Tissue Phenomics Blog

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Topics:  predictive test, image analysis, clinical trials, Immunotherapy, oncology, tumor microenvironment

Evaluation of the Tumor Microenvironment Using Image Analysis for Clinical Trials

May 8, 2017 7:30:00 AM

Advances in immunotherapy have dramatically changed the landscape for cancer researchers. With significant improvements in overall survival and recurrent free survival in some of the deadliest cancers, lives have been extended by years for some patients while other patients have not experienced benefit from immunotherapy treatment. For some cancer indications, such as non-small cell lung cancer, the FDA approved companion diagnostic test for PD-L1 by immunohistochemistry has predictive value on patient response. However, the test is not a perfect indicator of response as some PD-L1 negative patients will respond to therapy and, conversely, some PD-L1 positive patients will not. At the same time, the number of clinical trials examining immunotherapy and combination therapies with immunotherapy as a backbone has exceeded 1000 in 2017. The possibility of combination therapy brings many questions to light for the researcher and ultimately the physician on which therapy to choose, for how long, in what order and what potential combination. With these questions, comes the increasing need for predictive tests that can lead the physician to the right therapy at the right time.

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Topics:  image analysis, big data, digital pathology, machine learning, deep learning

Machine Learning in Digital Pathology: A Journey from Handcrafted Feature Descriptors to Deep Learning Approaches

Apr 3, 2017 5:00:00 AM

As outlined in the recent blog article by Dr. Ralf Huss, CMO of Definiens, we believe in big data and machine learning to significantly influence decision making in medicine. Already in digital histopathology, machine learning is a key component, for example, for the detection of regions of interest (e.g., tumor metastasis regions, stroma regions) or for the detection, segmentation and classification of objects of interest (e.g., nuclei, cells, mitosis, glomeruli and glands). The use of machine learning in digital pathology is motivated by the complexity and variety of the problems, and it has been enabled only recently by the availability of large amounts of raw data (e.g., the public TCGA database from the NIH) and of efficient algorithms or increased computing power. However, while big data is getting even bigger by the minute, we cannot accept algorithms without a medical plausibility check and robust clinical validation.

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Topics:  big data, artificial intelligence, machine learning

AI Makes Good Doctors Even Better Doctors

Mar 6, 2017 8:00:00 AM

Highly-paid doctors will not be obsolete and radiologists or anatomical pathologists will not be obsolete in the age of AI in medical practice. In a recent New England Journal of Medicine Paper (http://www.nejm.org/doi/full/10.1056/NEJMp1606181) Drs. Obermeyer and Emanuel (1) rightly suggest that big data will transform daily medical practice and computer-based algorithms will guide clinical decision making.

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Topics:  Immunotherapy, immunoscore, immuno-oncology, cancer therapy

The Top 3 Major Contributions to Immuno-oncology in 2016

Feb 6, 2017 8:29:57 AM

Immuno-oncology was a super-hot topic in 2016. Several thousand publications contributed to the scientific progress, so choosing the top 3 is a challenging undertaking. But there are a few common themes seen in many highly ranked publications: the use of the latest hard- and software to gain an improved mechanistic understanding, why immuno-oncology actually works, and guidance on how to both design and predict individual patient success of novel therapies.

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Topics:  image analysis, Immunotherapy, immuno-oncology, tissue phenomics, real world evidence

Three reasons image analysis should be incorporated into your immunotherapy real-world evidence development strategy

Jan 9, 2017 5:00:00 AM

Clinical trials are extremely important to assess the safety and effectiveness of a new therapy or of a currently-available therapy in a new indication. However, there are a few drawbacks to clinical trials, such as:

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