Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer
Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condit...
Saved in:
| Published in | Cancers Vol. 14; no. 3; p. 623 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI AG
26.01.2022
MDPI |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2072-6694 2072-6694 |
| DOI | 10.3390/cancers14030623 |
Cover
| Abstract | Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condition. These diagnostic approaches have, so far, mostly relied on conventional diagnostic methods and have made little use of the vast amounts of clinical and diagnostic data that are routinely being collected along the diagnostic pathway. Practitioners have lacked the tools to handle this ever-increasing flood of data. Only recently, the clinical field has opened up more for the opportunities that come with the systematic utilisation of high-dimensional computational data analysis. We aim to introduce the reader to the theoretical background of machine learning (ML) and elaborate on the established and potential use cases of ML algorithms in screening and early detection. Furthermore, we assess and comment on the relevant challenges and misconceptions of the applicability of ML-based diagnostic approaches. Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care. |
|---|---|
| AbstractList | Simple SummaryNon-communicable diseases in general, and cancer in particular, contribute greatly to the global burden of disease. Although significant advances have been made to address this burden, cancer is still among the top drivers of mortality, second only to cardiovascular diseases. Consensus has been established that a key factor to reduce the burden of disease from cancer is to improve screening for and the early detection of such conditions. To date, however, most approaches in this field relied on established screening methods, such as a clinical examination, radiographic imaging, tissue staining or biochemical markers. Yet, with the advances of information technology, new data-driven screening and diagnostic tools have been developed. This article provides a brief overview of the theoretical foundations of these data-driven approaches, highlights the promising use cases and underscores the challenges and limitations that come with the introduction of these approaches to the clinical field.AbstractDespite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condition. These diagnostic approaches have, so far, mostly relied on conventional diagnostic methods and have made little use of the vast amounts of clinical and diagnostic data that are routinely being collected along the diagnostic pathway. Practitioners have lacked the tools to handle this ever-increasing flood of data. Only recently, the clinical field has opened up more for the opportunities that come with the systematic utilisation of high-dimensional computational data analysis. We aim to introduce the reader to the theoretical background of machine learning (ML) and elaborate on the established and potential use cases of ML algorithms in screening and early detection. Furthermore, we assess and comment on the relevant challenges and misconceptions of the applicability of ML-based diagnostic approaches. Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care. Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condition. These diagnostic approaches have, so far, mostly relied on conventional diagnostic methods and have made little use of the vast amounts of clinical and diagnostic data that are routinely being collected along the diagnostic pathway. Practitioners have lacked the tools to handle this ever-increasing flood of data. Only recently, the clinical field has opened up more for the opportunities that come with the systematic utilisation of high-dimensional computational data analysis. We aim to introduce the reader to the theoretical background of machine learning (ML) and elaborate on the established and potential use cases of ML algorithms in screening and early detection. Furthermore, we assess and comment on the relevant challenges and misconceptions of the applicability of ML-based diagnostic approaches. Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care.Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condition. These diagnostic approaches have, so far, mostly relied on conventional diagnostic methods and have made little use of the vast amounts of clinical and diagnostic data that are routinely being collected along the diagnostic pathway. Practitioners have lacked the tools to handle this ever-increasing flood of data. Only recently, the clinical field has opened up more for the opportunities that come with the systematic utilisation of high-dimensional computational data analysis. We aim to introduce the reader to the theoretical background of machine learning (ML) and elaborate on the established and potential use cases of ML algorithms in screening and early detection. Furthermore, we assess and comment on the relevant challenges and misconceptions of the applicability of ML-based diagnostic approaches. Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care. Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condition. These diagnostic approaches have, so far, mostly relied on conventional diagnostic methods and have made little use of the vast amounts of clinical and diagnostic data that are routinely being collected along the diagnostic pathway. Practitioners have lacked the tools to handle this ever-increasing flood of data. Only recently, the clinical field has opened up more for the opportunities that come with the systematic utilisation of high-dimensional computational data analysis. We aim to introduce the reader to the theoretical background of machine learning (ML) and elaborate on the established and potential use cases of ML algorithms in screening and early detection. Furthermore, we assess and comment on the relevant challenges and misconceptions of the applicability of ML-based diagnostic approaches. Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care. |
| Author | Benning, Leo Peintner, Andreas Peintner, Lukas |
| AuthorAffiliation | 3 Institute of Molecular Medicine and Cell Research, Albert Ludwigs University of Freiburg, 79085 Freiburg, Germany 1 Health Care Supply Research and Data Mining Working Group, Emergency Department, University Medical Center Freiburg, 79106 Freiburg, Germany; leo.benning@uniklinik-freiburg.de 2 Databases and Information Systems, Department of Computer Science, Leopold-Franzens University of Innsbruck, 6020 Innsbruck, Austria; andreas.peintner@uibk.ac.at |
| AuthorAffiliation_xml | – name: 1 Health Care Supply Research and Data Mining Working Group, Emergency Department, University Medical Center Freiburg, 79106 Freiburg, Germany; leo.benning@uniklinik-freiburg.de – name: 3 Institute of Molecular Medicine and Cell Research, Albert Ludwigs University of Freiburg, 79085 Freiburg, Germany – name: 2 Databases and Information Systems, Department of Computer Science, Leopold-Franzens University of Innsbruck, 6020 Innsbruck, Austria; andreas.peintner@uibk.ac.at |
| Author_xml | – sequence: 1 givenname: Leo orcidid: 0000-0002-8429-9702 surname: Benning fullname: Benning, Leo – sequence: 2 givenname: Andreas surname: Peintner fullname: Peintner, Andreas – sequence: 3 givenname: Lukas orcidid: 0000-0002-0445-1445 surname: Peintner fullname: Peintner, Lukas |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35158890$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkc1v1DAQxS1UREvpmRuyxIVLqD9iJ-aAtCzlQ1oEEnCOHHvSdZW1Uzspyn-P0y1QVkJiLrbl93t6M_MYHfngAaGnlLzkXJFzo72BmGhJOJGMP0AnjFSskFKVR_fux-gspSuSi3NayeoROuaCirpW5ATNK3uz2CTsPNbe4nELeDUMvTO6db0bZxw6_EmbrfOAN6Cjd_6yeKMTWPzVRIDlfUte6NjP-C2MYEYX_OISQwazdxciXt-mfYVX-Et0O4hP0MNO9wnO7s5T9P3dxbf1h2Lz-f3H9WpTmJLRsVCqzaU7WtetlMJq4KyjStrSKlPXlpW0rEEI2wopLTESKmIJlYxQkbvl_BSRve_kBz3_0H3fDDmAjnNDSbMMsjkYZEZe75FhandgDfgx6j9Y0K75-8e7bXMZbpq65rzkKhu8uDOI4XqCNDY7lwz0vfYQptQwyRQRijKWpc8PpFdhij5PZFFVgsu8x6x6dj_R7yi_FpkF53uBiSGlCN1_NCkOCONGvWwut-T6f3I_AXnwxBQ |
| CitedBy_id | crossref_primary_10_1038_s41587_023_02051_9 crossref_primary_10_3389_fendo_2022_1011492 crossref_primary_10_3390_cancers14071819 crossref_primary_10_1002_1878_0261_13222 crossref_primary_10_1109_ACCESS_2025_3538566 crossref_primary_10_3934_mbe_2023457 crossref_primary_10_1007_s12672_025_02111_3 crossref_primary_10_1007_s11042_024_19510_3 crossref_primary_10_3390_cancers15041065 crossref_primary_10_3389_fbioe_2024_1456354 crossref_primary_10_1021_acsmaterialsau_3c00046 crossref_primary_10_3389_fonc_2024_1477166 crossref_primary_10_3390_cancers15030843 |
