Radiomics Analysis on Ultrasound for Prediction of Biologic Behavior in Breast Invasive Ductal Carcinoma
Radiomics Analysis on Ultrasound for Prediction of Biologic Behavior in Breast Invasive Ductal Carcinoma
复制标题
超声放射组学分析预测乳腺浸润性导管癌的生物学行为
DOI：
10.1016/j.clbc.2017.08.002
复制
发表时间：
2018-06-01
影响因子：
3.1
通讯作者：
Chang, Cai
中科院分区：
文献类型：
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作者：
Guo, Yi;Hu, Yuzhou;Chang, Cai
这项研究表明，肿瘤特征可以在遗传和细胞水平上通过医学图像捕获。对215例乳腺浸润性导管癌患者的临床资料进行分析。提出了一种自动放射组学方法来评估定量超声特征和生物学特征之间的关联。结果显示出很强的相关性。这种应用将有助于在早期阶段准确的预后。引言：在目前的临床实践中，浸润性导管癌总是使用医学影像技术进行筛查，并使用免疫组化进行诊断。最近的研究表明，放射组学方法提供了整个肿瘤的综合表征，并可以揭示图像和医疗结果之间的预测或预后关联。为了更好地揭示潜在的生物学，迫切需要改善客观图像特征和生物特征之间的理解。患者和方法：共有215例明确的组织学结果的患者参加了我们的研究。使用我们的基于相位的活动轮廓模型自动分割肿瘤。使用乳腺成像报告和数据系统设计和提取高通量放射组学特征，并使用Student t检验、特征间系数和套索回归模型进一步选择。采用三重交叉验证的支持向量机分类器对两者的关系进行了评价。结果：放射组学方法显示受体状态与亚型之间存在强相关性（P <0.05;曲线下面积0.760）。激素受体阳性癌症和人表皮生长因子受体2阴性癌症在超声扫描上的表现与三阴性癌症不同。结论：我们的方法可以帮助临床医生使用超声检查结果准确预测预后，从而进行早期医疗管理和治疗。
This study illustrates that tumor characteristics can be captured by medical images at the genetic and cellular levels. The data from 215 patients with breast invasive ductal carcinoma were analyzed. An automatic radiomics approach was proposed to assess the associations between quantitative ultrasound features and biologic characteristics. The results indicated a strong correlation. This application will be helpful for an accurate prognosis at an early stage.Introduction: In current clinical practice, invasive ductal carcinoma is always screened using medical imaging techniques and diagnosed using immunohistochemistry. Recent studies have illustrated that radiomics approaches provide a comprehensive characterization of entire tumors and can reveal predictive or prognostic associations between the images and medical outcomes. To better reveal the underlying biology, an improved understanding between objective image features and biologic characteristics is urgently required. Patients and Methods: A total of 215 patients with definite histologic results were enrolled in our study. The tumors were automatically segmented using our phase-based active contour model. The high-throughput radiomics features were designed and extracted using a breast imaging reporting and data system and further selected using Student's t test, interfeature coefficients and a lasso regression model. The support vector machine classifier with threefold cross-validation was used to evaluate the relationship. Results: The radiomics approach demonstrated a strong correlation between receptor status and subtypes (P < .05; area under the curve, 0.760). The appearance of hormone receptor-positive cancer and human epidermal growth factor receptor 2-negative cancer on ultrasound scans differs from that of triple-negative cancer. Conclusion: Our approach could assist clinicians with the accurate prediction of prognosis using ultrasound findings, allowing for early medical management and treatment.