Nuclear factor-κB predicts outcome in locally advanced rectal cancer patients receiving neoadjuvant radio-chemotherapy

2012 ◽  
Vol 44 (7) ◽  
pp. 617-622 ◽  
Author(s):  
Rossana Berardi ◽  
Elena Maccaroni ◽  
Alessandra Mandolesi ◽  
Giovanna Mantello ◽  
Azzurra Onofri ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Cancer Patients ◽  
Locally Advanced ◽  
Advanced Rectal Cancer ◽  
Nuclear Factor Κb ◽  
Locally Advanced Rectal Cancer ◽  
Nuclear Factor ◽  
Radio Chemotherapy ◽  
Neoadjuvant Radio Chemotherapy

scholarly journals 110: Improved Survival with The Use of Capecitabine in the Setting of Neoadjuvant Chemo-Radiation for Locally Advanced Rectal Cancer Patients

2020 ◽  
Vol 150 ◽  
pp. S48-S49
Author(s):  
Aswin George Abraham ◽  
Nawaid Usmani ◽  
Karen Mulder ◽  
JoAnn Thai ◽  
Sunita Ghosh ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Cancer Patients ◽  
Locally Advanced ◽  
Advanced Rectal Cancer ◽  
Locally Advanced Rectal Cancer

scholarly journals Radiomics features on radiotherapy treatment planning CT can predict patient survival in locally advanced rectal cancer patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jiazhou Wang ◽  
Lijun Shen ◽  
Haoyu Zhong ◽  
Zhen Zhou ◽  
Panpan Hu ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Cancer Patients ◽  
Treatment Planning ◽  
Clinical Features ◽  
Locally Advanced ◽  
Advanced Rectal Cancer ◽  
Locally Advanced Rectal Cancer ◽  
Neoadjuvant Chemoradiation ◽  
Radiotherapy Treatment Planning ◽  
Radiotherapy Treatment

Abstract This retrospective study was to investigate whether radiomics feature come from radiotherapy treatment planning CT can predict prognosis in locally advanced rectal cancer patients treated with neoadjuvant chemoradiation followed by surgery. Four-hundred-eleven locally advanced rectal cancer patients which were treated with neoadjuvant chemoradiation enrolled in this study. All patients’ radiotherapy treatment planning CTs were collected. Tumor was delineated on these CTs by physicians. An in-house radiomics software was used to calculate 271 radiomics features. The results of test-retest and contour-recontour studies were used to filter stable radiomics (Spearman correlation coefficient > 0.7). Twenty-one radiomics features were final enrolled. The performance of prediction model with the radiomics or clinical features were calculated. The clinical outcomes include local control, distant control, disease-free survival (DFS) and overall survival (OS). Model performance C-index was evaluated by C-index. Patients are divided into two groups by cluster results. The results of chi-square test revealed that the radiomics feature cluster is independent of clinical features. Patients have significant differences in OS (p = 0.032, log rank test) for these two groups. By supervised modeling, radiomics features can improve the prediction power of OS from 0.672 [0.617 0.728] with clinical features only to 0.730 [0.658 0.801]. In conclusion, the radiomics features from radiotherapy CT can potentially predict OS for locally advanced rectal cancer patients with neoadjuvant chemoradiation treatment.

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