The lancet oncology
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The lancet oncology · May 2019
ReviewExplaining the unexplainable: discrepancies in results from the CALGB/SWOG 80405 and FIRE-3 studies.
We propose a working hypothesis that integrates data from the CALGB/SWOG 80405 and FIRE-3 studies to explain apparent discrepancies in their results. Both trials assessed the combination of either cetuximab or bevacizumab with a different chemotherapy backbone: irinotecan in all patients in the FIRE-3 study, or oxaliplatin in 75% of the patients in the CALGB/SWOG 80405 study. The hypothesis is divided into three parts. ⋯ In a clinical setting, the optimal first-line combination of biological therapy and chemotherapy predetermines the crossover to a specific second-line treatment, which affects the overall survival of a patient with a specific tumour subtype. Our working hypothesis suggests that the CALGB/SWOG 80405 and FIRE-3 studies are complementary rather than discrepant, and it provides an explanation for their opposing interpretations. In conclusion, proper interpretation of the CALGB/SWOG 80405 and FIRE-3 results requires an in-depth examination of the complex interplay, not only between the targeted biological agents and chemotherapeutic drugs, but also between therapies and the tumour biology and microenvironment, for each line of treatment.
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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.