151
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van Seijen M, Jóźwiak K, Pinder SE, Hall A, Krishnamurthy S, Thomas JSJ, Collins LC, Bijron J, Bart J, Cohen D, Ng W, Bouybayoune I, Stobart H, Hudecek J, Schaapveld M, Thompson A, Lips EH, Wesseling J. Variability in grading of ductal carcinoma in situ among an international group of pathologists. J Pathol Clin Res 2021; 7:233-242. [PMID: 33620141 PMCID: PMC8073001 DOI: 10.1002/cjp2.201] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 12/11/2020] [Accepted: 01/08/2021] [Indexed: 01/04/2023]
Abstract
The prognostic value of cytonuclear grade in ductal carcinoma in situ (DCIS) is debated, partly due to high interobserver variability and the use of multiple guidelines. The aim of this study was to evaluate interobserver agreement in grading DCIS between Dutch, British, and American pathologists. Haematoxylin and eosin-stained slides of 425 women with primary DCIS were independently reviewed by nine breast pathologists based in the Netherlands, the UK, and the USA. Chance-corrected kappa (κma ) for association between pathologists was calculated based on a generalised linear mixed model using the ordinal package in R. Overall κma for grade of DCIS (low, intermediate, or high) was estimated to be 0.50 (95% confidence interval [CI] 0.44-0.56), indicating a moderate association between pathologists. When the model was adjusted for national guidelines, the association for grade did not change (κma = 0.53; 95% CI 0.48-0.57); subgroup analysis for pathologists using the UK pathology guidelines only had significantly higher association (κma = 0.58; 95% CI 0.56-0.61). To assess if concordance of grading relates to the expression of the oestrogen receptor (ER) and HER2, archived immunohistochemistry was analysed on a subgroup (n = 106). This showed that non-high grade according to the majority opinion was associated with ER positivity and HER2 negativity (100 and 89% of non-high grade cases, respectively). In conclusion, DCIS grade showed only moderate association using whole slide images scored by nine breast pathologists. As therapeutic decisions and inclusion in ongoing clinical trials are guided by DCIS grade, there is a pressing need to reduce interobserver variability in grading. ER and HER2 might be supportive to prevent the accidental and unwanted inclusion of high-grade DCIS in such trials.
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Affiliation(s)
- Maartje van Seijen
- Division of Molecular PathologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Katarzyna Jóźwiak
- Division of Molecular PathologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
- Institute of Biostatistics and Registry ResearchBrandenburg Medical School Theodor FontaneNeuruppinGermany
| | - Sarah E Pinder
- Comprehensive Cancer Centre at Guy's Hospital, School of Cancer & Pharmaceutical SciencesKings College LondonLondonUK
- Department of Cellular PathologyGuy's and St Thomas' NHS Foundation Trust LondonLondonUK
| | - Allison Hall
- Department of PathologyDuke University Medical CenterDurhamNCUSA
| | - Savitri Krishnamurthy
- Department of PathologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | | | - Laura C Collins
- Department of PathologyBeth Israel Deaconess Medical Center and Harvard Medical SchoolBostonMAUSA
| | - Jonathan Bijron
- Department of PathologyMartini HospitalGroningenThe Netherlands
| | - Joost Bart
- Department of PathologyIsala HospitalZwolleThe Netherlands
- Department of PathologyUniversity of Groningen, University Medical Center GroningenGroningenThe Netherlands
| | - Danielle Cohen
- Department of PathologyLeiden University Medical CenterLeidenThe Netherlands
| | - Wen Ng
- Department of Cellular PathologyGuy's and St Thomas' NHS Foundation Trust LondonLondonUK
| | - Ihssane Bouybayoune
- Comprehensive Cancer Centre at Guy's Hospital, School of Cancer & Pharmaceutical SciencesKings College LondonLondonUK
| | | | - Jan Hudecek
- Department of Research ITThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Michael Schaapveld
- Department of Psychosocial Research and EpidemiologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Alastair Thompson
- Dan L Duncan Comprehensive Cancer CenterBaylor College of MedicineHoustonTXUSA
| | - Esther H Lips
- Division of Molecular PathologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Jelle Wesseling
- Division of Molecular PathologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
- Department of PathologyIsala HospitalZwolleThe Netherlands
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152
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Improving DCIS diagnosis and predictive outcome by applying artificial intelligence. Biochim Biophys Acta Rev Cancer 2021; 1876:188555. [PMID: 33933557 DOI: 10.1016/j.bbcan.2021.188555] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 04/22/2021] [Accepted: 04/24/2021] [Indexed: 11/21/2022]
Abstract
Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that is considered to be a precursor to invasive breast cancer. Nevertheless, not all DCIS will progress to invasion. Current histopathological classification systems are unable to predict which cases will or will not progress, and therefore many women with DCIS may be overtreated. Artificial intelligence (AI) image-based analysis methods have potential to identify and analyze novel features that may facilitate tumor identification, prediction of disease outcome and response to treatment. Indeed, these methods prove promising for accurately identifying DCIS lesions, and show potential clinical utility in the therapeutic stratification of DCIS patients. Here, we review how AI techniques in histopathology may aid diagnosis and clinical decisions in regards to DCIS, and how such techniques could be incorporated into clinical practice.
