Prediction of Ki-67 expression and malignant potential in gastrointestinal stromal tumors: novel models based on CE-CT and serological indicators.

Autor: Tian J; Radiology Department, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China., Chen W; Radiology Department, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China. cghcwz@163.com.
Jazyk: angličtina
Zdroj: BMC cancer [BMC Cancer] 2024 Nov 16; Vol. 24 (1), pp. 1412. Date of Electronic Publication: 2024 Nov 16.
DOI: 10.1186/s12885-024-13172-y
Abstrakt: Purpose: To identify more reliable imaging and serological indicators for predicting Ki-67 expression and malignant potential in gastrointestinal stromal tumors, as well as to develop a preoperative prediction model with clinical utility.
Patients and Methods: This study retrospectively analyzed patients with gastrointestinal stromal tumors (GIST) diagnosed at the First Affiliated Hospital of Jinzhou Medical University between May 2018 and May 2024. Univariate logistic analyses, two-way stepwise regression, P-value stepwise regression, and LASSO regression were employed to screen for Ki-67 high expression and high malignant potential risk factors associated with GIST. Models were established using various regression methods; Nomograms, calibration curves, and clinical decision curves were generated for the two best prediction models.
Results: Two-way stepwise regression analysis revealed that diameter (P=0.037; OR=1.22; 95% CI: 1.01 - 1.46), growth pattern (extraluminal type: P=0.028; OR=3.54; 95% CI: 1.14 - 10.94), enhancement model (P=0.099; OR=0.39; 95% CI: 0.12 - 1.20), EVFDM (P=0.069; OR=0.43; 95% CI: 0.17 - 1.07), PLR (P=0.099; OR=3.06; 95% CI: 0.81 - 11.59), and OPNI (P=0.058; OR=2.38; 95% CI: 0.97 - 5.84) are identified as independent risk factors for Ki-67 expression. Utilizing the two-way stepwise regression model to predict Ki-67 expression, the area under the curve (AUC) for the training group was 0.865 (95% CI: 0.807-0.922), while for the validation group it was 0.784 (95% CI: 0.631-0.937). The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for the training group were 153.360 and 174.619, respectively. Two-way stepwise regression analysis revealed that volume (P < .001, OR = 1.06; 95% CI: 1.03 - 1.09), contour (P = 0.066; OR = 0.17; 95% CI: 0.05 - 0.62), ulcer (P = 0.094; OR = 0.16; 95% CI: 0.03 - 0.98), IBSC (P = 0.008; OR = 5.27; 95% CI: 1.57 - 17.69), and OPNI (P = 0.045; OR = 0.22; 95% CI: 0.05 - 0.96) are independent risk factors for malignant potential. Utilizing the two-way stepwise regression model to predict malignant potential, the AUC for the training group was 0.950 (95% CI: 0.920 - 0.980), while for the validation group it was 0.936 (95% CI: 0.867 - 1.000). The AIC and BIC values for the training group were 96.330 and 114.552, respectively.
Conclusion: Diameter, growth pattern, enhancement pattern, EVFDM, PLR, and OPNI are independent risk factors for GIST with high Ki-67 expression. Additionally, volume, contour, ulceration, IBSC, and OPNI serve as independent risk factors for GIST with high malignant potential. The preoperative models developed using CT images can predict the malignant potential and Ki-67 expression status of GIST to a certain extent. When combined with serological indicators, these models' predictive performance can be further enhanced.
Competing Interests: Declarations Ethics approval and consent to participate This study adhered to ethical standards and was approved by the Ethics Review Committee of the First Affiliated Hospital of Jinzhou Medical University (No: KYLL202445), which waived the need for consent to participate due to the retrospective design of this research. Consent for publication Not applicable. Competing interests The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
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