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Artificial intelligence cancer

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https://www.readbyqxmd.com/read/30335805/machine-learning-to-support-social-media-empowered-patients-in-cancer-care-and-cancer-treatment-decisions
#1
Daswin De Silva, Weranja Ranasinghe, Tharindu Bandaragoda, Achini Adikari, Nishan Mills, Lahiru Iddamalgoda, Damminda Alahakoon, Nathan Lawrentschuk, Raj Persad, Evgeny Osipov, Richard Gray, Damien Bolton
BACKGROUND: A primary variant of social media, online support groups (OSG) extend beyond the standard definition to incorporate a dimension of advice, support and guidance for patients. OSG are complementary, yet significant adjunct to patient journeys. Machine learning and natural language processing techniques can be applied to these large volumes of unstructured text discussions accumulated in OSG for intelligent extraction of patient-reported demographics, behaviours, decisions, treatment, side effects and expressions of emotions...
2018: PloS One
https://www.readbyqxmd.com/read/30324465/aso-author-reflections-enabling-optimised-delivery-of-patient-centred-cancer-care-using-artificial-intelligence-and-data-analytics
#2
Weranja Ranasinghe, Daswin de Silva, Tharindu Bandaragoda, Achini Adikari, Nathan Lawrentschuk, Damminda Alahakoon, Raj Persad, Damien Bolton
No abstract text is available yet for this article.
October 15, 2018: Annals of Surgical Oncology
https://www.readbyqxmd.com/read/30323963/multimodal-detection-of-pd-l1-reasonable-biomarkers-for-immune-checkpoint-inhibitor
#3
REVIEW
Qiao Yang, Zihan Xu, Linpeng Zheng, Luping Zhang, Qiai You, Jianguo Sun
Immune checkpoint inhibitor (ICI) therapy had achieved significant clinical benefit in multiple malignant solid tumors, such as non-small cell lung cancer, melanoma and urothelial cancer. ICI therapy not only revolutionarily altered the treatment strategy of malignant solid tumors, but also dramatically prolonged overall survival. However, the objective response rate (ORR) of ICI therapy in second line treatment remains 20% or less. How to find patients eligible for ICI therapy by effective biomarkers became hot nowadays...
2018: American Journal of Cancer Research
https://www.readbyqxmd.com/read/30317800/-advances-in-the-application-of-artificial-intelligence-in-cancer-diagnosis-and-treatment
#4
W D Liu, H B Zhou, H P Hu
The application of artificial intelligence is developing rapidly in various fields with the improvement of computing power, big data processing, and diversity of algorithms. It has a great potential value in the field of medical and healthcare, especially in the field of cancer diagnosis and treatment. In addition, it can analyze a large amount of data, information, and knowledge instantaneously. Therefore, it serves as a powerful tool for doctors to make the best treatment decisions. Notably, the development of science and technology truly transform into the actual interests of patients...
August 20, 2018: Zhonghua Gan Zang Bing za Zhi, Zhonghua Ganzangbing Zazhi, Chinese Journal of Hepatology
https://www.readbyqxmd.com/read/30314421/use-of-a-deep-belief-network-for-small-high-level-abstraction-data-sets-using-artificial-intelligence-with-rule-extraction
#5
Yoichi Hayashi
We describe a simple method to transfer from weights in deep neural networks (NNs) trained by a deep belief network (DBN) to weights in a backpropagation NN (BPNN) in the recursive-rule eXtraction (Re-RX) algorithm with J48graft (Re-RX with J48graft) and propose a new method to extract accurate and interpretable classification rules for rating category data sets. We apply this method to the Wisconsin Breast Cancer Data Set (WBCD), the Mammographic Mass Data Set, and the Dermatology Dataset, which are small, high-abstraction data sets with prior knowledge...
