ISSN 1004-4140
CN 11-3017/P
| Citation: |
BA W J, ZHU Y L, WEI M, et al. Advances in the Preoperative Prediction of the Spread of Lung Adenocarcinoma Through Air Spaces Using CT Features[J]. CT Theory and Applications, 2024, 33(1): 91-96. DOI: 10.15953/j.ctta.2022.236. (in Chinese).
|
The spread through air spaces (STAS) is one of the aggressive behaviors of lung adenocarcinoma. It is a risk factor for recurrence and an indicator of poor prognosis after sublobectomy. The association between computed tomography (CT)-based features and STAS can predict the STAS status of patients with lung adenocarcinoma, and thus, assist in the clinical selection of the appropriate type of surgery. This article reviewed the aggressiveness of STAS in lung adenocarcinoma and the current research on the preoperative CT prediction of STAS, as well as related new techniques.
| [1] |
SIEGEL R L, MILLER K D, FUCHS H E, et al. Cancer Statistics, 2021[J]. Ca-a Cancer Journal For Clinicians, 2021, 71(1): 7−33. doi: 10.3322/caac.21654
|
| [2] |
AMIN M B, TAMBOLI P, MERCHANT S H, et al. Micropapillary component in lung adenocarcinoma: A distinctive histologic feature with possible prognostic significance[J]. American Journal of Surgical Pathology, 2002, 26(3): 358−364. doi: 10.1097/00000478-200203000-00010
|
| [3] |
BLAAUWGEERS H, FLIEDER D, WARTH A, et al. A prospective study of loose tissue fragments in non-small cell lung Cancer resection specimens: An alternative view to “spread through air spaces”[J]. American Journal of Surgical Pathology, 2017, 41(9): 1226−1230. doi: 10.1097/PAS.0000000000000889
|
| [4] |
KADOTA K, NITADORI J, SIMA C S, et al. Tumor spread through air spaces is an important pattern of invasion and impacts the frequency and Location of recurrences after limited resection for small stage I lung adenocarcinomas[J]. Journal of Thoracic Oncology, 2015, 10(5): 806−814. doi: 10.1097/JTO.0000000000000486
|
| [5] |
TRAVIS W D, BRAMBILLA E, NICHOLSON A G, et al. The 2015 World Health Organization classification of lung tumors: Impact of genetic, clinical and radiologic advances since the 2004 classification[J]. Journal of Thoracic Oncology, 2015, 10(9): 1243−1260. doi: 10.1097/JTO.0000000000000630
|
| [6] |
WARTH A. Spread through air spaces (STAS): A comprehensive update[J]. Translational Lung Cancer Research, 2017, 6(5): 501−507. doi: 10.21037/tlcr.2017.06.08
|
| [7] |
DETTERBECK F C, BOFFA D J, KIM A W, et al. The eighth edition lung cancer stage classification[J]. Chest, 2017, 151(1): 193−203. doi: 10.1016/j.chest.2016.10.010
|
| [8] |
SHIONO S, ENDO M, SUZUKI K, et al. Spread through air spaces is a prognostic factor in sublobar resection of non-small cell lung Cancer[J]. Annals of Thoracic Surgery, 2018, 106(2): 354−360. doi: 10.1016/j.athoracsur.2018.02.076
|
| [9] |
LIU H, YIN Q, YANG G, et al. Prognostic impact of tumor spread through air spaces in non-small cell lung cancers: A Meta-analysis including 3564 patients[J]. Pathology & Oncology Research, 2019, 25(4): 1303−1310.
|
| [10] |
DAVID E A, ATAY S M, MCFADDEN P M, et al. Sublobar or suboptimal: Does tumor spread through air spaces signify the end of sublobar resections for T1 N0 adenocarcinomas?[J]. Journal of Thoracic Oncology. 2019, 14(1): 11-12.
|
| [11] |
DAI C, XIE H, SU H, et al. Tumor spread through air spaces affects the recurrence and overall survival in patients with lung? Adenocarcinoma >2 to 3 cm[J]. Journal of Thoracic Oncology, 2017, 12(7): 1052−1060. doi: 10.1016/j.jtho.2017.03.020
|
| [12] |
KADOTA K, KUSHIDA Y, KATSUKI N, et al. Tumor spread through air spaces is an independent predictor of recurrence-free survival in patients with resected lung squamous cell carcinoma[J]. American Journal of Surgical Pathology, 2017, 41(8): 1077−1086. doi: 10.1097/PAS.0000000000000872
|
| [13] |
YOKOYAMA S, MURAKAMI T, TAO H, et al. Tumor spread through air spaces identifies a distinct subgroup with poor prognosis in surgically resected lung pleomorphic carcinoma[J]. Chest, 2018, 154(4): 838−847. doi: 10.1016/j.chest.2018.06.007
|
| [14] |
CHEN D, MAO Y, WEN J, et al. Tumor spread through air spaces in non-small cell lung cancer: A systematic review and Meta-analysis[J]. Annals of Thoracic Surgery, 2019, 108(3): 945−954. doi: 10.1016/j.athoracsur.2019.02.045
|
| [15] |
KIM S K, KIM T J, CHUNG M J, et al. Lung adenocarcinoma: CT features associated with spread through air spaces[J]. Radiology, 2018, 289(3): 831−840. doi: 10.1148/radiol.2018180431
|
| [16] |
MASAI K, SAKURAI H, SUKEDA A, et al. Prognostic impact of margin distance and tumor spread through air spaces in limited resection for primary lung cancer[J]. Journal of Thoracic Oncology, 2017, 12(12): 1788−1797. doi: 10.1016/j.jtho.2017.08.015
|
| [17] |
江长思, 罗燕, 唐雪, 等. 基于CT机器学习模型预测肺腺癌气腔播散[J]. 中国医学影像技术, 2020,36(12): 1834−1838.
