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Bae Y, Byun J, Lee H, Han B. Comparative analysis of chronic progressive nephropathy (CPN) diagnosis in rat kidneys using an artificial intelligence deep learning model. Toxicol Res 2024; 40:551-559. [PMID: 39345736 PMCID: PMC11436530 DOI: 10.1007/s43188-024-00247-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 05/04/2024] [Accepted: 05/15/2024] [Indexed: 10/01/2024] Open
Abstract
With the development of artificial intelligence (AI), technologies based on machines and deep learning are being used in many academic fields. In toxicopathology, research is actively underway to analyze whole slide image (WSI)-level images using AI deep-learning models. However, few studies have been conducted on models for diagnosing complex lesions comprising multiple lesions. Therefore, this study used deep learning segmentation models (YOLOv8, Mask R-CNN, and SOLOv2) to identify three representative lesions (tubular basophilia with atrophy, mononuclear cell infiltration, and hyaline casts) of chronic progressive nephropathy of the kidney, a complex lesion observed in a non-clinical test using rats and selected an initial model appropriate for diagnosing complex lesions by analyzing the characteristics of each algorithm. Approximately 2000 images containing three lesions were extracted using 33 WSI of rat kidneys with chronic progressive nephropathy. Among them, 1701 images were divided into first and second rounds of learning. The loss and mAP50 values were measured twice to confirm the performances of the three algorithms. Loss measurements were stopped at an appropriate epoch to prevent overfitting, and the loss value decreased in the second round based on the data learned in the first round. After measuring the accuracy twice, detection using Mask R-CNN showed the highest mAP50 in all lesions among the three models and was considered sufficient as an initial model for diagnosing complex lesions. By contrast, the YOLOv8 and SOLOv2 models showed low accuracy for all three lesions and had difficulty with segmentation tasks. Therefore, this paper proposes a Mask R-CNN as the initial model for segmenting complex lesions. Precise diagnosis is possible if the model can be trained by increasing the input data, thereby providing greater accuracy in diagnosing pathological images.
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Affiliation(s)
- Yeji Bae
- Department of Pharmaceutical Engineering, Life Health College, Hoseo University, Asan City, Republic of Korea
| | - Jongsu Byun
- Pathology Team, Microscopic Examination, Dt&CRO, Yongin City, Republic of Korea
| | - Hangyu Lee
- Program Development Team, DeepSoft, Seoul, Republic of Korea
| | - Beomseok Han
- Department of Pharmaceutical Engineering, Life Health College, Hoseo University, Asan City, Republic of Korea
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Prezja F, Annala L, Kiiskinen S, Lahtinen S, Ojala T, Ruusuvuori P, Kuopio T. Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning. Heliyon 2024; 10:e37561. [PMID: 39309850 PMCID: PMC11415691 DOI: 10.1016/j.heliyon.2024.e37561] [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] [Received: 09/02/2024] [Accepted: 09/05/2024] [Indexed: 09/25/2024] Open
Abstract
In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for patient stratification and treatment selection is still being explored. The current gold standard relies on expensive and time-consuming genetic tests. However, recent research highlights the potential of convolutional neural networks (CNNs) to facilitate the extraction of clinically relevant biomarkers from these readily available images. These CNN-based biomarkers can predict patient outcomes comparably to golden standards, with the added advantages of speed, automation, and minimal cost. The predictive potential of CNN-based biomarkers fundamentally relies on the ability of CNNs to accurately classify diverse tissue types from whole slide microscope images. Consequently, enhancing the accuracy of tissue class decomposition is critical to amplifying the prognostic potential of imaging-based biomarkers. This study introduces a hybrid deep transfer learning and ensemble machine learning model that improves upon previous approaches, including a transformer and neural architecture search baseline for this task. We employed a pairing of the EfficientNetV2 architecture with a random forest classification head. Our model achieved 96.74% accuracy (95% CI: 96.3%-97.1%) on the external test set and 99.89% on the internal test set. Recognizing the potential of these models in the task, we have made them publicly available.
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Affiliation(s)
- Fabi Prezja
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
| | - Leevi Annala
- University of Helsinki, Faculty of Science, Department of Computer Science, Helsinki, Finland
- University of Helsinki, Faculty of Agriculture and Forestry, Department of Food and Nutrition, Helsinki, Finland
| | - Sampsa Kiiskinen
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
| | - Suvi Lahtinen
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
- University of Jyväskylä, Faculty of Mathematics and Science, Department of Biological and Environmental Science, Jyväskylä, 40014, Finland
| | - Timo Ojala
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
| | - Pekka Ruusuvuori
- University of Turku, Institute of Biomedicine, Cancer Research Unit, Turku, 20014, Finland
- Turku University Hospital, FICAN West Cancer Centre, Turku, 20521, Finland
| | - Teijo Kuopio
- University of Jyväskylä, Department of Biological and Environmental Science, Jyväskylä, 40014, Finland
- Hospital Nova of Central Finland, Department of Pathology, Jyväskylä, 40620, Finland
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潘 兴, 童 珂, 鄢 成, 罗 金, 杨 华, 丁 菊. [Research progress on colorectal cancer identification based on convolutional neural network]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2024; 41:854-860. [PMID: 39218614 PMCID: PMC11366468 DOI: 10.7507/1001-5515.202310027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 05/24/2024] [Indexed: 09/04/2024]
Abstract
Colorectal cancer (CRC) is a common malignant tumor that seriously threatens human health. CRC presents a formidable challenge in terms of accurate identification due to its indistinct boundaries. With the widespread adoption of convolutional neural networks (CNNs) in image processing, leveraging CNNs for automatic classification and segmentation holds immense potential for enhancing the efficiency of colorectal cancer recognition and reducing treatment costs. This paper explores the imperative necessity for applying CNNs in clinical diagnosis of CRC. It provides an elaborate overview on research advancements pertaining to CNNs and their improved models in CRC classification and segmentation. Furthermore, this work summarizes the ideas and common methods for optimizing network performance and discusses the challenges faced by CNNs as well as future development trends in their application towards CRC classification and segmentation, thereby promoting their utilization within clinical diagnosis.
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Affiliation(s)
- 兴亮 潘
- 四川轻化工大学 自动化与信息工程学院(四川自贡 643000)The School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China
- 四川轻化工大学 人工智能四川省重点实验室(四川自贡 643000)The Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China
| | - 珂 童
- 四川轻化工大学 自动化与信息工程学院(四川自贡 643000)The School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China
- 四川轻化工大学 人工智能四川省重点实验室(四川自贡 643000)The Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China
| | - 成东 鄢
- 四川轻化工大学 自动化与信息工程学院(四川自贡 643000)The School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China
- 四川轻化工大学 人工智能四川省重点实验室(四川自贡 643000)The Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China
| | - 金龙 罗
- 四川轻化工大学 自动化与信息工程学院(四川自贡 643000)The School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China
| | - 华 杨
- 四川轻化工大学 自动化与信息工程学院(四川自贡 643000)The School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China
| | - 菊容 丁
- 四川轻化工大学 自动化与信息工程学院(四川自贡 643000)The School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China
- 四川轻化工大学 人工智能四川省重点实验室(四川自贡 643000)The Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China
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Khalid M, Deivasigamani S, V S, Rajendran S. An efficient colorectal cancer detection network using atrous convolution with coordinate attention transformer and histopathological images. Sci Rep 2024; 14:19109. [PMID: 39154091 PMCID: PMC11330491 DOI: 10.1038/s41598-024-70117-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: 06/17/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024] Open
Abstract
The second most common type of malignant tumor worldwide is colorectal cancer. Histopathology image analysis offers crucial data for the clinical diagnosis of colorectal cancer. Currently, deep learning techniques are applied to enhance cancer classification and tumor localization in histopathological image analysis. Moreover, traditional deep learning techniques might loss integrated information in the image while evaluating thousands of patches recovered from whole slide images (WSIs). This research proposes a novel colorectal cancer detection network (CCDNet) that combines coordinate attention transformer with atrous convolution. CCDNet first denoises the input histopathological image using a Wiener based Midpoint weighted non-local means filter (WMW-NLM) for guaranteeing precise diagnoses and maintain image features. Also, a novel atrous convolution with coordinate attention transformer (AConvCAT) is introduced, which successfully combines the advantages of two networks to classify colorectal tissue at various scales by capturing local and global information. Further, coordinate attention model is integrated with a Cross-shaped window (CrSWin) transformer for capturing tiny changes in colorectal tissue from multiple angles. The proposed CCDNet achieved accuracy rates of 98.61% and 98.96%, on the colorectal histological image and NCT-CRC-HE-100 K datasets correspondingly. The comparison analysis demonstrates that the suggested framework performed better than the most advanced methods already in use. In hospitals, clinicians can use the proposed CCDNet to verify the diagnosis.
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Affiliation(s)
- Majdi Khalid
- Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, 21955, Makkah, Saudi Arabia
| | - Sugitha Deivasigamani
- Department of Computer Science and Engineering, University College of Engineering, A Constituent College of Anna University, Thirukkuvalai, Chennai, India
| | - Sathiya V
- Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, 600123, India
| | - Surendran Rajendran
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, Tamil Nadu, India.
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Vanitha K, R MT, Sree SS, Guluwadi S. Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning. BMC Med Inform Decis Mak 2024; 24:222. [PMID: 39112991 PMCID: PMC11304580 DOI: 10.1186/s12911-024-02628-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 08/01/2024] [Indexed: 08/11/2024] Open
Abstract
Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate diagnosis and treatment. This study addresses critical challenges in the diagnostic imaging of lung and colon cancers, which are among the leading causes of cancer-related deaths worldwide. Recognizing the limitations of existing diagnostic methods, which often suffer from overfitting and poor generalizability, our research introduces a novel deep learning framework that synergistically combines the Xception and MobileNet architectures. This innovative ensemble model aims to enhance feature extraction, improve model robustness, and reduce overfitting.Our methodology involves training the hybrid model on a comprehensive dataset of histopathological images, followed by validation against a balanced test set. The results demonstrate an impressive classification accuracy of 99.44%, with perfect precision and recall in identifying certain cancerous and non-cancerous tissues, marking a significant improvement over traditional approach.The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Mapping (Grad-CAM), the model offers enhanced interpretability, allowing clinicians to visualize the diagnostic reasoning process. This transparency is vital for clinical acceptance and enables more personalized, accurate treatment planning. Our study not only pushes the boundaries of medical imaging technology but also sets the stage for future research aimed at expanding these techniques to other types of cancer diagnostics.
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Affiliation(s)
- K Vanitha
- Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore, India
| | - Mahesh T R
- Department of Computer Science and Engineering, JAIN (Deemed-to-Be University), Bengaluru, 562112, India
| | - S Sathea Sree
- Department of Computer Science and Engineering, School of Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India
| | - Suresh Guluwadi
- Adama Science and Technology University, Adama, 302120, Ethiopia.
