Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features

Ji, Lin; Wang, Fu-Hai; Wang, Zu‐Kai; Wang, Jia-Bin; Zheng, Chao‐Hui; Li, Ping; Huang, Chang‐Ming; Xie, Jian‐Wei

doi:10.1007/s11547-023-01637-2

Cited by 12 publications

(3 citation statements)

References 50 publications

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“…An appealing target of radiomics in an oncological setting is the possibility to successfully predict a prognosis through the computational analysis of multiple preoperative parameters [ 39 , 40 , 41 , 42 ]. The ultimate purpose is to obtain individualized clinical decision-making and to increase patients’ survival rates [ 112 , 113 , 114 , 115 ].…”

Section: Resultsmentioning

confidence: 99%

Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and limits of Radiomics

De Muzio,

Pellegrino,

Fusco

et al. 2023

Diagnostics

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Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno–histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.

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Section: Resultsmentioning

confidence: 99%

Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and limits of Radiomics

De Muzio,

Pellegrino,

Fusco

et al. 2023

Diagnostics

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“…Previous studies based on enhanced-CT radiomics model for GIST had been reported. For instance, Lin et al developed a contrast-enhanced (CE)-CT-based preoperative risk stratification nomogram predicting GIST mitosis ( 18 ). Chen et al used a Residual Neural Network to build a model for predicting relapse-free survival (RFS) after surgical resection, achieving an area under the curve of 0.887 ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a computed tomography-based radiomics signature to predict “highest-risk” from patients with high-risk gastrointestinal stromal tumor

Zheng,

Liao,

Chen

et al. 2024

J Gastrointest Oncol

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Background Some patients with high-risk gastrointestinal stromal tumor (GIST) experience disease progression after complete resection and adjuvant therapy. It is of great significance to distinguish these patients among those with high-risk GIST. Radiomics has been demonstrated as a promising tool to predict various tumors prognosis. Methods From January 2006 to December 2018, a total of 100 high-risk GIST patients (training cohort: 60; validation cohort: 40) from Guangdong Provincial People’s Hospital with preoperative enhanced computed tomography (CT) images were enrolled. The radiomics features were extracted and a risk score was built using least absolute shrinkage and selection operator-Cox model. The clinicopathological factors were analyzed and a nomogram was established with and without radiomics risk score. The concordance index (C-index), calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomograms. Results We selected 11 radiomics features associated with recurrence or metastasis. The risk score was calculated and significantly associated with disease-free survival (DFS) in both the training and validation group. Cox regression analysis showed that Ki67 was an independent risk factor for DFS [P=0.004, hazard ratio 4.615, 95% confidence interval (CI): 1.624–13.114]. The combined radiomics nomogram, which integrated the radiomics risk score and significant clinicopathological factors, showed good performance in predicting DFS, with a C-index of 0.832 (95% CI: 0.761–0.903), which was better than the clinical nomogram (C-index 0.769, 95% CI: 0.679–0.859) in training cohort. The calibration curves and the DCA plot suggested satisfying accuracy and clinical utility of the model. Conclusions The CT-based radiomics nomogram, combined with the clinicopathological factors and risk score, has good potential to assess the recurrence or metastasis of patients with high-risk GIST.

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“…S8) continues to be a clinically promising strategy. Despite the study's limited inclusion of clinical variables, it remains an effective approach for feature selection, particularly in clinical scenarios involving high-dimensional characteristics like genomes 29,30 , proteomics 31,32 , and radiomics [14][15][16][17] .…”

Section: Discussionmentioning

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

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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.

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Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features

Cited by 12 publications

References 50 publications

Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and limits of Radiomics

Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and limits of Radiomics

Development and validation of a computed tomography-based radiomics signature to predict “highest-risk” from patients with high-risk gastrointestinal stromal tumor

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

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