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Li J, Zou L, Ma H, Zhao J, Wang C, Li J, Hu G, Yang H, Wang B, Xu D, Xia Y, Jiang Y, Jiang X, Li N. Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma. Abdom Radiol (NY) 2024:10.1007/s00261-024-04313-9. [PMID: 38703190 DOI: 10.1007/s00261-024-04313-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/23/2024] [Accepted: 03/25/2024] [Indexed: 05/06/2024]
Abstract
PURPOSE To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC). METHODS The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit. RESULTS 166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000-1.000) in the training set and 0.927 (95%CI: 0.837-0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837-0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908. CONCLUSION This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.
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Affiliation(s)
- Jie Li
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China
| | - Linxuan Zou
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, 264000, China
| | - Jifu Zhao
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China
| | - Chengyan Wang
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China
| | - Jun Li
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264000, China
| | - Guangchao Hu
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Laishan District, Yantai, 264003, China
| | - Haoran Yang
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Laishan District, Yantai, 264003, China
| | - Beizhong Wang
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China
| | - Donghao Xu
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Laishan District, Yantai, 264003, China
| | - Yuanhao Xia
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, 264000, China
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Laishan District, Yantai, 264003, China
| | - Yi Jiang
- Department of Vascular Interventional Surgery, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264000, China
| | - Xingyue Jiang
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China.
| | - Naixuan Li
- Department of Vascular Interventional Surgery, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264000, China.
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Peng Y, Tang H, Huang Y, Yuan X, Wang X, Ran Z, Deng W, Liu R, Lan X, Shen H, Zhang J. CT-derived extracellular volume and liver volumetry can predict posthepatectomy liver failure in hepatocellular carcinoma. Insights Imaging 2023; 14:145. [PMID: 37697217 PMCID: PMC10495294 DOI: 10.1186/s13244-023-01496-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 08/08/2023] [Indexed: 09/13/2023] Open
Abstract
OBJECTIVES Posthepatectomy liver failure (PHLF) is a severe complication of liver resection. We aimed to develop and validate a model based on extracellular volume (ECV) and liver volumetry derived from computed tomography (CT) for preoperative predicting PHLF in resectable hepatocellular carcinoma (HCC) patients. METHODS A total of 393 resectable HCC patients from two hospitals were enrolled and underwent multiphasic contrast-enhanced CT before surgery. A total of 281 patients from our hospital were randomly divided into a training cohort (n = 181) and an internal validation cohort (n = 100), and 112 patients from another hospital formed the external validation cohort. CT-derived ECV was measured on nonenhanced and equilibrium phase images, and liver volumetry was measured on portal phase images. The model is composed of independent predictors of PHLF. The under the receiver operator characteristic curve (AUC) and calibration curve were used to reflect the predictive performance and calibration of the model. Comparison of AUCs used the DeLong test. RESULTS CT-derived ECV, measured future liver remnant (mFLR) ratio, and serum albumin were independent predictors for PHLF in resectable HCC patients. The AUC of the model was significantly higher than that of the ALBI score in the training cohort, internal validation cohort, and external validation cohort (all p < 0.001). The calibration curve of the model showed good consistency in the training cohort and the internal and external validation cohorts. CONCLUSIONS The novel model contributes to the preoperative prediction of PHLF in resectable HCC patients. CRITICAL RELEVANCE STATEMENT The novel model combined CT-derived extracellular volume, measured future liver remnant ratio, and serum albumin outperforms the albumin-bilirubin score for predicting posthepatectomy liver failure in patients with resectable hepatocellular carcinoma. KEY POINTS • CT-derived ECV correlated well with the fibrosis stage of the background liver. • CT-derived ECV and mFLR ratio were independent predictors for PHLF in HCC. • The AUC of the model was higher than the CT-derived ECV and mFLR ratio. • The model showed a superior predictive performance than that of the ALBI score.
