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P1.11 - Screening and Early Detection (Not CME Accredited Session) (ID 943)
- Event: WCLC 2018
- Type: Poster Viewing in the Exhibit Hall
- Presentations: 1
- Coordinates: 9/24/2018, 16:45 - 18:00, Exhibit Hall
P1.11-08 - AI Based Malignancy Prediction of Indeterminate Pulmonary Nodules: Robustness to CT Contrast Media (ID 13904)
16:45 - 18:00 | Author(s): Heiko Peschl
Artificial Intelligence (AI) based malignancy prediction of indeterminate pulmonary nodules has been previously demonstrated to perform well on screen-detected nodules imaged with low-dose, non-contrast CT. This study aimed to assess the impact of contrast media on the classification performance of such a system.a9ded1e5ce5d75814730bb4caaf49419 Method
A Convolutional Neural Network (CNN) was trained on the US National Lung Screening Trial (NLST), which contained only low-dose non-contrast screening images, selecting all nodules 6mm and greater in size (14761 benign nodules from 5972 patients; 932 cancer from 575 patients). A CNN classifier was trained using Deep Learning on this data to produce a malignancy score per nodule.
For validation, an independent retrospective dataset of incidentally detected solid nodules was used. None of the patients had a cancer diagnosis within the past 5 years, and all had fewer than 5 nodules. The dataset contained 571 nodules from 505 patients, including 42 cancer from 39 patients.
A CT was considered to be contrasted if the mode HU value within an ROI placed at the aortic arch was greater than 60HU. This resulted in two groups: the non-contrast group had 313 nodules from 276 patients (16 cancer from 14 patients); the contrast group had 258 nodules from 229 patients (26 cancer from 25 patients). The overall efficacy was assessed using Area-Under-the-ROC-Curve analysis (AUC) for each group.
The AUC on the non-contrast CT group was 0.96 (95% CI 0.93 to 0.98) and 0.95 (95% CI 0.90 to 0.99) on the contrast CT group. Further analysis revealed that excluding high contrast cases, where the HU in the aortic arch was greater than 300HU (40 nodules from 38 patients; 4 cancer), resulted in an AUC of 0.97 (95% CI 0.92 to 1.00).8eea62084ca7e541d918e823422bd82e Conclusion
The CNN classifier seems to be robust to the presence of contrast media with only a moderate reduction in performance. Excluding cases with high contrast restored the performance, although, with only 38 nodules excluded by this, the result may be not statistically significant. These results indicate that a CNN developed to predict pulmonary nodule malignancy, that has been trained on low-dose, non-contrast enhanced CT images, may be used with CT images with moderate levels of contrast without retraining.6f8b794f3246b0c1e1780bb4d4d5dc53