keyword
MENU ▼
Read by QxMD icon Read
search

Artificial intelligence cancer

keyword
https://www.readbyqxmd.com/read/30120958/the-diagnostic-outcomes-of-esophageal-cancer-by-artificial-intelligence-using-convolutional-neural-networks
#1
Yoshimasa Horie, Toshiyuki Yoshio, Kazuharu Aoyama, Syouichi Yoshimizu, Yusuke Horiuchi, Akiyoshi Ishiyama, Toshiaki Hirasawa, Tomohiro Tuchida, 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 15, 2018: Gastrointestinal Endoscopy
https://www.readbyqxmd.com/read/30119234/current-trends-and-emerging-diagnostic-techniques-for-lung-cancer
#2
REVIEW
Bala Prabhakar, Pravin Shende, Steffi Augustine
Cancer is one of most fatal forms of disease with rapid, abnormal and uncontrolled division of cells which spreads into different organs in the body. The primary aim of this review is to showcase the current and emerging diagnostic techniques that are used in lung cancer detection. Lung cancer is a leading cause of death among smokers and it has been emerging in non-smokers due to passive smoke inhalation by non-smokers. The mortality rate of patients with lung cancer is very high due to the change in lifestyle and environmental factors...
July 27, 2018: Biomedicine & Pharmacotherapy, Biomédecine & Pharmacothérapie
https://www.readbyqxmd.com/read/30113824/computational-optics-enables-breast-cancer-profiling-in-point-of-care-settings
#3
Jouha Min, Hyungsoon Im, Matthew Allen, Phillip J McFarland, Ismail Degani, Hojeong Yu, Erica Normandin, Divya Pathania, Jaymin Patel, Cesar M Castro, Ralph Weissleder, Hakho Lee
The global burden of cancer, severe diagnostic bottlenecks in underserved regions, and underfunded health care systems are fueling the need for inexpensive, rapid and treatment-informative diagnostics. Based on advances in computational optics and deep learning, we have developed a low-cost digital system, termed AIDA (artificial intelligence diffraction analysis), for breast cancer diagnosis of fine needle aspirates. Here, we show high accuracy (>90%) in (i) recognizing cells directly from diffraction patterns and (ii) classifying breast cancer types using deep-learning based analysis of sample aspirates...
August 16, 2018: ACS Nano
https://www.readbyqxmd.com/read/30095205/predicting-cancer-outcome-artificial-intelligence-vs-pathologists
#4
Yasusei Kudo
Determining cancer prognosis in patients is crucial to controlling the suffering and death due to cancer. In general, the diagnosis of cancer is based on the histology. Histology of cancer is determined by the tissue obtained from patients via several sampling approaches, including excision or biopsy, fine-needle aspiration, and cytologic smears. As the pathologic diagnosis of cancer is an essential initial step for the determination of the line of therapy, pathologists face a great responsibility in the diagnosis of cancer...
August 10, 2018: Oral Diseases
https://www.readbyqxmd.com/read/30081101/predict-effective-drug-combination-by-deep-belief-network-and-ontology-fingerprints
#5
Guocai Chen, Alex Tsoi, Hua Xu, W Jim Zheng
The synergistic effect of drug combination is one of the most desirable properties for treating cancer. However, systematically predicting effective drug combination is a significant challenge. We report here a novel method based on deep belief network to predict drug synergy from gene expression, pathway and the Ontology Fingerprints-a literature derived ontological profile of genes. Using data sets provided by 2015 DREAM competition, our analysis shows that this integrative method outperforms published results from the DREAM website for 4999 drug pairs, demonstrating the feasibility of predicting drug synergy from literature and the -omics data using advanced artificial intelligence approach...
August 3, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30079779/improving-the-early-diagnosis-of-early-nodular-melanoma-can-we-do-better
#6
Paola Corneli, Iris Zalaudek, Giovanni Magaton-Rizzi, Nicola di Meo
Cutaneous melanoma is the 6th most common malignant cancer in the USA. Among different subtypes of melanoma, nodular melanoma (NM) accounts about 14% of all cases but is responsible for more than 40% of melanoma deaths. Early diagnosis is the best method to improve melanoma prognosis. Unfortunately, early diagnosis of NM is particularly challenging given that patients often lack identifiable risk factors such as many moles or freckles. Moreover, early NM may mimic a range of benign skin lesions that are not routinely excised or biopsied in every day practice...
August 6, 2018: Expert Review of Anticancer Therapy
https://www.readbyqxmd.com/read/30054121/abdominal-multi-organ-auto-contouring-method-for-online-adaptive-magnetic-resonance-guided-radiotherapy-an-intelligent-multi-level-fusion-approach
#7
Fan Liang, Pengjiang Qian, Kuan-Hao Su, Atallah Baydoun, Asha Leisser, Steven Van Hedent, Jung-Wen Kuo, Kaifa Zhao, Parag Parikh, Yonggang Lu, Bryan J Traughber, Raymond F Muzic
BACKGROUND: Manual contouring remains the most laborious task in radiation therapy planning and is a major barrier to implementing routine Magnetic Resonance Imaging (MRI) Guided Adaptive Radiation Therapy (MR-ART). To address this, we propose a new artificial intelligence-based, auto-contouring method for abdominal MR-ART modeled after human brain cognition for manual contouring. METHODS/MATERIALS: Our algorithm is based on two types of information flow, i.e. top-down and bottom-up...
