Mingyu Chen1,2,3, Jiasheng Cao1, Jiahao Hu1, Win Topatana3, Shijie Li1, Sarun Juengpanich3, Jian Lin4, Chenhao Tong5, Jiliang Shen1, Bin Zhang1, Jennifer Wu6, Christine Pocha7, Masatoshi Kudo8, Amedeo Amedei9, Franco Trevisani10, Pil Soo Sung11, Victor M Zaydfudim12, Tatsuo Kanda13, Xiujun Cai1,2,14. 1. Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China. 2. Engineering Research Center of Cognitive Healthcare of Zhejiang Province, Hangzhou, China. 3. Zhejiang University School of Medicine, Hangzhou, China. 4. General Surgery, Longyou People's Hospital, Quzhou, China. 5. General Surgery, Shaoxing People's Hospital, Shaoxing, China. 6. Perlmutter Cancer Center, NYU Langone Health, New York, New York, USA. 7. Avera McKennnan Hospital and University Medical Center, Sanford School of Medicine, University of South Dakota, Sioux Falls, South Dakota, USA. 8. Department of Gastroenterology and Hepatology, Kindai University School of Medicine, Osaka, Japan. 9. Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy. 10. Department of Medical and Surgical Sciences, Semeiotica Medica, Alma Mater Studiorum, University of Bologna, Bologna, Italy. 11. Department of Internal Medicine, College of Medicine, Eunpyeong St. Mary's Hospital, Seoul, Republic of Korea. 12. Department of Surgery, Section of Hepatobiliary and Pancreatic Surgery, University of Virginia, Charlottesville, Virginia, USA. 13. Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan. 14. Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, Hangzhou, China.
Abstract
BACKGROUND: The preoperative selection of patients with intermediate-stage hepatocellular carcinoma (HCC) who are likely to have an objective response to first transarterial chemoembolization (TACE) remains challenging. OBJECTIVE: To develop and validate a clinical-radiomic model (CR model) for preoperatively predicting treatment response to first TACE in patients with intermediate-stage HCC. METHODS: A total of 595 patients with intermediate-stage HCC were included in this retrospective study. A tumoral and peritumoral (10 mm) radiomic signature (TPR-signature) was constructed based on 3,404 radiomic features from 4 regions of interest. A predictive CR model based on TPR-signature and clinical factors was developed using multivariate logistic regression. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the model's performance. RESULTS: The final CR model consisted of 5 independent predictors, including TPR-signature (p < 0.001), AFP (p = 0.004), Barcelona Clinic Liver Cancer System Stage B (BCLC B) subclassification (p = 0.01), tumor location (p = 0.039), and arterial hyperenhancement (p = 0.050). The internal and external validation results demonstrated the high-performance level of this model, with internal and external AUCs of 0.94 and 0.90, respectively. In addition, the predicted objective response via the CR model was associated with improved survival in the external validation cohort (hazard ratio: 2.43; 95% confidence interval: 1.60-3.69; p < 0.001). The predicted treatment response also allowed for significant discrimination between the Kaplan-Meier curves of each BCLC B subclassification. CONCLUSIONS: The CR model had an excellent performance in predicting the first TACE response in patients with intermediate-stage HCC and could provide a robust predictive tool to assist with the selection of patients for TACE.
BACKGROUND: The preoperative selection of patients with intermediate-stage hepatocellular carcinoma (HCC) who are likely to have an objective response to first transarterial chemoembolization (TACE) remains challenging. OBJECTIVE: To develop and validate a clinical-radiomic model (CR model) for preoperatively predicting treatment response to first TACE in patients with intermediate-stage HCC. METHODS: A total of 595 patients with intermediate-stage HCC were included in this retrospective study. A tumoral and peritumoral (10 mm) radiomic signature (TPR-signature) was constructed based on 3,404 radiomic features from 4 regions of interest. A predictive CR model based on TPR-signature and clinical factors was developed using multivariate logistic regression. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the model's performance. RESULTS: The final CR model consisted of 5 independent predictors, including TPR-signature (p < 0.001), AFP (p = 0.004), Barcelona Clinic Liver Cancer System Stage B (BCLC B) subclassification (p = 0.01), tumor location (p = 0.039), and arterial hyperenhancement (p = 0.050). The internal and external validation results demonstrated the high-performance level of this model, with internal and external AUCs of 0.94 and 0.90, respectively. In addition, the predicted objective response via the CR model was associated with improved survival in the external validation cohort (hazard ratio: 2.43; 95% confidence interval: 1.60-3.69; p < 0.001). The predicted treatment response also allowed for significant discrimination between the Kaplan-Meier curves of each BCLC B subclassification. CONCLUSIONS: The CR model had an excellent performance in predicting the first TACE response in patients with intermediate-stage HCC and could provide a robust predictive tool to assist with the selection of patients for TACE.
Authors: L Kadalayil; R Benini; L Pallan; J O'Beirne; L Marelli; D Yu; A Hackshaw; R Fox; P Johnson; A K Burroughs; D H Palmer; T Meyer Journal: Ann Oncol Date: 2013-07-14 Impact factor: 32.976
Authors: Tai H Dou; Thibaud P Coroller; Joost J M van Griethuysen; Raymond H Mak; Hugo J W L Aerts Journal: PLoS One Date: 2018-11-02 Impact factor: 3.240
Authors: Vincenza Granata; Roberta Grassi; Roberta Fusco; Andrea Belli; Carmen Cutolo; Silvia Pradella; Giulia Grazzini; Michelearcangelo La Porta; Maria Chiara Brunese; Federica De Muzio; Alessandro Ottaiano; Antonio Avallone; Francesco Izzo; Antonella Petrillo Journal: Infect Agent Cancer Date: 2021-07-19 Impact factor: 2.965