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Adachi M, Taki T, Kojima M, Sakamoto N, Matsuura K, Hayashi R, Tabuchi K, Ishikawa S, Ishii G, Sakashita S. Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists. J Pathol Clin Res 2024; 10:e12392. [PMID: 39159053 PMCID: PMC11332396 DOI: 10.1002/2056-4538.12392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 07/07/2024] [Accepted: 07/16/2024] [Indexed: 08/21/2024]
Abstract
Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. We aimed to develop a highly accurate lymph node recurrence prediction model for clinical stage T1-2, N0 (cT1-2N0) tongue SCC by integrating histopathological artificial intelligence (AI) with clinicopathological information. A dataset from 148 patients with cT1-2N0 tongue SCC was divided into training and test sets. The prediction models were constructed using AI-extracted information from whole slide images (WSIs), human-assessed clinicopathological information, and both combined. Weakly supervised learning and machine learning algorithms were used for WSIs and clinicopathological information, respectively. The combination model utilised both algorithms. Highly predictive patches from the model were analysed for histopathological features. In the test set, the areas under the receiver operating characteristic (ROC) curve for the model using WSI, clinicopathological information, and both combined were 0.826, 0.835, and 0.991, respectively. The highest area under the ROC curve was achieved with the model combining WSI and clinicopathological factors. Histopathological feature analysis showed that highly predicted patches extracted from recurrence cases exhibited significantly more tumour cells, inflammatory cells, and muscle content compared with non-recurrence cases. Moreover, patches with mixed inflammatory cells, tumour cells, and muscle were significantly more prevalent in recurrence versus non-recurrence cases. The model integrating AI-extracted histopathological and human-assessed clinicopathological information demonstrated high accuracy in predicting lymph node recurrence in patients with cT1-2N0 tongue SCC.
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Affiliation(s)
- Masahiro Adachi
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Department of Otolaryngology, Head and Neck SurgeryUniversity of TsukubaTsukubaJapan
| | - Tetsuro Taki
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
| | - Motohiro Kojima
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
| | - Naoya Sakamoto
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
| | - Kazuto Matsuura
- Department of Head and Neck SurgeryNational Cancer Center Hospital EastKashiwaJapan
| | - Ryuichi Hayashi
- Department of Head and Neck SurgeryNational Cancer Center Hospital EastKashiwaJapan
| | - Keiji Tabuchi
- Department of Otolaryngology, Head and Neck SurgeryUniversity of TsukubaTsukubaJapan
| | - Shumpei Ishikawa
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
- Department of Preventive Medicine, Graduate School of MedicineThe University of TokyoTokyoJapan
| | - Genichiro Ishii
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of Innovative Pathology and Laboratory MedicineNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
| | - Shingo Sakashita
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
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Herdiantoputri RR, Komura D, Ochi M, Fukawa Y, Kayamori K, Tsuchiya M, Kikuchi Y, Ushiku T, Ikeda T, Ishikawa S. Benchmarking Deep Learning-Based Image Retrieval of Oral Tumor Histology. Cureus 2024; 16:e62264. [PMID: 39011227 PMCID: PMC11247249 DOI: 10.7759/cureus.62264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2024] [Indexed: 07/17/2024] Open
Abstract
INTRODUCTION Oral tumors necessitate a dependable computer-assisted pathological diagnosis system considering their rarity and diversity. A content-based image retrieval (CBIR) system using deep neural networks has been successfully devised for digital pathology. No CBIR system for oral pathology has been investigated because of the lack of an extensive image database and feature extractors tailored to oral pathology. MATERIALS AND METHODS This study uses a large CBIR database constructed from 30 categories of oral tumors to compare deep learning methods as feature extractors. RESULTS The highest average area under the receiver operating characteristic curve (AUC) was achieved by models trained on database images using self-supervised learning (SSL) methods (0.900 with SimCLR and 0.897 with TiCo). The generalizability of the models was validated using query images from the same cases taken with smartphones. When smartphone images were tested as queries, both models yielded the highest mean AUC (0.871 with SimCLR and 0.857 with TiCo). We ensured the retrieved image result would be easily observed by evaluating the top 10 mean accuracies and checking for an exact diagnostic category and its differential diagnostic categories. CONCLUSION Training deep learning models with SSL methods using image data specific to the target site is beneficial for CBIR tasks in oral tumor histology to obtain histologically meaningful results and high performance. This result provides insight into the effective development of a CBIR system to help improve the accuracy and speed of histopathology diagnosis and advance oral tumor research in the future.
