Detalhe da pesquisa
1.
Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas.
Radiology
; 290(3): 783-792, 2019 03.
Artigo
em Inglês
| MEDLINE | ID: mdl-30561278
2.
A tumor vasculature-based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors.
Sci Adv
; 8(47): eabq4609, 2022 Nov 25.
Artigo
em Inglês
| MEDLINE | ID: mdl-36427313
3.
4.
Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers.
Clin Cancer Res
; 28(20): 4410-4424, 2022 10 14.
Artigo
em Inglês
| MEDLINE | ID: mdl-35727603
5.
A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans.
Cancers (Basel)
; 13(11)2021 Jun 03.
Artigo
em Inglês
| MEDLINE | ID: mdl-34205005
6.
Integrated Clinical and CT Based Artificial Intelligence Nomogram for Predicting Severity and Need for Ventilator Support in COVID-19 Patients: A Multi-Site Study.
IEEE J Biomed Health Inform
; 25(11): 4110-4118, 2021 11.
Artigo
em Inglês
| MEDLINE | ID: mdl-34388099
7.
Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability.
Br J Ophthalmol
; 105(8): 1155-1160, 2021 08.
Artigo
em Inglês
| MEDLINE | ID: mdl-32816791
8.
Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade.
J Immunother Cancer
; 8(2)2020 10.
Artigo
em Inglês
| MEDLINE | ID: mdl-33051342
9.
Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer.
Cancer Immunol Res
; 8(1): 108-119, 2020 01.
Artigo
em Inglês
| MEDLINE | ID: mdl-31719058
10.
Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study.
Lung Cancer
; 142: 90-97, 2020 04.
Artigo
em Inglês
| MEDLINE | ID: mdl-32120229
11.
Author Correction: Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas.
Sci Rep
; 9(1): 15873, 2019 Oct 29.
Artigo
em Inglês
| MEDLINE | ID: mdl-31659229
12.
Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features.
Lung Cancer
; 135: 1-9, 2019 09.
Artigo
em Inglês
| MEDLINE | ID: mdl-31446979
13.
Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas.
Sci Rep
; 8(1): 15290, 2018 10 16.
Artigo
em Inglês
| MEDLINE | ID: mdl-30327507
14.
Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography.
J Med Imaging (Bellingham)
; 5(2): 024501, 2018 Apr.
Artigo
em Inglês
| MEDLINE | ID: mdl-29721515
15.
An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.
Med Phys
; 44(7): 3556-3569, 2017 Jul.
Artigo
em Inglês
| MEDLINE | ID: mdl-28295386
16.
Corrigendum to "Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features" [Lung Cancer 135 (September) (2019) 1-9].
Lung Cancer
; 136: 156, 2019 Oct.
Artigo
em Inglês
| MEDLINE | ID: mdl-31564290
17.
Segmentation of cell nuclei in heterogeneous microscopy images: a reshapable templates approach.
Comput Med Imaging Graph
; 37(7-8): 488-99, 2013.
Artigo
em Inglês
| MEDLINE | ID: mdl-24008033