Piotrzkowska Wróblewska Hanna, Karwat Piotr, Żyłka Agnieszka, Dobruch Sobczak Katarzyna, Dedecjus Marek, Litniewski Jerzy
Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland.
Department of Endocrine Oncology and Nuclear Medicine, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland.
Cancers (Basel). 2025 Aug 24;17(17):2761. doi: 10.3390/cancers17172761.
: Thyroid cancer encompasses distinct histological subtypes with varying biological behavior and treatment implications. Accurate preoperative subtype differentiation remains challenging. Although ultrasound (US) is widely used for thyroid nodule evaluation, qualitative assessment alone is often insufficient to distinguish between papillary (PTC), follicular (FTC), and medullary thyroid carcinoma (MTC). A retrospective analysis was performed on patients with histologically confirmed PTC, FTC, or MTC. A total of 224 standardized B-mode ultrasound images were analyzed. A set of fully quantitative features was extracted, including morphological characteristics (aspect ratio and perimeter-to-area ratio), internal echotexture (echogenicity and local entropy), boundary sharpness (gradient measures and KL divergence), and structural components (calcifications and cystic areas). Feature extraction was conducted using semi-automatic algorithms implemented in MATLAB. Statistical differences were assessed using the Kruskal-Wallis and Dunn-Šidák tests. A Random Forest classifier was trained and evaluated to determine the discriminatory performance of individual and combined features. Significant differences ( < 0.05) were found among subtypes for key features such as perimeter-to-area ratio, normalized echogenicity, and calcification pattern. The full-feature Random Forest model achieved an overall classification accuracy of 89.3%, with F1-scores of 93.4% for PTC, 85.7% for MTC, and 69.1% for FTC. A reduced model using the top 10 features yielded an even higher accuracy of 91.8%, confirming the robustness and clinical relevance of the selected parameters. Subtype classification of thyroid cancer was effectively performed using quantitative ultrasound features and machine learning. The results suggest that biologically interpretable image-derived metrics may assist in preoperative decision-making and potentially reduce the reliance on invasive diagnostic procedures.
甲状腺癌包含具有不同生物学行为和治疗意义的不同组织学亚型。准确的术前亚型分化仍然具有挑战性。尽管超声（US）广泛用于甲状腺结节评估，但仅靠定性评估往往不足以区分乳头状（PTC）、滤泡状（FTC）和髓样甲状腺癌（MTC）。对组织学确诊为PTC、FTC或MTC的患者进行了回顾性分析。共分析了224张标准化B超图像。提取了一组完全定量的特征，包括形态特征（长宽比和周长面积比）、内部回声纹理（回声性和局部熵）、边界清晰度（梯度测量和KL散度）以及结构成分（钙化和囊性区域）。使用MATLAB中实现的半自动算法进行特征提取。使用Kruskal-Wallis和Dunn-Šidák检验评估统计差异。训练并评估了随机森林分类器，以确定单个特征和组合特征的鉴别性能。在亚型之间发现了关键特征（如周长面积比、归一化回声性和钙化模式）的显著差异（<0.05）。全特征随机森林模型的总体分类准确率达到89.3%，PTC的F1分数为93.4%，MTC为85.7%，FTC为69.1%。使用前10个特征的简化模型产生了更高的91.8%的准确率，证实了所选参数的稳健性和临床相关性。使用定量超声特征和机器学习有效地进行了甲状腺癌的亚型分类。结果表明，具有生物学可解释性的图像衍生指标可能有助于术前决策，并有可能减少对侵入性诊断程序的依赖。