JIANG C S, LUO Y, TANG X, et al. CT-based machine learning model in prediction of spread through air space of lung adenocarcinoma[J]. Chinese Journal of Medical Imaging Technology, 2020, 36(12): 1834−1838. (in Chinese).
|
| [18] |
阙敬文, 刘涛, 罗达远, 等. 炎症指标及CT影像学对可手术肺腺癌患者出现气道播散的预测价值[J]. 四川医学, 2022,43(4): 339−344.
QUE J W, LIU T, LUO D Y, et al. The predictive value of inflammatory indexes and CT imaging in patients with operable lung adenocarcinoma with spread through air space (STAS)[J]. Sichuan Medical Journal, 2022, 43(4): 339−344. (in Chinese).
|
| [19] |
TOYOKAWA G, YAMADA Y, TAGAWA T, et al. Significance of spread through air spaces in resected pathological stage Ⅰ lung adenocarcinoma[J]. Annals of Thoracic Surgery, 2018, 105(6): 1655−1663. doi: 10.1016/j.athoracsur.2018.01.037
|
| [20] |
KOEZUKA S, MIKAMI T, TOCHIGI N, et al. Toward improving prognosis prediction in patients undergoing small lung adenocarcinoma resection: Radiological and pathological assessment of diversity and intratumor heterogeneity[J]. Lung Cancer, 2019, 135: 40−46. doi: 10.1016/j.lungcan.2019.06.023
|
| [21] |
尹柯, 巴文娟, 陶俊利, 等. 双能量CT定量参数预测实性肺腺癌气道播散[J]. 中国医学影像技术, 2022,38(10): 1514−1518.
YIN K, BA W J, TAO J L, et al. Dual-energy CT quantitative parameters for predicting spreading through air spaces of solid lung adenocarcinoma[J]. Chinese Journal of Medical Imaging Technology, 2022, 38(10): 1514−1518. (in Chinese).
|
| [22] |
GU Y, SHE Y, XIE D, et al. A texture analysis-based prediction model for lymph node metastasis in stage IA lung adenocarcinoma[J]. Annals of Thoracic Surgery, 2018, 106(1): 214−220. doi: 10.1016/j.athoracsur.2018.02.026
|
| [23] |
LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis[J]. European Journal of Cancer, 2012, 48(4): 441−446. doi: 10.1016/j.ejca.2011.11.036
|
| [24] |
COROLLER T P, AGRAWAL V, HUYNH E, et al. Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC[J]. Journal of Thoracic Oncology, 2017, 12(3): 467−476. doi: 10.1016/j.jtho.2016.11.2226
|
| [25] |
COROLLER T P, AGRAWAL V, NARAYAN V, et al. Radiomic phenotype features predict pathological response in non-small cell lung cancer[J]. Radiotherapy and Oncology, 2016, 119(3): 480−486. doi: 10.1016/j.radonc.2016.04.004
|
| [26] |
LEE G, PARK H, SOHN I, et al. Comprehensive computed tomography radiomics analysis of lung adenocarcinoma for prognostication[J]. Oncologist, 2018, 23(7): 806−813. doi: 10.1634/theoncologist.2017-0538
|
| [27] |
CHEN D, SHE Y, WANG T, et al. Radiomics-based prediction for tumour spread through air spaces in stage Ⅰ lung adenocarcinoma using machine learning[J]. Journal of Cardiothoracic Surgery, 2020, 58(1): 51−58. doi: 10.1093/ejcts/ezaa011
|
| [28] |
JIANG C, LUO Y, YUAN J, et al. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma[J]. European Radiology, 2020, 30(7): 4050−4057. doi: 10.1007/s00330-020-06694-z
|
| [29] |
HAN X, FAN J, ZHENG Y, et al. The value of CT-based radiomics for predicting spread through air spaces in stage IA lung adenocarcinoma[J]. Frontiers in Oncology, 2022, 12: 757389. doi: 10.3389/fonc.2022.757389
|
| [30] |
ZHUO Y, FENG M, YANG S, et al. Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma[J]. Translational Oncology, 2020, 13(10): 100820. doi: 10.1016/j.tranon.2020.100820
|
| [31] |
WU L, YANG X, CAO W, et al. Multiple level CT radiomics features preoperatively predict lymph node metastasis in esophageal cancer: A multicentre retrospective study[J]. Frontiers in Oncology, 2019, 9: 1548.
|
| [32] |
NAKAURA T, HIGAKI T, AWAI K, et al. A primer for understanding radiology articles about machine learning and deep learning[J]. Diagnostic and Interventional Imaging, 2020, 101(12): 765−770. doi: 10.1016/j.diii.2020.10.001
|
| [33] |
TAO J, LIANG C, YIN K. 3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer[J]. Diagnostic and Interventional Imaging, 2022, 103(11): 535-544.
|
Supported by: Beijing Renhe Information Technology Co. Ltd 
DownLoad: