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Li J, Dong P, Wang X, Zhang J, Zhao M, Shen H, Cai L, He J, Han M, Miao J, Liu H, Yang W, Han X, Liu Y. Artificial intelligence enhances whole-slide interpretation of PD-L1 CPS in triple-negative breast cancer: A multi-institutional ring study. Histopathology 2024; 85:451-467. [PMID: 38747491 DOI: 10.1111/his.15205] [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: 10/11/2023] [Revised: 03/11/2024] [Accepted: 04/21/2024] [Indexed: 08/09/2024]
Abstract
BACKGROUND AND AIMS Evaluation of the programmed cell death ligand-1 (PD-L1) combined positive score (CPS) is vital to predict the efficacy of the immunotherapy in triple-negative breast cancer (TNBC), but pathologists show substantial variability in the consistency and accuracy of the interpretation. It is of great importance to establish an objective and effective method which is highly repeatable. METHODS We proposed a model in a deep learning-based framework, which at the patch level incorporated cell analysis and tissue region analysis, followed by the whole-slide level fusion of patch results. Three rounds of ring studies (RSs) were conducted. Twenty-one pathologists of different levels from four institutions evaluated the PD-L1 CPS in TNBC specimens as continuous scores by visual assessment and our artificial intelligence (AI)-assisted method. RESULTS In the visual assessment, the interpretation results of PD-L1 (Dako 22C3) CPS by different levels of pathologists have significant differences and showed weak consistency. Using AI-assisted interpretation, there were no significant differences between all pathologists (P = 0.43), and the intraclass correlation coefficient (ICC) value was increased from 0.618 [95% confidence interval (CI) = 0.524-0.719] to 0.931 (95% CI = 0.902-0.955). The accuracy of interpretation result is further improved to 0.919 (95% CI = 0.886-0.947). Acceptance of AI results by junior pathologists was the highest among all levels, and 80% of the AI results were accepted overall. CONCLUSION With the help of the AI-assisted diagnostic method, different levels of pathologists achieved excellent consistency and repeatability in the interpretation of PD-L1 (Dako 22C3) CPS. Our AI-assisted diagnostic approach was proved to strengthen the consistency and repeatability in clinical practice.
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Affiliation(s)
- Jinze Li
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Pei Dong
- AI Lab, Tencent, Shenzhen, Guangdong, China
| | - Xinran Wang
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Jun Zhang
- AI Lab, Tencent, Shenzhen, Guangdong, China
| | - Meng Zhao
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | | | - Lijing Cai
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Jiankun He
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Mengxue Han
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Jiaxian Miao
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Hongbo Liu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Wei Yang
- AI Lab, Tencent, Shenzhen, Guangdong, China
| | - Xiao Han
- AI Lab, Tencent, Shenzhen, Guangdong, China
| | - Yueping Liu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
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Zhang XB, Fan YB, Jing R, Getu MA, Chen WY, Zhang W, Dong HX, Dakal TC, Hayat A, Cai HJ, Ashrafizadeh M, Abd El-Aty AM, Hacimuftuoglu A, Liu P, Li TF, Sethi G, Ahn KS, Ertas YN, Chen MJ, Ji JS, Ma L, Gong P. Gastroenteropancreatic neuroendocrine neoplasms: current development, challenges, and clinical perspectives. Mil Med Res 2024; 11:35. [PMID: 38835066 DOI: 10.1186/s40779-024-00535-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 05/07/2024] [Indexed: 06/06/2024] Open
Abstract
Neuroendocrine neoplasms (NENs) are highly heterogeneous and potentially malignant tumors arising from secretory cells of the neuroendocrine system. Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are the most common subtype of NENs. Historically, GEP-NENs have been regarded as infrequent and slow-growing malignancies; however, recent data have demonstrated that the worldwide prevalence and incidence of GEP-NENs have increased exponentially over the last three decades. In addition, an increasing number of studies have proven that GEP-NENs result in a limited life expectancy. These findings suggested that the natural biology of GEP-NENs is more aggressive than commonly assumed. Therefore, there is an urgent need for advanced researches focusing on the diagnosis and management of patients with GEP-NENs. In this review, we have summarized the limitations and recent advancements in our comprehension of the epidemiology, clinical presentations, pathology, molecular biology, diagnosis, and treatment of GEP-NETs to identify factors contributing to delays in diagnosis and timely treatment of these patients.
