The lancet oncology
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The lancet oncology · May 2019
Randomized Controlled Trial Multicenter Study Comparative StudyErlotinib plus bevacizumab versus erlotinib alone in patients with EGFR-positive advanced non-squamous non-small-cell lung cancer (NEJ026): interim analysis of an open-label, randomised, multicentre, phase 3 trial.
Resistance to first-generation or second-generation EGFR tyrosine kinase inhibitor (TKI) monotherapy develops in almost half of patients with EGFR-positive non-small-cell lung cancer (NSCLC) after 1 year of treatment. The JO25567 phase 2 trial comparing erlotinib plus bevacizumab combination therapy with erlotinib monotherapy established the activity and manageable toxicity of erlotinib plus bevacizumab in patients with NSCLC. We did a phase 3 trial to validate the results of the JO25567 study and report here the results from the preplanned interim analysis. ⋯ Chugai Pharmaceutical.
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In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. ⋯ Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.
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The lancet oncology · May 2019
ReviewBig data and machine learning algorithms for health-care delivery.
Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. ⋯ Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.
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The lancet oncology · May 2019
Multicenter StudyNiraparib monotherapy for late-line treatment of ovarian cancer (QUADRA): a multicentre, open-label, single-arm, phase 2 trial.
Late-line treatment options for patients with ovarian cancer are few, with the proportion of patients achieving an overall response typically less than 10%, and median overall survival after third-line therapy of 5-9 months. In this study (QUADRA), we investigated the activity of niraparib monotherapy as the fourth or later line of therapy. ⋯ Tesaro.