Individualized model for predicting COVID‐19 deterioration in patients with cancer: A multicenter retrospective study

Xu, Bin; Song, Ke; Yao, Yi; Dong, Xiao; Li, Lin Jun; Wang, Qun; Yang, Jiyuan; Hu, Wei; Xie, Zhi; Luo, Zhiguo; Luo, Xiu; Liu, Jing; Rao, Zhi Guo; Zhang, Hui Bo; Wu, Jie; Li, Lan; Gong, Hongyun; Chu, Qian; Song, Qi; Wang, Jie

doi:10.1111/cas.14882

Cited by 4 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous studies have reported the risk factors associated with severe COVID-19 cases [ 15 , 16 ]. However, most of these studies were based on patient populations outside China or before 2022 and may not accurately reflect the current pathogenicity of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Nomogram-based prediction model for survival of COVID-19 patients: A clinical study

Xu,

Zhang,

Cai

et al. 2023

Heliyon

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Nomogram-based prediction model for survival of COVID-19 patients: A clinical study

Xu,

Zhang,

Cai

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…Log-rank statistics was used to make a risk group stratification according to the total risk scores based on the nomogram, in order to illustrate the independent discrimination ability of BACH1-based model beyond BACH1 alone. Decision curve analysis (DCA) [ 14 , 15 ] was finally used to evaluate clinical usefulness of BACH1-based model beyond pathologic stage alone.…”

Section: Methodsmentioning

confidence: 99%

BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

Zhou

Liu

Zhang

et al. 2022

Oxidative Medicine and Cellular Longevity

Self Cite

View full text Add to dashboard Cite

Purpose. Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone. Results. BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t -AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves. Conclusion. Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.

show abstract

“…Additionally, COVID-19 infection and lung toxicities due to cancer treatments can present with similar radiologic abnormalities, such as ground glass opacities (GGO) or patchy consolidation [19][20][21] on chest CT, which poses further challenges for AI detection algorithms. Though pilot AI models have been developed to predict COVID-19 severity and deterioration specifically in cancer patients [22,23], they left out the quantitative imaging metrics. Further, it is unclear whether and to what degree these AI models built on the cancer population are different from the ones on the general population, which is what we aim to investigate in this study.…”

Section: Introductionmentioning

confidence: 99%

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

Aminu

Yadav

Hong

et al. 2022

Cancers

View full text Add to dashboard Cite

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.

show abstract

Individualized model for predicting COVID‐19 deterioration in patients with cancer: A multicenter retrospective study

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 4 publications

References 28 publications

Nomogram-based prediction model for survival of COVID-19 patients: A clinical study

Nomogram-based prediction model for survival of COVID-19 patients: A clinical study

BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

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

Contact Info

Product

Resources

About