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Affiliation(s)
| | - Daisuke Komura
- Department of Preventive Medicine, The University of Tokyo, Tokyo, JPN
| | - Mieko Ochi
- Department of Preventive Medicine, The University of Tokyo, Tokyo, JPN
| | - Yuki Fukawa
- Department of Oral Pathology, Tokyo Medical and Dental University, Tokyo, JPN
| | - Kou Kayamori
- Department of Oral Pathology, Tokyo Medical and Dental University, Tokyo, JPN
| | - Maiko Tsuchiya
- Department of Pathology, Teikyo University School of Medicine, Tokyo, JPN
| | - Yoshinao Kikuchi
- Department of Pathology, Teikyo University School of Medicine, Tokyo, JPN
| | - Tetsuo Ushiku
- Department of Pathology, The University of Tokyo, Tokyo, JPN
| | - Tohru Ikeda
- Department of Oral Pathology, Tokyo Medical and Dental University, Tokyo, JPN
| | - Shumpei Ishikawa
- Department of Preventive Medicine, The University of Tokyo, Tokyo, JPN
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Zha L, Matsu-ura T, Sluka JP, Murakawa T, Tsuta K. Morphological basis of the lung adenocarcinoma subtypes. iScience 2024; 27:109742. [PMID: 38706836 PMCID: PMC11066476 DOI: 10.1016/j.isci.2024.109742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/20/2024] [Accepted: 04/10/2024] [Indexed: 05/07/2024] Open
Abstract
Lung adenocarcinoma (LUAD), which accounts for a large proportion of lung cancers, is divided into five major subtypes based on histologic characteristics. The clinical characteristics, prognosis, and responses to treatments vary among subtypes. Here, we demonstrate that the variations of cell-cell contact energy result in the LUAD subtype-specific morphogenesis. We reproduced the morphologies of the papillary LUAD subtypes with the cellular Potts Model (CPM). Simulations and experimental validations revealed modifications of cell-cell contact energy changed the morphology from a papillary-like structure to micropapillary or solid subtype-like structures. Remarkably, differential gene expression analysis revealed subtype-specific expressions of genes relating to cell adhesion. Knockdown experiments of the micropapillary upregulated ITGA11 gene resulted in the morphological changes of the spheroids produced from an LUAD cell line PC9. This work shows the consequences of gene mutations and gene expressions on patient prognosis through differences in tissue composing physical forces among LUAD subtypes.
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Affiliation(s)
- Linjun Zha
- Department of Pathology, Kansai Medical University, Hirakata, Osaka 573-0033, Japan
| | - Toru Matsu-ura
- Department of Pathology, Kansai Medical University, Hirakata, Osaka 573-0033, Japan
| | - James P. Sluka
- Biocomplexity Institute, Indiana University, Bloomington, IN 47405-7105, USA
| | - Tomohiro Murakawa
- Department of Thoracic Surgery, Kansai Medical University, Hirakata, Osaka 573-0033, Japan
| | - Koji Tsuta
- Department of Pathology, Kansai Medical University, Hirakata, Osaka 573-0033, Japan
- Biocomplexity Institute, Indiana University, Bloomington, IN 47405-7105, USA
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Ochi M, Komura D, Onoyama T, Shinbo K, Endo H, Odaka H, Kakiuchi M, Katoh H, Ushiku T, Ishikawa S. Registered multi-device/staining histology image dataset for domain-agnostic machine learning models. Sci Data 2024; 11:330. [PMID: 38570515 PMCID: PMC10991301 DOI: 10.1038/s41597-024-03122-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/04/2024] [Indexed: 04/05/2024] Open
Abstract
Variations in color and texture of histopathology images are caused by differences in staining conditions and imaging devices between hospitals. These biases decrease the robustness of machine learning models exposed to out-of-domain data. To address this issue, we introduce a comprehensive histopathology image dataset named PathoLogy Images of Scanners and Mobile phones (PLISM). The dataset consisted of 46 human tissue types stained using 13 hematoxylin and eosin conditions and captured using 13 imaging devices. Precisely aligned image patches from different domains allowed for an accurate evaluation of color and texture properties in each domain. Variation in PLISM was assessed and found to be significantly diverse across various domains, particularly between whole-slide images and smartphones. Furthermore, we assessed the improvement in domain shift using a convolutional neural network pre-trained on PLISM. PLISM is a valuable resource that facilitates the precise evaluation of domain shifts in digital pathology and makes significant contributions towards the development of robust machine learning models that can effectively address challenges of domain shift in histological image analysis.
