Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19

Authors

Muhammad Aminu
Divya Yadav
Lingzhi Hong
Elliana Young
Paul Edelkamp
Maliazurina Saad
Morteza Salehjahromi
Pingjun Chen
Sheeba J Sujit
Melissa M Chen
Bradley Sabloff
Gregory Gladish
Patricia M de Groot
Myrna C B Godoy
Tina Cascone
Natalie I Vokes
Jianjun Zhang
Kristy K Brock
Naval Daver
Scott E Woodman
Hussein A Tawbi
Ajay Sheshadri
J Jack Lee
David Jaffray
D Code Team
Carol C Wu
Caroline Chung
Jia Wu

Publication Date

12-31-2022

Journal

Cancers

Abstract

OBJECTIVES: Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population.

METHODS: We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction.

RESULTS: These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts.

CONCLUSIONS: Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.

Keywords

COVID-19, habitat imaging, machine learning, diagnosis, prognosis

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Supplementary Materials

PMID: 36612278