AI Predicts Lung Cancer Risk (IMAGE)
Caption
Examples of CT images in nodules from the Danish Lung Cancer Screening Trial (DLCST) with (a-d) high and (e-h) low agreement between the deep learning (DL) algorithm and the clinicians for malignancy risk estimation. Numbers in rings on bottom left of each image are the algorithm's malignancy score, and numbers in rings on bottom right of each image are the clinicians' median malignancy score. The extent of the color filling is proportional to the malignancy risk (on a scale of 0 to 1, where 0 represents the lowest risk and 1 represents the highest risk). (a) Image shows a 15-mm spiculated and lobulated malignant nodule (arrow) classified correctly by the DL algorithm and clinicians. (b) Image shows an 11-mm smooth benign nodule (arrow) classified correctly by the DL algorithm and clinicians. (c) Image shows a 29-mm benign lesion (arrow) suspected to be a malignant nodule by both the DL algorithm and clinicians. This participant was diagnosed with pneumonia at clinical workup. (d) Image shows a 5-mm malignant nodule (arrow) called benign by both the DL algorithm and clinicians. The growth of the nodule can be seen from follow-up CT examinations. (e) Image shows a 15-mm part-solid malignant nodule (arrow) classified correctly by the DL algorithm and not suspected to be malignant by seven of 11 clinicians. (f) Image shows an 8-mm benign nodule (arrow) predicted to be moderately suspicious by the clinicians and called benign by the DL algorithm. (g) Image shows an 11-mm malignant nodule (arrow) predicted to be moderately suspicious by most clinicians but called benign by the DL algorithm. (h) Image shows a 16-mm benign lesion (arrow) classified correctly by the clinicians and predicted to be highly suspicious by the DL algorithm.
Credit
Radiological Society of North America
Usage Restrictions
May use with credit.
License
Licensed content