Literature DB >> 26473646

Role of CT and PET Imaging in Predicting Tumor Recurrence and Survival in Patients with Lung Adenocarcinoma: A Comparison with the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society Classification of Lung Adenocarcinoma.

Ho Yun Lee1, So Won Lee, Kyung Soo Lee, Ji Yun Jeong, Joon Young Choi, O Jung Kwon, So Hee Song, Eun Young Kim, Jhingook Kim, Young Mog Shim.   

Abstract

INTRODUCTION: Recently, a new lung adenocarcinoma classification scheme was published. The prognostic value of this new classification has not been elaborated together with the value of imaging biomarkers including computed tomography (CT) and positron emission tomography (PET).
METHODS: We reviewed pathologic specimens and imaging characteristics of primary tumors from 723 consecutive patients who underwent surgical resection for lung adenocarcinoma. On pathology, the predominant histologic subtype and pattern group were quantified. Tumor-shadow disappearance ratio (TDR) on CT and maximum standardized uptake value (SUVmax) on PET were assessed. The relationships between those variables and survival (overall survival [OS] and disease-free survival) were analyzed by using Kaplan-Meier curves and Cox regression analyses.
RESULTS: The median follow-up period was 3.8 years. There were 137 patients (19%) with recurrence and 167 patients (23%) with metastasis after surgical resection. Among 723 patients, 35 patients (4.8%) had adenocarcinoma in situ, 34 patients (4.7%) had minimally invasive adenocarcinoma, 125 patients (17.3%) had lepidic predominant, 314 patients (43.4%) had acinar predominant, 65 patients (9.0%) had papillary predominant, 23 patients (3.2%) had micropapillary predominant, 113 patients (15.6%) had solid predominant, and 14 patients (1.9%) had variant adenocarcinomas. OS and disease-free survival rates were significantly different according to TDR on CT and SUVmax on PET, predominant subtypes, and pattern groups. On multivariate analysis, the SUVmax (p < 0.001), TDR (p = 0.038), and pattern group (p = 0.015) were independent predictors of OS.
CONCLUSIONS: TDR on CT, SUVmax on PET, and the new histologic classification schemes appear to be promising parameters for the prognostic stratification of patients with lung adenocarcinomas, allowing for the triage of patients who necessitate further staging workup and adjuvant therapy.

Entities:  

Mesh:

Year:  2015        PMID: 26473646     DOI: 10.1097/JTO.0000000000000689

Source DB:  PubMed          Journal:  J Thorac Oncol        ISSN: 1556-0864            Impact factor:   15.609


  23 in total

1.  Survival after recurrence of stage I-III breast, colorectal, or lung cancer.

Authors:  Michael J Hassett; Hajime Uno; Angel M Cronin; Nikki M Carroll; Mark C Hornbrook; Paul Fishman; Debra P Ritzwoller
Journal:  Cancer Epidemiol       Date:  2017-07-12       Impact factor: 2.984

2.  Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes.

Authors:  Li-Wei Chen; Shun-Mao Yang; Hao-Jen Wang; Yi-Chang Chen; Mong-Wei Lin; Min-Shu Hsieh; Hsiang-Lin Song; Huan-Jang Ko; Chung-Ming Chen; Yeun-Chung Chang
Journal:  Eur Radiol       Date:  2021-01-03       Impact factor: 5.315

3.  Tumor spread through air space, the clinical implications for T factor and effects on the disease recurrence and prognosis.

Authors:  Takahiro Nakajima; Junichi Morimoto; Ichiro Yoshino
Journal:  J Thorac Dis       Date:  2018-02       Impact factor: 2.895

4.  Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer.

Authors:  Doohyun Park; Daejoong Oh; MyungHoon Lee; Shin Yup Lee; Kyung Min Shin; Johnson Sg Jun; Dosik Hwang
Journal:  Eur Radiol       Date:  2022-05-31       Impact factor: 5.315

5.  Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography.

Authors:  Li-Wei Chen; Shun-Mao Yang; Ching-Chia Chuang; Hao-Jen Wang; Yi-Chang Chen; Mong-Wei Lin; Min-Shu Hsieh; Mara B Antonoff; Yeun-Chung Chang; Carol C Wu; Tinsu Pan; Chung-Ming Chen
Journal:  Ann Surg Oncol       Date:  2022-07-05       Impact factor: 4.339

6.  Thin-section computed tomography findings of lung adenocarcinoma with inherent metastatic potential.

Authors:  Shigeki Suzuki; Keiju Aokage; Junji Yoshida; Genichiro Ishii; Yuki Matsumura; Tomohiro Haruki; Tomoyuki Hishida; Kanji Nagai
Journal:  Surg Today       Date:  2016-09-22       Impact factor: 2.549

Review 7.  Biomarker development in the precision medicine era: lung cancer as a case study.

Authors:  Ashley J Vargas; Curtis C Harris
Journal:  Nat Rev Cancer       Date:  2016-07-08       Impact factor: 60.716

8.  Investigating the association between ground-glass nodules glucose metabolism and the invasive growth pattern of early lung adenocarcinoma.

Authors:  Xiaoliang Shao; Xiaonan Shao; Rong Niu; Zhenxing Jiang; Mei Xu; Yuetao Wang
Journal:  Quant Imaging Med Surg       Date:  2021-08

9.  Prognostic factors for stage I lung adenocarcinoma and surgical management of subsolid nodules.

Authors:  Gökhan Kocaman; Mustafa Bülent Yenigün; Atilla Halil Elhan; Serpil Dizbay Sak; Elvin Hamzayev; Serkan Enön; Ayten Kayı Cangır; Cabir Yüksel
Journal:  Turk Gogus Kalp Damar Cerrahisi Derg       Date:  2018-09-16       Impact factor: 0.332

10.  Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer.

Authors:  Yuki Onozato; Takahiro Nakajima; Hajime Yokota; Jyunichi Morimoto; Akira Nishiyama; Takahide Toyoda; Terunaga Inage; Kazuhisa Tanaka; Yuichi Sakairi; Hidemi Suzuki; Takashi Uno; Ichiro Yoshino
Journal:  Sci Rep       Date:  2021-06-29       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.