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Introduction <p>Stage&#xa0;III cellulite is traditionally assessed through clinical inspection; however, visual scales often fail to capture the structural complexity of subcutaneous tissue. High-frequency ultrasound (HFUS) provides an objective, non-invasive method for visualizing the architecture of adipose tissue. This study aimed to develop and validate an ultrasound-based subclassification of stage&#xa0;III cellulite to improve diagnostic accuracy and facilitate personalized treatment strategies.</p> Methods <p>This observational cohort study included 150 female patients (ages 20–55&#xa0;years, BMI 18–32&#xa0;kg/m<sup>2</sup>) with a clinical diagnosis of stage&#xa0;III cellulite. Two physicians independently performed clinical staging (3A/3B) using the Nürnberger–Müller scale. A single physician conducted HFUS examinations of the subgluteal and trochanteric regions using a 20-MHz probe. Ultrasound features, including fat thickness, echotexture, fibrosis, and edema, served to classify patients into three phenotypes: 3A, 3B, and mixed.</p> Results <p>HFUS identified 50 patients in each subtype. The mean superficial fat thickness was 4.8 ± 1.1&#xa0;mm in 3A, 11.3 ± 2.4&#xa0;mm in 3B, and 8.6 ± 3.4&#xa0;mm in mixed subtype. Severe fibrosis occurred in 100.0% of patients with type&#xa0;3B, was absent in type&#xa0;3A, and was variable in the mixed type. Edema was mild in all patients with type&#xa0;3A (100.0%), severe in 44.0% of those with type&#xa0;3B, and variable in the mixed group. Discrepancies between clinical and ultrasound classifications appeared in 33.3% of cases. Agreement between classifications was moderate (Gwet’s AC1 = 0.444; <i>p</i> &lt; 0.001).</p> Conclusion <p>HFUS provides a valid and reproducible method for structurally assessing advanced cellulite. It enables the identification of a third, clinically unrecognized mixed phenotype, which has significant therapeutic implications by supporting more accurate treatment selection and improving treatment personalization.</p>

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High-Frequency Ultrasound Imaging for Stage III Cellulite: A Three-Subtype Structural Classification from an Observational Cohort Study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月25日

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  • Dora Intagliata,
  • Massimiliano Priolo,
  • Paola Molinari

摘要

Introduction

Stage III cellulite is traditionally assessed through clinical inspection; however, visual scales often fail to capture the structural complexity of subcutaneous tissue. High-frequency ultrasound (HFUS) provides an objective, non-invasive method for visualizing the architecture of adipose tissue. This study aimed to develop and validate an ultrasound-based subclassification of stage III cellulite to improve diagnostic accuracy and facilitate personalized treatment strategies.

Methods

This observational cohort study included 150 female patients (ages 20–55 years, BMI 18–32 kg/m2) with a clinical diagnosis of stage III cellulite. Two physicians independently performed clinical staging (3A/3B) using the Nürnberger–Müller scale. A single physician conducted HFUS examinations of the subgluteal and trochanteric regions using a 20-MHz probe. Ultrasound features, including fat thickness, echotexture, fibrosis, and edema, served to classify patients into three phenotypes: 3A, 3B, and mixed.

Results

HFUS identified 50 patients in each subtype. The mean superficial fat thickness was 4.8 ± 1.1 mm in 3A, 11.3 ± 2.4 mm in 3B, and 8.6 ± 3.4 mm in mixed subtype. Severe fibrosis occurred in 100.0% of patients with type 3B, was absent in type 3A, and was variable in the mixed type. Edema was mild in all patients with type 3A (100.0%), severe in 44.0% of those with type 3B, and variable in the mixed group. Discrepancies between clinical and ultrasound classifications appeared in 33.3% of cases. Agreement between classifications was moderate (Gwet’s AC1 = 0.444; p < 0.001).

Conclusion

HFUS provides a valid and reproducible method for structurally assessing advanced cellulite. It enables the identification of a third, clinically unrecognized mixed phenotype, which has significant therapeutic implications by supporting more accurate treatment selection and improving treatment personalization.