phenotypes were used to identify patients with mutations in KRAS and STK11, using linear SVM classifier: KRAS (squamous: 65% (AUC-0.61), non-squamous: 74% (AUC-0.63)), STK11 (squamous: 76% (AUC-0.62), non-squamous: 82% (AUC-0.68)). The radiomic phenotypes improve the prognostic value of clinical biomarkers in the prediction of overall survival and can identify patients with gene mutations that are significant in patient selection for PEMBRO-based therapy. Our future work involves building a multi-omic (radiomic, genomic, clinical) predictor of progression-free survival, to study patient response to PEMBRO-based therapy.

Proceedings Article | 7 April 2023 Poster + Paper

Radiomics analysis to diagnose tumor invasiveness of pulmonary sub-solid nodules from longitudinal pre-surgical CT scans

Apurva Singh, Hannah Horng, Leonid Roshkovan, Sharyn Katz, Despina Kontos, Jeffrey Thompson

Proceedings Volume 12465, 124652C (2023) https://doi.org/10.1117/12.2653949

KEYWORDS: Radiomics, Computed tomography, Brain-machine interfaces, Image segmentation, Diagnostics, Tumors, Performance modeling, Manufacturing, Feature extraction, Voxels

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Sub-solid lung nodules present unique diagnostic challenges. Pre-surgical characterization of the degree of invasiveness may be inaccurate even with tissue sampling. We hypothesize that the high-throughput information captured by radiomic descriptors on CT images can characterize the invasiveness of sub-solid nodules. In this study, we analyzed immediate pre-surgical CT scans of 72 nodules from 68 patients. Region of interest (ROI) segmentation on the scans was performed by our fellowship trained chest radiologists using the ITK-SNAP software. Feature extraction on ROIs was performed using the CaPTk toolkit. Scan parameter heterogeneity can affect radiomic features. To overcome this, image parameters including scanner manufacturer and voxel spacing parameters were harmonized at each time point using a nested ComBat harmonization technique. Clinical variables of ethnicity, BMI, smoking status and pathological category were protected during harmonization, to prevent the removal of biological variables of interest. Features with negative Mean Decrease in Accuracy (MDA) metric (non-optimal prognostic value) were dropped. Dimensionality of the feature set was reduced using the first radiomic principal component (PC) as a representative feature. Multiple logistic regression analysis using radiomic PC and clinical factors revealed only radiomic PC to be a significant predictor of nodule invasiveness (p < 0.05). A model containing clinical variables gave an accuracy of 73% (AUC=0.58) in identifying invasive sub-solid nodules. The accuracy increased to 93% (AUC-0.88) with the addition of radiomic PC. We further wanted to investigate if the change in the radiomic descriptors of the nodule properties over time, can improve the diagnosis of nodule invasiveness. Thus, from our original set of patients, we identified a subset of 40 nodules from 37 patients, with a total of 2 CT scans (immediate pre-surgical scan and 1 additional time point) and a subset of 34 nodules from 29 patients, with a total of 3 CT scans (immediate pre-surgical scan and 2 additional time points), each scan separated by at least a 12-month from each other. The features were harmonized at each time point. Delta radiomic features were computed (features from immediate pre-surgical scan- previous time point). Dimensionality of the delta radiomics feature set was reduced using the first radiomic principal component (PC) as a representative feature. In the case of longitudinal analysis using two scans, the model containing clinical variables gave an accuracy of 70% (AUC-0.55) in the diagnosis of nodule invasiveness. The accuracy increased to 82% (AUC- 0.65) upon the addition of delta radiomic PC (immediate pre-surgicalfirst time point). In the case of longitudinal analysis using three scans, the model containing clinical variables gave an accuracy of 68% (AUC-0.54) in the diagnosis of nodule invasiveness. The accuracy increased to 79% (AUC- 0.63) upon the addition of the first delta radiomic PC (immediate pre-surgical-first time point) and the second delta radiomic PC (immediate pre-surgical-second time point). Thus, the representative radiomic PCs, describing the change in the properties of the nodules over time, were found to be significant predictors of nodule invasiveness and augmented the performance of standard prognostic clinical factors. Thus, the ability of radiomic descriptors to predict nodule invasiveness could potentially be utilized to facilitate management of sub-solid nodules identified on chest CT imaging.