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Song H, Xiao X, Han X, Sun Y, Zheng G, Miao Q, Zhang Y, Tan J, Liu G, He Q, Zhou J, Zheng Z, Jiang G. Development and interpretation of a multimodal predictive model for prognosis of gastrointestinal stromal tumor. NPJ Precis Oncol 2024; 8:157. [PMID: 39060449 PMCID: PMC11282065 DOI: 10.1038/s41698-024-00636-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
Gastrointestinal stromal tumor (GIST) is the most common mesenchymal original tumor in gastrointestinal (GI) tract and is considered to have varying malignant potential. With the advancement of computer science, radiomics technology and deep learning had been applied in medical researches. It's vital to construct a more accurate and reliable multimodal predictive model for recurrence-free survival (RFS) aiding for clinical decision-making. A total of 254 patients underwent surgery and pathologically diagnosed with GIST in The First Hospital of China Medical University from 2019 to 2022 were included in the study. Preoperative contrast enhanced computerized tomography (CE-CT) and hematoxylin/eosin (H&E) stained whole slide images (WSI) were acquired for analysis. In the present study, we constructed a sum of 11 models while the multimodal model (average C-index of 0.917 on validation set in 10-fold cross validation) performed the best on external validation cohort with an average C-index of 0.864. The multimodal model also reached statistical significance when validated in the external validation cohort (n = 42) with a p-value of 0.0088 which pertained to the recurrence-free survival (RFS) comparison between the high and low groups using the optimal threshold on the predictive score. We also explored the biological significance of radiomics and pathomics features by visualization and quantitative analysis. In the present study, we constructed a multimodal model predicting RFS of GIST which was prior over unimodal models. We also proposed hypothesis on the correlation between morphology of tumor cell and prognosis.
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Affiliation(s)
- He Song
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China.
| | - XianHao Xiao
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xu Han
- Department of Pathology, The First Hospital and the College of Basic Medical Sciences of China Medical University, Shenyang, Liaoning, China
| | - YeFei Sun
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - GuoLiang Zheng
- Department of Gastric Surgery, Cancer Hospital of China Medical University; Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning, China
| | - Qi Miao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - YuLong Zhang
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - JiaYing Tan
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Gang Liu
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - QianRu He
- The state Key laboratory of Neurology and Oncology Drug Development, Jiangsu Simcere Diagnostics Co.,Ltd, Nanjing, China
| | - JianPing Zhou
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China.
| | - ZhiChao Zheng
- Department of Gastric Surgery, Cancer Hospital of China Medical University; Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning, China.
| | - GuiYang Jiang
- Department of Pathology, The College of Basic Medical Sciences and The First Hospital of China Medical University, Shenyang, Liaoning, China.
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Ozaki Y, Broughton P, Abdollahi H, Valafar H, Blenda AV. Integrating Omics Data and AI for Cancer Diagnosis and Prognosis. Cancers (Basel) 2024; 16:2448. [PMID: 39001510 PMCID: PMC11240413 DOI: 10.3390/cancers16132448] [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: 05/22/2024] [Revised: 06/27/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024] Open
Abstract
Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.
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Affiliation(s)
- Yousaku Ozaki
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
| | - Phil Broughton
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
| | - Hamed Abdollahi
- Department of Computer Science and Engineering, Molinaroli College of Engineering and Computing, Columbia, SC 29208, USA;
| | - Homayoun Valafar
- Department of Computer Science and Engineering, Molinaroli College of Engineering and Computing, Columbia, SC 29208, USA;
| | - Anna V. Blenda
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
- Prisma Health Cancer Institute, Prisma Health, Greenville, SC 29605, USA
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Wismayer R, Kiwanuka J, Wabinga H, Odida M. Dietary risk factors for colorectal cancer in Uganda: a case-control study. BMC Nutr 2024; 10:88. [PMID: 38898481 PMCID: PMC11186163 DOI: 10.1186/s40795-024-00894-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 06/05/2024] [Indexed: 06/21/2024] Open
Abstract
INTRODUCTION Low-income countries in East Africa have a lower incidence of colorectal cancer (CRC) than high-income countries; however, the incidence has steadily increased in the last few decades. In Uganda, the extent to which genetic and environmental factors, particularly dietary factors, contribute to the aetiology of CRC is unclear. Therefore, the objective of our study was to determine the relationship between dietary factors and CRC in Uganda. METHODS We conducted a case-control study and recruited 128 cases and 256 controls, matched for age (± 5 years) and sex. Data regarding the frequency of consumption of the dietary factors were obtained from all the participants using an interview-based questionnaire. The potential dietary risk factors and protective factors evaluated included the type and frequency of meat consumed and the type and frequency of high-fibre foods consumed. The frequency was either 4 or more times/week, 2-3 times/week, once/week or never. Conditional logistic regression analyses were used to determine the odds ratios associated with the different risk and protective factors. RESULTS The median age (IQR) for the case participants was 55.5 (43-67.5) years, and that of the control participants was 54 (42-65) years. The male-to-female ratio was 1:1 for all the participants. Factors significantly associated with CRC cases included:- the consumption of boiled beef 2-3 times/week (aOR:3.24; 95% CI: 1.08-9.69; p < 0.035). Consumption of high-fibre foods, including:- millet for ≥ 4 times/week (aOR: 0.23; 95% CI: 0.09-0.62; p = 0.003)), spinach for ≥ 4 times/week (aOR:0.32; 95% CI: 0.11-0.97; p = 0.043), and potatoes 2-3 times/week (aOR: 0.30; 95% CI: 0.09-0.97; p = 0.044), were protective against CRC. Boiled cassava showed a tendency to reduce the likelihood of CRC when consumed ≥ 4 times/week (aOR:0.38; 95% CI: 0.12-1.18) however this did not reach statistical significance (p = 0.093). CONCLUSIONS The consumption of boiled beef increases the risk of CRC, while the intake of high-fibre foods may reduce the risk of CRC among Ugandans. We recommend nutritional educational programmes to increase public awareness regarding the protective role of a high-fibre diet and to limit the intake of cooked meat in our Ugandan population.
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Affiliation(s)
- Richard Wismayer
- Department of Surgery, Masaka Regional Referral Hospital, Masaka, Uganda.
- Department of Surgery, Faculty of Health Sciences, Equator University for Science and Technology, Masaka, Uganda.
- Department of Surgery, Faculty of Health Sciences, Habib Medical School, IUIU University, Kampala, Uganda.
- Department of Pathology, School of Biomedical Sciences, College of Health Sciences, Makerere University, Kampala, Uganda.
| | - Julius Kiwanuka
- Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Henry Wabinga
- Department of Pathology, School of Biomedical Sciences, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Michael Odida
- Department of Pathology, School of Biomedical Sciences, College of Health Sciences, Makerere University, Kampala, Uganda
- Department of Pathology, Faculty of Medicine, Gulu University, Gulu, Uganda
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Chen J, Yang J, Wang J, Zhao Z, Wang M, Sun C, Song N, Feng S. Study on an Automatic Classification Method for Determining the Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-Scale Spatial-Spectral Fusion Features. SENSORS (BASEL, SWITZERLAND) 2024; 24:3803. [PMID: 38931588 PMCID: PMC11207485 DOI: 10.3390/s24123803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 06/05/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024]
Abstract
This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLMER-ResNet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial-spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3%) and a Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and treatment adjustment, by providing physicians with insights into the underlying pathological organization of gliomas.
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Affiliation(s)
- Jiaqi Chen
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (J.C.)
- University of Chinese Academy of Sciences, Beijing 130033, China
| | - Jin Yang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (J.C.)
| | - Jinyu Wang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (J.C.)
- University of Chinese Academy of Sciences, Beijing 130033, China
| | - Zitong Zhao
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (J.C.)
- University of Chinese Academy of Sciences, Beijing 130033, China
| | - Mingjia Wang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (J.C.)
| | - Ci Sun
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (J.C.)
| | - Nan Song
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (J.C.)
| | - Shulong Feng
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (J.C.)
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Zhang L, Wong C, Li Y, Huang T, Wang J, Lin C. Artificial intelligence assisted diagnosis of early tc markers and its application. Discov Oncol 2024; 15:172. [PMID: 38761260 PMCID: PMC11102422 DOI: 10.1007/s12672-024-01017-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 05/06/2024] [Indexed: 05/20/2024] Open
Abstract
Thyroid cancer (TC) is a common endocrine malignancy with an increasing incidence worldwide. Early diagnosis is particularly important for TC patients, because it allows patients to receive treatment as early as possible. Artificial intelligence (AI) provides great advantages for complex healthcare systems by analyzing big data based on machine learning. Nowadays, AI is widely used in the early diagnosis of cancer such as TC. Ultrasound detection and fine needle aspiration biopsy are the main methods for early diagnosis of TC. AI has been widely used in the detection of malignancy in thyroid nodules by ultrasound images, cytopathology images and molecular markers. It shows great potential in auxiliary medical diagnosis. The latest clinical trial has shown that the performance of AI models matches with the diagnostic efficiency of experienced clinicians, and more efficient AI tools will be developed in the future. Therefore, in this review, we summarized the recent advances in the application of AI algorithms in assessing the risk of malignancy in thyroid nodules. The objective of this review was to provide a data base for the clinical use of AI-assisted diagnosis in TC, as well as to provide new ideas for the next generation of AI-assisted diagnosis in TC.
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Affiliation(s)
- Laney Zhang
- Yale School of Public Health, New Haven, CT, USA
| | - Chinting Wong
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yungeng Li
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | | | - Jiawen Wang
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Chenghe Lin
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China.
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11
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McGenity C, Clarke EL, Jennings C, Matthews G, Cartlidge C, Freduah-Agyemang H, Stocken DD, Treanor D. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. NPJ Digit Med 2024; 7:114. [PMID: 38704465 PMCID: PMC11069583 DOI: 10.1038/s41746-024-01106-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 04/12/2024] [Indexed: 05/06/2024] Open
Abstract
Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.
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Affiliation(s)
- Clare McGenity
- University of Leeds, Leeds, UK.
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.
| | - Emily L Clarke
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Charlotte Jennings
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | | | | | | | - Darren Treanor
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Department of Clinical Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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12
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Yilmaz F, Brickman A, Najdawi F, Yakirevich E, Egger R, Resnick MB. Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies. J Transl Med 2024; 104:102043. [PMID: 38431118 DOI: 10.1016/j.labinv.2024.102043] [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: 10/30/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024] Open
Abstract
This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.
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Affiliation(s)
- Fazilet Yilmaz
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Arlen Brickman
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Fedaa Najdawi
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Evgeny Yakirevich
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | | | - Murray B Resnick
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island.
