Use of Patient-Specific 3D Models in Paediatric Surgery: Effect on Communication and Surgical Management

TL;DR

3D models help doctors explain complex pediatric conditions to families and improve surgical planning, despite being time-consuming to create.

Contribution

The study introduces an efficient workflow for 3D model creation and highlights their impact on family communication and clinical decisions.

Findings

Families showed improved understanding after viewing 3D models (mean score increased from 3.94 to 4.67).

Physicians rated the 3D models positively for their clinical utility.

An efficient post-processing workflow was established for model creation.

Abstract

Children with rare tumours and malformations may benefit from innovative imaging, including patient-specific 3D models that can enhance communication and surgical planning. The primary aim was to evaluate the impact of patient-specific 3D models on communication with families. The secondary aims were to assess their influence on medical management and to establish an efficient post-processing workflow. From 2021 to 2024, we prospectively included patients aged 3 months to 18 years with rare tumours or malformations. Families completed questionnaires before and after the presentation of a 3D model generated from MRI sequences, including peripheral nerve tractography. Treating physicians completed a separate questionnaire before surgical planning. Analyses were performed in R. Among 21 patients, diagnoses included 11 tumours, 8 malformations, 1 trauma, and 1 pancreatic pseudo-cyst. Likert scale responses showed improved family understanding after viewing the 3D model (mean score 3.94 to 4.67) and a high overall evaluation (mean 4.61). Physicians also rated the models positively. An efficient image post-processing workflow was defined. Although manual 3D reconstruction remains time-consuming, these preliminary results show that colourful, patient-specific 3D models substantially improve family communication and support clinical decision-making. They also highlight the need for supporting the development of MRI-based automated segmentation softwares using deep neural networks, which are clinically approved and usable in routine practice.

Figures