Zachary Abramson

Background/Objectives: Education of pediatric and adolescent patients with cancer and their families, regarding the malignancy, treatment plan, surgical plan, and risk for morbidities, is crucial. Physicians use imaging and drawings as visual aids to educate patients. However, it can be difficult for patients to understand radiological imaging. We aimed to assess the use of three‐dimensional (3D) reconstruction using virtual reality (VR) images of each patient's individual tumor to improve the quality of education during preoperative informed consent discussions. Methods: Patients with pediatric cancers and their parents from two institutions completed pre‐ and post‐test disease‐specific surveys after standard teaching and then after visualizing their tumor in VR. Survey questions included Likert scales, knowledge‐based, and open‐ended questions. The post‐test surveys also asked about their experience with VR as an educational modality. Results: Of participating families, 88.5% felt that VR should be used for all pre‐surgical consents, with the remainder of participants not answering the question. Likert‐scale ratings about VR education and free‐text comments were overall positive, and parents did not express distress when viewing the tumor in VR. Although families reported a better understanding of their child's cancer and planned surgery, answers to knowledge questions minimally increased. Conclusions: Families expressed high satisfaction with the use of VR and thought it should be used for all consent discussions. Answers to knowledge questions minimally improved, which may indicate a high level of understanding prior to VR or question and discussion content disparities. However, families reported feeling an increased understanding and felt that VR provided a positive learning environment.

Digital three-dimensional (3D) visualization is an evolving field that is being introduced into pediatric surgical oncology, where it addresses challenges related to complex anatomy and difficulties accurately defining surgical margins. This paper provides an overview of digital 3D modeling in pediatric surgical oncology and explores ways to further advance this field. Digital 3D models may improve various aspects of surgery, such as preoperative planning, intraoperative guidance, patient education, and surgical training. Traditionally, 3D models are manually or semi-automatically segmented. More recently, artificial intelligence (AI) can automatically segment pediatric solid tumors. For preoperative planning, 3D models improve anatomic understanding and help multidisciplinary communication. During surgery, these models can assist the surgeon in accurately localizing tumors and determining resection margins in preclinical research settings. In patient education, 3D models present an opportunity to improve patient understanding of disease, therapy, and surgical risks. Lastly, digital 3D models are used in surgical training, particularly for challenging cases. Although digital 3D technologies are evaluated using technical performance metrics, these measures do not necessarily result in improved clinical outcomes. Consequently, evidence demonstrating improved surgical outcomes remains limited, also due to the small patient cohorts. Several challenges continue to hinder clinical translation, including image quality and standardization, segmentation complexity, organ deformability during surgery, and insufficient clinical validation. Addressing these limitations will require progress in four key areas: AI integration, multicenter validation, the development of centralized infrastructures including digital twins, and scalable implementation strategies that support widespread clinical adoption.

Background: Three-dimensional (3-D) models are gaining interest for surgical planning in the field of pediatric surgical oncology. The diverse tumor types in pediatric patients and the non-uniform presentation and anatomy make 3D modeling in pediatric oncology particularly challenging. In addition, centers have variability in technique and experience with these approaches. Guidelines for model creation and quality assurance are notably absent. Objective: We developed national recommendations pertaining to image acquisition and model creation for 3-D renderings based on the existing literature and multidisciplinary expertise. Materials and Methods: The literature was critically appraised, and the authors developed minimum standard guidelines for imaging and modeling pediatric solid tumors. Strength of recommendation scores for each guideline were independently determined by the authors. Results: Twelve specific guidelines were developed and scored with overall strength of recommendation ranging from moderate to very strong. Guidelines focused on image acquisition were uniformly scored very strongly while scores for those pertaining to segmentation and model display were slightly less consistent. Conclusions: We propose minimum standards for image acquisition and modern 3-D modeling of pediatric solid tumors based on literature and expertise. The overall level of agreement among our multidisciplinary team was high.

Supplemental Table 3. Total cohort of 78 Patients with thyroid carcinoma, including those who did and did not undergo genetic testing.

Supplemental Table 4. Patient Cancer and Treatment History.

Supplemental Table 5. Thyroid carcinoma treatment and outcomes for patients with germline CPG PV.

Supplemental Table 2. Genes included on multigene panel tests.

Supplemental Table 1. Germline genetic testing completed.

Supplemental Table 4. Patient Cancer and Treatment History.