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13
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Yuan L, Zhou H, Xiao X, Zhang X, Chen F, Liu L, Liu J, Bao S, Tao K. Development and external validation of a transfer learning-based system for the pathological diagnosis of colorectal cancer: a large emulated prospective study. Front Oncol 2024; 14:1365364. [PMID: 38725622 PMCID: PMC11079287 DOI: 10.3389/fonc.2024.1365364] [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] [Received: 01/19/2024] [Accepted: 04/11/2024] [Indexed: 05/12/2024] Open
Abstract
Background The progress in Colorectal cancer (CRC) screening and management has resulted in an unprecedented caseload for histopathological diagnosis. While artificial intelligence (AI) presents a potential solution, the predominant emphasis on slide-level aggregation performance without thorough verification of cancer in each location, impedes both explainability and transparency. Effectively addressing these challenges is crucial to ensuring the reliability and efficacy of AI in histology applications. Method In this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm² grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center. Results Our results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of <0.01, signifying the dependability of the AI system to preclude diagnostic considerations. The positive likelihood ratio of 26.54, surpassing 10 at the grid level, underscores the imperative for meticulous scrutiny of any AI-generated highlights. Consequently, all four participating pathologists demonstrated statistically significant diagnostic improvements with AI assistance. Conclusion Our transfer learning approach has successfully yielded an algorithm that can be validated for CRC histological localizations in whole slide imaging. The outcome advocates for the integration of the AI system into histopathological diagnosis, serving either as a diagnostic exclusion application or a computer-aided detection (CADe) tool. This integration has the potential to alleviate the workload of pathologists and ultimately benefit patients.
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Affiliation(s)
- Liuhong Yuan
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Henghua Zhou
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | | | - Xiuqin Zhang
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Feier Chen
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Lin Liu
- Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | | | - Shisan Bao
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Kun Tao
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
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14
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Harasym J, Dziendzikowska K, Kopiasz Ł, Wilczak J, Sapierzyński R, Gromadzka-Ostrowska J. Consumption of Feed Supplemented with Oat Beta-Glucan as a Chemopreventive Agent against Colon Cancerogenesis in Rats. Nutrients 2024; 16:1125. [PMID: 38674816 PMCID: PMC11054053 DOI: 10.3390/nu16081125] [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: 03/01/2024] [Revised: 04/05/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Colorectal cancer (CRC) accounts for 30% of all cancer cases worldwide and is the second leading cause of cancer-related deaths. CRC develops over a long period of time, and in the early stages, pathological changes can be mitigated through nutritional interventions using bioactive plant compounds. Our study aims to determine the effect of highly purified oat beta-glucan on an animal CRC model. The study was performed on forty-five male Sprague-Dawley rats with azoxymethane-induced early-stage CRC, which consumed feed containing 1% or 3% low molar mass oat beta-glucan (OBG) for 8 weeks. In the large intestine, morphological changes, CRC signaling pathway genes (RT-PCR), and proteins (Western blot, immunohistochemistry) expression were analyzed. Whole blood hematology and blood redox status were also performed. Results indicated that the histologically confirmed CRC condition led to a downregulation of the WNT/β-catenin pathway, along with alterations in oncogenic and tumor suppressor gene expression. However, OBG significantly modulated these effects, with the 3% OBG showing a more pronounced impact. Furthermore, CRC rats exhibited elevated levels of oxidative stress and antioxidant enzyme activity in the blood, along with decreased white blood cell and lymphocyte counts. Consumption of OBG at any dose normalized these parameters. The minimal effect of OBG in the physiological intestine and the high activity in the pathological condition suggest that OBG is both safe and effective in early-stage CRC.
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Affiliation(s)
- Joanna Harasym
- Department of Biotechnology and Food Analysis, Wroclaw University of Economics and Business, 53-345 Wroclaw, Poland
| | - Katarzyna Dziendzikowska
- Department of Dietetics, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences, 02-776 Warsaw, Poland; (K.D.); (J.G.-O.)
| | - Łukasz Kopiasz
- Department of Dietetics, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences, 02-776 Warsaw, Poland; (K.D.); (J.G.-O.)
| | - Jacek Wilczak
- Department of Physiological Sciences, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-776 Warsaw, Poland;
| | - Rafał Sapierzyński
- Department of Pathology and Veterinary Diagnostic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-776 Warsaw, Poland;
| | - Joanna Gromadzka-Ostrowska
- Department of Dietetics, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences, 02-776 Warsaw, Poland; (K.D.); (J.G.-O.)
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15
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Sharkas M, Attallah O. Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform. Sci Rep 2024; 14:6914. [PMID: 38519513 PMCID: PMC10959971 DOI: 10.1038/s41598-024-56820-w] [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: 12/11/2022] [Accepted: 03/11/2024] [Indexed: 03/25/2024] Open
Abstract
Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it is complicated, time-consuming, and subjective. Computer-aided diagnostic (CAD) systems using digital pathology can help pathologists diagnose CRC faster and more accurately than manual histopathology examinations. Deep learning algorithms especially convolutional neural networks (CNNs) are advocated for diagnosis of CRC. Nevertheless, most previous CAD systems obtained features from one CNN, these features are of huge dimension. Also, they relied on spatial information only to achieve classification. In this paper, a CAD system is proposed called "Color-CADx" for CRC recognition. Different CNNs namely ResNet50, DenseNet201, and AlexNet are used for end-to-end classification at different training-testing ratios. Moreover, features are extracted from these CNNs and reduced using discrete cosine transform (DCT). DCT is also utilized to acquire spectral representation. Afterward, it is used to further select a reduced set of deep features. Furthermore, DCT coefficients obtained in the previous step are concatenated and the analysis of variance (ANOVA) feature selection approach is applied to choose significant features. Finally, machine learning classifiers are employed for CRC classification. Two publicly available datasets were investigated which are the NCT-CRC-HE-100 K dataset and the Kather_texture_2016_image_tiles dataset. The highest achieved accuracy reached 99.3% for the NCT-CRC-HE-100 K dataset and 96.8% for the Kather_texture_2016_image_tiles dataset. DCT and ANOVA have successfully lowered feature dimensionality thus reducing complexity. Color-CADx has demonstrated efficacy in terms of accuracy, as its performance surpasses that of the most recent advancements.
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Affiliation(s)
- Maha Sharkas
- Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt
| | - Omneya Attallah
- Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt.
- Wearables, Biosensing, and Biosignal Processing Laboratory, Arab Academy for Science, Technology and Maritime Transport, Alexandria, 21937, Egypt.
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16
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Neto PC, Montezuma D, Oliveira SP, Oliveira D, Fraga J, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Reinhard S, Zlobec I, Pinto IM, Cardoso JS. An interpretable machine learning system for colorectal cancer diagnosis from pathology slides. NPJ Precis Oncol 2024; 8:56. [PMID: 38443695 PMCID: PMC10914836 DOI: 10.1038/s41698-024-00539-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 02/08/2024] [Indexed: 03/07/2024] Open
Abstract
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.
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Affiliation(s)
- Pedro C Neto
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
| | - Diana Montezuma
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal.
- Cancer Biology and Epigenetics Group, Research Center of IPO Porto (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto) / Porto Comprehensive Cancer Center (Porto.CCC), R. Dr. António Bernardino de Almeida 865, Porto, 4200-072, Porto, Portugal.
- Doctoral Programme in Medical Sciences, School of Medicine and Biomedical Sciences - University of Porto (ICBAS-UP), R. Jorge de Viterbo Ferreira 228, Porto, 4050-313, Porto, Portugal.
| | - Sara P Oliveira
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
| | - Domingos Oliveira
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - João Fraga
- Department of Pathology, IPO-Porto, R. Dr. António Bernardino de Almeida 865, Porto, 4200-072, Porto, Portugal
| | - Ana Monteiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - João Monteiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Liliana Ribeiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Sofia Gonçalves
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Stefan Reinhard
- Institute of Pathology, University of Bern, Uni Bern, Murtenstrasse 31, Bern, 3008, Bern, Switzerland
| | - Inti Zlobec
- Institute of Pathology, University of Bern, Uni Bern, Murtenstrasse 31, Bern, 3008, Bern, Switzerland
| | - Isabel M Pinto
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Jaime S Cardoso
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal
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17
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Unger M, Kather JN. A systematic analysis of deep learning in genomics and histopathology for precision oncology. BMC Med Genomics 2024; 17:48. [PMID: 38317154 PMCID: PMC10845449 DOI: 10.1186/s12920-024-01796-9] [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: 08/03/2023] [Accepted: 01/02/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Digitized histopathological tissue slides and genomics profiling data are available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has been broadly used to extract clinically actionable information and biological knowledge from pathology slides and genomic data in cancer. In addition, a number of recent studies have introduced multimodal DL models designed to simultaneously process both images from pathology slides and genomic data as inputs. By comparing patterns from one data modality with those in another, multimodal DL models are capable of achieving higher performance compared to their unimodal counterparts. However, the application of these methodologies across various tumor entities and clinical scenarios lacks consistency. METHODS Here, we present a systematic survey of the academic literature from 2010 to November 2023, aiming to quantify the application of DL for pathology, genomics, and the combined use of both data types. After filtering 3048 publications, our search identified 534 relevant articles which then were evaluated by basic (diagnosis, grading, subtyping) and advanced (mutation, drug response and survival prediction) application types, publication year and addressed cancer tissue. RESULTS Our analysis reveals a predominant application of DL in pathology compared to genomics. However, there is a notable surge in DL incorporation within both domains. Furthermore, while DL applied to pathology primarily targets the identification of histology-specific patterns in individual tissues, DL in genomics is more commonly used in a pan-cancer context. Multimodal DL, on the contrary, remains a niche topic, evidenced by a limited number of publications, primarily focusing on prognosis predictions. CONCLUSION In summary, our quantitative analysis indicates that DL not only has a well-established role in histopathology but is also being successfully integrated into both genomic and multimodal applications. In addition, there is considerable potential in multimodal DL for harnessing further advanced tasks, such as predicting drug response. Nevertheless, this review also underlines the need for further research to bridge the existing gaps in these fields.
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Affiliation(s)
- Michaela Unger
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.
- Department of Medicine I, University Hospital Dresden, Dresden, Germany.
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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18
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Zhang S, Wu SQY, Hum M, Perumal J, Tan EY, Lee ASG, Teng J, Dinish US, Olivo M. Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis. RSC Adv 2024; 14:3599-3610. [PMID: 38264270 PMCID: PMC10804230 DOI: 10.1039/d3ra05723b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/17/2023] [Indexed: 01/25/2024] Open
Abstract
Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.
