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Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19.
Aminu, Muhammad; Yadav, Divya; Hong, Lingzhi; Young, Elliana; Edelkamp, Paul; Saad, Maliazurina; Salehjahromi, Morteza; Chen, Pingjun; Sujit, Sheeba J; Chen, Melissa M; Sabloff, Bradley; Gladish, Gregory; de Groot, Patricia M; Godoy, Myrna C B; Cascone, Tina; Vokes, Natalie I; Zhang, Jianjun; Brock, Kristy K; Daver, Naval; Woodman, Scott E; Tawbi, Hussein A; Sheshadri, Ajay; Lee, J Jack; Jaffray, David; Wu, Carol C; Chung, Caroline; Wu, Jia.
  • Aminu M; Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA.
  • Yadav D; Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Hong L; Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA.
  • Young E; Department of Enterprise Data Engineering & Analytics, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Edelkamp P; Department of Enterprise Data Engineering & Analytics, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Saad M; Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA.
  • Salehjahromi M; Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA.
  • Chen P; Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA.
  • Sujit SJ; Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA.
  • Chen MM; Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Sabloff B; Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Gladish G; Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
  • de Groot PM; Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Godoy MCB; Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Cascone T; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Vokes NI; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Zhang J; Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Brock KK; Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Daver N; Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Woodman SE; Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA.
  • Tawbi HA; Department of Leukemia, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Sheshadri A; Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Lee JJ; Department of Melanoma Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Jaffray D; Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • D Code Team; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Wu CC; Office of the Chief Technology and Digital Officer, MD Anderson Cancer Center, Houston, TX 77054, USA.
  • Wu J; Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
Cancers (Basel) ; 15(1)2022 Dec 31.
Article in English | MEDLINE | ID: covidwho-2238666
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

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials Language: English Year: 2022 Document Type: Article Affiliation country: Cancers15010275

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials Language: English Year: 2022 Document Type: Article Affiliation country: Cancers15010275