Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study

Peng An; Xiumei Li; Ping Qin; YingJian Ye; Junyan Zhang; Hongyan Guo; Peng Duan; Zhibing He; Ping Song; Mingqun Li; Jinsong Wang; Yan Hu; Guoyan Feng; Yong Lin; Peng An; Xiumei Li; Ping Qin; YingJian Ye; Junyan Zhang; Hongyan Guo; Peng Duan; Zhibing He; Ping Song; Mingqun Li; Jinsong Wang; Yan Hu; Guoyan Feng; Yong Lin

doi:10.3934/mbe.2023284

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 4: 6612-6629. doi: 10.3934/mbe.2023284

Previous Article Next Article

Research article Special Issues

Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study

1.
Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
2.
Department of Internal Medicine, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
3.
Department of Infectious Disease, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
4.
Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
5.
Department of Obstetrics and Gynecology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
† These authors contributed equally

Received: 07 December 2022 Revised: 15 January 2023 Accepted: 16 January 2023 Published: 02 February 2023

Objective
To predict COVID-19 severity by building a prediction model based on the clinical manifestations and radiomic features of the thymus in COVID-19 patients.

Method
We retrospectively analyzed the clinical and radiological data from 217 confirmed cases of COVID-19 admitted to Xiangyang NO.1 People's Hospital and Jiangsu Hospital of Chinese Medicine from December 2019 to April 2022 (including 118 mild cases and 99 severe cases). The data were split into the training and test sets at a 7:3 ratio. The cases in the training set were compared in terms of clinical data and radiomic parameters of the lasso regression model. Several models for severity prediction were established based on the clinical and radiomic features of the COVID-19 patients. The DeLong test and decision curve analysis (DCA) were used to compare the performances of several models. Finally, the prediction results were verified on the test set.

Result
For the training set, the univariate analysis showed that BMI, diarrhea, thymic steatosis, anorexia, headache, findings on the chest CT scan, platelets, LDH, AST and radiomic features of the thymus were significantly different between the two groups of patients (P < 0.05). The combination model based on the clinical and radiomic features of COVID-19 patients had the highest predictive value for COVID-19 severity [AUC: 0.967 (OR 0.0115, 95%CI: 0.925-0.989)] vs. the clinical feature-based model [AUC: 0.772 (OR 0.0387, 95%CI: 0.697-0.836), P < 0.05], laboratory-based model [AUC: 0.687 (OR 0.0423, 95%CI: 0.608-0.760), P < 0.05] and model based on CT radiomics [AUC: 0.895 (OR 0.0261, 95%CI: 0.835-0.938), P < 0.05]. DCA also confirmed the high clinical net benefits of the combination model. The nomogram drawn based on the combination model could help differentiate between the mild and severe cases of COVID-19 at an early stage. The predictions from different models were verified on the test set.

Conclusion
Severe cases of COVID-19 had a higher level of thymic involution. The thymic differentiation in radiomic features was related to disease progression. The combination model based on the radiomic features of the thymus could better promote early clinical intervention of COVID-19 and increase the cure rate.
- COVID-19,
- anorexia,
- diarrhea,
- thymus,
- computed tomography,
- radiomics,
- texture analysis,
- thymic steatosis,
- mild type,
- severe type,
- prediction model
Citation: Peng An, Xiumei Li, Ping Qin, YingJian Ye, Junyan Zhang, Hongyan Guo, Peng Duan, Zhibing He, Ping Song, Mingqun Li, Jinsong Wang, Yan Hu, Guoyan Feng, Yong Lin. Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 6612-6629. doi: 10.3934/mbe.2023284

Related Papers:

Abstract

Objective

To predict COVID-19 severity by building a prediction model based on the clinical manifestations and radiomic features of the thymus in COVID-19 patients.

