Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy
ObjectivesThis study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy.MethodsWe conducted a retrospective analysis involving 202 patients with surgically confirmed single...
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| Format: | Article |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1277698/full |
| _version_ | 1797299765427306496 |
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| author | Jia Li Yunhui Ma Chunyu Yang Ganbin Qiu Jingmu Chen Xiaoliang Tan Yue Zhao |
| author_facet | Jia Li Yunhui Ma Chunyu Yang Ganbin Qiu Jingmu Chen Xiaoliang Tan Yue Zhao |
| author_sort | Jia Li |
| collection | DOAJ |
| description | ObjectivesThis study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy.MethodsWe conducted a retrospective analysis involving 202 patients with surgically confirmed single HCC having undergone preoperative magnetic resonance imaging between 2018 and 2021 at two different institutions. 126 patients from Institution 1 were assigned to the training set, and 76 patients from Institution 2 were assigned to the validation set. A least absolute shrinkage and selection operator (LASSO) regularization was conducted to operate a logistic regression, then features were identified to construct a radiomic score (Rad-score). Uni- and multi-variable tests were used to assess the correlations of clinicopathological features and Rad-score with ER. We then established a combined model encompassing the optimal Rad-score and clinical-pathological risk factors. Additionally, we formulated and validated a predictive nomogram for predicting ER in HCC. The nomogram’s discrimination, calibration, and clinical utility were thoroughly evaluated.ResultsMultivariable logistic regression revealed the Rad-score, microvascular invasion (MVI), and α fetoprotein (AFP) level > 400 ng/mL as significant independent predictors of ER in HCC. We constructed a nomogram based on these significant factors. The areas under the receiver operator characteristic curve of the nomogram and precision-recall curve were 0.901 and 0.753, respectively, with an F1 score of 0.831 in the training set. These values in the validation set were 0.827, 0.659, and 0.808.ConclusionThe nomogram that integrates the radiomic score, MVI, and AFP demonstrates high predictive efficacy for estimating the risk of ER in HCC. It facilitates personalized risk classification and therapeutic decision-making for HCC patients. |
| first_indexed | 2024-03-07T22:55:25Z |
| format | Article |
| id | doaj.art-86a0da31594c477e9c051e6e9e3740d4 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-07T22:55:25Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-86a0da31594c477e9c051e6e9e3740d42024-02-23T04:55:37ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2024-02-011410.3389/fonc.2024.12776981277698Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomyJia Li0Yunhui Ma1Chunyu Yang2Ganbin Qiu3Jingmu Chen4Xiaoliang Tan5Yue Zhao6Department of Oncology, Central People’s Hospital of Zhanjiang, Zhanjiang, ChinaDepartment of Oncology, Central People’s Hospital of Zhanjiang, Zhanjiang, ChinaDepartment of Radiology, The First School of Clinical Medicine, Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, ChinaImaging Department of Zhaoqing Medical College, Zhaoqing, ChinaDepartment of Radiology, Central People’s Hospital of Zhanjiang, Zhanjiang, ChinaDepartment of Radiology, Central People’s Hospital of Zhanjiang, Zhanjiang, ChinaDepartment of Radiology, Central People’s Hospital of Zhanjiang, Zhanjiang, ChinaObjectivesThis study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy.MethodsWe conducted a retrospective analysis involving 202 patients with surgically confirmed single HCC having undergone preoperative magnetic resonance imaging between 2018 and 2021 at two different institutions. 126 patients from Institution 1 were assigned to the training set, and 76 patients from Institution 2 were assigned to the validation set. A least absolute shrinkage and selection operator (LASSO) regularization was conducted to operate a logistic regression, then features were identified to construct a radiomic score (Rad-score). Uni- and multi-variable tests were used to assess the correlations of clinicopathological features and Rad-score with ER. We then established a combined model encompassing the optimal Rad-score and clinical-pathological risk factors. Additionally, we formulated and validated a predictive nomogram for predicting ER in HCC. The nomogram’s discrimination, calibration, and clinical utility were thoroughly evaluated.ResultsMultivariable logistic regression revealed the Rad-score, microvascular invasion (MVI), and α fetoprotein (AFP) level > 400 ng/mL as significant independent predictors of ER in HCC. We constructed a nomogram based on these significant factors. The areas under the receiver operator characteristic curve of the nomogram and precision-recall curve were 0.901 and 0.753, respectively, with an F1 score of 0.831 in the training set. These values in the validation set were 0.827, 0.659, and 0.808.ConclusionThe nomogram that integrates the radiomic score, MVI, and AFP demonstrates high predictive efficacy for estimating the risk of ER in HCC. It facilitates personalized risk classification and therapeutic decision-making for HCC patients.https://www.frontiersin.org/articles/10.3389/fonc.2024.1277698/fullhepatocellular carcinomamagnetic resonance imagingearly recurrenceradiomics analysisnomogram |
| spellingShingle | Jia Li Yunhui Ma Chunyu Yang Ganbin Qiu Jingmu Chen Xiaoliang Tan Yue Zhao Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy Frontiers in Oncology hepatocellular carcinoma magnetic resonance imaging early recurrence radiomics analysis nomogram |
| title | Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy |
| title_full | Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy |
| title_fullStr | Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy |
| title_full_unstemmed | Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy |
| title_short | Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy |
| title_sort | radiomics analysis of r2 maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy |
| topic | hepatocellular carcinoma magnetic resonance imaging early recurrence radiomics analysis nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1277698/full |
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