Editorial comments for neoadjuvant chemo-radiotherapy in the treatment of locally advanced 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.

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REG I Expression Predicts Long-term Survival among Locally Advanced Thoracic Squamous Cell Esophageal Cancer Patients Treated with Neoadjuvant Chemoradiotherapy Followed by Esophagectomy

Annals of Surgical Oncology ◽

10.1245/s10434-006-9075-z ◽

2006 ◽

Vol 13 (12) ◽

pp. 1724-1731 ◽

Cited By ~ 19

Author(s):

Satoru Motoyama ◽

Toshihiro Sugiyama ◽

Yasuharu Ueno ◽

Hiroshi Okamoto ◽

Shin Takasawa ◽

...

Keyword(s):

Esophageal Cancer ◽

Cancer Patients ◽

Squamous Cell ◽

Locally Advanced ◽

Neoadjuvant Chemoradiotherapy ◽

Term Survival ◽

Long Term Survival ◽

Squamous Cell Esophageal Cancer ◽

Reg I

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Geophagia and risk of squamous cell esophageal cancer in the African esophageal cancer corridor ‐ Findings from the ESCCAPE multi‐country case‐control studies

International Journal of Cancer ◽

10.1002/ijc.33688 ◽

2021 ◽

Author(s):

Clement T Narh ◽

Charles P Dzamalala ◽

Blandina T Mmbaga ◽

Diana Menya ◽

Yohannie Mlombe ◽

...

Keyword(s):

Esophageal Cancer ◽

Squamous Cell ◽

Case Control ◽

Case Control Studies ◽

Squamous Cell Esophageal Cancer

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A Rare Case of Squamous Cell Esophageal Cancer Causing Malignant Hypercalcemia and Cardiac Dysrhythmia

10.1210/endo-meetings.2011.part4.p14.p3-763 ◽

2011 ◽

pp. P3-763-P3-763

Author(s):

Marian F Assal ◽

Greti Petersen

Keyword(s):

Esophageal Cancer ◽

Squamous Cell ◽

Rare Case ◽

Cardiac Dysrhythmia ◽

Squamous Cell Esophageal Cancer

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

British Journal of Radiology ◽

10.1259/bjr.20210525 ◽

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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