Published online Feb 14, 2024. doi: 10.3748/wjg.v30.i6.542
Peer-review started: October 20, 2023
First decision: December 5, 2023
Revised: December 18, 2023
Accepted: January 15, 2024
Article in press: January 15, 2024
Published online: February 14, 2024
Processing time: 107 Days and 23 Hours
The research background involves the critical role of lymphovascular invasion (LVI) and perineural invasion (PNI) as prognostic factors in gastric cancer (GC), indicating an increased risk of metastasis and poor patient outcomes. The ability to accurately predict LVI/PNI status preoperatively is significant for identifying high-risk patients and guiding treatment decisions. Conventional models using standard computed tomography (CT) images to predict these invasions have had limited success; thus, this study proposes a new approach using spectral CT imaging and machine learning to improve prediction accuracy.
The research is motivated by the necessity to improve preoperative predictions of LVI and PNI in GC patients, addressing the limitations of conventional CT imaging techniques. The primary objective is to develop a more precise predictive model by integrating spectral CT imaging parameters with clinical markers through machine learning algorithms. Successfully achieving this could refine preoperative assessments, aid in risk stratification, inform treatment planning, and potentially elevate future diagnostic strategies in the field of GC.
The primary objective of the research is to test the hypothesis that an optimal fusion of spectral CT parameters with clinical markers using a machine learning method can more accurately predict LVI or PNI status in GC patients before surgery. Specifically, the study analyzed a set of clinical indicators, such as preoperative CT evaluation of gastric wall invasion depth, lymph node metastasis, extramural vein invasion, and serum tumor markers, along with quantitative spectral CT parameters. The research aimed to develop a logistic regression (LR)-based nomogram model that integrates these clinical indicators with spectral CT parameters to predict histological LVI and PNI statuses in GC. Realizing these objectives has significant implications for improving preoperative staging and tailoring appropriate treatment plans for GC patients, thus advancing future research and diagnostic strategies in this field.
The research adopted a retrospective dataset and a LR-based nomogram model that incorporated clinical indicators with quantitative spectral CT parameters for the preoperative prediction of lymphovascular and PNI in GC patients. Methods included using statistical software for univariate analysis and correlation-based feature selection, along with 10-fold cross-validation and information gain ranking within a training-validation cohort framework to select significant features. The model’s performance was evaluated through receiver operating characteristic (ROC) analysis, calibration using the Hosmer-Lemeshow test and bootstrapping, and decision curve analysis to quantify potential net benefits. These methods highlighted novel approaches in integrating machine learning with available clinical and imaging data to potentially improve preoperative assessment and treatment planning.
The research results demonstrated that CT values and parameters such as iodine concentration and normalized iodine concentration were significantly higher in the LVI/PNI-positive group across all phases (arterial, venous, and equilibrium) when compared to the LVI/PNI-negative group, with statistical significance (P < 0.05). Good inter- and intra-observer agreement was observed for these spectral CT parameters, as indicated by the inter-observer intraclass correlation coefficients (ICC) values ranging from 0.766 to 0.955 and intra-observer ICC values from 0.759 to 0.945. This reproducibility led to their retention for feature selection in developing the predictive model. These findings contribute to the overall research in the field by introducing reproducible and quantifiable spectral CT parameters as reliable predictors for LVI/PNI status in GC patients. The study opens avenues for further investigation into refining and validating these spectral CT-based assessment methods, possibly addressing existing challenges in preoperative staging and treatment planning.
The study proposes a novel application of spectral CT imaging integrated with machine learning to preoperatively predict lymphovascular and PNI in patients with GC. Through the use of a logistic regression-based nomogram model, the research introduces a new method for combining clinical indicators with quantitative imaging parameters to improve the accuracy of preoperative assessments. This contributes to the field by proposing an alternative to the current postoperative pathology methods and could improve treatment planning by enabling non-invasive, individualized risk stratification prior to surgery.
The direction of future research following this study is anticipated to focus on validating the noninvasive spectral CT-based machine learning model in prospective multicenter studies to confirm its clinical utility in preoperative risk stratification. Additionally, further research may explore the integration of this model in routine clinical practice to assess its impact on patient management, particularly in the identification of those who may benefit from more aggressive treatment strategies preoperatively. By refining and expanding the predictive capabilities of spectral CT imaging, future research could pave the way for improved individualized treatment planning and outcomes in GC care.