TY - JOUR
T1 - 3D printing-assisted surgical planning versus traditional methods in complex liver resections
T2 - a systematic review
AU - Calle Gómez, Marco Antonio
AU - Fabara Vera, Mateo Daniel
AU - Gurumendi, Ingrid Esmeralda
AU - Durán Saraguro, Patricio Xavier
AU - Placencia Guartatanga, Paola Gissela
N1 - Publisher Copyright:
© 2025; Los autores.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Introduction: surgical resection remains a primary treatment for liver diseases, requiring precise preoperative planning due to the liver’s complex anatomy. Traditional imaging techniques like CT and MRI provide essential information but have limitations in spatial visualization. The emergence of 3D-printed liver models (3DPLMs) offers a novel approach to improving surgical planning and outcomes. Objective: this systematic review critically evaluates the outcomes of 3D printing assisted surgical planning versus traditional methods in complex liver resections. Method: a comprehensive search was conducted in PubMed, Embase, and Web of Science, yielding 11 studies that met inclusion criteria. Data extraction focused on surgical planning accuracy, strategy modification, outcomes, and educational value. Results: 3DPLMs improved surgical planning accuracy, with studies showing significant changes in surgical strategies in 16,7 % to 68 % of cases. Enhanced tumor detection rates, particularly for lesions ≤10 mm, were observed, improving pathological matching and staging. While 3DPLMs did not consistently reduce operative time or complications, they facilitated more precise resection proposals. Educationally, 3DPLMs increased satisfaction, comprehension, and surgical planning skills among trainees, outperforming traditional and virtual methods. Conclusion: 3DPLMs enhance surgical planning accuracy, modify strategies, and improve educational outcomes in complex liver resections. Despite mixed impacts on intraoperative outcomes, their utility in preoperative planning and education is evident, warranting further exploration.
AB - Introduction: surgical resection remains a primary treatment for liver diseases, requiring precise preoperative planning due to the liver’s complex anatomy. Traditional imaging techniques like CT and MRI provide essential information but have limitations in spatial visualization. The emergence of 3D-printed liver models (3DPLMs) offers a novel approach to improving surgical planning and outcomes. Objective: this systematic review critically evaluates the outcomes of 3D printing assisted surgical planning versus traditional methods in complex liver resections. Method: a comprehensive search was conducted in PubMed, Embase, and Web of Science, yielding 11 studies that met inclusion criteria. Data extraction focused on surgical planning accuracy, strategy modification, outcomes, and educational value. Results: 3DPLMs improved surgical planning accuracy, with studies showing significant changes in surgical strategies in 16,7 % to 68 % of cases. Enhanced tumor detection rates, particularly for lesions ≤10 mm, were observed, improving pathological matching and staging. While 3DPLMs did not consistently reduce operative time or complications, they facilitated more precise resection proposals. Educationally, 3DPLMs increased satisfaction, comprehension, and surgical planning skills among trainees, outperforming traditional and virtual methods. Conclusion: 3DPLMs enhance surgical planning accuracy, modify strategies, and improve educational outcomes in complex liver resections. Despite mixed impacts on intraoperative outcomes, their utility in preoperative planning and education is evident, warranting further exploration.
KW - 3D Printing
KW - Liver Neoplasm
KW - Liver Resection
KW - Three-dimensional imaging
KW - Tomography
KW - X-ray computed (CT)
UR - http://www.rte.espol.edu.ec/index.php/tecnologica/article/view/935
UR - https://sct.ageditor.ar/index.php/sct/article/view/1081
U2 - 10.56294/saludcyt20251081
DO - 10.56294/saludcyt20251081
M3 - Artículo de revisión
AN - SCOPUS:85209195587
SN - 2796-9711
VL - 5
SP - 1
EP - 11
JO - Salud, Ciencia y Tecnologia
JF - Salud, Ciencia y Tecnologia
M1 - 1081
ER -