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Guo J, Chen B, Cao H, Dai Q, Qin L, Zhang J, Zhang Y, Zhang H, Sui Y, Chen T, Yang D, Gong X, Li D. Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer. NPJ Precis Oncol 2024; 8:189. [PMID: 39237596 PMCID: PMC11377584 DOI: 10.1038/s41698-024-00678-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 08/26/2024] [Indexed: 09/07/2024] Open
Abstract
Pathological complete response (pCR) serves as a critical measure of the success of neoadjuvant chemotherapy (NAC) in breast cancer, directly influencing subsequent therapeutic decisions. With the continuous advancement of artificial intelligence, methods for early and accurate prediction of pCR are being extensively explored. In this study, we propose a cross-modal multi-pathway automated prediction model that integrates temporal and spatial information. This model fuses digital pathology images from biopsy specimens and multi-temporal ultrasound (US) images to predict pCR status early in NAC. The model demonstrates exceptional predictive efficacy. Our findings lay the foundation for developing personalized treatment paradigms based on individual responses. This approach has the potential to become a critical auxiliary tool for the early prediction of NAC response in breast cancer patients.
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Affiliation(s)
- Jianming Guo
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Baihui Chen
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Hongda Cao
- School of Computer, Beihang University, 100191, Beijing, China
| | - Quan Dai
- Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, 610041, Chengdu, China
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, 610041, Chengdu, China
| | - Ling Qin
- Department of Pathology, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Jinfeng Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Youxue Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Huanyu Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Yuan Sui
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Tianyu Chen
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Dongxu Yang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Xue Gong
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Dalin Li
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China.
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Ghezloo F, Chang OH, Knezevich SR, Shaw KC, Thigpen KG, Reisch LM, Shapiro LG, Elmore JG. Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01202-x. [PMID: 39122892 DOI: 10.1007/s10278-024-01202-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/24/2024] [Accepted: 07/05/2024] [Indexed: 08/12/2024]
Abstract
Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in whole slide images with diagnostic relevance rather than examining the entire slide, with a positive correlation between the time spent on these critical image regions and diagnostic accuracy. In this paper, a heatmap is generated to represent pathologists' viewing patterns during diagnosis and used to guide a deep learning architecture during training. The proposed system outperforms traditional approaches based on color and texture image characteristics, integrating pathologists' domain expertise to enhance region of interest detection without needing individual case annotations. Evaluating our best model, a U-Net model with a pre-trained ResNet-18 encoder, on a skin biopsy whole slide image dataset for melanoma diagnosis, shows its potential in detecting regions of interest, surpassing conventional methods with an increase of 20%, 11%, 22%, and 12% in precision, recall, F1-score, and Intersection over Union, respectively. In a clinical evaluation, three dermatopathologists agreed on the model's effectiveness in replicating pathologists' diagnostic viewing behavior and accurately identifying critical regions. Finally, our study demonstrates that incorporating heatmaps as supplementary signals can enhance the performance of computer-aided diagnosis systems. Without the availability of eye tracking data, identifying precise focus areas is challenging, but our approach shows promise in assisting pathologists in improving diagnostic accuracy and efficiency, streamlining annotation processes, and aiding the training of new pathologists.
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Affiliation(s)
- Fatemeh Ghezloo
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
| | - Oliver H Chang
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | | | | | | | - Lisa M Reisch
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Linda G Shapiro
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Joann G Elmore
- Department of Medicine, David Geffen School of Medicine, University of California, Los AngelesLos Angeles, CA, USA
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Priya C V L, V G B, B R V, Ramachandran S. Deep learning approaches for breast cancer detection in histopathology images: A review. Cancer Biomark 2024; 40:1-25. [PMID: 38517775 PMCID: PMC11191493 DOI: 10.3233/cbm-230251] [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] [Indexed: 03/24/2024]
Abstract
BACKGROUND Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images. OBJECTIVE To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques. METHODS This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models. RESULTS Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images. CONCLUSION This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.
