Can Lymphovascular Invasion be Predicted by Preoperative Contrast-Enhanced CT in Esophageal Squamous Cell Carcinoma?

Chen, Wei; Wang, Yating; Bai, Genji; Hu, Chunhong

doi:10.1177/15330338221111229

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…Based on these viewpoints, LVI has great clinical guiding significance for the selection of treatment strategies in patients with SESCC. More importantly, LVI is difficult to be diagnosed before surgery and is mostly identified by postoperative pathological examination ( 17 ). However, prior studies on LNM in patients with SESCC included LVI as one of the predictive risk factors in statistical analysis ( 31 – 34 ), which may greatly limit the clinical application of the findings from these studies.…”

Section: Discussionmentioning

confidence: 99%

“…Besides, LVI can greatly increase the risk of local malignant tumors micrometastasis ( 15 ) and lead to poor prognosis in patients with SESCC ( 16 ). However, almost all LVI is confirmed by postoperative histopathological examination, and it is extremely difficult to assess the status of LVI before surgery ( 17 , 18 ), which means that using LVI to make a preoperative prediction for LNM may be infeasible. So LNM and LVI should be integrated and considered as one outcome (it can be named LNM/LVI), and usable models for predicting the presence of LNM/LVI in patients with SESCC still have not been reported.…”

Section: Introductionmentioning

confidence: 99%

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Risk factors of lymph node metastasis or lymphovascular invasion for superficial esophageal squamous cell carcinoma: A practical and effective predictive nomogram based on a cancer hospital data

Tao

Chen

et al. 2022

Front. Med.

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BackgroundLymphovascular invasion (LVI) is mostly used as a preoperative predictor to establish lymph node metastasis (LNM) prediction models for superficial esophageal squamous cell carcinoma (SESCC). However, LVI still needs to be confirmed by postoperative pathology. In this study, we combined LNM and LVI as a unified outcome and named it LNM/LVI, and aimed to develop an LNM/LVI prediction model in SESCC using preoperative factors.MethodsA total of 512 patients who underwent radical resection of SESCC were retrospectively collected. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression were adopted to identify the predictive factors of LNM/LVI. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to select the potential predictive factors from the results of LASSO and logistic regression. A nomogram for predicting LNM/LVI was established by incorporating these factors. The efficacy, accuracy, and clinical utility of the nomogram were, respectively, assessed with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Finally, the random forest (RF) algorithm was used to further evaluate the impact of these factors included in the nomogram on LNM/LVI.ResultsTumor size, tumor location, tumor invasion depth, tumor differentiation, and macroscopic type were confirmed as independent risk factors for LNM/LVI according to the results of logistic regression, LASSO regression, IDI, and NRI analyses. A nomogram including these five variables showed a good performance in LNM/LVI prediction (AUC = 0.776). The calibration curve revealed that the predictive results of this nomogram were nearly consistent with actual observations. Significant clinical utility of our nomogram was demonstrated by DCA. The RF model with the same five variables also had similar predictive efficacy with the nomogram (AUC = 0.775).ConclusionThe nomogram was adopted as a final tool for predicting LNM/LVI because its risk score system made it more user-friendly and clinically useful than the random forest model, which can help clinicians make optimal treatment decisions for patients with SESCC.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk factors of lymph node metastasis or lymphovascular invasion for superficial esophageal squamous cell carcinoma: A practical and effective predictive nomogram based on a cancer hospital data

Tao

Chen

et al. 2022

Front. Med.

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“…Early recognition of high-risk recurrence patients is essential for the development of personalized ESCC treatment. Some studies ( 9 , 10 ) have used the image features of CT to predict the condition of LVI of esophageal cancer before surgery, but the accuracy of these morphological characteristics in predicting LVI is still not ideal, and it is hard to accurately reveal the tumor structural heterogeneity. Recently, radiomics and machine learning algorithms have been rapidly developed and widely used, which is an important development direction of tumor translational medicine in the future.…”

Section: Introductionmentioning

confidence: 99%

Machine learning model based on enhanced CT radiomics for the preoperative prediction of lymphovascular invasion in esophageal squamous cell carcinoma

Wang,

Bai,

Huang

et al. 2024

Front. Oncol.

