Definitive chemoradiotherapy with capecitabine and cisplatin for elder patients with locally advanced squamous cell esophageal cancer

2014 ◽  
Vol 140 (5) ◽  
pp. 867-872 ◽  
Author(s):  
Ligang Xing ◽  
Yemin Liang ◽  
Jiandong Zhang ◽  
Peipei Wu ◽  
Deguo Xu ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Squamous Cell ◽  
Locally Advanced ◽  
Definitive Chemoradiotherapy ◽  
Elder Patients ◽  
Squamous Cell Esophageal Cancer

Clinical prognostic factors associated with survival after definitive chemoradiotherapy in advanced squamous cell carcinoma of esophagus.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e15121-e15121
Author(s):  
Hyun-Jeong Shim ◽  
Won-young Choi ◽  
Dae-Eun Kim ◽  
Jun-Eul Hwang ◽  
Woo Kyun Bae ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Prognostic Factors ◽  
Squamous Cell ◽  
Locally Advanced ◽  
Maintenance Chemotherapy ◽  
Advanced Esophageal Cancer ◽  
Cell Type ◽  
Definitive Chemoradiotherapy ◽  
Risk Patients ◽  
Good Risk

e15121 Background: Definitive chemoradiotherapy (dCRT) has been used as a standard care for treating and palliating of symptoms in advanced esophageal cancer patients even if they have metastatic disease. The purpose of this study is to identify the treatment outcomes and prognostic factors for survival in squamous cell type of esophagus. Methods: We retrospectively studied the patients who treated with dCRT using 5-FU and cisplatin, and histologically proven squamous cell esophageal carcinoma of stage III and IV between January 2004 and December 2010. Results: In total 110 patients were enrolled in analysis of whom 64 patients were stage III and Iva (localized advanced) and 46 patients were stage IVb. The ratio of male/female was 109/1 and the median age was 65 years. T-stage was cT2=8%, cT3=75, cT4=17% and N-stage was cN0=5%, cN1=95%. The most common metastatic site was distant LN (60%). The median dose of radiation was 5400 cGy (range, 4400-6600). The median OS for locally advanced and metastatic disease group was 19.0 and 12.8 months, respectively (p=0.032). The median PFS for these groups was 8.3 and 6.9 months, respectively (p=0.208). The most common pattern of failure was locoregional area. In univariate analysis, cell type (poorly differentiation), dysphagia score, PS (ECOG 1-2 vs. 0), PFS (<5months) and CRP were significantly associated with OS and cell type, PS, PFS were shown significantly poor prognostic factors for OS in multivariate analysis. Among the patients who had locally advanced disease, maintenance chemotherapy after dCRT did not influence the OS. However, “good risk” patients who had less than 2 risk factors receiving maintenance chemotherapy showed better OS than those with no maintenance chemotherapy (40.9 vs. 12.3 months; p=0.011). Conclusions: dCRT has a major role in improving survival and palliation of dysphagia in inoperable advanced esophageal cancer even in metastatic stage. Cell type and PS have been still important prognostic factors in survival. In addition, maintenance chemotherapy after dCRT may be helpful for prolongation survival in good risk patients.

Neoadjuvant chemoradiotherapy (NCRT) compared with chemotherapy (NCT) in locally advanced squamous-cell cancer of the esophagus.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 202-202
Author(s):  
Xue Li ◽  
Daxuan Hao ◽  
Yuanyuan Yang ◽  
Xinyu Cheng ◽  
Yougai Zhang ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Squamous Cell ◽  
Postoperative Morbidity ◽  
Locally Advanced ◽  
Group Versus ◽  
Postoperative Mortality ◽  
Pathological Response ◽  
Complete Pathological Response ◽  
Squamous Cell Esophageal Cancer

202 Background: Neoadjuvant chemotherapy with or without radiotherapy are the common treatments for locally advanced squamous-cell esophageal cancer(ESCC). There is no sufficient data to choose between these two effective therapies. The aim of our retrospective study was to compare the clinical efficacy between these two strategies of complete pathological response (pCR), postoperative morbidity, mortality, and overall and disease-free survival in patients with locally advanced ESCC. Methods: Patients with stage T2-4N0-1M0 squamous-cell esophageal cancer at our institution were recruited, including patients who underwent NCRT (1 cycle of cisplatin and 5-fluorouracil with concurrent radiotherapy) or NCT (2 cycles of cisplatin and 5-fluorouracil only ) before esophagectomy. Results: From January 2009 to October 2015, a total of 177 patients were analyzed, with 72 received NCRT and the remaining 105 received NCT. The pathological complete response (pCR) rate was 22.2% (n = 16) in NCRT group and 9.5% (n = 10) in NCT group ( P= 0.019). The postoperative mortality was 1.4% in NCRT group, versus 4.8% in NCT group. The postoperative morbidity was 20.8% in NCRT group, versus 27.6% in NCT group. There was no significant difference in recurrence between the two groups ( P= 0.397). 1-,2-,3-year overall survival rates in NCRT and NCT group were 87%, 74%, 51% and 81%, 64%, 51%, respectively ( P= 0.527), and 1-,2-,3-year DFS rates were 77%, 54%, 50% and 65%, 54%, 46%, respectively( P= 0.379). Conclusions: For patients with locally advanced squamous-cell esophageal cancer, the addition of radiotherapy to neoadjuvant chemotherapy may result in higher complete pathological response with acceptable postoperative mortality and morbidity, while the long-term survival benefit is not significant.

A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning

2021 ◽  
pp. 20210525
Author(s):  
Daisuke Kawahara ◽  
Yuji Murakami ◽  
Shigeyuki Tani ◽  
Yasushi Nagata
Keyword(s):  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Esophageal Cancer ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Predictive Model ◽  
Squamous Cell ◽  
Locally Advanced ◽  
Degree Of Differentiation ◽  
Planning Ct

Objective: To propose the prediction model for degree of differentiation for locally advanced esophageal cancer patients from the planning CT image by radiomics analysis with machine learning. Methods: Data of 104 patients with esophagus cancer, who underwent chemoradiotherapy followed by surgery at the Hiroshima University hospital from 2003 to 2016 were analyzed. The treatment outcomes of these tumors were known prior to the study. The data were split into 3 sets: 57/16 tumors for the training/validation and 31 tumors for model testing. The degree of differentiation of squamous cell carcinoma was classified into two groups. The first group (Group I) was a poorly differentiated (POR) patients. The second group (Group II) was well and moderately differentiated patients. The radiomics feature was extracted in the tumor and around the tumor regions. A total number of 3480 radiomics features per patient image were extracted from radiotherapy planning CT scan. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors. The radiomics features were used for the input data in the machine learning. To build predictive models with radiomics features, neural network classifiers was used. The precision, accuracy, sensitivity by generating confusion matrices, the area under the curve (AUC) of receiver operating characteristic curve were evaluated. Results: By the LASSO analysis of the training data, we found 13 radiomics features from CT images for the classification. The accuracy of the prediction model was highest for using only CT radiomics features. The accuracy, specificity, and sensitivity of the predictive model were 85.4%, 88.6%, 80.0%, and the AUC was 0.92. Conclusion: The proposed predictive model showed high accuracy for the classification of the degree of the differentiation of esophagus cancer. Because of the good prediction ability of the method, the method may contribute to reducing the pathological examination by biopsy and predicting the local control. Advances in knowledge: For esophageal cancer, the differentiation of degree is the import indexes reflecting the aggressiveness. The current study proposed the prediction model for the differentiation of degree with radiomics analysis.

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