To enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection with the analysis of radiomics features.
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.
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).
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.
• 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.
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Akaike Information Criteria
Area under the curve
Gray-level co-occurrence matrix
Gray-level run-length matrix
Gray-level size zone matrix
Neighboring gray-level dependence matrix
National Lung Screening Trial
Positive predictive value
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A special thanks to the technologists Kawtar Nourani and Denise Guerra for their precious collaboration.
SOS Study team: Alberto Biggi (SC Medicina Nucleare), Andrea Campione, Mirella Fortunato (SC Anatomia Patologica), Adriano De Maggi, Stéphane Chauvie (SC Fisica Sanitaria), Ida Colantonio (SC Oncologia), Maurizio Grosso (SC Radiologia), Giulio Melloni, Federico Mazza, Alessia Stanzi (SC Chirurgia Toracica), Paolo Noceti (SC Pneumologia), Paolo Pellegrino (Direzione Sanitaria), Elvio Russi (SC Radioterapia)
This study has received funding from “Cassa di Risparmio di Cuneo” Foundation. Santa Croce e Carle, the hospital where the study was performed, provided logistic support, telephone lines, software, computer assistance, and an office free of charge.
The scientific guarantor of this publication is Maurizio Grosso.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
One of the authors has significant statistical expertise.
Written informed consent was obtained from all subjects (patients) in this study.
Institutional Review Board approval was obtained.
Study subjects or cohorts overlap
Some study subjects or cohorts have been previously reported in the results of the SOS study which have already been published as specified in the references. This study focuses on the application of AI to the data obtained during the trial.
• Diagnostic or prognostic study
• Performed at one institution
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Chauvie, S., De Maggi, A., Baralis, I. et al. Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial. Eur Radiol 30, 4134–4140 (2020). https://doi.org/10.1007/s00330-020-06783-z
- Thoracic radiography
- Lung cancer
- Artificial intelligence