| Cites_doi | 10.3322/caac.21652 10.1186/s12864-019-5489-4 10.3390/cancers13123047 10.1016/j.cmpb.2020.105584 10.1016/S0140-6736(19)30037-6 10.1109/ICASSP.2013.6638947 10.1109/TBME.2016.2613502 10.1186/s12911-020-01225-8 10.1016/j.acra.2019.09.017 10.1007/s10278-017-0009-z 10.1371/journal.pmed.1002730 10.1109/ICASSP.2019.8683352 10.1109/CVPR.2015.7298594 10.18653/v1/D18-1302 10.1117/12.2266335 10.1038/s41598-020-67960-0 10.1038/s41591-019-0583-3 10.1186/s12874-018-0482-1 10.1109/TPAMI.2016.2646371 10.1117/1.JMI.3.3.034501 10.1109/ICPR48806.2021.9412236 10.1038/nature21056 10.1016/S0140-6736(20)30925-9 10.1016/S2214-109X(18)30411-X 10.1016/S1470-2045(09)70145-7 10.1038/nrclinonc.2016.50 10.1002/mp.13361 10.1093/bioinformatics/btw427 10.1292/jvms.12-0233 10.3322/caac.21556 10.1038/nature14539 10.1016/j.icte.2020.04.009 10.1109/ICCV.2015.510 10.3390/cancers11091235 10.1016/j.media.2016.10.004 10.1109/BIBM.2015.7359868 10.1007/978-3-319-24574-4_28 10.1088/1361-6560/aa82ec 10.1038/s41389-019-0157-8 10.1016/j.neucom.2019.07.080 10.1038/s41746-018-0048-y 10.1038/s41467-019-13825-8 10.1016/j.tibtech.2017.10.012 10.1016/j.compmedimag.2016.11.004 10.1162/neco.1997.9.8.1735 10.1016/S2589-7500(21)00208-9 10.1109/CVPR.2015.7298965 10.1109/CVPR.2018.00175 10.1109/CVPR.2016.90 10.1038/nbt.3300 10.1016/j.ajpath.2019.05.007 10.1016/j.eururo.2013.12.062 10.1161/CIRCULATIONAHA.114.014508 10.1109/ICIP.2016.7532834 10.1109/ISBI.2017.7950686 10.3322/caac.21660 10.1093/oso/9780198538493.001.0001 10.1371/journal.pone.0195816 10.3115/v1/D14-1179 10.1001/jamainternmed.2015.5231 10.3390/biom10101460 10.1142/9789813235533_0031 10.1145/3065386 10.1038/s41416-020-01122-x 10.1038/s42003-020-0973-6 10.1200/JCO.2021.39.15_suppl.10577 10.1016/j.ophtha.2018.11.016 10.1126/science.aaw4399 10.1371/journal.pcbi.1006076 10.1038/s41598-020-61588-w 10.1001/jama.2019.10306 10.1016/j.csbj.2014.11.005 10.3390/cancers12030603 10.1109/EMBC.2016.7590782 |
| ContentType | Journal Article |
| Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION NPM 3V. 7T5 7TO 7XB 8FE 8FH 8FK 8G5 ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ GUQSH H94 HCIFZ LK8 M2O M7P MBDVC PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM ADTOC UNPAY |
| DOI | 10.3390/cancers14030623 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) Immunology Abstracts Oncogenes and Growth Factors Abstracts ProQuest Central (purchase pre-March 2016) ProQuest SciTech Collection ProQuest Natural Science Journals ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Research Library ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials Biological Science Collection ProQuest Central (New) Natural Science Collection ProQuest One ProQuest Central ProQuest Central Student ProQuest Research Library AIDS and Cancer Research Abstracts SciTech Collection (ProQuest) Biological Sciences ProQuest Research Library Biological Science Database Research Library (Corporate) ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database Research Library Prep ProQuest Central Student Oncogenes and Growth Factors Abstracts ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Natural Science Collection ProQuest Central Korea Biological Science Collection AIDS and Cancer Research Abstracts ProQuest Research Library ProQuest Central (New) ProQuest Biological Science Collection ProQuest Central Basic ProQuest One Academic Eastern Edition Biological Science Database ProQuest SciTech Collection ProQuest One Academic UKI Edition Immunology Abstracts ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic CrossRef PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 3 dbid: BENPR name: ProQuest Central url: http://www.proquest.com/pqcentral?accountid=15518 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2072-6694 |
| ExternalDocumentID | 10.3390/cancers14030623 PMC8833439 35158890 10_3390_cancers14030623 |
| Genre | Journal Article Review |
| GroupedDBID | --- 53G 5VS 8FE 8FH 8G5 AADQD AAFWJ AAYXX ABDBF ABUWG ACUHS ADBBV AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ CCPQU CITATION DIK DWQXO E3Z EBD ESX GNUQQ GUQSH GX1 HCIFZ HYE IAO IHR ITC KQ8 LK8 M2O M48 M7P MODMG M~E OK1 P6G PGMZT PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC RPM TUS NPM 3V. 7T5 7TO 7XB 8FK H94 MBDVC PKEHL PQEST PQUKI PRINS Q9U 7X8 PUEGO 5PM ADRAZ ADTOC C1A IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c421t-99bbbbaf188b665dae32f196d4d9c88d24148e55db566d0c6e70d016201517633 |
| IEDL.DBID | M48 |
| ISSN | 2072-6694 |