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Affiliation(s)
- Xian-Bin Zhang
- Department of General SurgeryInstitute of Precision Diagnosis and Treatment of Digestive System Tumors and Guangdong Provincial Key Laboratory of Chinese Medicine Ingredients and Gut Microbiomics, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Yi-Bao Fan
- Department of General SurgeryInstitute of Precision Diagnosis and Treatment of Digestive System Tumors and Guangdong Provincial Key Laboratory of Chinese Medicine Ingredients and Gut Microbiomics, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China
- School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, 518060, China
| | - Rui Jing
- Department of Radiology, Second Hospital of Shandong University, Jinan, Shandong, 250000, China
| | - Mikiyas Amare Getu
- Department of General SurgeryInstitute of Precision Diagnosis and Treatment of Digestive System Tumors and Guangdong Provincial Key Laboratory of Chinese Medicine Ingredients and Gut Microbiomics, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Wan-Ying Chen
- Department of General SurgeryInstitute of Precision Diagnosis and Treatment of Digestive System Tumors and Guangdong Provincial Key Laboratory of Chinese Medicine Ingredients and Gut Microbiomics, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China
- School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, 518060, China
| | - Wei Zhang
- Department of General SurgeryInstitute of Precision Diagnosis and Treatment of Digestive System Tumors and Guangdong Provincial Key Laboratory of Chinese Medicine Ingredients and Gut Microbiomics, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Hong-Xia Dong
- Department of Gastroenterology, General Hospital of Chinese PLA, Beijing, 100853, China
| | - Tikam Chand Dakal
- Department of Biotechnology, Mohanlal Sukhadia University, Udaipur, Rajasthan, 313001, India
| | - Akhtar Hayat
- Interdisciplinary Research Centre in Biomedical Materials (IRCBM), COMSATS University Islamabad, Lahore Campus, Lahore, 54000, Pakistan
| | - Hua-Jun Cai
- Department of General SurgeryInstitute of Precision Diagnosis and Treatment of Digestive System Tumors and Guangdong Provincial Key Laboratory of Chinese Medicine Ingredients and Gut Microbiomics, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Milad Ashrafizadeh
- Department of General SurgeryInstitute of Precision Diagnosis and Treatment of Digestive System Tumors and Guangdong Provincial Key Laboratory of Chinese Medicine Ingredients and Gut Microbiomics, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - A M Abd El-Aty
- Department of Pharmacology, Faculty of Veterinary Medicine, Cairo University, Giza, 12211, Egypt
- Department of Medical Pharmacology, Medical Faculty, Ataturk University, Erzurum, 25240, Turkey
| | - Ahmet Hacimuftuoglu
- Department of Medical Pharmacology, Medical Faculty, Ataturk University, Erzurum, 25240, Turkey
| | - Peng Liu
- Department of General SurgeryInstitute of Precision Diagnosis and Treatment of Digestive System Tumors and Guangdong Provincial Key Laboratory of Chinese Medicine Ingredients and Gut Microbiomics, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Tian-Feng Li
- Reproductive Medicine Center, Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, Guangdong, 518055, China
| | - Gautam Sethi
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore
| | - Kwang Seok Ahn
- Department of Science in Korean Medicine, Kyung Hee University, Seoul, 02447, Republic of Korea
| | - Yavuz Nuri Ertas
- ERNAM-Nanotechnology Research and Application Center, Erciyes University, Kayseri, 38039, Türkiye
- Department of Biomedical Engineering, Erciyes University, Kayseri, 38280, Türkiye
- UNAM-National Nanotechnology Research Center, Bilkent University, Ankara, 06800, Türkiye
| | - Min-Jiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, 323000, China
| | - Jian-Song Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, 323000, China
| | - Li Ma
- Department of Epidemiology, Dalian Medical University, Dalian, Liaoning, 116044, China
| | - Peng Gong
- Department of General SurgeryInstitute of Precision Diagnosis and Treatment of Digestive System Tumors and Guangdong Provincial Key Laboratory of Chinese Medicine Ingredients and Gut Microbiomics, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China.
