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:  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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Topics:  image analysis, clinical trials, drug development, Immunotherapy, immuno-oncology, predictive biomarkers, cancer treatment

How Image Analysis Can Improve the Results of Drug Development and Clinical Trials

Dec 12, 2016 4:00:00 AM

In recent years there have been several potentially life-saving medications approved for cancer treatment, including targeted molecular entities and biologics such as Opdivo (nivolumab) and Keytruda (pembrolizumab). Oncology drugs remain a pharmaceutical priority and investments into cancer account for 30% of all pre-clinical and phase 1 clinical development expenditures. There is an impressive list of close to 800 drugs and vaccines currently in the industry-wide development pipeline, many with promising results in early-stage clinical trials. However by historical measures only 10% or fewer of these drugs will ever make it through FDA approval and become part of routine patient care.

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Topics:  image analysis, personalized medicine, biomarker, immuno-oncology, tissue diagnostics, companion diagnostics, tissue biomarkers, diagnostics, diagnostic test, precision medicine, biomarker signature

3 ways tissue image analysis will shape the future of cancer treatment with immunotherapies

Nov 7, 2016 1:00:00 AM

As healthcare continues its transition towards precision medicine, physicians are eager to maximize both the quantity and quality of data obtained from their patients’ tissue samples.  In the current “one drug, one test” paradigm, physicians struggle to balance the number of tests required to make a treatment decision with the limited amount of tissue that is available.  Further complicating treatment decisions is the use of imperfect biomarkers associated with targeted therapies, as the patients who are identified to be ideal candidates for a targeted therapy respond only some of the time while patients who are not expected to respond do.  This dilemma is of great concern for those who are responsible for paying for these expensive therapies - payers and patients – who would like to avoid unnecessary expenses for drugs that may not work and possibly cause debilitating side effects.  Of course, of even greater concern is helping the patient identify the best therapeutic option that allows them the best opportunity to treat their disease and maximize their quality and quantity of life.

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