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Recent Research Hints At A Possibility Of Using AI For Cancer Detection

Published on December 8, 2023

Recently, an article published on Nature Medicine gave us insight into the possibilities of using AI for cancer detection.

Overview of PANDA’s development, evaluation and clinical translation

The article discusses a deep learning approach, named PANDA, for detecting and classifying pancreatic lesions.

The article highlights that Pancreatic ductal adenocarcinoma (PDAC), a deadly solid malignancy, is often detected late and at an inoperable stage. It mentions that Non-contrast computed tomography (CT), which is routinely performed for clinical indications, offers potential for large-scale screening.

The researchers developed PANDA to detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA was trained on a dataset of 3,208 patients from a single center. It achieved an area under the receiver operating characteristic curve (AUC) of 0.986–0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers.

PANDA outperformed the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification. In a real-world multi-scenario validation consisting of 20,530 consecutive patients, PANDA achieved a sensitivity of 92.9% and specificity of 99.9% for lesion detection.

The article concludes by stating that PANDA, when used with non-contrast CT, showed non-inferiority to radiology reports (using contrast-enhanced CT) in differentiating common pancreatic lesion subtypes. This suggests that PANDA shows promise in the early detection of pancreatic cancer using non-contrast CT scans.