Automated quantitative assessment of oncological disease progression using deep learning.
作者信息
Barash Yiftach, Klang Eyal
机构信息
Division of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel.
DeepVision Lab, Sheba Medical Center, Tel Hashomer, Israel.
出版信息
Ann Transl Med. 2019 Dec;7(Suppl 8):S379. doi: 10.21037/atm.2019.12.101.
Abstract
摘要
相似文献
1
Automated quantitative assessment of oncological disease progression using deep learning.使用深度学习对肿瘤疾病进展进行自动定量评估。
Ann Transl Med. 2019 Dec;7(Suppl 8):S379. doi: 10.21037/atm.2019.12.101.
2
A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images.基于深度学习的眼前节光学相干断层扫描图像中房角关闭自动检测系统。
Am J Ophthalmol. 2019 Jul;203:37-45. doi: 10.1016/j.ajo.2019.02.028. Epub 2019 Mar 6.
3
Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks.使用深度卷积神经网络从彩色眼底图像对年龄相关性黄斑变性进行自动分级
JAMA Ophthalmol. 2017 Nov 1;135(11):1170-1176. doi: 10.1001/jamaophthalmol.2017.3782.
4
Deep learning enables automated scoring of liver fibrosis stages.深度学习可实现肝纤维化分期的自动评分。
Sci Rep. 2018 Oct 30;8(1):16016. doi: 10.1038/s41598-018-34300-2.
5
Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.基于人工神经网络的 MRI 神经肿瘤学中肿瘤自动定量反应评估：多中心回顾性研究。
Lancet Oncol. 2019 May;20(5):728-740. doi: 10.1016/S1470-2045(19)30098-1. Epub 2019 Apr 2.
6
Assessment of Automated Identification of Phases in Videos of Cataract Surgery Using Machine Learning and Deep Learning Techniques.使用机器学习和深度学习技术评估白内障手术视频中的相位自动识别。
JAMA Netw Open. 2019 Apr 5;2(4):e191860. doi: 10.1001/jamanetworkopen.2019.1860.
7
Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning.基于深度学习的 OCT 中黄斑区液全自动化检测和定量分析
Ophthalmology. 2018 Apr;125(4):549-558. doi: 10.1016/j.ophtha.2017.10.031. Epub 2017 Dec 8.
8
Deep learning for automated skeletal bone age assessment in X-ray images.深度学习在 X 射线图像中进行自动骨骼骨龄评估。
Med Image Anal. 2017 Feb;36:41-51. doi: 10.1016/j.media.2016.10.010. Epub 2016 Oct 29.
9
Deep Learning-Based Algorithms in Screening of Diabetic Retinopathy: A Systematic Review of Diagnostic Performance.基于深度学习的算法在糖尿病视网膜病变筛查中的应用：诊断性能的系统评价
Ophthalmol Retina. 2019 Apr;3(4):294-304. doi: 10.1016/j.oret.2018.10.014. Epub 2018 Nov 3.
10
Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning.使用深度学习对儿科肌肉骨骼 X 光片进行自动语义标注。
Pediatr Radiol. 2019 Jul;49(8):1066-1070. doi: 10.1007/s00247-019-04408-2. Epub 2019 Apr 30.
引用本文的文献
1
An Innovative Approach to Target Lesion Progression in the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1: The Enaworu 25 mm Nadir Rule.实体瘤疗效评价标准（RECIST）1.1中针对靶病灶进展的创新方法：埃纳沃鲁25毫米最低点规则
Cureus. 2025 Apr 21;17(4):e82715. doi: 10.7759/cureus.82715. eCollection 2025 Apr.
2
From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.从手动临床标准到机器学习算法：比较源自不同电子健康记录数据模式的结局终点。
PLOS Digit Health. 2025 May 14;4(5):e0000755. doi: 10.1371/journal.pdig.0000755. eCollection 2025 May.
3
Reliability of Automated RECIST 1.1 and Volumetric RECIST Target Lesion Response Evaluation in Follow-Up CT-A Multi-Center, Multi-Observer Reading Study.自动RECIST 1.1和容积RECIST靶病灶反应评估在随访CT中的可靠性——一项多中心、多观察者阅片研究
Cancers (Basel). 2024 Nov 29;16(23):4009. doi: 10.3390/cancers16234009.
4
The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.深度学习在脊柱关节炎诊断成像中的作用：一项系统综述。
Eur Radiol. 2025 Jun;35(6):3661-3672. doi: 10.1007/s00330-024-11261-x. Epub 2024 Dec 10.
