|
1
|
Tsutsui S, Ashizawa K, Minami K, Tagawa T,
Nagayasu T, Hayashi T and Uetani M: Multiple focal pure
ground-glass opacities on high-resolution CT images: Clinical
significance in patients with lung cancer. AJR Am J Roentgenol.
195:W131–W138. 2010.PubMed/NCBI View Article : Google Scholar
|
|
2
|
Miller A, Markowitz S, Manowitz A and
Miller JA: Lung cancer screening using low-dose high-resolution CT
scanning in a high-risk workforce: 3500 nuclear fuel workers in
three US states. Chest. 125 (Suppl 5):152S–153S. 2004.PubMed/NCBI
|
|
3
|
Migliore M, Fornito M, Palazzolo M,
Criscione A, Gangemi M, Borrata F, Vigneri P, Nardini M and Dunning
J: Ground glass opacities management in the lung cancer screening
era. Ann Transl Med. 6(90)2018.PubMed/NCBI View Article : Google Scholar
|
|
4
|
Ye T, Deng L, Xiang J, Zhang Y, Hu H, Sun
Y, Li Y, Shen L, Wang S, Xie L and Chen H: Predictors of pathologic
tumor invasion and prognosis for ground glass opacity featured lung
adenocarcinoma. Ann Thorac Surg. 106:1682–1690. 2018.PubMed/NCBI View Article : Google Scholar
|
|
5
|
Travis WD, Brambilla E, Noguchi M,
Nicholson AG, Geisinger KR, Yatabe Y, Beer DG, Powell CA, Riely GJ,
Van Schil PE, et al: International association for the study of
lung cancer/American thoracic society/European respiratory society
international multidisciplinary classification of lung
adenocarcinoma. J Thorac Oncol. 6:244–285. 2011.PubMed/NCBI View Article : Google Scholar
|
|
6
|
Dai J, Yu G and Yu J: Can CT imaging
features of ground-glass opacity predict invasiveness? A
meta-analysis. Thorac Cancer. 9:452–458. 2018.PubMed/NCBI View Article : Google Scholar
|
|
7
|
Park CM, Goo JM, Lee HJ, Lee CH, Kim HC,
Chung DH and Im JG: CT findings of atypical adenomatous hyperplasia
in the lung. Korean J Radiol. 7:80–86. 2006.PubMed/NCBI View Article : Google Scholar
|
|
8
|
Nagao M, Murase K, Yasuhara Y, Ikezoe J,
Eguchi K, Mogami H, Mandai K, Nakata M and Ooshiro Y: Measurement
of localized ground-glass attenuation on thin-section computed
tomography images: Correlation with the progression of
bronchioloalveolar carcinoma of the lung. Invest Radiol.
37:692–697. 2002.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Van Schil PE, Asamura H, Rusch VW,
Mitsudomi T, Tsuboi M, Brambilla E and Travis WD: Surgical
implications of the new IASLC/ATS/ERS adenocarcinoma
classification. Eur Respir J. 39:478–486. 2012.PubMed/NCBI View Article : Google Scholar
|
|
10
|
Koike T, Togashi K, Shirato T, Sato S,
Hirahara H, Sugawara M, Oguma F, Usuda H and Emura I: Limited
resection for noninvasive bronchioloalveolar carcinoma diagnosed by
intraoperative pathologic examination. Ann Thorac Surg.
88:1106–1111. 2009.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Pedersen JH, Saghir Z, Wille MM, Thomsen
LH, Skov BG and Ashraf H: Ground-glass opacity lung nodules in the
era of lung cancer CT screening: Radiology, pathology and clinical
management. Oncology (Williston Park). 30:266–274. 2016.PubMed/NCBI
|
|
12
|
Chou HP, Lin KH, Huang HK, Lin LF, Chen
YY, Wu TH, Lee SC, Chang H and Huang TW: Prognostic value of
positron emission tomography in resected stage IA non-small cell
lung cancer. Eur Radiol. 31:8021–8029. 2021.PubMed/NCBI View Article : Google Scholar
|
|
13
|
Inoue M, Minami M, Sawabata N, Utsumi T,
Kadota Y, Shigemura N and Okumura M: Clinical outcome of resected
solid-type small-sized c-stage IA non-small cell lung cancer. Eur J
Cardiothorac Surg. 37:1445–1449. 2010.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Higuchi M, Yaginuma H, Yonechi A, Kanno R,
Ohishi A, Suzuki H and Gotoh M: Long-term outcomes after
video-assisted thoracic surgery (VATS) lobectomy versus lobectomy
via open thoracotomy for clinical stage IA non-small cell lung
cancer. J Cardiothorac Surg. 9(88)2014.PubMed/NCBI View Article : Google Scholar
|
|
15
|
Zhang J, Wu J, Tan Q, Zhu L and Gao W: Why
do pathological stage IA lung adenocarcinomas vary from prognosis?:
A clinicopathologic study of 176 patients with pathological stage
IA lung adenocarcinoma based on the IASLC/ATS/ERS classification. J
Thorac Oncol. 8:1196–1202. 2013.PubMed/NCBI View Article : Google Scholar
|
|
16
|
Lee SM, Park CM, Goo JM, Lee HJ, Wi JY and
Kang CH: Invasive pulmonary adenocarcinomas versus preinvasive
lesions appearing as ground-glass nodules: Differentiation by using
CT features. Radiology. 268:265–273. 2013.PubMed/NCBI View Article : Google Scholar
|
|
17
|
Lambin P, Rios-Velazquez E, Leijenaar R,
Carvalho S, van Stiphout RGPM, Granton P, Zegers CML, Gillies R,
Boellard R, Dekker A and Aerts HJWL: Radiomics: Extracting more
information from medical images using advanced feature analysis.