Method

We retrospectively analyzed the clinical and radiological data from 217 confirmed cases of COVID-19 admitted to Xiangyang NO.1 People's Hospital and Jiangsu Hospital of Chinese Medicine from December 2019 to April 2022 (including 118 mild cases and 99 severe cases). The data were split into the training and test sets at a 7:3 ratio. The cases in the training set were compared in terms of clinical data and radiomic parameters of the lasso regression model. Several models for severity prediction were established based on the clinical and radiomic features of the COVID-19 patients. The DeLong test and decision curve analysis (DCA) were used to compare the performances of several models. Finally, the prediction results were verified on the test set.

Result

For the training set, the univariate analysis showed that BMI, diarrhea, thymic steatosis, anorexia, headache, findings on the chest CT scan, platelets, LDH, AST and radiomic features of the thymus were significantly different between the two groups of patients (P < 0.05). The combination model based on the clinical and radiomic features of COVID-19 patients had the highest predictive value for COVID-19 severity [AUC: 0.967 (OR 0.0115, 95%CI: 0.925-0.989)] vs. the clinical feature-based model [AUC: 0.772 (OR 0.0387, 95%CI: 0.697-0.836), P < 0.05], laboratory-based model [AUC: 0.687 (OR 0.0423, 95%CI: 0.608-0.760), P < 0.05] and model based on CT radiomics [AUC: 0.895 (OR 0.0261, 95%CI: 0.835-0.938), P < 0.05]. DCA also confirmed the high clinical net benefits of the combination model. The nomogram drawn based on the combination model could help differentiate between the mild and severe cases of COVID-19 at an early stage. The predictions from different models were verified on the test set.

Conclusion

Severe cases of COVID-19 had a higher level of thymic involution. The thymic differentiation in radiomic features was related to disease progression. The combination model based on the radiomic features of the thymus could better promote early clinical intervention of COVID-19 and increase the cure rate.

