Rui Zhang1, Lei Xu2,3, Xue Wen4, Jiahui Zhang5, Pengfei Yang2,3, Lixia Zhang1, Xing Xue1, Xiaoli Wang1, Qiang Huang1, Chuangen Guo1, Yanjun Shi6, Tianye Niu2,3, Feng Chen1. 1. Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China. 2. Institute of Translational Medicine, College of Medicine, Zhejiang University, Hangzhou 310058, China. 3. Department of Radiology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou 310020, China. 4. Department of Pathology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China. 5. Department of Radiology, Hangzhou Third Hospital, Hangzhou 310009, China. 6. Department of Hepatobiliary and Pancreas Surgery, the Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310009, China.
Abstract
BACKGROUND: We aimed to develop and validate a nomogram combining bi-regional radiomics features from multimodal magnetic resonance imaging (MRI) and clinicoradiological characteristics to preoperatively predict microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHODS: A total of 267 HCC patients were divided into training (n=194) and validation (n=73) cohorts according to MRI data. Bi-regional features were extracted from whole tumors and peritumoral regions in multimodal MRI. The minimum redundancy maximum relevance (mRMR) algorithm was applied to select features and build signatures. The predictive performance of the optimal radiomics signature was further evaluated within subgroups defined by tumor size and alpha fetoprotein (AFP) level. Then, a radiomics nomogram including the optimal radiomics signature, radiographic descriptors, and clinical variables was developed using multivariable regression. The nomogram performance was evaluated based on its discrimination, calibration, and clinical utility. RESULTS: The fusion radiomics signature derived from triphasic dynamic contrast-enhanced (DCE) MR images can effectively classify MVI and non-MVI HCC patients, with an AUC of 0.784 (95% CI: 0.719-0.840) in the training cohort and 0.820 (95% CI: 0.713-0.900) in the validation cohort. The fusion radiomics signature also performed well in the subgroups defined by the two risk factors, respectively. The nomogram, consisting of the fusion radiomics signature, arterial peritumoral enhancement, and AFP level, outperformed the clinicoradiological prediction model in the validation cohort (AUCs: 0.858 vs. 0.729; P=0.022), fitting well in the calibration curves (P>0.05). Decision curves confirmed the clinical utility of the nomogram. CONCLUSIONS: The radiomics nomogram can serve as a visual predictive tool for MVI in HCCs, and thus assist clinicians in selecting optimal treatment strategies to improve clinical outcomes. 2019 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: We aimed to develop and validate a nomogram combining bi-regional radiomics features from multimodal magnetic resonance imaging (MRI) and clinicoradiological characteristics to preoperatively predict microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHODS: A total of 267 HCC patients were divided into training (n=194) and validation (n=73) cohorts according to MRI data. Bi-regional features were extracted from whole tumors and peritumoral regions in multimodal MRI. The minimum redundancy maximum relevance (mRMR) algorithm was applied to select features and build signatures. The predictive performance of the optimal radiomics signature was further evaluated within subgroups defined by tumor size and alpha fetoprotein (AFP) level. Then, a radiomics nomogram including the optimal radiomics signature, radiographic descriptors, and clinical variables was developed using multivariable regression. The nomogram performance was evaluated based on its discrimination, calibration, and clinical utility. RESULTS: The fusion radiomics signature derived from triphasic dynamic contrast-enhanced (DCE) MR images can effectively classify MVI and non-MVI HCC patients, with an AUC of 0.784 (95% CI: 0.719-0.840) in the training cohort and 0.820 (95% CI: 0.713-0.900) in the validation cohort. The fusion radiomics signature also performed well in the subgroups defined by the two risk factors, respectively. The nomogram, consisting of the fusion radiomics signature, arterial peritumoral enhancement, and AFP level, outperformed the clinicoradiological prediction model in the validation cohort (AUCs: 0.858 vs. 0.729; P=0.022), fitting well in the calibration curves (P>0.05). Decision curves confirmed the clinical utility of the nomogram. CONCLUSIONS: The radiomics nomogram can serve as a visual predictive tool for MVI in HCCs, and thus assist clinicians in selecting optimal treatment strategies to improve clinical outcomes. 2019 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Authors: John P Duffy; Andrew Vardanian; Elizabeth Benjamin; Melissa Watson; Douglas G Farmer; Rafik M Ghobrial; Gerald Lipshutz; Hasan Yersiz; David S K Lu; Charles Lassman; Myron J Tong; Jonathan R Hiatt; Ronald W Busuttil Journal: Ann Surg Date: 2007-09 Impact factor: 12.969
Authors: Seung Baek Hong; Sang Hyun Choi; So Yeon Kim; Ju Hyun Shim; Seung Soo Lee; Jae Ho Byun; Seong Ho Park; Kyung Won Kim; Suk Kim; Nam Kyung Lee Journal: Liver Cancer Date: 2021-03-11 Impact factor: 11.740