A Novel Nomogram for Individually Predicting of Vascular Invasion in Gastric Cancer

Meng, Yongsheng; Huang, Xiaoliang; Liu, Jungang; Chen, Jianhong; Bu, Zhaoting; Wu, Guan-Ling; Xie, Weishun; Jeen, Franco; Huang, Lingling; Tian, Chao; Mo, Xianwei; Tang, Weizhong

doi:10.1177/15330338211004924

Cited by 7 publications

(13 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meng Y et al. ( 12 ) proposed a nomogram consisted of clinical indicators with AUC of 0.774 in the testing datasets. Chen X et al.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Spectral CT for preoperative prediction of lymphovascular invasion in resectable gastric cancer: With external prospective validation

Wang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

ObjectivesTo develop and externally validate a spectral CT based nomogram for the preoperative prediction of LVI in patients with resectable GC.MethodsThe two centered study contained a retrospective primary dataset of 224 pathologically confirmed gastric adenocarcinomas (161 males, 63 females; mean age: 60.57 ± 10.81 years, range: 20-86 years) and an external prospective validation dataset from the second hospital (77 males and 35 females; mean age, 61.05 ± 10.51 years, range, 31 to 86 years). Triple-phase enhanced CT scans with gemstone spectral imaging mode were performed within one week before surgery. The clinicopathological characteristics were collected, the iodine concentration (IC) of the primary tumours at arterial phase (AP), venous phase (VP), and delayed phase (DP) were measured and then normalized to aorta (nICs). Univariable analysis was used to compare the differences of clinicopathological and IC values between LVI positive and negative groups. Independent predictors for LVI were screened by multivariable logistic regression analysis in primary dataset and used to develop a nomogram, and its performance was evaluated by using ROC analysis and tested in validation dataset. Its clinical use was evaluated by decision curve analysis (DCA).ResultsTumor thickness, Borrmann classification, CT reported lymph node (LN) status and nICDP were independent predictors for LVI, and the nomogram based on these indicators was significantly associated with LVI (P<0.001). It yielded an AUC of 0.825 (95% confidence interval [95% CI], 0.769-0.872) and 0.802 (95% CI, 0.716-0.871) in primary and validation datasets (all P<0.05), with promising clinical utility by DCA.ConclusionThis study presented a dual energy CT quantification based nomogram, which enables preferable preoperative individualized prediction of LVI in patients with GC.

show abstract

“…Meng Y et al. ( 12 ) proposed a nomogram consisted of clinical indicators with AUC of 0.774 in the testing datasets. Chen X et al.…”

Section: Discussionmentioning

confidence: 99%

“…Meng Y et al. ( 12 ) have developed a nomogram based on pre-operative features to predict LVI, but without involving quantitative indicators on enhanced CT. Ma Z et al. ( 13 ) found correlation of LVI with CT attenuation values on multiphasic enhanced CT, but without external validation of the results.…”

Section: Introductionmentioning

confidence: 99%

Spectral CT for preoperative prediction of lymphovascular invasion in resectable gastric cancer: With external prospective validation

Wang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, the Lauren classi cation and quantitative features were incorporated into the models to assist with predicting the LVI status for the rst time. Previous studies [14,[30][31][32][33] have shown that Lauren classi cation and LVI status are independent factors affecting the e cacy of and prognosis after chemotherapy, indicating that our research has important signi cance in the treatment and prognosis of GC.…”

Section: Discussionmentioning

confidence: 65%

“…Studies based on texture analysis of CT images have shown that CT radiomics features combined with clinicopathological factors such as T stage, N stage, and American Joint Committee on Cancer (AJCC) stage can be used to establish predictive models that can evaluate LVI status preoperatively [7]. Some studies used clinicopathological factors in combination with nomograms to predict vascular invasion in GC; however, these studies did not incorporate Lauren classi cation into their models as predictive factor, nor did they obtain quantitative information from CT images, such as the contrast enhancement rate [14]. Therefore, we established radiomics models based on CECT images to evaluate LVI status before surgery and proposed two hypotheses.…”