References

[1]	M. Herrero-Montes, C. Fernández-de-Las-Peñas, D. Ferrer-Pargada, S. Tello-Mena, I. Cancela-Cilleruelo, J. Rodríguez-Jiménez, et al., Prevalence of neuropathic component in post-COVID pain symptoms in previously hospitalized COVID-19 survivors, Int. J. Clin. Pract., 2022 (2022), 3532917. https://doi:10.1155/2022/3532917 doi: 10.1155/2022/3532917
[2]	S. Abuhammad, O. F. Khabour, K. H. Alzoubi, F. El-Zubi, S. H. Hamaieh, Respiratory infectious diseases and adherence to nonpharmacological interventions for overcoming COVID-19 pandemic: A self-reported study, Int. J. Clin. Pract., 2022 (2022), 4495806. https://doi:10.1155/2022/4495806 doi: 10.1155/2022/4495806
[3]	N. Demir, B. Yüzbasıoglu, T. Calhan, S. Ozturk, Prevalence and prognostic importance of high fibrosis-4 index in COVID-19 patients, Int. J. Clin. Pract., 2022 (2022), 1734896. https://doi:10.1155/2022/1734896 doi: 10.1155/2022/1734896
[4]	S. Tharwat, H. A. Abdelsalam, A. Abdelsalam, M. K. Nassar, COVID-19 vaccination intention and vaccine hesitancy among patients with autoimmune and autoinflammatory rheumatological diseases: A survey, Int. J. Clin. Pract., 2022 (2022), 5931506. https://doi:10.1155/2022/5931506 doi: 10.1155/2022/5931506
[5]	Y. Liu, Y. Pan, Z. Hu, M. Wu, C. Wang, Z. Feng, et al., Thymosin Alpha 1 reduces the mortality of severe coronavirus disease 2019 by restoration of lymphocytopenia and reversion of exhausted T cells, Clin. Infect. Dis., 71 (2020), 2150–2157. https://doi:10.1093/cid/ciaa630 doi: 10.1093/cid/ciaa630
[6]	V. J. Sharmila, D. Jemi Florinabel, Deep learning algorithm for COVID-19 classification using chest X-ray images, Comput. Math. Methods Med., 2021 (2021), 9269173. https://doi:10.1155/2021/9269173 doi: 10.1155/2021/9269173
[7]	W. C. Serena Low, J. H. Chuah, C. A. T. H. Tee, S. Anis, M. A. Shoaib, A. Faisal, et al., An overview of deep learning techniques on chest X-ray and CT scan identification of COVID-19, Comput. Math. Methods Med., 2021 (2021), 5528144. https://doi:10.1155/2021/5528144 doi: 10.1155/2021/5528144
[8]	M. Nakhaeizadeh, M. Chegeni, M. Adhami, H. Sharifi, M. A. Gohari, A. Iranpour, et al., Estimating the number of COVID-19 cases and impact of new COVID-19 variants and vaccination on the population in Kerman, Iran: A mathematical modeling study, Comput. Math. Methods Med., 2022 (2022), 6624471. https://doi:10.1155/2022/6624471 doi: 10.1155/2022/6624471
[9]	J. B. Ackman, B. Kovacina, B. W. Carter, C. C. Wu, A. Sharma, J. O. Shepard, et al., Sex difference in normal thymic appearance in adults 20–30 years of age, Radiology, 268 (2013), 245–253. https://doi:10.1148/radiol.13121104 doi: 10.1148/radiol.13121104
[10]	M. Takesh, S. Adams, Imaging comparison between (18) F-FDG-PET/CT and (18) F-Flouroethyl choline PET/CT in rare case of Thymus Carcinoma exhibiting a positive choline uptake, Case Rep. Oncol. Med., 2013 (2013), 464396. https://doi:10.1155/2013/464396 doi: 10.1155/2013/464396
[11]	N. Simanovsky, N. Hiller, N. Loubashevsky, K. Rozovsky, Normal CT characteristics of the thymus in adults, Eur. J. Radiol., 81 (2012), 3581–3586. https://doi:10.1016/j.ejrad.2011.12.015 doi: 10.1016/j.ejrad.2011.12.015
[12]	T. Araki, M. Nishino, W. Gao, J. Dupuis, G. M. Hunninghake, T. Murakami, et al., Normal thymus in adults: appearance on CT and associations with age, sex, BMI and smoking, Eur. Radiol., 26 (2016), 15–24. https://doi:10.1007/s00330-015-3796-y doi: 10.1007/s00330-015-3796-y
[13]	H. Zhou, R. Xu, H. Mei, L. Zhang, Q. Yu, R. Liu, et al., Application of enhanced T1WI of MRI Radiomics in Glioma grading, Int. J. Clin. Pract., 2022 (2022), 3252574. https://doi:10.1155/2022/3252574 doi: 10.1155/2022/3252574