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Affiliation(s)
- Mieko Ochi
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Daisuke Komura
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Takumi Onoyama
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
- Division of Gastroenterology and Nephrology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan
| | - Koki Shinbo
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Haruya Endo
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Hiroto Odaka
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Miwako Kakiuchi
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Hiroto Katoh
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Tetsuo Ushiku
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Shumpei Ishikawa
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
- Division of Pathology, National Cancer Center Exploratory Oncology Research & Clinical Trial Center, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
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5
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Chen RJ, Ding T, Lu MY, Williamson DFK, Jaume G, Song AH, Chen B, Zhang A, Shao D, Shaban M, Williams M, Oldenburg L, Weishaupt LL, Wang JJ, Vaidya A, Le LP, Gerber G, Sahai S, Williams W, Mahmood F. Towards a general-purpose foundation model for computational pathology. Nat Med 2024; 30:850-862. [PMID: 38504018 PMCID: PMC11403354 DOI: 10.1038/s41591-024-02857-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 02/05/2024] [Indexed: 03/21/2024]
Abstract
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.
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Affiliation(s)
- Richard J Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tong Ding
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Ming Y Lu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Drew F K Williamson
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Guillaume Jaume
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Andrew H Song
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bowen Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew Zhang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Daniel Shao
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Muhammad Shaban
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mane Williams
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Lukas Oldenburg
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Luca L Weishaupt
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Judy J Wang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anurag Vaidya
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Long Phi Le
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Georg Gerber
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sharifa Sahai
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Systems Biology, Harvard University, Cambridge, MA, USA
| | - Walt Williams
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
- Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA.
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6
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Adachi M, Taki T, Sakamoto N, Kojima M, Hirao A, Matsuura K, Hayashi R, Tabuchi K, Ishikawa S, Ishii G, Sakashita S. Extracting interpretable features for pathologists using weakly supervised learning to predict p16 expression in oropharyngeal cancer. Sci Rep 2024; 14:4506. [PMID: 38402356 PMCID: PMC10894206 DOI: 10.1038/s41598-024-55288-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/22/2024] [Indexed: 02/26/2024] Open
Abstract
One drawback of existing artificial intelligence (AI)-based histopathological prediction models is the lack of interpretability. The objective of this study is to extract p16-positive oropharyngeal squamous cell carcinoma (OPSCC) features in a form that can be interpreted by pathologists using AI model. We constructed a model for predicting p16 expression using a dataset of whole-slide images from 114 OPSCC biopsy cases. We used the clustering-constrained attention-based multiple-instance learning (CLAM) model, a weakly supervised learning approach. To improve performance, we incorporated tumor annotation into the model (Annot-CLAM) and achieved the mean area under the receiver operating characteristic curve of 0.905. Utilizing the image patches on which the model focused, we examined the features of model interest via histopathologic morphological analysis and cycle-consistent adversarial network (CycleGAN) image translation. The histopathologic morphological analysis evaluated the histopathological characteristics of image patches, revealing significant differences in the numbers of nuclei, the perimeters of the nuclei, and the intercellular bridges between p16-negative and p16-positive image patches. By using the CycleGAN-converted images, we confirmed that the sizes and densities of nuclei are significantly converted. This novel approach improves interpretability in histopathological morphology-based AI models and contributes to the advancement of clinically valuable histopathological morphological features.
