Lancet Digit Health . 2020 Jun;2(6):e271-e272. doi: 10.1016/S2589-7500(20)30105-9.
Linda C Chu, Elliot K Fishman
In The Lancet Digital Health, Kao-Lang Liu and colleagues1 describe the applications of a convolutional neural network (CNN) in distinguishing CT images of pancreatic cancer tissue from non-cancerous pancreatic tissue.1 Contrast-enhanced CT images of 370 patients with pancreatic cancer and 320 controls from a Taiwanese centre were manually segmented and CNN was trained to classify image patches as cancerous or non-cancerous. Performance of CNN was compared with radiology reports in the local test sets. Similar to results from previous studies on this topic,2, 3 CNN achieved a remarkable performance, with accuracy of 0·986–0·989 in the local test dataset. CNN achieved higher sensitivity than that of radiologists in the local test sets (0·983 vs 0·929; p=0·014). The three tumours that were missed by the CNN were 1·1–1·2 cm in size, of which two were correctly classified by radiologists. Impressively, CNN was able to correctly classify 11 of the 12 tumours that were missed by radiologists, which were 1·0–3·3 cm in size.1 These promising results show that CNN as a second reader can reduce misdiagnosis of pancreatic cancer and possibly lead to improved patient outcomes.
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