학술논문
CT features combined with RECIST 1.1 criteria improve progression assessments of sunitinib-treated gastrointestinal stromal tumors.
Document Type
Academic Journal
Author
Li J; Department of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.; Huang S; Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.; Zhu H; Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.; Shou C; Department of Gastrointestinal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.; Lin T; Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.; Yin X; Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China.; Zhu Q; Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.; Sun D; Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, China.; Li X; Department of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.; Shen L; Department of Gastrointestinal Oncology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.; Li J; Department of Gastrointestinal Oncology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China. oncogene@163.com.; Kou Y; Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, China. kouyw@sj-hospital.org.; Zhou Y; Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China. zhouyjbju@163.com.; Zhang B; Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China. zhangbo7310@126.com.; Qian H; Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China. qianhaoran@zju.edu.cn.; Yu J; Department of Gastrointestinal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. yujr0909@zju.edu.cn.; Zhou Y; Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai, China. zhouye79@126.com.; Tang L; Department of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China. tangl@bjcancer.org.; Zhang X; Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. zhangxinhua@mail.sysu.edu.cn.
Source
Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE
Subject
Language
English
Abstract
Objectives: To explore the auxiliary value of combining CT features with existing response evaluation criteria in the prediction of progressive disease (PD) in gastrointestinal stromal tumors (GIST) patients treated with sunitinib.
Material and Methods: Eighty-one patients with GISTs who received sunitinib were included in this retrospective multicenter study and divided into training and external validation cohorts. Progression at six months was determined as a reference standard. The predictive performance of the RECIST 1.1 and Choi criteria was compared. CT features at baseline and the first follow-up were analyzed. Logistic regression analyses were used to determine the most significant predictors and develop modified criteria.
Results: A total of 216 lesions showed a good response and 107 showed a poor response in 81 patients. The RECIST 1.1 criteria performed better than the Choi criteria in predicting progression (AUC, 0.75 vs. 0.69, p = 0.04). The expanded/intensified high-enhancement area, blurred tumor-tissue interface, and progressive enlarged vessels feeding or draining the mass (EVFDM) differed significantly between lesions with good and poor responses in the training cohort (p = 0.001, 0.003, and 0.000, respectively). Multivariate analysis revealed that the expanded/intensified high-enhancement area (p = 0.001), progressive EVFDM (p = 0.000), and RECIST PD (p = 0.000) were independent predictive factors. Modified RECIST (mRECIST) criteria were developed and showed significantly higher AUCs in the training and external validation cohorts than the RECIST 1.1 criteria (training: 0.81 vs. 0.73, p = 0.002; validation: 0.82 vs. 0.77, p = 0.04).
Conclusion: The mRECIST criteria, combining CT features with the RECIST 1.1 criteria, demonstrated superior performance in the prediction of early progression in GIST patients receiving sunitinib.
Clinical Relevance Statement: The mRECIST criteria, which combine CT features with the RECIST 1.1 criteria, may facilitate the early detection of progressive disease in GIST patients treated with sunitinib, thereby potentially guiding the timely switch to late-line medications or combination with surgical excision.
Key Points: • The RECIST 1.1 criteria outperformed the Choi criteria in identifying progression of GISTs in patients treated with sunitinib. • GISTs displayed different morphologic features on CT depending on how they responded to sunitinib. • Combining CT morphologic features with the RECIST 1.1 criteria allowed for the prompt and accurate identification of progressing GIST lesions.
(© 2023. The Author(s), under exclusive licence to European Society of Radiology.)
Material and Methods: Eighty-one patients with GISTs who received sunitinib were included in this retrospective multicenter study and divided into training and external validation cohorts. Progression at six months was determined as a reference standard. The predictive performance of the RECIST 1.1 and Choi criteria was compared. CT features at baseline and the first follow-up were analyzed. Logistic regression analyses were used to determine the most significant predictors and develop modified criteria.
Results: A total of 216 lesions showed a good response and 107 showed a poor response in 81 patients. The RECIST 1.1 criteria performed better than the Choi criteria in predicting progression (AUC, 0.75 vs. 0.69, p = 0.04). The expanded/intensified high-enhancement area, blurred tumor-tissue interface, and progressive enlarged vessels feeding or draining the mass (EVFDM) differed significantly between lesions with good and poor responses in the training cohort (p = 0.001, 0.003, and 0.000, respectively). Multivariate analysis revealed that the expanded/intensified high-enhancement area (p = 0.001), progressive EVFDM (p = 0.000), and RECIST PD (p = 0.000) were independent predictive factors. Modified RECIST (mRECIST) criteria were developed and showed significantly higher AUCs in the training and external validation cohorts than the RECIST 1.1 criteria (training: 0.81 vs. 0.73, p = 0.002; validation: 0.82 vs. 0.77, p = 0.04).
Conclusion: The mRECIST criteria, combining CT features with the RECIST 1.1 criteria, demonstrated superior performance in the prediction of early progression in GIST patients receiving sunitinib.
Clinical Relevance Statement: The mRECIST criteria, which combine CT features with the RECIST 1.1 criteria, may facilitate the early detection of progressive disease in GIST patients treated with sunitinib, thereby potentially guiding the timely switch to late-line medications or combination with surgical excision.
Key Points: • The RECIST 1.1 criteria outperformed the Choi criteria in identifying progression of GISTs in patients treated with sunitinib. • GISTs displayed different morphologic features on CT depending on how they responded to sunitinib. • Combining CT morphologic features with the RECIST 1.1 criteria allowed for the prompt and accurate identification of progressing GIST lesions.
(© 2023. The Author(s), under exclusive licence to European Society of Radiology.)