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Affiliation(s)
- Masahiro Adachi
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan
- Department of Otolaryngology, Head and Neck Surgery, University of Tsukuba, Tsukuba, Japan
| | - Tetsuro Taki
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan
| | - Naoya Sakamoto
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan
- Division of Pathology, National Cancer Center Exploratory Oncology Research and Clinical Trial Center, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Motohiro Kojima
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan
- Division of Pathology, National Cancer Center Exploratory Oncology Research and Clinical Trial Center, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Akihiko Hirao
- Division of Pathology, National Cancer Center Exploratory Oncology Research and Clinical Trial Center, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Kazuto Matsuura
- Department of Head and Neck Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Ryuichi Hayashi
- Department of Head and Neck Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Keiji Tabuchi
- Department of Otolaryngology, Head and Neck Surgery, University of Tsukuba, Tsukuba, Japan
| | - Shumpei Ishikawa
- Division of Pathology, National Cancer Center Exploratory Oncology Research and Clinical Trial Center, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Genichiro Ishii
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan
- Division of Innovative Pathology and Laboratory Medicine, National Cancer Center Exploratory Oncology Research and Clinical Trial Center, Kashiwa, Japan
| | - Shingo Sakashita
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan.
- Division of Pathology, National Cancer Center Exploratory Oncology Research and Clinical Trial Center, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
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7
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Lo CM, Jiang JK, Lin CC. Detecting microsatellite instability in colorectal cancer using Transformer-based colonoscopy image classification and retrieval. PLoS One 2024; 19:e0292277. [PMID: 38271352 PMCID: PMC10810505 DOI: 10.1371/journal.pone.0292277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 09/15/2023] [Indexed: 01/27/2024] Open
Abstract
Colorectal cancer (CRC) is a major global health concern, with microsatellite instability-high (MSI-H) being a defining characteristic of hereditary nonpolyposis colorectal cancer syndrome and affecting 15% of sporadic CRCs. Tumors with MSI-H have unique features and better prognosis compared to MSI-L and microsatellite stable (MSS) tumors. This study proposed establishing a MSI prediction model using more available and low-cost colonoscopy images instead of histopathology. The experiment utilized a database of 427 MSI-H and 1590 MSS colonoscopy images and vision Transformer (ViT) with different feature training approaches to establish the MSI prediction model. The accuracy of combining pre-trained ViT features was 84% with an area under the receiver operating characteristic curve of 0.86, which was better than that of DenseNet201 (80%, 0.80) in the experiment with support vector machine. The content-based image retrieval (CBIR) approach showed that ViT features can obtain a mean average precision of 0.81 compared to 0.79 of DenseNet201. ViT reduced the issues that occur in convolutional neural networks, including limited receptive field and gradient disappearance, and may be better at interpreting diagnostic information around tumors and surrounding tissues. By using CBIR, the presentation of similar images with the same MSI status would provide more convincing deep learning suggestions for clinical use.
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Affiliation(s)
- Chung-Ming Lo
- Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan
| | - Jeng-Kai Jiang
- Department of Surgery, Division of Colon and Rectal Surgery, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chun-Chi Lin
- Department of Surgery, Division of Colon and Rectal Surgery, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
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8
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Maedera S, Mizuno T, Kusuhara H. Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo. Comput Biol Med 2024; 168:107748. [PMID: 38016375 DOI: 10.1016/j.compbiomed.2023.107748] [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: 08/06/2023] [Revised: 10/25/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023]
Abstract
Toxicopathological images acquired during safety assessment elucidate an individual's biological responses to a given compound, and their numerization can yield valuable insights contributing to the assessment of compound properties. Currently, toxicopathological images are mainly encoded as pathological findings, evaluated by pathologists, which introduces challenges when used as input for modeling, specifically in terms of representation capability and comparability. In this study, we assessed the usefulness of latent representations extracted from toxicopathological images using Convolutional Neural Network (CNN) in estimating compound properties in vivo. Special emphasis was placed on examining the impact of learning pathological findings, the depth of frozen layers during learning, and the selection of the layer for latent representation. Our findings demonstrate that a machine learning model fed with the latent representation as input surpassed the performance of a model directly employing pathological findings as input, particularly in the classification of a compound's Mechanism of Action and in predicting late-phase findings from early-phase images in repeated-dose tests. While learning pathological findings did improve accuracy, the magnitude of improvement was relatively modest. Similarly, the effect of freezing layers during learning was also limited. Notably, the selection of the layer for latent representation had a substantial impact on the accurate estimation of compound properties in vivo.
