标题
Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer
作者
关键词
-
出版物
Frontiers in Oncology
Volume 12, Issue -, Pages -
出版商
Frontiers Media SA
发表日期
2022-08-09
DOI
10.3389/fonc.2022.913683
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Delta radiomics for rectal cancer response prediction using low field magnetic resonance guided radiotherapy: an external validation
- (2021) Davide Cusumano et al. Physica Medica-European Journal of Medical Physics
- Automatic segmentation of rectal tumor on diffusion‐weighted images by deep learning with U‐Net
- (2021) Hai‐Tao Zhu et al. Journal of Applied Clinical Medical Physics
- On dose cube pixel spacing pre-processing for features extraction stability in dosiomic studies
- (2021) L. Placidi et al. Physica Medica-European Journal of Medical Physics
- On the interplay between dosiomics and genomics in radiation-induced lymphopenia of lung cancer patients
- (2021) Serena Monti et al. RADIOTHERAPY AND ONCOLOGY
- A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer
- (2021) Weijun Chen et al. Scientific Reports
- Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer
- (2020) Jie Fu et al. PHYSICS IN MEDICINE AND BIOLOGY
- Radiomics: A primer for the radiation oncologist
- (2020) J.-E. Bibault et al. Cancer Radiotherapie
- CT‐based radiomic features to predict pathological response in rectal cancer: A retrospective cohort study
- (2020) Zhigang Yuan et al. Journal of Medical Imaging and Radiation Oncology
- Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases
- (2020) Aiqian Wu et al. ORAL ONCOLOGY
- Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy
- (2020) Sang Ho Lee et al. PHYSICS IN MEDICINE AND BIOLOGY
- The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
- (2020) Alex Zwanenburg et al. RADIOLOGY
- Clinical utility of radiomics at baseline rectal MRI to predict complete response of rectal cancer after chemoradiation therapy
- (2020) Iva Petkovska et al. Abdominal Radiology
- A Radiomics Model for Predicting the Response to Bevacizumab in Brain Necrosis after Radiotherapy
- (2020) Jinhua Cai et al. CLINICAL CANCER RESEARCH
- Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer
- (2020) Xiang Wang et al. EJSO
- Radiomics in neuro-oncology: Basics, workflow, and applications
- (2020) Philipp Lohmann et al. METHODS
- Artificial intelligence and radiomics in pediatric molecular imaging
- (2020) Matthias W. Wagner et al. METHODS
- Predicting poor response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer: Model constructed using pre-treatment MRI features of structured report template
- (2020) Xiaofeng Tang et al. RADIOTHERAPY AND ONCOLOGY
- Radiomics in radiation oncology—basics, methods, and limitations
- (2020) Philipp Lohmann et al. STRAHLENTHERAPIE UND ONKOLOGIE
- 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
- Radiomics of rectal cancer for predicting distant metastasis and overall survival
- (2020) Mou Li et al. WORLD JOURNAL OF GASTROENTEROLOGY
- Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma
- (2020) Yihuai Hu et al. RADIOTHERAPY AND ONCOLOGY
- Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure
- (2020) Giuditta Chiloiro et al. Frontiers in Oncology
- MRI-based delta-radiomics are predictive of pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer
- (2020) Lijuan Wan et al. ACADEMIC RADIOLOGY
- Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability
- (2019) Qingtao Qiu et al. QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
- MRI of Rectal Cancer: Tumor Staging, Imaging Techniques, and Management
- (2019) Natally Horvat et al. RADIOGRAPHICS
- Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer
- (2019) Seung Hyuck Jeon et al. Radiation Oncology
- Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics
- (2019) Xiance Jin et al. EUROPEAN RADIOLOGY
- MRI‐based radiomics signature for tumor grading of rectal carcinoma using random forest model
- (2019) Bo He et al. JOURNAL OF CELLULAR PHYSIOLOGY
- Locally recurrent rectal cancer: what the radiologist should know
- (2019) Dhakshinamoorthy Ganeshan et al. Abdominal Radiology
- Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis
- (2019) Bin Liang et al. Frontiers in Oncology