Proceedings Article | 7 April 2023 Presentation + Paper

A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

Chunrui Zou, Walter Mankowski, Lauren Pantalone, Shefali Setia Verma, Eduardo Mortani Barbosa, Tessa Cook, Peter Noel, Erica Carpenter, Jeffrey Thompson, Russell Shinohara, Sharyn Katz, Despina Kontos

Proceedings Volume 12465, 124650C (2023) https://doi.org/10.1117/12.2655506

KEYWORDS: Chest imaging, Radiomics, Feature extraction, Image segmentation, X-ray computed tomography, COVID 19, Lung, Medical imaging, Computed tomography, Deep learning

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Proceedings Article | 4 April 2022 Presentation + Paper

Iterative ComBat methods for harmonization of radiomic features

Hannah Horng, Apurva Singh, Bardia Yousefi, Eric Cohen, Babak Haghighi, Sharyn Katz, Peter Noël, Russell Shinohara, Despina Kontos

Proceedings Volume 12033, 120331F (2022) https://doi.org/10.1117/12.2610831

KEYWORDS: Spatial resolution, Lung, Computed tomography, Image segmentation, Data modeling, Visualization, Statistical analysis, Radiology

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

A comparison of feature selection methods for the development of a prognostic radiogenomic biomarker in non-small cell lung cancer patients

Apurva Singh, Florian Hölzl, Sharyn Katz, Despina Kontos

Proceedings Volume 12033, 1203328 (2022) https://doi.org/10.1117/12.2611489

KEYWORDS: Genomics, Feature selection, Tumors, Performance modeling, Cancer, Lung cancer, Data modeling, Image segmentation, Feature extraction, Tumor growth modeling

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This study aims at comparing methods for selecting optimal radiomic and gene expression features to develop a radiogenomic phenotype, that will be used to predict overall survival in non-small cell lung cancer (NSCLC) patients. Baseline CT images of 85 NSCLC patients (male/female: 58/27, event: death, adenocarcinoma/squamous cell carcinoma/unspecified: 41/32/12, in stages I/II/III/unspecified: 39/25/12/9) with gene expression profile (microarray data) of 33 genes were used from the NSCLC-Radiomics Genomics dataset, publicly available from the National Cancer Institute’s Cancer Imaging Archive (TCIA). The 33 genes were selected on the basis that they represent three major co-expression patterns (“signatures”) in the dataset. These included the histology, neuroendocrine (NE) and pulmonary surfactant systems (PSS) signature genes. ITKSNAP was used for 3D tumor volume segmentation from CT scans. Radiomic features (n=102) were extracted from the 3D tumor volume using the CaPTk software. The first approach performs the feature selection in two steps: intra-modal feature selection (select features within the radiomic and genomic modalities such that the features are not highly correlated with each other and do not have a skewed distribution, have a positive Mean Decrease in Accuracy (MDA) value and maximize the AUC in the prediction of overall survival) and inter-modal feature selection (select features that are not highly correlated with features from other modalities). The second approach builds upon the standard and widely used Principal Component Analysis but tries to improve on its poor performance for survival analysis by doing consensus clustering to determine the optimal number of feature clusters within the radiomic and genomic modalities. For each of the clusters, the first principal component is calculated and used as the representative feature for that highly correlative cluster. The third approach provides a supervised take on feature selection by fitting a Cox regression with lasso regularization on the radiomic and genomic features to obtain a correlation between the individual features and the overall survival outcome. The features which have the highest correlation with the outcome are selected. Consensus clustering with a 10% cutoff for minimum change in the cumulative distribution function is used to calculate the optimal multi-modal phenotypes from the optimal multi-modal features determined from these three approaches. The multi-modal phenotypes were combined with clinical factors of histology, stage and sex in five-fold cross-validated multivariate Cox proportional hazards models (200 iterations) of overall survival. In addition to the cross-validated cstatistics, we also built a model on the complete dataset, for each of the approaches, to evaluate the Kaplan Meier performance in separating participants above versus the median prognostic score. The first approach gives a survival prediction performance (0.61, [0.55,0.63]) that is comparable to the third approach (0.61, [0.56,0.65]). The second approach results in a model that has a comparably lower prognostic performance (0.54, [0.48,0.60]). All three approaches result in models that improve on the prognostic performance of the model built using only clinical covariates (0.53, [0.50,0.59]). This preliminary study aims to draw comparisons between the various methods used to select optimal features from multi-modal descriptors of tumor regions.

