Development and validation of a multiphase CT radiomics nomogram for the preoperative prediction of lymphovascular invasion in patients with gastric cancer

Dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics for preoperative prediction of lymph node metastasis in gastric cancer

OBJECTIVE

To determine whether intratumoral and peritumoral radiomics derived from dual-phase contrast-enhanced CT imaging could predict lymph node metastasis (LNM) in gastric cancer.

METHODS

Patients with gastric cancer from January 2017 to January 2022 were retrospectively collected and were randomly divided into training cohort (n = 287) and test cohort (n = 121) with a ratio of 7: 3. Clinical features and traditional radiological features were analyzed to construct clinical model. Radiomics features based on intratumoral (ITV) and peritumoral volumetric (PTV) regions of the tumor were extracted and screened to construct radiomics models. Clinical-radiomics combined model was constructed by the most predictive radiomics features and clinical independent predictors. The correlation between LNM predicted by the best model and 2-year disease-free survival (DFS) was evaluated by the Kaplan-Meier analysis.

RESULTS

CT-LNM and CT-T stage were independent predictors of LNM. Compared with other radiomics models, ITV + PTV on atrial and venous phase (ITV + PTV-AP + VP) radiomics model presented moderate AUCs of 0.679 and 0.670 in the training cohort and validation cohort, respectively. Among the models, clinical-radiomics combined model achieved the highest AUC of 0.894 and 0.872 in the training and test cohorts, and 0.744 and 0.784 in the T1-2 and T3-4 subgroups, respectively. Clinical-radiomics combined model based LNM could stratify patients into high-risk and low-risk groups, and 2-year DFS of high-risk group was significantly lower than that of low-risk group (p < 0.001).

CONCLUSION

Clinical-radiomics combined model integrating CT-LNM, CT-T stage, and ITV-PTV-AP + VP radiomics features could predict LNM, and this combined model based LNM was associated with 2-year DFS.

Dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics for preoperative prediction of lymphovascular invasion in gastric cancer

BACKGROUND

To develop and validate a dual-phase contrast-enhanced computed tomography (CT)-based intratumoral and peritumoral radiomics for the prediction of lymphovascular invasion (LVI) in patients with gastric cancer.

METHOD

Three hundred and eighty-three patients with gastric cancer (training cohort, 269 patients; test cohort, 114 patients) were retrospectively enrolled between January 2017 and June 2023. Radiomics features were extracted from the intratumoral volume (ITV) and peritumoral volume (PTV) on CT images at arterial phase (AP) and venous phase (VP), and selected by the least absolute shrinkage and selection operator. Radiomics models were constructed by logistic regression. The clinical-radiomics combined model incorporating the most predictive radiomics signature and clinical risk factors were developed with multivariate analysis. Receiver operating characteristic (ROC) curves were used to evaluate the prediction performance of models.

RESULTS

Clinical model comprised of three clinical risk factors including tumor differentiation, CT-reported lymph node metastasis status and CT-TNM staging showed good performance with an area under the ROC curve (AUC) of 0.804 and 0.825 in the training and test cohort, respectively. Compared with the other radiomics models, dual-phase (AP + VP) CT-based ITV + PTV radiomics model presented superior AUC of 0.844 and 0.835 in the training and test cohort, respectively. Clinical-radiomics combined model further improved the discriminatory performance (AUC, 0.903) in the training and test cohort (AUC, 0.901). Decision curve analysis confirmed the net benefit of clinical-radiomics combined model. Subgroup analyses showed that the clinical-radiomics nomogram showed the best performance with an AUC of 0.879 and 0.883 for predicting LVI in T1-T2 and T3-T4 gastric cancer compared with the clinical model and the ITV + PTV-AP + VP radiomics model, respectively.

CONCLUSIONS

Clinical-radiomics combined model integrating clinical risk factors and dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics signatures provided favorable performance for predicting LVI in gastric cancer.

Multiparametric MRI-based Radiomics Analysis for Prediction of Lymph Node Metastasis and Survival Outcome in Gastric Cancer: A Dual-center Study

RATIONALE AND OBJECTIVES

Gastric cancer (GC) is highly heterogeneous, and accurate preoperative assessment of lymph node status remains challenging. We aimed to develop a multiparametric MRI-based model for predicting lymph node metastasis (LNM) in GC and to explore its prognostic implications.

