A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer

Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at www.radiomics.io . With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research.

The health burden of cancer is increasing in China, with more than 1·6 million people being diagnosed and 1·2 million people dying of the disease each year. As in most other countries, breast cancer is now the most common cancer in Chinese women; cases in China account for 12·2% of all newly diagnosed breast cancers and 9·6% of all deaths from breast cancer worldwide. China's proportional contribution to global rates is increasing rapidly because of the population's rising socioeconomic status and unique reproductive patterns. In this Review we present an overview of present control measures for breast cancer across China, and emphasise epidemiological and socioeconomic diversities and disparities in access to care for various subpopulations. We describe demographic differences between China and high-income countries, and also within geographical and socioeconomic regions of China. These disparities between China and high-income countries include younger age at onset of breast cancer; the unique one-child policy; lower rates of provision and uptake for screening for breast cancer; delays in diagnosis that result in more advanced stage of disease at presentation; inadequate resources; and a lack of awareness about breast cancer in the Chinese population. Finally, we recommend key measures that could contribute to improved health outcomes for patients with breast cancer in China. Copyright © 2014 Elsevier Ltd. All rights reserved.