October 12, 2018: Neural Computation
https://www.readbyqxmd.com/read/30295070/artificial-intelligence-based-breast-cancer-nodal-metastasis-detection
#6
Yun Liu, Timo Kohlberger, Mohammad Norouzi, George E Dahl, Jenny L Smith, Arash Mohtashamian, Niels Olson, Lily H Peng, Jason D Hipp, Martin C Stumpe
CONTEXT.—: Nodal metastasis of a primary tumor influences therapy decisions for a variety of cancers. Histologic identification of tumor cells in lymph nodes can be laborious and error-prone, especially for small tumor foci. OBJECTIVE.—: To evaluate the application and clinical implementation of a state-of-the-art deep learning-based artificial intelligence algorithm (LYmph Node Assistant or LYNA) for detection of metastatic breast cancer in sentinel lymph node biopsies...
October 8, 2018: Archives of Pathology & Laboratory Medicine
https://www.readbyqxmd.com/read/30257820/using-artificial-intelligence-watson-for-oncology-for-treatment-recommendations-amongst-chinese-patients-with-lung-cancer-feasibility-study
#7
Chaoyuan Liu, Xianling Liu, Fang Wu, Mingxuan Xie, Yeqian Feng, Chunhong Hu
BACKGROUND: Artificial intelligence (AI) is developing quickly in the medical field and can benefit both medical staff and patients. The clinical decision support system Watson for Oncology (WFO) is an outstanding representative AI in the medical field, and it can provide to cancer patients prompt treatment recommendations comparable with ones made by expert oncologists. WFO is increasingly being used in China, but limited reports on whether WFO is suitable for Chinese patients, especially patients with lung cancer, exist...
September 25, 2018: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/30254144/a-convolutional-neural-network-uses-microscopic-images-to-differentiate-between-mouse-and-human-cell-lines-and-their-radioresistant-clones
#8
Masayasu Toratani, Masamitsu Konno, Ayumu Asai, Jun Koseki, Koichi Kawamoto, Keisuke Tamari, Zhihao Li, Daisuke Sakai, Toshihiro Kudo, Taroh Satoh, Katsutoshi Sato, Daisuke Motooka, Daisuke Okuzaki, Yuichiro Doki, Masaki Mori, Kazuhiko Ogawa, Hideshi Ishii
Artificial intelligence (AI) trained with a convolutional neural network (CNN) is a recent technological advancement. Previously, several attempts have been made to train AI using medical images for clinical applications. However, whether AI can distinguish microscopic images of mammalian cells has remained debatable. This study assesses the accuracy of image recognition techniques using the CNN to identify microscopic images. We also attempted to distinguish between mouse and human cells and their radioresistant clones...
September 25, 2018: Cancer Research
https://www.readbyqxmd.com/read/30247662/radiomics-with-artificial-intelligence-for-precision-medicine-in-radiation-therapy
#9
Hidetaka Arimura, Mazen Soufi, Kamezawa, Kenta Ninomiya, Masahiro Yamada
Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine...
September 22, 2018: Journal of Radiation Research
https://www.readbyqxmd.com/read/30216858/a-review-of-computer-aided-detection-in-mammography
#10
REVIEW
Janine Katzen, Katerina Dodelzon
Breast screening with mammography is widely recognized as the most effective method of detecting early breast cancer and has consistently demonstrated a 20-40% decrease in mortality among screened women. Despite this, the sensitivity of mammography ranges between 70 and 90%. Computer aided detection (CAD) is an artificial intelligence (AI) technique that utilizes pattern recognition to highlight suspicious features on imaging and marks them for the radiologist to review and interpret. It aims to decrease oversights made by interpreting radiologists...
September 7, 2018: Clinical Imaging
https://www.readbyqxmd.com/read/30213409/utilizing-big-data-in-cancer-care
#11
REVIEW
Cary Jo R Schlick, Joshua P Castle, David J Bentrem
Clinical research has boomed over the past decade, with the development of multiple clinical datasets that are available for retrospective review. However, data remain incomplete based on fragmented reporting, provider change, and loss of follow-up. New technologies are being developed to assist with this limitation, by joining health care systems' medical records, and tracking Medicare claims files. The future of health care will rely more heavily on these systems, and artificial intelligence to quickly pull relevant clinical and genomic data regarding particular diagnoses, as a means to personalize medicine...
