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Advance articles Annals of Oncology

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Accepted Manuscript Editorial 28 May 2018 Artificial intelligence for melanoma diagnosis: How can we deliver on the promise? Published: 28 May 2018 Section: Editorial Melanoma Corrected Proof Research Article 28 May 2018 Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists H A Haenssle; C Fink; R Schneiderbauer; F Toberer; T Buhl ... Annals of Oncology, mdy166, https://doi.org/10.1093/annonc/mdy166 Published: 28 May 2018 Section: Original Article Corrected Proof Review Article 28 May 2018 Gastrointestinal stromal tumours: ESMO–EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up P G Casali; N Abecassis; S Bauer; R Biagini; S Bielack ... Annals of Oncology, mdy095, https://doi.org/10.1093/annonc/mdy095 Published: 28 May 2018 Section: clinical practice guidelines Corrected Proof Review Article 28 May 2018 Soft tissue and visceral sarcomas: ESMO–EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up P G Casali; N Abecassis; S Bauer; R Biagini; S Bielack ... Annals of Oncology, mdy096, https://doi.org/10.1093/annonc/mdy096 Published: 28 May 2018 Section: clinical practice guidelines Corrected Proof Review Article 23 May 2018 Hodgkin lymphoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up D A Eichenauer; B M P Aleman; M André; M Federico; M Hutchings ... Annals of Oncology, mdy080, https://doi.org/10.1093/annonc/mdy080 Published: 23 May 2018 Section: clinical practice guidelines Accepted Manuscript Review Article 22 May 2018 Advances in the systemic treatment of melanoma brain metastases I C Glitza Oliva; G Schvartsman; H Tawbi Annals of Oncology, mdy185, https://doi.org/10.1093/annonc/mdy185


Innovative AI Breath Analyzer Diagnoses Diseases by "Smell"

#artificialintelligence

Imagine being able to know if you have Parkinson's disease, multiple sclerosis, liver failure, Crohn's diseases, pulmonary hypertension, chronic kidney disease, or any number of cancers based on a simple, non-invasive test of your breath. Breath analyzers to detect alcohol have been around for well over half a century--why not apply the same concept to detect diseases? A global team of scientists from universities in Israel, France, Latvia, China and the United States have developed an artificial intelligence (AI) system to detect 17 diseases from exhaled breath with 86 percent accuracy. The research team led by Professor Hassam Haick of the Technion-Israel Institute of Technology collected breath samples from 1404 subjects with either no disease (healthy control) or one of 17 different diseases. The disease conditions include lung cancer, colorectal cancer, head and neck cancer, ovarian cancer, bladder cancer, prostate cancer, kidney cancer, gastric cancer, Crohn's disease, ulcerative colitis, irritable bowel syndrome, idiopathic Parkinson's, atypical Parkinson ISM, multiple sclerosis, pulmonary hypertension, pre-eclampsia toxemia, and chronic kidney disease.


Predicting drug response of tumors from integrated genomic profiles by deep neural networks

arXiv.org Machine Learning

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.


Resistance to Medical Artificial Intelligence

#artificialintelligence

Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A–3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for consumers' unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to ...


Notable Labs launches rolling blood cancer trial to test its AI system

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Precision oncology firm Notable Labs is launching its first self-sponsored clinical trial, designed from the ground up to help validate its cancer patient matching platform over the long term. The observational study--which also represents the company's largest trial to date--aims to enroll up to 1,000 participants with a variety of blood cancers and will follow them for at least one year as they receive physician-led standard-of-care therapies at different sites across the U.S. and Canada. Separately, Notable's phenotypic and artificial intelligence-powered platform will be tested against multiple patient samples collected over time to provide a longitudinal view of its predictive value based on cancer mutations, drug responses and the outcomes of each participant. It will also search for patterns useful in the development of new treatments. The company combines AI approaches with automated lab processes to determine which drugs or combinations will be most effective for specific cancers.