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Affiliation(s)
- Shuyan Zhang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Steve Qing Yang Wu
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Melissa Hum
- Division of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre Singapore (NCCS) 30 Hospital Boulevard Singapore 168583 Republic of Singapore
| | - Jayakumar Perumal
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Ern Yu Tan
- Breast & Endocrine Surgery, Tan Tock Seng Hospital (TTSH) 11 Jln Tan Tock Seng Singapore 308433 Republic of Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Republic of Singapore
| | - Ann Siew Gek Lee
- Division of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre Singapore (NCCS) 30 Hospital Boulevard Singapore 168583 Republic of Singapore
- SingHealth Duke-NUS Oncology Academic Clinical Programme (ONCO ACP), Duke-NUS Medical School Singapore 169857 Republic of Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore Singapore 117593 Republic of Singapore
| | - Jinghua Teng
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - U S Dinish
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Malini Olivo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
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Li JW, Wang LM, Ichimasa K, Lin KW, Ngu JCY, Ang TL. Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth? Clin Endosc 2024; 57:24-35. [PMID: 37743068 PMCID: PMC10834280 DOI: 10.5946/ce.2023.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 05/11/2023] [Indexed: 09/26/2023] Open
Abstract
The field of artificial intelligence is rapidly evolving, and there has been an interest in its use to predict the risk of lymph node metastasis in T1 colorectal cancer. Accurately predicting lymph node invasion may result in fewer patients undergoing unnecessary surgeries; conversely, inadequate assessments will result in suboptimal oncological outcomes. This narrative review aims to summarize the current literature on deep learning for predicting the probability of lymph node metastasis in T1 colorectal cancer, highlighting areas of potential application and barriers that may limit its generalizability and clinical utility.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Academic Medicine Center, Duke-NUS Medical School, Singapore
| | - Lai Mun Wang
- Department of Laboratory Medicine, Changi General Hospital, Singapore Health Services, Singapore
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kenneth Weicong Lin
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Academic Medicine Center, Duke-NUS Medical School, Singapore
| | - James Chi-Yong Ngu
- Department of General Surgery, Changi General Hospital, Singapore Health Services, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Academic Medicine Center, Duke-NUS Medical School, Singapore
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20
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Solovev IA. [Artificial intelligence in pathological anatomy]. Arkh Patol 2024; 86:65-71. [PMID: 38591909 DOI: 10.17116/patol20248602165] [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: 04/10/2024]
Abstract
The review presents key concepts and global developments in the field of artificial intelligence used in pathological anatomy. The work examines two types of artificial intelligence (AI): weak and strong ones. A review of experimental algorithms using both deep machine learning and computer vision technologies to work with WSI images of preparations, diagnose and make a prognosis for various malignant neoplasms is carried out. It has been established that weak artificial intelligence at this stage of development of computer (digital) pathological anatomy shows significantly better results in speeding up and refining diagnostic procedures than strong artificial intelligence having signs of general intelligence. The article also discusses three options for the further development of AI assistants for pathologists based on the technologies of large language models (strong AI) ChatGPT (PathAsst), Flan-PaLM2 and LIMA. As a result of the analysis of the literature, key problems in the field were identified: the equipment of pathology institutions, the lack of experts in training neural networks, the lack of strict criteria for the clinical viability of AI diagnostic technologies.
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Affiliation(s)
- I A Solovev
- Pitirim Sorokin Syktyvkar State University, Syktyvkar, Russia
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21
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Griem J, Eich ML, Schallenberg S, Pryalukhin A, Bychkov A, Fukuoka J, Zayats V, Hulla W, Munkhdelger J, Seper A, Tsvetkov T, Mukhopadhyay A, Sanner A, Stieber J, Fuchs M, Babendererde N, Schömig-Markiefka B, Klein S, Buettner R, Quaas A, Tolkach Y. Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens. Mod Pathol 2023; 36:100327. [PMID: 37683932 DOI: 10.1016/j.modpat.2023.100327] [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: 06/02/2023] [Revised: 08/11/2023] [Accepted: 08/31/2023] [Indexed: 09/10/2023]
Abstract
Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artificial intelligence (AI) tool for precise multiclass tissue segmentation in colorectal specimens (resections and biopsies) and clinically validate the tool for tumor detection in biopsy specimens. The training data set included 241 precisely manually annotated whole-slide images (WSIs) from multiple institutions. The algorithm was trained for semantic segmentation of 11 tissue classes with an additional module for biopsy WSI classification. Six case cohorts from 5 pathology departments (4 countries) were used for formal and clinical validation, digitized by 4 different scanning systems. The developed algorithm showed high precision of segmentation of different tissue classes in colorectal specimens with composite multiclass Dice score of up to 0.895 and pixel-wise tumor detection specificity and sensitivity of up to 0.958 and 0.987, respectively. In the clinical validation study on multiple external cohorts, the AI tool reached sensitivity of 1.0 and specificity of up to 0.969 for tumor detection in biopsy WSI. The AI tool analyzes most biopsy cases in less than 1 minute, allowing effective integration into clinical routine. We developed and extensively validated a highly accurate, clinical-grade tool for assistive diagnostic processing of colorectal specimens. This tool allows for quantitative deciphering of colorectal cancer tissue for development of prognostic and predictive biomarkers and personalization of oncologic care. This study is a foundation for a SemiCOL computational challenge. We open-source multiple manually annotated and weakly labeled test data sets, representing a significant contribution to the colorectal cancer computational pathology field.
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Affiliation(s)
- Johanna Griem
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | - Marie-Lisa Eich
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | | | - Alexey Pryalukhin
- Institute of Pathology, State Hospital Wiener Neustadt, Wiener Neustadt, Austria
| | - Andrey Bychkov
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan; Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | - Junya Fukuoka
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan; Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | - Vitaliy Zayats
- Laboratory for Medical Artificial Intelligence, The Resource Center for Universal Design and Rehabilitation Technologies (RCUD and RT), Moscow, Russia
| | - Wolfgang Hulla
- Institute of Pathology, State Hospital Wiener Neustadt, Wiener Neustadt, Austria
| | | | - Alexander Seper
- Danube Private University, Medical Faculty, Krems-Stein, Austria
| | - Tsvetan Tsvetkov
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | | | | | | | - Moritz Fuchs
- Technical University Darmstadt, Darmstadt, Germany
| | | | | | - Sebastian Klein
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | - Reinhard Buettner
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | - Alexander Quaas
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | - Yuri Tolkach
- Institute of Pathology, University Hospital Cologne, Cologne, Germany.
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22
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Adiwinata R, Tandarto K, Arifputra J, Waleleng BJ, Gosal F, Rotty L, Winarta J, Waleleng A, Simadibrata P, Simadibrata M. The Impact of Artificial Intelligence in Improving Polyp and Adenoma Detection Rate During Colonoscopy: Systematic-Review and Meta-Analysis. Asian Pac J Cancer Prev 2023; 24:3655-3663. [PMID: 38019222 PMCID: PMC10772777 DOI: 10.31557/apjcp.2023.24.11.3655] [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: 06/25/2023] [Accepted: 11/10/2023] [Indexed: 11/30/2023] Open
Abstract
INTRODUCTION Colonoscopy may detect colorectal polyp and facilitate its removal in order to prevent colorectal cancer. However, substantial miss rate for colorectal adenomas detection still occurred during screening colonoscopy procedure. Nowadays, artificial intelligence (AI) have been employed in trials to improve polyp detection rate (PDR) and adenoma detection rate (ADR). Therefore, we would like to determine the impact of AI in increasing PDR and ADR. METHODS The present study adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 (PRISMA 2020) statement. To identify relevant literature, comprehensive searches were conducted on major scientific databases, including Pubmed, EBSCO-host, and Proquest. The search was limited to articles published up to November 30, 2022. Inclusion criteria for the study encompassed full-text accessibility, articles written in the English language, and randomized controlled trials (RCTs) that reported both ADR and PDR values, comparing conventional diagnostic methods with AI-aided approaches. To synthesize the data, we computed the combined pooled odds ratio (OR) using a random-effects model. This model was chosen due to the expectation of considerable heterogeneity among the selected studies. To evaluate potential publication bias, the Begg's funnel diagram was employed. RESULTS A total of 13 studies were included in this study. Colonoscopy with AI had significantly higher PDR compared to without AI (pooled OR 1.46, 95% CI 1.13-1.89, p = 0.003) and higher ADR (pooled OR 1.58, 95% CI 1.37-1.82, p < 0.00001). PDR analysis showed moderate heterogeneity between included studies (p = 0.004; I2=63%). Furthermore, ADR analysis showed moderate heterogeneity (p < 0.007; I2 = 57%). Additionally, the funnels plot of ADR and PDR analysis showed an asymmetry plot and low publication bias. CONCLUSION AI may improve colonoscopy result quality through improving PDR and ADR.
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Affiliation(s)
- Randy Adiwinata
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Kevin Tandarto
- S.K Lerik Regional Public Hospital, Kupang, East Nusa Tenggara, Indonesia.
| | - Jonathan Arifputra
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Bradley Jimmy Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Fandy Gosal
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Luciana Rotty
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Jeanne Winarta
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Andrew Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Paulus Simadibrata
- Department of Internal Medicine, Abdi Waluyo Hospital, Jakarta, Indonesia.
| | - Marcellus Simadibrata
- Division of Gastroenterology, Pancreatobiliary and Digestive Endoscopy, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia.
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23
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Bilal M, Tsang YW, Ali M, Graham S, Hero E, Wahab N, Dodd K, Sahota H, Wu S, Lu W, Jahanifar M, Robinson A, Azam A, Benes K, Nimir M, Hewitt K, Bhalerao A, Eldaly H, Raza SEA, Gopalakrishnan K, Minhas F, Snead D, Rajpoot N. Development and validation of artificial intelligence-based prescreening of large-bowel biopsies taken in the UK and Portugal: a retrospective cohort study. Lancet Digit Health 2023; 5:e786-e797. [PMID: 37890902 DOI: 10.1016/s2589-7500(23)00148-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 07/10/2023] [Accepted: 07/25/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND Histopathological examination is a crucial step in the diagnosis and treatment of many major diseases. Aiming to facilitate diagnostic decision making and improve the workload of pathologists, we developed an artificial intelligence (AI)-based prescreening tool that analyses whole-slide images (WSIs) of large-bowel biopsies to identify typical, non-neoplastic, and neoplastic biopsies. METHODS This retrospective cohort study was conducted with an internal development cohort of slides acquired from a hospital in the UK and three external validation cohorts of WSIs acquired from two hospitals in the UK and one clinical laboratory in Portugal. To learn the differential histological patterns from digitised WSIs of large-bowel biopsy slides, our proposed weakly supervised deep-learning model (Colorectal AI Model for Abnormality Detection [CAIMAN]) used slide-level diagnostic labels and no detailed cell or region-level annotations. The method was developed with an internal development cohort of 5054 biopsy slides from 2080 patients that were labelled with corresponding diagnostic categories assigned by pathologists. The three external validation cohorts, with a total of 1536 slides, were used for independent validation of CAIMAN. Each WSI was classified into one of three classes (ie, typical, atypical non-neoplastic, and atypical neoplastic). Prediction scores of image tiles were aggregated into three prediction scores for the whole slide, one for its likelihood of being typical, one for its likelihood of being non-neoplastic, and one for its likelihood of being neoplastic. The assessment of the external validation cohorts was conducted by the trained and frozen CAIMAN model. To evaluate model performance, we calculated area under the convex hull of the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and specificity compared with our previously published iterative draw and rank sampling (IDaRS) algorithm. We also generated heat maps and saliency maps to analyse and visualise the relationship between the WSI diagnostic labels and spatial features of the tissue microenvironment. The main outcome of this study was the ability of CAIMAN to accurately identify typical and atypical WSIs of colon biopsies, which could potentially facilitate automatic removing of typical biopsies from the diagnostic workload in clinics. FINDINGS A randomly selected subset of all large bowel biopsies was obtained between Jan 1, 2012, and Dec 31, 2017. The AI training, validation, and assessments were done between Jan 1, 2021, and Sept 30, 2022. WSIs with diagnostic labels were collected between Jan 1 and Sept 30, 2022. Our analysis showed no statistically significant differences across prediction scores from CAIMAN for typical and atypical classes based on anatomical sites of the biopsy. At 0·99 sensitivity, CAIMAN (specificity 0·5592) was more accurate than an IDaRS-based weakly supervised WSI-classification pipeline (0·4629) in identifying typical and atypical biopsies on cross-validation in the internal development cohort (p<0·0001). At 0·99 sensitivity, CAIMAN was also more accurate than IDaRS for two external validation cohorts (p<0·0001), but not for a third external validation cohort (p=0·10). CAIMAN provided higher specificity than IDaRS at some high-sensitivity thresholds (0·7763 vs 0·6222 for 0·95 sensitivity, 0·7126 vs 0·5407 for 0·97 sensitivity, and 0·5615 vs 0·3970 for 0·99 sensitivity on one of the external validation cohorts) and showed high classification performance in distinguishing between neoplastic biopsies (AUROC 0·9928, 95% CI 0·9927-0·9929), inflammatory biopsies (0·9658, 0·9655-0·9661), and atypical biopsies (0·9789, 0·9786-0·9792). On the three external validation cohorts, CAIMAN had AUROC values of 0·9431 (95% CI 0·9165-0·9697), 0·9576 (0·9568-0·9584), and 0·9636 (0·9615-0·9657) for the detection of atypical biopsies. Saliency maps supported the representation of disease heterogeneity in model predictions and its association with relevant histological features. INTERPRETATION CAIMAN, with its high sensitivity in detecting atypical large-bowel biopsies, might be a promising improvement in clinical workflow efficiency and diagnostic decision making in prescreening of typical colorectal biopsies. FUNDING The Pathology Image Data Lake for Analytics, Knowledge and Education Centre of Excellence; the UK Government's Industrial Strategy Challenge Fund; and Innovate UK on behalf of UK Research and Innovation.