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Affiliation(s)
- Yangling Peng
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, People's Republic of China
| | - Hao Tang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, People's Republic of China
| | - Yuanying Huang
- Department of Hematology, Chongqing General Hospital, University of the Chinese Academy of Sciences, Chongqing, People's Republic of China
| | - Xiaoqian Yuan
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, People's Republic of China
| | - Xing Wang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, People's Republic of China
| | - Zijuan Ran
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, People's Republic of China
| | - Wei Deng
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, People's Republic of China
| | - Renwei Liu
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, People's Republic of China
| | - Xiaosong Lan
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, People's Republic of China
| | - Hesong Shen
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, People's Republic of China.
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, People's Republic of China.
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Xu X, Xing Z, Xu Z, Tong Y, Wang S, Liu X, Ren Y, Liang X, Yu Y, Ying H. A deep learning model for prediction of post hepatectomy liver failure after hemihepatectomy using preoperative contrast-enhanced computed tomography: a retrospective study. Front Med (Lausanne) 2023; 10:1154314. [PMID: 37448800 PMCID: PMC10336538 DOI: 10.3389/fmed.2023.1154314] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/08/2023] [Indexed: 07/15/2023] Open
Abstract
Objective Post-hepatectomy liver failure (PHLF) remains clinical challenges after major hepatectomy. The aim of this study was to establish and validate a deep learning model to predict PHLF after hemihepatectomy using preoperative contrast-enhancedcomputed tomography with three phases (Non-contrast, arterial phase and venous phase). Methods 265 patients undergoing hemihepatectomy in Sir Run Run Shaw Hospital were enrolled in this study. The primary endpoint was PHLF, according to the International Study Group of Liver Surgery's definition. In this study, to evaluate the proposed method, 5-fold cross-validation technique was used. The dataset was split into 5 folds of equal size, and each fold was used as a test set once, while the other folds were temporarily combined to form a training set. Performance metrics on the test set were then calculated and stored. At the end of the 5-fold cross-validation run, the accuracy, precision, sensitivity and specificity for predicting PHLF with the deep learning model and the area under receiver operating characteristic curve (AUC) were calculated. Results Of the 265 patients, 170 patients with left liver resection and 95 patients with right liver resection. The diagnosis had 6 types: hepatocellular carcinoma, intrahepatic cholangiocarcinoma, liver metastases, benign tumor, hepatolithiasis, and other liver diseases. Laparoscopic liver resection was performed in 187 patients. The accuracy of prediction was 84.15%. The AUC was 0.7927. In 170 left hemihepatectomy cases, the accuracy was 89.41% (152/170), and the AUC was 82.72%. The accuracy was 77.47% (141/182) with liver mass, 78.33% (47/60) with liver cirrhosis and 80.46% (70/87) with viral hepatitis. Conclusion The deep learning model showed excellent performance in prediction of PHLF and could be useful for identifying high-risk patients to modify the treatment planning.