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Mooghal M, Khan MAA, Samar MR, Shaikh H, Valimohammad AT, Idrees R, Abdul Rashid Y, Sattar AK. Association Between Ki-67 Proliferative Index and Oncotype-Dx Recurrence Score in Hormone Receptor-Positive, HER2-Negative Early Breast Cancers. A Systematic Review of the Literature. Breast Cancer (Auckl) 2024; 18:11782234241255211. [PMID: 38779417 PMCID: PMC11110513 DOI: 10.1177/11782234241255211] [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: 01/15/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024] Open
Abstract
Background Oncotype-Dx (ODx) is a 21-gene assay used as a prognostic and predictive tool for hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative, node-negative, or 1 to 3 lymph node-positive early breast cancers (EBCs). The cost of the test, which is not available in low-middle income countries (LMICs), is not within the means of most individuals. The Ki-67 index is a marker of tumor proliferation that is cost-effective and easily performed and has been substituted in many cases to obtain prognostic information. Objective We aimed to identify the correlation between the ODx recurrence score (RS) and the Ki-67 index in HR-positive EBCs and to determine whether Ki-67, like the ODx, can help facilitate clinical decision-making. Design Systematic review correlating Ki-67 index and ODx in HR-positive and HER2-negative EBCs as per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data sources and methods We searched different databases between January 2010 and May 2023 and included retrospective/prospective cohorts, clinical trials, case-control, and cross-sectional studies involving HR-positive and HER2-negative EBCs correlating the Ki-67 index and ODx RS categories. Results Of the 18 studies included, 16 indicated a positive or weakly positive correlation between ODx and the Ki-67 index. The combined P value of the included studies is <0.05 (P = .000), which shows a statistical significance between the 2. Our review also discusses the potential of machine learning and artificial intelligence (AI) in Ki-67 assessment, offering a cost-effective and reproducible alternative. Conclusion Even although there are limitations, studies indicate a favorable association between ODx and the Ki-67 index in specific situations. This implies that Ki-67 can offer important predictive details, especially regarding the likelihood of relapse in HR-positive EBC. This is particularly significant in LMICs where financial constraints often hinder the availability of costly diagnostic tests.
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Affiliation(s)
- Mehwish Mooghal
- Section of Breast Surgery, Department of Surgery, The Aga Khan University Hospital, Karachi, Pakistan
| | | | - Mirza Rameez Samar
- Section of Medical Oncology, Department of Oncology, The Aga Khan University Hospital, Karachi, Pakistan
| | - Hafsa Shaikh
- Section of Breast Surgery, Department of Surgery, The Aga Khan University Hospital, Karachi, Pakistan
| | - Azmina Tajdin Valimohammad
- Section of Medical Oncology, Department of Oncology, The Aga Khan University Hospital, Karachi, Pakistan
| | - Romana Idrees
- Department of Pathology, The Aga Khan University Hospital, Karachi, Pakistan
| | - Yasmin Abdul Rashid
- Section of Medical Oncology, Department of Oncology, The Aga Khan University Hospital, Karachi, Pakistan
| | - Abida K Sattar
- Section of Breast Surgery, Department of Surgery, The Aga Khan University Hospital, Karachi, Pakistan
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Dy A, Nguyen NNJ, Meyer J, Dawe M, Shi W, Androutsos D, Fyles A, Liu FF, Done S, Khademi A. AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer. Sci Rep 2024; 14:1283. [PMID: 38218973 PMCID: PMC10787826 DOI: 10.1038/s41598-024-51723-2] [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: 10/27/2023] [Accepted: 01/09/2024] [Indexed: 01/15/2024] Open
Abstract
The Ki-67 proliferation index (PI) guides treatment decisions in breast cancer but suffers from poor inter-rater reproducibility. Although AI tools have been designed for Ki-67 assessment, their impact on pathologists' work remains understudied. 90 international pathologists were recruited to assess the Ki-67 PI of ten breast cancer tissue microarrays with and without AI. Accuracy, agreement, and turnaround time with and without AI were compared. Pathologists' perspectives on AI were collected. Using AI led to a significant decrease in PI error (2.1% with AI vs. 5.9% without AI, p < 0.001), better inter-rater agreement (ICC: 0.70 vs. 0.92; Krippendorff's α: 0.63 vs. 0.89; Fleiss' Kappa: 0.40 vs. 0.86), and an 11.9% overall median reduction in turnaround time. Most pathologists (84%) found the AI reliable. For Ki-67 assessments, 76% of respondents believed AI enhances accuracy, 82% said it improves consistency, and 83% trust it will improve efficiency. This study highlights AI's potential to standardize Ki-67 scoring, especially between 5 and 30% PI-a range with low PI agreement. This could pave the way for a universally accepted PI score to guide treatment decisions, emphasizing the promising role of AI integration into pathologist workflows.