Proceedings Article | 4 April 2022 Presentation + Paper

Resampling and harmonization for mitigation of heterogeneity in imaging parameters: a comparative study

Apurva Singh, Hannah Horng, Leonid Roshkovan, Michelle Hershman, Russell Shinohara, Sharyn Katz, Despina Kontos

Proceedings Volume 12033, 1203336 (2022) https://doi.org/10.1117/12.2611467

KEYWORDS: Image acquisition, Statistical modeling, Feature extraction, Tumors, Image contrast enhancement, Medical imaging, Image enhancement, CT reconstruction, Resolution enhancement technologies, Image resolution

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We compare techniques for addressing heterogeneity in image physical dimensions and acquisition parameters, and how these methods affect the predictive performance of radiomic features. We further combine radiomic signatures with established clinical prognostic factors to predict progression-free survival (PFS) in stage four NSCLC patients undergoing first-line immunotherapy. Our study includes 124 stage 4 NSCLC patients treated with pembrolizumab (monotherapy:30.65%, combination therapy:69.35%). The Captk software was used to extract radiomic features (n=102) from 3D tumor volumes segmented from lung CT scans with ITK-SNAP. The ability of the following approaches to mitigate the heterogeneity in image physical dimensions (voxel spacing parameters) and acquisition parameters (contrast enhancement and CT reconstruction kernel) were evaluated: resampling the images (to minimum/maximum voxel spacing parameters), harmonization of radiomic features using a nested ComBat technique (taking voxel spacing and/or image acquisition parameters as batch variables) or a combination of resampling the images to the minimum voxel spacing parameters and applying nested harmonization by image acquisition parameters. Two radiomic phenotypes were identified using unsupervised hierarchical clustering of the extracted radiomic features derived from each of these scenarios. Established prognostic factors, including PDL1 expression, ECOG status, BMI and smoking status, were combined with radiomic phenotypes in five-fold cross-validated multivariate Cox proportional hazards models (200 iterations) of progression-free survival. A Cox model based only on clinical factors had a cstatistic (mean, 95% CI) of 0.53[0.50,0.57], which increased to 0.62[0.55,0.64] upon the addition of radiomic phenotypes derived from images which had been resampled to minimum voxel spacing and harmonized by image acquisition parameters. In addition to the cross-validated cstatistics, we also built a model on the complete dataset of features corresponding to each of the approaches to evaluate the Kaplan Meier performance in separating patients above versus below the median prognostic score. This preliminary study aims to draw comparisons between the various techniques used to address the issue of reproducibility in radiomic features derived from medical images with heterogeneous parameters.

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Development of a radiogenomic biomarker for tumor characterization and prognosis in non-small cell lung cancer patients

Apurva Singh, Zhuoyang Wang, Sharyn Katz, Bardia Yousefi, Despina Kontos

Proceedings Volume 11597, 115972W (2021) https://doi.org/10.1117/12.2582142

KEYWORDS: Tumor growth modeling, Tumors, Genomics, Lung cancer, Performance modeling, Cancer, Statistical modeling, Principal component analysis, Feature extraction, Data modeling

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This study aims at developing a radiogenomic model to identify high-risk non-small cell lung cancer (NSCLC) patients and predict overall survival. Baseline CT images of 85 NSCLC patients (male/female: 58/27, event: death, adenocarcinoma/squamous cell carcinoma/unspecified: 41/32/12, in stages I/II/III/unspecified: 39/25/12/9) with gene expression profile (microarray data) of 33 genes were used from the NSCLC-Radiomics Genomics dataset, publicly available from the National Cancer Institute’s Cancer Imaging Archive (TCIA). The 33 genes were selected on the basis that they represent three major co-expression patterns in the dataset- histology, neuroendocrine (NE), and pulmonary surfactant systems (PSS) signature genes. Radiomic features (429) characterizing the primary tumor were extracted from the 3D tumor volume using PyRadiomics. As the first step to our analysis, we used the Mann-Whitney U test to identify a sub-set of 224 features that were robust (p-value < 0.05) to scan differences in slice thickness, reconstruction kernel, and contrast enhancement. Principal Component Analysis was used to extract ten principal components, capturing 85% of the variance, from the radiomic and genomic features, respectively. The following three models were created, to assess the prognostic performance of the radiomic and genomic PCs- Model 1: consists of the first five radiomic PCs; Model 2: consists of the first five genomic PCs; and Model 3: consists of the first three radiomic PCs and the first two genomic PCs. For these models, a five-fold crossvalidated multivariate Cox proportional hazards model (200 iterations) was used to compute the concordance index which measures the ability of the models to predict overall survival. The concordance index values for the Cox model are (mean (min, max)): Model 1: 0.59(0.57,0.62), Model 2: 0.57(0.54,0.61), Model 3: 0.62(0.61,0.64). In addition to the cross-validated c statistics, we also built a model on the complete dataset, for each of the three sets of predictors, to evaluate Kaplan Meier's performance in separating participants above versus below the median prognostic score. We constructed separate radiogenomic signatures for the NSCLC patients based on their histology- LUSC and LUAD and conducted similar survival analysis on them, showing that the combined radiogenomic model was the only one with statistically significant curve separation (p<0.05). This preliminary study suggests that a combination of radiomics and genomics information could give a more comprehensive assessment of the tumor’s characteristics at baseline and generate a better prognostic model in comparison to models formed using radiomics or genomics information alone.

