70基因特征（70-GS，MammaPrint）测试已被主要指南推荐用于评估激素受体阳性人表皮受体2阴性（HR  /Her2-）早期乳腺癌（BC）的预后和化疗获益。然而，这种昂贵的检测方法并不总是在全世界范围内都能获得和负担得起。基于我们之前的研究，我们建立了列线图模型来预测 70-GS 的二元和四分位分类风险。我们回顾性分析了 150 名 HR  /Her2- BC 且符合 70-GS 测试条件的女性患者的连续队列。对70-GS试验的高风险(N = 62)和低风险(N = 88)患者进行患者病史危险因素、影像学特征、临床病理特征等40个参数的比较，并根据建立的模型进行风险计算还比较了 70-GS 的高与低二元风险之间以及超高 (N = 12)、高 ( 70-GS 的 N = 50）、低（N = 65）和超低（N = 23）四分位数分类风险。将150名患者的数据按4：1的比例随机分配，训练组120名患者，测试组30名患者。通过单因素分析和多因素Logistic回归建立两种列线图模型来预测70-GS的二元和四分位分类风险。与70-GS低风险患者相比，高风险患者的心血管合并症明显较少(p = 0.034)，3级BC较多(p = 0.006)，黄体酮受体(PR)阳性百分比较低(p = 0.007)，Ki67高BC较多(≥ 20%，p < 0.001)，并且所有组中无显着差异超声和乳房X光检查的成像参数。 IHC3 风险和 NPI 计算得分与二元和四分位数 70-GS 风险分类显着相关（均 p<0.001）。二元风险预测列线图的受试者工作曲线 (ROC) 的曲线下面积 (AUC) 训练数据集为 0.826（C 指数 0.903, 0.799-1.000），验证数据集为 0.737（C 指数 0.785, 0.700-0.870）分别。四分位风险预测列线图的 ROC AUC 训练集为 0.870（C 指数 0.854、0.746-0.962），测试集为 0.592（C 指数 0.769、0.703-0.835）。四分位分类风险组列线图的预测准确度为 55.0%（似然比检验，p < 0.001），训练和验证的预测准确度为 53.3% (p = 0.04)，是基线概率 25% 的两倍多。 据我们所知，我们是第一个建立易于使用的列线图来预测 70-GS 测试的个性化二元（高与低）和四分位数分类（超高、高、低和超低）风险分类，性能良好，这可能为那些无法进行 70-GS 测试的人提供治疗选择信息。© 2023。作者。

The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2-) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS.We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2- BC and eligible 70-GS test. Comparison of 40 parameters including the patients' medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS.Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799-1.000) for training and 0.737 (C-index 0.785, 0.700-0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746-0.962) for training and 0.592 (C-index 0.769, 0.703-0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%.To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing.© 2023. The Author(s).