MATERIALS AND METHODS

In this dual-center retrospective study, 479 GC patients undergoing preoperative multiparametric MRI before radical gastrectomy were enrolled. 1595 imaging features were extracted from T2-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted imaging (cT1WI), respectively. Feature selection steps, including the Boruta and Simulated Annealing algorithms, were conducted to identify key features. Different radiomics models (RMs) based on the single- and multiple-sequence were constructed. The performance of various RMs in predicting LNM was assessed in terms of discrimination, calibration, and clinical usefulness. Additionally, Kaplan-Meier survival curves were employed to estimate differences in disease-free survival (DFS) and overall survival (OS).

RESULTS

The multi-sequence radiomics model (MRM) achieved area under the curves (AUCs) of 0.774 [95 % confidence interval (CI), 0.703-0.845], 0.721 (95 % CI, 0.593-0.850), and 0.720 (95 % CI, 0.639-0.801) in the training and two validation cohorts, respectively, outperforming the single-sequence RMs. Notably, the RM derived from cT1WI demonstrated superior performance compared to the other two single-sequence models. Furthermore, the proposed MRM exhibited a significant association with DFS and OS in GC patients (both P < 0.05).

CONCLUSION

The multiparametric MRI-based radiomics model, derived from primary lesions, demonstrated moderate performance in predicting LNM and survival outcomes in patients with GC, which could provide valuable insights for personalized treatment strategies.

Construction of a predictive model for gastric cancer neuroaggression and clinical validation analysis: A single-center retrospective study

BACKGROUND

This study investigated the construction and clinical validation of a predictive model for neuroaggression in patients with gastric cancer. Gastric cancer is one of the most common malignant tumors in the world, and neuroinvasion is the key factor affecting the prognosis of patients. However, there is a lack of systematic analysis on the construction and clinical application of its prediction model. This study adopted a single-center retrospective study method, collected a large amount of clinical data, and applied statistics and machine learning technology to build and verify an effective prediction model for neuroaggression, with a view to providing scientific basis for clinical treatment decisions and improving the treatment effect and survival rate of patients with gastric cancer.

AIM

To investigate the value of a model based on clinical data, spectral computed tomography (CT) parameters and image omics characteristics for the preoperative prediction of nerve invasion in patients with gastric cancer.

METHODS

A retrospective analysis was performed on 80 gastric cancer patients who underwent preoperative energy spectrum CT at our hospital between January 2022 and August 2023, these patients were divided into a positive group and a negative group according to their pathological results. Clinicopathological data were collected, the energy spectrum parameters of primary gastric cancer lesions were measured, and single factor analysis was performed. A total of 214 image omics features were extracted from two-phase mixed energy images, and the features were screened by single factor analysis and a support vector machine. The variables with statistically significant differences were included in logistic regression analysis to construct a prediction model, and the performance of the model was evaluated using the subject working characteristic curve.

RESULTS

There were statistically significant differences in sex, carbohydrate antigen 199 expression, tumor thickness, Lauren classification and Borrmann classification between the two groups (all P < 0.05). Among the energy spectrum parameters, there were statistically significant differences in the single energy values (CT60-CT110 keV) at the arterial stage between the two groups (all P < 0.05) and statistically significant differences in CT values, iodide group values, standardized iodide group values and single energy values except CT80 keV at the portal vein stage between the two groups (all P < 0.05). The support vector machine model with the largest area under the curve was selected by image omics analysis, and its area under the curve, sensitivity, specificity, accuracy, P value and parameters were 0.843, 0.923, 0.714, 0.925, < 0.001, and c:g 2.64:10.56, respectively. Finally, based on the logistic regression algorithm, a clinical model, an energy spectrum CT model, an imaging model, a clinical + energy spectrum model, a clinical + imaging model, an energy spectrum + imaging model, and a clinical + energy spectrum + imaging model were established, among which the clinical + energy spectrum + imaging model had the best efficacy in diagnosing gastric cancer nerve invasion. The area under the curve, optimal threshold, Youden index, sensitivity and specificity were 0.927 (95%CI: 0.850-1.000), 0.879, 0.778, 0.778, and 1.000, respectively.

CONCLUSION

The combined model based on clinical features, spectral CT parameters and imaging data has good value for the preoperative prediction of gastric cancer neuroinvasion.

