FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3124164081> ?p ?o ?g. }

• W3124164081 endingPage "823" @default.
• W3124164081 startingPage "823" @default.
• W3124164081 abstract "Despite several studies having identified factors associated with successful treatment outcomes in locally advanced cervical cancer, there is the lack of accurate predictive modeling for progression-free survival (PFS) in patients who undergo radical hysterectomy after neoadjuvant chemotherapy (NACT). Here we investigated whether machine learning (ML) may have the potential to provide a tool to predict neoadjuvant treatment response as PFS. In this retrospective observational study, we analyzed patients with locally advanced cervical cancer (FIGO stages IB2, IB3, IIA1, IIA2, IIB, and IIIC1) who were followed in a tertiary center from 2010 to 2018. Demographic and clinical characteristics were collected at either treatment baseline or at 24-month follow-up. Furthermore, we recorded data about magnetic resonance imaging (MRI) examinations and post-surgery histopathology. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with 10-fold cross-validation to predict 24-month PFS. Our analysis included n. 92 patients. The attribute core set used to train machine learning algorithms included the presence/absence of fornix infiltration at pre-treatment MRI as well as of either parametrium invasion and lymph nodes involvement at post-surgery histopathology. RFF showed the best performance (accuracy 82.4%, precision 83.4%, recall 96.2%, area under receiver operating characteristic curve (AUROC) 0.82). We developed an accurate ML model to predict 24-month PFS." @default.
• W3124164081 created "2021-02-01" @default.
• W3124164081 creator A5004761260 @default.
• W3124164081 creator A5006821514 @default.
• W3124164081 creator A5039199774 @default.
• W3124164081 creator A5039618749 @default.
• W3124164081 creator A5043268030 @default.
• W3124164081 creator A5045632024 @default.
• W3124164081 creator A5065326829 @default.
• W3124164081 creator A5086391070 @default.
• W3124164081 creator A5088023314 @default.
• W3124164081 date "2021-01-17" @default.
• W3124164081 modified "2023-09-27" @default.
• W3124164081 title "A Machine Learning Tool to Predict the Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Cervical Cancer" @default.
• W3124164081 cites W1957754569 @default.
• W3124164081 cites W1977125071 @default.
• W3124164081 cites W1987272616 @default.
• W3124164081 cites W1988905767 @default.
• W3124164081 cites W2010279428 @default.
• W3124164081 cites W2012035409 @default.
• W3124164081 cites W2019607817 @default.
• W3124164081 cites W2049037184 @default.
• W3124164081 cites W2050539540 @default.
• W3124164081 cites W2075094580 @default.
• W3124164081 cites W2087335959 @default.
• W3124164081 cites W2097516082 @default.
• W3124164081 cites W2103851997 @default.
• W3124164081 cites W2116376711 @default.
• W3124164081 cites W2127060407 @default.
• W3124164081 cites W2131231823 @default.
• W3124164081 cites W2151287754 @default.
• W3124164081 cites W2154290668 @default.
• W3124164081 cites W2171627975 @default.
• W3124164081 cites W2262895780 @default.
• W3124164081 cites W2339740514 @default.
• W3124164081 cites W2554140915 @default.
• W3124164081 cites W2586607563 @default.
• W3124164081 cites W2605278070 @default.
• W3124164081 cites W2611332446 @default.
• W3124164081 cites W2765495938 @default.
• W3124164081 cites W2774840889 @default.
• W3124164081 cites W2782769512 @default.
• W3124164081 cites W2807593075 @default.
• W3124164081 cites W2810174094 @default.
• W3124164081 cites W2884307502 @default.
• W3124164081 cites W2899020268 @default.
• W3124164081 cites W2905889700 @default.
• W3124164081 cites W2919115771 @default.
• W3124164081 cites W2947683972 @default.
• W3124164081 cites W2993250673 @default.
• W3124164081 cites W2997273696 @default.
• W3124164081 cites W2999082368 @default.
• W3124164081 cites W3006506024 @default.
• W3124164081 cites W3036507077 @default.
• W3124164081 cites W3039746624 @default.
• W3124164081 cites W3090442497 @default.
• W3124164081 cites W3114235910 @default.
• W3124164081 cites W4211132630 @default.
• W3124164081 cites W4255356077 @default.
• W3124164081 cites W4298106286 @default.
• W3124164081 doi "https://doi.org/10.3390/app11020823" @default.
• W3124164081 hasPublicationYear "2021" @default.
• W3124164081 type Work @default.
• W3124164081 sameAs 3124164081 @default.
• W3124164081 citedByCount "15" @default.
• W3124164081 countsByYear W31241640812021 @default.
• W3124164081 countsByYear W31241640812022 @default.
• W3124164081 countsByYear W31241640812023 @default.
• W3124164081 crossrefType "journal-article" @default.
• W3124164081 hasAuthorship W3124164081A5004761260 @default.
• W3124164081 hasAuthorship W3124164081A5006821514 @default.
• W3124164081 hasAuthorship W3124164081A5039199774 @default.
• W3124164081 hasAuthorship W3124164081A5039618749 @default.
• W3124164081 hasAuthorship W3124164081A5043268030 @default.
• W3124164081 hasAuthorship W3124164081A5045632024 @default.
• W3124164081 hasAuthorship W3124164081A5065326829 @default.
• W3124164081 hasAuthorship W3124164081A5086391070 @default.
• W3124164081 hasAuthorship W3124164081A5088023314 @default.
• W3124164081 hasBestOaLocation W31241640811 @default.
• W3124164081 hasConcept C119857082 @default.
• W3124164081 hasConcept C121608353 @default.
• W3124164081 hasConcept C126322002 @default.
• W3124164081 hasConcept C143998085 @default.
• W3124164081 hasConcept C146357865 @default.
• W3124164081 hasConcept C151730666 @default.
• W3124164081 hasConcept C151956035 @default.
• W3124164081 hasConcept C154945302 @default.
• W3124164081 hasConcept C169258074 @default.
• W3124164081 hasConcept C2778220009 @default.
• W3124164081 hasConcept C2778292576 @default.
• W3124164081 hasConcept C2779808985 @default.
• W3124164081 hasConcept C41008148 @default.
• W3124164081 hasConcept C530470458 @default.
• W3124164081 hasConcept C58471807 @default.
• W3124164081 hasConcept C71924100 @default.
• W3124164081 hasConcept C86803240 @default.
• W3124164081 hasConceptScore W3124164081C119857082 @default.
• W3124164081 hasConceptScore W3124164081C121608353 @default.

• next