Classification of intrinsic subtypes and histological grade for breast cancers by multimodality images
Classification of intrinsic subtypes and histological grade for breast cancers by multimodality images
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DOI：
10.1117/12.2625871
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
C. Muramatsu;Takumi Iwasaki;M. Oiwa;T. Kawasaki;H. Fujita
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文献类型：
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作者：
C. Muramatsu;Takumi Iwasaki;M. Oiwa;T. Kawasaki;H. Fujita
乳腺癌治疗的成功受到各种因素的影响，包括癌症阶段和癌症等级。最好的治疗方法是根据癌症的特征选择。希望通过诊断成像准确，迅速地预测癌症的特征和预后因素。该研究的目的是研究多模式诊断图像在预测乳腺癌亚型中的使用以帮助诊断和治疗计划。在这项研究中，我们将病变分类为分子亚型，并通过乳房X线摄影和乳房超声图像同时预测癌症的组织学等级和侵入性。比较具有不同体系结构的模型，包括单个头部和多个头部模型的单输入和多输入层。结果表明，使用多模式图像比使用单个模态更具预测性。使用多模式图像的自动亚型分类可以支持及时的治疗计划和适当的患者护理。
Success of breast cancer treatment is subject to various factors, including cancer stage and cancer grade. The best treatment is selected based on the characteristic of cancer. It is desirable to predict the cancer characteristics and prognostic factors accurately and promptly by diagnostic imaging. The purpose of the study is to investigate the use of multimodality diagnostic images in predicting breast cancer subtypes to assist diagnosis and treatment planning. In this study, we classify lesions into molecular subtypes and simultaneously predict histological grades and invasiveness of the cancers by mammography and breast ultrasound images. Models with different architectures including single input and multi-input layers with single head and multiple head models are compared. The results indicate that use of multimodality images is more predictive than using single modalities. The automatic subtype classification using multimodality images may support a prompt treatment planning and proper patient care.