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Affiliation(s)
- Xiaoqing Xu
- Department of Nursing, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zijian Xing
- Deepwise Artificial Intelligence Laboratory, Hangzhou, China
| | - Zhiyao Xu
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yifan Tong
- Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shuxin Wang
- Deepwise Artificial Intelligence Laboratory, Hangzhou, China
| | - Xiaoqing Liu
- Deepwise Artificial Intelligence Laboratory, Hangzhou, China
| | - Yiyue Ren
- Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Liang
- Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yizhou Yu
- Faculty of Engineering, The University of Hong Kong, Hong Kong, China
| | - Hanning Ying
- Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Luo Y, Liu L, Liu D, Shen H, Wang X, Fan C, Zeng Z, Zhang J, Tan Y, Zhang X, Wu J, Zhang J. Extracellular volume fraction determined by equilibrium contrast-enhanced CT for the prediction of the pathological complete response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Eur Radiol 2022; 33:4042-4051. [PMID: 36462046 DOI: 10.1007/s00330-022-09307-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/31/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022]
Abstract
OBJECTIVES To determine the extracellular volume (ECV) fraction derived from equilibrium contrast-enhanced CT for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC). METHODS The ECV fraction before NCRT (ECVpre) and/or ECV after NCRT (ECVpost) of rectal tumors was assessed, and ECVΔ was calculated as ECVpost - ECVpre. The histopathologic tumor regression grading (TRG) was assessed. pCR (TRG 0 grade) was defined as the absence of viable tumor cells in the primary tumor and lymph nodes. Demographic and clinicopathological characteristics and ECV fraction were compared between the pCR and non-pCR groups. A mixed model was constructed by logistic regression. The performance for predicting pCR was assessed with the area under the receiver-operator curve (AUC). The AUCs of the different methods were compared by the method proposed by DeLong et al. RESULTS: Seventy-five patients were included; 17 achieved pCR, and 58 achieved non-pCR. The ECVpost (17.05 ± 2.36% vs. 29.94 ± 1.20%; p < 0.001) and ECVΔ (- 17.01 ± 3.01% vs. 0.44 ± 1.45%; p < 0.001) values in the pCR group were significantly lower than those in the non-pCR group. The mixed model that combined ECVpost with ECVΔ achieved an AUC of 0.92 (95% confidence interval (CI) = 0.81-0.98), which was higher than that of ECVpost (AUC, 0.91 (95% CI = 0.80-0.97); p = 0.60) or ECVΔ (AUC, 0.90 (95% CI = 0.79-0.97); p = 0.61). CONCLUSIONS ECVpost and ECVΔ determined by using equilibrium contrast-enhanced CT were useful in distinguishing between pCR and non-pCR patients with LARC who received NCRT. KEY POINTS • ECVpost and ECVΔ (ECVpost - ECVpre) differed significantly between the non-pCR and pCR groups. • ECVpre cannot be used to predict the efficacy of neoadjuvant chemoradiotherapy. • ECVpost combined with ECVΔ had the best performance with an AUC of 0.92 for predicting pCR after NCRT in LARC.
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Yaghmai V. CT-derived extracellular volume to predict post-hepatectomy liver failure: a simple approach to a very complex problem. Eur Radiol 2022; 32:8527-8528. [PMID: 36074264 DOI: 10.1007/s00330-022-09100-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 07/25/2022] [Accepted: 08/04/2022] [Indexed: 01/13/2023]
Affiliation(s)
- Vahid Yaghmai
- Department of Radiological Sciences, University of California, Irvine, 101 The City Drive South, Bldg. 1, Rte 140, Orange, CA, 92868, USA.
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Mesoporous Nanoparticles for Diagnosis and Treatment of Liver Cancer in the Era of Precise Medicine. Pharmaceutics 2022; 14:pharmaceutics14091760. [PMID: 36145508 PMCID: PMC9500788 DOI: 10.3390/pharmaceutics14091760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/11/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
Primary liver cancer is the seventh-most-common cancer worldwide and the fourth-leading cause of cancer mortality. In the current era of precision medicine, the diagnosis and management of liver cancer are full of challenges and prospects. Mesoporous nanoparticles are often designed as specific carriers of drugs and imaging agents because of their special morphology and physical and chemical properties. In recent years, the design of the elemental composition and morphology of mesoporous nanoparticles have greatly improved their drug-loading efficiency, biocompatibility and biodegradability. Especially in the field of primary liver cancer, mesoporous nanoparticles have been modified as highly tumor-specific imaging contrast agents and targeting therapeutic medicine. Various generations of complexes and structures have been determined for the complicated clinical management requirements. In this review, we summarize these advanced mesoporous designs in the different diagnostic and therapeutic fields of liver cancer and discuss the relevant advantages and disadvantages of transforming applications. By comparing the material properties, drug-delivery characteristics and application methods of different kinds of mesoporous materials in liver cancer, we try to help determine the most suitable drug carriers and information media for future clinical trials. We hope to improve the fabrication of biomedical mesoporous nanoparticles and provide direct evidence for specific cancer management.
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