@article {334,
	title = {NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images.},
	journal = {Med Image Anal},
	volume = {78},
	year = {2022},
	month = {2022 May},
	pages = {102384},
	abstract = {<p>Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.</p>
},
	keywords = {Breast Neoplasms, Female, Humans, Prognosis, Tumor Microenvironment},
	issn = {1361-8423},
	doi = {10.1016/j.media.2022.102384},
	author = {Jim{\'e}nez-S{\'a}nchez, Daniel and Ariz, Mikel and Chang, Hang and Matias-Guiu, Xavier and de Andrea, Carlos E and Ortiz-de-Sol{\'o}rzano, Carlos}
}
@article {326,
	title = {A new platform for ultra-high dose rate radiobiological research using the BELLA PW laser proton beamline.},
	journal = {Sci Rep},
	volume = {12},
	year = {2022},
	month = {2022 01 27},
	pages = {1484},
	abstract = {<p>Radiotherapy is the current standard of care for more than 50\% of all cancer patients. Improvements in radiotherapy (RT) technology have increased tumor targeting and normal tissue sparing. Radiations at ultra-high dose rates required for FLASH-RT effects have sparked interest in potentially providing additional differential therapeutic benefits. We present a new experimental platform that is the first one to deliver petawatt laser-driven proton pulses of 2\&nbsp;MeV energy at 0.2\&nbsp;Hz repetition rate by means of a compact, tunable active plasma lens beamline to biological samples. Cell monolayers grown over a 10\&nbsp;mm diameter field were exposed to clinically relevant proton doses ranging from 7 to 35\&nbsp;Gy at ultra-high instantaneous dose rates of 10\&nbsp;Gy/s. Dose-dependent cell survival measurements of human normal and tumor cells exposed to LD protons showed significantly higher cell survival of normal-cells compared to tumor-cells for total doses of 7\&nbsp;Gy and higher, which was not observed to the same extent for X-ray reference irradiations at clinical dose rates. These findings provide preliminary evidence that compact LD proton sources enable a new and promising platform for investigating the physical, chemical and biological mechanisms underlying the FLASH effect.</p>
},
	keywords = {Cell Line, Humans, Lasers, Monte Carlo Method, Neoplasms, Proton Therapy, Radiation Oncology, Radiobiology, Radiometry, Radiotherapy Dosage, Synchrotrons},
	issn = {2045-2322},
	doi = {10.1038/s41598-022-05181-3},
	author = {Bin, Jianhui and Obst-Huebl, Lieselotte and Mao, Jian-Hua and Nakamura, Kei and Geulig, Laura D and Chang, Hang and Ji, Qing and He, Li and De Chant, Jared and Kober, Zachary and Gonsalves, Anthony J and Bulanov, Stepan and Celniker, Susan E and Schroeder, Carl B and Geddes, Cameron G R and Esarey, Eric and Simmons, Blake A and Schenkel, Thomas and Blakely, Eleanor A and Steinke, Sven and Snijders, Antoine M}
}
@conference {24,
	title = {Nuclei segmentation via sparsity constrained convolutional regression},
	booktitle = {12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015},
	year = {2015},
	doi = {10.1109/ISBI.2015.7164109},
	url = {http://dx.doi.org/10.1109/ISBI.2015.7164109},
	author = {Yin Zhou and Hang Chang and Kenneth E. Barner and Bahram Parvin}
}
