Department of Diagnostic Imaging, Chaim Sheba Medical Center, 2 Sheba Road, 5266202, Ramat Gan, Israel.
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Eur Radiol. 2022 Sep;32(9):5921-5929. doi: 10.1007/s00330-022-08725-3. Epub 2022 Apr 6.
OBJECTIVES: To evaluate if radiomics with machine learning can differentiate between F-18-fluorodeoxyglucose (FDG)-avid breast cancer metastatic lymphadenopathy and FDG-avid COVID-19 mRNA vaccine-related axillary lymphadenopathy. MATERIALS AND METHODS: We retrospectively analyzed FDG-positive, pathology-proven, metastatic axillary lymph nodes in 53 breast cancer patients who had PET/CT for follow-up or staging, and FDG-positive axillary lymph nodes in 46 patients who were vaccinated with the COVID-19 mRNA vaccine. Radiomics features (110 features classified into 7 groups) were extracted from all segmented lymph nodes. Analysis was performed on PET, CT, and combined PET/CT inputs. Lymph nodes were randomly assigned to a training (n = 132) and validation cohort (n = 33) by 5-fold cross-validation. K-nearest neighbors (KNN) and random forest (RF) machine learning models were used. Performance was evaluated using an area under the receiver-operator characteristic curve (AUC-ROC) score. RESULTS: Axillary lymph nodes from breast cancer patients (n = 85) and COVID-19-vaccinated individuals (n = 80) were analyzed. Analysis of first-order features showed statistically significant differences (p < 0.05) in all combined PET/CT features, most PET features, and half of the CT features. The KNN model showed the best performance score for combined PET/CT and PET input with 0.98 (± 0.03) and 0.88 (± 0.07) validation AUC, and 96% (± 4%) and 85% (± 9%) validation accuracy, respectively. The RF model showed the best result for CT input with 0.96 (± 0.04) validation AUC and 90% (± 6%) validation accuracy. CONCLUSION: Radiomics features can differentiate between FDG-avid breast cancer metastatic and FDG-avid COVID-19 vaccine-related axillary lymphadenopathy. Such a model may have a role in differentiating benign nodes from malignant ones. KEY POINTS: • Patients who were vaccinated with the COVID-19 mRNA vaccine have shown FDG-avid reactive axillary lymph nodes in PET-CT scans. • We evaluated if radiomics and machine learning can distinguish between FDG-avid metastatic axillary lymphadenopathy in breast cancer patients and FDG-avid reactive axillary lymph nodes. • Combined PET and CT radiomics data showed good test AUC (0.98) for distinguishing between metastatic axillary lymphadenopathy and post-COVID-19 vaccine-associated axillary lymphadenopathy. Therefore, the use of radiomics may have a role in differentiating between benign from malignant FDG-avid nodes.
目的：评估放射组学与机器学习是否可区分 F-18-氟脱氧葡萄糖（FDG）阳性乳腺癌转移性淋巴结和 FDG 阳性 COVID-19 mRNA 疫苗相关腋窝淋巴结。
材料与方法：我们回顾性分析了 53 例经 PET/CT 随访或分期检查证实的 FDG 阳性、经病理证实的转移性腋窝淋巴结的患者和 46 例接种 COVID-19 mRNA 疫苗后 FDG 阳性腋窝淋巴结的患者。从所有分割的淋巴结中提取放射组学特征（分为 7 组的 110 个特征）。对 PET、CT 和联合 PET/CT 输入进行分析。通过 5 折交叉验证，将淋巴结随机分配到训练集（n=132）和验证集（n=33）。使用 K-最近邻（KNN）和随机森林（RF）机器学习模型进行分析。使用受试者工作特征曲线（AUC-ROC）下面积评估性能。
结果：分析了来自乳腺癌患者（n=85）和接种 COVID-19 疫苗患者（n=80）的腋窝淋巴结。对一阶特征的分析表明，所有联合 PET/CT 特征、大多数 PET 特征和一半 CT 特征均存在统计学显著差异（p＜0.05）。KNN 模型在联合 PET/CT 和 PET 输入方面表现出最佳性能评分，验证 AUC 分别为 0.98（±0.03）和 0.88（±0.07），验证准确率分别为 96%（±4%）和 85%（±9%）。RF 模型在 CT 输入方面表现出最佳结果，验证 AUC 为 0.96（±0.04），验证准确率为 90%（±6%）。
结论：放射组学特征可区分 FDG 阳性乳腺癌转移性和 FDG 阳性 COVID-19 疫苗相关腋窝淋巴结。该模型可能在区分良性淋巴结和恶性淋巴结方面具有一定作用。
重点：
Clin Nucl Med. 2021-12-1
Ann Nucl Med. 2024-6
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