纹理和放射学分析参数在预测乳腺癌患者肿瘤组织病理学参数中的作用。

IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Rutuja Kote, Mudalsha Ravina, Harish Goyal, Debajyoti Mohanty, Rakesh Gupta, Arvind Kumar Shukla, Moulish Reddy, Pratheek N Prasanth
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引用次数: 0

摘要

导读:纹理和放射线组学分析可以描述肿瘤的表型,并对其微环境进行定量评估:纹理和放射学分析可描述肿瘤的表型,并对其微环境进行定量评估。本研究旨在探讨纹理和放射学分析参数在预测乳腺癌患者组织病理学因素方面的作用:212 名原发性乳腺癌患者接受了 18F-FDG PET/计算机断层扫描分期检查。图像由市面上销售的纹理分析软件处理。以 40% 的阈值在原发肿瘤上绘制 ROI,并进一步处理以得出纹理和放射学参数。然后将这些参数与肿瘤的组织病理学因素进行比较。用 P 值进行受体运算特征分析 结果:对 212 名原发性乳腺癌患者进行了回顾性研究。在所有重要参数中,发现 SUVmin、SUVmean、SUVstd、SUVmax、离散化 HISTO_Entropy 和灰度共现矩阵_Contrast 与导管癌类型显著相关。四个参数(SUVmin、SUVmean、SUVstd 和 SUVmax)在区分肿瘤的管腔亚型方面具有重要意义。五个参数(SUVmin、SUVmean、SUVstd、SUVmax 和 SUV kurtosis)在预测肿瘤分级方面具有重要意义。在使用机器学习算法进行测试时,这些参数显示了预测多个组织病理学参数的强大能力:尽管纹理分析无法预测激素受体状态、淋巴管侵犯状态、神经周围侵犯状态、肿瘤微钙化状态以及肿瘤的所有分子亚型,但它可以预测肿瘤的组织学类型、三阴亚型以及肿瘤的无创评分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Role of textural and radiomic analysis parameters in predicting histopathological parameters of the tumor in breast cancer patients.

Introduction: Texture and radiomic analysis characterizes the tumor's phenotype and evaluates its microenvironment in quantitative terms. This study aims to investigate the role of textural and radiomic analysis parameters in predicting histopathological factors in breast cancer patients.

Materials and methods: Two hundred and twelve primary breast cancer patients underwent 18F-FDG PET/computed tomography for staging. The images were processed in a commercially available textural analysis software. ROI was drawn over the primary tumor with a 40% threshold and was processed further to derive textural and radiomic parameters. These parameters were then compared with histopathological factors of tumor. Receiver-operating characteristic analysis was performed with a P-value <0.05 for statistical significance. The significant parameters were subsequently utilized in various machine learning models to assess their predictive accuracy.

Results: A retrospective study of 212 primary breast cancer patients was done. Among all the significant parameters, SUVmin, SUVmean, SUVstd, SUVmax, discretized HISTO_Entropy, and gray level co-occurrence matrix_Contrast were found to be significantly associated with ductal carcinoma type. Four parameters (SUVmin, SUVmean, SUVstd, and SUVmax) were significant in differentiating the luminal subtypes of the tumor. Five parameters (SUVmin, SUVmean, SUVstd, SUVmax, and SUV kurtosis) were significant in predicting the grade of the tumor. These parameters showcased robust capabilities in predicting multiple histopathological parameters when tested using machine learning algorithms.

Conclusion: Though textural analysis could not predict hormonal receptor status, lymphovascular invasion status, perineural invasion status, microcalcification status of tumor, and all the molecular subtypes of the tumor, it could predict the tumor's histologic type, triple-negative subtype, and score of the tumor noninvasively.

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来源期刊
CiteScore
2.20
自引率
6.70%
发文量
212
审稿时长
3-8 weeks
期刊介绍: Nuclear Medicine Communications, the official journal of the British Nuclear Medicine Society, is a rapid communications journal covering nuclear medicine and molecular imaging with radionuclides, and the basic supporting sciences. As well as clinical research and commentary, manuscripts describing research on preclinical and basic sciences (radiochemistry, radiopharmacy, radiobiology, radiopharmacology, medical physics, computing and engineering, and technical and nursing professions involved in delivering nuclear medicine services) are welcomed, as the journal is intended to be of interest internationally to all members of the many medical and non-medical disciplines involved in nuclear medicine. In addition to papers reporting original studies, frankly written editorials and topical reviews are a regular feature of the journal.
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