Dual-Phase deep learning Enhances detection of incidental small pancreatic cystic lesion (0.5-3 cm) on...

Develop a dual-phase deep learning (DPDL) model using arterial/portal-phase CT to detect incidental small (0.5-3 cm) pancreatic cystic lesions (PCLs).

Contrast-enhanced CT images of 437 incidental small PCLs, including 201 subcentimeter cysts (0.5-1 cm) and 193 normal pancreases were retrospectively collected (January 2018 - December 2020) and randomly divided into training, validation and testing cohorts. Detection sensitivity, specificity, any-false-positive rate (AFPR) and accuracy of the DPDL model were compared with a portal single-phase deep learning (SPDL) model and the senior and junior radiologists in the testing cohort. Factors potentially affecting detection were analyzed using logistic regression analysis.

In the validation cohort, the DPDL model exceeded the SPDL model in sensitivity (91.7 % vs. 82.1 %; P = 0.021). In the testing cohort, it surpassed the junior radiologist in sensitivity (92.7 % vs. 74.0 %; P < 0.001) and accuracy (86.2 % vs. 69.7 %; P = 0.003), while performing comparably to the senior radiologist (all P > 0.05). Subgroup analysis confirmed DPDL's superiority for subcentimeter PCLs than the junior radiologist. With DPDL assistance, sensitivity of radiologists was significantly improved, while detection time and AFPR of the junior radiologist were significantly reduced (all P < 0.05). None of study factors affected DPDL's performance, whereas SPDL and radiologists were influenced by multiple factors. Notably, DPDL model identified all 18 incidental PCLs that progressed during follow-up (3 malignant) while the junior radiologist missed 2 in testing cohort.

The DPDL model exhibited superior and more robust detection performance for small PCLs than the SPDL model and the junior radiologist, potentially narrowing performance gaps between experience levels and improving early diagnosis of high-risk lesions.