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Personalized 3D-Printed Transparent Liver Model Using the Hepatobiliary Phase MRI: Usefulness in the Lesion-by-Lesion Imaging-Pathologic Matching of Focal Liver Lesions-Preliminary Results.

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작성자 profile_image최고관리자 작성일 19-04-18 16:15
48회 0건

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Abstract


PURPOSE:

The aim of this study was to investigate the usefulness of a personalized, 3-dimensional (3D)-printed, transparent liver model with focal liver lesions (FLLs) for lesion-by-lesion imaging-pathologic matching.


MATERIALS AND METHODS:

This preliminary, prospective study was approved by our institutional review board, and written informed consent was obtained. Twenty patients (male-to-female ratio, 13:7; mean age, 56 years) with multiple FLLs, including at least one presumed malignant, or an indeterminate lesion 10 mm or less on the preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI), were included. After digital segmentation of hepatobiliary phase MRI, a transparent, 3D-printed liver model with colored anatomical structures and FLLs was produced. During the gross examination of the liver specimen, the per-lesion detection rates were compared between those without (routine protocol) and those with the aid of the 3D-printed liver model.


RESULTS:

Among 98 MRI-detected FLLs (11.5 ± 12.5 mm), the per-lesion detection rate on gross examination using the 3D-printed liver model was 99.0% (97/98), which was significantly higher than that obtained on routine examination (82.7% [81/98]; P < 0.001). In the subgroup analysis, according to the tumor size, 23.9% (16/67) of FLLs 10 mm or less were additionally detected using the liver model, whereas none were additionally detected in greater than 10 mm. The additionally detected 16 FLLs in 12 patients included histologic diagnoses of viable metastases, pathologic complete response of metastases, hepatocellular carcinomas, focal nodular hyperplasia-like nodules, and hemangiomas.


CONCLUSIONS:

A personalized, 3D-printed liver model with FLLs may improve the lesion-by-lesion imaging-pathologic matching for small FLLs, thus leading to accurate pathologic tumor staging and obtaining a reliable reference for imaging-detected FLLs.



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