201 related articles for article (PubMed ID: 37370621)
21. Radiomic Detection of EGFR Mutations in NSCLC.
Rossi G; Barabino E; Fedeli A; Ficarra G; Coco S; Russo A; Adamo V; Buemi F; Zullo L; Dono M; De Luca G; Longo L; Dal Bello MG; Tagliamento M; Alama A; Cittadini G; Pronzato P; Genova C
Cancer Res; 2021 Feb; 81(3):724-731. PubMed ID: 33148663
[TBL] [Abstract][Full Text] [Related]
22. The deep radiomic analytics pipeline.
Currie G; Rohren E
Vet Radiol Ultrasound; 2022 Dec; 63 Suppl 1():889-896. PubMed ID: 36468301
[TBL] [Abstract][Full Text] [Related]
23. Deep Learning Approaches Towards Skin Lesion Segmentation and Classification from Dermoscopic Images - A Review.
Baig R; Bibi M; Hamid A; Kausar S; Khalid S
Curr Med Imaging; 2020; 16(5):513-533. PubMed ID: 32484086
[TBL] [Abstract][Full Text] [Related]
24. Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction.
Amyar A; Modzelewski R; Vera P; Morard V; Ruan S
J Imaging; 2022 May; 8(5):. PubMed ID: 35621894
[TBL] [Abstract][Full Text] [Related]
25. A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes.
Shen H; Chen L; Liu K; Zhao K; Li J; Yu L; Ye H; Zhu W
Quant Imaging Med Surg; 2021 Jul; 11(7):2918-2932. PubMed ID: 34249623
[TBL] [Abstract][Full Text] [Related]
26. Identifying pathological subtypes of non-small-cell lung cancer by using the radiomic features of
Sha X; Gong G; Qiu Q; Duan J; Li D; Yin Y
Transl Cancer Res; 2019 Sep; 8(5):1741-1749. PubMed ID: 35116924
[TBL] [Abstract][Full Text] [Related]
27. Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP).
Ziegelmayer S; Kaissis G; Harder F; Jungmann F; Müller T; Makowski M; Braren R
J Clin Med; 2020 Dec; 9(12):. PubMed ID: 33322559
[TBL] [Abstract][Full Text] [Related]
28. Homology-based radiomic features for prediction of the prognosis of lung cancer based on CT-based radiomics.
Kadoya N; Tanaka S; Kajikawa T; Tanabe S; Abe K; Nakajima Y; Yamamoto T; Takahashi N; Takeda K; Dobashi S; Takeda K; Nakane K; Jingu K
Med Phys; 2020 Jun; 47(5):2197-2205. PubMed ID: 32096876
[TBL] [Abstract][Full Text] [Related]
29. Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?
Pasini G; Stefano A; Russo G; Comelli A; Marinozzi F; Bini F
Diagnostics (Basel); 2023 Mar; 13(6):. PubMed ID: 36980475
[TBL] [Abstract][Full Text] [Related]
30. A computational pipeline for quantification of pulmonary infections in small animal models using serial PET-CT imaging.
Bagci U; Foster B; Miller-Jaster K; Luna B; Dey B; Bishai WR; Jonsson CB; Jain S; Mollura DJ
EJNMMI Res; 2013 Jul; 3(1):55. PubMed ID: 23879987
[TBL] [Abstract][Full Text] [Related]
31. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.
Alilou M; Beig N; Orooji M; Rajiah P; Velcheti V; Rakshit S; Reddy N; Yang M; Jacono F; Gilkeson RC; Linden P; Madabhushi A
Med Phys; 2017 Jul; 44(7):3556-3569. PubMed ID: 28295386
[TBL] [Abstract][Full Text] [Related]
32. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.
Lin YC; Lin CH; Lu HY; Chiang HJ; Wang HK; Huang YT; Ng SH; Hong JH; Yen TC; Lai CH; Lin G
Eur Radiol; 2020 Mar; 30(3):1297-1305. PubMed ID: 31712961
[TBL] [Abstract][Full Text] [Related]
33. Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on
Koyasu S; Nishio M; Isoda H; Nakamoto Y; Togashi K
Ann Nucl Med; 2020 Jan; 34(1):49-57. PubMed ID: 31659591
[TBL] [Abstract][Full Text] [Related]
34. Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics.
Li Y; Gao X; Tang X; Lin S; Pang H
Front Oncol; 2023; 13():1013085. PubMed ID: 36910615
[TBL] [Abstract][Full Text] [Related]
35. A Machine Learning Approach Using PET/CT-based Radiomics for Prediction of PD-L1 Expression in Non-small Cell Lung Cancer.
Lim CH; Koh YW; Hyun SH; Lee SJ
Anticancer Res; 2022 Dec; 42(12):5875-5884. PubMed ID: 36456151
[TBL] [Abstract][Full Text] [Related]
36. An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images.
Caballo M; Boone JM; Mann R; Sechopoulos I
Med Phys; 2018 Jun; 45(6):2542-2559. PubMed ID: 29676025
[TBL] [Abstract][Full Text] [Related]
37. Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features.
Li G; Zhang X; Song X; Duan L; Wang G; Xiao Q; Li J; Liang L; Bai L; Bai S
Quant Imaging Med Surg; 2023 Mar; 13(3):1605-1618. PubMed ID: 36915317
[TBL] [Abstract][Full Text] [Related]
38. Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan.
Xia X; Gong J; Hao W; Yang T; Lin Y; Wang S; Peng W
Front Oncol; 2020; 10():418. PubMed ID: 32296645
[TBL] [Abstract][Full Text] [Related]
39. A radiomics approach for lung nodule detection in thoracic CT images based on the dynamic patterns of morphological variation.
Lin FY; Chang YC; Huang HY; Li CC; Chen YC; Chen CM
Eur Radiol; 2022 Jun; 32(6):3767-3777. PubMed ID: 35020016
[TBL] [Abstract][Full Text] [Related]
40. 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]
[Previous] [Next] [New Search]