| IngestDate | Sun Oct 26 01:26:40 EDT 2025 Tue Sep 30 16:37:25 EDT 2025 Fri Sep 05 12:48:13 EDT 2025 Fri Jul 25 12:00:23 EDT 2025 Mon Jul 21 05:45:48 EDT 2025 Thu Oct 16 04:43:46 EDT 2025 Thu Apr 24 22:59:36 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | deep learning CNN DNN cancer diagnostics machine learning high throughput artificial intelligence |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c421t-99bbbbaf188b665dae32f196d4d9c88d24148e55db566d0c6e70d016201517633 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 These authors contributed equally to this work. |
| ORCID | 0000-0002-0445-1445 0000-0002-8429-9702 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.3390/cancers14030623 |
| PMID | 35158890 |
| PQID | 2627536072 |
| PQPubID | 2032421 |
| ParticipantIDs | unpaywall_primary_10_3390_cancers14030623 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8833439 proquest_miscellaneous_2629059122 proquest_journals_2627536072 pubmed_primary_35158890 crossref_primary_10_3390_cancers14030623 crossref_citationtrail_10_3390_cancers14030623 |
| PublicationCentury | 2000 |
| PublicationDate | 20220126 |
| PublicationDateYYYYMMDD | 2022-01-26 |
| PublicationDate_xml | – month: 1 year: 2022 text: 20220126 day: 26 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Cancers |
| PublicationTitleAlternate | Cancers (Basel) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Han (ref_48) 2017; 62 Courtiol (ref_65) 2019; 25 ref_91 Han (ref_58) 2020; 196 ref_12 ref_56 ref_55 Weller (ref_9) 2009; 10 ref_52 Elmore (ref_3) 2021; 71 ref_19 ref_18 ref_17 ref_16 ref_15 Collins (ref_80) 2015; 131 Loeb (ref_10) 2014; 65 Krizhevsky (ref_14) 2017; 60 Dou (ref_59) 2017; 64 ref_61 ref_60 Heo (ref_67) 2013; 75 Kocarnik (ref_4) 2021; 39 Shieh (ref_8) 2016; 13 ref_25 ref_24 ref_68 ref_23 Benning (ref_43) 2020; 10 ref_21 ref_20 ref_63 Collins (ref_82) 2019; 393 ref_62 Yabroff (ref_5) 2019; 69 ref_29 ref_28 Echle (ref_66) 2021; 124 ref_27 ref_26 Sayres (ref_74) 2019; 126 Lai (ref_64) 2020; 10 ref_71 LeCun (ref_22) 2015; 521 Yang (ref_44) 2019; 366 Vaka (ref_45) 2020; 6 Korfiatis (ref_57) 2017; 30 ref_34 Wang (ref_36) 2019; 189 ref_78 ref_77 Kourou (ref_13) 2015; 13 ref_32 ref_76 Ghassemi (ref_89) 2021; 3 ref_31 ref_75 ref_30 Hintze (ref_88) 2018; 2 Lagies (ref_11) 2020; 3 ref_39 Sung (ref_7) 2021; 71 Jiao (ref_53) 2020; 11 ref_37 Wang (ref_51) 2017; 57 Esteva (ref_72) 2017; 542 Hua (ref_50) 2015; 8 Alipanahi (ref_69) 2015; 33 Lehman (ref_73) 2015; 175 Vos (ref_2) 2020; 396 Kamnitsas (ref_38) 2017; 36 Harvey (ref_87) 2020; 27 ref_83 Hochreiter (ref_33) 1997; 9 Shah (ref_79) 2019; 322 Huynh (ref_40) 2016; 3 Shi (ref_35) 2017; 39 Byra (ref_49) 2019; 46 ref_47 ref_46 Peccoud (ref_90) 2018; 36 ref_42 ref_86 ref_41 Cao (ref_6) 2018; 6 ref_84 ref_1 Gao (ref_54) 2019; 8 Finlayson (ref_85) 2019; 363 Keane (ref_81) 2018; 1 Singh (ref_70) 2016; 32 |
| References_xml | – volume: 71 start-page: 107 year: 2021 ident: ref_3 article-title: Blueprint for cancer research: Critical gaps and opportunities publication-title: CA Cancer J. Clin. doi: 10.3322/caac.21652 – ident: ref_71 doi: 10.1186/s12864-019-5489-4 – ident: ref_55 doi: 10.3390/cancers13123047 – volume: 196 start-page: 105584 year: 2020 ident: ref_58 article-title: Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2020.105584 – ident: ref_26 – volume: 393 start-page: 1577 year: 2019 ident: ref_82 article-title: Reporting of artificial intelligence prediction models publication-title: Lancet doi: 10.1016/S0140-6736(19)30037-6 – ident: ref_32 doi: 10.1109/ICASSP.2013.6638947 – volume: 64 start-page: 1558 year: 2017 ident: ref_59 article-title: Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2016.2613502 – ident: ref_68 – ident: ref_84 – ident: ref_16 – ident: ref_63 doi: 10.1186/s12911-020-01225-8 – volume: 27 start-page: 58 year: 2020 ident: ref_87 article-title: How the FDA Regulates AI publication-title: Acad. Radiol. doi: 10.1016/j.acra.2019.09.017 – ident: ref_1 – volume: 30 start-page: 622 year: 2017 ident: ref_57 article-title: Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status publication-title: J. Digit. Imaging doi: 10.1007/s10278-017-0009-z – volume: 2 start-page: 145 year: 2018 ident: ref_88 article-title: Comparing the benefits of pseudonymisation and anonymisation under the GDPR publication-title: J. Data Prot. Priv. – ident: ref_56 doi: 