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1.
Artificial Intelligence-Powered Hematoxylin and Eosin Analyzer Reveals Distinct Immunologic and Mutational Profiles among Immune Phenotypes in Non-Small-Cell Lung Cancer.
Am J Pathol;
192(4): 701-711, 2022 04.
Artículo
en Inglés
| MEDLINE | ID: mdl-35339231
2.
Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer.
JCO Precis Oncol;
8: e2300556, 2024 May.
Artículo
en Inglés
| MEDLINE | ID: mdl-38723233
3.
Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms.
Diagnostics (Basel);
12(10)2022 Sep 27.
Artículo
en Inglés
| MEDLINE | ID: mdl-36292028
4.
Tumor-infiltrating lymphocyte enrichment predicted by CT radiomics analysis is associated with clinical outcomes of non-small cell lung cancer patients receiving immune checkpoint inhibitors.
Front Immunol;
13: 1038089, 2022.
Artículo
en Inglés
| MEDLINE | ID: mdl-36660547
5.
Artificial intelligence-powered programmed death ligand 1 analyser reduces interobserver variation in tumour proportion score for non-small cell lung cancer with better prediction of immunotherapy response.
Eur J Cancer;
170: 17-26, 2022 07.
Artículo
en Inglés
| MEDLINE | ID: mdl-35576849
6.
Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non-Small-Cell Lung Cancer.
J Clin Oncol;
40(17): 1916-1928, 2022 06 10.
Artículo
en Inglés
| MEDLINE | ID: mdl-35271299
7.
Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients.
Sci Rep;
11(1): 17363, 2021 08 30.
Artículo
en Inglés
| MEDLINE | ID: mdl-34462515
8.
Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses.
Diagn Pathol;
15(1): 80, 2020 Jul 04.
Artículo
en Inglés
| MEDLINE | ID: mdl-32622359
9.
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.
Med Image Anal;
54: 111-121, 2019 05.
Artículo
en Inglés
| MEDLINE | ID: mdl-30861443
10.
From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.
IEEE Trans Med Imaging;
38(2): 550-560, 2019 02.
Artículo
en Inglés
| MEDLINE | ID: mdl-30716025
11.
Author Correction: Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients.
Sci Rep;
11(1): 21043, 2021 Oct 20.
Artículo
en Inglés
| MEDLINE | ID: mdl-34671078
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