Lymphovascular invasion and extracapsular invasion are risk factors for distant recurrence after preoperative chemoradiotherapy and oesophagectomy in patients with oesophageal squamous cell carcinoma

Hsu, Po‐Kuei; Chien, Ling-I; Wang, Lei-Chi; Chou, Teh-Ying

doi:10.1093/ejcts/ezx029

Cited by 19 publications

(18 citation statements)

References 22 publications

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“…In various situations for ESCC, LVI can serve as an indicator of highly aggressive behavior (12). Patients with LVI have a high risk of recurrence, so they must be treated with effective systemic therapy and intensive care (13). Therefore, identifying esophageal cancer with a high risk of recurrence, especially in patients with early recurrence, is crucial for an individualized treatment approach (3).…”

Section: Introductionmentioning

confidence: 99%

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Wang

Yang

et al. 2021

Front. Oncol.

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ObjectivesTo develop a radiomics model based on contrast-enhanced CT (CECT) to predict the lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC) and provide decision-making support for clinicians.Patients and MethodsThis retrospective study enrolled 334 patients with surgically resected and pathologically confirmed ESCC, including 96 patients with LVI and 238 patients without LVI. All enrolled patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3, with the training cohort containing 234 patients (68 patients with LVI and 166 without LVI) and the testing cohort containing 100 patients (28 patients with LVI and 72 without LVI). All patients underwent preoperative CECT scans within 2 weeks before operation. Quantitative radiomics features were extracted from CECT images, and the least absolute shrinkage and selection operator (LASSO) method was applied to select radiomics features. Logistic regression (Logistic), support vector machine (SVM), and decision tree (Tree) methods were separately used to establish radiomics models to predict the LVI status in ESCC, and the best model was selected to calculate Radscore, which combined with two clinical CT predictors to build a combined model. The clinical model was also developed by using logistic regression. The receiver characteristic curve (ROC) and decision curve (DCA) analysis were used to evaluate the model performance in predicting the LVI status in ESCC.ResultsIn the radiomics model, Sphericity and gray-level non-uniformity (GLNU) were the most significant radiomics features for predicting LVI. In the clinical model, the maximum tumor thickness based on CECT (cThick) in patients with LVI was significantly greater than that in patients without LVI (P<0.001). Patients with LVI had higher clinical N stage based on CECT (cN stage) than patients without LVI (P<0.001). The ROC analysis showed that both the radiomics model (AUC values were 0.847 and 0.826 in the training and testing cohort, respectively) and the combined model (0.876 and 0.867, respectively) performed better than the clinical model (0.775 and 0.798, respectively), with the combined model exhibiting the best performance.ConclusionsThe combined model incorporating radiomics features and clinical CT predictors may potentially predict the LVI status in ESCC and provide support for clinical treatment decisions.

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Section: Introductionmentioning

confidence: 99%

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Wang

Yang

et al. 2021

Front. Oncol.

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“…To our knowledge, this is the first report to apply CNN to predict specific histopathological features in esophageal cancer. The significance of LVI and PNI has been wellestablished in the literature (5,6), and patients with these features have been shown to have worse outcomes and a higher risk of recurrence after treatment. However, information about these two histological features is currently impossible to gather prior to major surgical resection.…”

Section: Discussionmentioning

confidence: 99%

“…Measurements of esophageal cancer aggressiveness are primarily based on the anatomical extent of the disease, including tumor length, depth, and involvement of lymph node or distant organs, which are obtained from clinical examinations, such as computed tomography, esophagogastroscopy, and esophageal endoscopic ultrasound (4). Non-anatomic factors, such as the presence of lymphovascular invasion (LVI) and perineural invasion (PNI), have also been shown to have a large impact on patient survival; however, such information is not available before tumor specimens are collected by surgical resection (5,6). The only nonanatomical assessment of esophageal cancer that can be obtained prior to surgical resection is positron emission tomography (PET), a nuclear medicine imaging technique based on the measurement of gamma rays emitted by a positron-emitting radiotracer, such as 18 F-fludeoxyglucose (FDG) (7).…”

Section: Introductionmentioning

confidence: 99%

Predicting aggressive histopathological features in esophageal cancer with positron emission tomography using a deep convolutional neural network

