Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus N 8200, Denmark.
Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus N 8200, Denmark; Department of Pathology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, Aarhus N 8200, Denmark.
Pathol Res Pract. 2024 Oct;262:155543. doi: 10.1016/j.prp.2024.155543. Epub 2024 Aug 13.
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.
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.
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).
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.
在这项概念验证研究中,我们提出了一种新的方法,用于自动数字化定量 PRAME(黑色素瘤优先表达抗原),作为辅助诊断以区分良性和恶性黑素细胞病变。该方法利用 PRAME 和 SOX10 的免疫组织化学虚拟双核染色,精确识别感兴趣的黑素细胞,结合数字图像分析来定量 PRAME 指数。
我们的研究包括 10 例复合痣、3 例 halo 痣和 10 例黑色素瘤。组织切片用 PRAME 染色、扫描,去除盖玻片,用 SOX10 再次染色,最后进行数字分析。数字化定量的 PRAME 指数与皮肤科病理学家使用标准 PRAME 评分系统进行的手动定性评估进行比较。
数字化定量的 PRAME 指数在区分黑色素瘤与良性病变方面具有 70%的敏感性和 100%的特异性。手动定性 PRAME 评分的敏感性为 60%,特异性为 100%。使用 ROC 分析比较两种方法,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)相当。
我们发现,我们的新数字定量方法在分类病变为良性或恶性方面至少与当前的手动定性评估一样准确。我们的方法具有不依赖操作者、客观和可重复的优点。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。鉴于样本量较小,需要进一步研究来验证我们的发现。