Mr. Sean Johnson Profile

Sean Johnson

at Univ of California Los Angeles

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Proceedings Article | 3 April 2024 Presentation + Paper

Using integrated radiomic and pathomic-based models to predict progression-free survival in early-stage lung adenocarcinoma

Tengyue Zhang, Anil Yadav, Ruiwen Ding, Sean Johnson, Denise Aberle, Ashley Prosper, Erika Rodriguez, Ana Cristina Araujo Lemos da Silva, William Hsu

Proceedings Volume 12933, 129330V (2024) https://doi.org/10.1117/12.3006499

KEYWORDS: Radiomics, Lung, Computed tomography, Statistical modeling, Lung cancer, Data fusion

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Early-stage non-small cell lung cancer (NSCLC) patients have a relatively high recurrence rate within the first five years of surgery, reflecting a need to predict post-surgical recurrence and offer personalized adjuvant therapies. Quantitative features extracted from radiology and pathology images can provide valuable information for the NSCLC recurrence prediction task, with radiomic features capturing global tumor phenotypes and pathomic features capturing local cellular and tumor microenvironment information. In this study, we propose to combine radiomic and pathomic features to predict progression-free survival within five years of curative resection in early-stage lung adenocarcinoma (LUAD), the most common subtype of NSCLC. Using 106 cases from the National Lung Screening Trial dataset, we extracted radiomic features from lung nodules on pre-surgery computed tomography (CT) scans guided by radiologist’s segmentation and pathomic features from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of the resected tissue. We leveraged both hand-crafted and deep features in each modality and used a Cox proportional hazards model. Models were trained with 5-fold cross-validation with ten repetitions, and metrics such as the concordance index (C-index) were calculated by the mean performance on the test set. The fused model using combined radiomic and pathomic features has a C-index of 0.634. Our study shows that combining radiomic and pathomic features results in a more accurate progression-free survival prediction model as compared to only using radiomic features (C-index=0.612), pathomic features (C-index=0.584), or clinical features (C-index= 0.477).

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