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Affiliation(s)
- Mohsin Bilal
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK; Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan
| | - Yee Wah Tsang
- Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK
| | - Mahmoud Ali
- Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK
| | - Simon Graham
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK; Histofy, Birmingham, UK
| | - Emily Hero
- Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK; Department of Pathology, University Hospitals of Leicester National Health Service Trust, Leicester, UK
| | - Noorul Wahab
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Katherine Dodd
- Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK
| | - Harvir Sahota
- Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK
| | - Shaobin Wu
- Department of Pathology, East Suffolk and North Essex National Health Service Foundation Trust, Colchester, UK
| | - Wenqi Lu
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Mostafa Jahanifar
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Andrew Robinson
- Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK
| | - Ayesha Azam
- Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK
| | - Ksenija Benes
- Department of Pathology, The Royal Wolverhampton National Health Service Trust, Wolverhampton, UK
| | - Mohammed Nimir
- Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK
| | - Katherine Hewitt
- Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK
| | - Abhir Bhalerao
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Hesham Eldaly
- Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK
| | - Shan E Ahmed Raza
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Kishore Gopalakrishnan
- Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - David Snead
- Warwick Medical School, University of Warwick, Coventry, UK; Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK; Histofy, Birmingham, UK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK; Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Trust, Coventry, UK; Histofy, Birmingham, UK; The Alan Turing Institute, London, UK.
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24
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Beniwal SS, Lamo P, Kaushik A, Lorenzo-Villegas DL, Liu Y, MohanaSundaram A. Current Status and Emerging Trends in Colorectal Cancer Screening and Diagnostics. BIOSENSORS 2023; 13:926. [PMID: 37887119 PMCID: PMC10605407 DOI: 10.3390/bios13100926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/27/2023] [Accepted: 10/07/2023] [Indexed: 10/28/2023]
Abstract
Colorectal cancer (CRC) is a prevalent and potentially fatal disease categorized based on its high incidences and mortality rates, which raised the need for effective diagnostic strategies for the early detection and management of CRC. While there are several conventional cancer diagnostics available, they have certain limitations that hinder their effectiveness. Significant research efforts are currently being dedicated to elucidating novel methodologies that aim at comprehending the intricate molecular mechanism that underlies CRC. Recently, microfluidic diagnostics have emerged as a pivotal solution, offering non-invasive approaches to real-time monitoring of disease progression and treatment response. Microfluidic devices enable the integration of multiple sample preparation steps into a single platform, which speeds up processing and improves sensitivity. Such advancements in diagnostic technologies hold immense promise for revolutionizing the field of CRC diagnosis and enabling efficient detection and monitoring strategies. This article elucidates several of the latest developments in microfluidic technology for CRC diagnostics. In addition to the advancements in microfluidic technology for CRC diagnostics, the integration of artificial intelligence (AI) holds great promise for further enhancing diagnostic capabilities. Advancements in microfluidic systems and AI-driven approaches can revolutionize colorectal cancer diagnostics, offering accurate, efficient, and personalized strategies to improve patient outcomes and transform cancer management.
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Affiliation(s)
| | - Paula Lamo
- Escuela Superior de Ingeniería y Tecnología, Universidad Internacional de La Rioja, 26006 Logroño, Spain
| | - Ajeet Kaushik
- NanoBioTech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, FL 33805, USA
| | | | - Yuguang Liu
- Departments of Physiology and Biomedical Engineering, Immunology and Surgery, Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
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25
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Jing Y, Li C, Du T, Jiang T, Sun H, Yang J, Shi L, Gao M, Grzegorzek M, Li X. A comprehensive survey of intestine histopathological image analysis using machine vision approaches. Comput Biol Med 2023; 165:107388. [PMID: 37696178 DOI: 10.1016/j.compbiomed.2023.107388] [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: 06/08/2023] [Revised: 08/06/2023] [Accepted: 08/25/2023] [Indexed: 09/13/2023]
Abstract
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.
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Affiliation(s)
- Yujie Jing
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
| | - Tianming Du
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Tao Jiang
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China; International Joint Institute of Robotics and Intelligent Systems, Chengdu University of Information Technology, Chengdu, China
| | - Hongzan Sun
- Shengjing Hospital of China Medical University, Shenyang, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Liyu Shi
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Minghe Gao
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany; Department of Knowledge Engineering, University of Economics in Katowice, Katowice, Poland
| | - Xiaoyan Li
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China.
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26
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Prezja F, Äyrämö S, Pölönen I, Ojala T, Lahtinen S, Ruusuvuori P, Kuopio T. Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions. Sci Rep 2023; 13:15879. [PMID: 37741820 PMCID: PMC10517936 DOI: 10.1038/s41598-023-42357-x] [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: 02/24/2023] [Accepted: 09/08/2023] [Indexed: 09/25/2023] Open
Abstract
Hematoxylin and eosin-stained biopsy slides are regularly available for colorectal cancer patients. These slides are often not used to define objective biomarkers for patient stratification and treatment selection. Standard biomarkers often pertain to costly and slow genetic tests. However, recent work has shown that relevant biomarkers can be extracted from these images using convolutional neural networks (CNNs). The CNN-based biomarkers predicted colorectal cancer patient outcomes comparably to gold standards. Extracting CNN-biomarkers is fast, automatic, and of minimal cost. CNN-based biomarkers rely on the ability of CNNs to recognize distinct tissue types from microscope whole slide images. The quality of these biomarkers (coined 'Deep Stroma') depends on the accuracy of CNNs in decomposing all relevant tissue classes. Improving tissue decomposition accuracy is essential for improving the prognostic potential of CNN-biomarkers. In this study, we implemented a novel training strategy to refine an established CNN model, which then surpassed all previous solutions . We obtained a 95.6% average accuracy in the external test set and 99.5% in the internal test set. Our approach reduced errors in biomarker-relevant classes, such as Lymphocytes, and was the first to include interpretability methods. These methods were used to better apprehend our model's limitations and capabilities.
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Affiliation(s)
- Fabi Prezja
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland.
- Digital Health Intelligence Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland.
| | - Sami Äyrämö
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Digital Health Intelligence Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Ilkka Pölönen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Spectral Imaging Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Timo Ojala
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Digital Health Intelligence Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Suvi Lahtinen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Department of Biological and Environmental Science, Faculty of Mathematics and Science, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Pekka Ruusuvuori
- Institute of Biomedicine, Cancer Research Unit, University of Turku, Turku, 20014, Finland
- FICAN West Cancer Centre, Turku University Hospital, Turku, 20521, Finland
| | - Teijo Kuopio
- Department of Education and Research, Hospital Nova of Central Finland, Jyväskylä, 40620, Finland
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, 40014, Finland
- Department of Pathology, Hospital Nova of Central Finland, Jyväskylä, 40620, Finland
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27
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Gu Q, Meroueh C, Levernier J, Kroneman T, Flotte T, Hart S. Using an anomaly detection approach for the segmentation of colorectal cancer tumors in whole slide images. J Pathol Inform 2023; 14:100336. [PMID: 37811333 PMCID: PMC10550750 DOI: 10.1016/j.jpi.2023.100336] [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] [Received: 07/17/2023] [Revised: 09/13/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
Colorectal cancer (CRC) is the second most commonly diagnosed cancer in the United States. Genetic testing is critical in assisting in the early detection of CRC and selection of individualized treatment plans, which have shown to improve the survival rate of CRC patients. The tissue slide review (TSR), a tumor tissue macro-dissection procedure, is a required pre-analytical step to perform genetic testing. Due to the subjective nature of the process, major discrepancies in CRC diagnostics by pathologists are reported, and metrics for quality are often only qualitative. Progressive context encoder anomaly detection (P-CEAD) is an anomaly detection approach to detect tumor tissue from whole slide images (WSIs), since tumor tissue is by its nature, an anomaly. P-CEAD-based CRC tumor segmentation achieves a 71% 26% sensitivity, 92% 7% specificity, and 63% 23% F1 score. The proposed approach provides an automated CRC tumor segmentation pipeline with a quantitatively reproducible quality compared with the conventional manual tumor segmentation procedure.
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Affiliation(s)
- Qiangqiang Gu
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55901, United States
| | - Chady Meroueh
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55901, United States
| | - Jacob Levernier
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55901, United States
| | - Trynda Kroneman
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55901, United States
| | - Thomas Flotte
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55901, United States
| | - Steven Hart
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55901, United States
- Department of Quantitative Health Science, Mayo Clinic, Rochester, MN 55901, United States
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28
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Gunesli GN, Bilal M, Raza SEA, Rajpoot NM. A Federated Learning Approach to Tumor Detection in Colon Histology Images. J Med Syst 2023; 47:99. [PMID: 37715855 DOI: 10.1007/s10916-023-01994-5] [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: 06/06/2023] [Accepted: 09/07/2023] [Indexed: 09/18/2023]
Abstract
Federated learning (FL), a relatively new area of research in medical image analysis, enables collaborative learning of a federated deep learning model without sharing the data of participating clients. In this paper, we propose FedDropoutAvg, a new federated learning approach for detection of tumor in images of colon tissue slides. The proposed method leverages the power of dropout, a commonly employed scheme to avoid overfitting in neural networks, in both client selection and federated averaging processes. We examine FedDropoutAvg against other FL benchmark algorithms for two different image classification tasks using a publicly available multi-site histopathology image dataset. We train and test the proposed model on a large dataset consisting of 1.2 million image tiles from 21 different sites. For testing the generalization of all models, we select held-out test sets from sites that were not used during training. We show that the proposed approach outperforms other FL methods and reduces the performance gap (to less than 3% in terms of AUC on independent test sites) between FL and a central deep learning model that requires all data to be shared for centralized training, demonstrating the potential of the proposed FedDropoutAvg model to be more generalizable than other state-of-the-art federated models. To the best of our knowledge, ours is the first study to effectively utilize the dropout strategy in a federated setting for tumor detection in histology images.