July 24, 2018: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/30044653/overview-of-precision-oncology-trials-challenges-and-opportunities
#8
Elena Fountzilas, Apostolia M Tsimberidou
In recent years, the therapeutic management of selected patients with cancer has shifted toward the 'precision medicine' approach based on patient's mechanisms of tumorigenesis, and their baseline characteristics and comorbidities. Complete tumor and cell-free DNA profiling using next-generation sequencing, proteomic and RNA analysis, and immune mechanisms should to be taken into consideration and accurate bioinformatic analysis is essential to optimize patient's treatment. Areas covered: The challenges and opportunities of conducting clinical trials in precision oncology are summarized...
August 10, 2018: Expert Review of Clinical Pharmacology
https://www.readbyqxmd.com/read/30029596/rna-seq-assistant-machine-learning-based-methods-to-identify-more-transcriptional-regulated-genes
#9
Likai Wang, Yanpeng Xi, Sibum Sung, Hong Qiao
BACKGROUND: Although different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectability for certain differentially expressed genes (DEGs). Whether the transcriptional dynamics of a gene can be captured accurately depends on experimental design/operation and the following data analysis processes. The workflow of subsequent data processing, such as reads alignment, transcript quantification, normalization, and statistical methods for ultimate identification of DEGs can influence the accuracy and sensitivity of DEGs analysis, producing a certain number of false-positivity or false-negativity...
July 20, 2018: BMC Genomics
https://www.readbyqxmd.com/read/30022630/artificial-intelligence-weights-the-importance-of-factors-predicting-complete-cytoreduction-at-secondary-cytoreductive-surgery-for-recurrent-ovarian-cancer
#10
Giorgio Bogani, Diego Rossetti, Antonino Ditto, Fabio Martinelli, Valentina Chiappa, Lavinia Mosca, Umberto Leone Roberti Maggiore, Stefano Ferla, Domenica Lorusso, Francesco Raspagliesi
OBJECTIVE: Accumulating evidence support that complete cytoreduction (CC) at the time of secondary cytoreductive surgery (SCS) improves survival in patients affected by recurrent ovarian cancer (ROC). Here, we aimed to determine whether artificial intelligence (AI) might be useful in weighting the importance of clinical variables predicting CC and survival. METHODS: This is a retrospective study evaluating 194 patients having SCS for ROC. Using artificial neuronal network (ANN) analysis was estimated the importance of different variables, used in predicting CC and survival...
September 2018: Journal of Gynecologic Oncology
https://www.readbyqxmd.com/read/30017637/explaining-the-dynamics-of-tumor-aggressiveness-at-the-crossroads-between-biology-artificial-intelligence-and-complex-systems
#11
REVIEW
Caterina A M La Porta, Stefano Zapperi
Facing metastasis is the most pressing challenge of cancer research. In this review, we discuss recent advances in understanding phenotypic plasticity of cancer cells, highlighting the kinetics of cancer stem cell and the role of the epithelial mesenchymal transition for metastasis. It appears that the tumor micro-environment plays a crucial role in triggering phenotypic transitions, as we illustrate discussing the challenges posed by macrophages and cancer associated fibroblasts. To disentangle the complexity of environmentally induced phenotypic transitions, there is a growing need for novel advanced algorithms as those proposed in our recent work combining single cell data analysis and numerical simulations of gene regulatory networks...
July 11, 2018: Seminars in Cancer Biology
https://www.readbyqxmd.com/read/30003283/diffusion-weighted-breast-imaging
#12
REVIEW
K Deike-Hofmann, T Kuder, F König, D Paech, C Dreher, S Delorme, H-P Schlemmer, S Bickelhaupt
Magnetic resonance imaging (MRI) of the breast represents one of the most sensitive imaging modalities in breast cancer detection. Diffusion-weighted imaging (DWI) is a sequence variation introduced as a complementary MRI technique that relies on mapping the diffusion process of water molecules thereby providing additional information about the underlying tissue. Since water diffusion is more restricted in most malignant tumors than in benign ones owing to the higher cellularity of the rapidly proliferating neoplasia, DWI has the potential to contribute to the identification and characterization of suspicious breast lesions...