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Affiliation(s)
- Lakshmi Priya C V
- Department of Electronics and Communication Engineering, College of Engineering Trivandrum, Kerala, India
| | - Biju V G
- Department of Electronics and Communication Engineering, College of Engineering Munnar, Kerala, India
| | - Vinod B R
- Department of Electronics and Communication Engineering, College of Engineering Trivandrum, Kerala, India
| | - Sivakumar Ramachandran
- Department of Electronics and Communication Engineering, Government Engineering College Wayanad, Kerala, India
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Rakaee M, Andersen S, Giannikou K, Paulsen EE, Kilvaer TK, Busund LTR, Berg T, Richardsen E, Lombardi AP, Adib E, Pedersen MI, Tafavvoghi M, Wahl SGF, Petersen RH, Bondgaard AL, Yde CW, Baudet C, Licht P, Lund-Iversen M, Grønberg BH, Fjellbirkeland L, Helland Å, Pøhl M, Kwiatkowski DJ, Donnem T. Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial. Ann Oncol 2023; 34:578-588. [PMID: 37100205 DOI: 10.1016/j.annonc.2023.04.005] [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: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND We aim to implement an immune cell score model in routine clinical practice for resected non-small-cell lung cancer (NSCLC) patients (NCT03299478). Molecular and genomic features associated with immune phenotypes in NSCLC have not been explored in detail. PATIENTS AND METHODS We developed a machine learning (ML)-based model to classify tumors into one of three categories: inflamed, altered, and desert, based on the spatial distribution of CD8+ T cells in two prospective (n = 453; TNM-I trial) and retrospective (n = 481) stage I-IIIA NSCLC surgical cohorts. NanoString assays and targeted gene panel sequencing were used to evaluate the association of gene expression and mutations with immune phenotypes. RESULTS Among the total of 934 patients, 24.4% of tumors were classified as inflamed, 51.3% as altered, and 24.3% as desert. There were significant associations between ML-derived immune phenotypes and adaptive immunity gene expression signatures. We identified a strong association of the nuclear factor-κB pathway and CD8+ T-cell exclusion through a positive enrichment in the desert phenotype. KEAP1 [odds ratio (OR) 0.27, Q = 0.02] and STK11 (OR 0.39, Q = 0.04) were significantly co-mutated in non-inflamed lung adenocarcinoma (LUAD) compared to the inflamed phenotype. In the retrospective cohort, the inflamed phenotype was an independent prognostic factor for prolonged disease-specific survival and time to recurrence (hazard ratio 0.61, P = 0.01 and 0.65, P = 0.02, respectively). CONCLUSIONS ML-based immune phenotyping by spatial distribution of T cells in resected NSCLC is able to identify patients at greater risk of disease recurrence after surgical resection. LUADs with concurrent KEAP1 and STK11 mutations are enriched for altered and desert immune phenotypes.
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Affiliation(s)
- M Rakaee
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Clinical Pathology, University Hospital of North Norway, Tromso; Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso.
| | - S Andersen
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso; Department of Oncology, University Hospital of North Norway, Tromso, Norway
| | - K Giannikou
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Division of Hematology and Oncology, Cancer and Blood Disease Institute, Children's Hospital Los Angeles, Los Angeles, USA
| | - E-E Paulsen
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso; Department of Pulmonology, University Hospital of North Norway, Tromso
| | - T K Kilvaer
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso; Department of Oncology, University Hospital of North Norway, Tromso, Norway
| | - L-T R Busund
- Department of Clinical Pathology, University Hospital of North Norway, Tromso; Department of Medical Biology, UiT The Arctic University of Norway, Tromso, Norway
| | - T Berg
- Department of Clinical Pathology, University Hospital of North Norway, Tromso; Department of Medical Biology, UiT The Arctic University of Norway, Tromso, Norway
| | - E Richardsen
- Department of Clinical Pathology, University Hospital of North Norway, Tromso; Department of Medical Biology, UiT The Arctic University of Norway, Tromso, Norway
| | - A P Lombardi
- Department of Medical Biology, UiT The Arctic University of Norway, Tromso, Norway
| | - E Adib
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - M I Pedersen
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso
| | - M Tafavvoghi
- Department of Community Medicine, UiT The Arctic University of Norway, Tromso
| | - S G F Wahl
- Department of Oncology, St. Olav's Hospital, Trondheim University Hospital, Trondheim; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - R H Petersen
- Department of Cardiothoracic Surgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen; Department of Clinical Medicine, University of Copenhagen, Copenhagen
| | - A L Bondgaard
- Department of Pathology, Copenhagen University Hospital, Rigshospitalet, Copenhagen
| | - C W Yde
- Center for Genomic Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen
| | - C Baudet
- Center for Genomic Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen
| | - P Licht
- Department of Cardiothoracic Surgery, Odense University Hospital, Odense, Denmark
| | - M Lund-Iversen
- Department of Pathology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo
| | - B H Grønberg
- Department of Oncology, St. Olav's Hospital, Trondheim University Hospital, Trondheim; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - L Fjellbirkeland
- Department of Respiratory Medicine, Oslo University Hospital, University of Oslo, Oslo
| | - Å Helland
- Department of Cancer Genetics, Institute for Cancer Research, Norwegian Radium Hospital, Oslo University Hospital, Oslo; Department of Oncology, Oslo University Hospital, Oslo; Department of Clinical Medicine, University of Oslo, Oslo, Norway
| | - M Pøhl
- Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - D J Kwiatkowski
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA
| | - T Donnem
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso; Department of Oncology, University Hospital of North Norway, Tromso, Norway
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Ogundokun RO, Misra S, Akinrotimi AO, Ogul H. MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:656. [PMID: 36679455 PMCID: PMC9863875 DOI: 10.3390/s23020656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/02/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Abstract
Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients' recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model "MobileNet-SVM", which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.