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Affiliation(s)
- Gozde N Gunesli
- The Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK.
| | - Mohsin Bilal
- The Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Shan E Ahmed Raza
- The Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Nasir M Rajpoot
- The Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK.
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Al-Thelaya K, Gilal NU, Alzubaidi M, Majeed F, Agus M, Schneider J, Househ M. Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey. J Pathol Inform 2023; 14:100335. [PMID: 37928897 PMCID: PMC10622844 DOI: 10.1016/j.jpi.2023.100335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 11/07/2023] Open
Abstract
Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by "engineered" methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology.
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Affiliation(s)
- Khaled Al-Thelaya
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Nauman Ullah Gilal
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Mahmood Alzubaidi
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Fahad Majeed
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Marco Agus
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Jens Schneider
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Mowafa Househ
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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Das R, Bose S, Chowdhury RS, Maulik U. Dense Dilated Multi-Scale Supervised Attention-Guided Network for histopathology image segmentation. Comput Biol Med 2023; 163:107182. [PMID: 37379615 DOI: 10.1016/j.compbiomed.2023.107182] [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: 03/03/2023] [Revised: 05/24/2023] [Accepted: 06/13/2023] [Indexed: 06/30/2023]
Abstract
Over the last couple of decades, the introduction and proliferation of whole-slide scanners led to increasing interest in the research of digital pathology. Although manual analysis of histopathological images is still the gold standard, the process is often tedious and time consuming. Furthermore, manual analysis also suffers from intra- and interobserver variability. Separating structures or grading morphological changes can be difficult due to architectural variability of these images. Deep learning techniques have shown great potential in histopathology image segmentation that drastically reduces the time needed for downstream tasks of analysis and providing accurate diagnosis. However, few algorithms have clinical implementations. In this paper, we propose a new deep learning model Dense Dilated Multiscale Supervised Attention-Guided (D2MSA) Network for histopathology image segmentation that makes use of deep supervision coupled with a hierarchical system of novel attention mechanisms. The proposed model surpasses state-of-the-art performance while using similar computational resources. The performance of the model has been evaluated for the tasks of gland segmentation and nuclei instance segmentation, both of which are clinically relevant tasks to assess the state and progress of malignancy. Here, we have used histopathology image datasets for three different types of cancer. We have also performed extensive ablation tests and hyperparameter tuning to ensure the validity and reproducibility of the model performance. The proposed model is available at www.github.com/shirshabose/D2MSA-Net.
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Affiliation(s)
- Rangan Das
- Department of Computer Science Engineering, Jadavpur University, Kolkata, 700032, West Bengal, India.
| | - Shirsha Bose
- Department of Informatics, Technical University of Munich, Munich, Bavaria 85748, Germany.
| | - Ritesh Sur Chowdhury
- Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, 700032, West Bengal, India.
| | - Ujjwal Maulik
- Department of Computer Science Engineering, Jadavpur University, Kolkata, 700032, West Bengal, India.
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31
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Chlorogiannis DD, Verras GI, Tzelepi V, Chlorogiannis A, Apostolos A, Kotis K, Anagnostopoulos CN, Antzoulas A, Davakis S, Vailas M, Schizas D, Mulita F. Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation? PRZEGLAD GASTROENTEROLOGICZNY 2023; 18:353-367. [PMID: 38572457 PMCID: PMC10985751 DOI: 10.5114/pg.2023.130337] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/20/2023] [Indexed: 04/05/2024]
Abstract
Colorectal cancer is one of the most prevalent types of cancer, with histopathologic examination of biopsied tissue samples remaining the gold standard for diagnosis. During the past years, artificial intelligence (AI) has steadily found its way into the field of medicine and pathology, especially with the introduction of whole slide imaging (WSI). The main outcome of interest was the composite balanced accuracy (ACC) as well as the F1 score. The average reported ACC from the collected studies was 95.8 ±3.8%. Reported F1 scores reached as high as 0.975, with an average of 89.7 ±9.8%, indicating that existing deep learning algorithms can achieve in silico distinction between malignant and benign. Overall, the available state-of-the-art algorithms are non-inferior to pathologists for image analysis and classification tasks. However, due to their inherent uniqueness in their training and lack of widely accepted external validation datasets, their generalization potential is still limited.
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Affiliation(s)
| | | | - Vasiliki Tzelepi
- Department of Pathology, School of Medicine, University of Patras, Patras, Greece
| | | | - Anastasios Apostolos
- First Department of Cardiology, Hippokration Hospital, University of Athens, Athens, Greece
| | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
| | | | - Andreas Antzoulas
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | - Spyridon Davakis
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Michail Vailas
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Dimitrios Schizas
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Francesk Mulita
- Department of Surgery, General University Hospital of Patras, Patras, Greece
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32
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Wu Y, Li Y, Xiong X, Liu X, Lin B, Xu B. Recent advances of pathomics in colorectal cancer diagnosis and prognosis. Front Oncol 2023; 13:1094869. [PMID: 37538112 PMCID: PMC10396402 DOI: 10.3389/fonc.2023.1094869] [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] [Received: 11/10/2022] [Accepted: 06/13/2023] [Indexed: 08/05/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most common malignancies, with the third highest incidence and the second highest mortality in the world. To improve the therapeutic outcome, the risk stratification and prognosis predictions would help guide clinical treatment decisions. Achieving these goals have been facilitated by the fast development of artificial intelligence (AI) -based algorithms using radiological and pathological data, in combination with genomic information. Among them, features extracted from pathological images, termed pathomics, are able to reflect sub-visual characteristics linking to better stratification and prediction of therapeutic responses. In this paper, we review recent advances in pathological image-based algorithms in CRC, focusing on diagnosis of benign and malignant lesions, micro-satellite instability, as well as prediction of neoadjuvant chemoradiotherapy and the prognosis of CRC patients.
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Affiliation(s)
- Yihan Wu
- School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Yi Li
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
- Bioengineering College, Chongqing University, Chongqing, China
| | - Xiaomin Xiong
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
- Bioengineering College, Chongqing University, Chongqing, China
| | - Xiaohua Liu
- Bioengineering College, Chongqing University, Chongqing, China
| | - Bo Lin
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Bo Xu
- School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
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33
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Bilal M, Jewsbury R, Wang R, AlGhamdi HM, Asif A, Eastwood M, Rajpoot N. An aggregation of aggregation methods in computational pathology. Med Image Anal 2023; 88:102885. [PMID: 37423055 DOI: 10.1016/j.media.2023.102885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 05/02/2023] [Accepted: 06/28/2023] [Indexed: 07/11/2023]
Abstract
Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this paper, we present a review of existing literature on various types of aggregation methods with a view to help guide future research in the area of computational pathology (CPath). We propose a general CPath workflow with three pathways that consider multiple levels and types of data and the nature of computation to analyse WSIs for predictive modelling. We categorize aggregation methods according to the context and representation of the data, features of computational modules and CPath use cases. We compare and contrast different methods based on the principle of multiple instance learning, perhaps the most commonly used aggregation method, covering a wide range of CPath literature. To provide a fair comparison, we consider a specific WSI-level prediction task and compare various aggregation methods for that task. Finally, we conclude with a list of objectives and desirable attributes of aggregation methods in general, pros and cons of the various approaches, some recommendations and possible future directions.
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Affiliation(s)
- Mohsin Bilal
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK; School of Computing, National University of Computer and Emerging Sciences, Islamabad, Pakistan
| | - Robert Jewsbury
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK
| | - Ruoyu Wang
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK
| | - Hammam M AlGhamdi
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK
| | - Amina Asif
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK
| | - Mark Eastwood
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK; The Alan Turing Institute, UK; Department of Pathology, University Hospitals Coventry and Warwickshire, UK.
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34
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Khazaee Fadafen M, Rezaee K. Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework. Sci Rep 2023; 13:8823. [PMID: 37258631 DOI: 10.1038/s41598-023-35431-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/17/2023] [Indexed: 06/02/2023] Open
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer death in the world, so digital pathology is essential for assessing prognosis. Due to the increasing resolution and quantity of whole slide images (WSIs), as well as the lack of annotated information, previous methodologies cannot be generalized as effective decision-making systems. Since deep learning (DL) methods can handle large-scale applications, they can provide a viable alternative to histopathology image (HI) analysis. DL architectures, however, may not be sufficient to classify CRC tissues based on anatomical histopathology data. A dilated ResNet (dResNet) structure and attention module are used to generate deep feature maps in order to classify multiple tissues in HIs. In addition, neighborhood component analysis (NCA) overcomes the constraint of computational complexity. Data is fed into a deep support vector machine (SVM) based on an ensemble learning algorithm called DeepSVM after the features have been selected. CRC-5000 and NCT-CRC-HE-100 K datasets were analyzed to validate and test the hybrid procedure. We demonstrate that the hybrid model achieves 98.75% and 99.76% accuracy on CRC datasets. The results showed that only pathologists' labels could successfully classify unseen WSIs. Furthermore, the hybrid deep learning method outperforms state-of-the-art approaches in terms of computational efficiency and time. Using the proposed mechanism for tissue analysis, it will be possible to correctly predict CRC based on accurate pathology image classification.
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Affiliation(s)
- Masoud Khazaee Fadafen
- Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran
| | - Khosro Rezaee
- Department of Biomedical Engineering, Meybod University, Meybod, Iran.
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35
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Karnachoriti M, Stathopoulos I, Kouri M, Spyratou E, Orfanoudakis S, Lykidis D, Lambropoulou Μ, Danias N, Arkadopoulos N, Efstathopoulos EP, Raptis YS, Seimenis I, Kontos AG. Biochemical differentiation between cancerous and normal human colorectal tissues by micro-Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 299:122852. [PMID: 37216817 DOI: 10.1016/j.saa.2023.122852] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/29/2023] [Accepted: 05/08/2023] [Indexed: 05/24/2023]
Abstract
Human colorectal tissues obtained by ten cancer patients have been examined by multiple micro-Raman spectroscopic measurements in the 500-3200 cm-1 range under 785 nm excitation. Distinct spectral profiles are recorded from different spots on the samples: a predominant 'typical' profile of colorectal tissue, as well as those from tissue topologies with high lipid, blood or collagen content. Principal component analysis identified several Raman bands of amino acids, proteins and lipids which allow the efficient discrimination of normal from cancer tissues, the first presenting plurality of Raman spectral profiles while the last showing off quite uniform spectroscopic characteristics. Tree-based machine learning experiment was further applied on all data as well as on filtered data keeping only those spectra which characterize the largely inseparable data clusters of 'typical' and 'collagen-rich' spectra. This purposive sampling evidences statistically the most significant spectroscopic features regarding the correct identification of cancer tissues and allows matching spectroscopic results with the biochemical changes induced in the malignant tissues.
