Development and validation of a preoperative risk nomogram prediction model for gastric gastrointestinal stromal tumors

被引:1
|
作者
Liu, Zide [1 ]
Gao, Jiaxin [1 ]
Zeng, Chunyan [1 ,2 ]
Chen, Youxiang [1 ,2 ]
机构
[1] Nanchang Univ, Digest Dis Hosp, Affiliated Hosp 1, Jiangxi Med Coll,Dept Gastroenterol, 17 Yongwaizheng St, Nanchang 330006, Jiangxi, Peoples R China
[2] Jiangxi Clin Res Ctr Gastroenterol, Nanchang, Jiangxi, Peoples R China
来源
SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES | 2024年 / 38卷 / 04期
关键词
Gastric GIST; Malignant potential; Risk stratification; Nomogram; Prediction model; MUTATIONS; PATHOLOGY; CANCER; CELLS;
D O I
10.1007/s00464-024-10674-5
中图分类号
R61 [外科手术学];
学科分类号
摘要
Background and study aimsGastrointestinal stromal tumors (GIST) carry a potential risk of malignancy, and the treatment of GIST varies for different risk levels. However, there is no systematic preoperative assessment protocol to predict the malignant potential of GIST. The aim of this study was to develop a reliable and clinically applicable preoperative nomogram prediction model to predict the malignant potential of gastric GIST.Patients and methodsPatients with a pathological diagnosis of gastric GIST from January 2015 to December 2021 were screened retrospectively. Univariate and multivariate logistic analyses were used to identify independent risk factors for gastric GIST with high malignancy potential. Based on these independent risk factors, a nomogram model predicting the malignant potential of gastric GIST was developed and the model was validated in the validation group.ResultsA total of 494 gastric GIST patients were included in this study and allocated to a development group (n = 345) and a validation group (n = 149). In the development group, multivariate logistic regression analysis revealed that tumor size, tumor ulceration, CT growth pattern and monocyte-to- lymphocyte ratio (MLR) were independent risk factors for gastric GIST with high malignancy potential. The AUC of the model were 0.932 (95% CI 0.890-0.974) and 0.922 (95% CI 0.868-0.977) in the development and validation groups, respectively. The best cutoff value for the development group was 0.184, and the sensitivity and specificity at this value were 0.895 and 0.875, respectively. The calibration curves indicated good agreement between predicted and actual observed outcomes, while the DCA indicated that the nomogram model had clinical application.ConclusionsTumor size, tumor ulceration, CT growth pattern and MLR are independent risk factors for high malignancy potential gastric GIST, and a nomogram model developed based on these factors has a high ability to predict the malignant potential of gastric GIST.
引用
收藏
页码:1933 / 1943
页数:11
相关论文
共 50 条
  • [41] Development and validation of a CT-based radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma
    Jia Huang
    Huasheng Yao
    Yexing Li
    Mengyi Dong
    Chu Han
    Lan He
    Xiaomei Huang
    Ting Xia
    Zongjian Yi
    Huihui Wang
    Yuan Zhang
    Jian He
    Changhong Liang
    Zaiyi Liu
    ChineseJournalofCancerResearch, 2021, 33 (01) : 69 - 79
  • [42] Development and validation of a CT-based radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma
    Huang, Jia
    Yao, Huasheng
    Li, Yexing
    Dong, Mengyi
    Han, Chu
    He, Lan
    Huang, Xiaomei
    Xia, Ting
    Yi, Zongjian
    Wang, Huihui
    Zhang, Yuan
    He, Jian
    Liang, Changhong
    Liu, Zaiyi
    CHINESE JOURNAL OF CANCER RESEARCH, 2021, 33 (01) : 69 - +
  • [43] Application of radiomics in predicting the preoperative risk stratification of gastric stromal tumors
    Yang, Li
    Ma, Chong-Fei
    Li, Yang
    Zhang, Chun-Ran
    Ren, Jia-Liang
    Shi, Gao-Feng
    DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY, 2022, 28 (06): : 532 - 539
  • [44] Artificial Intelligence in the Prediction of Gastrointestinal Stromal Tumors on Endoscopic Ultrasonography Images: Development, Validation and Comparison with Endosonographers
    Lu, Yi
    Wu, Jiachuan
    Hu, Minhui
    Zhong, Qinghua
    Er, Limian
    Shi, Huihui
    Cheng, Weihui
    Chen, Ke
    Liu, Yuan
    Qiu, Bingfeng
    Xu, Qiancheng
    Lai, Guangshun
    Wang, Yufeng
    Luo, Yuxuan
    Mu, Jinbao
    Zhang, Wenjie
    Zhi, Min
    Sun, Jiachen
    GUT AND LIVER, 2023, 17 (06) : 874 - 883
  • [45] Preoperative Predictive Factors for Gastrointestinal Stromal Tumors: Analysis of 375 Surgically Resected Gastric Subepithelial Tumors
    Min, Yang Won
    Park, Ha Na
    Min, Byung-Hoon
    Choi, Dongil
    Kim, Kyoung-Mee
    Kim, Sung
    JOURNAL OF GASTROINTESTINAL SURGERY, 2015, 19 (04) : 631 - 638
  • [46] Preoperative Predictive Factors for Gastrointestinal Stromal Tumors: Analysis of 375 Surgically Resected Gastric Subepithelial Tumors
    Yang Won Min
    Ha Na Park
    Byung-Hoon Min
    Dongil Choi
    Kyoung-Mee Kim
    Sung Kim
    Journal of Gastrointestinal Surgery, 2015, 19 : 631 - 638
  • [47] Development and validation of a nomogram prediction model for early mortality in patients with primary malignant cardiac tumors
    Wang, Shaojun
    Jing, Hui
    Yang, Zhiyong
    ANNALS OF TRANSLATIONAL MEDICINE, 2021, 9 (22)
  • [48] RISK FACTORS FOR MUCOSAL ULCERATION IN GASTRIC GASTROINTESTINAL STROMAL TUMORS (GIST)
    Ariam, Eran
    Bermont, Anton
    Matalon, Shay
    Bard, Slava
    Avidan, Benjamin
    Leshno, Moshe
    Broide, Efrat
    Shirin, Haim
    GASTROENTEROLOGY, 2018, 154 (06) : S514 - S514
  • [49] Association between calcification and risk stratification in gastric gastrointestinal stromal tumors
    Luo, Xiao
    Chen, Jinyao
    Fang, Yicheng
    Xu, Qinhui
    Jiang, Fei
    Wang, Guanliang
    ABDOMINAL RADIOLOGY, 2025, 50 (02) : 579 - 588
  • [50] Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study
    Zhong, Jiaojiao
    He, Yunan
    Ma, Jianchi
    Lu, Siyao
    Wu, Yushi
    Zhang, Junmin
    PEERJ, 2021, 9