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Artificial Intelligence in Digital Breast Pathology: Techniques and Applications

Asmaa Ibrahim, Paul Gamble, Ronnachai Jaroensri, Mohammed M. Abdelsamea, Craig H. Mermel, Po-Hsuan Cameron Chen, Emad A. Rakha
<span title="2019-12-19">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ryhbxrsjvffeflobfr2i5psfou" style="color: black;">Breast</a> </i> &nbsp;
, classification and prediction of behaviour of breast tumours.  ...  The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis.  ...  Then, we do a deep dive into the applications of machine learning to digital pathology for breast cancer, including both diagnostic and prognostic applications.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.breast.2019.12.007">doi:10.1016/j.breast.2019.12.007</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31935669">pmid:31935669</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7375550/">pmcid:PMC7375550</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u24dfeb6zndvpcsx56cgkhcifm">fatcat:u24dfeb6zndvpcsx56cgkhcifm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210523164937/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7375550&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4f/2f/4f2f039824e3959379686c23d090720b6599819a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.breast.2019.12.007"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375550" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Breast cancer outcome prediction with tumour tissue images and machine learning

Riku Turkki, Dmitrii Byckhov, Mikael Lundin, Jorma Isola, Stig Nordling, Panu E. Kovanen, Clare Verrill, Karl von Smitten, Heikki Joensuu, Johan Lundin, Nina Linder
<span title="2019-05-22">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/leczobeksfd3pe5z3xcrxwb2jy" style="color: black;">Breast Cancer Research and Treatment</a> </i> &nbsp;
Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.  ...  The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients.  ...  We thank the Digital Microscopy and Molecular Pathology unit at FIMM, supported by the Helsinki Institute of Life Science and Biocenter Finland for providing slide scanning services.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10549-019-05281-1">doi:10.1007/s10549-019-05281-1</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31119567">pmid:31119567</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6647903/">pmcid:PMC6647903</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3guvw4nfjnggtnuas3o4nj2ady">fatcat:3guvw4nfjnggtnuas3o4nj2ady</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200311143708/https://link.springer.com/content/pdf/10.1007%2Fs10549-019-05281-1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d1/3e/d13e857051fa85493c0e7bc34e78ee5470a1a681.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10549-019-05281-1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6647903" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Machine learning-based prediction of breast cancer growth rate in vivo

Shristi Bhattarai, Sergey Klimov, Mohammed A. Aleskandarany, Helen Burrell, Anthony Wormall, Andrew R. Green, Padmashree Rida, Ian O. Ellis, Remus M. Osan, Emad A. Rakha, Ritu Aneja
<span title="2019-08-09">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/k7cos7qqgzazliffoxrrx3awka" style="color: black;">British Journal of Cancer</a> </i> &nbsp;
We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was  ...  Determining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice.  ...  Janssen for discussions and help with editing of the paper. We also thank the Nottingham Health Science Biobank for providing tissue samples.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41416-019-0539-x">doi:10.1038/s41416-019-0539-x</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31395950">pmid:31395950</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6738119/">pmcid:PMC6738119</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kyellcaxxfaffm4owjzwc66mbe">fatcat:kyellcaxxfaffm4owjzwc66mbe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108091815/https://www.nature.com/articles/s41416-019-0539-x.pdf?error=cookies_not_supported&amp;code=18723783-17be-4567-8221-16afbf313102" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/00/df/00df8db4251778af680f078e690f8b00015ff8ab.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41416-019-0539-x"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6738119" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Radiomics: from qualitative to quantitative imaging

William Rogers, Sithin Thulasi Seetha, Turkey A. G. Refaee, Relinde I. Y. Lieverse, Renée W. Y. Granzier, Abdalla Ibrahim, Simon A. Keek, Sebastian Sanduleanu, Sergey P. Primakov, Manon P. L. Beuque, Damiënne Marcus, Alexander M. A. van der Wiel (+5 others)
<span title="2020-02-26">2020</span> <i title="British Institute of Radiology"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vngwqxkavfdxxnv7qnd3n2eoli" style="color: black;">British Journal of Radiology</a> </i> &nbsp;
As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes  ...  The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages.  ...  An example of fivefold cross-validation which can be used to evaluate machine learning models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1259/bjr.20190948">doi:10.1259/bjr.20190948</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32101448">pmid:32101448</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mnhaur7dyrhanio63v6kdbdthm">fatcat:mnhaur7dyrhanio63v6kdbdthm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210427195419/https://cris.maastrichtuniversity.nl/ws/files/53401857/Lambin_2020_Radiomics_from_qualitative_to_quantitative_imaging.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/dc/53/dc53bf02505c2ae4af603513cce034bd598689f5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1259/bjr.20190948"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Machine learning versus mechanistic modeling for prediction of metastatic relapse in breast cancer [article]