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Affiliation(s)
- M Karnachoriti
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece; Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - I Stathopoulos
- 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - M Kouri
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece; Medical Physics Program, University of Massachusetts Lowell, MA 01854, United States
| | - E Spyratou
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - S Orfanoudakis
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece; Alpha Information Technology S.A., Software & System Development, 68131 Alexandroupolis, Greece
| | - D Lykidis
- Laboratory of Histology-Embryology, Medical Department, Democritus University of Thrace, Alexandroupolis, Greece
| | - Μ Lambropoulou
- Laboratory of Histology-Embryology, Medical Department, Democritus University of Thrace, Alexandroupolis, Greece
| | - N Danias
- 4(th) Department of Surgery, School of Medicine, Attikon University Hospital, Univ. of Athens, 12462 Athens, Greece
| | - N Arkadopoulos
- 4(th) Department of Surgery, School of Medicine, Attikon University Hospital, Univ. of Athens, 12462 Athens, Greece
| | - E P Efstathopoulos
- 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Y S Raptis
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece
| | - I Seimenis
- Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - A G Kontos
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece.
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36
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Lan J, Chen M, Wang J, Du M, Wu Z, Zhang H, Xue Y, Wang T, Chen L, Xu C, Han Z, Hu Z, Zhou Y, Zhou X, Tong T, Chen G. Using less annotation workload to establish a pathological auxiliary diagnosis system for gastric cancer. Cell Rep Med 2023; 4:101004. [PMID: 37044091 PMCID: PMC10140598 DOI: 10.1016/j.xcrm.2023.101004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/20/2022] [Accepted: 03/17/2023] [Indexed: 04/14/2023]
Abstract
Pathological diagnosis of gastric cancer requires pathologists to have extensive clinical experience. To help pathologists improve diagnostic accuracy and efficiency, we collected 1,514 cases of stomach H&E-stained specimens with complete diagnostic information to establish a pathological auxiliary diagnosis system based on deep learning. At the slide level, our system achieves a specificity of 0.8878 while maintaining a high sensitivity close to 1.0 on 269 biopsy specimens (147 malignancies) and 163 surgical specimens (80 malignancies). The classified accuracy of our system is 0.9034 at the slide level for 352 biopsy specimens (201 malignancies) from 50 medical centers. With the help of our system, the pathologists' average false-negative rate and average false-positive rate on 100 biopsy specimens (50 malignancies) are reduced to 1/5 and 1/2 of the original rates, respectively. At the same time, the average uncertainty rate and the average diagnosis time are reduced by approximately 22% and 20%, respectively.
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Affiliation(s)
- Junlin Lan
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China; Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou University, Fuzhou, Fujian 350108, China
| | - Musheng Chen
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, China; Fujian Key Laboratory of Translational Cancer Medicine, Fuzhou, Fujian 350014, China
| | - Jianchao Wang
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, China; Fujian Key Laboratory of Translational Cancer Medicine, Fuzhou, Fujian 350014, China
| | - Min Du
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China; Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou University, Fuzhou, Fujian 350108, China
| | - Zhida Wu
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, China; Fujian Key Laboratory of Translational Cancer Medicine, Fuzhou, Fujian 350014, China
| | - Hejun Zhang
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, China; Fujian Key Laboratory of Translational Cancer Medicine, Fuzhou, Fujian 350014, China
| | - Yuyang Xue
- School of Engineering, University of Edinburgh, Edinburgh EH8 9JU, UK
| | - Tao Wang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China; Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou University, Fuzhou, Fujian 350108, China
| | - Lifan Chen
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, China; Fujian Key Laboratory of Translational Cancer Medicine, Fuzhou, Fujian 350014, China
| | - Chaohui Xu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China; Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou University, Fuzhou, Fujian 350108, China
| | - Zixin Han
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China; Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou University, Fuzhou, Fujian 350108, China
| | - Ziwei Hu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China; Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou University, Fuzhou, Fujian 350108, China
| | - Yuanbo Zhou
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China; Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou University, Fuzhou, Fujian 350108, China
| | - Xiaogen Zhou
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China; Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou University, Fuzhou, Fujian 350108, China
| | - Tong Tong
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China; Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou University, Fuzhou, Fujian 350108, China; Imperial Vision Technology, Fuzhou, Fujian 350100, China.
| | - Gang Chen
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350014, China; Fujian Key Laboratory of Translational Cancer Medicine, Fuzhou, Fujian 350014, China.
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37
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Soons E, Siersema PD, van Lierop LMA, Bisseling TM, van Kouwen MCA, Nagtegaal ID, van der Post RS, Atsma F. Laboratory variation in the grading of dysplasia of duodenal adenomas in familial adenomatous polyposis patients. Fam Cancer 2023; 22:177-186. [PMID: 36401146 PMCID: PMC10020317 DOI: 10.1007/s10689-022-00320-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/06/2022] [Indexed: 11/20/2022]
Abstract
To prevent duodenal and ampullary cancer in familial adenomatous polyposis (FAP) patients, a diagnosis of high grade dysplasia (HGD) plays an important role in the clinical management. Previous research showed that FAP patients are both over- and undertreated after a misdiagnosis of HGD, indicating unwarranted variation. We aimed to investigate the laboratory variation in dysplasia grading of duodenal adenomas and explore possible explanations for this variation. We included data from all Dutch pathology laboratories between 1991 and 2020 by retrieving histology reports from upper endoscopy specimens of FAP patients from the Dutch nationwide pathology databank (PALGA). Laboratory variation was investigated by comparing standardized proportions of HGD. To describe the degree of variation between the laboratories a factor score was calculated. A funnel plot was used to identify outliers. A total of 3050 specimens from 25 laboratories were included in the final analyses. The mean observed HGD proportion was 9.4%. The top three HGD-diagnosing laboratories diagnosed HGD 3.9 times more often than the lowest three laboratories, even after correcting for case-mix. No outliers were identified. Moderate laboratory variation was found in HGD diagnoses of duodenal tissue of FAP patients after adjusting for case-mix. Despite the fact that no outliers were observed, there may well be room for quality improvement. Concentration of these patients in expertise centers may decrease variation. To further reduce unwarranted variation, we recommend (inter)national guidelines to become more uniform in their recommendations regarding duodenal tissue sampling and consequences of HGD diagnoses.
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Affiliation(s)
- E Soons
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - P D Siersema
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - L M A van Lierop
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - T M Bisseling
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - M C A van Kouwen
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - I D Nagtegaal
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - R S van der Post
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - F Atsma
- Department of IQ Healthcare, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
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38
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Sun K, Chen Y, Bai B, Gao Y, Xiao J, Yu G. Automatic Classification of Histopathology Images across Multiple Cancers Based on Heterogeneous Transfer Learning. Diagnostics (Basel) 2023; 13:diagnostics13071277. [PMID: 37046497 PMCID: PMC10093253 DOI: 10.3390/diagnostics13071277] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/07/2023] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
Background: Current artificial intelligence (AI) in histopathology typically specializes on a single task, resulting in a heavy workload of collecting and labeling a sufficient number of images for each type of cancer. Heterogeneous transfer learning (HTL) is expected to alleviate the data bottlenecks and establish models with performance comparable to supervised learning (SL). Methods: An accurate source domain model was trained using 28,634 colorectal patches. Additionally, 1000 sentinel lymph node patches and 1008 breast patches were used to train two target domain models. The feature distribution difference between sentinel lymph node metastasis or breast cancer and CRC was reduced by heterogeneous domain adaptation, and the maximum mean difference between subdomains was used for knowledge transfer to achieve accurate classification across multiple cancers. Result: HTL on 1000 sentinel lymph node patches (L-HTL-1000) outperforms SL on 1000 sentinel lymph node patches (L-SL-1-1000) (average area under the curve (AUC) and standard deviation of L-HTL-1000 vs. L-SL-1-1000: 0.949 ± 0.004 vs. 0.931 ± 0.008, p value = 0.008). There is no significant difference between L-HTL-1000 and SL on 7104 patches (L-SL-2-7104) (0.949 ± 0.004 vs. 0.948 ± 0.008, p value = 0.742). Similar results are observed for breast cancer. B-HTL-1008 vs. B-SL-1-1008: 0.962 ± 0.017 vs. 0.943 ± 0.018, p value = 0.008; B-HTL-1008 vs. B-SL-2-5232: 0.962 ± 0.017 vs. 0.951 ± 0.023, p value = 0.148. Conclusions: HTL is capable of building accurate AI models for similar cancers using a small amount of data based on a large dataset for a certain type of cancer. HTL holds great promise for accelerating the development of AI in histopathology.
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Yin Z, Yao C, Zhang L, Qi S. Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect. Front Med (Lausanne) 2023; 10:1128084. [PMID: 36968824 PMCID: PMC10030915 DOI: 10.3389/fmed.2023.1128084] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/13/2023] [Indexed: 03/29/2023] Open
Abstract
In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.
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Affiliation(s)
- Zugang Yin
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenhui Yao
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shaohua Qi
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
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Peyret R, Pozin N, Sockeel S, Kammerer-Jacquet SF, Adam J, Bocciarelli C, Ditchi Y, Bontoux C, Depoilly T, Guichard L, Lanteri E, Sockeel M, Prévot S. Multicenter automatic detection of invasive carcinoma on breast whole slide images. PLOS DIGITAL HEALTH 2023; 2:e0000091. [PMID: 36854026 PMCID: PMC9974110 DOI: 10.1371/journal.pdig.0000091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 12/12/2022] [Indexed: 03/02/2023]
Abstract
Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms that can be trusted by practitioners, whatever the medical center. We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. The network was trained on a dataset extracted from a reference acquisition center. We then performed a calibration step based on transfer learning to maintain the performance when translating on a new target acquisition center by using a limited amount of additional training data. Performance was evaluated using classical binary measures (accuracy, recall, precision) for both centers (referred to as "test reference dataset" and "test target dataset") and at two levels: patch and slide level. At patch level, accuracy, recall, and precision of the model on the reference and target test sets were 92.1% and 96.3%, 95% and 87.8%, and 73.9% and 70.6%, respectively. At slide level, accuracy, recall, and precision were 97.6% and 92.0%, 90.9% and 100%, and 100% and 70.8% for test sets 1 and 2, respectively. The high performance of the algorithm at both centers shows that the calibration process is efficient. This is performed using limited training data from the new target acquisition center and requires that the model is trained beforehand on a large database from a reference center. This methodology allows the implementation of AI diagnostic tools to help in routine pathology practice.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Sophie Prévot
- Bicêtre hospital, APHP, Paris Saclay University, France
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Mansur A, Saleem Z, Elhakim T, Daye D. Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions. Front Oncol 2023; 13:1065402. [PMID: 36761957 PMCID: PMC9905815 DOI: 10.3389/fonc.2023.1065402] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 01/09/2023] [Indexed: 01/26/2023] Open
Abstract
Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.