July 12, 2018: Der Radiologe
https://www.readbyqxmd.com/read/29998596/position-paper-telemedicine-in-occupational-dermatology-current-status-and-perspectives
#13
REVIEW
Peter Elsner, Andrea Bauer, Thomas Ludwig Diepgen, Hans Drexler, Manigé Fartasch, Swen Malte John, Sibylle Schliemann, Wolfgang Wehrmann, Jörg Tittelbach
Teledermatology is the use of telecommunication technologies to exchange medical information for diagnosis, consultation, treatment and teaching in dermatology. While its use has been evaluated in a wide range of dermatological diagnoses, only few studies exist on its validity, diagnostic precision, feasibility, and cost-effectiveness in occupational dermatology. However, these studies show a considerable potential for diagnosis, prevention, treatment support and follow-up of patients with occupational skin diseases...
July 11, 2018: Journal der Deutschen Dermatologischen Gesellschaft, Journal of the German Society of Dermatology: JDDG
https://www.readbyqxmd.com/read/29994489/weakly-supervised-biomedical-image-segmentation-by-reiterative-learning
#14
Qiaokang Liang, Yang Nan, Gianmarc Coppola, Kunlin Zou, Wei Sun, Dan Zhang, Guanzhen Yu
Recent advances in deep learning have produced encouraging results for biomedical image segmentation; however, outcomes rely heavily on comprehensive annotation. In this paper, we propose a neural network architecture and a new algorithm, known as overlapped region forecast, for the automatic segmentation of gastric cancer images. To the best of our knowledge, this report describes the first time that deep learning has been applied to the segmentation of gastric cancer images. Moreover, a reiterative learning framework that achieves superior performance without pre-training or further manual annotation is presented to train a simple network on weakly annotated biomedical images...
June 25, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/29970965/cancer-risk-assessment-in-modern-radiotherapy-workflow-with-medical-big-data
#15
REVIEW
Fu Jin, Huan-Li Luo, Juan Zhou, Ya-Nan He, Xian-Feng Liu, Ming-Song Zhong, Han Yang, Chao Li, Qi-Cheng Li, Xia Huang, Xiu-Mei Tian, Da Qiu, Guang-Lei He, Li Yin, Ying Wang
Modern radiotherapy (RT) is being enriched by big digital data and intensive technology. Multimodality image registration, intelligence-guided planning, real-time tracking, image-guided RT (IGRT), and automatic follow-up surveys are the products of the digital era. Enormous digital data are created in the process of treatment, including benefits and risks. Generally, decision making in RT tries to balance these two aspects, which is based on the archival and retrieving of data from various platforms. However, modern risk-based analysis shows that many errors that occur in radiation oncology are due to failures in workflow...
2018: Cancer Management and Research
https://www.readbyqxmd.com/read/29953901/novel-therapeutic-strategy-for-cancer-and-autoimmune-conditions-modulating-cell-metabolism-and-redox-capacity
#16
REVIEW
Xing-Xing Fan, Hu-Dan Pan, Ying Li, Rui-Jin Guo, Elaine Lai-Han Leung, Liang Liu
Dysregulation of cell metabolism and redox balance is implicated in the pathogenesis and progression of cancer and autoimmune diseases. Because the cell proliferation and apoptotic regulatory pathways are interconnected with metabolic and redox signalling pathways, the current mono-target treatment is ineffective, and multi-drug resistance remains common. Complex diseases are often implicated in a network-based context of pathology; therefore, a new holistic intervention approach is required to block multi-crosstalk in such complicated circumstances...
June 25, 2018: Pharmacology & Therapeutics
https://www.readbyqxmd.com/read/29944567/cancer-on-a-chip-and-artificial-intelligence-tomorrow-s-cancer-management
#17
Mohammed Elmusrati, Nureddin Ashammakhi
No abstract text is available yet for this article.
June 25, 2018: Journal of Craniofacial Surgery
https://www.readbyqxmd.com/read/29893330/breast-cancer-tumor-type-recognition-using-graph-feature-selection-technique-and-radial-basis-function-neural-network-with-optimal-structure
#18
Payam Zarbakhsh, Abdoljalil Addeh
Context: Breast cancer is a major cause of mortality in young women in the developing countries. Early diagnosis is the key to improve survival rate in cancer patients. Aims: In this paper an intelligent system is proposed to breast cancer tumor type recognition. Settings and Design: The proposed system includes three main module: The feature selection module, the classifier module and the optimization module. Feature selection plays an important role in pattern recognition systems...
April 2018: Journal of Cancer Research and Therapeutics
https://www.readbyqxmd.com/read/29799486/natural-products-for-drug-discovery-in-the-21st-century-innovations-for-novel-drug-discovery
#19
REVIEW
Nicholas Ekow Thomford, Dimakatso Alice Senthebane, Arielle Rowe, Daniella Munro, Palesa Seele, Alfred Maroyi, Kevin Dzobo
The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures...
May 25, 2018: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/29787940/survey-on-deep-learning-for-radiotherapy
#20
REVIEW
Philippe Meyer, Vincent Noblet, Christophe Mazzara, Alex Lallement
More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning...
July 1, 2018: Computers in Biology and Medicine
keyword
keyword
163425
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"