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本文引用的文献

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Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening.用于预测低剂量CT筛查发现的小实性肺结节恶性程度的定量放射组学模型。
Quant Imaging Med Surg. 2019 Feb;9(2):263-272. doi: 10.21037/qims.2019.02.02.
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Stereotactic body radiation therapy for non-small cell lung cancer: A review.立体定向体部放射治疗非小细胞肺癌:综述
World J Clin Oncol. 2019 Jan 10;10(1):14-27. doi: 10.5306/wjco.v10.i1.14.
3
Radiomics: the facts and the challenges of image analysis.放射组学:图像分析的现状与挑战
Eur Radiol Exp. 2018 Nov 14;2(1):36. doi: 10.1186/s41747-018-0068-z.
4
A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram.预测实性肺腺癌淋巴结转移的新方法:一种影像组学列线图
J Thorac Dis. 2018 Apr;10(Suppl 7):S807-S819. doi: 10.21037/jtd.2018.03.126.
5
Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma.基于影像组学的肺腺癌隐匿性纵隔淋巴结转移预测方法
AJR Am J Roentgenol. 2018 Jul;211(1):109-113. doi: 10.2214/AJR.17.19074. Epub 2018 Apr 18.
6
Radiomics and radiogenomics in lung cancer: A review for the clinician.肺癌的影像组学和放射组学:临床医生的综述。
Lung Cancer. 2018 Jan;115:34-41. doi: 10.1016/j.lungcan.2017.10.015. Epub 2017 Nov 8.
7
Development and clinical application of radiomics in lung cancer.放射组学在肺癌中的发展与临床应用。
Radiat Oncol. 2017 Sep 15;12(1):154. doi: 10.1186/s13014-017-0885-x.
8
Radiomics-based Assessment of Radiation-induced Lung Injury After Stereotactic Body Radiotherapy.基于放射组学的立体定向体部放射治疗后放射性肺损伤评估。
Clin Lung Cancer. 2017 Nov;18(6):e425-e431. doi: 10.1016/j.cllc.2017.05.014. Epub 2017 May 25.
9
Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall-cell lung cancer patients treated with stereotactic body radiotherapy.术前 CT 扫描的影像学特征与接受立体定向体部放射治疗的非小细胞肺癌患者的临床结局相关。
Med Phys. 2017 Aug;44(8):4341-4349. doi: 10.1002/mp.12309. Epub 2017 Jun 24.
10
Early Assessment of Treatment Responses During Radiation Therapy for Lung Cancer Using Quantitative Analysis of Daily Computed Tomography.使用每日计算机断层扫描的定量分析对肺癌放射治疗期间的治疗反应进行早期评估。
Int J Radiat Oncol Biol Phys. 2017 Jun 1;98(2):463-472. doi: 10.1016/j.ijrobp.2017.02.032. Epub 2017 Feb 21.

肿瘤学家视角下的肺癌影像组学

Radiomics in lung cancer for oncologists.

作者信息

de la Pinta Carolina, Barrios-Campo Nuria, Sevillano David

机构信息

Department of Radiation Oncology, Ramón y Cajal Hospital, Madrid, Spain.

Department of Biomedical Engineering, Madrid Polytechnic University, Madrid, Spain.

出版信息

J Clin Transl Res. 2020 Sep 2;6(4):127-134. eCollection 2020 Oct 29.

PMID:33521373
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7837741/
Abstract

UNLABELLED

Radiomics has revolutionized the world of medical imaging. The aim of this review is to guide oncologists in radiomics and its applications in diagnosis, prediction of response and damage, prediction of survival, and prognosis in lung cancer. In this review, we analyzed published literature on PubMed and MEDLINE with papers published in the last 10 years. We included papers in English language with information about radiomics features and diagnostic, predictive, and prognosis of radiomics in lung cancer. All citations were evaluated for relevant content and validation.

RELEVANCE FOR PATIENTS

The evolution of technology allows the development of computer algorithms that facilitate the diagnosis and evaluation of response after different oncological treatments and their non-invasive follow-up.

摘要

未标注

放射组学已经彻底改变了医学成像领域。本综述的目的是指导肿瘤学家了解放射组学及其在肺癌诊断、反应和损伤预测、生存预测及预后方面的应用。在本综述中,我们分析了过去10年在PubMed和MEDLINE上发表的文献。我们纳入了英文论文,这些论文包含有关放射组学特征以及肺癌放射组学的诊断、预测和预后方面的信息。对所有引用文献进行了相关内容和验证评估。

对患者的意义

技术的发展使得计算机算法得以开发,这些算法有助于不同肿瘤治疗后的诊断和反应评估以及非侵入性随访。