10.1371/journal.pmed.1002730 – ident: ref_46 doi: 10.1109/ICASSP.2019.8683352 – ident: ref_27 doi: 10.1109/CVPR.2015.7298594 – ident: ref_78 doi: 10.18653/v1/D18-1302 – volume: 8 start-page: 2015 year: 2015 ident: ref_50 article-title: Computer-aided classification of lung nodules on computed tomography images via deep learning technique publication-title: OncoTargets Ther. – ident: ref_39 doi: 10.1117/12.2266335 – volume: 10 start-page: 11071 year: 2020 ident: ref_43 article-title: Automated spheroid generation, drug application and efficacy screening using a deep learning classification: A feasibility study publication-title: Sci. Rep. doi: 10.1038/s41598-020-67960-0 – volume: 25 start-page: 1519 year: 2019 ident: ref_65 article-title: Deep learning-based classification of mesothelioma improves prediction of patient outcome publication-title: Nat. Med. doi: 10.1038/s41591-019-0583-3 – ident: ref_61 doi: 10.1186/s12874-018-0482-1 – volume: 39 start-page: 2298 year: 2017 ident: ref_35 article-title: An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2016.2646371 – volume: 3 start-page: 034501 year: 2016 ident: ref_40 article-title: Digital mammographic tumor classification using transfer learning from deep convolutional neural networks publication-title: J. Med. Imaging doi: 10.1117/1.JMI.3.3.034501 – ident: ref_83 doi: 10.1109/ICPR48806.2021.9412236 – volume: 542 start-page: 115 year: 2017 ident: ref_72 article-title: Dermatologist-level classification of skin cancer with deep neural networks publication-title: Nature doi: 10.1038/nature21056 – volume: 396 start-page: 1204 year: 2020 ident: ref_2 article-title: Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019 publication-title: Lancet doi: 10.1016/S0140-6736(20)30925-9 – volume: 6 start-page: e1288 year: 2018 ident: ref_6 article-title: Effect on longevity of one-third reduction in premature mortality from non-communicable diseases by 2030: A global analysis of the Sustainable Development Goal health target publication-title: Lancet Glob. Health doi: 10.1016/S2214-109X(18)30411-X – volume: 10 start-page: 693 year: 2009 ident: ref_9 article-title: Uptake in cancer screening programmes publication-title: Lancet Oncol. doi: 10.1016/S1470-2045(09)70145-7 – volume: 13 start-page: 550 year: 2016 ident: ref_8 article-title: Population-based screening for cancer: Hope and hype publication-title: Nat. Rev. Clin. Oncol. doi: 10.1038/nrclinonc.2016.50 – volume: 46 start-page: 746 year: 2019 ident: ref_49 article-title: Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion publication-title: Med. Phys. doi: 10.1002/mp.13361 – volume: 32 start-page: i639 year: 2016 ident: ref_70 article-title: DeepChrome: Deep-learning for predicting gene expression from histone modifications publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw427 – volume: 75 start-page: 299 year: 2013 ident: ref_67 article-title: Canonical Wnt signaling pathway plays an essential role in N-methyl-N-nitrosurea induced gastric tumorigenesis of mice publication-title: J. Vet. Med. Sci. doi: 10.1292/jvms.12-0233 – ident: ref_20 – volume: 69 start-page: 166 year: 2019 ident: ref_5 article-title: Minimizing the burden of cancer in the United States: Goals for a high-performing health care system publication-title: CA Cancer J. Clin. doi: 10.3322/caac.21556 – volume: 521 start-page: 436 year: 2015 ident: ref_22 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – ident: ref_28 – volume: 6 start-page: 320 year: 2020 ident: ref_45 article-title: Breast cancer detection by leveraging Machine Learning publication-title: ICT Express doi: 10.1016/j.icte.2020.04.009 – ident: ref_29 doi: 10.1109/ICCV.2015.510 – ident: ref_25 doi: 10.3390/cancers11091235 – ident: ref_76 – volume: 36 start-page: 61 year: 2017 ident: ref_38 article-title: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation publication-title: Med. Image Anal. doi: 10.1016/j.media.2016.10.004 – ident: ref_41 doi: 10.1109/BIBM.2015.7359868 – ident: ref_31 doi: 10.1007/978-3-319-24574-4_28 – volume: 62 start-page: 7714 year: 2017 ident: ref_48 article-title: A deep learning framework for supporting the classification of breast lesions in ultrasound images publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/aa82ec – ident: ref_86 – volume: 8 start-page: 44 year: 2019 ident: ref_54 article-title: DeepCC: A novel deep learning-based