Literature DB >> 32234911

Nomogram for Predicting the Pathological Tumor Response from Pre-treatment Clinical Characteristics in Rectal Cancer.

Byung-Hee Kang1, Changhoon Song2, Sung-Bum Kang3, Keun-Wook Lee4, Hye Seung Lee5, Jae-Sung Kim6.   

Abstract

BACKGROUND/AIM: To develop a nomogram for predicting the pathological tumor response to preoperative chemoradiotherapy (CRT) for locally advanced rectal cancer based on pre-treatment magnetic resonance imaging (MRI) and blood test characteristics. PATIENTS AND METHODS: This retrospective study included 514 patients who underwent MRI and received preoperative CRT followed by surgical resection. Pathological tumor response was assessed as good [Dworak tumor regression grade (TRG) 3 or 4] or poor (TRG 0-2). A nomogram for good response was developed using stepwise logistic regression analysis.
RESULTS: A nomogram based on longitudinal tumor diameter, extramural tumor invasion depth, carcinoembryonic antigen and hemoglobin levels, age, and interval between CRT and surgery gave an area under the receiver operating characteristic curve for a good response of 0.721 (95%CI=0.676-0.768).
CONCLUSION: Our nomogram based on pre-treatment clinical characteristics can predict the tumor response to CRT, which may help identify patients who can benefit most from CRT. Copyright
© 2020, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Entities:  

Keywords:  Rectal cancer; chemoradiation; clinical predictor; pathologic response; prediction nomogram

Mesh:

Year:  2020        PMID: 32234911     DOI: 10.21873/anticanres.14177

Source DB:  PubMed          Journal:  Anticancer Res        ISSN: 0250-7005            Impact factor:   2.480


  3 in total

1.  Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy.

Authors:  Silin Chen; Yuan Tang; Ning Li; Jun Jiang; Liming Jiang; Bo Chen; Hui Fang; Shunan Qi; Jing Hao; Ningning Lu; Shulian Wang; Yongwen Song; Yueping Liu; Yexiong Li; Jing Jin
Journal:  Front Oncol       Date:  2021-11-15       Impact factor: 6.244

2.  Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost.

Authors:  Xijie Chen; Wenhui Wang; Junguo Chen; Liang Xu; Xiaosheng He; Ping Lan; Jiancong Hu; Lei Lian
Journal:  Int J Colorectal Dis       Date:  2022-06-15       Impact factor: 2.796

3.  3.0 T MRI IVIM-DWI for predicting the efficacy of neoadjuvant chemoradiation for locally advanced rectal cancer.

Authors:  Hongbo Hu; Huijie Jiang; Song Wang; Hao Jiang; Sheng Zhao; Wenbin Pan
Journal:  Abdom Radiol (NY)       Date:  2021-01
  3 in total

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