Eur J Cancer. 48:441–446. 2012.PubMed/NCBI View Article : Google Scholar
|
|
18
|
Wilson R and Devaraj A: Radiomics of
pulmonary nodules and lung cancer. Transl Lung Cancer Res. 6:86–91.
2017.PubMed/NCBI View Article : Google Scholar
|
|
19
|
Han F, Wang H, Zhang G, Han H, Song B, Li
L, Moore W, Lu H, Zhao H and Liang Z: Texture feature analysis for
computer-aided diagnosis on pulmonary nodules. J Digit Imaging.
28:99–115. 2015.PubMed/NCBI View Article : Google Scholar
|
|
20
|
Kumar V, Gu Y, Basu S, Berglund A,
Eschrich SA, Schabath MB, Forster K, Aerts HJWL, Dekker A,
Fenstermacher D, et al: Radiomics: The process and the challenges.
Magn Reson Imaging. 30:1234–1248. 2012.PubMed/NCBI View Article : Google Scholar
|
|
21
|
Zhao S, Ren W, Zhuang Y and Wang Z: The
influence of different segmentation methods on the extraction of
imaging histological features of hepatocellular carcinoma CT. J Med
Syst. 43:1–7. 2019.PubMed/NCBI View Article : Google Scholar
|
|
22
|
Çinarer G, Gürsel B and Haşim A:
Prediction of glioma grades using deep learning with wavelet
radiomic features. Appl Sci. 10(6296)2020.
|
|
23
|
Korpershoek YJ, Slot JC, Effing TW,
Schuurmans MJ and Trappenburg JC: Self-management behaviors to
reduce exacerbation impact in COPD patients: A Delphi study. Int J
Chron Obstruct Pulmon Dis. 12:2735–2746. 2017.PubMed/NCBI View Article : Google Scholar
|
|
24
|
Dang Y, Wang R, Qian K, Lu J, Zhang H and
Zhang Y: Clinical and radiological predictors of epidermal growth
factor receptor mutation in nonsmall cell lung cancer. J Appl Clin
Med Phys. 22:271–280. 2021.PubMed/NCBI View Article : Google Scholar
|
|
25
|
Dong M, Hou G, Li S, Li N, Zhang L and Xu
K: Preoperatively estimating the malignant potential of mediastinal
lymph nodes: A pilot study toward establishing a robust radiomics
model based on contrast-enhanced CT imaging. Front Oncol.
10(558428)2021.PubMed/NCBI View Article : Google Scholar
|
|
26
|
Vickers AJ and Elkin BB: Decision curve
analysis: A novel method for evaluating prediction models. Med
Decis Making. 26:565–574. 2006.PubMed/NCBI View Article : Google Scholar
|
|
27
|
Sun F, Xi J, Zhan C, Yang X, Wang L, Shi
Y, Jiang W and Wang Q: Ground glass opacities: Imaging, pathology,
and gene mutations. J Thorac Cardiovasc Surg. 156:808–813.
2018.PubMed/NCBI View Article : Google Scholar
|
|
28
|
Shimizu K, Ikeda N, Tsuboi M, Hirano T and
Kato H: Percutaneous CT- guided fine needle aspiration for lung
cancer smaller than 2 cm and revealed by ground-glass opacity at
CT. Lung Cancer. 51:173–179. 2006.PubMed/NCBI View Article : Google Scholar
|
|
29
|
Ikezawa Y, Shinagawa N, Sukoh N, Morimoto
M, Kikuchi H, Watanabe M, Nakano K, Oizumi S and Nishimura M:
Usefulness of endobronchial ultrasonography with a guide sheath and
virtual bronchoscopic navigation for ground-glass opacity lesions.
Ann Thorac Surg. 103:470–475. 2017.PubMed/NCBI View Article : Google Scholar
|
|
30
|
Zhang Y, Qiang JW, Ye JD, Ye XD and Zhang
J: High resolution CT in differentiating minimally invasive
component in early lung adenocarcinoma. Lung Cancer. 84:236–241.