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153
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Munien C, Viriri S. Classification of Hematoxylin and Eosin-Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:5580914. [PMID: 33897774 PMCID: PMC8052174 DOI: 10.1155/2021/5580914] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 03/15/2021] [Accepted: 03/29/2021] [Indexed: 12/19/2022]
Abstract
Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Computer-aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine-tuned and evaluated on their ability to classify images into four classes: normal, benign, in situ carcinoma, and invasive carcinoma. Moreover, two standard stain normalization techniques, Reinhard and Macenko, were observed to measure the impact of stain normalization on performance. The outcome of this approach reveals that the EfficientNet-B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity of 96.67% using the Macenko stain normalization method. These satisfactory results indicate that transferring generic features from natural images to medical images through fine-tuning on EfficientNets can achieve satisfactory results.
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Affiliation(s)
- Chanaleä Munien
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 217013433, South Africa
| | - Serestina Viriri
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 217013433, South Africa
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154
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Suarez-Zamora DA, Mustafa RA, Estrada-Orozco K, Rodriguez-Urrego PA, Torres-Franco F, Barreto-Hauzeur L, Mora-Ochoa H, Di Tanna GL, Yepes-Nuñez JJ. Intraoperative sub-areolar frozen section analysis for detecting nipple involvement in candidates for nipple-sparing mastectomy. Hippokratia 2021. [DOI: 10.1002/14651858.cd014702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- David A Suarez-Zamora
- Department of Pathology and Laboratories; Fundación Santa Fe de Bogotá University Hospital; Bogota Colombia
| | - Reem A Mustafa
- Department of Internal Medicine, Division of Nephrology and Hypertension; University of Kansas Medical Center; Kansas City Missouri USA
- Department of Health Research Methods, Evidence and Impact; McMaster University; Hamilton Canada
| | - Kelly Estrada-Orozco
- Health Technologies and Politics Assessment Group, Clinical Research Institute; National University of Colombia; Bogota Colombia
| | - Paula A Rodriguez-Urrego
- Department of Pathology and Laboratories; Fundación Santa Fe de Bogotá University Hospital; Bogota Colombia
| | - Fabio Torres-Franco
- Division of Breast Surgery; Fundación Santa Fe de Bogotá University Hospital; Bogota Colombia
| | - Lisette Barreto-Hauzeur
- Division of Plastic and Reconstructive Surgery; Fundación Santa Fe de Bogotá University Hospital; Bogota Colombia
| | | | - Gian Luca Di Tanna
- Faculty of Medicine; University of New South Wales; Sydney Australia
- Statistics Division; The George Institute for Global Health; Newtown Australia
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155
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Gavrielides MA, Ronnett BM, Vang R, Sheikhzadeh F, Seidman JD. Selection of Representative Histologic Slides in Interobserver Reproducibility Studies: Insights from Expert Review for Ovarian Carcinoma Subtype Classification. J Pathol Inform 2021; 12:15. [PMID: 34012719 PMCID: PMC8112350 DOI: 10.4103/jpi.jpi_56_20] [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: 06/25/2020] [Revised: 09/02/2020] [Accepted: 10/28/2020] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Observer studies in pathology often utilize a limited number of representative slides per case, selected and reported in a nonstandardized manner. Reference diagnoses are commonly assumed to be generalizable to all slides of a case. We examined these issues in the context of pathologist concordance for histologic subtype classification of ovarian carcinomas (OCs). MATERIALS AND METHODS A cohort of 114 OCs consisting of 72 cases with a single representative slide (Group 1) and 42 cases with multiple representative slides (148 slides, 2-6 sections per case, Group 2) was independently reviewed by three experts in gynecologic pathology (case-based review). In a follow-up study, each individual slide was independently reviewed in a randomized order by the same pathologists (section-based review). RESULTS Average interobserver concordance varied from 100% for Group 