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

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

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.

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.

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.

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.

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