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Affiliation(s)
- Amanda Dy
- Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
| | | | - Julien Meyer
- School of Health Services Management, Toronto Metropolitan University, Toronto, ON, Canada
| | - Melanie Dawe
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Wei Shi
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Dimitri Androutsos
- Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
| | - Anthony Fyles
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Fei-Fei Liu
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Susan Done
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - April Khademi
- Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
- Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Network, Toronto, ON, Canada
- Institute for Biomedical Engineering, Science Tech (iBEST), A Partnership Between St. Michael's Hospital and Toronto Metropolitan University, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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5
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Liu Y, Zhen T, Fu Y, Wang Y, He Y, Han A, Shi H. AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images. Cancers (Basel) 2023; 16:167. [PMID: 38201594 PMCID: PMC10778369 DOI: 10.3390/cancers16010167] [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: 11/28/2023] [Revised: 12/24/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
AIMS The automation of quantitative evaluation for breast immunohistochemistry (IHC) plays a crucial role in reducing the workload of pathologists and enhancing the objectivity of diagnoses. However, current methods face challenges in achieving fully automated immunohistochemistry quantification due to the complexity of segmenting the tumor area into distinct ductal carcinoma in situ (DCIS) and invasive carcinoma (IC) regions. Moreover, the quantitative analysis of immunohistochemistry requires a specific focus on invasive carcinoma regions. METHODS AND RESULTS In this study, we propose an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images (WSIs). Our method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional H&E and P63 staining slides. In addition, we introduced an advanced semi-supervised learning algorithm, allowing efficient training of the model using unlabeled data. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 618 IHC-stained WSIs from 170 cases, including four types of staining (ER, PR, HER2, and Ki-67). Notably, the model demonstrated an impressive intersection over union (IoU) score exceeding 80% on the test set. Furthermore, to ascertain the practical utility of our model in IHC quantitative evaluation, we constructed a fully automated Ki-67 scoring system based on the model's predictions. Comparative experiments convincingly demonstrated that our system exhibited high consistency with the scores given by experienced pathologists. CONCLUSIONS Our developed model excels in accurately distinguishing between DCIS and invasive carcinoma regions in breast cancer immunohistochemistry WSIs. This method paves the way for a clinically available, fully automated immunohistochemistry quantitative scoring system.
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Affiliation(s)
- Yiqing Liu
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China; (Y.L.); (Y.F.); (Y.W.); (Y.H.)
| | - Tiantian Zhen
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China;
| | - Yuqiu Fu
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China; (Y.L.); (Y.F.); (Y.W.); (Y.H.)
| | - Yizhi Wang
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China; (Y.L.); (Y.F.); (Y.W.); (Y.H.)
| | - Yonghong He
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China; (Y.L.); (Y.F.); (Y.W.); (Y.H.)
| | - Anjia Han
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China;
| | - Huijuan Shi
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China;
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Mandair D, Reis-Filho JS, Ashworth A. Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology. NPJ Breast Cancer 2023; 9:21. [PMID: 37024522 PMCID: PMC10079681 DOI: 10.1038/s41523-023-00518-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 02/27/2023] [Indexed: 04/08/2023] Open
Abstract
Breast cancer remains a highly prevalent disease with considerable inter- and intra-tumoral heterogeneity complicating prognostication and treatment decisions. The utilization and depth of genomic, transcriptomic and proteomic data for cancer has exploded over recent times and the addition of spatial context to this information, by understanding the correlating morphologic and spatial patterns of cells in tissue samples, has created an exciting frontier of research, histo-genomics. At the same time, deep learning (DL), a class of machine learning algorithms employing artificial neural networks, has rapidly progressed in the last decade with a confluence of technical developments - including the advent of modern graphic processing units (GPU), allowing efficient implementation of increasingly complex architectures at scale; advances in the theoretical and practical design of network architectures; and access to larger datasets for training - all leading to sweeping advances in image classification and object detection. In this review, we examine recent developments in the application of DL in breast cancer histology with particular emphasis of those producing biologic insights or novel biomarkers, spanning the extraction of genomic information to the use of stroma to predict cancer recurrence, with the aim of suggesting avenues for further advancing this exciting field.
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Affiliation(s)
- Divneet Mandair
- UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, 94158, USA
| | | | - Alan Ashworth
- UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, 94158, USA.
