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Artificial intelligence cancer

Youichi Kumagai, Kaiyo Takubo, Kenro Kawada, Kazuharu Aoyama, Yuma Endo, Tsuyoshi Ozawa, Toshiaki Hirasawa, Toshiyuki Yoshio, Soichiro Ishihara, Mitsuhiro Fujishiro, Jun-Ichi Tamaru, Erito Mochiki, Hideyuki Ishida, Tomohiro Tada
BACKGROUND AND AIMS: The endocytoscopic system (ECS) helps in virtual realization of histology and can aid in confirming histological diagnosis in vivo. We propose replacing biopsy-based histology for esophageal squamous cell carcinoma (ESCC) by using the ECS. We applied deep-learning artificial intelligence (AI) to analyse ECS images of the esophagus to determine whether AI can support endoscopists for the replacement of biopsy-based histology. METHODS: A convolutional neural network-based AI was constructed based on GoogLeNet and trained using 4715 ECS images of the esophagus (1141 malignant and 3574 non-malignant images)...
December 13, 2018: Esophagus: Official Journal of the Japan Esophageal Society
Ian S Boon, Tracy P T Au Yong, Cheng S Boon
The fields of radiotherapy and clinical oncology have been rapidly changed by the advances of technology. Improvement in computer processing power and imaging quality heralded precision radiotherapy allowing radiotherapy to be delivered efficiently, safely and effectively for patient benefit. Artificial intelligence (AI) is an emerging field of computer science which uses computer models and algorithms to replicate human-like intelligence and perform specific tasks which offers a huge potential to healthcare...
December 11, 2018: Medicines (Basel, Switzerland)
Jennifer Abbasi
No abstract text is available yet for this article.
December 11, 2018: JAMA: the Journal of the American Medical Association
Luis M Seijo, Nir Peled, Daniel Ajona, Mattia Boeri, John K Field, Gabriella Sozzi, Ruben Pio, Javier J Zulueta, Avrum Spira, Pierre P Massion, Peter J Mazzone, Luis M Montuenga
The present review is an update of the research and development efforts regarding the use of molecular biomarkers in the lung cancer screening setting. The two main unmet clinical needs, namely, the refinement of risk in order to improve the selection of individuals undergoing screening and the characterization of undetermined nodules found during the CT-based screening process are the object of the biomarkers described in the present review. We first propose some principles to optimize lung cancer biomarker discovery projects...
December 4, 2018: Journal of Thoracic Oncology
Omer F Ahmad, Antonio S Soares, Evangelos Mazomenos, Patrick Brandao, Roser Vega, Edward Seward, Danail Stoyanov, Manish Chand, Laurence B Lovat
Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates for polyps are as high as 22%, and associated interval colorectal cancers after colonoscopy are of concern. Optical biopsy, whereby in-vivo classification of polyps based on enhanced imaging replaces histopathology, has not been incorporated into routine practice because it is limited by interobserver variability and generally only meets accepted standards in expert settings. Real-time decision-support software has been developed to detect and characterise polyps, and also to offer feedback on the technical quality of inspection...
January 2019: Lancet. Gastroenterology & Hepatology
Vasant Kearney, Jason W Chan, Gilmer Valdes, Timothy D Solberg, Sue S Yom
Artificial intelligence (AI) is beginning to transform IMRT treatment planning for head and neck patients. However, the complexity and novelty of AI algorithms make them susceptible to misuse by researchers and clinicians. Understanding nuances of new technologies could serve to mitigate potential clinical implementation pitfalls. This article is intended to facilitate integration of AI into the radiotherapy clinic by providing an overview of AI algorithms, including support vector machines (SVMs), random forests (RF), gradient boosting (GB), and several variations of deep learning...
December 2018: Oral Oncology
Lavinia Ferrante di Ruffano, Yemisi Takwoingi, Jacqueline Dinnes, Naomi Chuchu, Susan E Bayliss, Clare Davenport, Rubeta N Matin, Kathie Godfrey, Colette O'Sullivan, Abha Gulati, Sue Ann Chan, Alana Durack, Susan O'Connell, Matthew D Gardiner, Jeffrey Bamber, Jonathan J Deeks, Hywel C Williams
BACKGROUND: Early accurate detection of all skin cancer types is essential to guide appropriate management and to improve morbidity and survival. Melanoma and cutaneous squamous cell carcinoma (cSCC) are high-risk skin cancers which have the potential to metastasise and ultimately lead to death, whereas basal cell carcinoma (BCC) is usually localised with potential to infiltrate and damage surrounding tissue. Anxiety around missing early curable cases needs to be balanced against inappropriate referral and unnecessary excision of benign lesions...