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Affiliation(s)
- Shotaro Maedera
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan
| | - Tadahaya Mizuno
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan.
| | - Hiroyuki Kusuhara
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan
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9
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Sauter D, Lodde G, Nensa F, Schadendorf D, Livingstone E, Kukuk M. Deep learning in computational dermatopathology of melanoma: A technical systematic literature review. Comput Biol Med 2023; 163:107083. [PMID: 37315382 DOI: 10.1016/j.compbiomed.2023.107083] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/10/2023] [Accepted: 05/27/2023] [Indexed: 06/16/2023]
Abstract
Deep learning (DL) has become one of the major approaches in computational dermatopathology, evidenced by a significant increase in this topic in the current literature. We aim to provide a structured and comprehensive overview of peer-reviewed publications on DL applied to dermatopathology focused on melanoma. In comparison to well-published DL methods on non-medical images (e.g., classification on ImageNet), this field of application comprises a specific set of challenges, such as staining artifacts, large gigapixel images, and various magnification levels. Thus, we are particularly interested in the pathology-specific technical state-of-the-art. We also aim to summarize the best performances achieved thus far with respect to accuracy, along with an overview of self-reported limitations. Accordingly, we conducted a systematic literature review of peer-reviewed journal and conference articles published between 2012 and 2022 in the databases ACM Digital Library, Embase, IEEE Xplore, PubMed, and Scopus, expanded by forward and backward searches to identify 495 potentially eligible studies. After screening for relevance and quality, a total of 54 studies were included. We qualitatively summarized and analyzed these studies from technical, problem-oriented, and task-oriented perspectives. Our findings suggest that the technical aspects of DL for histopathology in melanoma can be further improved. The DL methodology was adopted later in this field, and still lacks the wider adoption of DL methods already shown to be effective for other applications. We also discuss upcoming trends toward ImageNet-based feature extraction and larger models. While DL has achieved human-competitive accuracy in routine pathological tasks, its performance on advanced tasks is still inferior to wet-lab testing (for example). Finally, we discuss the challenges impeding the translation of DL methods to clinical practice and provide insight into future research directions.
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Affiliation(s)
- Daniel Sauter
- Department of Computer Science, Fachhochschule Dortmund, 44227 Dortmund, Germany.
