Dr. Leonid Roshkovan Profile

Dr. Leonid Roshkovan

at Hospital of the Univ of Pennsylvania

SPIE Involvement:

Author

Publications (8)

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

Read Abstract +

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 | 3 April 2024 Poster + Paper

Radiomic phenotypes of the background lung parenchyma from [18]F-FDG PET/CT images can augment tumor radiomics and clinical factors in predicting response after surgical resection of tumors in patients with non-small cell lung cancer

Andrew William Chen, Ohm Patel, Eric Cohen, Leonid Roshkovan, Despina Kontos

Proceedings Volume 12927, 129272R (2024) https://doi.org/10.1117/12.3006944

KEYWORDS: Radiomics, Tumors, Lung, Positron emission tomography, Computed tomography, Lung cancer, Biological imaging

Read Abstract +

Proceedings Article | 1 April 2024 Presentation + Paper

Lifelike PixelPrint phantoms for assessing clinical image quality and dose reduction capabilities of a deep learning CT reconstruction algorithm

Jessica Im, Sandra Halliburton, Kai Mei, Amy Perkins, Eddy Wong, Leonid Roshkovan, Grace Gang, Peter Noël

Proceedings Volume 12925, 129251O (2024) https://doi.org/10.1117/12.3006547

KEYWORDS: Image quality, Reconstruction algorithms, Computed tomography, Lung, Diagnostics, Image sharpness, CT reconstruction, Medical image reconstruction, Deep learning, Tunable filters

Read Abstract +

Proceedings Article | 7 April 2023 Poster + Paper

PixelPrint: a collection of three-dimensional printed CT phantoms of different respiratory diseases

Kai Mei, Leonid Roshkovan, Pouyan Pasyar, Nadav Shapira, Grace Gang, J. Webster Stayman, Michael Geagan, Peter Noël

Proceedings Volume 12463, 124633Q (2023) https://doi.org/10.1117/12.2654343

KEYWORDS: Lung, Computed tomography, Pulmonary disorders, Printing, Windows, X-ray computed tomography, Image resolution, Attenuation, Tissues, Histograms

Read Abstract +

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

Read Abstract +

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 | 4 April 2022 Presentation + Paper

PixelPrint: three-dimensional printing of realistic patient-specific lung phantoms for CT imaging

Nadav Shapira, Kevin Donovan, Kai Mei, Michael Geagan, Leonid Roshkovan, Harold Litt, Grace Gang, Web Stayman, Russell Shinohara, Peter Noël

Proceedings Volume 12031, 120310N (2022) https://doi.org/10.1117/12.2611805

KEYWORDS: Computed tomography, Lung, Printing, Signal attenuation, Image segmentation, 3D printing, 3D modeling, Spatial resolution, 3D image processing, Manufacturing

Read Abstract +

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

Read Abstract +

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

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

Read Abstract +

Showing 5 of 8 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years