基于超声射频时间序列分析预测乳腺癌腋窝淋巴结转移。

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Acta radiologica Pub Date : 2024-10-01 Epub Date: 2024-09-02 DOI:10.1177/02841851241268463
Pengfei Sun, Ruifang Guo, Xiangdong Hu, Andre Dekker, Alberto Traverso, Linxue Qian, Zhixiang Wang
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引用次数: 0

摘要

背景:腋窝淋巴结(ALN)的状态在乳腺癌患者的治疗中起着至关重要的作用。目的:评估超声射频(URF)时间序列参数结合临床数据预测乳腺癌患者腋窝淋巴结转移的效果:我们前瞻性地收集了乳腺癌患者的临床病理和超声波数据。我们利用所有可用特征开发了各种机器学习(ML)模型,以确定最有效的诊断模型。随后,使用最优的 ML 模型创建了不同的预测模型,并对其诊断性能进行了评估和比较:研究涵盖 240 名患者,其中 88 人有淋巴结转移。采用留一交叉验证法(LOOCV)将整个数据集分为训练子集和测试子集。随机森林 ML 模型的表现优于其他算法,其曲线下面积(AUC)为 0.92。在测试集中,基于临床、超声波、URF 参数、临床 + 超声波、临床 + URF 和超声波 + URF 参数的预测模型的 AUC 分别为 0.56、0.79、0.78、0.90、0.80 和 0.84。综合诊断模型(临床+超声+URF参数)显示出强大的诊断能力,在测试集中的AUC为0.94,超过了任何单一预测模型:结论:综合模型(临床+超声+URF参数)可用于术前预测淋巴结状态,为个体化手术方法的设计提供有价值的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting axillary lymph node metastasis in breast cancer based on ultrasound radiofrequency time-series analysis.

Background: The status of axillary lymph nodes (ALN) plays a critical role in the management of patients with breast cancer. It is an urgent demand to develop highly accurate, non-invasive methods for predicting ALN status.

Purpose: To evaluate the efficacy of ultrasound radiofrequency (URF) time-series parameters, in combination with clinical data, in predicting ALN metastasis in patients with breast cancer.

Material and methods: We prospectively gathered clinicopathologic and ultrasonic data from patients diagnosed with breast cancer. Various machine-learning (ML) models were developed using all available features to determine the most efficient diagnostic model. Subsequently, distinct prediction models were created using the optimal ML model, and their diagnostic performances were evaluated and compared.

Results: The study encompassed 240 patients, of whom 88 had lymph node metastases. A leave-one-out cross-validation (LOOCV) method was used to split the entire dataset into training and testing subsets. The random forest ML model outperformed the other algorithms, with an area under the curve (AUC) of 0.92. Prediction models based on clinical, ultrasonic, URF parameters, clinical + ultrasonic, clinical + URF, and ultrasonic + URF parameters had AUCs of 0.56, 0.79, 0.78, 0.90, 0.80, and 0.84, respectively, in the testing set. The comprehensive diagnostic model (clinical + ultrasonic + URF parameters) demonstrated strong diagnostic capability, with an AUC of 0.94 in the testing set, exceeding any single prediction model.

Conclusion: The combined model (clinical + ultrasonic + URF parameters) could be used preoperatively to predict lymph node status, offering valuable input for the design of individualized surgical approaches.

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来源期刊
Acta radiologica
Acta radiologica 医学-核医学
CiteScore
2.70
自引率
0.00%
发文量
170
审稿时长
3-8 weeks
期刊介绍: Acta Radiologica publishes articles on all aspects of radiology, from clinical radiology to experimental work. It is known for articles based on experimental work and contrast media research, giving priority to scientific original papers. The distinguished international editorial board also invite review articles, short communications and technical and instrumental notes.
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