framework for cancer molecular subtype classification publication-title: Oncogenesis doi: 10.1038/s41389-019-0157-8 – volume: 366 start-page: 46 year: 2019 ident: ref_44 article-title: EMS-Net: Ensemble of Multiscale Convolutional Neural Networks for Classification of Breast Cancer Histology Images publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.07.080 – volume: 1 start-page: 40 year: 2018 ident: ref_81 article-title: With an eye to AI and autonomous diagnosis publication-title: NPJ Digit. Med. doi: 10.1038/s41746-018-0048-y – volume: 11 start-page: 728 year: 2020 ident: ref_53 article-title: A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns publication-title: Nat. Commun. doi: 10.1038/s41467-019-13825-8 – volume: 36 start-page: 4 year: 2018 ident: ref_90 article-title: Cyberbiosecurity: From Naive Trust to Risk Awareness publication-title: Trends Biotechnol. doi: 10.1016/j.tibtech.2017.10.012 – ident: ref_18 – volume: 57 start-page: 10 year: 2017 ident: ref_51 article-title: Lung nodule classification using deep feature fusion in chest radiography publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2016.11.004 – ident: ref_21 – volume: 9 start-page: 1735 year: 1997 ident: ref_33 article-title: Long Short-Term Memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 3 start-page: e745 year: 2021 ident: ref_89 article-title: The false hope of current approaches to explainable artificial intelligence in health care publication-title: Lancet Digit. Health doi: 10.1016/S2589-7500(21)00208-9 – ident: ref_30 doi: 10.1109/CVPR.2015.7298965 – ident: ref_77 doi: 10.1109/CVPR.2018.00175 – ident: ref_17 doi: 10.1109/CVPR.2016.90 – ident: ref_75 – volume: 33 start-page: 831 year: 2015 ident: ref_69 article-title: Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning publication-title: Nat. Biotechnol. doi: 10.1038/nbt.3300 – volume: 189 start-page: 1686 year: 2019 ident: ref_36 article-title: Pathology Image Analysis Using Segmentation Deep Learning Algorithms publication-title: Am. J. Pathol. doi: 10.1016/j.ajpath.2019.05.007 – volume: 65 start-page: 1046 year: 2014 ident: ref_10 article-title: Overdiagnosis and Overtreatment of Prostate Cancer publication-title: Eur. Urol. doi: 10.1016/j.eururo.2013.12.062 – volume: 131 start-page: 211 year: 2015 ident: ref_80 article-title: Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.114.014508 – ident: ref_12 – ident: ref_47 doi: 10.1109/ICIP.2016.7532834 – ident: ref_52 doi: 10.1109/ISBI.2017.7950686 – volume: 71 start-page: 209 year: 2021 ident: ref_7 article-title: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries publication-title: CA Cancer J. Clin. doi: 10.3322/caac.21660 – ident: ref_23 doi: 10.1093/oso/9780198538493.001.0001 – ident: ref_42 doi: 10.1371/journal.pone.0195816 – ident: ref_34 doi: 10.3115/v1/D14-1179 – volume: 175 start-page: 1828 year: 2015 ident: ref_73 article-title: Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection publication-title: JAMA Intern. Med. doi: 10.1001/jamainternmed.2015.5231 – ident: ref_15 – ident: ref_62 doi: 10.3390/biom10101460 – ident: ref_91 – ident: ref_19 doi: 10.1142/9789813235533_0031 – volume: 60 start-page: 84 year: 2017 ident: ref_14 article-title: ImageNet classification with deep convolutional neural networks publication-title: Commun. ACM doi: 10.1145/3065386 – volume: 124 start-page: 686 year: 2021 ident: ref_66 article-title: Deep learning in cancer pathology: A new generation of clinical biomarkers publication-title: Brit. J. Cancer doi: 10.1038/s41416-020-01122-x – volume: 3 start-page: 246 year: 2020 ident: ref_11 article-title: Cells grown in three-dimensional spheroids mirror in vivo metabolic response of epithelial cells publication-title: Commun. Biol. doi: 10.1038/s42003-020-0973-6 – volume: 39 start-page: 10577 year: 2021 ident: ref_4 article-title: The global burden of 29 cancer groups from 2010 to 2019: A systematic analysis for the Global Burden of Disease study 2019 publication-title: J. Clin. Oncol. doi: 10.1200/JCO.2021.39.15_suppl.10577 – volume: 126 start-page: 552 year: 2019 ident: ref_74 article-title: Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy publication-title: Ophthalmology doi: 10.1016/j.ophtha.2018.11.016 – volume: 363 start-page: 1287 year: 2019 ident: ref_85 article-title: Adversarial attacks on medical machine learning publication-title: