Prof. Pratik Chaudhari Profile

Prof. Pratik Chaudhari

at Univ of Pennsylvania

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Publications (2)

Proceedings Article | 3 April 2024 Presentation + Paper

Investigating causal genetic effects on overall survival of glioblastoma patients using normalizing flow and structural causal model

Fanyang Yu, Rongguang Wang, Pratik Chaudhari, Christos Davatzikos

Proceedings Volume 12927, 129271F (2024) https://doi.org/10.1117/12.3005434

KEYWORDS: Genetics, Radiomics, Data modeling, Tumors, Biological samples, Statistical modeling

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Glioblastoma (GBM) is the most common and aggressive brain tumor with short overall survival (OS) of about 15 months. Understanding the causal factors affecting the patient survival is crucial for disease prognosis and treatment planning. Although previous efforts on survival prediction using multi-omics data has yielded useful predictive models, the causation of the correlated genetic risk factors has not been addressed. Recent advances in causal deep learning models enable the study of causality from complex dataset. In this paper, we leverage the recently proposed structural causal model (SCM) with normalizing flows parameterized by deep networks to perform the counterfactual query to investigate the causal relationship between gene mutation and OS with the presence of other confounders including sex, age and radiomics features. The query amounts to the question that what the survival days will be if the gene mutation status has been changed, i.e., from mutant to non-mutant and vice versa. The trained causal model will infer the counterfactual outcome given the intervention on specific gene mutation. We apply multivariate Cox-PH model to find the genes associated with survival, and investigate the causal genetic effect by comparing the original and counterfactual survival days in a bi-directional fashion. Particularly, the following two scenarios are considered: (1) intervention on a specific gene with non-mutant status to generate the counterfactual survival days as if the gene is mutant, with which the original survival days of the subjects with that mutant gene will be compared; (2) intervention on the gene with mutant status and perform the comparison with survival days of subjects with that non-mutant gene. Our experimental results show that no causation of two correlated genes (NF1, RB1) was revealed in the cohort (n=181), while their genetic effects on OS in terms of prolonging or shortening are generally in accordance with clinical findings.

Proceedings Article | 2 April 2024 Presentation + Paper

Automated estimation of microcirculation capillary density using relative perfusion maps

Rohit Jena, Yifan Wu, John Greenwood, Pratik Chaudhari, James Gee

Proceedings Volume 12926, 129260U (2024) https://doi.org/10.1117/12.3000736

KEYWORDS: Capillaries, Video, Image segmentation, Education and training, Red blood cells, Plasma, Binary data, Video microscopy, Surgery, Neural networks

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