Cuiping Zhou1, Xiaohui Duan2, Xiang Zhang1, Huijun Hu2, Dongye Wang2, Jun Shen3. 1. Department of Radiology, The Huizhou Central municipal Hospital, No. 41 Eling Rood North, Huizhou, 516001, Guangdong, China. 2. Department of Radiology, Sun Yat-Sen Memorial Hospital, SunYat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China. 3. Department of Radiology, Sun Yat-Sen Memorial Hospital, SunYat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China. shenjun@mail.sysu.edu.cn.
Abstract
PURPOSE: To determine the predictive CT imaging features for risk stratifications in patients with primary gastrointestinal stromal tumours (GISTs). MATERIALS AND METHODS: One hundred and twenty-nine patients with histologically confirmed primary GISTs (diameter >2 cm) were enrolled. CT imaging features were reviewed. Tumour risk stratifications were determined according to the 2008 NIH criteria where GISTs were classified into four categories according to the tumour size, location, mitosis count, and tumour rupture. The association between risk stratifications and CT features was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. RESULTS: CT imaging features including tumour margin, size, shape, tumour growth pattern, direct organ invasion, necrosis, enlarged vessels feeding or draining the mass (EVFDM), lymphadenopathy, and contrast enhancement pattern were associated with the risk stratifications, as determined by univariate analysis (P < 0.05). Only lesion size, growth pattern and EVFDM remained independent risk factors in multinomial logistic regression analysis (OR = 3.480-100.384). ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.806 (95 % CI: 0.727-0.885). CONCLUSION: CT features including lesion size, tumour growth pattern, and EVFDM were predictors of the risk stratifications for GIST. KEY POINTS: • CT features were of predictive value for risk stratification of GISTs. • Tumour size, growth patterns, and EVFDM were risk predictors of GISTs. • Large size, mixed growth pattern, or EVFDM indicated high risk GIST.
PURPOSE: To determine the predictive CT imaging features for risk stratifications in patients with primary gastrointestinal stromal tumours (GISTs). MATERIALS AND METHODS: One hundred and twenty-nine patients with histologically confirmed primary GISTs (diameter >2 cm) were enrolled. CT imaging features were reviewed. Tumour risk stratifications were determined according to the 2008 NIH criteria where GISTs were classified into four categories according to the tumour size, location, mitosis count, and tumour rupture. The association between risk stratifications and CT features was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. RESULTS: CT imaging features including tumour margin, size, shape, tumour growth pattern, direct organ invasion, necrosis, enlarged vessels feeding or draining the mass (EVFDM), lymphadenopathy, and contrast enhancement pattern were associated with the risk stratifications, as determined by univariate analysis (P < 0.05). Only lesion size, growth pattern and EVFDM remained independent risk factors in multinomial logistic regression analysis (OR = 3.480-100.384). ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.806 (95 % CI: 0.727-0.885). CONCLUSION: CT features including lesion size, tumour growth pattern, and EVFDM were predictors of the risk stratifications for GIST. KEY POINTS: • CT features were of predictive value for risk stratification of GISTs. • Tumour size, growth patterns, and EVFDM were risk predictors of GISTs. • Large size, mixed growth pattern, or EVFDM indicated high risk GIST.
Authors: Alvin C Silva; Brian G Morse; Amy K Hara; Robert G Paden; Norio Hongo; William Pavlicek Journal: Radiographics Date: 2011 Jul-Aug Impact factor: 5.333
Authors: Anthony Paul Conley; Annie Guerin; Medha Sasane; Genevieve Gauthier; Frances Schwiep; Christopher Hunt Keir; Eric Q Wu Journal: J Gastrointest Cancer Date: 2014-12
Authors: Agnieszka Wozniak; Piotr Rutkowski; Patrick Schöffski; Isabelle Ray-Coquard; Isabelle Hostein; Hans-Ulrich Schildhaus; Axel Le Cesne; Elzbieta Bylina; Janusz Limon; Jean-Yves Blay; Janusz A Siedlecki; Eva Wardelmann; Raf Sciot; Jean-Michel Coindre; Maria Debiec-Rychter Journal: Clin Cancer Res Date: 2014-10-07 Impact factor: 12.531
Authors: Christopher D M Fletcher; Jules J Berman; Christopher Corless; Fred Gorstein; Jerzy Lasota; B Jack Longley; Markku Miettinen; Timothy J O'Leary; Helen Remotti; Brian P Rubin; Barry Shmookler; Leslie H Sobin; Sharon W Weiss Journal: Hum Pathol Date: 2002-05 Impact factor: 3.466
Authors: Guy J C Burkill; Mohammed Badran; Omar Al-Muderis; J Meirion Thomas; Ian R Judson; Cyril Fisher; Eleanor C Moskovic Journal: Radiology Date: 2003-02 Impact factor: 11.105
Authors: Martijn P A Starmans; Milea J M Timbergen; Melissa Vos; Michel Renckens; Dirk J Grünhagen; Geert J L H van Leenders; Roy S Dwarkasing; François E J A Willemssen; Wiro J Niessen; Cornelis Verhoef; Stefan Sleijfer; Jacob J Visser; Stefan Klein Journal: J Digit Imaging Date: 2022-01-27 Impact factor: 4.056