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A Scoping Review of Deep Learning in Cancer Nursing Combined with Augmented Reality: the Era of Intelligent Nursing is Coming. Asia Pac J Oncol Nurs 2022; 9:100135. [PMID: 36276884 PMCID: PMC9579790 DOI: 10.1016/j.apjon.2022.100135] [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: 05/21/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022] Open
Abstract
Artificial intelligence has been developing greatly in the field of medicine. As a new research hotspot of artificial intelligence, deep learning (DL) has been widely applied in the fields of cancer risk assessment, symptom recognition, and cancer detection. Therefore, nursing care issues in terms of consuming time and energy, lower accuracy, and lower efficiency can be solved with applying DL in caring cancer patients. In addition, augmented reality (AR) has great navigation potential through combining computer-generated virtual elements with the real world. Thus, DL + AR may facilitate patients with cancer to possess a brand-new model of nursing care that is more intelligent, mobile, and adapted to the information age, compared to traditional nursing. With the advent of the era of intelligent nursing, future nursing models can not only learn from the DL + AR model to meet the needs of patients with cancer but also reduce nursing workload, save healthcare resources, and improve work efficiency, the quality of nursing care, as well as the quality of life for cancer patients.
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Liao J, Chen X, Ding G, Dong P, Ye H, Wang H, Zhang Y, Yao J. Deep learning-based single-shot autofocus method for digital microscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:314-327. [PMID: 35154873 PMCID: PMC8803042 DOI: 10.1364/boe.446928] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/07/2021] [Accepted: 12/07/2021] [Indexed: 06/14/2023]
Abstract
Digital pathology is being transformed by artificial intelligence (AI)-based pathological diagnosis. One major challenge for correct AI diagnoses is to ensure the focus quality of captured images. Here, we propose a deep learning-based single-shot autofocus method for microscopy. We use a modified MobileNetV3, a lightweight network, to predict the defocus distance with a single-shot microscopy image acquired at an arbitrary image plane without secondary camera or additional optics. The defocus prediction takes only 9 ms with a focusing error of only ∼1/15 depth of field. We also provide implementation examples for the augmented reality microscope and the whole slide imaging (WSI) system. Our proposed technique can perform real-time and accurate autofocus which will not only support pathologists in their daily work, but also provide potential applications in the life sciences, material research, and industrial automatic detection.
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Affiliation(s)
| | - Xu Chen
- Tencent AI Lab, Shenzhen 518054, China
| | - Ge Ding
- Tencent AI Lab, Shenzhen 518054, China
| | - Pei Dong
- Tencent AI Lab, Shenzhen 518054, China
| | - Hu Ye
- Tencent AI Lab, Shenzhen 518054, China
| | - Han Wang
- Tencent AI Lab, Shenzhen 518054, China
| | - Yongbing Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
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Should Ki-67 be adopted to select breast cancer patients for treatment with adjuvant abemaciclib? Ann Oncol 2021; 33:234-238. [PMID: 34942341 DOI: 10.1016/j.annonc.2021.12.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/12/2021] [Indexed: 01/09/2023] Open
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Bongiovanni L, Andriessen A, Silvestri S, Porcellato I, Brachelente C, de Bruin A. H2AFZ: A Novel Prognostic Marker in Canine Melanoma and a Predictive Marker for Resistance to CDK4/6 Inhibitor Treatment. Front Vet Sci 2021; 8:705359. [PMID: 34485433 PMCID: PMC8415453 DOI: 10.3389/fvets.2021.705359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Uncontrolled proliferation is a key feature of tumor progression and malignancy. This suggests that cell-cycle related factors could be exploited as cancer biomarkers and that pathways specifically involved in the cell cycle, such as the Rb-E2F pathway, could be targeted as an effective anti-tumor therapy. We investigated 34 formalin-fixed paraffin-embedded (FFPE) tissue samples of canine cutaneous melanocytoma, cutaneous melanoma, and oral melanoma. Corresponding clinical follow-up data were used to determine the prognostic value of the mRNA expression levels of several cell cycle regulated E2F target genes (E2F1, DHFR, CDC6, ATAD2, MCM2, H2AFZ, GINS2, and survivin/BIRC5). Moreover, using four canine melanoma cell lines, we explored the possibility of blocking the Rb-E2F pathway by using a CDK4/6 inhibitor (Palbociclib) as a potential anti-cancer therapy. We investigated the expression levels of the same E2F target gene transcripts before and after treatment to determine the potential utility of these molecules as predictive markers. The E2F target gene H2AFZ was expressed in 91.43% of the primary tumors and H2AFZ expression was significantly higher in cases with unfavorable clinical outcome. Among the other tested genes, survivin/BIRC5 showed as well-promising results as a prognostic marker in canine melanoma. Three of the four tested melanoma cell lines were sensitive to the CDK4/6 inhibitor. The resistant cell line displayed higher expression levels of H2AFZ before treatment compared to the CDK4/6 inhibitor-sensitive cell lines. The present results suggest that CDK4/6 inhibitors could potentially be used as a new anti-cancer treatment for canine melanoma and that H2AFZ could serve as a prognostic and predictive marker for patient selection.