Science doi: 10.1126/science.aaw4399 – ident: ref_60 doi: 10.1371/journal.pcbi.1006076 – volume: 10 start-page: 4679 year: 2020 ident: ref_64 article-title: Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning publication-title: Sci. Rep. doi: 10.1038/s41598-020-61588-w – volume: 322 start-page: 1351 year: 2019 ident: ref_79 article-title: Making Machine Learning Models Clinically Useful publication-title: JAMA doi: 10.1001/jama.2019.10306 – volume: 13 start-page: 8 year: 2015 ident: ref_13 article-title: Machine learning applications in cancer prognosis and prediction publication-title: Comput. Struct. Biotechnol. J. doi: 10.1016/j.csbj.2014.11.005 – ident: ref_24 doi: 10.3390/cancers12030603 – ident: ref_37 doi: 10.1109/EMBC.2016.7590782 |
| SSID | ssj0000331767 |
| Score | 2.3436391 |
| SecondaryResourceType | review_article |
| Snippet | Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and... Simple SummaryNon-communicable diseases in general, and cancer in particular, contribute greatly to the global burden of disease. Although significant advances... |
| SourceID | unpaywall pubmedcentral proquest pubmed crossref |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 623 |
| SubjectTerms | Artificial intelligence Biochemical markers Breast cancer Cancer Cardiovascular diseases Cellular biology Computer applications Deep learning Disease Learning algorithms Machine learning Medical screening Mortality Neural networks Prostate cancer Review Skin cancer Trends |
| SummonAdditionalLinks | – databaseName: ProQuest Central (New) dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3daxQxEB_qFdQX8bOuVonggz7E7iXZ3K4gcq0tRehR1ELflmSTaOHYO-8Duf_emeyHnkXdh4UlH0uSSeaXzOQ3AC-tT1PvlOEuc44rJzy3psi4DTbgdFIkSORtMdGnF-rjZXa5A5PuLgy5VXZrYlyo3ayiM_IDQXS6Uqcj8X7-nVPUKLKudiE0TBtawb2LFGM3YFcQM9YAdg-PJ-ef-lOXVKK-1KOG40fifv-gos5dLIm2LtVCbquna5jzuuvkrXU9N5sfZjr9TS-d3IU7LaBk40YC7sGOr-_DzbPWZP4ANuPGyr9kVzUztWOI-Ni4MVtHx9gNmwV2Fn0qPWvpVr_yQ9Rujn2uyC0Hv2PJSIbMPvhVdN-qqZZ4HwvrRujLjmIb37IxO6eYAYuHcHFy_OXolLfxFnilxHDFi8LiY8Iwz63WmTNeioAz1ClXVHnuUNmr3GeZs4gBXVppP0odQkbEEBn2qpSPYFDPav8YmCKWH4QHwWupjCiMDNY6q00QJuArgTddN5dVS0ZOMTGmJW5KaFzKP8YlgVd9gXnDw_H3rPvduJXthFyWv8QngRd9Mk4lso-Y2s_WMU-BaHMoMM9eM8z9vyTivjwv0gRGWwLQZyCa7u2U-upbpOumcM4I-xJ43YvK_5rw5N9NeAq3Bd3CSIdc6H0YrBZr_wyx0co-bwX-J7QUEko priority: 102 providerName: ProQuest – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1db9MwFLVQJwEvY3wHBjISD_DgJrEdN-EFZYNpQto0CSqNp8iO7VFRpVWbbup-PdeOG9FNCEEeIkW5zpfvtY_tk3MReqtMkhjNJdGZ1oRraoiSRUaUVRbCiTtHcmyLU3E85l_Os_Mw4bYMtEoYik98I02TESVCFDxOecxi6KnjubYfL8NMEiB95kZYDJrgHZEBFh-gnfHpWfndZZTblO30fBiM7ePafcjF0knUJYKy7a7oFr68TZO8t2rmcn0lp9Pf-qCjB6jaPH1HPfk5XLVqWF_fEHb8_9fbQ7sBnuKy86eH6I5pHqG7J2EB_jFalx1nYIknDZaNxoAfcdktgnua7RrPLD7xDE2Dg3jrBTmAvlLjr7Uj-cCxL-mllfEn03oyWOOu4v_ugmsDkMaH_it-wCU-cxkIFk_Q-Ojzt8NjErI3kJrTtCVFoWCTNs1zJUSmpWHUQrxrros6zzVAB56bLNMKEKVOamFGiQYACogkS6HVY0_RoJk15jnC3GkGAdiwRjAuaSGZVUorIS2VFnYRGm4qsqqDtLnLsDGtYIjjar66UfMRetcXmHeqHn823d94RhXCe1lRp-3MBNRfhN70pyEw3WqLbMxs5W0KwK4pBZtnnSP192KAIvO8SCI02nKx3sCJfm-faSY_vPi3Sw4NIDJC73tn_NsrvPgH25foPnU_eCQpoWIfDdrFyrwC2NWq1yG2fgFhHir1 priority: 102 providerName: Unpaywall |
| Title | Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35158890 https://www.proquest.com/docview/2627536072 https://www.proquest.com/docview/2629059122 https://pubmed.ncbi.nlm.nih.gov/PMC8833439 https://www.mdpi.com/2072-6694/14/3/623/pdf?version=1643338332 |
| UnpaywallVersion | publishedVersion |
| Volume | 14 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 2072-6694 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000331767 issn: 2072-6694 databaseCode: KQ8 dateStart: 20090101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVEBS databaseName: Academic Search Ultimate - eBooks customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 2072-6694 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000331767 issn: 2072-6694 databaseCode: ABDBF dateStart: 20100901 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 2072-6694 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000331767 issn: 2072-6694 databaseCode: DIK dateStart: 20090101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 2072-6694 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000331767 issn: 2072-6694 databaseCode: GX1 dateStart: 20090101 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2072-6694 