Chiara Nicolo, Cynthia Perier, Melanie Prague, Gregoire MacGrogan, Olivier Saut, Sebastien Benzekry
<span title="2019-05-10">2019</span> <i title="Cold Spring Harbor Laboratory"> biorxiv/medrxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Using the breast cancer database from the Bergonie Institute (n=1057 patients), we investigated the potential use of machine learning algorithms for predicting 5-years metastatic relapse.  ...  Predicting the probability of metastatic relapse for patients diagnosed with early-stage breast cancer is essential for decision of adjuvant therapy.  ...  values of the primary tumour size (diameter) at diagnosis Predictions of the mechanistic model for individual patients Prediction of 5-years metastatic relapse using machine learning classification algorithms  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/634428">doi:10.1101/634428</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/i7h5y3mjh5bjpl7ledomltqsw4">fatcat:i7h5y3mjh5bjpl7ledomltqsw4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715123934/https://oskar-bordeaux.fr/bitstream/handle/20.500.12278/26182/BPH_JCOCCI_2020_Nicolo.pdf;jsessionid=D33C6AEFD8C1E6881D7EA50AD76AC69A?sequence=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d9/5f/d95f27a4b8f076fa4a67c9b70baabd0b9edb1310.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/634428"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

BRENDA-Score, a Highly Significant, Internally and Externally Validated Prognostic Marker for Metastatic Recurrence: Analysis of 10,449 Primary Breast Cancer Patients

Manfred Wischnewsky, Lukas Schwentner, Joachim Diessner Diessner, Amelie De Gregorio, Ralf Joukhadar, Dayan Davut, Jessica Salmen, Inga Bekes, Matthias Kiesel, Max Müller-Reiter, Maria Blettner, Wolfgang Janni (+3 others)
<span title="2021-06-22">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2zwku6u6nfdcri773tpisi6ldi" style="color: black;">Cancers</a> </i> &nbsp;
Bootstrapping was used for internal validation and an independent dataset of 1883 patients for external validation. The predictive accuracy was checked by Harrell's c-index.  ...  Results: Intrinsic subtypes, tumour size, grading, and nodal status were identified as statistically significant prognostic factors in the multivariate analysis.  ...  Acknowledgments: We would like to express our gratitude to Engel and Schrodi of the Tumorcenter Munich for preparing the Dachau database.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/cancers13133121">doi:10.3390/cancers13133121</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34206581">pmid:34206581</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8268855/">pmcid:PMC8268855</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wzvqwvuxgfgxnfgts4fpi3d36m">fatcat:wzvqwvuxgfgxnfgts4fpi3d36m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210629110246/https://res.mdpi.com/d_attachment/cancers/cancers-13-03121/article_deploy/cancers-13-03121.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/89/1a/891a872f83d7e49fb18e0d5d2962beecf51d937d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/cancers13133121"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268855" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

Sunil Gupta, Truyen Tran, Wei Luo, Dinh Phung, Richard Lee Kennedy, Adam Broad, David Campbell, David Kipp, Madhu Singh, Mustafa Khasraw, Leigh Matheson, David M Ashley (+1 others)
<span title="2014-03-17">2014</span> <i title="BMJ"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cadiaxy7y5alle7oylyd7rqmnu" style="color: black;">BMJ Open</a> </i> &nbsp;
Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data.  ...  Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems.  ...  The second analysis evaluated the added discriminative power provided by EAR, by comparing the best machine-learning models using three sets of predicting variables: variables from ECO (box 1), variables  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1136/bmjopen-2013-004007">doi:10.1136/bmjopen-2013-004007</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24643167">pmid:24643167</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3963101/">pmcid:PMC3963101</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/keqywacklndflayedyldxryvjm">fatcat:keqywacklndflayedyldxryvjm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190502070255/https://bmjopen.bmj.com/content/bmjopen/4/3/e004007.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/54/38/5438286c85244b486704ec9391133becfc3b2aca.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1136/bmjopen-2013-004007"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> bmj.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3963101" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Detect Breast Cancer using Fuzzy C means Techniques in Wisconsin Prognostic Breast Cancer (WPBC) Data Sets

