Dr. Jeffrey C. Thompson Profile

Dr. Jeffrey C. Thompson

at Univ of Pennsylvania

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Publications (3)

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 | 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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