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Affiliation(s)
- Laura Bongiovanni
- Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - Anneloes Andriessen
- Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | | | - Ilaria Porcellato
- Department of Veterinary Medicine, University of Perugia, Perugia, Italy
| | - Chiara Brachelente
- Department of Veterinary Medicine, University of Perugia, Perugia, Italy
| | - Alain de Bruin
- Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands.,Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
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Cserni B, Bori R, Csörgő E, Oláh-Németh O, Pancsa T, Sejben A, Sejben I, Vörös A, Zombori T, Nyári T, Cserni G. The additional value of ONEST (Observers Needed to Evaluate Subjective Tests) in assessing reproducibility of oestrogen receptor, progesterone receptor, and Ki67 classification in breast cancer. Virchows Arch 2021; 479:1101-1109. [PMID: 34415429 PMCID: PMC8724065 DOI: 10.1007/s00428-021-03172-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/06/2021] [Accepted: 07/25/2021] [Indexed: 11/28/2022]
Abstract
The reproducibility of assessing potential biomarkers is crucial for their implementation. ONEST (Observers Needed to Evaluate Subjective Tests) has been recently introduced as a new additive evaluation method for the assessment of reliability, by demonstrating how the number of observers impact on interobserver agreement. Oestrogen receptor (ER), progesterone receptor (PR), and Ki67 proliferation marker immunohistochemical stainings were assessed on 50 core needle biopsy and 50 excision samples from breast cancers by 9 pathologists according to daily practice. ER and PR statuses based on the percentages of stained nuclei were the most consistently assessed parameters (intraclass correlation coefficients, ICC 0.918-0.996), whereas Ki67 with 5 different theoretical or St Gallen Consensus Conference-proposed cut-off values demonstrated moderate to good reproducibility (ICC: 0.625-0.760). ONEST highlighted that consistent tests like ER and PR assessment needed only 2 or 3 observers for optimal evaluation of reproducibility, and the width between plots of the best and worst overall percent agreement values for 100 randomly selected permutations of observers was narrow. In contrast, with less consistently evaluated tests of Ki67 categorization, ONEST suggested at least 5 observers required for more trustful assessment of reliability, and the bandwidth of the best and worst plots was wider (up to 34% difference between two observers). ONEST has additional value to traditional calculations of the interobserver agreement by not only highlighting the number of observers needed to trustfully evaluate reproducibility but also by highlighting the rate of agreement with an increasing number of observers and disagreement between the better and worse ratings.
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Affiliation(s)
| | - Rita Bori
- Department of Pathology, Bács-Kiskun County Teaching Hospital, Kecskemét, Hungary
| | - Erika Csörgő
- Department of Pathology, Bács-Kiskun County Teaching Hospital, Kecskemét, Hungary
| | | | - Tamás Pancsa
- Department of Pathology, University of Szeged, Szeged, Hungary
| | - Anita Sejben
- Department of Pathology, University of Szeged, Szeged, Hungary
| | - István Sejben
- Department of Pathology, Bács-Kiskun County Teaching Hospital, Kecskemét, Hungary
| | - András Vörös
- Department of Pathology, University of Szeged, Szeged, Hungary
| | - Tamás Zombori
- Department of Pathology, University of Szeged, Szeged, Hungary
| | - Tibor Nyári
- Department of Medical Physics and Informatics, University of Szeged, Szeged, Hungary
| | - Gábor Cserni
- Department of Pathology, Bács-Kiskun County Teaching Hospital, Kecskemét, Hungary. .,Department of Pathology, University of Szeged, Szeged, Hungary.
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