December 3, 2018: Cochrane Database of Systematic Reviews
Naomi Chuchu, Yemisi Takwoingi, Jacqueline Dinnes, Rubeta N Matin, Oliver Bassett, Jacqueline F Moreau, Susan E Bayliss, Clare Davenport, Kathie Godfrey, Susan O'Connell, Abhilash Jain, Fiona M Walter, Jonathan J Deeks, Hywel C Williams
BACKGROUND: Melanoma accounts for a small proportion of all skin cancer cases but is responsible for most skin cancer-related deaths. Early detection and treatment can improve survival. Smartphone applications are readily accessible and potentially offer an instant risk assessment of the likelihood of malignancy so that the right people seek further medical attention from a clinician for more detailed assessment of the lesion. There is, however, a risk that melanomas will be missed and treatment delayed if the application reassures the user that their lesion is low risk...
December 3, 2018: Cochrane Database of Systematic Reviews
Gajanan V Sherbet, Wai Lok Woo, Satnam Dlay
Artificial intelligence was recognised many years ago as a potential and powerful tool to predict disease outcome in many clinical situations. The conventional approaches using statistical methods have provided much information, but are subject to limitations imposed by the complexity of medical data. The structures of the important variants of the machine learning system artificial neural networks (ANN) are discussed and emphasis is given to the powerful analytical support that could be provided by ANN for the prediction of cancer progression and prognosis...
December 2018: Anticancer Research
Yoshiko Ariji, Motoki Fukuda, Yoshitaka Kise, Michihito Nozawa, Yudai Yanashita, Hiroshi Fujita, Akitoshi Katsumata, Eiichiro Ariji
OBJECTIVE: Although the deep learning system has been applied to interpretation of medical images, its application to the diagnosis of cervical lymph nodes in patients with oral cancer has not yet been reported. The purpose of this study was to evaluate the performance of deep learning image classification for diagnosis of lymph node metastasis. STUDY DESIGN: The imaging data used for evaluation consisted of computed tomography (CT) images of 127 histologically proven positive cervical lymph nodes and 314 histologically proven negative lymph nodes from 45 patients with oral squamous cell carcinoma...
October 15, 2018: Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology
Kota Itahashi, Shunsuke Kondo, Takashi Kubo, Yutaka Fujiwara, Mamoru Kato, Hitoshi Ichikawa, Takahiko Koyama, Reitaro Tokumasu, Jia Xu, Claudia S Huettner, Vanessa V Michelini, Laxmi Parida, Takashi Kohno, Noboru Yamamoto
Background: Oncologists increasingly rely on clinical genome sequencing to pursue effective, molecularly targeted therapies. This study assesses the validity and utility of the artificial intelligence Watson for Genomics (WfG) for analyzing clinical sequencing results. Methods: This study identified patients with solid tumors who participated in in-house genome sequencing projects at a single cancer specialty hospital between April 2013 and October 2016. Targeted genome sequencing results of these patients' tumors, previously analyzed by multidisciplinary specialists at the hospital, were reanalyzed by WfG...
2018: Frontiers in Medicine
Viktor H Koelzer, Korsuk Sirinukunwattana, Jens Rittscher, Kirsten D Mertz
Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment...
November 23, 2018: Virchows Archiv: An International Journal of Pathology
Aritrick Chatterjee, Aytekin Oto
Multiparametric MR imaging is being used for prostate cancer diagnosis. However, many cancers are missed and the performance needs to improve before it can be used for population-level screening. We can expect standardization of multiparametric MR imaging and increased use of quantitative multiparametric MR imaging, which will lead to more reproducible results and improved interpretation. The development and integration of new acquisition techniques and use of artificial intelligence for image interpretation can lead to implementation of new clinical MR methods...
February 2019: Magnetic Resonance Imaging Clinics of North America
Jason J Lau, Soumya Gayen, Asma Ben Abacha, Dina Demner-Fushman
Radiology images are an essential part of clinical decision making and population screening, e.g., for cancer. Automated systems could help clinicians cope with large amounts of images by answering questions about the image contents. An emerging area of artificial intelligence, Visual Question Answering (VQA) in the medical domain explores approaches to this form of clinical decision support. Success of such machine learning tools hinges on availability and design of collections composed of medical images augmented with question-answer pairs directed at the content of the image...
