Department of Electronic Engineering, Fudan University, Shanghai, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China.
Department of Electronic Engineering, Fudan University, Shanghai, China.
Clin Breast Cancer. 2018 Jun;18(3):e335-e344. doi: 10.1016/j.clbc.2017.08.002. Epub 2017 Aug 18.
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.
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.
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.
Our approach could assist clinicians with the accurate prediction of prognosis using ultrasound findings, allowing for early medical management and treatment.
在当前的临床实践中，浸润性导管癌通常通过医学影像学技术进行筛查，并通过免疫组织化学进行诊断。最近的研究表明，放射组学方法可以全面描述整个肿瘤，并揭示图像与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机制，迫切需要在客观的图像特征和生物学特征之间建立更好的理解。
本研究共纳入 215 名具有明确组织学结果的患者。使用我们基于相位的主动轮廓模型对肿瘤进行自动分割。使用乳腺影像报告和数据系统设计并提取高通量放射组学特征，并进一步使用学生 t 检验、特征间系数和套索回归模型进行选择。使用三倍交叉验证的支持向量机分类器来评估相关性。
放射组学方法显示受体状态与亚型之间具有很强的相关性（P<0.05；曲线下面积，0.760）。激素受体阳性癌和人表皮生长因子受体 2 阴性癌在超声扫描中的表现与三阴性癌不同。
我们的方法可以帮助临床医生通过超声结果准确预测预后，从而进行早期的医疗管理和治疗。
