Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial.
Objective: To enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection with the analysis of radiomics features.Method: The investigation was carried out within the SOS clinical trial (NCT03645018) for lung cancer screening with DTS. Lung nodules w...
| Publicado en: | European Radiology Vol. 30; no. 7; pp. 4134 - 4141 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
| Formato: | clinical trial research Journal Article |
| Publicado: |
Springer Nature
Jul2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143855667&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143855667 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jul2020 vid: 30 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143855667 143855667 143992219 NLM32166491 143855667 10.1007/s00330-020-06783-z NLM32166491 143855667 ppf: 4134 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial. aug: au: Chauvie, Stéphane De Maggi, Adriano Baralis, Ilaria Dalmasso, Federico Berchialla, Paola Priotto, Roberto Violino, Paolo Mazza, Federico Melloni, Giulio Grosso, Maurizio SOS Study team Biggi, Alberto Campione, Andrea Fortunato, Mirella Colantonio, Ida Stanzi, Alessia Noceti, Paolo Pellegrino, Paolo Russi, Elvio affil: Medical Physics Division, Santa Croce e Carle Hospital, via Coppino 26, 12100, Cuneo, Italy sug: subj: Image Processing, Computer Assisted Tomography, X-Ray Computed Methods Artificial Intelligence Lung Neoplasms Diagnosis Algorithms Semantics Specialties, Medical Reproducibility of Results Aged Middle Age Human Logistic Regression Lung Neoplasms Early Detection of Cancer Methods Clinical Trials Validation Studies Comparative Studies Evaluation Research Multicenter Studies Psychological Tests Aged: 65+ years Middle Aged: 45-64 years ab: Objective: To enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection with the analysis of radiomics features.Method: The investigation was carried out within the SOS clinical trial (NCT03645018) for lung cancer screening with DTS. Lung nodules were identified by visual analysis and then classified using the diameter and the radiological aspect of the nodule following lung-RADS. Haralick texture features were extracted from the segmented nodules. Both semantic variables and radiomics features were used to build a predictive model using logistic regression on a subset of variables selected with backward feature selection and using two machine learning: a Random Forest and a neural network with the whole subset of variables. The methods were applied to a train set and validated on a test set where diagnostic accuracy metrics were calculated.Results: Binary visual analysis had a good sensitivity (0.95) but a low PPV (0.14). Lung-RADS classification increased the PPV (0.19) but with an unacceptable low sensitivity (0.65). Logistic regression showed a mildly increased PPV (0.29) but a lower sensitivity (0.20). Random Forest demonstrated a moderate PPV (0.40) but with a low sensitivity (0.30). Neural network demonstrated to be the best predictor with a high PPV (0.95) and a high sensitivity (0.90).Conclusions: The neural network demonstrated the best PPV. The use of visual analysis along with neural network could help radiologists to reduce the number of false positive in DTS.Key Points: • We investigated several approaches to enhance the positive predictive value of chest digital tomosynthesis in the lung cancer detection. • Neural network demonstrated to be the best predictor with a nearly perfect PPV. • Neural network could help radiologists to reduce the number of false positive in DTS. pubtype: Academic Journal doctype: clinical trial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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