November 20, 2018: Scientific Data
Alejandro Rodríguez-Ruiz, Elizabeth Krupinski, Jan-Jurre Mordang, Kathy Schilling, Sylvia H Heywang-Köbrunner, Ioannis Sechopoulos, Ritse M Mann
Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act-qualified radiologists, once with and once without AI support...
November 20, 2018: Radiology
Rut Cañas, Isabel Linares, Ferran Guedea, Miguel Ángel Berenguer
Radiological Oncology, like the rest of medical specialties, is beginning to provide can personalized therapies. The ongoing scientific advances enable a great degree of precision in diagnoses and therapies. To fight cancer, from a radiotherapy unit, requires up-to-date equipment, professionals with different specialties working in synchrony (doctors, physicists, biologists, etc.) and a lot of research. Some of the new therapeutic tendencies are immunotherapy, nanoparticles, gene therapy, biomarkers, artificial intelligence, etc...
January 2019: Reports of Practical Oncology and Radiotherapy
Yan Zhu, Qiu-Cheng Wang, Mei-Dong Xu, Zhen Zhang, Jing Chen, Yun-Shi Zhong, Yi-Qun Zhang, Wei-Feng Chen, Li-Qing Yao, Ping-Hong Zhou, Quan-Lin Li
BACKGROUND AND AIMS: According to guidelines, endoscopic resection should only be performed for patients whose early gastric cancer invasion depth is within the mucosa or submucosa of the stomach regardless of lymph node involvement. The accurate prediction of invasion depth based on endoscopic images is crucial for screening patients for endoscopic resection. We constructed a convolutional neural network computer-aided detection (CNN-CAD) system based on endoscopic images to determine invasion depth and screen patients for endoscopic resection...
November 16, 2018: Gastrointestinal Endoscopy
George Simon, Courtney D DiNardo, Koichi Takahashi, Tina Cascone, Cynthia Powers, Rick Stevens, Joshua Allen, Mara B Antonoff, Daniel Gomez, Pat Keane, Fernando Suarez Saiz, Quynh Nguyen, Emily Roarty, Sherry Pierce, Jianjun Zhang, Emily Hardeman Barnhill, Kate Lakhani, Kenna Shaw, Brett Smith, Stephen Swisher, Rob High, P Andrew Futreal, John Heymach, Lynda Chin
BACKGROUND: Rapid advances in science challenge the timely adoption of evidence-based care in community settings. To bridge the gap between what is possible and what is practiced, we researched approaches to developing an artificial intelligence (AI) application that can provide real-time patient-specific decision support. MATERIALS AND METHODS: The Oncology Expert Advisor (OEA) was designed to simulate peer-to-peer consultation with three core functions: patient history summarization, treatment options recommendation, and management advisory...
November 16, 2018: Oncologist
Fabian L Kriegel, Ralf Köhler, Jannike Bayat-Sarmadi, Simon Bayerl, Anja E Hauser, Raluca Niesner, Andreas Luch, Zoltan Cseresnyes
The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to the site of inflammation and are activated to prevent a further spreading of the invasion. This is also reflected by changes in the behavior and the morphological appearance of the immune cells. In cancerous tissue, similar morphokinetic changes have been observed in the behavior of microglial cells: intra-tumoral microglia have less complex 3-dimensional shapes, having less-branched cellular processes, and move more rapidly than those in healthy tissue...
October 28, 2018: Journal of Visualized Experiments: JoVE
Tanvi Vaidya, Archi Agrawal, Shivani Mahajan, Meenakshi H Thakur, Abhishek Mahajan
The present era of precision medicine sees 'cancer' as a consequence of molecular derangements occurring at the commencement of the disease process, with morphologic changes happening much later in the process of tumorigenesis. Conventional imaging techniques, such as computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI), play an integral role in the detection of disease at a macroscopic level. However, molecular functional imaging (MFI) techniques entail the visualisation and quantification of biochemical and physiological processes occurring during tumorigenesis, and thus has the potential to play a key role in heralding the transition from the concept of 'one size fits all' to 'precision medicine'...
November 8, 2018: Molecular Diagnosis & Therapy
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