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ObjectiveTo evaluate the value of a machine learning model using enhanced CT radiomics features in the prediction of lymphovascular invasion (LVI) of esophageal squamous cell carcinoma (ESCC) before treatment.MethodsWe reviewed and analyzed the enhanced CT images of 258 ESCC patients from June 2017 to December 2019. We randomly assigned the patients in a ratio of 7:3 to a training set (182 cases) and a validation (76 cases) set. Clinical risk factors and CT image characteristics were recorded, and multifactor logistic regression was used to screen independent risk factors of LVI of ESCC patients. We extracted the CT radiomics features using the FAE software and screened radiomics features using maximum relevance and minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) algorithms, and finally, the radiomics labels of each patient were established. Five machine learning algorithms, namely, support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), Gauss naive Bayes (GNB), and multilayer perceptron (MLP), were used to construct the model of radiomics labels, and its clinical features were screened. The predictive efficacy of the machine learning model for LVI of ESCC was evaluated using the receiver operating characteristic (ROC) curve.ResultsTumor thickness [OR = 1.189, 95% confidence interval (CI) 1.060–1.351, P = 0.005], tumor-to-normal wall enhancement ratio (TNR) (OR = 2.966, 95% CI 1.174–7.894, P = 0.024), and clinical N stage (OR = 5.828, 95% CI 1.752–20.811, P = 0.005) were determined as independent risk factors of LVI. We extracted 1,316 features from preoperative enhanced CT images and selected 14 radiomics features using MRMR and LASSO to construct the radiomics labels. In the test set, SVM, KNN, LR, and GNB showed high predictive performance, while the MLP model had poor performance. In the training set, the area under the curve (AUC) values were 0.945 and 0.905 in the KNN and SVM models, but these decreased to 0.866 and 0.867 in the validation set, indicating significant overfitting. The GNB and LR models had AUC values of 0.905 and 0.911 in the training set and 0.900 and 0.893 in the validation set, with stable performance and good fitting and predictive ability. The MLP model had AUC values of 0.658 and 0.674 in the training and validation sets, indicating poor performance. A multiscale combined model constructed using multivariate logistic regression has an AUC of 0.911 (0.870–0.951) and 0.893 (0.840–0.962), accuracy of 84.4% and 79.7%, sensitivity of 90.8% and 87.1%, and specificity of 80.5% and 79.0% in the training and validation sets, respectively.ConclusionMachine learning models can preoperatively predict the condition of LVI effectively in patients with ESCC based on enhanced CT radiomics features. The GNB and LR models exhibit good stability and may bring a new way for the non-invasive prediction of LVI condition in ESCC patients before treatment.

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“…Computed tomography (CT) scanning is a commonly employed modality for the diagnosis and assessment of solid tumors [16]. The arterial phase images contain valuable information on tumor hemodynamics, which is closely linked to tumor activity and may offer insights into residual tumor survival based on contrast uptake [17][18][19]. Previous studies have indicated that radiomic features may be associated with the biological characteristics of the tumors [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma

Ruan,

Ma,

et al. 2024

J Transl Med

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Background Neoadjuvant immunochemotherapy (NICT) plus esophagectomy has emerged as a promising treatment option for locally advanced esophageal squamous cell carcinoma (LA-ESCC). Pathologic complete response (pCR) is a key indicator associated with great efficacy and overall survival (OS). However, there are insufficient indicators for the reliable assessment of pCR. Methods 192 patients with LA-ESCC treated with NICT from December 2019 to October 2023 were recruited. According to pCR status, patients were categorized into pCR group (22.92%) and non-pCR group (77.08%). Radiological features of pretreatment and preoperative CT images were extracted. Logistic and COX regressions were trained to predict pathological response and prognosis, respectively. Results Four of the selected radiological features were combined to construct an ESCC preoperative imaging score (ECPI-Score). Logistic models revealed independent associations of ECPI-Score and vascular sign with pCR, with AUC of 0.918 in the training set and 0.862 in the validation set, respectively. After grouping by ECPI-Score, a higher proportion of pCR was observed among the high-ECPI group and negative vascular sign. Kaplan Meier analysis demonstrated that recurrence-free survival (RFS) with negative vascular sign was significantly better than those with positive (P = 0.038), but not for OS (P = 0.310). Conclusions This study demonstrates dynamic radiological features are independent predictors of pCR for LA-ESCC treated with NICT. It will guide clinicians to make accurate treatment plans.

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Can Lymphovascular Invasion be Predicted by Preoperative Contrast-Enhanced CT in Esophageal Squamous Cell Carcinoma?

Cited by 4 publications

References 39 publications

Risk factors of lymph node metastasis or lymphovascular invasion for superficial esophageal squamous cell carcinoma: A practical and effective predictive nomogram based on a cancer hospital data

Risk factors of lymph node metastasis or lymphovascular invasion for superficial esophageal squamous cell carcinoma: A practical and effective predictive nomogram based on a cancer hospital data

Machine learning model based on enhanced CT radiomics for the preoperative prediction of lymphovascular invasion in esophageal squamous cell carcinoma

Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma

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