Predicting the outcomes of hepatocellular carcinoma downstaging with the use of clinical and radiomics features
Abstract Background Downstaging of hepatocellular carcinoma (HCC) makes it possible for patients beyond the criteria to have the chance of liver transplantation (LT) and improved outcomes. Thus, a procedure to predict the prognosis of the treatment is an urgent requisite. The present study aimed to...
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-023-11386-0 |
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| author | Si-Yuan Wang Kai Sun Shuo Jin Kai-Yu Wang Nan Jiang Si-Qiao Shan Qian Lu Guo-Yue Lv Jia-Hong Dong |
| author_facet | Si-Yuan Wang Kai Sun Shuo Jin Kai-Yu Wang Nan Jiang Si-Qiao Shan Qian Lu Guo-Yue Lv Jia-Hong Dong |
| author_sort | Si-Yuan Wang |
| collection | DOAJ |
| description | Abstract Background Downstaging of hepatocellular carcinoma (HCC) makes it possible for patients beyond the criteria to have the chance of liver transplantation (LT) and improved outcomes. Thus, a procedure to predict the prognosis of the treatment is an urgent requisite. The present study aimed to construct a comprehensive framework with clinical information and radiomics features to accurately predict the prognosis of downstaging treatment. Methods Specifically, three-dimensional (3D) tumor segmentation from contrast-enhanced computed tomography (CT) is employed to extract spatial information of the lesions. Then, the radiomics features within the segmented region are calculated. Combining radiomics features and clinical data prompts the development of feature selection to enhance the robustness and generalizability of the model. Finally, we adopt the support vector machine (SVM) algorithm to establish a classification model for predicting HCC downstaging outcomes. Results Herein, a comparative study was conducted on three different models: a radiomics features-based model (R model), a clinical features-based model (C model), and a joint radiomics clinical features-based model (R-C model). The average accuracy of the three models was 0.712, 0.792, and 0.844, and the average area under the receiver-operating characteristic (AUROC) of the three models was 0.775, 0.804, and 0.877, respectively. Conclusions The novel and practical R-C model accurately predicted the downstaging outcomes, which could be utilized to guide the HCC downstaging toward LT treatment. |
| first_indexed | 2024-03-10T17:40:22Z |
| format | Article |
| id | doaj.art-f7e44e8c68024bfe834a57a074284e7a |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-03-10T17:40:22Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-f7e44e8c68024bfe834a57a074284e7a2023-11-20T09:43:52ZengBMCBMC Cancer1471-24072023-09-0123111110.1186/s12885-023-11386-0Predicting the outcomes of hepatocellular carcinoma downstaging with the use of clinical and radiomics featuresSi-Yuan Wang0Kai Sun1Shuo Jin2Kai-Yu Wang3Nan Jiang4Si-Qiao Shan5Qian Lu6Guo-Yue Lv7Jia-Hong Dong8Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityHepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityHepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityHepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityHepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityHepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityHepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityDepartment of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin UniversityHepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityAbstract Background Downstaging of hepatocellular carcinoma (HCC) makes it possible for patients beyond the criteria to have the chance of liver transplantation (LT) and improved outcomes. Thus, a procedure to predict the prognosis of the treatment is an urgent requisite. The present study aimed to construct a comprehensive framework with clinical information and radiomics features to accurately predict the prognosis of downstaging treatment. Methods Specifically, three-dimensional (3D) tumor segmentation from contrast-enhanced computed tomography (CT) is employed to extract spatial information of the lesions. Then, the radiomics features within the segmented region are calculated. Combining radiomics features and clinical data prompts the development of feature selection to enhance the robustness and generalizability of the model. Finally, we adopt the support vector machine (SVM) algorithm to establish a classification model for predicting HCC downstaging outcomes. Results Herein, a comparative study was conducted on three different models: a radiomics features-based model (R model), a clinical features-based model (C model), and a joint radiomics clinical features-based model (R-C model). The average accuracy of the three models was 0.712, 0.792, and 0.844, and the average area under the receiver-operating characteristic (AUROC) of the three models was 0.775, 0.804, and 0.877, respectively. Conclusions The novel and practical R-C model accurately predicted the downstaging outcomes, which could be utilized to guide the HCC downstaging toward LT treatment.https://doi.org/10.1186/s12885-023-11386-0Hepatocellular carcinomaDownstagingPredicting modelMachine learningRadiomics |
| spellingShingle | Si-Yuan Wang Kai Sun Shuo Jin Kai-Yu Wang Nan Jiang Si-Qiao Shan Qian Lu Guo-Yue Lv Jia-Hong Dong Predicting the outcomes of hepatocellular carcinoma downstaging with the use of clinical and radiomics features BMC Cancer Hepatocellular carcinoma Downstaging Predicting model Machine learning Radiomics |
| title | Predicting the outcomes of hepatocellular carcinoma downstaging with the use of clinical and radiomics features |
| title_full | Predicting the outcomes of hepatocellular carcinoma downstaging with the use of clinical and radiomics features |
| title_fullStr | Predicting the outcomes of hepatocellular carcinoma downstaging with the use of clinical and radiomics features |
| title_full_unstemmed | Predicting the outcomes of hepatocellular carcinoma downstaging with the use of clinical and radiomics features |
| title_short | Predicting the outcomes of hepatocellular carcinoma downstaging with the use of clinical and radiomics features |
| title_sort | predicting the outcomes of hepatocellular carcinoma downstaging with the use of clinical and radiomics features |
| topic | Hepatocellular carcinoma Downstaging Predicting model Machine learning Radiomics |
| url | https://doi.org/10.1186/s12885-023-11386-0 |
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