October 2018: Surgical Oncology Clinics of North America
https://www.readbyqxmd.com/read/30191140/immuno-oncology-emerging-targets-and-combination-therapies
#12
REVIEW
Henry T Marshall, Mustafa B A Djamgoz
Host immunity recognizes and eliminates most early tumor cells, yet immunological checkpoints, exemplified by CTLA-4, PD-1, and PD-L1, pose a significant obstacle to effective antitumor immune responses. T-lymphocyte co-inhibitory pathways influence intensity, inflammation and duration of antitumor immunity. However, tumors and their immunosuppressive microenvironments exploit them to evade immune destruction. Recent PD-1 checkpoint inhibitors yielded unprecedented efficacies and durable responses across advanced-stage melanoma, showcasing potential to replace conventional radiotherapy regimens...
2018: Frontiers in Oncology
https://www.readbyqxmd.com/read/30190664/unraveling-the-bioactivity-of-anticancer-peptides-as-deduced-from-machine-learning
#13
REVIEW
Watshara Shoombuatong, Nalini Schaduangrat, Chanin Nantasenamat
Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. In spite of advancements in cancer therapy, the low success rate and recurrence of tumor has necessitated the ongoing search for new therapeutic agents. Aside from drugs based on small molecules and protein-based biopharmaceuticals, there has been an intense effort geared towards the development of peptide-based therapeutics owing to its favorable and intrinsic properties of being relatively small, highly selective, potent, safe and low in production costs...
2018: EXCLI Journal
https://www.readbyqxmd.com/read/30181359/genetics-and-biology-of-prostate-cancer
#14
REVIEW
Guocan Wang, Di Zhao, Denise J Spring, Ronald A DePinho
Despite the high long-term survival in localized prostate cancer, metastatic prostate cancer remains largely incurable even after intensive multimodal therapy. The lethality of advanced disease is driven by the lack of therapeutic regimens capable of generating durable responses in the setting of extreme tumor heterogeneity on the genetic and cell biological levels. Here, we review available prostate cancer model systems, the prostate cancer genome atlas, cellular and functional heterogeneity in the tumor microenvironment, tumor-intrinsic and tumor-extrinsic mechanisms underlying therapeutic resistance, and technological advances focused on disease detection and management...
September 1, 2018: Genes & Development
https://www.readbyqxmd.com/read/30181315/concordance-study-between-ibm-watson-for-oncology-and-clinical-practice-for-patients-with-cancer-in-china
#15
Na Zhou, Chuan-Tao Zhang, Hong-Ying Lv, Chen-Xing Hao, Tian-Jun Li, Jing-Juan Zhu, Hua Zhu, Man Jiang, Ke-Wei Liu, He-Lei Hou, Dong Liu, Ai-Qin Li, Guo-Qing Zhang, Zi-Bin Tian, Xiao-Chun Zhang
BACKGROUND: IBM Watson for Oncology (WFO), which can use natural language processing to evaluate data in structured and unstructured formats, has begun to be used in China. It provides physicians with evidence-based treatment options and ranks them in three categories for treatment decision support. This study was designed to examine the concordance between the treatment recommendation proposed by WFO and actual clinical decisions by oncologists in our cancer center, which would reflect the differences of cancer treatment between China and the U...
