Radiomics and Quantitative MDA Criteria in Breast Cancer with Bone Metastases by MRI: Examples of Calculation Algorithms and Their Practical Use.

Sovremennye tekhnologii v meditsine Pub Date : 2024-01-01 Epub Date: 2024-06-28 DOI:10.17691/stm2024.16.3.01
V Steinhauer, G Hartung
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Abstract

In the practical work of a radiologist or oncologist, especially in the context of individualized treatment, there is a need for fast and in-depth diagnostics. Radiomics and artificial intelligence can be of great help here. Quantitative and qualitative assessments obtained in this way act as decision support for the physician. The aim of the study is to enhance the ability of quantitative and qualitative assessment of metastatic spinal lesions in breast cancer, to better evaluate the nature of changes under the influence of therapy, in addition to MDA.

Materials and methods: We used MRI data in sagittal projection for a patient diagnosed with T2N3M1 breast cancer when treated according to accepted clinical protocols. Metastases to the spine were assessed by a radiologist and by machine analysis using the described software: image internal structure extraction operators and recognition based on traditional neural networks. Fragments of the program codes used are also given.

Results: The structure of metastatically changed vertebrae in sagittal projection was analysed using machine operators of image analysis. Subtle changes in structure such as several types of "calderas" and the pattern of change in image complexity as treatment with CDK 4/6 inhibitors were detected. Measurements were supported by metastasis recognition using neural networks, to increase the reliability of the estimates. In addition to the ability to record response to therapy, a fundamental ability to assess the degree of action compared to previous therapy was identified.

Conclusion: The study showed high efficiency of using image structure analysis algorithms, good correlation of the results obtained with the radiologist's opinion and with clinical and laboratory data, and allowed to approach the analysis of subtle effects to obtain not only quantitative characteristics in addition to MDA, but also to obtain new qualitative results.

MRI显示乳腺癌骨转移的放射组学和定量MDA标准:计算算法的例子及其实际应用。
在放射科医生或肿瘤科医生的实际工作中,特别是在个体化治疗的背景下,需要快速和深入的诊断。放射组学和人工智能在这方面可以提供很大的帮助。通过这种方式获得的定量和定性评估为医生提供决策支持。本研究的目的是为了提高乳腺癌脊柱转移性病变的定量和定性评估能力,更好地评估除MDA外治疗影响下的变化性质。材料和方法:我们对一名确诊为T2N3M1乳腺癌的患者进行矢状位MRI数据分析,并根据公认的临床方案进行治疗。脊柱转移由放射科医生评估,并使用所描述的软件进行机器分析:图像内部结构提取算子和基于传统神经网络的识别。还给出了所用程序代码的片段。结果:应用图像分析的机器算子对矢状位转移椎体的结构进行了分析。在使用cdk4 /6抑制剂治疗后,检测到结构的细微变化,如几种类型的“火山口”和图像复杂性的变化模式。测量支持转移识别使用神经网络,以增加估计的可靠性。除了记录治疗反应的能力外,还确定了与先前治疗相比评估作用程度的基本能力。结论:本研究使用图像结构分析算法效率高,所得结果与放射科医师意见及临床、实验室数据相关性好,可以对细微效应进行分析,不仅可以获得定量特征,还可以获得新的定性结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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