Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial

Abstract

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.

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Abbreviations

AIC:

Akaike Information Criteria

AUC:

Area under the curve

CAD:

Computer-aided detection

CT:

Computed tomography

DTS:

Digital tomosynthesis

GGO:

Ground-glass opacity

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size zone matrix

LR:

Logistic regression

NGLDM:

Neighboring gray-level dependence matrix

NLST:

National Lung Screening Trial

NNET:

Neural network

PPV:

Positive predictive value

RF:

Random Forest

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Acknowledgments

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)

Funding

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.

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Correspondence to Stéphane Chauvie.

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Guarantor

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.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

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.

Methodology

• Prospective

• 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

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Keywords

  • Thoracic radiography
  • Lung cancer
  • Artificial intelligence