September 4, 2018: Oncologist
https://www.readbyqxmd.com/read/30149185/the-utility-of-artificial-neural-networks-and-classification-and-regression-trees-for-the-prediction-of-endometrial-cancer-in-postmenopausal-women
#16
V Pergialiotis, A Pouliakis, C Parthenis, V Damaskou, C Chrelias, N Papantoniou, I Panayiotides
OBJECTIVE: Artificial neural networks (ANNs) and classification and regression trees (CARTs) have been previously used for the prediction of cancer in several fields. In our study, we aim to investigate the diagnostic accuracy of three different methodologies (i.e. logistic regression, ANNs and CARTs) for the prediction of endometrial cancer in postmenopausal women with vaginal bleeding or endometrial thickness ≥5 mm, as determined by ultrasound examination. STUDY DESIGN: We conducted a retrospective case-control study based on data from analysis of pathology reports of curettage specimens in postmenopausal women...
August 24, 2018: Public Health
https://www.readbyqxmd.com/read/30140854/how-should-artificial-intelligence-screen-for-skin-cancer-and-deliver-diagnostic-predictions-to-patients
#17
George A Zakhem, Catherine C Motosko, Roger S Ho
No abstract text is available yet for this article.
August 22, 2018: JAMA Dermatology
https://www.readbyqxmd.com/read/30133035/clinical-applications-of-carbon-nanomaterials-in-diagnostics-and-therapy
#18
REVIEW
Kian Ping Loh, Dean Ho, Gigi Ngar Chee Chiu, David Tai Leong, Giorgia Pastorin, Edward Kai-Hua Chow
Nanomaterials have the potential to improve how patients are clinically treated and diagnosed. While there are a number of nanomaterials that can be used toward improved drug delivery and imaging, how these nanomaterials confer an advantage over other nanomaterials, as well as current clinical approaches is often application or disease specific. How the unique properties of carbon nanomaterials, such as nanodiamonds, carbon nanotubes, carbon nanofibers, graphene, and graphene oxides, make them promising nanomaterials for a wide range of clinical applications are discussed herein, including treating chemoresistant cancer, enhancing magnetic resonance imaging, and improving tissue regeneration and stem cell banking, among others...
August 21, 2018: Advanced Materials
https://www.readbyqxmd.com/read/30125150/insights-into-pathogenic-interactions-among-environment-host-and-tumor-at-the-crossroads-of-molecular-pathology-and-epidemiology
#19
Shuji Ogino, Jonathan A Nowak, Tsuyoshi Hamada, Danny A Milner, Reiko Nishihara
Evidence indicates that diet, nutrition, lifestyle, the environment, the microbiome, and other exogenous factors have pathogenic roles and also influence the genome, epigenome, transcriptome, proteome, and metabolome of tumor and nonneoplastic cells, including immune cells. With the need for big-data research, pathology must transform to integrate data science fields, including epidemiology, biostatistics, and bioinformatics. The research framework of molecular pathological epidemiology (MPE) demonstrates the strengths of such an interdisciplinary integration, having been used to study breast, lung, prostate, and colorectal cancers...
August 20, 2018: Annual Review of Pathology
https://www.readbyqxmd.com/read/30120958/diagnostic-outcomes-of-esophageal-cancer-by-artificial-intelligence-using-convolutional-neural-networks
#20
Yoshimasa Horie, Toshiyuki Yoshio, Kazuharu Aoyama, Shoichi Yoshimizu, Yusuke Horiuchi, Akiyoshi Ishiyama, Toshiaki Hirasawa, Tomohiro Tsuchida, Tsuyoshi Ozawa, Soichiro Ishihara, Youichi Kumagai, Mitsuhiro Fujishiro, Iruru Maetani, Junko Fujisaki, Tomohiro Tada
BACKGROUND AND AIMS: The prognosis of esophageal cancer is relatively poor. Patients are usually diagnosed at an advanced stage when it is often too late for effective treatment. Recently, artificial intelligence (AI) using deep learning has made remarkable progress in medicine. However, there are no reports on its application for diagnosing esophageal cancer. Here, we demonstrate the diagnostic ability of AI to detect esophageal cancer including squamous cell carcinoma and adenocarcinoma...
August 16, 2018: Gastrointestinal Endoscopy
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