Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings.
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| Title: | Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings. |
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| Authors: | Buosi, Samuele1 (AUTHOR) samuele.buosi@universityofgalway.ie, Timilsina, Mohan1 (AUTHOR) mohan.timilsina@universityofgalway.ie, Janik, Adrianna2 (AUTHOR) adrianna.janik@accenture.com, Costabello, Luca2 (AUTHOR) luca.costabello@accenture.com, Torrente, Maria3 (AUTHOR) maria.torrente@salud.madrid.org, Provencio, Mariano3 (AUTHOR) mariano.provencio@salud.madrid.org, Fey, Dirk4 (AUTHOR) dirk.fey@ucd.ie, Nováček, Vít1,5,6 (AUTHOR) vit.novacek@insight-centre.org |
| Source: | Expert Systems with Applications. Jan2024, Vol. 235, pN.PAG-N.PAG. 1p. |
| Subjects: | Non-small-cell lung carcinoma, Knowledge graphs, Multiple imputation (Statistics), Machine learning, Cancer patients, Aneuploidy, Lung cancer, Machine theory, Missing data (Statistics) |
| Abstract: | Low-stage lung cancer is known to recur unpredictably, and patients receiving various treatment methods like radiation, chemotherapy, and immunotherapies have been seen to respond very differently. Identifying a priori if a patient is going to relapse or not could make a difference in terms of saving lives and personalized care offered. In this work, we provide an answer to the following research question: Is it possible to enhance the machine learning (ML) of the estimated probability of relapse in early-stage non-small-cell lung cancer (NSCLC) patients with aneuploidy imputation scores? To predict recurrence in 1,348 early-stage (I–II) NSCLC patients, we train graph ML models utilizing the Spanish pulmonary cancer group knowledge graph enriched with triples from pathway imputation. ML models trained on Knowledge graph data enriched with triples from pathway score imputation present an 82% Precision and 91% Specificity in predicting relapse over 200 patients from a held-out test set. ML models trained using graphs data could prove useful supplemental tool in the TNM classification systems and improve a lung cancer patient's prognosis. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 173175533 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Buosi%2C+Samuele%22">Buosi, Samuele</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> samuele.buosi@universityofgalway.ie</i><br /><searchLink fieldCode="AR" term="%22Timilsina%2C+Mohan%22">Timilsina, Mohan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mohan.timilsina@universityofgalway.ie</i><br /><searchLink fieldCode="AR" term="%22Janik%2C+Adrianna%22">Janik, Adrianna</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> adrianna.janik@accenture.com</i><br /><searchLink fieldCode="AR" term="%22Costabello%2C+Luca%22">Costabello, Luca</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> luca.costabello@accenture.com</i><br /><searchLink fieldCode="AR" term="%22Torrente%2C+Maria%22">Torrente, Maria</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> maria.torrente@salud.madrid.org</i><br /><searchLink fieldCode="AR" term="%22Provencio%2C+Mariano%22">Provencio, Mariano</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> mariano.provencio@salud.madrid.org</i><br /><searchLink fieldCode="AR" term="%22Fey%2C+Dirk%22">Fey, Dirk</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> dirk.fey@ucd.ie</i><br /><searchLink fieldCode="AR" term="%22Nováček%2C+Vít%22">Nováček, Vít</searchLink><relatesTo>1,5,6</relatesTo> (AUTHOR)<i> vit.novacek@insight-centre.org</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Jan2024, Vol. 235, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Non-small-cell+lung+carcinoma%22">Non-small-cell lung carcinoma</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+imputation+%28Statistics%29%22">Multiple imputation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+patients%22">Cancer patients</searchLink><br /><searchLink fieldCode="DE" term="%22Aneuploidy%22">Aneuploidy</searchLink><br /><searchLink fieldCode="DE" term="%22Lung+cancer%22">Lung cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+theory%22">Machine theory</searchLink><br /><searchLink fieldCode="DE" term="%22Missing+data+%28Statistics%29%22">Missing data (Statistics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Low-stage lung cancer is known to recur unpredictably, and patients receiving various treatment methods like radiation, chemotherapy, and immunotherapies have been seen to respond very differently. Identifying a priori if a patient is going to relapse or not could make a difference in terms of saving lives and personalized care offered. In this work, we provide an answer to the following research question: Is it possible to enhance the machine learning (ML) of the estimated probability of relapse in early-stage non-small-cell lung cancer (NSCLC) patients with aneuploidy imputation scores? To predict recurrence in 1,348 early-stage (I–II) NSCLC patients, we train graph ML models utilizing the Spanish pulmonary cancer group knowledge graph enriched with triples from pathway imputation. ML models trained on Knowledge graph data enriched with triples from pathway score imputation present an 82% Precision and 91% Specificity in predicting relapse over 200 patients from a held-out test set. ML models trained using graphs data could prove useful supplemental tool in the TNM classification systems and improve a lung cancer patient's prognosis. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.eswa.2023.121127 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Non-small-cell lung carcinoma Type: general – SubjectFull: Knowledge graphs Type: general – SubjectFull: Multiple imputation (Statistics) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Cancer patients Type: general – SubjectFull: Aneuploidy Type: general – SubjectFull: Lung cancer Type: general – SubjectFull: Machine theory Type: general – SubjectFull: Missing data (Statistics) Type: general Titles: – TitleFull: Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Buosi, Samuele – PersonEntity: Name: NameFull: Timilsina, Mohan – PersonEntity: Name: NameFull: Janik, Adrianna – PersonEntity: Name: NameFull: Costabello, Luca – PersonEntity: Name: NameFull: Torrente, Maria – PersonEntity: Name: NameFull: Provencio, Mariano – PersonEntity: Name: NameFull: Fey, Dirk – PersonEntity: Name: NameFull: Nováček, Vít IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09574174 Numbering: – Type: volume Value: 235 Titles: – TitleFull: Expert Systems with Applications Type: main |
| ResultId | 1 |
