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Histoanatomic Features Distinguishing Aganglionosis in Hirschsprung’s Disease : Toward a Diagnostic Algorithm

Fransson, Emma LU ; Evertsson, Maria LU ; Lundberg, Tyra ; Hawez, Tebin LU orcid ; Andersson, Gustav ; Granéli, Christina LU ; Cinthio, Magnus LU orcid ; Erlöv, Tobias LU and Stenström, Pernilla LU orcid (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.

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author
; ; ; ; ; ; ; and
organization
publishing date
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 &lt; 0.001), and a higher muscularis interna/muscularis externa ratio (2.047 mm versus 1.287 mm, CI −0.954–(−0.565), p &lt; 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}},
}