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Affiliation(s)
- Roseline Oluwaseun Ogundokun
- Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Department of Computer Science, Landmark University, Omu Aran 251103, Kwara, Nigeria
| | - Sanjay Misra
- Department of Computer Science and Communication, Østfold University College, 1757 Halden, Norway
| | | | - Hasan Ogul
- Department of Computer Science and Communication, Østfold University College, 1757 Halden, Norway
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Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer. Nat Commun 2022; 13:6753. [PMID: 36347854 PMCID: PMC9643479 DOI: 10.1038/s41467-022-34275-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 10/18/2022] [Indexed: 11/10/2022] Open
Abstract
Programmed death ligand-1 (PD-L1) has been recently adopted for breast cancer as a predictive biomarker for immunotherapies. The cost, time, and variability of PD-L1 quantification by immunohistochemistry (IHC) are a challenge. In contrast, hematoxylin and eosin (H&E) is a robust staining used routinely for cancer diagnosis. Here, we show that PD-L1 expression can be predicted from H&E-stained images by employing state-of-the-art deep learning techniques. With the help of two expert pathologists and a designed annotation software, we construct a dataset to assess the feasibility of PD-L1 prediction from H&E in breast cancer. In a cohort of 3,376 patients, our system predicts the PD-L1 status in a high area under the curve (AUC) of 0.91 - 0.93. Our system is validated on two external datasets, including an independent clinical trial cohort, showing consistent prediction performance. Furthermore, the proposed system predicts which cases are prone to pathologists miss-interpretation, showing it can serve as a decision support and quality assurance system in clinical practice.
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BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images. Bioengineering (Basel) 2022; 9:bioengineering9060261. [PMID: 35735504 PMCID: PMC9220285 DOI: 10.3390/bioengineering9060261] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/15/2022] [Accepted: 06/15/2022] [Indexed: 11/17/2022] Open
Abstract
Breast cancer is one of the most common types of cancer and is the leading cause of cancer-related death. Diagnosis of breast cancer is based on the evaluation of pathology slides. In the era of digital pathology, these slides can be converted into digital whole slide images (WSIs) for further analysis. However, due to their sheer size, digital WSIs diagnoses are time consuming and challenging. In this study, we present a lightweight architecture that consists of a bilinear structure and MobileNet-V3 network, bilinear MobileNet-V3 (BM-Net), to analyze breast cancer WSIs. We utilized the WSI dataset from the ICIAR2018 Grand Challenge on Breast Cancer Histology Images (BACH) competition, which contains four classes: normal, benign, in situ carcinoma, and invasive carcinoma. We adopted data augmentation techniques to increase diversity and utilized focal loss to remove class imbalance. We achieved high performance, with 0.88 accuracy in patch classification and an average 0.71 score, which surpassed state-of-the-art models. Our BM-Net shows great potential in detecting cancer in WSIs and is a promising clinical tool.
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Zhu J, Liu M, Li X. Progress on deep learning in digital pathology of breast cancer: a narrative review. Gland Surg 2022; 11:751-766. [PMID: 35531111 PMCID: PMC9068546 DOI: 10.21037/gs-22-11] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/04/2022] [Indexed: 01/26/2024]
Abstract
BACKGROUND AND OBJECTIVE Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology. METHODS A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied. KEY CONTENT AND FINDINGS DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches. CONCLUSIONS Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.
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Affiliation(s)
- Jingjin Zhu
- School of Medicine, Nankai University, Tianjin, China
| | - Mei Liu
- Department of Pathology, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Xiru Li
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
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Utilizing digital pathology to quantify stromal caveolin-1 expression in malignant and benign ovarian tumors: Associations with clinicopathological parameters and clinical outcomes. PLoS One 2021; 16:e0256615. [PMID: 34813586 PMCID: PMC8610269 DOI: 10.1371/journal.pone.0256615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 11/09/2021] [Indexed: 11/19/2022] Open
Abstract
Loss of stromal caveolin-1 (Cav-1) is a biomarker of a cancer-associated fibroblast (CAF) phenotype and is related to progression, metastasis, and poor outcomes in several cancers. The objective of this study was to evaluate the clinical significance of Cav-1 expression in invasive epithelial ovarian cancer (OvCa). Epithelial and stromal Cav-1 expression were quantified in serous OvCa and benign ovarian tissue in two, independent cohorts–one quantified expression using immunohistochemistry (IHC) and the other using multiplex immunofluorescence (IF) with digital image analysis designed to target CAF-specific expression. Cav-1 expression was significantly downregulated in OvCa stroma compared to non-neoplastic stroma using both the IHC (p = 0.002) and IF (p = 1.8x10-13) assays. OvCa stroma showed Cav-1 downregulation compared to tumor epithelium with IHC (p = 1.2x10-24). Conversely, Cav-1 expression was higher in OvCa stroma compared to tumor epithelium with IF (p = 0.002). There was moderate correlation between IHC and IF methods for stromal Cav-1 expression (r2 = 0.69, p = 0.006) whereas there was no correlation for epithelial expression (r2 = 0.006, p = 0.98). Irrespective of the staining assay, neither response to therapy or overall survival correlated with the expression level of Cav-1 in the stroma or tumor epithelium. Our findings demonstrate a loss of stromal Cav-1 expression in ovarian serous carcinomas. Studies are needed to replicate these findings and explore therapeutic implications, particularly for immunotherapy response.
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