<![CDATA[Background: Breast cancer is one of the most common carcinomas worldwide and remains the leading cause of cancer-related death among women. A key biomarker used to guide treatment decisions in breast carcinoma is the expression of human epidermal growth factor receptor 2 (HER2). However, the assessment of HER2 expression through immunocytochemical staining can be highly subjective and inconsistent. This study aims to evaluate the accuracy and consistency of HER2 assessment using a slide scanner with a computer, with a microscope and digital camera as an additional device. Methods: This study analyzed 51 cases of breast carcinoma registered at Mohammad Hoesin Hospital Palembang during February 2023, focusing on HER2 expression using assessment standards according to ASCO/CAP 2023. The computer-assisted pathology interpretation (CPI) protocol utilizes two complementary modalities for assessing HER2 expression, including digital slide scanning with the Ventana DP200 system and manual microscopy using an Olympus CX21 microscope paired with an Indomicro digital camera. These methods were compared to assess consistency and accuracy in HER2 scoring. Data were analyzed using Cohen’s Kappa test and the McNemar test. Results: Statistical analysis using Cohen’s kappa test showed a moderate agreement rate between the two methods (κ = 0.434). McNemar’s test revealed a statistically significant difference in classification (χ² = 5.06; p = 0.024), with Method 1 (digital imaging system) identifying 17 HER2-positive cases (score 3+), identical to Method 2, but classifying a greater number of HER2-low cases (1+ and 2+). A total of 31 cases compared to 16 by Method 2. Conclusion: The digital scanner method demonstrated moderate agreement with manual microscopy, yet showed potential advantages in workflow efficiency and classification consistency. While it may aid in streamlining HER2 assessment, manual microscopy remains essential for verifying ambiguous or borderline cases where algorithmic interpretation may be limited. Integrating both methods can enhance diagnostic accuracy and support more informed therapeutic decision-making for breast cancer patients.]]>
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