Live Coverage: MedCity Converge 2018 Philadelphia: AI in Cancer and Keynote Address
Reporter: Stephen J. Williams, PhD
8:30 AM -9:15
Practical Applications of AI in Cancer
We are far from machine learning dictating clinical decision making, but AI has important niche applications in oncology. Hear from a panel of innovative startups and established life science players about how machine learning and AI can transform different aspects in healthcare, be it in patient recruitment, data analysis, drug discovery or care delivery.
Moderator: , Advanced Analytics Specialist Leader, Deloitte Consulting LLP
Speakers:
, CEO and Co-Founder, Deep 6 AI @woutbrusselaers
, Chair of Breast Medical Oncology and Department of Oncology Chair of IT, Mayo Clinic
, Head of Corporate Development, Arterys @carlaleibowitz
, Professor and Director of the Center for Cancer Computational Biology, Dana-Farber Cancer Institute
Ayan: working at IBM and Thompon Rueters with structured datasets and having gone through his own cancer battle, he is now working in healthcare AI which has an unstructured dataset(s)
Carla: collecting medical images over the world, mainly tumor and calculating tumor volumetrics
Tufia: drug resistant breast cancer clinician but interested in AI and healthcareIT at Mayo
John: taking large scale datasets but a machine learning skeptic
moderator: how has imaging evolved?
Carla: ten times images but not ten times radiologists so stressed field needs help with image analysis; they have seen measuring lung tumor volumetrics as a therapeutic diagnostic has worked
moderator: how has AI affected patient recruitment?
Tufia: majority of patients are receiving great care but AI can offer profiles and determine which patients can benefit from tertiary care;
John: 1980 paper on no free lunch theorem; great enthusiasm about optimization algortihisms fell short in application; can extract great information from e.g. images
moderator: how is AI for healthcare delivery working at mayo?
Tufia: for every hour with patient two hours of data mining. for care delivery hope to use the systems to leverage the cognitive systems to do the data mining
John: problem with irreproducible research which makes a poor dataset: also these care packages are based on population data not personalized datasets; challenges to AI is moving correlation to causation
Carla: algorithisms from on healthcare network is not good enough, Google tried and it failed
John: curation very important; good annotation is needed; needed to go in and develop, with curators, a systematic way to curate medial records; need standardization and reproducibility; applications in radiometrics can be different based on different data collection machines; developed a machine learning model site where investigators can compare models on a hub; also need to communicate with patients on healthcare information and quality information
Ayan: Australia and Canada has done the most concerning AI and lifescience, healthcare space; AI in most cases is cognitive learning: really two types of companies 1) the Microsofts, Googles, and 2) the startups that may be more pure AI
Final Notes: We are at a point where collecting massive amounts of healthcare related data is simple, rapid, and shareable. However challenges exist in quality of datasets, proper curation and annotation, need for collaboration across all healthcare stakeholders including patients, and dissemination of useful and accurate information
9:15 AM–9:45 AM
Opening Keynote: Dr. Joshua Brody, Medical Oncologist, Mount Sinai Health System
The Promise and Hype of Immunotherapy
Immunotherapy is revolutionizing oncology care across various types of cancers, but it is also necessary to sort the hype from the reality. In his keynote, Dr. Brody will delve into the history of this new therapy mode and how it has transformed the treatment of lymphoma and other diseases. He will address the hype surrounding it, why so many still don’t respond to the treatment regimen and chart the way forward—one that can lead to more elegant immunotherapy combination paths and better outcomes for patients.
Speaker:
, Assistant Professor, Mount Sinai School of Medicine @joshuabrodyMD
Director Lymphoma therapy at Mt. Sinai
- lymphoma a cancer with high PD-L1 expression
- hodgkin’s lymphoma best responder to PD1 therapy (nivolumab) but hepatic adverse effects
- CAR-T (chimeric BCR and TCR); a long process which includes apheresis, selection CD3/CD28 cells; viral transfection of the chimeric; purification
- complete remissions of B cell lymphomas (NCI trial) and long term remissions past 18 months
- side effects like cytokine release (has been controlled); encephalopathy (he uses a hand writing test to see progression of adverse effect)
Vaccines
- teaching the immune cells as PD1 inhibition exhausting T cells so a vaccine boost could be an adjuvant to PD1 or checkpoint therapy
- using Flt3L primed in-situ vaccine (using a Toll like receptor agonist can recruit the dendritic cells to the tumor and then activation of T cell response); therefore vaccine does not need to be produced ex vivo; months after the vaccine the tumor still in remission
- versus rituximab, which can target many healthy B cells this in-situ vaccine strategy is very specific for the tumorigenic B cells
- HoWEVER they did see resistant tumor cells which did not overexpress PD-L1 but they did discover a novel checkpoint (cannot be disclosed at this point)
Please follow on Twitter using the following #hashtags and @pharma_BI
#MCConverge
#AI
#cancertreatment
#immunotherapy
#healthIT
#innovation
#precisionmedicine
#healthcaremodels
#personalizedmedicine
#healthcaredata
And at the following handles:
@pharma_BI
@medcitynews
Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal
https://pharmaceuticalintelligence.com/press-coverage/
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