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Rodríguez-Candela Mateos M, Azmat M, Santiago-Freijanes P, Galán-Moya EM, Fernández-Delgado M, Aponte RB, Mosquera J, Acea B, Cernadas E, Mayán MD. Software BreastAnalyser for the semi-automatic analysis of breast cancer immunohistochemical images. Sci Rep 2024; 14:2995. [PMID: 38316810 PMCID: PMC10844656 DOI: 10.1038/s41598-024-53002-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 01/25/2024] [Indexed: 02/07/2024] Open
Abstract
Breast cancer is the most diagnosed cancer worldwide and represents the fifth cause of cancer mortality globally. It is a highly heterogeneous disease, that comprises various molecular subtypes, often diagnosed by immunohistochemistry. This technique is widely employed in basic, translational and pathological anatomy research, where it can support the oncological diagnosis, therapeutic decisions and biomarker discovery. Nevertheless, its evaluation is often qualitative, raising the need for accurate quantitation methodologies. We present the software BreastAnalyser, a valuable and reliable tool to automatically measure the area of 3,3'-diaminobenzidine tetrahydrocholoride (DAB)-brown-stained proteins detected by immunohistochemistry. BreastAnalyser also automatically counts cell nuclei and classifies them according to their DAB-brown-staining level. This is performed using sophisticated segmentation algorithms that consider intrinsic image variability and save image normalization time. BreastAnalyser has a clean, friendly and intuitive interface that allows to supervise the quantitations performed by the user, to annotate images and to unify the experts' criteria. BreastAnalyser was validated in representative human breast cancer immunohistochemistry images detecting various antigens. According to the automatic processing, the DAB-brown area was almost perfectly recognized, being the average difference between true and computer DAB-brown percentage lower than 0.7 points for all sets. The detection of nuclei allowed proper cell density relativization of the brown signal for comparison purposes between the different patients. BreastAnalyser obtained a score of 85.5 using the system usability scale questionnaire, which means that the tool is perceived as excellent by the experts. In the biomedical context, the connexin43 (Cx43) protein was found to be significantly downregulated in human core needle invasive breast cancer samples when compared to normal breast, with a trend to decrease as the subtype malignancy increased. Higher Cx43 protein levels were significantly associated to lower cancer recurrence risk in Oncotype DX-tested luminal B HER2- breast cancer tissues. BreastAnalyser and the annotated images are publically available https://citius.usc.es/transferencia/software/breastanalyser for research purposes.
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Affiliation(s)
- Marina Rodríguez-Candela Mateos
- Institute of Biomedical Research of A Coruña (INIBIC), Complexo Hospitalario Universitario A Coruña (CHUAC), SERGAS, A Coruña, Spain
| | - Maria Azmat
- CiTIUS - Centro Singular de Investigación en Tecnoloxías Intelixentes da USC, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Paz Santiago-Freijanes
- Institute of Biomedical Research of A Coruña (INIBIC), Complexo Hospitalario Universitario A Coruña (CHUAC), SERGAS, A Coruña, Spain
- Department of Pathology, Complexo Hospitalario Universitario A Coruña (CHUAC), SERGAS, A Coruña, Spain
| | - Eva María Galán-Moya
- Physiology and Cell Dynamics, Centro Regional de Investigaciones Biomédicas (CRIB) and Faculty of Nursing, Universidad de Castilla-La Mancha, Albacete, Spain
- Grupo Mixto de Oncología Traslacional UCLM-GAI Albacete, Universidad de Castilla-La Mancha, Servicio de Salud de Castilla-La Mancha, Ciudad Real, Spain
| | - Manuel Fernández-Delgado
- CiTIUS - Centro Singular de Investigación en Tecnoloxías Intelixentes da USC, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Rosa Barbella Aponte
- Anatomic Pathology Unit, Hospital General Universitario de Albacete, Albacete, Spain
| | - Joaquín Mosquera
- Institute of Biomedical Research of A Coruña (INIBIC), Complexo Hospitalario Universitario A Coruña (CHUAC), SERGAS, A Coruña, Spain
- Breast Unit, Complexo Hospitalario Universitario A Coruña (CHUAC), SERGAS, A Coruña, Spain
| | - Benigno Acea
- Institute of Biomedical Research of A Coruña (INIBIC), Complexo Hospitalario Universitario A Coruña (CHUAC), SERGAS, A Coruña, Spain
- Breast Unit, Complexo Hospitalario Universitario A Coruña (CHUAC), SERGAS, A Coruña, Spain
| | - Eva Cernadas
- CiTIUS - Centro Singular de Investigación en Tecnoloxías Intelixentes da USC, Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
| | - María D Mayán
- Institute of Biomedical Research of A Coruña (INIBIC), Complexo Hospitalario Universitario A Coruña (CHUAC), SERGAS, A Coruña, Spain.
