136 related articles for article (PubMed ID: 38769829)
1. [Clinical Multi-features Analysis of Cystic Lung Adenocarcinoma
and Construction of Invasive Risk Prediction Model].
Wang Q; Fu C; Wang K; Ren Q; Chen A; Xu X; Chen L; Zhu Q
Zhongguo Fei Ai Za Zhi; 2024 Apr; 27(4):266-275. PubMed ID: 38769829
[TBL] [Abstract][Full Text] [Related]
2. CT-Assisted Improvements in the Accuracy of the Intraoperative Frozen Section Examination of Ground-Glass Density Nodules.
Xinli W; Xiaoshuang S; Chengxin Y; Qiang Z
Comput Math Methods Med; 2022; 2022():8967643. PubMed ID: 35035526
[TBL] [Abstract][Full Text] [Related]
3. CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction.
Li X; Zhang W; Yu Y; Zhang G; Zhou L; Wu Z; Liu B
BMC Cancer; 2020 Jan; 20(1):60. PubMed ID: 31992239
[TBL] [Abstract][Full Text] [Related]
4. [Value of CT Features on Differential Diagnosis of Pulmonary Subsolid Nodules and Degree of invasion Prediction in Pulmonary Adenocarcinoma].
Guo F; Li X; Wang X; Zheng W; Wang Q; Song W; Yu T; Fan Y; Wang Y
Zhongguo Fei Ai Za Zhi; 2018 Jun; 21(6):451-457. PubMed ID: 29945703
[TBL] [Abstract][Full Text] [Related]
5. Lung adenocarcinoma associated with cystic airspaces: Predictive value of CT features in assessing pathologic invasiveness.
Zhu H; Zhang L; Huang Z; Chen J; Sun L; Chen Y; Huang G; Chen Q; Yu H
Eur J Radiol; 2023 Aug; 165():110947. PubMed ID: 37392546
[TBL] [Abstract][Full Text] [Related]
6. [CT diagnosis of different pathological types of ground-glass nodules].
Gao F; Ge XJ; Li M; Chen Y; Lyu F; Hua Y; Ren Q; Qi L
Zhonghua Zhong Liu Za Zhi; 2014 Mar; 36(3):188-92. PubMed ID: 24785278
[TBL] [Abstract][Full Text] [Related]
7. Discriminating invasive adenocarcinoma among lung pure ground-glass nodules: a multi-parameter prediction model.
Hu F; Huang H; Jiang Y; Feng M; Wang H; Tang M; Zhou Y; Tan X; Liu Y; Xu C; Ding N; Bai C; Hu J; Yang D; Zhang Y
J Thorac Dis; 2021 Sep; 13(9):5383-5394. PubMed ID: 34659805
[TBL] [Abstract][Full Text] [Related]
8. A computerized tomography-based radiomic model for assessing the invasiveness of lung adenocarcinoma manifesting as ground-glass opacity nodules.
Zhu M; Yang Z; Wang M; Zhao W; Zhu Q; Shi W; Yu H; Liang Z; Chen L
Respir Res; 2022 Apr; 23(1):96. PubMed ID: 35429974
[TBL] [Abstract][Full Text] [Related]
9. [HRCT features differentiate synchronous multiple primary lung adenocarcinomas from intrapulmonary metastases].
Zhou LN; Wu N; Zhao SJ; Yang L; Wang YL; Wang RB; Yang SX
Zhonghua Zhong Liu Za Zhi; 2020 Jun; 42(6):449-455. PubMed ID: 32575939
[No Abstract] [Full Text] [Related]
10. CT Characteristics for Predicting Invasiveness in Pulmonary Pure Ground-Glass Nodules.
Chu ZG; Li WJ; Fu BJ; Lv FJ
AJR Am J Roentgenol; 2020 Aug; 215(2):351-358. PubMed ID: 32348187
[No Abstract] [Full Text] [Related]
11. [Establishment and analysis of prediction model for invasive subsolid pulmonary nodules based on radiomics].
Wu XL; Xu QZ; Chen T; Wang FL; Jiang WH; Lyu GM; Lu G
Zhonghua Yi Xue Za Zhi; 2022 Jan; 102(3):209-215. PubMed ID: 35042290
[No Abstract] [Full Text] [Related]
12. Computed Tomography Findings for Predicting Invasiveness of Lung Adenocarcinomas Manifesting as Pure Ground-Glass Nodules.
Park J; Doo KW; Sung YE; Jung JI; Chang S
Can Assoc Radiol J; 2023 Feb; 74(1):137-146. PubMed ID: 35840350
[No Abstract] [Full Text] [Related]
13. Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study.
Dong H; Yin LK; Qiu YG; Wang XB; Yang JJ; Lou CC; Ye XD
Eur Radiol; 2023 Jun; 33(6):3931-3940. PubMed ID: 36600124
[TBL] [Abstract][Full Text] [Related]
14. [A Retrospective Study of Mean Computed Tomography Value to Predict
the Tumor Invasiveness in AAH and Clinical Stage Ia Lung Cancer].
Wu H; Liu C; Xu M; Xiong R; Xu G; Li C; Xie M
Zhongguo Fei Ai Za Zhi; 2018 Mar; 21(3):190-196. PubMed ID: 29587938
[TBL] [Abstract][Full Text] [Related]
15. Qualitative and quantitative imaging features of pulmonary subsolid nodules: differentiating invasive adenocarcinoma from minimally invasive adenocarcinoma and preinvasive lesions.
Qi L; Lu W; Yang L; Tang W; Zhao S; Huang Y; Wu N; Wang J
J Thorac Dis; 2019 Nov; 11(11):4835-4846. PubMed ID: 31903274
[TBL] [Abstract][Full Text] [Related]
16. Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact.
Jiang Y; Che S; Ma S; Liu X; Guo Y; Liu A; Li G; Li Z
Cancer Imaging; 2021 Jan; 21(1):1. PubMed ID: 33407884
[TBL] [Abstract][Full Text] [Related]
17. Nomogram for Predicting the Risk of Invasive Pulmonary Adenocarcinoma for Pure Ground-Glass Nodules.
Wang L; Shen W; Xi Y; Liu S; Zheng D; Jin C
Ann Thorac Surg; 2018 Apr; 105(4):1058-1064. PubMed ID: 29452996
[TBL] [Abstract][Full Text] [Related]
18. Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs.
Fang W; Zhang G; Yu Y; Chen H; Liu H
Biosci Rep; 2022 Jan; 42(1):. PubMed ID: 35005775
[TBL] [Abstract][Full Text] [Related]
19. CT features differentiating pre- and minimally invasive from invasive adenocarcinoma appearing as mixed ground-glass nodules: mass is a potential imaging biomarker.
Wang XW; Chen WF; He WJ; Yang ZM; Li M; Xiao L; Hua YQ
Clin Radiol; 2018 Jun; 73(6):549-554. PubMed ID: 29525515
[TBL] [Abstract][Full Text] [Related]
20. A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence.
Gao R; Gao Y; Zhang J; Zhu C; Zhang Y; Yan C
J Cancer Res Clin Oncol; 2023 Nov; 149(17):15323-15333. PubMed ID: 37624396
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
