Can lymphovascular invasion be predicted by preoperative multiphasic dynamic CT in patients with advanced gastric cancer?

Ma, Zhanjun; Liang, Changhong; Huang, Yanqi; Liang, Chun-Ling; Chen, Xin; Huang, Xiaomei; Xiong, Yabing; Liu, Zaiyi

doi:10.1007/s00330-016-4695-6

Cited by 39 publications

(39 citation statements)

References 30 publications

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“…The current AJCC/UICC guidelines do not include LVI as an independent prognostic indicator of GC in the TNM staging system. However, many studies have shown that LVI is an independent risk factor for survival in GC patients [11,13,15,27,28]. Patients with LVI had been reported to be associated with poorer prognosis, and we also obtained similar results.…”

Section: Discussionsupporting

confidence: 83%

“…As a common preoperative examination, CECT was an effective tool for differential diagnosis, preoperative assessment, therapeutic and prognostic evaluation in patients with GC [3,13,18,21]. In this study, we built and validated radiomics models based on CECT for the noninvasive, preoperative individualized prediction of histological LVI status were associated with disease specific recurrence, suggesting that our findings can play an important role in the clinical treatment of GC.…”

Section: Discussionmentioning

confidence: 75%

“…In addition, lymphovascular invasion (LVI) is another important prognostic factor for GC after surgical treatment [9][10][11] and is associated with early recurrence [12]. LVI, which refers to lymphatic and/or blood vessel invasion [13], can only be postoperatively diagnosed by histopathology. The preoperative prediction of LVI is remains difficult.…”

Section: Introductionmentioning

confidence: 99%

“…studied the correlation of the contrast enhancement ratio in multiphase CT images with tumor differentiation and intratumoral microvascular/lymphatic invasion. The study from Ma et.al [13]. showed that LVI is related to the quantitative enhancement parameters of multiphasic dynamic CT. Another study focused on predicting LVI using texture parameters, such as energy and entropy, extracted from contrastenhanced CT images [15].…”

Section: Introductionmentioning

confidence: 99%

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Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study

Chen

Yang

et al. 2020

Cancer Imaging

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Background: To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients. Methods: In total, 160 surgically resected patients were retrospectively analyzed, and seven predictive models were constructed. Three radiomics predictive models were built from radiomics features based on arterial (A), venous (V) and combination of two phase (A + V) images. Then, three Radscores (A-Radscore, V-Radscore and A + V-Radscore) were obtained. Another four predictive models were constructed by the three Radscores and clinical risk factors through multivariate logistic regression. A nomogram was developed to predict LVI by incorporating A + V-Radscore and clinical risk factors. Kaplan-Meier curve and log-rank test were utilized to analyze the outcome of LVI. Results: Radiomics related to tumor size and intratumoral inhomogeneity were the top-ranked LVI predicting features. The related Radscores showed significant differences according to LVI status (P < 0.01). Univariate logistic analysis identified three clinical features (T stage, N stage and AJCC stage) and three Radscores as LVI predictive factors. The Clinical-Radscore (namely, A + V + C) model that used all these factors showed a higher performance (AUC = 0.856) than the clinical (namely, C, including T stage, N stage and AJCC stage) model (AUC = 0.810) and the A + V-Radscore model (AUC = 0.795) in the train cohort. For patients without LVI and with LVI, the median progression-free survival (PFS) was 11.5 and 8.0 months (P < 0.001),and the median OS was 20.2 and 17.0 months (P = 0.3), respectively. In the Clinical-Radscore-predicted LVI absent and LVI present groups, the median PFS was 11.0 and 8.0 months (P = 0.03), and the median OS was 20.0 and 18.0 months (P = 0.05), respectively. N stage, LVI status and Clinical-Radscore-predicted LVI status were associated with disease-specific recurrence or mortality. Conclusions: Radiomics features based on CECT may serve as potential markers to successfully predict LVI and PFS, but no evidence was found that these features were related to OS. Considering that it is a single central study, multi-center validation studies will be required in the future to verify its clinical feasibility.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study

Chen

Yang

et al. 2020

Cancer Imaging

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“…Previous studies indicated that diffuse type (histopathological type according to Lauren classification) and Borrmann type IV (histopathological type according to Borrmann classification) patients were associated with poor prognosis (7,12–14). Besides, several studies demonstrated that lymphatic vascular infiltration (LVI) of the tumor is an additional prognostic marker (6,7,15,16). Taken together, the histopathological status is an important risk factor and exerts excellent ability to classify survival of patients with GCs.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning–Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer

Feng

et al. 2019

Clin Transl Gastroenterol

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INTRODUCTION:Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale imaging factors, especially radiomic features at contrast-enhanced computed tomography, to predict AHS and clinical outcomes of patients with GC.METHODS:Five hundred fifty-four patients with GC (370 training and 184 test) undergoing gastrectomy were retrospectively included. Six radiomic scores (R-scores) related to pT stage, pN stage, Lauren & Borrmann (L&B) classification, World Health Organization grade, lymphatic vascular infiltration, and an overall histopathologic score (H-score) were, respectively, built from 7,000+ radiomic features. R-scores and radiographic factors were then integrated into prediction models to assess AHS. The developed AHS-based Cox model was compared with the American Joint Committee on Cancer (AJCC) eighth stage model for predicting survival outcomes.RESULTS:Radiomics related to tumor gray-level intensity, size, and inhomogeneity were top-ranked features for AHS. R-scores constructed from those features reflected significant difference between AHS-absent and AHS-present groups (P < 0.001). Regression analysis identified 5 independent predictors for pT and pN stages, 2 predictors for Lauren & Borrmann classification, World Health Organization grade, and lymphatic vascular infiltration, and 3 predictors for H-score, respectively. Area under the curve of models using those predictors was training/test 0.93/0.94, 0.85/0.83, 0.63/0.59, 0.66/0.63, 0.71/0.69, and 0.84/0.77, respectively. The AHS-based Cox model produced higher area under the curve than the eighth AJCC staging model for predicting survival outcomes. Furthermore, adding AHS-based scores to the eighth AJCC staging model enabled better net benefits for disease outcome stratification.DISCUSSION:The developed computational approach demonstrates good performance for successfully decoding AHS of GC and preoperatively predicting disease clinical outcomes.

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Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables

Fan

Zhang

et al. 2022

Abdom Radiol

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Can lymphovascular invasion be predicted by preoperative multiphasic dynamic CT in patients with advanced gastric cancer?

Cited by 39 publications

References 30 publications

Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study

Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study

Machine Learning–Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer

Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables

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