- CELLCOM Research Group. Biomedical Research Center (CINBIO) and Institute of Biomedical Research of Ourense-Pontevedra-Vigo (IBI), University of Vigo. Edificio Olimpia Valencia, Campus Universitario Lagoas Marcosende, 36310, Pontevedra, Spain.
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Konugolu Venkata Sekar S, Ma H, Komolibus K, Dumlupinar G, Mickert MJ, Krawczyk K, Andersson-Engels S. High contrast breast cancer biomarker semi-quantification and immunohistochemistry imaging using upconverting nanoparticles. BIOMEDICAL OPTICS EXPRESS 2024; 15:900-909. [PMID: 38404324 PMCID: PMC10890842 DOI: 10.1364/boe.504939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/21/2023] [Accepted: 01/09/2024] [Indexed: 02/27/2024]
Abstract
Breast cancer is the second leading cause of cancer death in women. Current clinical treatment stratification practices open up an avenue for significant improvements, potentially through advancements in immunohistochemistry (IHC) assessments of biopsies. We report a high contrast upconverting nanoparticles (UCNP) labeling to distinguish different levels of human epidermal growth factor receptor 2 (HER2) in HER2 control pellet arrays (CPAs) and HER2-positive breast cancer tissue. A simple Fourier transform algorithm trained on CPAs was sufficient to provide a semi-quantitative HER2 assessment tool for breast cancer tissues. The UCNP labeling had a signal-to-background ratio of 40 compared to the negative control.
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Affiliation(s)
| | - Hui Ma
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork,
Ireland
- Department of Physics,
University College Cork, College Road,
Cork, T12 K8AF, Ireland
| | - Katarzyna Komolibus
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork,
Ireland
| | - Gokhan Dumlupinar
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork,
Ireland
- Department of Physics,
University College Cork, College Road,
Cork, T12 K8AF, Ireland
| | | | | | - Stefan Andersson-Engels
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork,
Ireland
- Department of Physics,
University College Cork, College Road,
Cork, T12 K8AF, Ireland
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Gallo M, Krajňanský V, Nenutil R, Holub P, Brázdil T. Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability. N Biotechnol 2023; 78:52-67. [PMID: 37793603 DOI: 10.1016/j.nbt.2023.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 08/29/2023] [Accepted: 09/30/2023] [Indexed: 10/06/2023]
Abstract
Diagnostic histopathology faces increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure. The learning models for histopathology are inherently complex and opaque from the user's perspective. Hence different methods have been developed to interpret their behavior. However, relatively limited attention has been devoted to the connection between interpretation methods and the knowledge of experienced pathologists. The main contribution of this paper is a method for comparing morphological patterns used by expert pathologists to detect cancer with the patterns identified as important for inference of learning models. Given the patch-based nature of processing large-scale histopathological imaging, we have been able to show statistically that the VGG16 model could utilize all the structures that are observable by the pathologist, given the patch size and scan resolution. The results show that the neural network approach to recognizing prostatic cancer is similar to that of a pathologist at medium optical resolution. The saliency maps identified several prevailing histomorphological features characterizing carcinoma, e.g., single-layered epithelium, small lumina, and hyperchromatic nuclei with halo. A convincing finding was the recognition of their mimickers in non-neoplastic tissue. The method can also identify differences, i.e., standard patterns not used by the learning models and new patterns not yet used by pathologists. Saliency maps provide added value for automated digital pathology to analyze and fine-tune deep learning systems and improve trust in computer-based decisions.
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Affiliation(s)
- Matej Gallo
- Faculty of Informatics, Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic.
| | - Vojtěch Krajňanský
- Faculty of Informatics, Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic
| | - Rudolf Nenutil
- Department of Pathology, Masaryk Memorial Cancer Institute, Žlutý kopec 7, 656 53 Brno, Czech Republic
| | - Petr Holub
- Institute of Computer Science, Masaryk University, Šumavská 416/15, 602 00 Brno, Czech Republic
| | - Tomáš Brázdil
- Faculty of Informatics, Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic
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