Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis

Jia, Xiaoxuan; Wan, Lijuan; Chen, Xiaoshan; Ji, Wan-Ying; Huang, Shaoqing; Qi, Yuangang; Cui, Jingjing; Wei, Shengcai; Cheng, Jin; Chai, Fan; Feng, Caizhen; Liu, Yulu; Zhang, Hongmei; Sun, Ying‐Shi; Hong, Nan; Rao, Shengxiang; Zhang, Xinhua; Xiao, Y.; Ye, Yingjiang; Tang, Lei; Wang, Yi

doi:10.1007/s00330-022-09228-x

Cited by 15 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of interest, one study tried to identify CT variables that could help discriminate between very-low and intermediate risk among 151 small gastric GISTs (with a size between 1 and 2 cm). 62 This study found that radiomics features showed better performances (AUCs of .866, .812, and .766, in the training, validation, and testing cohorts, respectively) than visual evaluation using morphological high-risk features, such as calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration. 62 In the same line, Song et al built a radiomics nomogram derived from CT-based texture analysis features that achieved an AUC of .905 in the validation cohort to discriminate between very low-risk and intermediate-high risk GIST.…”

Section: Risk Stratification and Survival Predictionmentioning

confidence: 74%

“…62 This study found that radiomics features showed better performances (AUCs of .866, .812, and .766, in the training, validation, and testing cohorts, respectively) than visual evaluation using morphological high-risk features, such as calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration. 62 In the same line, Song et al built a radiomics nomogram derived from CT-based texture analysis features that achieved an AUC of .905 in the validation cohort to discriminate between very low-risk and intermediate-high risk GIST. 63 Similarly, Palatresi et al found significant association between radiomics features from CT data and Miettinen classification.…”

Section: Risk Stratification and Survival Predictionmentioning

confidence: 74%

See 1 more Smart Citation

CT and MRI of Gastrointestinal Stromal Tumours: New Trends and Perspectives

et al. 2023

View full text Add to dashboard Cite

Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c- KIT proto-oncogene antigen. Expression of the c- KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.

show abstract

Section: Risk Stratification and Survival Predictionmentioning

confidence: 74%

Section: Risk Stratification and Survival Predictionmentioning

confidence: 74%

CT and MRI of Gastrointestinal Stromal Tumours: New Trends and Perspectives

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Radiomics is an artificial intelligence technology that extracts features such as shape, intensity, texture, and wavelet from images based on images and converts them into high-dimensional quantifiable quantitative feature data to further reflect the biological information of lesions. It can provide relevant information for disease diagnosis, prognosis evaluation, and efficacy prediction (20)(21)(22). To date, few studies have used radiomics to solve the problem of pneumonia identification.…”

Section: Discussionmentioning

confidence: 99%

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia

Meng,

Wang,

Zhuo

et al. 2024

Front. Med.

View full text Add to dashboard Cite

PurposeThis study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs.Materials and methodsIn a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively.ResultsFor the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35.ConclusionThe combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.

show abstract

“…The traditional design of the cox nomogram normally involves a two-stage process of single-factor and multi-factor selection before nomogram construction 7 – 11 . However, with the widespread usage of Least Absolute Shrinkage and Selection Operator (LASSO) in clinical research as a feature selection and standalone model, Penalized Models have steadily developed as a unique clinical modeling strategy to replace the old two-stage method 14 – 17 . In the area of survival prediction for gastric GISTs, whether the Penalized Cox Regression Model 18 can replace the usual two-stage modeling technique has not been compared in any study.…”

Section: Introductionmentioning

confidence: 99%

Evaluating nomogram models for predicting survival outcomes in gastric gastrointestinal stromal tumors with SEER database analysis

Yong,

Li,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Gastrointestinal stromal tumors (GISTs) predominantly develop in the stomach. While nomogram offer tremendous therapeutic promise, there is yet no ideal nomogram comparison customized specifically for handling categorical data and model selection related gastric GISTs. (1) We selected 5463 patients with gastric GISTs from the SEER Research Plus database spanning from 2000 to 2020; (2) We proposed an advanced missing data imputation algorithm specifically designed for categorical variables; (3) We constructed five Cox nomogram models, each employing distinct methods for the selection and modeling of categorical variables, including Cox (Two-Stage), Lasso-Cox, Ridge-Cox, Elastic Net-Cox, and Cox With Lasso; (4) We conducted a comprehensive comparison of both overall survival (OS) and cancer-specific survival (CSS) tasks at six different time points; (5) To ensure robustness, we performed 50 randomized splits for each task, maintaining a 7:3 ratio between the training and test cohorts with no discernible statistical differences. Among the five models, the Cox (Two-Stage) nomogram contains the fewest features. Notably, at Near-term, Mid-term, and Long-term intervals, the Cox (Two-Stage) model attains the highest Area Under the Curve (AUC), top-1 ratio, and top-3 ratio in both OS and CSS tasks. For the prediction of survival in patients with gastric GISTs, the Cox (Two-Stage) nomogram stands as a simple, stable, and accurate predictive model with substantial promise for clinical application. To enhance the clinical utility and accessibility of our findings, we have deployed the nomogram model online, allowing healthcare professionals and researchers worldwide to access and utilize this predictive tool.

show abstract

Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis

Cited by 15 publications

References 32 publications

CT and MRI of Gastrointestinal Stromal Tumours: New Trends and Perspectives

CT and MRI of Gastrointestinal Stromal Tumours: New Trends and Perspectives

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia

Evaluating nomogram models for predicting survival outcomes in gastric gastrointestinal stromal tumors with SEER database analysis

Contact Info

Product

Resources

About