Huanhuan Liu1, Caiyuan Zhang1, Lijun Wang1, Ran Luo1, Jinning Li1, Hui Zheng1, Qiufeng Yin1, Zhongyang Zhang1, Shaofeng Duan2, Xin Li2, Dengbin Wang3. 1. Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China. 2. GE Healthcare, Pudong New Town, No.1, Huatuo Road, Shanghai, 210000, China. 3. Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China. wangdengbin@xinhuamed.com.cn.
Abstract
OBJECTIVES: To investigate the value of MRI radiomics based on T2-weighted (T2W) images in predicting preoperative synchronous distant metastasis (SDM) in patients with rectal cancer. METHODS: This retrospective study enrolled 177 patients with histopathology-confirmed rectal adenocarcinoma (123 patients in the training cohort and 54 in the validation cohort). A total of 385 radiomics features were extracted from pretreatment T2W images. Five steps, including univariate statistical tests and a random forest algorithm, were performed to select the best preforming features for predicting SDM. Multivariate logistic regression analysis was conducted to build the clinical and clinical-radiomics combined models in the training cohort. The predictive performance was validated by receiver operating characteristics curve (ROC) analysis and clinical utility implementing a nomogram and decision curve analysis. RESULTS: Fifty-nine patients (33.3%) were confirmed to have SDM. Six radiomics features and four clinical characteristics were selected for predicting SDM. The clinical-radiomics combined model performed better than the clinical model in both the training and validation datasets. A threshold of 0.44 yielded an area under the ROC (AUC) value of 0.827 (95% confidence interval (CI), 0.6963-0.9580), a sensitivity of 72.2%, a specificity of 94.4%, and an accuracy of 87.0% in the validation cohort for the combined model. A clinical-radiomics nomogram and decision curve analysis confirmed the clinical utility of the combined model. CONCLUSIONS: Our proposed clinical-radiomics combined model could be utilized as a noninvasive biomarker for identifying patients at high risk of SDM, which could aid in tailoring treatment strategies. KEY POINTS: • T2WI-based radiomics analysis helps predict synchronous distant metastasis (SDM) of rectal cancer. • The clinical-radiomics combined model could be utilized as a noninvasive biomarker for predicting SDM. • Personalized treatment can be carried out with greater confidence based on the risk stratification for SDM in rectal cancer.
OBJECTIVES: To investigate the value of MRI radiomics based on T2-weighted (T2W) images in predicting preoperative synchronous distant metastasis (SDM) in patients with rectal cancer. METHODS: This retrospective study enrolled 177 patients with histopathology-confirmed rectal adenocarcinoma (123 patients in the training cohort and 54 in the validation cohort). A total of 385 radiomics features were extracted from pretreatment T2W images. Five steps, including univariate statistical tests and a random forest algorithm, were performed to select the best preforming features for predicting SDM. Multivariate logistic regression analysis was conducted to build the clinical and clinical-radiomics combined models in the training cohort. The predictive performance was validated by receiver operating characteristics curve (ROC) analysis and clinical utility implementing a nomogram and decision curve analysis. RESULTS: Fifty-nine patients (33.3%) were confirmed to have SDM. Six radiomics features and four clinical characteristics were selected for predicting SDM. The clinical-radiomics combined model performed better than the clinical model in both the training and validation datasets. A threshold of 0.44 yielded an area under the ROC (AUC) value of 0.827 (95% confidence interval (CI), 0.6963-0.9580), a sensitivity of 72.2%, a specificity of 94.4%, and an accuracy of 87.0% in the validation cohort for the combined model. A clinical-radiomics nomogram and decision curve analysis confirmed the clinical utility of the combined model. CONCLUSIONS: Our proposed clinical-radiomics combined model could be utilized as a noninvasive biomarker for identifying patients at high risk of SDM, which could aid in tailoring treatment strategies. KEY POINTS: • T2WI-based radiomics analysis helps predict synchronous distant metastasis (SDM) of rectal cancer. • The clinical-radiomics combined model could be utilized as a noninvasive biomarker for predicting SDM. • Personalized treatment can be carried out with greater confidence based on the risk stratification for SDM in rectal cancer.
Entities:
Keywords:
Magnetic resonance imaging; Metastasis; Radiomics; Rectal neoplasm
Authors: Virendra Kumar; Yuhua Gu; Satrajit Basu; Anders Berglund; Steven A Eschrich; Matthew B Schabath; Kenneth Forster; Hugo J W L Aerts; Andre Dekker; David Fenstermacher; Dmitry B Goldgof; Lawrence O Hall; Philippe Lambin; Yoganand Balagurunathan; Robert A Gatenby; Robert J Gillies Journal: Magn Reson Imaging Date: 2012-08-13 Impact factor: 2.546
Authors: Jean M Butte; Mithat Gonen; Peirong Ding; Karyn A Goodman; Peter J Allen; Garrett M Nash; Jose Guillem; Philip B Paty; Leonard B Saltz; Nancy E Kemeny; Ronald P Dematteo; Yuman Fong; William R Jarnagin; Martin R Weiser; Michael I D'Angelica Journal: Cancer Date: 2012-04-19 Impact factor: 6.860
Authors: Alexandre Ho-Pun-Cheung; Eric Assenat; Caroline Bascoul-Mollevi; Frédéric Bibeau; Florence Boissière-Michot; Dominic Cellier; David Azria; Philippe Rouanet; Pierre Senesse; Marc Ychou; Evelyne Lopez-Crapez Journal: Int J Cancer Date: 2010-10-26 Impact factor: 7.396
Authors: Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts Journal: Eur J Cancer Date: 2012-01-16 Impact factor: 9.162
Authors: Hyuk Hur; Yong Taek Ko; Byung Soh Min; Kyung Sik Kim; Jin Sub Choi; Seung Kook Sohn; Chang Hwan Cho; Heung Kyu Ko; Jong Tai Lee; Nam Kyu Kim Journal: Am J Surg Date: 2008-09-11 Impact factor: 2.565
Authors: Fiona G M Taylor; Philip Quirke; Richard J Heald; Brendan J Moran; Lennart Blomqvist; Ian R Swift; David Sebag-Montefiore; Paris Tekkis; Gina Brown Journal: J Clin Oncol Date: 2013-11-25 Impact factor: 44.544
Authors: Gena P Kanas; Aliki Taylor; John N Primrose; Wendy J Langeberg; Michael A Kelsh; Fionna S Mowat; Dominik D Alexander; Michael A Choti; Graeme Poston Journal: Clin Epidemiol Date: 2012-11-07 Impact factor: 4.790
Authors: Ji Eun Park; Donghyun Kim; Ho Sung Kim; Seo Young Park; Jung Youn Kim; Se Jin Cho; Jae Ho Shin; Jeong Hoon Kim Journal: Eur Radiol Date: 2019-07-26 Impact factor: 5.315