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dc.contributor.authorRundo, Leonardo
dc.contributor.authorLedda, Roberta Eufrasia
dc.contributor.authordi Noia, Christian
dc.contributor.authorSala, Evis
dc.contributor.authorMauri, Giancarlo
dc.contributor.authorMilanese, Gianluca
dc.contributor.authorSverzellati, Nicola
dc.contributor.authorApolone, Giovanni
dc.contributor.authorGilardi, Maria Carla
dc.contributor.authorMessa, Maria Cristina
dc.contributor.authorCastiglioni, Isabella
dc.contributor.authorPastorino, Ugo
dc.identifier.citationDiagnostics (Basel, Switzerland), volume 11, issue 9
dc.description.abstractLung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.
dc.rightsAttribution 4.0 International
dc.sourceessn: 2075-4418
dc.sourcenlmid: 101658402
dc.subjectMachine Learning
dc.subjectLow-dose Computed Tomography
dc.subjectPulmonary Nodules
dc.subjectLung Cancer Screening
dc.subjectLung Cancer Risk Stratification
dc.titleA Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules.
dc.contributor.orcidRundo, Leonardo [0000-0003-3341-5483]
dc.contributor.orcidMauri, Giancarlo [0000-0003-3520-4022]
dc.contributor.orcidApolone, Giovanni [0000-0001-5179-104X]
dc.contributor.orcidCastiglioni, Isabella [0000-0001-7191-5417]
dc.contributor.orcidPastorino, Ugo [0000-0001-9974-7902]
pubs.funder-project-idMark Foundation For Cancer Research (C9685/A25177)
pubs.funder-project-idCancer Research UK (C42780/A27066)
pubs.funder-project-idAssociazione Italiana per la Ricerca sul Cancro (IG 12162, IG 11991, IG 18812)
pubs.funder-project-idMinistero della Salute (RF 2010-2306232, 2010-2310201)
pubs.funder-project-idIstituto Nazionale dei Tumori di Milano (EDRN UO1 CA166905)
pubs.funder-project-idWellcome Trust (RG98755)

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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International