Deep Learning Plus Three-Dimensional Printing in the Management of Giant (>15 cm) Sporadic Renal Angiomyolipoma: An Initial Report

Abstract

ObjectiveTo evaluate the feasibility and effectivity of deep learning (DL) plus three-dimensional (3D) printing in the management of giant sporadic renal angiomyolipoma (RAML).MethodsThe medical records of patients with giant (>15 cm) RAML were retrospectively reviewed from January 2011 to December 2020. 3D visualized and printed kidney models were performed by DL algorithms and 3D printing technology, respectively. Patient demographics and intra- and postoperative outcomes were compared between those with 3D-assisted surgery (3D group) or routine ones (control group).ResultsAmong 372 sporadic RAML patients, 31 with giant ones were eligible for analysis. The median age was 40.6 (18–70) years old, and the median tumor size was 18.2 (15–28) cm. Seventeen of 31 (54.8%) had a surgical kidney removal. Overall, 11 underwent 3D-assisted surgeries and 20 underwent routine ones. A significant higher success rate of partial nephrectomy (PN) was noted in the 3D group (72.7% vs. 30.0%). Patients in the 3D group presented a lower reduction in renal function but experienced a longer operation time, a greater estimated blood loss, and a higher postoperative morbidity. Subgroup analysis was conducted between patients undergoing PN with or without 3D assistance. Despite no significant difference, patients with 3D-assisted PN had a slightly larger tumor size and higher nephrectomy score, possibly contributing to a relatively higher rate of complications. However, 3D-assisted PN lead to a shorter warm ischemia time and a lower renal function loss without significant difference. Another subgroup analysis between patients under 3D-assisted PN or 3D-assisted RN showed no statistically significant difference. However, the nearness of tumor to the second branch of renal artery was relatively shorter in 3D-assisted PN subgroup than that in 3D-assisted RN subgroup, and the difference between them was close to significant.Conclusions3D visualized and printed kidney models appear to be additional tools to assist operational management and avoid a high rate of kidney removal for giant sporadic RAMLs.