A Clinical Aid for Detecting Skin Cancer: The Triage Amalgamated Dermoscopic Algorithm (TADA)

T. Rogers, MFA; M. L. Marino, MD; S. W. Dusza, DrPH; S. Bajaj, MD; R. P. Usatine, MD; M. A. Marchetti, MD; A. A. Marghoob, MD

Disclosures

J Am Board Fam Med. 2016;29(6):694-701. 

In This Article

Materials and Methods

Study Population

This study was approved by the Memorial Sloan Kettering Institutional Review Board without requirement for written informed consent in accordance with the Helsinki Declaration. The study was open to all attendees of a dermoscopy course and was not restricted by specialty type or prior dermoscopy training or experience.

Selection of Skin Lesions

The study lesions were selected retrospectively from dermoscopically imaged neoplasms seen by author AAM at Memorial Sloan Kettering Cancer Center in Hauppauge, NY. Sixty-two lesions that were deemed as representative examples of benign and malignant neoplasms by dermoscopists AAM and MAM were selected. Twelve of the 62 lesions were excluded due to suboptimal image quality or lack of polarized dermoscopic images, resulting in 50 study lesions. Melanomas, basal cell carcinomas, and squamous cell carcinomas were histopathologically proven as malignant (27 total: 16 malignant melanoma, including 3 nodular melanoma and 1 amelanotic melanoma, 7 basal cell carcinoma, 4 squamous cell carcinoma). Benign lesions were either biopsied or were known to be stable by comparison with prior images (23 total: 8 nevi, 5 seborrheic keratoses, 5 angioma, 4 dermatofibroma, 1 clear cell acanthoma). Lesions were imaged with contact polarized dermoscopy at a magnification factor of 10.

Evaluation of Cases and Data Collection

The study took place on the second day of a 3-day dermoscopy course, after participants had received 1 day of basic dermoscopy training. This included classroom sessions that covered the published dermoscopic features of common benign and malignant skin lesions, as well as the dermoscopic criteria used in the TADA algorithm. Participants were given quizzes and partook in unknown lesion identification sessions to reinforce the covered topics. Before the study commenced, participants also received a brief tutorial on how to use the TADA algorithm and fill out the provided worksheets. Although clinical images of the study lesions were not provided, clinical information related to palpation (ie, rough, firm, dimpling with lateral pressure) was provided when relevant. Dimpling with lateral pressure, for instance, is a common feature of dermatofibroma that can aid in their identification in the clinical setting.

TADA consists of 3 levels, which participants worked through in a stepwise manner for each study lesion. In level 1, participants were asked to determine whether the lesion was an unequivocal example of 1 of 3 benign neoplasms (angioma, dermatofibroma, or seborrheic keratosis). After excluding these 3, participants then moved to level 2, in which they assessed for the presence of architectural disorder. For lesions deemed as ordered/organized/symmetric, participants then moved to level 3 to evaluate for the remaining criteria. The presence of any 1 criterion from level 2 or 3 was considered to be suggestive of malignancy and indicated the need for a biopsy or specialist referral. After a participant identified any 1 criterion, they were instructed to stop filling out the worksheet and to wait for the next case. If none of the criteria were noted, the participants were led to the management decision of monitoring the lesion. Worksheets were collected and data entered into an electronic database that was used to estimate sensitivities and specificities.

Statistics

Descriptive statistics and graphical methods were used to assess and describe the characteristics of the study participants, the lesions used in the evaluations, and the individual dermoscopic characteristics of the lesions. A TADA classification variable was created from the responses for each lesion evaluation and coded as dichotomous (TADA positive = 1; TADA negative = 0). Level of experience in dermoscopy was assessed as a dichotomous variable with those reporting "any dermoscopic training" coded as 1 and no dermoscopic training coded as 0. In addition, level of experience with dermatology and dermoscopy was assessed as years practicing dermoscopy. To quantify the association for the presence/absence of each TADA feature with malignant status, tabular cross classifications and χ2 statistics were calculated. Measures of diagnostic accuracy along with 95% confidence intervals (CIs) were estimated using a general estimating equations approach for binary outcomes with the TADA evaluation response as the dependent variable and with the main independent variable being benign/malignant status. Two separate models were independently used to estimate the sensitivity and specificity. Given that study participants contributed multiple observations to the data set, and responses from a given individual have some level of correlation, an exchangeable correlation structure was used in the modeling approach. All analyses were performed with Stata v. 14.0, StataCorp, College Station, TX.

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