Yeshua Talia, Ben-Haim Yoav, Schwarz Yehuda, Attal Pierre, Nezri Eliyahu, Grinzaig Shlomo, Ben-David Eliel
Jerusalem College of Technology, Jerusalem, Israel.
Hebrew University of Jerusalem, Jerusalem, Israel.
Pediatr Radiol. 2025 Jun 24. doi: 10.1007/s00247-025-06282-7.
Congenital nasal pyriform aperture stenosis is a rare neonatal condition that causes respiratory distress and potentially requires surgery. Current diagnosis relies on clinical assessment and manual computed tomography (CT) measurements of the pyriform aperture width, which may not fully capture obstruction severity.
To evaluate the severity of pyriform aperture stenosis using automatic three-dimensional (3-D) analysis and identify parameters discriminating between conservative and surgical cases.
This retrospective study analyzed CT scans of neonatal airways using a novel automated 3-D segmentation algorithm. We collected 22 CT scans (2010-2022) of newborns aged 0-35 days: 12 controls, four moderate cases treated conservatively, and six severe cases requiring surgery. The algorithm measured pyriform aperture width, nasal volumes, surface area, and cross-sectional areas.
The algorithm achieved high accuracy (Dice coefficient, 0.961 ± 0.005) and aligned well with manual measurements of the pyriform aperture (average difference, -0.05 ± 0.77 mm, -0.7 ± 9.1%). All cases with stenosis showed anterior narrowing, while only severe cases exhibited stenosis along the entire mid-nasal cavity. Mid-nasal cavity volume, surface area, and cross-sectional areas at 50% and 75% of the mid-nasal cavity emerged as potential surgical predictors, with cross-sectional area at 75% being the most discriminating (moderate, 68.6 ± 7.5 mm; severe, 33.5 ± 13.7 mm; P<0.01).
Automated 3-D CT analysis quantifies pyriform aperture stenosis severity by measuring nasal airway dimensions. The study suggests objective parameters that may assist in surgical decisions and highlights the importance of considering the entire 3-D nasal cavity when planning surgical interventions. A multi-center study with a larger cohort is recommended to validate these findings.
先天性鼻梨状孔狭窄是一种罕见的新生儿疾病，可导致呼吸窘迫，可能需要手术治疗。目前的诊断依赖于临床评估和梨状孔宽度的手动计算机断层扫描（CT）测量，这可能无法完全反映梗阻的严重程度。
使用自动三维（3-D）分析评估梨状孔狭窄的严重程度，并确定区分保守治疗和手术治疗病例的参数。
这项回顾性研究使用一种新型的自动3-D分割算法分析新生儿气道的CT扫描图像。我们收集了2010年至2022年期间0至35天新生儿的22份CT扫描图像：12例对照，4例保守治疗的中度病例，以及6例需要手术的重度病例。该算法测量了梨状孔宽度、鼻腔容积、表面积和横截面积。
该算法具有很高的准确性（骰子系数，0.961±0.005），与梨状孔的手动测量结果吻合良好（平均差异，-0.05±0.77mm，-0.7±9.1%）。所有狭窄病例均表现为前部狭窄，而只有重度病例在整个鼻腔中部出现狭窄。鼻腔中部容积、表面积以及鼻腔中部50%和75%处的横截面积成为潜在的手术预测指标，其中75%处的横截面积区分度最高（中度，68.6±7.5mm；重度，33.5±13.7mm；P<0.01）。
自动3-D CT分析通过测量鼻气道尺寸来量化梨状孔狭窄的严重程度。该研究提出了可能有助于手术决策的客观参数，并强调了在规划手术干预时考虑整个三维鼻腔的重要性。建议进行一项更大样本量的多中心研究来验证这些发现。
