Search Results (4)

Background: Breast cancer remains a critical health concern worldwide, with histopathological analysis of tissue biopsies serving as the clinical gold standard for diagnosis. Manual evaluation of histopathology images is time-intensive and requires specialized expertise, often resulting in variability in diagnostic outcomes. In silver in situ hybridization (SISH) images, accurate nuclei detection is essential for precise histo-scoring of HER2 gene expression, directly impacting treatment decisions. Methods: This study presents a scalable and automated deep learning framework for nuclei detection in HER2-SISH whole slide images (WSIs), utilizing a novel dataset of 100 expert-marked regions extracted from 20 WSIs collected at the University of Malaya Medical Center (UMMC). The proposed two-stage approach combines a pretrained Stardist model with image processing-based annotations, followed by fine tuning on our domain-specific dataset to improve generalization. Results: The fine-tuned model achieved substantial improvements over both the pretrained Stardist model and a conventional watershed segmentation baseline. Quantitatively, the proposed method attained an average F1-score of 98.1% for visual assessments and 97.4% for expert-marked nuclei, outperforming baseline methods across all metrics. Additionally, training and validation performance curves demonstrate stable model convergence over 100 epochs. Conclusions: These results highlight the robustness of our approach in handling the complex morphological characteristics of SISH-stained nuclei. Our framework supports pathologists by offering reliable, automated nuclei detection in HER2 scoring workflows, contributing to diagnostic consistency and efficiency in clinical pathology. Full article

Fluorescence in situ hybridization (FISH) is widely regarded as the gold standard for evaluating human epidermal growth factor receptor 2 (HER2) status in breast cancer; however, it poses challenges such as the need for specialized training and issues related to signal degradation from dye quenching. Silver-enhanced in situ hybridization (SISH) serves as an automated alternative, employing permanent staining suitable for bright-field microscopy. Determining HER2 status involves distinguishing between “Amplified” and “Non-Amplified” regions by assessing HER2 and centromere 17 (CEN17) signals in SISH-stained slides. This study is the first to leverage deep learning for classifying Normal, Amplified, and Non-Amplified regions within HER2-SISH whole slide images (WSIs), which are notably more complex to analyze compared to hematoxylin and eosin (H&E)-stained slides. Our proposed approach consists of a two-stage process: first, we evaluate deep-learning models on annotated image regions, and then we apply the most effective model to WSIs for regional identification and localization. Subsequently, pseudo-color maps representing each class are overlaid, and the WSIs are reconstructed with these mapped regions. Using a private dataset of HER2-SISH breast cancer slides digitized at 40× magnification, we achieved a patch-level classification accuracy of $99.9\%$ and a generalization accuracy of $78.8\%$ by applying transfer learning with a Vision Transformer (ViT) model. The robustness of the model was further evaluated through k-fold cross-validation, yielding an average performance accuracy of $98\%$, with metrics reported alongside $95\%$ confidence intervals to ensure statistical reliability. This method shows significant promise for clinical applications, particularly in assessing HER2 expression status in HER2-SISH histopathology images. It provides an automated solution that can aid pathologists in efficiently identifying HER2-amplified regions, thus enhancing diagnostic outcomes for breast cancer treatment. Full article

Ovarian cancer is the deadliest gynecologic cancer. Although human epidermal growth factor receptor-2 (HER2) overexpression, a poor prognostic molecular marker in ovarian cancer, is found in almost 30% of ovarian cancer cases, there are no established therapies for HER2-overexpressing ovarian cancer. In this study, we investigated the efficacy of combined samfenet, a biosimilar compound of trastuzumab, and natural killer (NK) cells in preclinical model of HER2-overexpressing ovarian cancer. Firstly, we screened the HER2 expression in three ovarian cancer cell lines and eight ovarian cancer patient-derived tumor xenograft (PDTX) samples. Then, immunohistochemistry and silver in situ hybridization (SISH) were performed following clinical criteria. HER2-overexpressing cells exhibited the highest sensitivity to samfenet compared with low-HER2-expressing cells. In addition, the combination of samfenet with natural killer (NK) cells resulted in significantly enhanced sensitivity to HER2-overexpressing cells and showed a significant antitumor effect on PDTX mice compared with monotherapy. It is known that anti-HER2-humanized IgG1 monoclonal antibodies, including trastuzumab, induce antibody-dependent cellular cytotoxicity (ADCC). Consequently, the combination of samfenet with NK cells demonstrated NK cell-mediated ADCC, as confirmed using an in vitro NK cytotoxicity assay and in vivo antitumor efficacy. A transferase dUTP nick end labeling (TUNEL) assay using xenografted tumors further supported the ADCC effects based on the increase in the number of apoptotic cells in the combination group. Furthermore, high HER2 expression was associated with shorter progression-free survival and overall survival based on public mRNA expression data. In this study, we demonstrated that the combination of samfenet and NK cell therapy could be a promising treatment strategy for patients with HER2-overexpressing ovarian cancer, through ADCC effects. Therefore, this study supports a rationale for further clinical studies of the combination of samfenet and NK cells as a therapy for patients with HER2-overexpressing ovarian cancer. Full article

Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20–25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on HER2 gene amplification. Literature from 1997 to 2023 is analyzed, emphasizing silver-enhanced in situ hybridization (SISH) and its integration with image processing and machine learning techniques. Both conventional machine learning approaches and recent advances in deep learning are compared. The review reveals that automated ISH analysis in combination with bright-field microscopy provides a cost-effective and scalable solution for routine pathology. The integration of deep learning techniques shows promise in improving accuracy over conventional methods, although there are limitations related to data variability and computational demands. Automated ISH analysis can reduce manual labor and increase diagnostic accuracy. Future research should focus on refining these computational methods, particularly in handling the complex nature of HER2 status evaluation, and integrate best practices to further enhance clinical adoption of these techniques. Full article