Literature DB >> 28709645

Usefulness of computed tomography density of a tumor in predicting the response of advanced esophageal cancer to preoperative chemotherapy.

Kohei Wakatsuki1, Sohei Matsumoto2, Kazuhiro Migita2, Masahiro Ito2, Tomohiro Kunishige2, Hiroshi Nakade2, Mitsuhiro Nakatani2, Mutsuko Kitano2, Masato Takano3, Chiho Obayashi3, Masayuki Sho2.   

Abstract

BACKGROUND: In Japan, preoperative chemotherapy is considered essential for resectable stage II or III esophageal cancers. It is important to identify nonresponders for preoperative chemotherapy because continuing ineffective chemotherapy is not beneficial for them. We investigated the correlation between the computed tomography number of tumor and the effect of preoperative chemotherapy in patients with esophageal cancer.
METHODS: This retrospective study included 50 patients receiving preoperative chemotherapy with docetaxel, cisplatin, and 5-fluorouracil for stage II or III esophageal cancer. The computed tomography number of tumor was measured as the mean of Hounsfield Units of the primary lesion on a plain computed tomography measured within a freehand region of interest drawn around the tumor border. For analysis, the patients were classified into responders and nonresponders to chemotherapy, with the pathologic response evaluated using the Japanese and Mandard classification. We analyzed the associations between the computed tomography number of tumor and clinical factors; histopathologic features, including the tumor size, depth of tumor invasion, capillary invasion, Ki-67, p53, and CK5/6 expression; the pathologic response to chemotherapy and prognosis.
RESULTS: There was a significant association between the computed tomography number of tumor and the response to chemotherapy. The cut-off value of the computed tomography number of tumor in predicting responders to chemotherapy was 40 Hounsfield Units (area under the receiver operating characteristic curve = 0.73, P = .009); patients with computed tomography number of tumor greater than this value significantly responded to chemotherapy (P = .02 in the Japanese and P = .009 in the Mandard classification) with good postoperative prognosis (P = .04). Only Ki-67 expression among the histopathogic features were associated with the computed tomography number of tumor in histopathologic features (P = .01).
CONCLUSION: The computed tomography number of tumor may be useful to predict the efficacy of preoperative chemotherapy and subsequent prognosis for patients with advanced esophageal cancer.
Copyright © 2017 Elsevier Inc. All rights reserved.

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Year:  2017        PMID: 28709645     DOI: 10.1016/j.surg.2017.06.003

Source DB:  PubMed          Journal:  Surgery        ISSN: 0039-6060            Impact factor:   3.982


  5 in total

1.  Impact of CT-assessed changes in tumor size after neoadjuvant chemotherapy on pathological response and survival of patients with esophageal squamous cell carcinoma.

Authors:  Sohei Matsumoto; Kohei Wakatsuki; Hiroshi Nakade; Tomohiro Kunishige; Shintaro Miyao; Akinori Tsujimoto; Takanari Tatsumi; Masayuki Sho
Journal:  Langenbecks Arch Surg       Date:  2022-01-06       Impact factor: 3.445

2.  DCE-MRI radiomics nomogram can predict response to neoadjuvant chemotherapy in esophageal cancer.

Authors:  Jinrong Qu; Ling Ma; Yanan Lu; Zhaoqi Wang; Jia Guo; Hongkai Zhang; Xu Yan; Hui Liu; Ihab R Kamel; Jianjun Qin; Hailiang Li
Journal:  Discov Oncol       Date:  2022-01-08

Review 3.  Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.

Authors:  Zhenwei Shi; Zhen Zhang; Zaiyi Liu; Lujun Zhao; Zhaoxiang Ye; Andre Dekker; Leonard Wee
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-12-23       Impact factor: 10.057

4.  Clinical and radiological characteristics of central pulmonary adenocarcinoma: a comparison with central squamous cell carcinoma and small cell lung cancer and the impact on treatment response.

Authors:  Zhe Wang; Minghuan Li; Yong Huang; Li Ma; Hui Zhu; Li Kong; Jinming Yu
Journal:  Onco Targets Ther       Date:  2018-05-04       Impact factor: 4.147

5.  Constructing a risk prediction model for anastomotic leakage after esophageal cancer resection.

Authors:  Zhong-Wen Sun; Hui Du; Jia-Rui Li; Hui-Ying Qin
Journal:  J Int Med Res       Date:  2020-04       Impact factor: 1.671

  5 in total

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