Cargando…

Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images

Identification of patients with early stage non-small cell lung cancer (NSCLC) with high risk of recurrence could help identify patients who would receive additional benefit from adjuvant therapy. In this work, we present a computational histomorphometric image classifier using nuclear orientation,...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wang, Xiangxue, Janowczyk, Andrew, Zhou, Yu, Thawani, Rajat, Fu, Pingfu, Schalper, Kurt, Velcheti, Vamsidhar, Madabhushi, Anant
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2017
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648794/ https://www.ncbi.nlm.nih.gov/pubmed/29051570 http://dx.doi.org/10.1038/s41598-017-13773-7

Ejemplares similares

A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study
por: Lu, Cheng, et al.
Publicado: (2020)

A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer
por: Wang, Xiangxue, et al.
Publicado: (2021)

Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors
por: Kumar, Neeraj, et al.
Publicado: (2022)

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors
por: Wang, Xiangxue, et al.
Publicado: (2022)

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
por: Janowczyk, Andrew, et al.
Publicado: (2016)

Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis
por: Chen, Yijiang, et al.
Publicado: (2020)

Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer
por: Barrera, Cristian, et al.
Publicado: (2023)

A tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors
por: Alilou, Mehdi, et al.
Publicado: (2022)

Quick Annotator: an open‐source digital pathology based rapid image annotation tool
por: Miao, Runtian, et al.
Publicado: (2021)

Objective measurement and clinical significance of IDO1 protein in hormone receptor-positive breast cancer
por: Carvajal-Hausdorf, Daniel E., et al.
Publicado: (2017)

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer
por: Whitney, Jon, et al.
Publicado: (2018)

Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral from stromal regions
por: Janowczyk, Andrew, et al.
Publicado: (2013)

Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance
por: Chandramouli, Sacheth, et al.
Publicado: (2020)

CT radiomic signature predicts survival and chemotherapy benefit in stage I and II HPV-associated oropharyngeal carcinoma
por: Song, Bolin, et al.
Publicado: (2023)

Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation
por: Arabyarmohammadi, Sara, et al.
Publicado: (2022)

Expression and clinical significance of PD-L1, B7-H3, B7-H4 and TILs in human small cell lung Cancer (SCLC)
por: Carvajal-Hausdorf, Daniel, et al.
Publicado: (2019)

Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade
por: Vaidya, Pranjal, et al.
Publicado: (2020)

High SOX2 Levels Predict Better Outcome in Non-Small Cell Lung Carcinomas
por: Velcheti, Vamsidhar, et al.
Publicado: (2013)

Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome
por: Ding, Ruiwen, et al.
Publicado: (2022)

Clinical significance of PD-L1 protein expression on tumor-associated macrophages in lung cancer
por: Schalper, Kurt Alex, et al.
Publicado: (2015)

Role of tumor infiltrating lymphocytes and spatial immune heterogeneity in sensitivity to PD-1 axis blockers in non-small cell lung cancer
por: Lopez de Rodas, Miguel, et al.
Publicado: (2022)

Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings
por: Ghose, Soumya, et al.
Publicado: (2017)

Novel pharmacologic approach to enhance the epigenetic and immune priming effect of decitabine in patients with advanced non-small cell lung cancer
por: Velcheti, Vamsidhar, et al.
Publicado: (2015)

A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans
por: Alilou, Mehdi, et al.
Publicado: (2021)

Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study
por: Leo, Patrick, et al.
Publicado: (2021)

Computational image features of immune architecture is associated with clinical benefit and survival in gynecological cancers across treatment modalities
por: Azarianpour, Sepideh, et al.
Publicado: (2022)

Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images
por: Romo-Bucheli, David, et al.
Publicado: (2016)

Response to: Correspondence on 'Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab' by Zheng et al
por: Viswanathan, Vidya Sankar, et al.
Publicado: (2022)

Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer
por: Chen, Yuli, et al.
Publicado: (2023)

Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma?—A Feasibility Study
por: Ismail, Marwa, et al.
Publicado: (2020)

Digital pathology and computational image analysis in nephropathology
por: Barisoni, Laura, et al.
Publicado: (2020)

A Case of a Patient with Idiopathic Pulmonary Fibrosis with Lung Squamous Cell Carcinoma Treated with Nivolumab
por: Khunger, Monica, et al.
Publicado: (2017)

Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study
por: Vaidya, Pranjal, et al.
Publicado: (2022)

Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas
por: Alilou, Mehdi, et al.
Publicado: (2018)

Author Correction: Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas
por: Alilou, Mehdi, et al.
Publicado: (2019)

Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer()
por: Zhou, Yufei, et al.
Publicado: (2023)

History of the SPIE Medical Imaging Digital Pathology Conference
por: Madabhushi, Anant, et al.
Publicado: (2022)

Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study
por: Prasanna, Prateek, et al.
Publicado: (2019)

Management of patients with advanced non-small cell lung cancer: role of gefitinib
por: Velcheti, Vamsidhar, et al.
Publicado: (2010)

Role of immune-checkpoint inhibitors in lung cancer
por: Jain, Prantesh, et al.
Publicado: (2018)

Cannot write session to /tmp/vufind_sessions/sess_njqm309tnt101ht16kr8s6u5lc