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000331767 issn: 2072-6694 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 2072-6694 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000331767 issn: 2072-6694 databaseCode: RPM dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 2072-6694 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000331767 issn: 2072-6694 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal Journals: Open Access customDbUrl: eissn: 2072-6694 dateEnd: 20250930 omitProxy: true ssIdentifier: ssj0000331767 issn: 2072-6694 databaseCode: M48 dateStart: 20091201 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3daxQxEB9sC7Uv0vp5Wo8IPuhD6m7243aFItvaWoQ7DvXgfFqSTaKFI1fvA73_3pns3ra1FcF9WNjNF0lmMr-QyW8AXioTBEbHkutEax5rYbiSecKVVRbVKSZBIm-LQXo2ij-Ok_FlOKBmAOe3bu0ontRoNjn49WP1DhX-kHacuGV_U9H4zObEPBegNd-ALTRTOcVx6DdY3y_LEZrKtFfT-9xWbge2I7TuWUbr81UjdQN53nSgvLt0F3L1U04mV6zT6S7ca2AlK2o52IM7xt2H7X5zcP4AVkV91j9n545JpxniPlbUh9fePXbFppb1vWelYQ3p6jd-hDZOs88VOefgty_pKZHZe7PwTlyOavG3srBuBMDs2Hf3LSvYkCIHzB7C6PTky_EZb6Iu8CoW4YLnucJH2jDLVJomWppIWNRTHeu8yjKNJj_OTJJohUhQB1VqeoFG4IhIIsEBjqJHsOmmzjwBFhPXD4IEa9IoliKXkVVKq1RaIS2-OnCwHuayaijJKTLGpMStCU1R-ccUdeBVW-CiZuP4e9b99byVa6kqBXEyR2nQw6ZftMmoUHRKIp2ZLn2eHDFnKDDP43qa27bW8tGB3jUBaDMQWff1FHf-3ZN2U1BnBH8deN2Kyr-68PS_W3kGO4KuaQQhF-k-bC5mS_McwdNCdWHr6GQw_NSFjQ_jsOtVBP-NBsPi6288VyEx |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Zb9QwEB6VVqK8IG5SChgJJHgIdWzHmyBVaHtpS7urClqpb8GOHVpplV32ULV_jt_G2DlgqYCn5iFS5COyZ-z5bI-_AXitLaXWCBWa2JhQGGZDrdI41IUucDgJp0jO22Ige2fi03l8vgI_mrswzq2ymRP9RG1Gudsj32KOTpdL2mEfx99DFzXKna42ITRUHVrBbHuKsfpix5FdXOESbrp9uIfyfsPYwf7pbi-sowyEuWDRLExTjY8qoiTRUsZGWc4K1EsjTJoniUETJxIbx0Yj8jE0l7ZDDQIltJxxhKOTY723YE1wkeLib21nf3Dyud3loRzts-xUnEKcp3Qrd8KcTB1NHpWML5vDaxj3uqvm-rwcq8WVGg5_s4MH9-BuDWBJt9K4-7Biywdwu18f0T-ERbfyKpiSy5Ko0hBEmKRbHZN7R9wFGRWk7304LanpXb-FO2hNDfmSOzcg_PYlPfky2bMz7y5Wulr8_S-sG6E22fVt_EC65MTFKJg8grMb6fnHsFqOSvsUiHCsQghHCiu5UCxVvNDaaKkKpgp8BfC-6eYsr8nPXQyOYYaLICeX7A-5BPC2LTCueD_-nnWzkVtWTwDT7Je6BvCqTcah685jVGlHc58nRXQbMczzpBJz-y-OODNJUhpAZ0kB2gyOFnw5pby88PTgLnw0wswA3rWq8r8mbPy7CS9hvXfaP86ODwdHz-AOczdAaBQyuQmrs8ncPkdcNtMvauUn8PWmx9tPg6VOTA |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELfGJg1eEN8EBhgJJHgwdezETZAm1K2rNsaqCpi0t2DHNkyq0tIPTf0X-as4O06gTMDT8hApiuPIuTvfz7nz7xB6oQylRieS6FRrkmhmiJJ5SpRVFswpcYrksi2G4vA0eX-Wnm2gH81eGJdW2cyJfqLWk9L9I-8wR6fLBe2yjg1pEaP-4N30O3EVpFyktSmnIUOZBb3r6cbCJo9js7qA5dx896gPsn_J2ODg8_4hCRUHSJmweEHyXMEhbZxlSohUS8OZBR3Vic7LLNPg7pLMpKlWgII0LYXpUg2gCbxoGoOlcuj3GtpywS-YJLb2Doajj-0fH8rBV4tuzS_EeU47pRPsbO4o86hgfN01XsK7l9M2ry-rqVxdyPH4N584uIVuBjCLe7X23UYbprqDtk9CuP4uWvXqDIM5Pq-wrDQGtIl7dcjcJ-Wu8MTiE5_PaXCgev1K9sCzavypdClBcO2f9ETMuG8WPnWscr34vWDQN8BuvO_H-Bb38MjVK5jdQ6dX8uXvo81qUpmHCCeOYQigiTWCJ5LlklultBLSMmnhFKE3zWcuykCE7upxjAtYEDm5FH_IJUKv2gemNQfI35vuNHIrwmQwL36pboSet7fBjF1sRlZmsvRtckC6MYM2D2oxt-_igDmzLKcR6q4pQNvAUYSv36nOv3mqcFdKGiBnhF63qvK_ITz69xCeoW2wu-LD0fD4MbrB3GYQGhMmdtDmYrY0TwCiLdTToPsYfblqc_sJ2khSew |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1db9MwFLVQJwEvY3wHBjISD_DgJrEdN-EFZYNpQto0CSqNp8iO7VFRpVWbbup-PdeOG9FNCEEeIkW5zpfvtY_tk3MReqtMkhjNJdGZ1oRraoiSRUaUVRbCiTtHcmyLU3E85l_Os_Mw4bYMtEoYik98I02TESVCFDxOecxi6KnjubYfL8NMEiB95kZYDJrgHZEBFh-gnfHpWfndZZTblO30fBiM7ePafcjF0knUJYKy7a7oFr68TZO8t2rmcn0lp9Pf-qCjB6jaPH1HPfk5XLVqWF_fEHb8_9fbQ7sBnuKy86eH6I5pHqG7J2EB_jFalx1nYIknDZaNxoAfcdktgnua7RrPLD7xDE2Dg3jrBTmAvlLjr7Uj-cCxL-mllfEn03oyWOOu4v_ugmsDkMaH_it-wCU-cxkIFk_Q-Ojzt8NjErI3kJrTtCVFoWCTNs1zJUSmpWHUQrxrros6zzVAB56bLNMKEKVOamFGiQYACogkS6HVY0_RoJk15jnC3GkGAdiwRjAuaSGZVUorIS2VFnYRGm4qsqqDtLnLsDGtYIjjar66UfMRetcXmHeqHn823d94RhXCe1lRp-3MBNRfhN70pyEw3WqLbMxs5W0KwK4pBZtnnSP192KAIvO8SCI02nKx3sCJfm-faSY_vPi3Sw4NIDJC73tn_NsrvPgH25foPnU_eCQpoWIfDdrFyrwC2NWq1yG2fgFhHir1 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Advances+in+and+the+Applicability+of+Machine+Learning-Based+Screening+and+Early+Detection+Approaches+for+Cancer%3A+A+Primer&rft.jtitle=Cancers&rft.au=Benning%2C+Leo&rft.au=Peintner%2C+Andreas&rft.au=Peintner%2C+Lukas&rft.date=2022-01-26&rft.pub=MDPI&rft.eissn=2072-6694&rft.volume=14&rft.issue=3&rft_id=info:doi/10.3390%2Fcancers14030623&rft_id=info%3Apmid%2F35158890&rft.externalDocID=PMC8833439 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-6694&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-6694&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-6694&client=summon |