Objectives: within this investigation we investigated several approaches to enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection Methods: 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 a modified lung-RADS classification.Haralick texture features were extracted from the segmented nodules. Both semantic variables and radiomics features were used to build a predictive model using two approaches: logistic regression model on a sub-set of variables selected with backward feature selection or machine learning using the whole sub-set of variables. We used two machine learning methods: a Random Forest and a neural network. Machine learning methods were applied to a training set and validated on a test set. Methods were compared using diagnostic accuracy metrics.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). Analogously, logistic regression showed a mildly increased PPV (0.22) and a low sensitivity (0.67). Random Forest demonstrated a low accuracy with a moderate PPV (0.40) but with a dramatically low sensitivity (0.30). Neural network demonstrated to be the best predictor with a nearly perfect PPV (0.95) and a high sensitivity (0.90).Conclusions: among the various technique to reduce the false positive rates of DTS the neural network demonstrated a very high PPV. The use of visual analysis along with neural network could help radiologists to depict a follow-up strategy after a positive DTS.
Introduction: Stereotactic body radiation therapy is increasingly used in the treatment of early-stage lung cancers. Guidelines provide indications regarding the constraints to the organs at risk (OARs) and the minimum coverage of the planning target volume but do not suggest optimal dose distribution. Data on dose distribution from the different published series are not comparable due to different prescription modalities and reported dose parameters. Methods: We conducted a review of the published data on dose prescription, focusing on the role of homogeneity on local tumor control, and present suggestions on how to specify and report the prescriptions to permit comparisons between studies or between cases from different centers. Conclusions: To identify the dose-prescription modality that better correlates with oncologic outcomes, future studies should guarantee a close uniformity of dose distribution between cases and complete dose parameters reporting for treatment volumes and OARs.
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