An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning
Authors
Keywords
-
Journal
Cancer Radiotherapie
Volume -, Issue -, Pages -
Publisher
Elsevier BV
Online
2023-11-04
DOI
10.1016/j.canrad.2023.05.005
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Cancer statistics, 2022
- (2022) Rebecca L. Siegel et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Predicting chemotherapy response in non-small-cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined?
- (2022) Runsheng Chang et al. Frontiers in Oncology
- Deep multiple instance learning for predicting chemotherapy response in non-small cell lung cancer using pretreatment CT images
- (2022) Runsheng Chang et al. Scientific Reports
- Cancer Statistics, 2021
- (2021) Rebecca L. Siegel et al. CA-A CANCER JOURNAL FOR CLINICIANS
- A review on the attention mechanism of deep learning
- (2021) Zhaoyang Niu et al. NEUROCOMPUTING
- Predictive Radiomic Models for the Chemotherapy Response in Non-Small-Cell Lung Cancer based on Computerized-Tomography Images
- (2021) Runsheng Chang et al. Frontiers in Oncology
- Radiomics and deep learning in lung cancer
- (2020) Michele Avanzo et al. STRAHLENTHERAPIE UND ONKOLOGIE
- Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study
- (2020) Ahmad Algohary et al. Cancers
- Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives
- (2020) Madhurima R. Chetan et al. EUROPEAN RADIOLOGY
- Cancer statistics, 2019
- (2019) Rebecca L. Siegel et al. CA-A CANCER JOURNAL FOR CLINICIANS
- CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation
- (2019) Quan-yuan Shan et al. CANCER IMAGING
- Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging
- (2019) Yiwen Xu et al. CLINICAL CANCER RESEARCH
- Radiomics: Data Are Also Images
- (2019) Mathieu Hatt et al. JOURNAL OF NUCLEAR MEDICINE
- Non–Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications
- (2018) Mu Zhou et al. RADIOLOGY
- Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
- (2018) Freddie Bray et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
- (2018) Nicholas Bien et al. PLOS MEDICINE
- Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
- (2018) Ahmed Hosny et al. PLOS MEDICINE
- Radiation Therapy Advances for Treatment of Anal Cancer
- (2017) Joseph M. Pepek et al. Journal of the National Comprehensive Cancer Network
- RECIST 1.1—Update and clarification: From the RECIST committee
- (2016) Lawrence H. Schwartz et al. EUROPEAN JOURNAL OF CANCER
- The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer
- (2016) Peter Goldstraw et al. Journal of Thoracic Oncology
- Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models
- (2016) Philipp Kickingereder et al. RADIOLOGY
- Early-Stage Non–Small Cell Lung Cancer: Quantitative Imaging Characteristics of18F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis
- (2016) Jia Wu et al. RADIOLOGY
- MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays
- (2016) Hui Li et al. RADIOLOGY
- Radiomics: Images Are More than Pictures, They Are Data
- (2016) Robert J. Gillies et al. RADIOLOGY
- Multiple instance learning with bag dissimilarities
- (2015) Veronika Cheplygina et al. PATTERN RECOGNITION
- Machine Learning methods for Quantitative Radiomic Biomarkers
- (2015) Chintan Parmar et al. Scientific Reports
- 3D Slicer as an image computing platform for the Quantitative Imaging Network
- (2012) Andriy Fedorov et al. MAGNETIC RESONANCE IMAGING
- Anal Cancer: An Examination of Radiotherapy Strategies
- (2011) Rob Glynne-Jones et al. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
- Chemotherapy, chemoresistance and the changing treatment landscape for NSCLC
- (2010) Alex Chang LUNG CANCER
- New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)
- (2008) E.A. Eisenhauer et al. EUROPEAN JOURNAL OF CANCER
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search