Dual-region radiomics signature: Integrating primary tumor and lymph node computed tomography features improves survival prediction in esophageal squamous cell cancer

2021 ◽  
pp. 106287
Author(s):  
Nian Lu ◽  
Weijing Zhang ◽  
Lu Dong ◽  
Junying Chen ◽  
Yanlin Zhu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lymph Node ◽  
Squamous Cell ◽  
Primary Tumor ◽  
Squamous Cell Cancer ◽  
Cell Cancer ◽  
Esophageal Squamous Cell Cancer ◽  
Survival Prediction ◽  
Radiomics Signature

scholarly journals Radiomics Signature Facilitates Organ-Saving Strategy in Patients With Esophageal Squamous Cell Cancer Receiving Neoadjuvant Chemoradiotherapy

2021 ◽  
Vol 10 ◽  
Author(s):  
Yue Li ◽  
Jun Liu ◽  
Hong-xuan Li ◽  
Xu-wei Cai ◽  
Zhi-gang Li ◽  
...  
Keyword(s):  
Squamous Cell ◽  
Squamous Cell Cancer ◽  
Cell Cancer ◽  
Locally Advanced ◽  
Neoadjuvant Chemoradiotherapy ◽  
Esophageal Squamous Cell Cancer ◽  
Clinical Model ◽  
Clinical Variables ◽  
Treatment Plans ◽  
Radiomics Signature

After neoadjuvant chemoradiotherapy (NCRT) in locally advanced esophageal squamous cell cancer (ESCC), roughly 40% of the patients may achieve pathologic complete response (pCR). Those patients may benefit from organ-saving strategy if the probability of pCR could be correctly identified before esophagectomy. A reliable approach to predict pathological response allows future studies to investigate individualized treatment plans.MethodAll eligible patients treated in our center from June 2012 to June 2019 were retrospectively collected. Radiomics features extracted from pre-/post-NCRT CT images were selected by univariate logistic and LASSO regression. A radiomics signature (RS) developed with selected features was combined with clinical variables to construct RS+clinical model with multivariate logistic regression, which was internally validated by bootstrapping. Performance and clinical usefulness of RS+clinical model were assessed by receiver operating characteristic (ROC) curves and decision curve analysis, respectively.ResultsAmong the 121 eligible patients, 51 achieved pCR (42.1%) after NCRT. Eighteen radiomics features were selected and incorporated into RS. The RS+clinical model has improved prediction performance for pCR compared with the clinical model (corrected area under the ROC curve, 0.84 vs. 0.70). At the 60% probability threshold cutoff (i.e., the patient would opt for observation if his probability of pCR was >60%), net 13% surgeries could be avoided by RS+clinical model, equivalent to implementing organ-saving strategy in 31.37% of the 51 true-pCR cases.ConclusionThe model built with CT radiomics features and clinical variables shows the potential of predicting pCR after NCRT; it provides significant clinical benefit in identifying qualified patients to receive individualized organ-saving treatment plans.

CRP Genetic Polymorphism Is Associated with Lymph Node Metastasis in Thoracic Esophageal Squamous Cell Cancer

2009 ◽  
Vol 16 (9) ◽  
pp. 2479-2485 ◽  
Author(s):  
Satoru Motoyama ◽  
Masatomo Miura ◽  
Yudai Hinai ◽  
Kiyotomi Maruyama ◽  
Shuetsu Usami ◽  
...  
Keyword(s):  
Lymph Node ◽  
Genetic Polymorphism ◽  
Lymph Node Metastasis ◽  
Squamous Cell ◽  
Squamous Cell Cancer ◽  
Cell Cancer ◽  
Esophageal Squamous Cell Cancer ◽  
Node Metastasis

scholarly journals Postoperative chemoradiotherapy improves survival in esophageal squamous cell cancer with extracapsular lymph node extension

Neoplasma ◽  
2014 ◽  
Vol 61 (06) ◽  
pp. 732-738 ◽  
Author(s):  
Z. W. WANG ◽  
Z. P. LUAN ◽  
W. ZHANG ◽  
W. DONG ◽  
C. R. FU ◽  
...  
Keyword(s):  
Lymph Node ◽  
Squamous Cell ◽  
Squamous Cell Cancer ◽  
Cell Cancer ◽  
Esophageal Squamous Cell Cancer

Appraisal of a Revised Lymph Node Classification System for Esophageal Squamous Cell Cancer

2007 ◽  
Vol 83 (4) ◽  
pp. 1265-1272 ◽  
Author(s):  
Dipok Kumar Dhar ◽  
Shinji Hattori ◽  
Yasuhito Tonomoto ◽  
Tadakazu Shimoda ◽  
Hoichi Kato ◽  
...  
Keyword(s):  
Lymph Node ◽  
Squamous Cell ◽  
Classification System ◽  
Squamous Cell Cancer ◽  
Cell Cancer ◽  
Esophageal Squamous Cell Cancer ◽  
Node Classification
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