Tintu P B, Paulin. R
<span title="2013-09-01">2013</span> <i title="Association of Technology and Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lhvdmzuqxreinc3m4ancngr4qi" style="color: black;">International Journal of Computer Applications Technology and Research</a> </i> &nbsp;
Results on breast cancer diagnosis data set from UCI machine learning repository show that this approach would be capable of classifying cancer cases with high accuracy rate in addition to adequate interpretability  ...  In this research work, using intelligent techniques of data mining is Fuzzy C Means; we have focused on breast cancer diagnosis by fuzzy systems.  ...  DATA SET OF BREAST CANCER DISEASE The WDBC and WPBC datasets are made at the University of Wisconsin Hospital for the diagnosis and prognosis of breast tumours solely based on FNA test.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7753/ijcatr0205.1017">doi:10.7753/ijcatr0205.1017</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rsayia5yujfl3g2ume5xwqcegq">fatcat:rsayia5yujfl3g2ume5xwqcegq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180603164334/http://www.ijcat.com/archives/volume2/issue5/ijcatr02051017.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a9/f1/a9f1a87428ee6e21e20f4863b0edfbe032d12839.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7753/ijcatr0205.1017"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Brief Review of Breast Cancer Detection via Computer Aided Deep Learning Methods

Ayush Dogra, Srinivas Institute of Technology
<span title="2019-12-23">2019</span> <i title="ESRSA Publications Pvt. Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3j6n6lpsjndinobmibtprywohe" style="color: black;">International Journal of Engineering Research and</a> </i> &nbsp;
The CAD (computer aided diagnosis) have surfaced as an assistive medical diagnostic tool which help a great deal in patient care and management by reducing the ratio of false positive results and by enabling  ...  In this manuscript we have presented a brief review of the most recent trends in deep learning based breast cancer detection methods.  ...  In [50] authors developed an integrated approach of machine learning and deep learning model which is trained on health records and linked mammograms.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17577/ijertv8is120191">doi:10.17577/ijertv8is120191</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7ox3sg7zjrdjboe5ec3xcjoy6y">fatcat:7ox3sg7zjrdjboe5ec3xcjoy6y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210512232113/https://www.ijert.org/research/a-brief-review-of-breast-cancer-detection-via-computer-aided-deep-learning-methods-IJERTV8IS120191.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/41/d3/41d36062aacd2058b2ecf7ae3c03e23fac7224f3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17577/ijertv8is120191"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Radiomics for the Discrimination of Infiltrative vs In Situ Breast Cancer

Luana Conte
<span title="2019-12-18">2019</span> <i title="Biomedical Research Network, LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7skyhy7ghvel3niw4nxm3qyija" style="color: black;">Biomedical Journal of Scientific &amp; Technical Research</a> </i> &nbsp;
Breast cancer is the most common malignant tumor in women worldwide. Its early diagnosis relies on radiology and clinical evaluation, supplemented by biopsy confirmation.  ...  Radiomics signatures in DCE-MRI and machine learning, aimed to investigate the feasibility of distinguishing infiltrating cancer from ductal carcinoma in situ (DCIS) diagnosed by preoperative core needle  ...  [27] tried a deep learning approach on breast MRI for predicting of invasive disease following the diagnosis of DCIS.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.26717/bjstr.2019.24.003983">doi:10.26717/bjstr.2019.24.003983</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yuy7t4iipvgdhnx4a43sbl52nm">fatcat:yuy7t4iipvgdhnx4a43sbl52nm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200507000811/https://biomedres.us/pdfs/BJSTR.MS.ID.003983.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0c/dc/0cdc3859ade2506f37727077c4d99dd58a970016.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.26717/bjstr.2019.24.003983"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Multi-Agent Based Diagnostic Model for Breast Tumour Classification

Yusuf Musa Malgwi, Gregory Maksha Wajiga, Etemi Joshua Garba
<span title="">2019</span> <i title="Science Publishing Group"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/uorrparrl5ajxogtkifvwtbhe4" style="color: black;">American Journal of Data Mining and Knowledge Discovery</a> </i> &nbsp;
This study focused on developing a multi-agent based model for diagnosis of breast tumours using the k-Nearest Neighbor (k-NN) algorithm by classifying the nature of the tumours based on their associated  ...  patterns of symptoms and other risk factors of Cancer diseases.  ...  Walaa propose a method to enhance the diagnosis of breast cancer by integrating an unsupervised learning method K-means with Support Vector Machine (SVM) which is a supervised learning method running on  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11648/j.ajdmkd.20190401.11">doi:10.11648/j.ajdmkd.20190401.11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6drcqxnmengr7nlcw7yloljjse">fatcat:6drcqxnmengr7nlcw7yloljjse</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321105511/http://article.ajdmkd.org/pdf/10.11648.j.ajdmkd.20190401.11.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c1/4a/c14a6ff1c8c6b2b38f36c2f8cdc3f043b3230f1b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11648/j.ajdmkd.20190401.11"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Impact of time to local recurrence on the occurrence of metastasis in breast cancer patients treated with neoadjuvant chemotherapy: A random forest survival approach

