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Total 60669 results found since Jan 2013.

Deep Learning and Machine Learning with Grid Search to Predict Later Occurrence of Breast Cancer Metastasis Using Clinical Data
CONCLUSIONS: Our results show that deep learning with grid search overall performs at least as well as other machine learning methods when using non-image clinical data. It is interesting to note that some of the other machine learning methods, such as XGB, RF, and SVM, are very strong competitors of DFNN when incorporating grid search. It is also worth noting that the computation time required to do grid search with DFNN is much more than that required to do grid search with the other nine machine learning methods.PMID:36233640 | DOI:10.3390/jcm11195772
Source: Clinical Breast Cancer - October 14, 2022 Category: Cancer & Oncology Authors: Xia Jiang Chuhan Xu Source Type: research

Readability Analysis of Online Breast Cancer Surgery Patient Education Materials from National Cancer Institute-Designated Cancer Centers Compared with Top Internet Search Results
CONCLUSIONS: Patient education materials on breast cancer from both NCI-DCC and top Internet search results were written several reading grade levels higher than the NIH recommendation. Materials should be revised to enhance patient comprehension of breast cancer surgical treatment and guide patients in this important decision-making process to ultimately improve health outcomes.PMID:37707665 | DOI:10.1245/s10434-023-14279-5
Source: Ann Oncol - September 14, 2023 Category: Cancer & Oncology Authors: Anna Rauzi Lauren E Powell McKenzie White Saranya Prathibha Jane Yuet Ching Hui Source Type: research

Visual search in breast imaging: A review.
Abstract Breast cancer is the most common cancer among females worldwide and large volumes of breast images are produced and interpreted annually. As long as radiologists interpret these images, the diagnostic accuracy will be limited by human factors and both false-positive and false-negative errors might occur. By understanding visual search in breast images, we may be able to identify causes of diagnostic errors, find ways to reduce them, and also provide a better education to radiology residents. Many visual search studies in breast radiology have been devoted to mammography. These studies showed that 70% of m...
Source: The British Journal of Radiology - July 8, 2019 Category: Radiology Authors: Gandomkar Z, Mello-Thoms C Tags: Br J Radiol Source Type: research

Feasibility of Feature-based Indexing, Clustering, and Search of Clinical Trials. A Case Study of Breast Cancer Trials from ClinicalTrials.gov.
Conclusions: It is feasible to develop feature-based indexing and clustering methods for clinical trials to identify trials with similar target populations and to improve trial search efficiency. PMID: 23666475 [PubMed - as supplied by publisher]
Source: Methods of Information in Medicine - May 13, 2013 Category: Information Technology Authors: Boland MR, Miotto R, Gao J, Weng C Tags: Methods Inf Med Source Type: research

The Effect of Patient Narratives on Information Search in a Web-Based Breast Cancer Decision Aid: An Eye-Tracking Study
Conclusions: The observed increase in fixation duration with video patient testimonials is consistent with the idea that the vividness of the video content could cause greater elaboration of the message, thereby encouraging greater information search. Conversely, because reading requires more effortful processing than watching, reading patient narratives may have decreased participant motivation to engage in more reading in the remaining sections of the Web decision aid. These findings suggest that the format of patient stories may be equally as important as their content in determining their effect on decision making. Mor...
Source: Journal of Medical Internet Research - December 17, 2013 Category: Journals (General) Authors: Victoria A ShafferJustin OwensBrian J Zikmund-Fisher Source Type: research

Relationship Between State-Level Google Online Search Volume and Cancer Incidence in the United States: Retrospective Study
Conclusions: Cancer incidence is correlated with online search volume at the state level. Search patterns were temporally associated with cancer awareness months and celebrity announcements. Online searches reflect public awareness. Advancing understanding of online search patterns could augment traditional epidemiologic surveillance, provide opportunities for targeted patient engagement, and allow public information campaigns to be evaluated in ways previously unable to be measured.
Source: Journal of Medical Internet Research - January 8, 2018 Category: General Medicine Authors: Charles A. Phillips Allison Barz Leahy Yimei Li Marilyn M. Schapira L. Charles Bailey Raina M. Merchant Source Type: research

Hypothesis driven single nucleotide polymorphism search (HyDn-SNP-S).
Abstract The advent of complete-genome genotyping across phenotype cohorts has provided a rich source of information for bioinformaticians. However the search for SNPs from this data is generally performed on a study-by-study case without any specific hypothesis of the location for SNPs that are predictive for the phenotype. We have designed a method whereby very large SNP lists (several gigabytes in size), combining several genotyping studies at once, can be sorted and traced back to their ultimate consequence in protein structure. Given a working hypothesis, researchers are able to easily search whole genome gen...
Source: DNA Repair - July 4, 2013 Category: Genetics & Stem Cells Authors: Swett RJ, Elias A, Miller JA, Dyson GE, Andrés Cisneros G Tags: DNA Repair (Amst) Source Type: research

Prevalence in Visual Search: From the Clinic to the Lab and Back Again
Abstract One of the most important applications of visual search is in the interpretation of medical images. Like many applications of visual search, medical image interpretation is typically characterized as a low “prevalence” context, meaning that targets are relatively rare. Breast cancer screening, for example, has a prevalence of around 0.5%. In this review, I present a survey of research on the effects of prevalence on visual search from both the medical image perception and basic psychological science perspectives. In order to provide some quantitative rigor, I include a meta‐analysis of the effects of low pre...
Source: Japanese Psychological Research - April 7, 2017 Category: Psychiatry & Psychology Authors: Todd S. Horowitz Tags: Invited Review Source Type: research

The search for biomarkers in the critically ill: a cautionary tale.
Authors: Moran JL, Solomon PJ Abstract The search for biomarkers has been described as a dismal patchwork of fragmented research. We review biomarkers in sepsis in the critically ill in terms of conventional single circulating proteins. Despite sepsis biomarker publications trebling over the past 6 years, currently only one, procalcitonin, has materialised promise. We survey genomic biomarker initiatives, single nucleotide polymorphisms (SNPs) and gene signatures. Despite many SNP associations with sepsis susceptibility and a limited number of genome-wide association studies, the status of these associations is tha...
Source: Critical Care and Resuscitation - June 3, 2018 Category: Intensive Care Tags: Crit Care Resusc Source Type: research

Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction
Conclusion: The experimental results show that the accuracy of the proposed method proves to be encouraging for detection of masses. Thus, it provides a better assistance to the radiologists in diagnosing breast cancer at an early stage. PMID: 30678427 [PubMed - in process]
Source: Asian Pacific Journal of Cancer Prevention - January 28, 2019 Category: Cancer & Oncology Tags: Asian Pac J Cancer Prev Source Type: research

Can a Machine Learn from Radiologists ’ Visual Search Behaviour and Their Interpretation of Mammograms—a Deep-Learning Study
AbstractVisual search behaviour and the interpretation of mammograms have been studied for errors in breast cancer detection. We aim to ascertain whether machine-learning models can learn about radiologists ’ attentional level and the interpretation of mammograms. We seek to determine whether these models are practical and feasible for use in training and teaching programmes. Eight radiologists of varying experience levels in reading mammograms reviewed 120 two-view digital mammography cases (59 canc ers). Their search behaviour and decisions were captured using a head-mounted eye-tracking device and software allowing th...
Source: Journal of Digital Imaging - August 12, 2019 Category: Radiology Source Type: research