[14]	J. Wang, J. Zeng, H. Li, X. Yu, A deep learning radiomics analysis for survival prediction in Esophageal cancer, J. Healthcare Eng., 2022 (2022), 4034404. https://doi:10.1155/2022/4034404 doi: 10.1155/2022/4034404
[15]	Y. Wang, G. Feng, J. Wang, P. An, P. Duan, Y. Hu, et al., Contrast-enhanced ultrasound-magnetic resonance imaging radiomics based model for predicting the biochemical recurrence of prostate cancer: A feasibility study, Comput. Math. Methods Med., 2022 (2022), 8090529. https://doi:10.1155/2022/8090529 doi: 10.1155/2022/8090529
[16]	I. Malinauskaite, J. Hofmeister, S. Burgermeister, A. Neroladaki, M. Hamard, X. Montet, et al., Radiomics and machine learning differentiate soft-tissue lipoma and liposarcoma better than musculoskeletal radiologists, Sarcoma, 2020 (2022), 7163453. https://doi:10.1155/2020/7163453 doi: 10.1155/2020/7163453
[17]	P. An, J. Zhang, Y. Li, P. Duan, Y. Hu, X. Li, et al., Clinical and imaging data-based model for predicting Reversible Posterior Leukoencephalopathy Syndrome (RPLS) in pregnant women with severe preeclampsia or eclampsia and analysis of perinatal outcomes, Int. J. Clin. Pract., 2022 (2022), 6990974. https://doi:10.1155/2022/6990974 doi: 10.1155/2022/6990974
[18]	P. An, J. Zhang, F. Yang, Z. Wang, Y. Hu, X. Li, USMRI features and clinical data-based model for predicting the degree of placenta accreta spectrum disorders and developing prediction models, Int. J. Clin. Pract., 2022 (2022), 9527412. https://doi:10.1155/2022/9527412 doi: 10.1155/2022/9527412
[19]	P. An, W. Gu, S. Luo, M. Zhang, Y. Wang, Q. X. Li, Radiological changes on chest CT following COVID-19 infection, Ann. Acad. Med. Singapore, 50 (2021), 346–348. https://doi:10.47102/annals-acadmedsg.2020208 doi: 10.47102/annals-acadmedsg.2020208
[20]	P. An, P. Song, Y. Wang, B. Liu, Asymptomatic patients with novel coronavirus disease (COVID-19), Balkan Med. J., 37 (2020), 229–230. https://doi:10.4274/balkanmedj.galenos.2020.2020.4.20 doi: 10.4274/balkanmedj.galenos.2020.2020.4.20
[21]	P. An, P. Song, K. Lian, Y. Wang, CT manifestations of novel coronavirus pneumonia: A case report, Balkan Med. J., 37 (2020), 163–165. https://doi:10.4274/balkanmedj.galenos.2020.2020.2.15 doi: 10.4274/balkanmedj.galenos.2020.2020.2.15
[22]	P. An, B. J. Wood, W. Li, M. Zhang, Y. Ye, Postpartum exacerbation of antenatal COVID-19 pneumonia in 3 women, CMAJ, 192 (2020), E603–E606. https://doi:10.1503/cmaj.200553 doi: 10.1503/cmaj.200553
[23]	P. An, Y. Ye, M. Chen, Y. Chen, W. Fan, Y. Wang, Management strategy of novel coronavirus (COVID-19) pneumonia in the radiology department: a Chinese experience, Diagn. Interv. Radiol., 26 (2020), 200–203. https://doi:10.5152/dir.2020.20167 doi: 10.5152/dir.2020.20167
[24]	C. Kellogg, O. Equils, The role of the thymus in COVID-19 disease severity: implications for antibody treatment and immunization, Hum. Vaccines Immunother., 17 (2021), 638–643. https://doi:10.1080/21645515.2020.1818519 doi: 10.1080/21645515.2020.1818519
[25]	P. Cuvelier, H. Roux, A. Couëdel-Courteille, J. Dutrieux, C. Naudin, B. C. de Muylder, et al., Protective reactive thymus hyperplasia in COVID-19 acute respiratory distress syndrome, Crit. Care., 25 (2021), 4. https://doi:10.1186/s13054-020-03440-1 doi: 10.1186/s13054-020-03440-1
[26]	R. Thomas, W. Wang, D. M. Su, Contributions of age-related thymic involution to immunosenescence and inflammaging, Immun. Ageing, 17 (2020), 2. https://doi:10.1186/s12979-020-0173-8 doi: 10.1186/s12979-020-0173-8
[27]	W. Wang, R. Thomas, J. Oh, D. M. Su, Thymic aging may be associated with COVID-19 pathophysiology in the elderly, Cells, 10 (2021), 628. https://doi:10.3390/cells10030628 doi: 10.3390/cells10030628
