• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

利用红外显微光谱法预测口腔鳞状细胞癌的预后

Prediction of prognosis in oral squamous cell carcinoma using infrared microspectroscopy.

作者信息

Whitley Conor A, Ellis Barnaby G, Triantafyllou Asterios, Gunning Philip J, Gardner Peter, Barrett Steve D, Shaw Richard J, Smith Caroline I, Weightman Peter, Risk Janet M

机构信息

Department of Physics, University of Liverpool, Liverpool, UK.

Department of Pathology, Liverpool Clinical Laboratories, University of Liverpool, Liverpool, UK.

出版信息

Cancer Med. 2024 Mar;13(5):e7094. doi: 10.1002/cam4.7094.

DOI:10.1002/cam4.7094
PMID:38468595
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10928453/
Abstract

BACKGROUND

Estimation of prognosis of oral squamous cell carcinoma (OSCC) is inaccurate prior to surgery, only being effected following subsequent pathological analysis of the primary tumour and excised lymph nodes. Consequently, a proportion of patients are overtreated, with an increase in morbidity, or undertreated, with inadequate margins and risk of recurrence. We hypothesise that it is possible to accurately characterise clinical outcomes from infrared spectra arising from diagnostic biopsies. In this first step, we correlate survival with IR spectra derived from the primary tumour.

METHODS

Infrared spectra were collected from tumour tissue from 29 patients with OSCC and subject to classification modelling.

RESULTS

The model had a median AUROC of 0.89 with regard to prognosis, a median specificity of 0.83, and a hazard ratio of 6.29 in univariate Cox proportional hazard modelling.

CONCLUSION

The data suggest that FTIR spectra may be a useful early biomarker of prognosis in OSCC.

摘要

背景

口腔鳞状细胞癌(OSCC)术前预后评估不准确,只有在对原发肿瘤和切除的淋巴结进行后续病理分析后才能确定。因此,一部分患者接受了过度治疗,导致发病率增加,或者接受了治疗不足,切缘不充分且有复发风险。我们假设可以通过诊断性活检产生的红外光谱准确地描述临床结果。在第一步中,我们将生存率与源自原发肿瘤的红外光谱进行关联。

方法

收集了29例OSCC患者肿瘤组织的红外光谱,并进行分类建模。

结果

在单变量Cox比例风险建模中,该模型的预后中位受试者工作特征曲线下面积(AUROC)为0.89,中位特异性为0.83,风险比为6.29。

结论

数据表明傅里叶变换红外光谱(FTIR)可能是OSCC预后的一种有用的早期生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2f4/10928453/20b0a7b5919d/CAM4-13-e7094-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2f4/10928453/5c1f817421ca/CAM4-13-e7094-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2f4/10928453/f6cb6a1c82aa/CAM4-13-e7094-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2f4/10928453/20b0a7b5919d/CAM4-13-e7094-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2f4/10928453/5c1f817421ca/CAM4-13-e7094-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2f4/10928453/f6cb6a1c82aa/CAM4-13-e7094-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2f4/10928453/20b0a7b5919d/CAM4-13-e7094-g001.jpg

相似文献

1
Prediction of prognosis in oral squamous cell carcinoma using infrared microspectroscopy.利用红外显微光谱法预测口腔鳞状细胞癌的预后
Cancer Med. 2024 Mar;13(5):e7094. doi: 10.1002/cam4.7094.
2
Role of hyaluronan mediated motility receptor gene in oral squamous cell carcinoma and clinical prognosis.透明质酸介导的运动受体基因在口腔鳞状细胞癌及临床预后中的作用。
Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2021 Dec 28;46(12):1315-1324. doi: 10.11817/j.issn.1672-7347.2021.200955.
3
Extracellular vesicles miR-210 as a potential biomarker for diagnosis and survival prediction of oral squamous cell carcinoma patients.细胞外囊泡 miR-210 作为口腔鳞状细胞癌患者诊断和生存预测的潜在生物标志物。
J Oral Pathol Med. 2022 Apr;51(4):350-357. doi: 10.1111/jop.13263. Epub 2021 Dec 1.
4
Tumour mismatch repair protein loss is associated with advanced stage in oral cavity squamous cell carcinoma.口腔鳞状细胞癌中肿瘤错配修复蛋白缺失与晚期相关。
Pathology. 2019 Dec;51(7):688-695. doi: 10.1016/j.pathol.2019.08.005. Epub 2019 Oct 18.
5
Comprehensive survival analysis of oral squamous cell carcinoma patients undergoing initial radical surgery.口腔鳞状细胞癌患者初始根治性手术的综合生存分析。
BMC Oral Health. 2024 Aug 9;24(1):919. doi: 10.1186/s12903-024-04690-z.
6
Prognostic significance of tumor infiltrating immune cells in oral squamous cell carcinoma.肿瘤浸润免疫细胞在口腔鳞状细胞癌中的预后意义
BMC Cancer. 2017 May 26;17(1):375. doi: 10.1186/s12885-017-3317-2.
7
Corneodesmosin as a potential target of oral squamous cell carcinoma.桥粒芯糖蛋白作为口腔鳞状细胞癌的一个潜在靶点。
Medicine (Baltimore). 2022 Sep 30;101(39):e28397. doi: 10.1097/MD.0000000000030851.
8
Overexpression of caldesmon is associated with lymph node metastasis and poorer prognosis in patients with oral cavity squamous cell carcinoma.钙调蛋白过表达与口腔鳞状细胞癌患者的淋巴结转移和预后不良相关。
Cancer. 2013 Nov 15;119(22):4003-11. doi: 10.1002/cncr.28300. Epub 2013 Aug 20.
9
Revelation of comprehensive cell profiling of primary and metastatic tumour ecosystems in oral squamous cell carcinoma by single-cell transcriptomic analysis.单细胞转录组分析揭示口腔鳞状细胞癌原发和转移肿瘤生态系统的全面细胞特征。
Int J Med Sci. 2024 Aug 26;21(12):2293-2304. doi: 10.7150/ijms.97404. eCollection 2024.
10
Zinc finger AN1-type containing 4 is a novel marker for predicting metastasis and poor prognosis in oral squamous cell carcinoma.含锌指 AN1 结构域蛋白 4 是预测口腔鳞状细胞癌转移和预后不良的新型标志物。
J Clin Pathol. 2018 May;71(5):436-441. doi: 10.1136/jclinpath-2017-204770. Epub 2017 Oct 26.