2014.PubMed/NCBI View Article : Google Scholar
|
|
31
|
Lee HJ, Lee CH, Jeong YJ, Chung DH, Goo
JM, Park CM and Austin JHM: IASLC/ATS/ERS international
multidisciplinary classification of lung adenocarcinoma: Novel
concepts and radiologic implications. J Thorac Imaging. 27:340–353.
2012.PubMed/NCBI View Article : Google Scholar
|
|
32
|
Kobayashi Y, Ambrogio C and Mitsudomi T:
Ground-glass nodules of the lung in never-smokers and smokers:
Clinical and genetic insights. Transl Lung Cancer Res. 7:487–497.
2018.PubMed/NCBI View Article : Google Scholar
|
|
33
|
Eguchi T, Yoshizawa A, Kawakami S, Kumeda
H, Umesaki T, Agatsuma H, Sakaizawa T, Tominaga Y, Toishi M and
Hashizume M: Tumor size and computed tomography attenuation of
pulmonary pure ground-glass nodules are useful for predicting
pathological invasiveness. PLoS One. 9(e97867)2014.PubMed/NCBI View Article : Google Scholar
|
|
34
|
Li M, Wang Y, Chen Y and Zhang Z:
Identification of preoperative prediction factors of tumor subtypes
for patients with solitary ground-glass opacity pulmonary nodules.
J Cardiothorac Surg. 13(9)2018.PubMed/NCBI View Article : Google Scholar
|
|
35
|
Chen PH, Chang KM, Tseng WC, Chen CH and
Chao JI: Invasiveness and surgical timing evaluation by clinical
features of ground-glass opacity nodules in lung cancers. Thorac
Cancer. 10:2133–2141. 2019.PubMed/NCBI View Article : Google Scholar
|
|
36
|
Saji H, Matsubayashi J, Akata S, Shimada
Y, Kato Y, Kudo Y, Nagao T, Park J, Kakihana M, Kajiwara N, et al:
Correlation between whole tumor size and solid component size on
high-resolution computed tomography in the prediction of the degree
of pathologic malignancy and the prognostic outcome in primary lung
adenocarcinoma. Acta Radiol. 56:1187–1195. 2015.PubMed/NCBI View Article : Google Scholar
|
|
37
|
Chae HD, Park CM, Park SJ, Lee SM, Kim KG
and Goo JM: Computerized texture analysis of persistent part-solid
ground-glass nodules: Differentiation of preinvasive lesions from
invasive pulmonary adenocarcinomas. Radiology. 273:285–293.
2014.PubMed/NCBI View Article : Google Scholar
|
|
38
|
Li W, Wang X, Zhang Y, Li X, Li Q and Ye
Z: Radiomic analysis of pulmonary ground-glass opacity nodules for
distinction of preinvasive lesions, invasive pulmonary
adenocarcinoma and minimally invasive adenocarcinoma based on
quantitative texture analysis of CT. Chin J Cancer Res. 30:415–424.
2018.PubMed/NCBI View Article : Google Scholar
|
|
39
|
Xue X, Yang Y, Huang Q, Cui F, Lian Y,
Zhang S, Yao L, Peng W, Li X, Pang P, et al: Use of a radiomics
model to predict tumor invasiveness of pulmonary adenocarcinomas
appearing as pulmonary ground-glass nodules. Biomed Res Int.
2018(6803971)2018.PubMed/NCBI View Article : Google Scholar
|
|
40
|
Li M, Narayan V, Gill RR, Jagannathan JP,
Barile MF, Gao F, Bueno R and Jayender J: Computer-aided diagnosis
of ground-glass opacity nodules using open-source software for
quantifying tumor heterogeneity. Am J Roentgenol. 209:1216–1227.
2017.PubMed/NCBI View Article : Google Scholar
|
|
41
|
Haralick RM, Shanmugam K and Dinstein I:
Textural features for image classification. IEEE Trans Systems Man
Cybernetics. 6:610–621. 1973.PubMed/NCBI View Article : Google Scholar
|
|
42
|
Zhang Y, Ko CC, Chen JH, Chang KT, Chen
TY, Lim SW, Tsui YK and Su MY: Radiomics approach for prediction of
recurrence in non-functioning pituitary macroadenomas. Front Oncol.
10(590083)2020.PubMed/NCBI View Article : Google Scholar
|
|
43
|
Batur A, Kılınçer A, Ateş F, Demir NA and
Ergün R: Evaluation of systemic involvement of Coronavirus disease
2019 through spleen; size and texture analysis. Turk J Med Sci.
51:972–980. 2021.PubMed/NCBI View Article : Google Scholar
|
|
44
|
Chen ZW, Tang K, Zhao YF, Chen YZ, Tang
LJ, Li G, Huang OY, Wang XD, Targher G, Byrne CD, et al: Radiomics
based on fluoro-deoxyglucose positron emission tomography predicts
liver fibrosis in biopsy-proven MAFLD: A pilot study. Int J Med
Sci. 18:3624–3630. 2021.PubMed/NCBI View Article : Google Scholar
|