5
Artificial intelligence in neuro-oncology.神经肿瘤学中的人工智能
Front Neurosci. 2023 Dec 14;17:1217629. doi: 10.3389/fnins.2023.1217629. eCollection 2023.
6
Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis.人工智能在 X 射线舟状骨骨折检测中的应用：系统评价和诊断试验准确性的荟萃分析。
Eur Radiol. 2024 Jul;34(7):4341-4351. doi: 10.1007/s00330-023-10473-x. Epub 2023 Dec 15.
7
Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper.二十年回顾：实体瘤疗效评估标准（RECIST）作为实体瘤反应生物标志物——欧洲癌症研究与治疗组织（EORTC）影像组与欧洲肿瘤内科学会（ESOI）联合论文
Front Oncol. 2022 Jan 10;11:800547. doi: 10.3389/fonc.2021.800547. eCollection 2021.
8
Capsule Endoscopy in Crohn's Disease-From a Relative Contraindication to Habitual Monitoring Tool.克罗恩病中的胶囊内镜检查——从相对禁忌证到常规监测工具
Diagnostics (Basel). 2021 Sep 22;11(10):1737. doi: 10.3390/diagnostics11101737.
9
A Convolutional Neural Network Deep Learning Model Trained on CD Ulcers Images Accurately Identifies NSAID Ulcers.在克罗恩病溃疡图像上训练的卷积神经网络深度学习模型可准确识别非甾体抗炎药溃疡。
Front Med (Lausanne). 2021 Aug 27;8:656493. doi: 10.3389/fmed.2021.656493. eCollection 2021.
10
Differentiation Between Malignant and Benign Endoscopic Images of Gastric Ulcers Using Deep Learning.利用深度学习鉴别胃溃疡的恶性与良性内镜图像
Clin Exp Gastroenterol. 2021 May 5;14:155-162. doi: 10.2147/CEG.S292857. eCollection 2021.
本文引用的文献
1
Classification of Pulmonary Nodular Findings based on Characterization of Change using Radiology Reports.基于放射学报告中变化特征的肺结节发现分类
AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:285-294. eCollection 2019.
2
Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.基于人工神经网络的 MRI 神经肿瘤学中肿瘤自动定量反应评估：多中心回顾性研究。
Lancet Oncol. 2019 May;20(5):728-740. doi: 10.1016/S1470-2045(19)30098-1. Epub 2019 Apr 2.
3
Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide.卷积神经网络在放射影像中的应用：放射科医师指南。
Radiology. 2019 Mar;290(3):590-606. doi: 10.1148/radiol.2018180547. Epub 2019 Jan 29.
4
Deep learning and medical imaging.深度学习与医学成像。
J Thorac Dis. 2018 Mar;10(3):1325-1328. doi: 10.21037/jtd.2018.02.76.
5
Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice.欧洲脑胶质瘤影像学：220 个中心的调查及最佳临床实践建议。
Eur Radiol. 2018 Aug;28(8):3306-3317. doi: 10.1007/s00330-018-5314-5. Epub 2018 Mar 13.
6
Response Assessment in Neuro-Oncology Clinical Trials.神经肿瘤学临床试验中的疗效评估
J Clin Oncol. 2017 Jul 20;35(21):2439-2449. doi: 10.1200/JCO.2017.72.7511. Epub 2017 Jun 22.
7
RECIST 1.1-Update and clarification: From the RECIST committee.RECIST 1.1更新与说明：来自RECIST委员会。
Eur J Cancer. 2016 Jul;62:132-7. doi: 10.1016/j.ejca.2016.03.081. Epub 2016 May 14.
8
Semiautomated volumetric measurement on postcontrast MR imaging for analysis of recurrent and residual disease in glioblastoma multiforme.在多形性胶质母细胞瘤中，基于增强后磁共振成像的半自动容积测量用于分析复发和残留疾病。
AJNR Am J Neuroradiol. 2014 Mar;35(3):498-503. doi: 10.3174/ajnr.A3724. Epub 2013 Aug 29.
9
Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas.神经肿瘤学中的反应评估（ RANO 小组的报告）：弥漫性低级别胶质瘤试验的结果评估。
Lancet Oncol. 2011 Jun;12(6):583-93. doi: 10.1016/S1470-2045(11)70057-2. Epub 2011 Apr 5.
10
Revised RECIST guideline version 1.1: What oncologists want to know and what radiologists need to know.修订版 RECIST 指南 1.1：肿瘤学家想知道的和放射科医生需要知道的。
AJR Am J Roentgenol. 2010 Aug;195(2):281-9. doi: 10.2214/AJR.09.4110.
Zotero 插件