1c:I[419806,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/30036bedc5269936.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/cd5e572bc3e4eb92.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/d4ac9dcf27f6ff52.js","/_next/static/chunks/294b9525248c8e5f.js"],"default"] 1d:T49c,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 noninvasive 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. Cancer Res; 77(21); e104-7. ©2017 AACR.1e:T4f3,Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution^1-9. Most clinical strategies rely on histopathological stratification of tumour subtypes, yet the spatial context of single-cell phenotypes within these stratified subgroups is poorly understood. Here we apply imaging mass cytometry to characterize the tumour and immunological landscape of samples from 416 patients with lung adenocarcinoma across five histological patterns. We resolve more than 1.6 million cells, enabling spatial analysis of immune lineages and activation states with distinct clinical correlates, including survival. Using deep learning, we can predict with high accuracy those patients who will progress after surgery using a single 1-mm² tumour core, which could be informative for clinical management following surgical resection. Our dataset represents a valuable resource for the non-small cell lung cancer research community and exemplifies the utility of spatial resolution within single-cell analyses. This study also highlights how artificial intelligence can improve our understanding of microenvironmental features that underlie cancer progression and may influence future clinical practice.

	Year	Citations

References