| | - Georg Lodde
- Department of Dermatology, University Hospital Essen, 45147 Essen, Germany
| | - Felix Nensa
- Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany; Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
| | - Dirk Schadendorf
- Department of Dermatology, University Hospital Essen, 45147 Essen, Germany
| | | | - Markus Kukuk
- Department of Computer Science, Fachhochschule Dortmund, 44227 Dortmund, Germany
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10
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Asif A, Rajpoot K, Graham S, Snead D, Minhas F, Rajpoot N. Unleashing the potential of AI for pathology: challenges and recommendations. J Pathol 2023; 260:564-577. [PMID: 37550878 PMCID: PMC10952719 DOI: 10.1002/path.6168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 08/09/2023]
Abstract
Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer-reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre-clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Amina Asif
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
| | - Kashif Rajpoot
- School of Computer ScienceUniversity of BirminghamBirminghamUK
| | - Simon Graham
- Histofy Ltd, Birmingham Business ParkBirminghamUK
| | - David Snead
- Histofy Ltd, Birmingham Business ParkBirminghamUK
- Department of PathologyUniversity Hospitals Coventry & Warwickshire NHS TrustCoventryUK
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
- Cancer Research CentreUniversity of WarwickCoventryUK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
- Histofy Ltd, Birmingham Business ParkBirminghamUK
- Cancer Research CentreUniversity of WarwickCoventryUK
- The Alan Turing InstituteLondonUK
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11
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Fu X, Sahai E, Wilkins A. Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response. J Pathol 2023; 260:578-591. [PMID: 37551703 PMCID: PMC10952145 DOI: 10.1002/path.6153] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/25/2023] [Accepted: 06/07/2023] [Indexed: 08/09/2023]
Abstract
In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Xiao Fu
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
- Biomolecular Modelling LaboratoryThe Francis Crick InstituteLondonUK
| | - Erik Sahai
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
| | - Anna Wilkins
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
- Division of Radiotherapy and ImagingInstitute of Cancer ResearchLondonUK
- Royal Marsden Hospitals NHS TrustLondonUK
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12
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Komura D, Ishikawa S. Meet the authors: Daisuke Komura and Shumpei Ishikawa. PATTERNS (NEW YORK, N.Y.) 2023; 4:100794. [PMID: 37521039 PMCID: PMC10382963 DOI: 10.1016/j.patter.2023.100794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
In this People of Data, Cell Press Community Review Scientific Editor Leia Judge talks to lead author Dr. Daisuke Komura and Principal Investigator Prof. Shumpei Ishikawa about their paper "Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists," which was published in the February issue of Patterns, and their experiences with Cell Press Community Review.
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Affiliation(s)
- Daisuke Komura
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shumpei Ishikawa
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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13
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Hatanaka S, Takahashi M, Nakano M. Discrimination of early HCC using single cell nucleus image and visualization of feature distribution in whole slide images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082928 DOI: 10.1109/embc40787.2023.10340502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Among hepatocellular carcinoma (HCC), early HCC such as well-differentiated hepatocellular carcinoma is more difficult to distinguish from non-cancer than other cancers. In particular, very well-differentiated hepatocellular carcinoma is even more difficult to distinguish, and it is difficult for pathologists to distinguish between cancer and non-cancer from a single nucleus image. If a function to distinguish cancer with a single cell nucleus image is realized, it may be possible to find new features related to nuclei that are useful for differentiating early HCC. The function will also be very helpful in needle biopsy where the area that can be observed is limited.In this study, we investigated the potential to discriminate cancer/non-cancer from an image of a single hepatocyte nucleus using CNN. The results indicated that discrimination was achievable with a correct rate of around 70%.The probability of cancer/non-cancer was visualized on WSI. The visualization results indicated a difference between cancerous and non-cancerous areas in 71% of the cases, which will help pathologists distinguish region of interest. Grouping sections with similar features also proved useful in improving accuracy and visualization results.
