Tissue Phenomics Blog

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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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Topics:  image analysis, IHC, H&E, breast cancer, Ki-67, HER-2

Image Analysis in Breast Cancer

Oct 10, 2016 7:00:00 AM

Breast cancer is the most prevalent form of cancer among woman, and image analysis methods which target the disease have a great potential to reduce the workload in a typical pathology lab and to improve the quality of interpretation.

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Topics:  personalized medicine, big data, tissue phenomics, oncology, digital pathology, colorectal cancer

Towards Personalised Pathology Through Tissue Phenomics®

Sep 14, 2016 3:00:00 AM

Path to personalised medicine

Pathology is one of the main driving forces behind personalised or precision medicine. In fact it has always striven towards the accurate diagnosis and prognosis of a patient’s disease through the observation of tissue architecture under the microscope. Through the application of international staging guidelines, such as the Tissue, Node, Metastasis (TNM) system in the majority of cancers, pathologists are very good at predicting prognosis at the population scale but not so good at predicting a prognosis for the individual patient. For example, if a patient presents with stage II colorectal cancer (CRC) they are predicted to have 20-30% chance of succumbing to their disease. However, it is currently difficult to accurately identify if an individual patient will be within that 20-30% group.

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