Digital quantification of PRAME for distinguishing melanoma from nevi compared to manual assessment

IF 2.9 4区 医学 Q2 PATHOLOGY Pathology, research and practice Pub Date : 2024-08-13 DOI:10.1016/j.prp.2024.155543
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Abstract

Aims

In this proof-of-concept study, we propose a new method for automated digital quantification of PRAME (PReferentially expressed Antigen of MElanoma) as a diagnostic aid to distinguish between benign and malignant melanocytic lesions. The proposed method utilizes immunohistochemical virtual double nuclear staining for PRAME and SOX10 to precisely identify the melanocytic cells of interest, which is combined with digital image analyse to quantify a PRAME-index.

Methods

Our study included 10 compound nevi, 3 halo nevi, and 10 melanomas. Tissue slides were stained with PRAME, scanned, the cover glass removed, stained with SOX10, scanned again, and finally analysed digitally. The digitally quantified PRAME-index was compared with a manual qualitative assessment by a dermatopathologist using the standard PRAME-scoring system.

Results

The digitally quantified PRAME-index showed a sensitivity of 70 % and a specificity of 100 % for separating melanomas from benign lesions. The manual qualitative PRAME-score showed a sensitivity of 60 % and a specificity of 100 %. Comparing the two methods using ROC-analyses, our digital quantitative method (AUC: 0.931, 95 % CI: 0.834;1.00, SD: 0.050) remains on par with the manual qualitative method (AUC: 0.877, 95 % CI: 0.725;1.00, SD: 0.078).

Conclusion

We found our novel digital quantitative method was at least as precise at classifying lesions as benign or malignant as the current manual qualitative assessment. Our method has the advantages of being operator-independent, objective, and replicable. Furthermore, our method can easily be implemented in an already digitalized pathology department. Given the small cohort size, more studies are to be done to validate our findings.

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用于区分黑色素瘤和痣的 PRAME 数字量化与人工评估的比较
目的在这项概念验证研究中,我们提出了一种自动数字量化 PRAME(黑素瘤干扰素表达抗原)的新方法,作为区分良性和恶性黑素细胞病变的辅助诊断方法。该方法利用 PRAME 和 SOX10 的免疫组化虚拟双核染色来精确识别相关的黑色素细胞,并结合数字图像分析来量化 PRAME 指数。对组织切片进行 PRAME 染色、扫描、去除盖玻片、SOX10 染色、再次扫描,最后进行数字分析。结果数字量化的 PRAME 指数在区分黑色素瘤和良性病变方面的灵敏度为 70%,特异度为 100%。人工定性 PRAME 评分的灵敏度为 60%,特异性为 100%。使用 ROC 分析比较这两种方法,我们的数字定量方法(AUC:0.931,95 % CI:0.834;1.00,SD:0.050)与手动定性方法(AUC:0.877,95 % CI:0.725;1.00,SD:0.078)不相上下。我们的方法具有独立于操作者、客观、可复制等优点。此外,我们的方法可以很容易地在已经数字化的病理科实施。由于样本量较小,还需要更多的研究来验证我们的发现。
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来源期刊
CiteScore
5.00
自引率
3.60%
发文量
405
审稿时长
24 days
期刊介绍: Pathology, Research and Practice provides accessible coverage of the most recent developments across the entire field of pathology: Reviews focus on recent progress in pathology, while Comments look at interesting current problems and at hypotheses for future developments in pathology. Original Papers present novel findings on all aspects of general, anatomic and molecular pathology. Rapid Communications inform readers on preliminary findings that may be relevant for further studies and need to be communicated quickly. Teaching Cases look at new aspects or special diagnostic problems of diseases and at case reports relevant for the pathologist''s practice.
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