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14
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Iwaki T, Akiyama Y, Nosato H, Kinjo M, Niimi A, Taguchi S, Yamada Y, Sato Y, Kawai T, Yamada D, Sakanashi H, Kume H, Homma Y, Fukuhara H. Deep Learning Models for Cystoscopic Recognition of Hunner Lesion in Interstitial Cystitis. EUR UROL SUPPL 2023; 49:44-50. [PMID: 36874607 PMCID: PMC9975003 DOI: 10.1016/j.euros.2022.12.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/22/2022] [Indexed: 01/27/2023] Open
Abstract
Background Accurate cystoscopic recognition of Hunner lesions (HLs) is indispensable for better treatment prognosis in managing patients with Hunner-type interstitial cystitis (HIC), but frequently challenging due to its varying appearance. Objective To develop a deep learning (DL) system for cystoscopic recognition of a HL using artificial intelligence (AI). Design setting and participants A total of 626 cystoscopic images collected from January 8, 2019 to December 24, 2020, consisting of 360 images of HLs from 41 patients with HIC and 266 images of flat reddish mucosal lesions resembling HLs from 41 control patients including those with bladder cancer and other chronic cystitis, were used to create a dataset with an 8:2 ratio of training images and test images for transfer learning and external validation, respectively. AI-based five DL models were constructed, using a pretrained convolutional neural network model that was retrained to output 1 for a HL and 0 for control. A five-fold cross-validation method was applied for internal validation. Outcome measurements and statistical analysis True- and false-positive rates were plotted as a receiver operating curve when the threshold changed from 0 to 1. Accuracy, sensitivity, and specificity were evaluated at a threshold of 0.5. Diagnostic performance of the models was compared with that of urologists as a reader study. Results and limitations The mean area under the curve of the models reached 0.919, with mean sensitivity of 81.9% and specificity of 85.2% in the test dataset. In the reader study, the mean accuracy, sensitivity, and specificity were, respectively, 83.0%, 80.4%, and 85.6% for the models, and 62.4%, 79.6%, and 45.2% for expert urologists. Limitations include the diagnostic nature of a HL as warranted assertibility. Conclusions We constructed the first DL system that recognizes HLs with accuracy exceeding that of humans. This AI-driven system assists physicians with proper cystoscopic recognition of a HL. Patient summary In this diagnostic study, we developed a deep learning system for cystoscopic recognition of Hunner lesions in patients with interstitial cystitis. The mean area under the curve of the constructed system reached 0.919 with mean sensitivity of 81.9% and specificity of 85.2%, demonstrating diagnostic accuracy exceeding that of human expert urologists in detecting Hunner lesions. This deep learning system assists physicians with proper diagnosis of a Hunner lesion.
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Affiliation(s)
- Takuya Iwaki
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Department of Urology, Center Hospital of the National Center for Global Health and Medicine, Tokyo, Japan.,Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | - Yoshiyuki Akiyama
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hirokazu Nosato
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | - Manami Kinjo
- Department of Urology, Kyorin University School of Medicine, Tokyo, Japan
| | - Aya Niimi
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Department of Urology, New Tokyo Hospital, Matsudo, Japan
| | - Satoru Taguchi
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yuta Yamada
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yusuke Sato
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Taketo Kawai
- Department of Urology, Teikyo University School of Medicine, Tokyo, Japan
| | - Daisuke Yamada
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hidenori Sakanashi
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | - Haruki Kume
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yukio Homma
- Japanese Red Cross Medical Center, Tokyo, Japan
| | - Hiroshi Fukuhara
- Department of Urology, Kyorin University School of Medicine, Tokyo, Japan
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15
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De Luca F, Roda E, Ratto D, Desiderio A, Venuti MT, Ramieri M, Bottone MG, Savino E, Rossi P. Fighting secondary triple-negative breast cancer in cerebellum: A powerful aid from a medicinal mushrooms blend. Biomed Pharmacother 2023; 159:114262. [PMID: 36657301 DOI: 10.1016/j.biopha.2023.114262] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/05/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Breast cancer (BC) is the second most common cause of brain metastasis onset in patients, with the cerebellum accounting for the 33% of cases. In the current study, using a 4T1 triple-negative mouse BC model, we revealed that an orally administered medicinal mushrooms (MM) blend, rich in β-glucans, played a direct and specific anti-cancer action on cerebellar metastases, also bettering locomotor performances. The neuroprotective effect of the MM blend plays through (i) a direct and specific inhibition of cerebellar metastatization pattern typical of TNBC (with an induced reduction of about 50% of metastases density) and (ii) the regulation of apoptosis and proliferation-related genes, as suggested by expression changes of specific molecular markers, i.e. PCNA, p53, Bcl2, BAX, CASP9, CASP3, Hsp70 and AIF. Therefore, inhibiting the metastatization process, triggering a significant apoptosis increase, and lessening cell proliferation, this MM supplement, employed as adjuvant treatment in association with conventional therapy, could represent a promising approach, in the field of Integrative Oncology, for patients' management in both prevention and treatment of brain metastases from BC.