Energy spectrum computed tomography multi-parameter imaging in preoperative assessment of vascular and neuroinvasive status in gastric cancer

BACKGROUND

Vascular and nerve infiltration are important indicators for the progression and prognosis of gastric cancer (GC), but traditional imaging methods have some limitations in preoperative evaluation. In recent years, energy spectrum computed tomography (CT) multiparameter imaging technology has been gradually applied in clinical practice because of its advantages in tissue contrast and lesion detail display.

AIM

To explore and analyze the value of multiparameter energy spectrum CT imaging in the preoperative assessment of vascular invasion (LVI) and nerve invasion (PNI) in GC patients.

METHODS

Data from 62 patients with GC confirmed by pathology and accompanied by energy spectrum CT scanning at our hospital between September 2022 and September 2023, including 46 males and 16 females aged 36-71 (57.5 ± 9.1) years, were retrospectively collected. The patients were divided into a positive group (42 patients) and a negative group (20 patients) according to the presence of LVI/PNI. The CT values (CT40 keV, CT70 keV), iodine concentration (IC), and normalized IC (NIC) of lesions in the upper energy spectrum CT images of the arterial phase, venous phase, and delayed phase 40 and 70 keV were measured, and the slopes of the energy spectrum curves [K (40-70)] from 40 to 70 keV were calculated. Arterial phase combined parameter, venous phase combined parameters (VP-ALLs), and delayed phase association parameters were calculated for patients with late-stage disease. The differences in the energy spectrum parameters between the positive and negative groups were compared, receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC), sensitivity, specificity, and optimal threshold were calculated to measure the diagnostic efficiency of each parameter.

RESULTS

In the delayed phase, the CT40 keV, CT70 keV, K (40-70), IC, NIC, and CT70 keV and the NIC in the upper arterial and venous phases of energy spectrum CT were greater in the LVI/PNI-positive group than in the LVI-negative group. The representative parameters for the arterial phase NIC were 0.14 ± 0.04 in the positive group and 0.12 ± 0.04 in the negative group. The venous phase NIC was 0.5 (0.5, 0.6) in the positive group and 0.4 (0.4, 0.5) in the negative group. Last, for the delayed phase NIC, it was 0.6 ± 0.1 in the positive group and 0.5 ± 0.1 in the negative group (all P values are less than 0.05). ROC curve analysis demonstrated that the diagnostic efficacy of each parameter during the venous stage was superior to that during the arterial and delayed stages. Furthermore, the diagnostic efficacy of the combined parameter throughout all three stages was superior to that of any single parameter. The AUC, sensitivity, and specificity of the optimal parameter, VP-ALL, were 0.931 (95% confidence interval: 0.872-0.990), 80.95%, and 95.00%, respectively.

CONCLUSION

When assessing the condition of LVI and PNI (perineural invasion) in patients with GC prior to surgery, the ability to diagnose these conditions using venous stage parameters was superior to that using arterial stage and delayed stage parameters. Furthermore, the diagnostic accuracy of using a combination of parameters was better than that of using individual parameters alone.

A nomogram model of spectral CT quantitative parameters and clinical characteristics predicting lymphovascular invasion of gastric cancer

Objective

The study established a nomogram based on quantitative parameters of spectral computed tomography (CT) and clinical characteristics, aiming to evaluate its predictive value for preoperative lymphovascular invasion (LVI) in gastric cancer (GC).

Methods

From December 2019 to December 2021, 171 patients with pathologically confirmed GC were retrospectively collected with corresponding clinical data and spectral CT quantitative data. Patients were divided into LVI-positive and LVI-negative groups based on their pathological results. The univariate and multivariate logistic regression analyses were used to identify the risk factors and construct a nomogram. The calibration curve and receiver operating characteristic (ROC) curve were adopted to evaluate the predictive accuracy of nomogram.

Results

Four clinical characteristics or spectral CT quantitative parameters, including Borrmann classification (P = 0.039), CA724 (P = 0.007), tumor thickness (P = 0.031), and iodine concentration in the venous phase (VIC) (P = 0.004) were identified as independent factors for LVI in GC patients. The nomogram was established based on the four factors, which had a potent predictive accuracy in the training, internal validation and external validation cohorts, with the area under the ROC curve (AUC) of 0.864 (95% CI, 0.798-0.930), 0.964 (95% CI, 0.903-1.000) and 0.877 (95% CI, 0.759-0.996), respectively.

Conclusion

This study constructed a comprehensive nomogram consisting spectral CT quantitative parameters and clinical characteristics of GC, which exhibited a robust efficiency in predicting LVI in GC patients.