Detalhe da pesquisa
1.
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.
PLoS Med
; 15(11): e1002711, 2018 11.
Artigo
em Inglês
| MEDLINE | ID: mdl-30500819
2.
VertXNet: an ensemble method for vertebral body segmentation and identification from cervical and lumbar spinal X-rays.
Sci Rep
; 14(1): 3341, 2024 02 09.
Artigo
em Inglês
| MEDLINE | ID: mdl-38336974
3.
Methodology for Good Machine Learning with Multi-Omics Data.
Clin Pharmacol Ther
; 115(4): 745-757, 2024 04.
Artigo
em Inglês
| MEDLINE | ID: mdl-37965805
4.
A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs).
PLoS One
; 18(7): e0280316, 2023.
Artigo
em Inglês
| MEDLINE | ID: mdl-37410795
5.
The impact of quantitative CT-based tumor volumetric features on the outcomes of patients with limited stage small cell lung cancer.
Radiat Oncol
; 15(1): 14, 2020 Jan 14.
Artigo
em Inglês
| MEDLINE | ID: mdl-31937336
6.
Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging.
Clin Cancer Res
; 25(11): 3266-3275, 2019 06 01.
Artigo
em Inglês
| MEDLINE | ID: mdl-31010833
7.
Antibody-targeting of ultra-small nanoparticles enhances imaging sensitivity and enables longitudinal tracking of multiple myeloma.
Nanoscale
; 11(43): 20485-20496, 2019 Nov 21.
Artigo
em Inglês
| MEDLINE | ID: mdl-31650133
8.
Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC.
PLoS One
; 13(11): e0206108, 2018.
Artigo
em Inglês
| MEDLINE | ID: mdl-30388114
9.
Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT.
PLoS One
; 12(1): e0169172, 2017.
Artigo
em Inglês
| MEDLINE | ID: mdl-28046060
10.
Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC.
J Thorac Oncol
; 12(3): 467-476, 2017 03.
Artigo
em Inglês
| MEDLINE | ID: mdl-27903462
11.
Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non-Small Cell Lung Cancer.
J Nucl Med
; 58(4): 569-576, 2017 04.
Artigo
em Inglês
| MEDLINE | ID: mdl-27688480
12.
Lymph node volume predicts survival but not nodal clearance in Stage IIIA-IIIB NSCLC.
PLoS One
; 12(4): e0174268, 2017.
Artigo
em Inglês
| MEDLINE | ID: mdl-28426673
13.
Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer.
Cancer Res
; 77(14): 3922-3930, 2017 07 15.
Artigo
em Inglês
| MEDLINE | ID: mdl-28566328
14.
Radiographic prediction of meningioma grade by semantic and radiomic features.
PLoS One
; 12(11): e0187908, 2017.
Artigo
em Inglês
| MEDLINE | ID: mdl-29145421
15.
CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer.
Radiother Oncol
; 120(2): 258-66, 2016 08.
Artigo
em Inglês
| MEDLINE | ID: mdl-27296412
16.
Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients.
Front Oncol
; 6: 72, 2016.
Artigo
em Inglês
| MEDLINE | ID: mdl-27066454
17.
Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction.
Phys Med Biol
; 61(2): 906-22, 2016 Jan 21.
Artigo
em Inglês
| MEDLINE | ID: mdl-26738433
18.
Radiomic phenotype features predict pathological response in non-small cell lung cancer.
Radiother Oncol
; 119(3): 480-6, 2016 06.
Artigo
em Inglês
| MEDLINE | ID: mdl-27085484
19.
Radiologic-pathologic correlation of response to chemoradiation in resectable locally advanced NSCLC.
Lung Cancer
; 102: 1-8, 2016 12.
Artigo
em Inglês
| MEDLINE | ID: mdl-27987576
20.
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
Radiother Oncol
; 114(3): 345-50, 2015 Mar.
Artigo
em Inglês
| MEDLINE | ID: mdl-25746350