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Rundo L, Ledda RE, di Noia C, Sala E, Mauri G, Milanese G, Sverzellati N, Apolone G, Gilardi MC, Messa MC, Castiglioni I, Pastorino U. A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules. Diagnostics (Basel) 2021; 11:1610. [PMID: 34573951 PMCID: PMC8471292 DOI: 10.3390/diagnostics11091610] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/25/2021] [Accepted: 08/30/2021] [Indexed: 12/25/2022] Open
Abstract
Lung 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.
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Affiliation(s)
- Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK;
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Roberta Eufrasia Ledda
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
| | - Christian di Noia
- Department of Physics “Giuseppe Occhialini”, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK;
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Gianluca Milanese
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
| | - Nicola Sverzellati
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
| | - Giovanni Apolone
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
| | - Maria Carla Gilardi
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (M.C.G.); (M.C.M.)
| | - Maria Cristina Messa
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (M.C.G.); (M.C.M.)
- Institute of Biomedical Imaging and Physiology, Italian National Research Council (IBFM-CNR), Segrate, 20090 Milan, Italy
- Fondazione Tecnomed, University of Milano-Bicocca, 20900 Monza, Italy
| | - Isabella Castiglioni
- Department of Physics “Giuseppe Occhialini”, University of Milano-Bicocca, 20126 Milan, Italy;
- Institute of Biomedical Imaging and Physiology, Italian National Research Council (IBFM-CNR), Segrate, 20090 Milan, Italy
| | - Ugo Pastorino
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
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