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乳腺癌是女性最常见恶性肿瘤之一,其发病率在女性恶性肿瘤中一直高居首位;在我国,乳腺癌死亡率高居女性恶性肿瘤致死率的第2位[1]。腋窝淋巴结转移(axillary lymph node metastasis,ALNM)不仅是乳腺癌预后的重要影响因素之一,而且与病人的手术方案制定有密切联系[2]。因此,术前准确判断乳腺癌病人有无ALNM具有重要临床意义。然而,目前常用的方式均为有创方式,包括腋窝淋巴结清扫术(axillary lymph node dissection,ALND)和前哨淋巴结清扫术(sentinel lymph node dissection,SLND),这些方式可能具有一定的并发症风险,如神经和血管损伤、感染、血肿等[3-4]。由于MRI的无创性、无电离辐射、多序列成像、高软组织分辨率等优势,其在术前无创评估ALNM中具有巨大的应用潜力。然而既往研究[5-6]显示单一的MRI序列在评估ALNM中的能力有限。影像组学可以将数字医学图像转换为可挖掘的数据,分析数据,提取特征,并利用这些特征来直观、定量地描述病灶的生物学特点,全面评估肿瘤的异质性,从而提高诊断、预后和预测准确性,近年来被广泛应用于辅助疾病诊断、预测肿瘤病人的治疗疗效、预后等方面[7]。因此,本文旨在将一种基于双模态MRI的影像组学用于术前预测浸润性乳腺癌ALNM,以协助乳腺癌病人治疗方式的制定,改善病人预后。
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淋巴结转移组病人的肿瘤长径大于无淋巴结转移组(P < 0.01),年龄和病理分级差异无统计学意义(P > 0.05);年龄、肿瘤长径及病理分级在训练组和验证组病人之间差异均无统计学意义(P > 0.05)(见表 1)。
分组 n 年龄
(x±s)/岁肿瘤长径
(x±s)/cm病理分级 Ⅰ Ⅱ Ⅲ ALN+ 64 47.88±9.67 2.82±1.98 12 30 22 ALN- 104 49.24±8.75 2.02±0.7 26 53 25 t — 0.94 3.12 2.33* P — > 0.05 < 0.01 > 0.05 训练组 134 48.68±8.98 2.34±1.53 34 63 37 验证组 34 48.62±9.76 2.29±0.81 3 20 11 t — 0.04 0.16 1.35* P — > 0.05 > 0.05 > 0.05 *示χ2值 表 1 不同组间临床病理资料的比较
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MRI图像中提取的影像组学特征共1 878个。用最小最大值归一化、Select K best和LASSO回归算法及迭代筛选特征进行特征筛选,筛选出与浸润性乳腺癌ALNM相关的影像组学特征:T2WI序列4个特征(1个一阶特征、1个形状特征、1个纹理和1个小波特征);DCE-MRI序列4个特征(1个形状特征、1个纹理特征和2个小波特征);T2WI联合DCE序列5个特征(1个一阶特、1个形状特征、1个纹理特征和2个小波特征)。运用logistic回归构建的T2WI、DCE和T2WI联合DCE影像组学预测模型在训练组中的AUC和95%CI分别为0.75(0.63,0.88)、0.75(0.63,0.86)、0.80(0.68,0.92);在验证组中分别为0.75(0.51,1.00)、0.73(0.44,0.99)、0.79(0.58,1.00);T2WI联合DCE的AUC在训练组及验证组中均最高,高于单独T2WI和单独DCE预测模型,T2WI联合DCE预测模型在预测浸润性乳腺癌ALNM中预测效能最佳(见图 3)。依据最优模型生成列线图(见图 4),该列线图可以实现浸润性乳腺癌病人发生ALNM的个体化预测,计算出的数值越高,病人发生ALNM的可能性越高。
基于双模态MRI影像组学术前预测浸润性乳腺癌腋窝淋巴结转移
The preoperative prediction value based on dual-mode MRI image omics in axillary lymph node metastasis of invasive breast cancer
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摘要:
目的探讨T2WI联合DCE-MRI的影像组学特征术前预测浸润性乳腺癌腋窝淋巴结转移的价值。 方法回顾性分析经手术病理证实的168例浸润性乳腺癌病人的临床病理资料及MRI图像资料。根据手术病理结果,将其分为淋巴结转移组(n=64)和无淋巴结转移组(n=104),并按8:2的比例将病人随机分为训练组(n=134)与验证组(n=34)。在T2WI和DCE两个序列手动勾画ROI进行图像分割和影像组学特征提取,利用Select K Best、LASSO回归及迭代筛选特征对高维组学特征进行降维,保留与腋窝淋巴结转移高度相关的特征。采用logistic回归建立T2WI、DCE和T2WI联合DCE三个影像组学预测模型,利用ROC曲线下面积(AUC)评估模型的效能,并以最优模型生成列线图。 结果T2WI、DCE和T2WI联合DCE的影像组学预测模型在训练组的AUC分别为0.75、0.75和0.80;验证组的AUC分别为0.75、0.73和0.79。T2WI联合DCE模型的预测效能最佳。 结论T2WI联合DCE影像组学预测模型在术前对浸润性乳腺癌腋窝淋巴结转移的预测具有一定的价值,能够无创、准确地预测腋窝淋巴结转移状态。 Abstract:ObjectiveTo investigate the value of T2WI combined with DCE-MRI radiomics features in the preoperative prediction of axillary lymph node metastasis of invasive breast cancer. MethodsThe clinicopathological and MRI data of 168 patients with invasive breast cancer confirmed by surgical pathology were retrospectively analyzed. According to the pathological results, the patients were divided into the lymph node metastasis group(n=64) and non-lymph node metastasis group(n=104), and randomly divided into the training group(n=134) and verification group(n=34) in an 8:2 ratio. The ROI was manually delineated on T2WI and DCE sequences for image segmentation and image omics feature extraction. The Select K Best, LASSO regression and iterative screening features were used to reduce the dimensionality of high-dimensional omics features and retain the high associated features with axillary lymph node metastasis. The three imaging omics prediction models of T2WI, DCE and T2WI combined WITH DCE were established using the logistic regression. The area under the ROC curve(AUC) was used to evaluate the effectiveness of models, and the optimal model was used to generate a column chart. ResultsThe AUC of T2WI, DCE, and T2WI combined with DCE in the training group was 0.75, 0.75, 0.80, respectively. The AUC of T2WI, DCE, and T2WI combined with DCE in the validation group was 0.75, 0.73 and 0.79, respectively. The predictive performance in T2WI combined with DCE predictive model was the best. ConclusionsThe predictive model of T2WI combined with DCE has certain value in the preoperative prediction of axillary lymph node metastasis of invasive breast cancer. It can accurately and noninvasively predict the status of axillary lymph node metastasis. -
Key words:
- invasive breast cancer /
- T2WI /
- DCE-MRI /
- radiomics /
- axillary lymph node
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表 1 不同组间临床病理资料的比较
分组 n 年龄
(x±s)/岁肿瘤长径
(x±s)/cm病理分级 Ⅰ Ⅱ Ⅲ ALN+ 64 47.88±9.67 2.82±1.98 12 30 22 ALN- 104 49.24±8.75 2.02±0.7 26 53 25 t — 0.94 3.12 2.33* P — > 0.05 < 0.01 > 0.05 训练组 134 48.68±8.98 2.34±1.53 34 63 37 验证组 34 48.62±9.76 2.29±0.81 3 20 11 t — 0.04 0.16 1.35* P — > 0.05 > 0.05 > 0.05 *示χ2值 -
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