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Affiliation(s)
- Arian Mansur
- Harvard Medical School, Boston, MA, United States
| | | | - Tarig Elhakim
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
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Artificial intelligence-based triage of large bowel biopsies can improve workflow. J Pathol Inform 2023; 14:100181. [PMID: 36687528 PMCID: PMC9852686 DOI: 10.1016/j.jpi.2022.100181] [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] [Received: 11/20/2022] [Revised: 12/03/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023] Open
Abstract
Background Large bowel biopsies are one of the commonest types of biopsy specimen. We describe a service evaluation study to test the feasibility of using artificial intelligence (AI) to triage large bowel biopsies from a reporting backlog and prioritize those that require more urgent reporting. Methods The pathway was developed in the UK by National Health Service (NHS) laboratory staff working in a medium-sized general hospital. The AI platform was interfaced with the slide scanner software and the reporting platform's software, so that pathologists could correct the AI label and reinforce the training set as they reported the cases. Results he AI classifier achieved a sensitivity of 97.56% and specificity of 93.02% for the case-level-diagnosis of neoplasia (adenoma and adenocarcinoma) and for an AI diagnosis of any significant pathology (i.e., adenomas, adenocarcinomas, inflammation, hyperplastic polyps, and sessile serrated lesions) sensitivity was 95.65% and specificity 92.96%. The automated AI diagnostic classification pathway took approximately 175 s per slide to download and process the scanned whole slide image (WSI) and return an AI diagnostic classification. Biopsies with an AI diagnosis of neoplasia or inflammation were prioritized for reporting while the remainder followed the routine reporting pathway. The AI triaged pathway resulted in a significantly shorter reporting turnaround time for pathologist verified neoplastic cases (P < 0.001) and inflammation (P < 0.05). The project's costs amounted to £14800, excluding laboratory staff salaries. More time and resources were spent on developing the interface between the AI platform and laboratory IT systems than on the development of the AI platform itself. Conclusions NHS laboratory staff were able to implement an AI solution to accurately triage large bowel biopsies into several diagnostic classes and this improved reporting turnaround times for cases with neoplasia or with inflammation.
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Kumar A, Vishwakarma A, Bajaj V. CRCCN-Net: Automated framework for classification of colorectal tissue using histopathological images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Qiao Y, Zhao L, Luo C, Luo Y, Wu Y, Li S, Bu D, Zhao Y. Multi-modality artificial intelligence in digital pathology. Brief Bioinform 2022; 23:6702380. [PMID: 36124675 PMCID: PMC9677480 DOI: 10.1093/bib/bbac367] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/27/2022] [Accepted: 08/05/2022] [Indexed: 12/14/2022] Open
Abstract
In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.
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Affiliation(s)
- Yixuan Qiao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lianhe Zhao
- Corresponding authors: Yi Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences; Shandong First Medical University & Shandong Academy of Medical Sciences. Tel.: +86 10 6260 0822; Fax: +86 10 6260 1356; E-mail: ; Lianhe Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences. Tel.: +86 18513983324; E-mail:
| | - Chunlong Luo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yufan Luo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Shengtong Li
- Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Yi Zhao
- Corresponding authors: Yi Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences; Shandong First Medical University & Shandong Academy of Medical Sciences. Tel.: +86 10 6260 0822; Fax: +86 10 6260 1356; E-mail: ; Lianhe Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences. Tel.: +86 18513983324; E-mail:
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Zhou W, Deng Z, Liu Y, Shen H, Deng H, Xiao H. Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191811597. [PMID: 36141871 PMCID: PMC9517580 DOI: 10.3390/ijerph191811597] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 06/13/2023]
Abstract
Cancer has become a major threat to global health care. With the development of computer science, artificial intelligence (AI) has been widely applied in histopathological images (HI) analysis. This study analyzed the publications of AI in HI from 2001 to 2021 by bibliometrics, exploring the research status and the potential popular directions in the future. A total of 2844 publications from the Web of Science Core Collection were included in the bibliometric analysis. The country/region, institution, author, journal, keyword, and references were analyzed by using VOSviewer and CiteSpace. The results showed that the number of publications has grown rapidly in the last five years. The USA is the most productive and influential country with 937 publications and 23,010 citations, and most of the authors and institutions with higher numbers of publications and citations are from the USA. Keyword analysis showed that breast cancer, prostate cancer, colorectal cancer, and lung cancer are the tumor types of greatest concern. Co-citation analysis showed that classification and nucleus segmentation are the main research directions of AI-based HI studies. Transfer learning and self-supervised learning in HI is on the rise. This study performed the first bibliometric analysis of AI in HI from multiple indicators, providing insights for researchers to identify key cancer types and understand the research trends of AI application in HI.
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Affiliation(s)
- Wentong Zhou
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410031, China
| | - Ziheng Deng
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410031, China
| | - Yong Liu
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410031, China
| | - Hui Shen
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University School, New Orleans, LA 70112, USA
| | - Hongwen Deng
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University School, New Orleans, LA 70112, USA
| | - Hongmei Xiao
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410031, China
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A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images. Comput Biol Med 2022; 147:105680. [DOI: 10.1016/j.compbiomed.2022.105680] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/06/2022] [Accepted: 05/30/2022] [Indexed: 12/15/2022]
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47
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Beeche C, Singh JP, Leader JK, Gezer S, Oruwari AP, Dansingani KK, Chhablani J, Pu J. Super U-Net: a modularized generalizable architecture. PATTERN RECOGNITION 2022; 128:108669. [PMID: 35528144 PMCID: PMC9070860 DOI: 10.1016/j.patcog.2022.108669] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OBJECTIVE To develop and validate a novel convolutional neural network (CNN) termed "Super U-Net" for medical image segmentation. METHODS Super U-Net integrates a dynamic receptive field module and a fusion upsampling module into the classical U-Net architecture. The model was developed and tested to segment retinal vessels, gastrointestinal (GI) polyps, skin lesions on several image types (i.e., fundus images, endoscopic images, dermoscopic images). We also trained and tested the traditional U-Net architecture, seven U-Net variants, and two non-U-Net segmentation architectures. K-fold cross-validation was used to evaluate performance. The performance metrics included Dice similarity coefficient (DSC), accuracy, positive predictive value (PPV), and sensitivity. RESULTS Super U-Net achieved average DSCs of 0.808±0.0210, 0.752±0.019, 0.804±0.239, and 0.877±0.135 for segmenting retinal vessels, pediatric retinal vessels, GI polyps, and skin lesions, respectively. The Super U-net consistently outperformed U-Net, seven U-Net variants, and two non-U-Net segmentation architectures (p < 0.05). CONCLUSION Dynamic receptive fields and fusion upsampling can significantly improve image segmentation performance.
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Affiliation(s)
- Cameron Beeche
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jatin P Singh
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Joseph K Leader
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Sinem Gezer
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Amechi P Oruwari
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Kunal K Dansingani
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jiantao Pu
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Deep Neural Network Models for Colon Cancer Screening. Cancers (Basel) 2022; 14:cancers14153707. [PMID: 35954370 PMCID: PMC9367621 DOI: 10.3390/cancers14153707] [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: 07/04/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Deep learning models have been shown to achieve high performance in diagnosing colon cancer compared to conventional image processing and hand-crafted machine learning methods. Hence, several studies have focused on developing hybrid learning, end-to-end, and transfer learning techniques to reduce manual interaction and for labelling the regions of interest. However, these weak learning techniques do not always provide a clear diagnosis. Therefore, it is necessary to develop a clear explainable learning method that can highlight factors and form the basis of clinical decisions. However, there has been little research carried out employing such transparent approaches. This study discussed the aforementioned models for colon cancer diagnosis. Abstract Early detection of colorectal cancer can significantly facilitate clinicians’ decision-making and reduce their workload. This can be achieved using automatic systems with endoscopic and histological images. Recently, the success of deep learning has motivated the development of image- and video-based polyp identification and segmentation. Currently, most diagnostic colonoscopy rooms utilize artificial intelligence methods that are considered to perform well in predicting invasive cancer. Convolutional neural network-based architectures, together with image patches and preprocesses are often widely used. Furthermore, learning transfer and end-to-end learning techniques have been adopted for detection and localization tasks, which improve accuracy and reduce user dependence with limited datasets. However, explainable deep networks that provide transparency, interpretability, reliability, and fairness in clinical diagnostics are preferred. In this review, we summarize the latest advances in such models, with or without transparency, for the prediction of colorectal cancer and also address the knowledge gap in the upcoming technology.
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Neto PC, Oliveira SP, Montezuma D, Fraga J, Monteiro A, Ribeiro L, Gonçalves S, Pinto IM, Cardoso JS. iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images. Cancers (Basel) 2022; 14:cancers14102489. [PMID: 35626093 PMCID: PMC9139905 DOI: 10.3390/cancers14102489] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Nowadays, colorectal cancer is the third most incident cancer worldwide and, although it can be detected by imaging techniques, diagnosis is always based on biopsy samples. This assessment includes neoplasia grading, a subjective yet important task for pathologists. With the growing availability of digital slides, the development of robust and high-performance computer vision algorithms can help to tackle such a task. In this work, we propose an approach to automatically detect and grade lesions in colorectal biopsies with high sensitivity. The presented model attempts to support slide decision reasoning in terms of the spatial distribution of lesions, focusing the pathologist’s attention on key areas. Thus, it can be integrated into clinical practice as a second opinion or as a flag for details that may have been missed at first glance. Abstract Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.
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Affiliation(s)
- Pedro C. Neto
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
- Correspondence:
| | - Sara P. Oliveira
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
| | - Diana Montezuma
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
- School of Medicine and Biomedical Sciences, University of Porto (ICBAS), 4050-313 Porto, Portugal
- Cancer Biology and Epigenetics Group, IPO-Porto, 4200-072 Porto, Portugal
| | - João Fraga
- Department of Pathology, IPO-Porto, 4200-072 Porto, Portugal;
| | - Ana Monteiro
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Liliana Ribeiro
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Sofia Gonçalves
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Isabel M. Pinto
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Jaime S. Cardoso
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
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Exploration on College Ideological and Political Education Integrating Artificial Intelligence-Intellectualized Information Technology. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4844565. [PMID: 35634053 PMCID: PMC9132633 DOI: 10.1155/2022/4844565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/14/2022] [Accepted: 03/21/2022] [Indexed: 11/17/2022]
Abstract
In recent years, with the vigorous development and application of Artificial Intelligence (AI), the application of AI in education is becoming more and more extensive. This study makes a theoretical analysis of AI-Intellectualized Information Technology (IT). Discrete Cosine Transform (DCT)-Based Speech Recognition (SR) and Genetic Algorithm (GA)-Based Image Recognition (IR) are used to analyze the College Ideological and Political Education (IAPE). The research findings prove that the advantages of integrating AI-intellectualized IT on College IAPE outweigh the disadvantages. The improvement of technological development, which accounts for 71.17% of undergraduate gains, is the most significant, and the smallest gain is technology coverage, which is 36.80%. Overall, 57.21% are interested in new technology, and the students' enthusiasm accounts for 30.77%. Most of the students focus on the innovation performance of technology, accounting for 75.92%. With an average influence of 89.04% on undergraduates, technology has the largest impact, followed by 85.78% on students with masters or higher degrees. The largest impact of diversified teaching methods for all students is 62.48%. This study provides some reference values for AI-intellectualized IT research and analysis, as well as students' IAPE.
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