Jia Huang1,2, Huasheng Yao2,3, Yexing Li1,2, Mengyi Dong2,4, Chu Han2, Lan He2, Xiaomei Huang2,4, Ting Xia2,5, Zongjian Yi2,6, Huihui Wang1,2, Yuan Zhang2,4, Jian He1,7, Changhong Liang2, Zaiyi Liu2. 1. Graduate College, Shantou University Medical College, Shantou 515041, China. 2. Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China. 3. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510006, China. 4. Graduate College, Southern Medical University, Guangzhou 510515, China. 5. School of Medicine, South China University of Technology, Guangzhou 510006, China. 6. School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou 510006, China. 7. Department of Interventional Radiology, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Abstract
OBJECTIVES: To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma (GA). METHODS: This retrospective study enrolled 592 patients with clinicopathologically confirmed GA (low-grade: n=154; high-grade: n=438) from January 2008 to March 2018 who were divided into training (n=450) and validation (n=142) sets according to the time of computed tomography (CT) examination. Radiomic features were extracted from the portal venous phase CT images. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression model were used for feature selection, data dimension reduction and radiomics signature construction. Multivariable logistic regression analysis was applied to develop the prediction model. The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration and discrimination. RESULTS: A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA (P<0.001 for both training and validation sets). A nomogram including the radiomics signature and tumor location as predictors was developed. The model showed both good calibration and good discrimination, in which C-index in the training set, 0.752 [95% confidence interval (95% CI): 0.701-0.803]; C-index in the validation set, 0.793 (95% CI: 0.711-0.874). CONCLUSIONS: This study developed a radiomics nomogram that incorporates tumor location and radiomics signatures, which can be useful in facilitating preoperative individualized prediction of histologic grade of GA.
OBJECTIVES: To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma (GA). METHODS: This retrospective study enrolled 592 patients with clinicopathologically confirmed GA (low-grade: n=154; high-grade: n=438) from January 2008 to March 2018 who were divided into training (n=450) and validation (n=142) sets according to the time of computed tomography (CT) examination. Radiomic features were extracted from the portal venous phase CT images. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression model were used for feature selection, data dimension reduction and radiomics signature construction. Multivariable logistic regression analysis was applied to develop the prediction model. The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration and discrimination. RESULTS: A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA (P<0.001 for both training and validation sets). A nomogram including the radiomics signature and tumor location as predictors was developed. The model showed both good calibration and good discrimination, in which C-index in the training set, 0.752 [95% confidence interval (95% CI): 0.701-0.803]; C-index in the validation set, 0.793 (95% CI: 0.711-0.874). CONCLUSIONS: This study developed a radiomics nomogram that incorporates tumor location and radiomics signatures, which can be useful in facilitating preoperative individualized prediction of histologic grade of GA.
Authors: Marco Gerlinger; Andrew J Rowan; Stuart Horswell; James Larkin; David Endesfelder; Eva Gronroos; Pierre Martinez; Nicholas Matthews; Aengus Stewart; Charles Swanton; M Math; Patrick Tarpey; Ignacio Varela; Benjamin Phillimore; Sharmin Begum; Neil Q McDonald; Adam Butler; David Jones; Keiran Raine; Calli Latimer; Claudio R Santos; Mahrokh Nohadani; Aron C Eklund; Bradley Spencer-Dene; Graham Clark; Lisa Pickering; Gordon Stamp; Martin Gore; Zoltan Szallasi; Julian Downward; P Andrew Futreal Journal: N Engl J Med Date: 2012-03-08 Impact factor: 91.245
Authors: Salah-Eddin Al-Batran; Nils Homann; Claudia Pauligk; Gerald Illerhaus; Uwe M Martens; Jan Stoehlmacher; Harald Schmalenberg; Kim B Luley; Nicole Prasnikar; Matthias Egger; Stephan Probst; Helmut Messmann; Markus Moehler; Wolfgang Fischbach; Jörg T Hartmann; Frank Mayer; Heinz-Gert Höffkes; Michael Koenigsmann; Dirk Arnold; Thomas W Kraus; Kersten Grimm; Stefan Berkhoff; Stefan Post; Elke Jäger; Wolf Bechstein; Ulrich Ronellenfitsch; Stefan Mönig; Ralf D Hofheinz Journal: JAMA Oncol Date: 2017-09-01 Impact factor: 31.777
Authors: Iris D Nagtegaal; Robert D Odze; David Klimstra; Valerie Paradis; Massimo Rugge; Peter Schirmacher; Kay M Washington; Fatima Carneiro; Ian A Cree Journal: Histopathology Date: 2019-11-13 Impact factor: 5.087