Literature DB >> 27144416

Pleomorphic Carcinoma of the Lung: Relationship Between CT Findings and Prognosis.

Akitaka Fujisaki1, Takatoshi Aoki1, Takahiko Kasai2,3, Shunsuke Kinoshita1, Yoshinori Tomoda4, Fumihiro Tanaka5, Kazuhiro Yatera6, Hiroshi Mukae6, Yukunori Korogi1.   

Abstract

OBJECTIVE: The objective of our study was to assess the radiologic and clinical findings of pleomorphic carcinoma (PC) of the lung and to evaluate whether there are any characteristic features that can be used to predict prognosis.
MATERIALS AND METHODS: Forty-four consecutive patients whose diagnosis of PC was histologically confirmed through resection of the lung tumor were included in this study. The clinical and CT findings of these patients were retrospectively reviewed. Two thoracic radiologists evaluated the CT findings including the size, location, internal characteristics, and margin characteristics of the tumors and the presence of chest wall invasion, mediastinal invasion, and surrounding lung abnormalities. A multivariate analysis by the Cox proportional hazards regression model was used to identify variables that can be used to predict overall survival and disease-free survival.
RESULTS: In the patients with PC, a central low-attenuation area or cavity (40/44, 91%), chest wall invasion (19/44, 43%), and pulmonary emphysema (30/44, 68%) were frequently observed on CT. On multivariate analysis, a massive central low-attenuation area or cavity (> 25% of the lesion) on CT indicating necrosis was the only significant independent factor for overall survival and disease-free survival (p < 0.05). Clinical findings, the presence of lymph node metastasis at surgery, and postoperative pathologic stage were not significant predictors of overall survival and disease-free survival.
CONCLUSION: A massive central low-attenuation area or cavity on CT was the only predictor of overall survival and disease-free survival in patients with lung PC.

Entities:  

Keywords:  CT; lung cancer; pleomorphic carcinoma; prognosis

Mesh:

Year:  2016        PMID: 27144416     DOI: 10.2214/AJR.15.15542

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  6 in total

Review 1.  Tumor necrosis by pretreatment breast MRI: association with neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC).

Authors:  Abeer H Abdelhafez; Benjamin C Musall; Wei T Yang; Gaiane M Rauch; Beatriz E Adrada; KennethR Hess; Jong Bum Son; Ken-Pin Hwang; Rosalind P Candelaria; Lumarie Santiago; Gary J Whitman; Huong T Le-Petross; Tanya W Moseley; Elsa Arribas; Deanna L Lane; Marion E Scoggins; Jessica W T Leung; Hagar S Mahmoud; Jason B White; Elizabeth E Ravenberg; Jennifer K Litton; Vicente Valero; Peng Wei; Alastair M Thompson; Stacy L Moulder; Mark D Pagel; Jingfei Ma
Journal:  Breast Cancer Res Treat       Date:  2020-09-13       Impact factor: 4.872

2.  Clinicopathological characteristics and prognosis of pulmonary pleomorphic carcinoma: a population-based retrospective study using SEER data.

Authors:  Jiacheng Yin; Yong Yang; Ke Ma; Xiaodong Yang; Tao Lu; Shuai Wang; Yu Shi; Cheng Zhan; Yimeng Zhu; Qun Wang
Journal:  J Thorac Dis       Date:  2018-07       Impact factor: 2.895

3.  Air bronchogram in pleomorphic carcinoma of the lung is associated with favorable prognosis.

Authors:  Hideko Onoda; Tokuhiro Kimura; Hiroyuki Tao; Kazunori Okabe; Tsuneo Matsumoto; Eiji Ikeda
Journal:  Thorac Cancer       Date:  2018-04-06       Impact factor: 3.500

4.  Pulmonary Pleomorphic Carcinoma Mimicking Primary Sarcoma of the Neck: A Case Report and Literature Review.

Authors:  Daishi Ogawa; Masahisa Arahata; Masato Kuriyama; Shunji Shinagawa; Gakuto Tomizawa; Yukihiro Shimizu
Journal:  Clin Interv Aging       Date:  2021-02-23       Impact factor: 4.458

5.  Ringed fluorodeoxyglucose uptake predicted poor prognosis after resection of pulmonary pleomorphic carcinoma.

Authors:  Yutaka Shishido; Akihiro Aoyama; Shigeo Hara; Yuki Sato; Keisuke Tomii; Hiroshi Hamakawa; Yutaka Takahashi
Journal:  J Cardiothorac Surg       Date:  2022-03-21       Impact factor: 1.637

6.  Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging.

Authors:  Wenyi Yue; Hongtao Zhang; Juan Zhou; Guang Li; Zhe Tang; Zeyu Sun; Jianming Cai; Ning Tian; Shen Gao; Jinghui Dong; Yuan Liu; Xu Bai; Fugeng Sheng
Journal:  Front Oncol       Date:  2022-08-11       Impact factor: 5.738

  6 in total

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