- Prognostic value of the texture analysis parameters of the initial computed tomographic scan for response to neoadjuvant chemoradiation therapy in patients with locally advanced rectal cancer
- (2019) Benjamin Vandendorpe et al. RADIOTHERAPY AND ONCOLOGY
- MRI-Based Radiomics Predicts Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer
- (2019) Xiaoping Yi et al. Frontiers in Oncology
- Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules
- (2019) Fatima-Zohra Mokrane et al. EUROPEAN RADIOLOGY
- Radiomics and Machine Learning With Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas
- (2019) Hexiang Wang et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning
- (2019) Li Sun et al. Frontiers in Neuroscience
- Radiomics for diagnosis of dual-phenotype hepatocellular carcinoma using Gd-EOB-DTPA-enhanced MRI and patient prognosis
- (2019) Xialing Huang et al. JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
- Radiomics features on radiotherapy treatment planning CT can predict patient survival in locally advanced rectal cancer patients
- (2019) Jiazhou Wang et al. Scientific Reports
- An investigation of machine learning methods in delta-radiomics feature analysis
- (2019) Yushi Chang et al. PLoS One
- Repeatability and reproducibility of radiomic features: A systematic review
- (2018) Alberto Traverso et al. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
- Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer
- (2018) Yankai Meng et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- Feature selection for the classification of traced neurons
- (2018) José D. López-Cabrera et al. JOURNAL OF NEUROSCIENCE METHODS
- Towards precision medicine: from quantitative imaging to radiomics
- (2018) U. Rajendra Acharya et al. Journal of Zhejiang University-SCIENCE B
- MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy
- (2018) Natally Horvat et al. RADIOLOGY
- PO-0932: Combining deep learning and radiomics to predict HPV status in oropharyngeal squamous cell carcinoma
- (2018) A. Jochems et al. RADIOTHERAPY AND ONCOLOGY
- Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer
- (2018) Yanfen Cui et al. EUROPEAN RADIOLOGY
- Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients
- (2018) Jung Youn Kim et al. NEURO-ONCOLOGY
- Texture analysis of 3D dose distributions for predictive modelling of toxicity rates in radiotherapy
- (2018) Linda Rossi et al. RADIOTHERAPY AND ONCOLOGY
- Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer
- (2018) Jean-Emmanuel Bibault et al. Scientific Reports
- Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer
- (2018) Xiaochun Meng et al. EUROPEAN RADIOLOGY
- Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach
- (2018) Luca Boldrini et al. Radiologia Medica
- Radiomics: the bridge between medical imaging and personalized medicine
- (2017) Philippe Lambin et al. Nature Reviews Clinical Oncology
- CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: A potential imaging biomarker for treatment response and prognosis
- (2017) Choong Guen Chee et al. PLoS One
- A Review of Neoadjuvant Chemoradiotherapy for Locally Advanced Rectal Cancer
- (2016) Yi Li et al. International Journal of Biological Sciences
- Model selection and overfitting
- (2016) Jake Lever et al. NATURE METHODS
- MRI of Rectal Cancer: An Overview and Update on Recent Advances
- (2015) Kartik S. Jhaveri et al. AMERICAN JOURNAL OF ROENTGENOLOGY
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Variability of clinical target volume delineation for definitive radiotherapy in cervix cancer
- (2015) Gemma Eminowicz et al. RADIOTHERAPY AND ONCOLOGY
- Diagnostic Performance of Multidetector Row Computed Tomography for Assessment of Lymph Node Metastasis in Patients with Distal Rectal Cancer
- (2014) Hirotoshi Kobayashi et al. ANNALS OF SURGICAL ONCOLOGY
- Colorectal cancer: Current imaging methods and future perspectives for the diagnosis, staging and therapeutic response evaluation
- (2013) Maka Kekelidze WORLD JOURNAL OF GASTROENTEROLOGY
- Radiomics: the process and the challenges
- (2012) Virendra Kumar et al. MAGNETIC RESONANCE IMAGING
- New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)
- (2008) E.A. Eisenhauer et al. EUROPEAN JOURNAL OF CANCER
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started