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Affiliation(s)
- Fabrizio De Luca
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, 27100 Pavia, Italy.
| | - Elisa Roda
- Laboratory of Clinical & Experimental Toxicology, Pavia Poison Centre, National Toxicology Information Centre, Toxicology Unit, Istituti Clinici Scientifici Maugeri IRCCS, 27100 Pavia, Italy.
| | - Daniela Ratto
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, 27100 Pavia, Italy.
| | - Anthea Desiderio
- Department of Earth and Environmental Science, University of Pavia, 27100 Pavia, Italy.
| | - Maria Teresa Venuti
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, 27100 Pavia, Italy.
| | - Martino Ramieri
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, 27100 Pavia, Italy.
| | - Maria Grazia Bottone
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, 27100 Pavia, Italy.
| | - Elena Savino
- Department of Earth and Environmental Science, University of Pavia, 27100 Pavia, Italy.
| | - Paola Rossi
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, 27100 Pavia, Italy.
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16
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Abe H, Kurose Y, Takahama S, Kume A, Nishida S, Fukasawa M, Yasunaga Y, Ushiku T, Ninomiya Y, Yoshizawa A, Murao K, Sato S, Kitsuregawa M, Harada T, Kitagawa M, Fukayama M. Development and multi‐institutional validation of an artificial intelligence‐based diagnostic system for gastric biopsy. Cancer Sci 2022; 113:3608-3617. [PMID: 36068652 PMCID: PMC9530856 DOI: 10.1111/cas.15514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/10/2022] [Accepted: 07/21/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Hiroyuki Abe
- Department of Pathology, Graduate School of Medicine the University of Tokyo Tokyo Japan
- Japanese Society of Pathology Tokyo Japan
| | - Yusuke Kurose
- Research Center for Advanced Science and Technology the University of Tokyo Tokyo Japan
- Center for Advanced Intelligence Project RIKEN Tokyo Japan
| | - Shusuke Takahama
- Graduate School of Information Science and Technology the University of Tokyo Tokyo Japan
| | - Ayako Kume
- Department of Pathology, Graduate School of Medicine the University of Tokyo Tokyo Japan
| | - Shu Nishida
- Department of Pathology, Graduate School of Medicine the University of Tokyo Tokyo Japan
| | - Miyako Fukasawa
- Department of Pathology, Graduate School of Medicine the University of Tokyo Tokyo Japan
| | - Yoichi Yasunaga
- Department of Pathology, Graduate School of Medicine the University of Tokyo Tokyo Japan
| | - Tetsuo Ushiku
- Department of Pathology, Graduate School of Medicine the University of Tokyo Tokyo Japan
| | - Youichiro Ninomiya
- Research Center for Medical Bigdata National Institute of Informatics Tokyo Japan
| | - Akihiko Yoshizawa
- Japanese Society of Pathology Tokyo Japan
- Department of Diagnostic Pathology, Graduate School of Medicine Kyoto University Kyoto Japan
| | - Kohei Murao
- Research Center for Medical Bigdata National Institute of Informatics Tokyo Japan
| | - Shin’ichi Sato
- Research Center for Medical Bigdata National Institute of Informatics Tokyo Japan
| | - Masaru Kitsuregawa
- Research Center for Medical Bigdata National Institute of Informatics Tokyo Japan
- Institute of Industrial Science the University of Tokyo Tokyo Japan
| | - Tatsuya Harada
- Research Center for Advanced Science and Technology the University of Tokyo Tokyo Japan
- Center for Advanced Intelligence Project RIKEN Tokyo Japan
- Research Center for Medical Bigdata National Institute of Informatics Tokyo Japan
| | - Masanobu Kitagawa
- Japanese Society of Pathology Tokyo Japan
- Department of Comprehensive Pathology, Graduate School of Medical and Dental Sciences Tokyo Medical and Dental University Tokyo Japan
| | - Masashi Fukayama
- Department of Pathology, Graduate School of Medicine the University of Tokyo Tokyo Japan
- Japanese Society of Pathology Tokyo Japan
- Asahi TelePathology Center Asahi General Hospital Chiba Japan
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