引用本文的文献

1
Full fingerprint hyperspectral imaging of prostate cancer tissue microarrays within clinical timeframes using quantum cascade laser microscopy.使用量子级联激光显微镜在临床时间范围内对前列腺癌组织微阵列进行全指纹高光谱成像。
Analyst. 2025 Apr 22;150(9):1741-1753. doi: 10.1039/d5an00046g.

本文引用的文献

1
Infrared micro-spectroscopy coupled with multivariate and machine learning techniques for cancer classification in tissue: a comparison of classification method, performance, and pre-processing technique.用于组织中癌症分类的红外显微光谱结合多元和机器学习技术:分类方法、性能及预处理技术的比较
Analyst. 2022 Aug 8;147(16):3709-3722. doi: 10.1039/d2an00775d.
2
Prediction of malignant transformation in oral epithelial dysplasia using infrared absorbance spectra.利用红外吸收光谱预测口腔上皮异型增生的恶变。
PLoS One. 2022 Mar 25;17(3):e0266043. doi: 10.1371/journal.pone.0266043. eCollection 2022.
3
Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review.
应用机器学习预测口腔癌结局的模型:系统评价。
Int J Med Inform. 2021 Oct;154:104557. doi: 10.1016/j.ijmedinf.2021.104557. Epub 2021 Aug 18.
4
Fourier Transform Infrared Spectroscopy in Oral Cancer Diagnosis.傅里叶变换红外光谱技术在口腔癌诊断中的应用。
Int J Mol Sci. 2021 Jan 26;22(3):1206. doi: 10.3390/ijms22031206.
5
Comparison of machine learning algorithms for the prediction of five-year survival in oral squamous cell carcinoma.机器学习算法在预测口腔鳞状细胞癌五年生存率中的比较。
J Oral Pathol Med. 2021 Apr;50(4):378-384. doi: 10.1111/jop.13135. Epub 2020 Dec 15.
6
The diagnostic performance of CT and MRI for detecting extranodal extension in patients with head and neck squamous cell carcinoma: a systematic review and diagnostic meta-analysis.CT 和 MRI 诊断头颈部鳞状细胞癌患者结外侵犯的诊断性能:系统评价和诊断荟萃分析。
Eur Radiol. 2021 Apr;31(4):2048-2061. doi: 10.1007/s00330-020-07281-y. Epub 2020 Sep 19.
7
Optimizing Treatment De-Escalation in Head and Neck Cancer: Current and Future Perspectives.优化头颈部癌症的降阶梯治疗:现状与未来展望。
Oncologist. 2021 Jan;26(1):40-48. doi: 10.1634/theoncologist.2020-0303. Epub 2020 Sep 21.
8
SciPy 1.0: fundamental algorithms for scientific computing in Python.SciPy 1.0:Python 中的科学计算基础算法。
Nat Methods. 2020 Mar;17(3):261-272. doi: 10.1038/s41592-019-0686-2. Epub 2020 Feb 3.
9
Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.多机构验证深度学习对头颈鳞状细胞癌术前外侵犯的识别。
J Clin Oncol. 2020 Apr 20;38(12):1304-1311. doi: 10.1200/JCO.19.02031. Epub 2019 Dec 9.
10
Oral cancer-associated fibroblasts predict poor survival: Systematic review and meta-analysis.口腔癌相关成纤维细胞预测不良预后:系统评价和荟萃分析。
Oral Dis. 2020 May;26(4):733-744. doi: 10.1111/odi.13140. Epub 2019 Jul 15.