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Radiomic features predict local failure-free survival in stage III NSCLC adenocarcinoma treated with chemoradiation

José Marcio Luna, Andrew Barsky, Russell Shinohara, Alexandra Dreyfuss, Leonid Roshkovan, Michelle Hershman, Babak Haghighi, Bardia Yousefi, Peter Noel, Keith Cengel, Sharyn Katz, Eric Diffenderfer, Despina Kontos

Proceedings Volume 11597, 1159720 (2021) https://doi.org/10.1117/12.2581873

KEYWORDS: Computed tomography, Tumors, Lung cancer, Feature extraction, Principal component analysis, Failure analysis, Biopsy

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Proceedings Article | 15 March 2019 Paper

Correlative hierarchical clustering-based low-rank dimensionality reduction of radiomics-driven phenotype in non-small cell lung cancer

Bardia Yousefi, Nariman Jahani, Michael LaRiviere , Eric Cohen, Meng-Kang Hsieh, José Marcio Luna, Rhea Chitalia, Jeffrey Thompson , Erica Carpenter , Sharyn Katz, Despina Kontos

Proceedings Volume 10954, 1095416 (2019) https://doi.org/10.1117/12.2515609

KEYWORDS: Lung cancer, Tumors, Feature extraction, Principal component analysis, Medicine, Feature selection, Computed tomography, Cancer, Wavelets, Image segmentation

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Background: Lung cancer is one of the most common cancers in the United States and the most fatal, with 142,670 deaths in 2019. Accurately determining tumor response is critical to clinical treatment decisions, ultimately impacting patient survival. To better differentiate between non-small cell lung cancer (NSCLC) responders and non-responders to therapy, radiomic analysis is emerging as a promising approach to identify associated imaging features undetectable by the human eye. However, the plethora of variables extracted from an image may actually undermine the performance of computer-aided prognostic assessment, known as the curse of dimensionality. In the present study, we show that correlative-driven hierarchical clustering improves high-dimensional radiomics-based feature selection and dimensionality reduction, ultimately predicting overall survival in NSCLC patients. Methods: To select features for high-dimensional radiomics data, a correlation-incorporated hierarchical clustering algorithm automatically categorizes features into several groups. The truncation distance in the resulting dendrogram graph is used to control the categorization of the features, initiating low-rank dimensionality reduction in each cluster, and providing descriptive features for Cox proportional hazards (CPH)-based survival analysis. Using a publicly available non- NSCLC radiogenomic dataset of 204 patients’ CT images, 429 established radiomics features were extracted. Low-rank dimensionality reduction via principal component analysis (PCA) was employed (𝒌 = 𝟏, 𝒏 < 𝟏) to find the representative components of each cluster of features and calculate cluster robustness using the relative weighted consistency metric. Results: Hierarchical clustering categorized radiomic features into several groups without primary initialization of cluster numbers using the correlation distance metric (as a function) to truncate the resulting dendrogram into different distances. The dimensionality was reduced from 429 to 67 features (for truncation distance of 0.1). The robustness within the features in clusters was varied from -1.12 to -30.02 for truncation distances of 0.1 to 1.8, respectively, which indicated that the robustness decreases with increasing truncation distance when smaller number of feature classes (i.e., clusters) are selected. The best multivariate CPH survival model had a C-statistic of 0.71 for truncation distance of 0.1, outperforming conventional PCA approaches by 0.04, even when the same number of principal components was considered for feature dimensionality. Conclusions: Correlative hierarchical clustering algorithm truncation distance is directly associated with robustness of the clusters of features selected and can effectively reduce feature dimensionality while improving outcome prediction.

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