Farberg Aaron S, Winkelmann Richard R, Tucker Natalie, White Richard, Rigel Darrell S
Dr. Farberg is with the Icahn School of Medicine, Dermatology, at Mount Sinai in New York, New York.
Dr. Winkelmann is with Ohio Health, Dermatology, Columbus, Ohio.
J Clin Aesthet Dermatol. 2017 Sep;10(9):24-26. Epub 2017 Sep 1.
Early diagnosis of melanoma is critical to survival. New technologies, such as a multi-spectral digital skin lesion analysis (MSDSLA) device [MelaFind, STRATA Skin Sciences, Horsham, Pennsylvania] may be useful to enhance clinician evaluation of concerning pigmented skin lesions. Previous studies evaluated the effect of only the binary output. The objective of this study was to determine how decisions dermatologists make regarding pigmented lesion biopsies are impacted by providing both the underlying classifier score (CS) and associated probability risk provided by multi-spectral digital skin lesion analysis. This outcome was also compared against the improvement reported with the provision of only the binary output. Dermatologists attending an educational conference evaluated 50 pigmented lesions (25 melanomas and 25 benign lesions). Participants were asked if they would biopsy the lesion based on clinical images, and were asked this question again after being shown multi-spectral digital skin lesion analysis data that included the probability graphs and classifier score. Data were analyzed from a total of 160 United States board-certified dermatologists. Biopsy sensitivity for melanoma improved from 76 percent following clinical evaluation to 92 percent after quantitative multi-spectral digital skin lesion analysis information was provided (<0.0001). Specificity improved from 52 percent to 79 percent (<0.0001). The positive predictive value increased from 61 percent to 81 percent (<0.01) when the quantitative data were provided. Negative predictive value also increased (68% vs. 91%, p<0.01), and overall biopsy accuracy was greater with multi-spectral digital skin lesion analysis (64% vs. 86%, <0.001). Interrater reliability improved (intraclass correlation 0.466 before, 0.559 after). Incorporating the classifier score and probability data into physician evaluation of pigmented lesions led to both increased sensitivity and specificity, thereby resulting in more accurate biopsy decisions.
黑色素瘤的早期诊断对生存至关重要。新技术，如多光谱数字皮肤病变分析（MSDSLA）设备[MelaFind，STRATA皮肤科学公司，宾夕法尼亚州霍舍姆]，可能有助于加强临床医生对可疑色素沉着性皮肤病变的评估。以往的研究仅评估了二元输出的效果。本研究的目的是确定提供基础分类器评分（CS）和多光谱数字皮肤病变分析提供的相关概率风险如何影响皮肤科医生对色素沉着性病变活检的决策。该结果还与仅提供二元输出时报告的改善情况进行了比较。参加一次教育会议的皮肤科医生评估了50个色素沉着性病变（25个黑色素瘤和25个良性病变）。参与者被问及是否会根据临床图像对病变进行活检，并在查看包括概率图和分类器评分的多光谱数字皮肤病变分析数据后再次被问及这个问题。对来自美国160名获得委员会认证的皮肤科医生的数据进行了分析。黑色素瘤的活检敏感性从临床评估后的76%提高到提供定量多光谱数字皮肤病变分析信息后的92%（<0.0001）。特异性从52%提高到79%（<0.0001）。提供定量数据时，阳性预测值从61%增加到81%（<0.01）。阴性预测值也增加了（68%对91%，p<0.01），多光谱数字皮肤病变分析的总体活检准确性更高（64%对86%，<0.001）。评分者间信度提高（组内相关系数之前为0.466，之后为0.559）。将分类器评分和概率数据纳入医生对色素沉着性病变的评估中，可提高敏感性和特异性，从而做出更准确的活检决策。
