Ying Ming1, Xinyi Chen2, Jingxu Xu3, Haiyu Zhan4, Jie Zhang1, Teng Ma1, Chencui Huang3, Zhiling Liu1, Zhaoqin Huang5. 1. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan, 250012, Shandong, China. 2. Department of Nuclear Medicine, Jinling Hospital, School of Medicine, Nanjing University, Nanjing, 210018, China. 3. Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co. Ltd, Beijing, 100080, China. 4. Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014, Shandong, China. 5. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan, 250012, Shandong, China. devin813@163.com.
Abstract
PURPOSE: To investigate and validate the prognostic value of nomogram models for predicting disease-free survival (DFS) and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC). METHODS: In this retrospective study, 223 patients (age 54.38 ± 10.93 years) with pathologically confirmed ccRCC who underwent resection and lymph node dissection between March 2010 and September 2018 were investigated. All patients were randomly divided into training (n = 155) and validation (n = 68) cohorts. Radiomics features were extracted from computed tomography (CT) images in the unenhanced, corticomedullary, and nephrographic phases. Radiomic score was calculated and combined with clinicopathological factors for model construction and nomogram development. Clinicopathological factors and imaging features were collected at initial diagnosis. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the relationship between the radiomics signature and prognosis outcomes. RESULTS: There were four prognostic factors for predicting DFS and five factors for predicting OS in our nomogram model (P < 0.05). The radiomics signature correlated independently with DFS (hazard ratio = 27; P < 0.001) and OS (hazard ratio = 25; P < 0.001). The nomogram showed excellent performance (C-index = 0.825) for predicting DFS. The combined nomogram also showed the highest C-index for OS (C-index = 0.943), which was verified in the validation dataset. CONCLUSION: The combined nomogram model based on radiomics, clinicopathological factors, and preoperative CT features can accurately perform prognosis and survival analysis and can potentially be used for preoperative non-invasive survival prediction in ccRCC patients.
PURPOSE: To investigate and validate the prognostic value of nomogram models for predicting disease-free survival (DFS) and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC). METHODS: In this retrospective study, 223 patients (age 54.38 ± 10.93 years) with pathologically confirmed ccRCC who underwent resection and lymph node dissection between March 2010 and September 2018 were investigated. All patients were randomly divided into training (n = 155) and validation (n = 68) cohorts. Radiomics features were extracted from computed tomography (CT) images in the unenhanced, corticomedullary, and nephrographic phases. Radiomic score was calculated and combined with clinicopathological factors for model construction and nomogram development. Clinicopathological factors and imaging features were collected at initial diagnosis. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the relationship between the radiomics signature and prognosis outcomes. RESULTS: There were four prognostic factors for predicting DFS and five factors for predicting OS in our nomogram model (P < 0.05). The radiomics signature correlated independently with DFS (hazard ratio = 27; P < 0.001) and OS (hazard ratio = 25; P < 0.001). The nomogram showed excellent performance (C-index = 0.825) for predicting DFS. The combined nomogram also showed the highest C-index for OS (C-index = 0.943), which was verified in the validation dataset. CONCLUSION: The combined nomogram model based on radiomics, clinicopathological factors, and preoperative CT features can accurately perform prognosis and survival analysis and can potentially be used for preoperative non-invasive survival prediction in ccRCC patients.
Authors: Wenya Linda Bi; Ahmed Hosny; Matthew B Schabath; Maryellen L Giger; Nicolai J Birkbak; Alireza Mehrtash; Tavis Allison; Omar Arnaout; Christopher Abbosh; Ian F Dunn; Raymond H Mak; Rulla M Tamimi; Clare M Tempany; Charles Swanton; Udo Hoffmann; Lawrence H Schwartz; Robert J Gillies; Raymond Y Huang; Hugo J W L Aerts Journal: CA Cancer J Clin Date: 2019-02-05 Impact factor: 508.702
Authors: Alex Zwanenburg; Martin Vallières; Mahmoud A Abdalah; Hugo J W L Aerts; Vincent Andrearczyk; Aditya Apte; Saeed Ashrafinia; Spyridon Bakas; Roelof J Beukinga; Ronald Boellaard; Marta Bogowicz; Luca Boldrini; Irène Buvat; Gary J R Cook; Christos Davatzikos; Adrien Depeursinge; Marie-Charlotte Desseroit; Nicola Dinapoli; Cuong Viet Dinh; Sebastian Echegaray; Issam El Naqa; Andriy Y Fedorov; Roberto Gatta; Robert J Gillies; Vicky Goh; Michael Götz; Matthias Guckenberger; Sung Min Ha; Mathieu Hatt; Fabian Isensee; Philippe Lambin; Stefan Leger; Ralph T H Leijenaar; Jacopo Lenkowicz; Fiona Lippert; Are Losnegård; Klaus H Maier-Hein; Olivier Morin; Henning Müller; Sandy Napel; Christophe Nioche; Fanny Orlhac; Sarthak Pati; Elisabeth A G Pfaehler; Arman Rahmim; Arvind U K Rao; Jonas Scherer; Muhammad Musib Siddique; Nanna M Sijtsema; Jairo Socarras Fernandez; Emiliano Spezi; Roel J H M Steenbakkers; Stephanie Tanadini-Lang; Daniela Thorwarth; Esther G C Troost; Taman Upadhaya; Vincenzo Valentini; Lisanne V van Dijk; Joost van Griethuysen; Floris H P van Velden; Philip Whybra; Christian Richter; Steffen Löck Journal: Radiology Date: 2020-03-10 Impact factor: 29.146