[28]	S. Rehman, T. Majeed, M. A. Ansari, U. Ali, H. Sabit, E. A. Al-Suhaimi, Current scenario of COVID-19 in pediatric age group and physiology of immune and thymus response, Saudi J. Biol. Sci., 27 (2020), 2567–2573. https://doi:10.1016/j.sjbs.2020.05.024 doi: 10.1016/j.sjbs.2020.05.024
[29]	K. A. Harrington, D. S. Kennedy, B. Tang, C. Hickie, E. Phelan, W. Torreggiani, et al., Computed tomographic evaluation of the thymus-does obesity affect thymic fatty involution in a healthy young adult population, Br. J. Radiol., 91 (2018), 20170609. https://doi:10.1259/bjr.20170609 doi: 10.1259/bjr.20170609
[30]	F. Nasseri, F. Eftekhari, Clinical and radiologic review of the normal and abnormal thymus: pearls and pitfalls, Radiographics, 30 (2010), 413–428. https://doi:10.1148/rg.302095131 doi: 10.1148/rg.302095131
[31]	J. L. Zhang, Y. H. Li, L. L. Wang, H. Q. Liu, S. Y. Lu, Y. Liu, et al., Azvudine is a thymus-homing anti-SARS-CoV-2 drug effective in treating COVID-19 patients, Signal Transduction Targeted Ther., 6 (2021), 414. https://doi:10.1038/s41392-021-00835-6 doi: 10.1038/s41392-021-00835-6
[32]	M. E. Mayerhoefer, A. Materka, G. Langs, I. Häggström, P. Szczypiński, P. Gibbs, et al., Introduction to radiomics, J. Nucl. Med., 61 (2020), 488–495. https://doi:10.2967/jnumed.118.222893 doi: 10.2967/jnumed.118.222893
[33]	M. R. Chetan, F. V. Gleeson, Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives, Eur. Radiol., 31 (2021), 1049–1058. https://doi:10.1007/s00330-020-07141-9 doi: 10.1007/s00330-020-07141-9
[34]	M. Seyit, E. Avci, A. Yilmaz, H. Senol, M. Ozen, A. Oskay, Predictive values of coagulation parameters to monitor COVID-19 patients, Int. J. Clin. Pract., 2022 (2022), 8436248. https://doi:10.1155/2022/8436248 doi: 10.1155/2022/8436248
[35]	I. Tsougos, A. Vamvakas, C. Kappas, I. Fezoulidis, K. Vassiou, Application of radiomics and decision support systems for breast mr differential diagnosis, Comput. Math. Methods Med., 2018 (2018), 7417126. https://doi:10.1155/2018/7417126 doi: 10.1155/2018/7417126
[36]	M. Umesh Pai, A. A. Ardakani, A. Kamath, U. Raghavendra, A. Gudigar, N. Venkatesh, et al., Novel radiomics features for automated detection of cardiac abnormality in patients with pacemaker, Comput. Math. Methods Med., 2022 (2022), 1279749. https://doi:10.1155/2022/1279749 doi: 10.1155/2022/1279749
[37]	S. A. Harmon, T. H. Sanford, S. Xu, E. B. Turkbey, H. Roth, Z. Xu, et al., Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets, Nat. Commun., 11 (2020), 4080. https://doi:10.1038/s41467-020-17971-2 doi: 10.1038/s41467-020-17971-2
[38]	S. Cournane, R. Conway, D. Byrne, D. O'Riordan, B. Silke, Predicting outcomes in emergency medical admissions using a laboratory only nomogram, Comput. Math. Methods Med., 2017 (2017), 5267864. https://doi:10.1155/2017/5267864 doi: 10.1155/2017/5267864
[39]	S. Tian, Y. Guo, J. Fu, Z. Li, J. Li, X. Tian, Prognostic value of immunotyping combined with targeted therapy in patients with non-small-cell lung cancer and establishment of nomogram model, Comput. Math. Methods Med., 2022 (2022), 3049619. https://doi:10.1155/2022/3049619 doi: 10.1155/2022/3049619
[40]	R. Qin, H. Zhang, L. Jiang, K. Qiao, J. Hai, J. Chen, et al., Multicenter computer-aided diagnosis for lymph nodes using unsupervised domain-adaptation networks based on cross-domain confounding representations, Comput. Math. Methods Med., 2020 (2020), 3709873. https://doi:10.1155/2020/3709873 doi: 10.1155/2020/3709873
[41]	M. Brambilla, R. Matheoud, C. Basile, C. Bracco, I. Castiglioni, C. Cavedon, et al., An adaptive thresholding method for BTV estimation incorporating PET reconstruction parameters: A multicenter study of the robustness and the reliability, Comput. Math. Methods Med., 2015 (2015), 571473. https://doi:10.1155/2015/571473 doi: 10.1155/2015/571473

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)