Collaborating Authors

Advance articles Annals of Oncology


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, 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, 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, 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, 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,

Innovative AI Breath Analyzer Diagnoses Diseases by "Smell"


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.

Intel and Penn Medicine are developing an AI to spot brain tumors


We've seen AI outperform doctors in spotting breast cancer, lung cancer and skin cancer. Now, researchers from Intel and the University of Pennsylvania are turning their attention to brain tumors. Using Intel's AI hardware and software, Penn Medicine will lead 29 international healthcare and research institutions in creating an AI model trained on the largest brain tumor dataset ever -- and will do so without sharing sensitive patient data. The project is based on a technique called federated learning, which trains an algorithm across decentralized servers, so that hospitals can work together without actually sharing patient data. This will allow the institutions -- from the US, Canada, the UK, Germany, the Netherlands, Switzerland and India -- to create a much larger data set than any one institution would be able to on its own.

Automated histologic diagnosis of CNS tumors with machine learning


A new mass discovered in the CNS is a common reason for referral to a neurosurgeon. CNS masses are typically discovered on MRI or computed tomography (CT) scans after a patient presents with new neurologic symptoms. Presenting symptoms depend on the location of the tumor and can include headaches, seizures, difficulty expressing or comprehending language, weakness affecting extremities, sensory changes, bowel or bladder dysfunction, gait and balance changes, vision changes, hearing loss and endocrine dysfunction. A mass in the CNS has a broad differential diagnosis, including tumor, infection, inflammatory or demyelinating process, infarct, hemorrhage, vascular malformation and radiation treatment effect. The most likely diagnoses can be narrowed based on patient demographics, medical history, imaging characteristics and adjunctive laboratory studies. However, accurate histopathologic interpretation of tissue obtained at the time of surgery is frequently required to make a diagnosis and guide intraoperative decision making. Over half of CNS tumors in adults are metastases from systemic cancer originating elsewhere in the body [1]. An estimated 9.6% of adults with lung cancer, melanoma, breast cancer, renal cell carcinoma and colorectal cancer have brain metastases [2].

Paige touts paper in Nature Medicine on AI for pathology


Pathology artificial intelligence (AI) software developer Paige is highlighting a paper published July 15 in Nature Medicine that indicates the company's technology can be used to develop AI algorithms with "near-perfect accuracy" for analyzing pathology slides for prostate cancer, skin cancer, and breast cancer. In the paper, Chief Scientific Officer Thomas Fuchs, PhD, of Memorial Sloan Kettering Cancer Center and colleagues describe how a series of deep-learning algorithms for clinical decision support in pathology were developed with an automated training and testing technique. Fuchs is the senior author on the paper, with his student Gabriele Campanella as the first author. The deployment of clinical decision support for pathology has been hindered by the need to curate large, manually annotated datasets to test and train AI algorithms, the authors noted. Instead, Campanella et al present a system in which algorithms are trained using only the reported diagnoses.