121 related articles for article (PubMed ID: 38113804)
1. Multimodal deep learning for personalized renal cell carcinoma prognosis: Integrating CT imaging and clinical data.
Mahootiha M; Qadir HA; Bergsland J; Balasingham I
Comput Methods Programs Biomed; 2024 Feb; 244():107978. PubMed ID: 38113804
[TBL] [Abstract][Full Text] [Related]
2. Deep learning-assisted survival prognosis in renal cancer: A CT scan-based personalized approach.
Mahootiha M; Qadir HA; Aghayan D; Fretland ÅA; von Gohren Edwin B; Balasingham I
Heliyon; 2024 Jan; 10(2):e24374. PubMed ID: 38298725
[TBL] [Abstract][Full Text] [Related]
3. A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.
Nie P; Yang G; Wang Y; Xu Y; Yan L; Zhang M; Zhao L; Wang N; Zhao X; Li X; Cheng N; Wang Y; Chen C; Wang N; Duan S; Wang X; Wang Z
Eur Radiol; 2023 Dec; 33(12):8858-8868. PubMed ID: 37389608
[TBL] [Abstract][Full Text] [Related]
4. Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.
Shu J; Wen D; Xi Y; Xia Y; Cai Z; Xu W; Meng X; Liu B; Yin H
Eur J Radiol; 2019 Dec; 121():108738. PubMed ID: 31756634
[TBL] [Abstract][Full Text] [Related]
5. A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma.
Lin F; Ma C; Xu J; Lei Y; Li Q; Lan Y; Sun M; Long W; Cui E
Eur J Radiol; 2020 Aug; 129():109079. PubMed ID: 32526669
[TBL] [Abstract][Full Text] [Related]
6. An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma.
Yang H; Wu K; Liu H; Wu P; Yuan Y; Wang L; Liu Y; Zeng H; Li J; Liu W; Wu S
Eur Radiol; 2023 Nov; 33(11):7532-7541. PubMed ID: 37289245
[TBL] [Abstract][Full Text] [Related]
7. A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: A multicenter study.
Nie P; Liu S; Zhou R; Li X; Zhi K; Wang Y; Dai Z; Zhao L; Wang N; Zhao X; Li X; Cheng N; Wang Y; Chen C; Xu Y; Yang G
Eur J Radiol; 2023 Sep; 166():111018. PubMed ID: 37562222
[TBL] [Abstract][Full Text] [Related]
8. Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.
Coy H; Hsieh K; Wu W; Nagarajan MB; Young JR; Douek ML; Brown MS; Scalzo F; Raman SS
Abdom Radiol (NY); 2019 Jun; 44(6):2009-2020. PubMed ID: 30778739
[TBL] [Abstract][Full Text] [Related]
9. Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.
Lee H; Hong H; Kim J; Jung DC
Med Phys; 2018 Apr; 45(4):1550-1561. PubMed ID: 29474742
[TBL] [Abstract][Full Text] [Related]
10. Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics.
Cui E; Li Z; Ma C; Li Q; Lei Y; Lan Y; Yu J; Zhou Z; Li R; Long W; Lin F
Eur Radiol; 2020 May; 30(5):2912-2921. PubMed ID: 32002635
[TBL] [Abstract][Full Text] [Related]
11. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.
Nazari M; Shiri I; Hajianfar G; Oveisi N; Abdollahi H; Deevband MR; Oveisi M; Zaidi H
Radiol Med; 2020 Aug; 125(8):754-762. PubMed ID: 32193870
[TBL] [Abstract][Full Text] [Related]
12. Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT.
Han D; Yu Y; Yu N; Dang S; Wu H; Jialiang R; He T
Br J Radiol; 2020 Oct; 93(1114):20200131. PubMed ID: 32706977
[TBL] [Abstract][Full Text] [Related]
13. A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images.
Wu K; Wu P; Yang K; Li Z; Kong S; Yu L; Zhang E; Liu H; Guo Q; Wu S
Eur Radiol; 2022 Apr; 32(4):2255-2265. PubMed ID: 34800150
[TBL] [Abstract][Full Text] [Related]
14. Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study.
Yao H; Tian L; Liu X; Li S; Chen Y; Cao J; Zhang Z; Chen Z; Feng Z; Xu Q; Zhu J; Wang Y; Guo Y; Chen W; Li C; Li P; Wang H; Luo J
J Cancer Res Clin Oncol; 2023 Nov; 149(17):15827-15838. PubMed ID: 37672075
[TBL] [Abstract][Full Text] [Related]
15. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma.
Ding J; Xing Z; Jiang Z; Chen J; Pan L; Qiu J; Xing W
Eur J Radiol; 2018 Jun; 103():51-56. PubMed ID: 29803385
[TBL] [Abstract][Full Text] [Related]
16. Phase recognition in contrast-enhanced CT scans based on deep learning and random sampling.
Dao BT; Nguyen TV; Pham HH; Nguyen HQ
Med Phys; 2022 Jul; 49(7):4518-4528. PubMed ID: 35428990
[TBL] [Abstract][Full Text] [Related]
17. The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method.
Han S; Hwang SI; Lee HJ
J Digit Imaging; 2019 Aug; 32(4):638-643. PubMed ID: 31098732
[TBL] [Abstract][Full Text] [Related]
18. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.
Hosny A; Parmar C; Coroller TP; Grossmann P; Zeleznik R; Kumar A; Bussink J; Gillies RJ; Mak RH; Aerts HJWL
PLoS Med; 2018 Nov; 15(11):e1002711. PubMed ID: 30500819
[TBL] [Abstract][Full Text] [Related]
19. Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma.
Gao Y; Wang X; Zhao X; Zhu C; Li C; Li J; Wu X
BMC Cancer; 2023 Oct; 23(1):953. PubMed ID: 37814228
[TBL] [Abstract][Full Text] [Related]
20. Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.
Zabihollahy F; Schieda N; Krishna S; Ukwatta E
Eur Radiol; 2020 Sep; 30(9):5183-5190. PubMed ID: 32350661
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]