Section: Introductionmentioning

confidence: 99%

Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in patients with gastric cancer with Lauren classification

Wang

Chen

Han

et al. 2022

Preprint

View full text Add to dashboard Cite

Objective Lymphovascular invasion (LVI) is an independent risk factor of gastric cancer (GC) prognosis; however, LVI cannot be determined preoperatively. We explored whether a model based on contrast-enhanced computed tomography (CECT) radiomics features combined with clinicopathological factors can evaluate preoperative LVI in patients with GC with a clear Lauren classification. Methods We retrospectively analyzed 495 patients with GC, including 288 with LVI. The extracted CECT features were standardized, followed by consistency testing, correlation analysis, univariate analysis, and multivariate least absolute shrinkage and selection operator (LASSO) regression analysis. The radiomics score (Radcore) was calculated for each patient. Univariate (p < 0.10) and multivariate (p < 0.050) analyses were used to identify the clinical risk factors associated with LVI. Accordingly, three prediction models were established: Clinical + Arterial phase_Radcore, Clinical + Venous phase_Radcore, and a combined model. The prediction performances of the models were verified using receiver operating characteristics, decision curve analysis, and calibration curves in the testing dataset. The relationship between Lauren classification and LVI was analyzed using a histogram. Results The areas under the curve of the combined model were 0.8629 (95% confidence interval [CI], 0.8247–0.9011) and 0.8343 (95% CI, 0.7673–0.9012) in the training and testing datasets, respectively. The combined model had superior performance compared with the other models. Diffuse-type GC according to the Lauren classification accounted for 43.4% of LVI cases. Conclusions CECT-based radiomics models can effectively predict the preoperative LVI status in patients with GC with Lauren classification. The prediction ability of the models was effectively improved by incorporating clinicopathological factors.

show abstract

“…Microvascular invasion (MVI) is an early pathological manifestation of LN and other organ metastasis in malignant tumors. It involves the invasion of tumor cells or tumor thrombus into the blood vessels and/or lymphatic walls near the tumor, playing a key role in tumor cell proliferation and metastasis [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Prediction of microvascular invasion based on CT in gastric cancer

Liu,

Ding,

Guo

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Microvascular invasion (MVI) is an important step in cancer cell migration and invasion, and it is also a significant factor in predicting tumor recurrence and prognosis. Building a nomogram based on CT image features and clinicopathological data to predict preoperative MVI in gastric cancer (GC). Methods Retrospective study enrolled 358 patients with surgically proven GC. Univariate and multivariate logistic regression analyses were performed to identify the predictors for the model and establish a nomogram for MVI. The performance of the model was evaluated using ROC, accuracy, and C index. Internal validation of the model was conducted using the bootstrap resampling method. Difference in the area under the curve (AUC) between the two models was evaluated using the Delong test. Random forest algorithm is used to extract important risk factors for MVI. Results Mural stratification, Lauren classification and Albumin (Alb) were found to be independent influencing factors for MVI. The nomogram model incorporating these three factors showed significantly better performance compared to the original model that did not include CT parameters (P < 0.05). The AUC of the model was 0.779 (95% CI 0.774–0.868), and the average AUC of the bootstrap sample was 0.813. The sensitivity, specificity, and accuracy of the model were 65.6%, 86.0%, and 70.7%, respectively. Conclusion The nomogram based on CT image features and clinicopathological data demonstrated good predictive value for MVI in GC. This nomogram can provide valuable baseline information for individualized treatment of GC.

show abstract

A Novel Nomogram for Individually Predicting of Vascular Invasion in Gastric Cancer

Cited by 7 publications

References 52 publications

Spectral CT for preoperative prediction of lymphovascular invasion in resectable gastric cancer: With external prospective validation

Spectral CT for preoperative prediction of lymphovascular invasion in resectable gastric cancer: With external prospective validation

Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in patients with gastric cancer with Lauren classification

Prediction of microvascular invasion based on CT in gastric cancer

Contact Info

Product

Resources

About