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下咽癌管理中的影像组学:最新综述

Radiomics in Hypopharyngeal Cancer Management: A State-of-the-Art Review.

作者信息

Chiesa-Estomba Carlos M, Mayo-Yanez Miguel, Guntinas-Lichius Orlando, Vander-Poorten Vincent, Takes Robert P, de Bree Remco, Halmos Gyorgy B, Saba Nabil F, Nuyts Sandra, Ferlito Alfio

机构信息

Otorhinolaryngology-Head & Neck Surgery Department, Hospital Universitario Donostia, Biodonostia Research Institute, Faculty of Medicine, Deusto University, 20014 San Sebastian, Spain.

Otorhinolaryngology-Head and Neck Surgery Department, Complexo Hospitalario Universitario A Coruña (CHUAC), 15006 A Coruña, Spain.

出版信息

Biomedicines. 2023 Mar 6;11(3):805. doi: 10.3390/biomedicines11030805.

DOI:10.3390/biomedicines11030805
PMID:36979783
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10045560/
Abstract

(1) Background: Hypopharyngeal squamous cell carcinomas usually present with locally advanced disease and a correspondingly poor prognosis. Currently, efforts are being made to improve tumor characterization and provide insightful information for outcome prediction. Radiomics is an emerging area of study that involves the conversion of medical images into mineable data; these data are then used to extract quantitative features based on shape, intensity, texture, and other parameters; (2) Methods: A systematic review of the peer-reviewed literature was conducted; (3) Results: A total of 437 manuscripts were identified. Fifteen manuscripts met the inclusion criteria. The main targets described were the evaluation of textural features to determine tumor-programmed death-ligand 1 expression; a surrogate for microvessel density and heterogeneity of perfusion; patient stratification into groups at high and low risk of progression; prediction of early recurrence, 1-year locoregional failure and survival outcome, including progression-free survival and overall survival, in patients with locally advanced HPSCC; thyroid cartilage invasion, early disease progression, recurrence, induction chemotherapy response, treatment response, and prognosis; and (4) Conclusions: our findings suggest that radiomics represents a potentially useful tool in the diagnostic workup as well as during the treatment and follow-up of patients with HPSCC. Large prospective studies are essential to validate this technology in these patients.

摘要

(1) 背景:下咽鳞状细胞癌通常表现为局部晚期疾病,预后相应较差。目前,人们正在努力改善肿瘤特征描述,并为预后预测提供有见地的信息。放射组学是一个新兴的研究领域,涉及将医学图像转换为可挖掘的数据;然后利用这些数据基于形状、强度、纹理和其他参数提取定量特征;(2) 方法:对同行评审文献进行了系统综述;(3) 结果:共鉴定出437篇手稿。15篇手稿符合纳入标准。所描述的主要目标包括评估纹理特征以确定肿瘤程序性死亡配体1的表达;微血管密度和灌注异质性的替代指标;将患者分层为进展高风险和低风险组;预测局部晚期下咽鳞状细胞癌患者的早期复发、1年局部区域失败和生存结果,包括无进展生存和总生存;甲状腺软骨侵犯、早期疾病进展、复发、诱导化疗反应、治疗反应和预后;以及(4) 结论:我们的研究结果表明,放射组学在诊断检查以及下咽鳞状细胞癌患者的治疗和随访过程中是一种潜在有用的工具。大型前瞻性研究对于在这些患者中验证该技术至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa2e/10045560/f1cccf6e84b7/biomedicines-11-00805-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa2e/10045560/f1cccf6e84b7/biomedicines-11-00805-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa2e/10045560/f1cccf6e84b7/biomedicines-11-00805-g001.jpg

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本文引用的文献

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Treatment options for resectable hypopharyngeal squamous cell carcinoma: A systematic review and meta-analysis of randomized controlled trials.可切除下咽鳞状细胞癌的治疗选择:一项随机对照试验的系统评价和荟萃分析。
PLoS One. 2022 Nov 29;17(11):e0277460. doi: 10.1371/journal.pone.0277460. eCollection 2022.
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The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer.基于机器学习评估临床及治疗前[F]-FDG-PET/CT影像组学特征对下咽癌预后预测的价值
Mol Imaging Biol. 2023 Apr;25(2):303-313. doi: 10.1007/s11307-022-01757-7. Epub 2022 Jul 21.
3
CT-based radiomics signature analysis for evaluation of response to induction chemotherapy and progression-free survival in locally advanced hypopharyngeal carcinoma.
基于 CT 的放射组学特征分析在评估局部晚期下咽癌诱导化疗反应及无进展生存期中的应用。
Eur Radiol. 2022 Nov;32(11):7755-7766. doi: 10.1007/s00330-022-08859-4. Epub 2022 May 24.
4
Computed tomography-based radiomics signature as a pretreatment predictor of progression-free survival in locally advanced hypopharyngeal carcinoma with a different response to induction chemotherapy.基于计算机断层扫描的放射组学特征可作为诱导化疗后局部晚期下咽癌无进展生存期的预测因子。
Eur Arch Otorhinolaryngol. 2022 Jul;279(7):3551-3562. doi: 10.1007/s00405-022-07306-w. Epub 2022 Feb 25.
5
Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers.用于预测局部晚期头颈癌放疗后局部失败的CT和PET影像组学机器学习框架
J Med Phys. 2021 Jul-Sep;46(3):181-188. doi: 10.4103/jmp.JMP_6_21. Epub 2021 Sep 8.
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Eur Radiol. 2022 Mar;32(3):1548-1557. doi: 10.1007/s00330-021-08292-z. Epub 2021 Oct 19.
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