Korean J Radiol.  2019 Oct;20(10):1409-1421. 10.3348/kjr.2019.0241.

Ultrasound Feature-Based Diagnostic Model Focusing on the “Submarine Sign” for Epidermal Cysts among Superficial Soft Tissue Lesions

• Lee DH
• Yoon CS
• Lim BJ
• Lee HS
• Kim S
• Choi AL
• Kim S

• KMID: 2459160
• DOI: http://doi.org/10.3348/kjr.2019.0241

Abstract

OBJECTIVE
To develop a diagnostic model for superficial soft tissue lesions to differentiate epidermal cyst (EC) from other lesions based on ultrasound (US) features.
MATERIALS AND METHODS
This retrospective study included 205 patients who had undergone US examinations for superficial soft tissue lesions and subsequent surgical excision. The study population was divided into the derivation set (n = 112) and validation set (n = 93) according to the imaging date. The following US features were analyzed to determine those that could discriminate EC from other lesions: more-than-half-depth involvement of the dermal layer, "submarine sign" (focal projection of the hypoechoic portion to the epidermis), posterior acoustic enhancement, posterior wall enhancement, morphology, shape, echogenicity, vascularity, and perilesional fat change. Using multivariable logistic regression, a diagnostic model was constructed and visualized as a nomogram. The performance of the diagnostic model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic curve and calibration plot in both the derivation and validation sets.
RESULTS
More-than-half-depth involvement of the dermal layer (odds ratio [OR] = 3.35; p = 0.051), "submarine sign" (OR = 12.2; p < 0.001), and morphology (OR = 5.44; p = 0.002) were features that outweighed the others when diagnosing EC. The diagnostic model based on these features showed good discrimination ability in both the derivation set (AUC = 0.888, 95% confidence interval [95% CI] = 0.825-0.950) and validation set (AUC = 0.902, 95% CI = 0.832-0.972).
CONCLUSION
More-than-half-depth of involvement of the dermal layer, "submarine sign," and morphology are relatively better US features than the others for diagnosing EC.

Keyword

Epidermal cyst; Submarine sign; Ultrasound; Nomogram

MeSH Terms

Acoustics
Calibration
Discrimination (Psychology)
Epidermal Cyst*
Humans
Logistic Models
Nomograms
Retrospective Studies
ROC Curve
Ultrasonography*

Figure

• 

  Fig. 1 Flow chart illustrating patient selection.EC = epidermal cyst, US = ultrasound

• 

  Fig. 2 “Submarine sign” of EC.A. Illustration demonstrating “submarine sign,” which is defined when lesion shows focal protrusion of hypoechoic portion (b) from main mass (a) to epidermis (arrowhead). B. Photomicrograph (original magnification, × 400; hematoxylin-eosin stain) shows focal tract-like appearance (arrows) of EC and epidermis.

• 

  Fig. 3 Representative case of “submarine sign” in 54-year-old female with EC in back.Focal protrusion of hypoechoic portion (arrowhead) from main mass appears as “submarine sign.” Arrows indicate delineation from adjacent hypodermal fat.

• 

  Fig. 4 Another representative case of “submarine sign” in 32-year-old male with EC in posterior neck.Focal protrusion of hypoechoic portion (arrowheads) from main mass appears like “tract-to-skin sign.” It also counted as “submarine sign.” Arrows indicate delineation from adjacent hypodermal fat.

• 

  Fig. 5 Algorithm showing statistical analysis schema.AUC = area under curve, ROC = receiver operating characteristic

• 

  Fig. 6 Nomogram visualizing diagnostic model for predicting of EC.Point corresponding to each US feature can be determined using at uppermost scale (more-than-half-depth involvement of dermal layer = 48 [solid arrow]; “submarine sign” = 100 [dashed arrow]; and morphology = 68 [dotted and solid arrow]). Then, sum of all points is matched at scale of total point. Further, line drawn down from at that point helps calculate probability of EC. If calculated probability exceeds 0.44, it is considered highly likely to be EC.

• 

  Fig. 7 True positive example identified using developed nomogram.A. US exam obtained from 26-year-old male patient revealed subcutaneous nodule in anterior chest wall involving more-than-half-depth of dermal layer, “submarine sign” (arrowhead), and no posterior acoustic enhancement. Its morphology does not fit any of morphological criteria. B. Total point which is taken from nomogram of patient is 148 (48 + 100 + 0 = 148). This point is converted to predicted probability of 0.756 by using calculation of equation. Similar numerical value is obtained via visual assessment of nomogram (bold arrow). This lesion is pathologically confirmed as EC.

• 

  Fig. 8 True negative example identified using developed nomogram.A. Subcutaneous nodule in left calf of 30-year-old male patient is noted with internal cystic change and posterior acoustic enhancement on US exam. However, this lesion shows less than half-depth involvement of dermis and no “submarine sign.” Morphology of this lesion does not fit any of known typical morphology of EC. This mass was excised and was found to be schwannoma with cystic degeneration and hemorrhage. B. Total point obtained from nomogram of patient is 0 (0 + 0 + 0 = 0). As it yields predicted probability of 0.07 by using equation, visually assessed probability based on nomogram is less than 0.10 and lower than cut-off value of 0.44.

• 

  Fig. 9 Internal validation of diagnostic model.A. Discrimination performance. Following ROC curve analysis for diagnostic model of EC was obtained in derivation set. AUC was 0.888 (95% CI, 0.825–0.950). B. Calibration ability. Dashed line was reference line where ideal diagnostic model would lie. Dotted line was calibration ability of diagnostic model, while solid line corrects for bias. 95% CI = 95% confidence interval

• 

  Fig. 10 External validation of diagnostic model.A. Discrimination performance. In independent validation set, it was shown as following ROC curve analysis for diagnostic model of EC. AUC was 0.902 (95% CI, 0.832–0.972). B. Calibration ability. Soft and thickly drown diagonal line represents reference line where ideal diagnostic model would lie. Lines, that stand for calibration ability of diagnostic model in independent validation set with both logistic calibration (solid line) and nonparametric calibration (dotted line, using lowess), almost coincide with ideal reference.

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