Enora Laas, Anne-Sophie Hamy, Anne-Sophie Michel, Nabilah Panchbhaya, Matthieu Faron, Thanh Lam, Sophie Carrez, Jean-Yves Pierga, Roman Rouzier, Florence Lerebours, Jean-Guillaume Feron, Fabien Reyal (+1 others)
<span title="2019-01-23">2019</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
We studied the relationship between time to ipsilateral breast tumor recurrence (IBTR) and distant metastasis-free survival (DMFS) in patients with breast cancer treated by neoadjuvant chemotherapy (NAC  ...  The funding source had no role in data analysis and interpretation or in the writing of the manuscript. AS Hamy was supported by an ITMO-INSERM-AVIESAN cancer translational research grant.  ...  Acknowledgments We thank Roche � France for financial support for the construction of the Institut Curie neoadjuvant database (NEOREP).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0208807">doi:10.1371/journal.pone.0208807</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30673703">pmid:30673703</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6344020/">pmcid:PMC6344020</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aotg2fnt6zh4fkocow7h6tw44a">fatcat:aotg2fnt6zh4fkocow7h6tw44a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190503094719/https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208807&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/74/61/746103e56ec017bc2b70ed36855d06523b763c9f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0208807"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6344020" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Breast Cancer Prediction by Logistic Regression with CUDA Parallel Programming Support

Alessandro Peretti, Francesco Amenta
<span title="">2016</span> <i title="OMICS Publishing Group"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bws6ldjh2bhllgwrklwjm6yaou" style="color: black;">Breast Cancer Current Research</a> </i> &nbsp;
Objective: The present article shows the development and the simulation of a machine learning model created with logistic regression to predict breast cancer tumor.  ...  It uses Python programming language and Nvidia CUDA parallel GPU programming mechanism. It uses Nvidia CUDA programming approach to take advantage of multiple GPUs.  ...  Large number of risk prediction models have been developed that evaluate different types of risk factors for breast cancer tumor and not only.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4172/2572-4118.1000111">doi:10.4172/2572-4118.1000111</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vt6cznpt3fci3gm6nlv6dweltm">fatcat:vt6cznpt3fci3gm6nlv6dweltm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180724153717/https://www.omicsonline.org/open-access/breast-cancer-prediction-by-logistic-regression-with-cuda-parallel-programming-support-bccr-1000111.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d5/ed/d5ed5ae1b21f59ceae19b97fd4324b9bf9be574a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4172/2572-4118.1000111"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

The Role of Artificial Intelligence in Early Cancer Diagnosis

Benjamin Hunter, Sumeet Hindocha, Richard W. Lee
<span title="2022-03-16">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2zwku6u6nfdcri773tpisi6ldi" style="color: black;">Cancers</a> </i> &nbsp;
We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis  ...  In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges.  ...  Acknowledgments: The authors would like to thank Stan Kaye for his invaluable support and feedback on this manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/cancers14061524">doi:10.3390/cancers14061524</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35326674">pmid:35326674</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8946688/">pmcid:PMC8946688</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bzcgndsievgzhajxh2sumudlqe">fatcat:bzcgndsievgzhajxh2sumudlqe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220318150014/https://mdpi-res.com/d_attachment/cancers/cancers-14-01524/article_deploy/cancers-14-01524.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8c/e8/8ce8fff8f2e1459b7daf74ccbadbb88c8dd5085b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/cancers14061524"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8946688" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology

Rajit Rattan, Tejinder Kataria, Susovan Banerjee, Shikha Goyal, Deepak gupta, Akshi Pandita, Shyam Bisht, Kushal Narang, Soumya Ranjan Mishra
<span title="2019-04-24">2019</span> <i title="British Institute of Radiology"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/atqsetzdnbgipc6yu6qpl6efle" style="color: black;">BJR|Open</a> </i> &nbsp;
AI with the use of machine learning and artificial neural networks has come up with faster and more accurate solutions for the problems faced by oncologist.  ...  Whether it's the screening modalities, or diagnosis or the prognostic assays, AI has come with more accurately defining results and survival of patients.  ...  Machine learning is now used for knowledge-based planning that implies use of software tool to predict the dose-volume histogram of critical organs in relationship to the tumour.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1259/bjro.20180031">doi:10.1259/bjro.20180031</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33178922">pmid:33178922</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7592433/">pmcid:PMC7592433</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vyiar3mglzfulkuklrjj6hrleq">fatcat:vyiar3mglzfulkuklrjj6hrleq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210527004759/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7592433&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4e/d4/4ed4bfe7ad12654c755593d686fccdbfe43f3d57.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1259/bjro.20180031"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592433" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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