ArticleCancers2022
Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- The deubiquitinase USP5 mediates anti-PD-L1 resistance in breast cancer by stabilizing FOXM1 to upregulate Nectin2.Human cell · 2026Article
- Illness perceptions and symptom burden in chemotherapy-induced peripheral neuropathy: a moderated mediation analysis from a self-regulatory perspective.Journal of cancer survivorship : research and practice · 2026Article
- Heterogeneity Analyzed by CT-Based Habitat Analysis for Clinical Management of Cancers: A Narrative Review.The Kaohsiung journal of medical sciences · 2026Review
- HLA Class II Alleles DRB1*11:01 and DQB1*03:01 Unmask Immunogenetic Susceptibility to Anti-Nivolumab Antibodies in Combination with Ipilimumab.The AAPS journal · 2026Review
- Determinants of long-term quality of life in nasopharyngeal carcinoma survivors: the roles of household income and performance status.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Reshaping the robotic learning curve in anatomic lung resection: the role of prior video-assisted thoracoscopic surgery experience in a multi-surgeon single-center series.Journal of robotic surgery · 2025Article
- Application of multimodal fusion technology in early recurrence prediction and pathological analysis of hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- MRI-based habitat imaging in cancer treatment: current technology, applications, and challenges.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers.Patterns (New York, N.Y.) · 2023Article
- Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia: proof of concept.Frontiers in immunology · 2023Article
Corrections and comments
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28 authors.
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Abstract
objectivesCancer 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.
methodsWe 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.
resultsThese 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.
conclusionsTaken 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.
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