CT-based radiomic nomogram for predicting the severity of patients with COVID-19

Shi, Hai; Xu, Zhihua; Cheng, Guohua; Ji, Hongli; He, Li; Zhu, Jing; Hu, Hanjin; Xie, Zongyu; Ao, Weiqun; Wang, Jian

doi:10.1186/s40001-022-00634-x

Cited by 14 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most radiomics studies conducted to date have involved the use of multilayer manual delineation for feature extraction [ 22 ]. Manual delineation is labor-intensive, time-consuming, and subject to subjective influence.…”

Section: Discussionmentioning

confidence: 99%

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

et al. 2023

Self Cite

View full text Add to dashboard Cite

Background To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa). Materials The data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient’s prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated. Results Seven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly. Conclusion The multiple easy-to-use deep learning–based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among these works, many researches [22][23][24][25] focused on the lesions, not used the patients as subjects. Their socalled thousands of cases were actually thousands of lesions; many researches [26][27][28][29][30][31] built models to predict the COVID-19 patients' current severity status, not the potential severe risk. There were also some works [32][33][34] similar to our work in research ideas and methods.…”

Section: Discussionmentioning

confidence: 99%

Prediction of potential severe coronavirus disease 2019 patients based on CT radiomics: A retrospective study

Xiao

Sun

et al. 2022

Medical Physics

View full text Add to dashboard Cite

Purpose Coronavirus disease 2019 (COVID‐19) is a recently declared worldwide pandemic. Triaging of patients into severe and non‐severe could further help in targeted management. “Potential severe patients” is a category of patients who did not have severe symptoms at their initial diagnosis, but eventually progressed to be severe patients and are easily overlooked in the early stage. This work aimed to develop and evaluate a CT‐based radiomics signature for the prediction of these potential severe COVID‐19 patients. Methods One hundred fifty COVID‐19 patients were enrolled and randomly divided into cross‐validation and independent test sets. First, their clinical characteristics were screened using the univariate and multivariate logistic regression step by step. Then, radiomics features were extracted from the lesions on their chest CT images. Subsequently, the inter‐ and intra‐class correlation coefficients (ICC) analysis, minimum‐redundancy maximum‐relevance (mRMR) selection, and the least absolute shrinkage and selection operator (LASSO) algorithm were used step by step for feature selection and construction of a radiomics signature. Finally, the screened clinical risk factors and constructed radiomics signature were combined for the combined model and Radiomics+Clinics nomogram construction. The predictive performance of the Radiomics and Combined models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Hosmer–Lemeshow test and Delong test. Results Clinical characteristics analysis resulted in the screening of five clinical risk factors. The combination of ICC, mRMR, and LASSO methods resulted in the selection of ten radiomics features, which made up of the radiomics signature. The differences in the radiomics signature between the potential severe and non‐severe groups in cross‐validation set and test sets were both p < 0.001. All Radiomics and Combined models showed a very good predictive performance with the accuracy and AUC of nearly or above 0.9. Additionally, we found no significant difference in the predictive performance between these two models. Conclusions A CT‐based radiomics signature for the prediction of potential severe COVID‐19 patients was constructed and evaluated. Constructed Radiomics and Combined model showed good feasibility and accuracy. The Radiomics+Clinical nomogram, acted as a useful tool, may assist clinicians to better identify potential severe cases to target their management in the COVID‐19 pandemic prevention and control.

show abstract

“…Within the automatically segmented lung region and regions of interest/lesion regions, it calculates several metrics to quantify lung lesions: volumes and densities of the entire lung, individual left and right lungs, and separate lung lobes; lesion volumes, counts, densities, solid-to-total ratio, ground glass opacity ratio, as well as the ratios of bilateral lung ground glass opacity and consolidation volumes to the total lung volume. The implementation process and accuracy of this system have been validated in previously published studies [17].…”

Section: Ct Image Acquisition and Volume Analysismentioning

confidence: 99%

End-expiratory lung volumes as a potential indicator for COVID-19 associated acute respiratory distress syndrome: a retrospective study

Hao,

Wei,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

Background End-expiratory lung volume (EELV) has been observed to decrease in acute respiratory distress syndrome (ARDS). Yet, research investigating EELV in patients with COVID-19 associated ARDS (CARDS) remains limited. It is unclear EELV serve as a potential metric for monitoring disease progression and identifying patients with ARDS at increased risk of adverse outcomes. Study Design and Methods: This retrospective study included mechanically ventilated patients with CARDS during the initial phase of epidemic control in Shanghai. EELV was measured within 48 hours post-intubation, followed by regular assessments every 3–4 days. Chest CT scans, performed within a 24-hour window around each EELV measurement, were analyzed using AI software. Differences in patient demographics, clinical data, respiratory mechanics, EELV, and chest CT findings were assessed using linear mixed models (LMM). Results Out of the 38 enrolled patients, 26.3% survived until discharge from the ICU. In the survivor group, EELV, EELV/PBW and EELV/preFRC were significantly higher than those in the non-survivor group (survivor group vs non-survivor group: EELV: 1455 vs 1162 ml, P = 0.049; EELV/PBW: 24.1 vs 18.5 ml/kg, P = 0.011; EELV/preFRC: 0.45 vs 0.34, P = 0.005). Follow-up assessments showed a sustained elevation of EELV/PBW and EELV/preFRC among the survivors. Additionally, EELV exhibited a positive correlation with total lung volume and residual lung volume, while demonstrating a negative correlation with lesion volume determined through chest CT scans analyzed using AI software. Conclusion EELV is a useful indicator for assessing disease severity and monitoring the prognosis of patients with CARDS.

show abstract

CT-based radiomic nomogram for predicting the severity of patients with COVID-19

Cited by 14 publications

References 30 publications

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

Prediction of potential severe coronavirus disease 2019 patients based on CT radiomics: A retrospective study

End-expiratory lung volumes as a potential indicator for COVID-19 associated acute respiratory distress syndrome: a retrospective study

Contact Info

Product

Resources

About