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Background <p>The absence of objective classification criteria for dorsocervical fibrofatty hump (buffalo hump) results in suboptimal surgical outcomes due to inappropriate technique selection.</p> Objective <p>To develop a data-driven diagnostic algorithm based on histopathology, shear-wave elastography (SWE), and B-mode ultrasound (B-US) fibrous assessment for dorsocervical fibrofatty hump sub-clustering, thereby facilitating therapeutic decision-making.</p> Methods <p>In 86 patients, collagen percentage was quantified from Masson-stained histology with ImageJ and classified via <i>K</i>-means clustering into three sub-clusters: adipose-dominant (&lt;&#xa0;23.3% collagen), mixed (23.3–38.4%), and fibrous-dominant (&gt;&#xa0;38.4%) subtypes. Diagnostic thresholds for SWE (kPa) and B-US (fibrous percentage) were determined through decision tree analysis against histologic subtypes, and their classification accuracy was systematically evaluated.</p> Results <p><i>K</i>-means clustering revealed three distinct collagen subgroups (Silhouette score: 0.661).</p> <p>B-US fibrous score exhibited excellent discriminatory capacity (training/test accuracy: 93.4%/92.0%), while SWE showed low accuracy (67.2%/48.0%) and overlapping classifications.</p> Conclusions <p>A collagen percentage-based classification is reliable for dorsocervical fibrofatty hump classification, and B-US fibrous assessment is a reliable noninvasive method for preoperative planning, with adipose-dominant lesions being optimal for modalities such as suction-assisted liposuction (SAL) or ultrasound-assisted liposuction (UAL), and fibrous-dominant cases requiring excision or limited-open approaches.</p> Level of Evidence IV <p>This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors &#xa0;<a href="http://www.springer.com/00266">www.springer.com/00266</a>.</p>

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A Pathologically Validated B-Mode Ultrasound Classification System for Surgical Planning in Dorsocervical Fibrofatty Humps

  • 期刊论文

  • 发表日期：

    2026年2月9日

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  • Angang Ding,
  • Qirui Wang,
  • Dongze Lyu,
  • Ping Xiong,
  • Renpeng Zhou

摘要

Background

The absence of objective classification criteria for dorsocervical fibrofatty hump (buffalo hump) results in suboptimal surgical outcomes due to inappropriate technique selection.

Objective

To develop a data-driven diagnostic algorithm based on histopathology, shear-wave elastography (SWE), and B-mode ultrasound (B-US) fibrous assessment for dorsocervical fibrofatty hump sub-clustering, thereby facilitating therapeutic decision-making.

Methods

In 86 patients, collagen percentage was quantified from Masson-stained histology with ImageJ and classified via K-means clustering into three sub-clusters: adipose-dominant (< 23.3% collagen), mixed (23.3–38.4%), and fibrous-dominant (> 38.4%) subtypes. Diagnostic thresholds for SWE (kPa) and B-US (fibrous percentage) were determined through decision tree analysis against histologic subtypes, and their classification accuracy was systematically evaluated.

Results

K-means clustering revealed three distinct collagen subgroups (Silhouette score: 0.661).

B-US fibrous score exhibited excellent discriminatory capacity (training/test accuracy: 93.4%/92.0%), while SWE showed low accuracy (67.2%/48.0%) and overlapping classifications.

Conclusions

A collagen percentage-based classification is reliable for dorsocervical fibrofatty hump classification, and B-US fibrous assessment is a reliable noninvasive method for preoperative planning, with adipose-dominant lesions being optimal for modalities such as suction-assisted liposuction (SAL) or ultrasound-assisted liposuction (UAL), and fibrous-dominant cases requiring excision or limited-open approaches.

Level of Evidence IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors  www.springer.com/00266.