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1. Biologie
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3.1.1 Prévention - Tabac - e-cigs
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4.10 Dép., diag. & prono. - FDA, EMA, NICE,...
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4.12 Biopsies liquides
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4.2 Dép., diag. & prono. - Génome
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Taking uncertainty out of cancer prognosis [Cold Spring Harbor Lab]
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An
analysis of nearly 20,000 cancer patient histories and genetic data has
revealed that knowing the genetic cause of a cancer does not help
predict how deadly the disease will be. Instead, researchers from CSHL
have discovered that copy number variations in specific gene sites are
far more informative, providing new opportunities to improve prognosis.
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4.5 Dép., diag. & prono. - Colorectal
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4.7 Dép., diag. & prono. - Col de l'utérus
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5. Traitements
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5.1 Traitements - Pré-clinique
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5.10 Traitements - Essais
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5.12 Immunothérapies
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Tumor-Specific Antigens From Way Out There [In the Pipeline]
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Identifying
and validating these things is still going to be labor-intensive
(although the paper has some suggestions on how to make the workflow
less onerous). But the results could be worth it – there are so many
tumor types for which we have no real antigen candidates at all.
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5.12.5 Immunothérapies - Pharma
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5.2 Pharma
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5.6 ESMO
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6. Lutte contre les cancers
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6.10.1 Politiques (USA)
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6.4 Médico-éco
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Measuring the Value of Matched Therapy [Foundation Medicine]
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In
new research recently published in JCO Precision Oncology, we
quantified the costs and benefits of genomically matching patients with
metastatic cancer to therapy. This cost overlay study is the first to
report anticancer drug costs and associated cost drivers for matched and
unmatched therapies in these types of patients.
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6.7.1 IA/bioinformatique
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A new AI method can train on medical records without revealing patient data [MIT Technology Review]
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The
idea is hospitals and other medical institutions would be able to train
their models partway with their patients’ data locally, then each send
their half-trained model to a centralized location to complete the final
stages of training with their models together. The centralized
location, whether that be the cloud services of Google or another
company, would never see the raw patient data; they would only see the
output of the half-baked model plus the model itself.
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