Yeh¹,

Yu²,

Yang³

et al. 2021

Ann Transl Med

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Background: The presence of lymphovascular invasion (LVI) and perineural invasion (PNI) are of great prognostic importance in esophageal squamous cell carcinoma. Currently, positron emission tomography (PET) scans are the only means of functional assessment prior to treatment. We aimed to predict the presence of LVI and PNI in esophageal squamous cell carcinoma using PET imaging data by training a three-dimensional convolution neural network (3D-CNN).Methods: Seven hundred and ninety-eight PET scans of patients with esophageal squamous cell carcinoma and 309 PET scans of patients with stage I lung cancer were collected. In the first part of this study, we built a 3D-CNN based on a residual network, ResNet, for a task to classify the scans into esophageal cancer or lung cancer. In the second stage, we collected the PET scans of 278 patients undergoing esophagectomy for a task to classify and predict the presence of LVI/PNI.Results: In the first part, the model performance attained an area under the receiver operating characteristic curve (AUC) of 0.860. In the second part, we randomly split 80%, 10%, and 10% of our dataset into training set, validation set and testing set, respectively, for a task to classify the scans into the presence of LVI/PNI and evaluated the model performance on the testing set. Our 3D-CNN model attained an AUC of 0.668 in the testing set, which shows a better discriminative ability than random guessing.Conclusions: A 3D-CNN can be trained, using PET imaging datasets, to predict LNV/PNI in esophageal cancer with acceptable accuracy.

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“…HNSCC patients with high tumor grade, lymphovascular invasion (LVI), or perineural invasion (PNI) tend to demonstrate a poor prognosis in clinical series. Thus, the identification of histopathological features, including tumor grade, LVI, and PNI, before treatment could be used to stratify the prognosis of patients with HNSCC (2)(3)(4)(5)(6)(7) and to identify patients who should receive more aggressive treatments (8).…”

Section: Introductionmentioning

confidence: 99%

Using quantitative parameters derived from pretreatment dual-energy computed tomography to predict histopathologic features in head and neck squamous cell carcinoma

Shen¹,

Huang²,

Yuan³

et al. 2022

Quant Imaging Med Surg

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Background: Head and neck squamous cell carcinoma (HNSCC) patients with a high tumor grade, lymphovascular invasion (LVI), or perineural invasion (PNI) tend to demonstrate a poor prognosis in clinical series. Thus, the identification of histopathological features, including tumor grade, LVI, and PNI, before treatment could be used to stratify the prognosis of patients with HNSCC. This study aimed to assess whether quantitative parameters derived from pretreatment dual-energy computed tomography (DECT) can predict the histopathological features of patients with HNSCC.Methods: In this study, 72 consecutive patients with pathologically confirmed HNSCC were enrolled and underwent dual-phase (noncontrast-enhanced phase and contrast-enhanced phase) DECT examinations.Normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit curve (λ HU ), and normalized effective atomic number (NZ eff ) were calculated. The attenuation values on 40-140 keV noiseoptimized virtual monoenergetic images [VMIs (+)] in the contrast-enhanced phase were recorded. The diagnostic performance of the quantitative parameters for predicting histopathological features, including tumor grade, LVI, and PNI, was assessed by receiver operating characteristic curves. Results:The NIC, λ HU , NZ eff , and attenuation value on the VMIs (+) at 40 keV (A 40 ) in the grade III group, LVI-positive group, and PNI-positive group were significantly higher than those in the grade I and II groups, the LVI-negative group, and the PNI-negative group (all P values <0.05). A multivariate logistic regression model combining these 4 quantitative parameters improved the diagnostic performance of the model in predicting tumor grade, LVI, and PNI (areas under the curve: 0.969, 0.944, and 0.931, respectively).Conclusions: Quantitative parameters derived from pretreatment DECT, including NIC, λ HU , NZ eff , and A 4,0 were found to be imaging markers for predicting the histopathological characteristics of HNSCC.Combining all these characteristics improved the predictive performance of the model.

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Lymphovascular invasion and extracapsular invasion are risk factors for distant recurrence after preoperative chemoradiotherapy and oesophagectomy in patients with oesophageal squamous cell carcinoma

Abstract: Patients with lymphovascular invasion and extracapsular invasion are at a high risk of distant recurrence after preoperative chemoradiotherapy and oesophagectomy. Effective systemic therapy and intensive surveillance are necessary in this group of patients.

Cited by 19 publications

References 22 publications

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Predicting aggressive histopathological features in esophageal cancer with positron emission tomography using a deep convolutional neural network

Using quantitative parameters derived from pretreatment dual-energy computed tomography to predict histopathologic features in head and neck squamous cell carcinoma

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