Histoanatomic Features Distinguishing Aganglionosis in Hirschsprung’s Disease : Toward a Diagnostic Algorithm
(2025) In Diseases 13(8).- Abstract
Background/Objectives: Intraoperative frozen biopsies are essential during surgery for Hirschsprung’s disease (HD). However, this method has several limitations with the need for a faster and real-time diagnostic alternative. For this, consistent histoanatomical and morphometric differences between aganglionic and ganglionic bowel must be established. The primary objective was to compare dimensions of bowel wall layers between aganglionic and ganglionic segments histopathologically in resected rectosigmoid specimens from children with HD. Secondary objectives were to design a diagnostic algorithm to distinguish aganglionosis from ganglionosis and assess whether full bowel wall thickness correlates with patient weight and age. Methods:... (More)
Background/Objectives: Intraoperative frozen biopsies are essential during surgery for Hirschsprung’s disease (HD). However, this method has several limitations with the need for a faster and real-time diagnostic alternative. For this, consistent histoanatomical and morphometric differences between aganglionic and ganglionic bowel must be established. The primary objective was to compare dimensions of bowel wall layers between aganglionic and ganglionic segments histopathologically in resected rectosigmoid specimens from children with HD. Secondary objectives were to design a diagnostic algorithm to distinguish aganglionosis from ganglionosis and assess whether full bowel wall thickness correlates with patient weight and age. Methods: Each histoanatomic bowel wall layer—mucosa, submucosa, and muscularis propria’s layers—was delineated manually on histopathological images. Mean thicknesses were calculated automatically using an in-house image analysis software. Paired parametric tests compared measurements in aganglionic and ganglionic segments. Results: Resected specimens from 30 children with HD were included. Compared to aganglionic bowel, ganglionic bowel showed a thicker muscularis interna (mean 0.666 mm versus 0.461 mm, CI −0.257–(−0.153), p < 0.001), and a higher muscularis interna/muscularis externa ratio (2.047 mm versus 1.287 mm, CI −0.954–(−0.565), p < 0.001). An algorithm based on these features achieved 100% accuracy in distinguishing aganglionosis from ganglionosis. No significant difference in full bowel wall thickness was found between aganglionic and ganglionic segments, nor any correlation with patient weight or age. Conclusions: Histoanatomic layer thickness differs between aganglionic and ganglionic bowel, forming the basis of a diagnostic algorithm. Full bowel wall thickness was independent of patient weight and age.
(Less)
- author
- Fransson, Emma
LU
; Evertsson, Maria
LU
; Lundberg, Tyra
; Hawez, Tebin
LU
; Andersson, Gustav
; Granéli, Christina
LU
; Cinthio, Magnus
LU
; Erlöv, Tobias
LU
and Stenström, Pernilla
LU
- organization
- publishing date
- 2025-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- diagnostics, frozen biopsy, Hirschsprung’s disease, histopathology, ultra-high frequency ultrasonography
- in
- Diseases
- volume
- 13
- issue
- 8
- article number
- 264
- publisher
- MDPI AG
- external identifiers
-
- scopus:105014410579
- pmid:40863237
- ISSN
- 2079-9721
- DOI
- 10.3390/diseases13080264
- language
- English
- LU publication?
- yes
- id
- bbb69147-cb95-42bf-8256-3f92e6d1f367
- date added to LUP
- 2025-11-07 10:04:50
- date last changed
- 2025-11-08 03:00:09
@article{bbb69147-cb95-42bf-8256-3f92e6d1f367,
abstract = {{<p>Background/Objectives: Intraoperative frozen biopsies are essential during surgery for Hirschsprung’s disease (HD). However, this method has several limitations with the need for a faster and real-time diagnostic alternative. For this, consistent histoanatomical and morphometric differences between aganglionic and ganglionic bowel must be established. The primary objective was to compare dimensions of bowel wall layers between aganglionic and ganglionic segments histopathologically in resected rectosigmoid specimens from children with HD. Secondary objectives were to design a diagnostic algorithm to distinguish aganglionosis from ganglionosis and assess whether full bowel wall thickness correlates with patient weight and age. Methods: Each histoanatomic bowel wall layer—mucosa, submucosa, and muscularis propria’s layers—was delineated manually on histopathological images. Mean thicknesses were calculated automatically using an in-house image analysis software. Paired parametric tests compared measurements in aganglionic and ganglionic segments. Results: Resected specimens from 30 children with HD were included. Compared to aganglionic bowel, ganglionic bowel showed a thicker muscularis interna (mean 0.666 mm versus 0.461 mm, CI −0.257–(−0.153), p < 0.001), and a higher muscularis interna/muscularis externa ratio (2.047 mm versus 1.287 mm, CI −0.954–(−0.565), p < 0.001). An algorithm based on these features achieved 100% accuracy in distinguishing aganglionosis from ganglionosis. No significant difference in full bowel wall thickness was found between aganglionic and ganglionic segments, nor any correlation with patient weight or age. Conclusions: Histoanatomic layer thickness differs between aganglionic and ganglionic bowel, forming the basis of a diagnostic algorithm. Full bowel wall thickness was independent of patient weight and age.</p>}},
author = {{Fransson, Emma and Evertsson, Maria and Lundberg, Tyra and Hawez, Tebin and Andersson, Gustav and Granéli, Christina and Cinthio, Magnus and Erlöv, Tobias and Stenström, Pernilla}},
issn = {{2079-9721}},
keywords = {{diagnostics; frozen biopsy; Hirschsprung’s disease; histopathology; ultra-high frequency ultrasonography}},
language = {{eng}},
number = {{8}},
publisher = {{MDPI AG}},
series = {{Diseases}},
title = {{Histoanatomic Features Distinguishing Aganglionosis in Hirschsprung’s Disease : Toward a Diagnostic Algorithm}},
url = {{http://dx.doi.org/10.3390/diseases13080264}},
doi = {{10.3390/diseases13080264}},
volume = {{13}},
year = {{2025}},
}