1 to 64.3% for Group 2 (86.8% across all cases). Across Group 2, 19 cases (45.2%) had at least one slide classified as a different subtype than the subtype assigned from case-based review, demonstrating the impact of intratumoral heterogeneity. Section-based concordance across individual sections from Group 2 was comparable to case-based concordance for those cases indicating diagnostic challenges at the individual section level. Findings demonstrate the increased diagnostic complexity of heterogeneous tumors that require multiple section sampling and its impact on pathologist performance. CONCLUSIONS The proportion of cases with multiple representative slides in cohorts used in validation studies, such as those conducted to evaluate artificial intelligence/machine learning tools, can influence diagnostic performance, and if not accounted for, can cause disparities between research and real-world observations and between research studies. Case selection in validation studies should account for tumor heterogeneity to create balanced datasets in terms of diagnostic complexity.
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Affiliation(s)
- Marios A. Gavrielides
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA, (Currently at AstraZeneca, Precision Medicine and Biosamples, Gaithersburg, Maryland, USA)
| | - Brigitte M. Ronnett
- Department of Pathology and Gynecology and Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Russell Vang
- Department of Pathology and Gynecology and Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Fahime Sheikhzadeh
- Electrical and Computer Engineering Department, University of British Columbia, Vancouver, Canada, (Currently at Roche Diagnostics, San Francisco, California, USA)
| | - Jeffrey D Seidman
- Division of Molecular Genetics and Pathology, Office of In Vitro Diagnostics and Radiological Health, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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156
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Sohail A, Khan A, Wahab N, Zameer A, Khan S. A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images. Sci Rep 2021; 11:6215. [PMID: 33737632 PMCID: PMC7973714 DOI: 10.1038/s41598-021-85652-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 02/16/2021] [Indexed: 12/24/2022] Open
Abstract
The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework "MP-MitDet" for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier "MitosRes-CNN" to filter false mitoses. The performance of the proposed "MitosRes-CNN" is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.
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Affiliation(s)
- Anabia Sohail
- Pattern Recognition Lab, DCIS, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, 45650, Pakistan
| | - Asifullah Khan
- Pattern Recognition Lab, DCIS, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, 45650, Pakistan.
- Deep Learning Lab, Centre for Mathematical Sciences, Pakistan Institute of Engineering and Applied Sciences, (PIEAS), Nilore, Islamabad, 45650, Pakistan.
| | - Noorul Wahab
- Department of Computer Science, Tissue Image Analytics (TIA) Lab, University of Warwick, Coventry, UK
| | - Aneela Zameer
- Pattern Recognition Lab, DCIS, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, 45650, Pakistan
| | - Saranjam Khan
- Department of Physics, Islamia College Peshawar, Peshawar, Pakistan
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157
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Polónia A, Campelos S, Ribeiro A, Aymore I, Pinto D, Biskup-Fruzynska M, Veiga RS, Canas-Marques R, Aresta G, Araújo T, Campilho A, Kwok S, Aguiar P, Eloy C. Artificial Intelligence Improves the Accuracy in Histologic Classification of Breast Lesions. Am J Clin Pathol 2021; 155:527-536. [PMID: 33118594 DOI: 10.1093/ajcp/aqaa151] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVES This study evaluated the usefulness of artificial intelligence (AI) algorithms as tools in improving the accuracy of histologic classification of breast tissue. METHODS Overall, 100 microscopic photographs (test A) and 152 regions of interest in whole-slide images (test B) of breast tissue were classified into 4 classes: normal, benign, carcinoma in situ (CIS), and invasive carcinoma. The accuracy of 4 pathologists and 3 pathology residents were evaluated without and with the assistance of algorithms. RESULTS In test A, algorithm A had accuracy of 0.87, with the lowest accuracy in the benign class (0.72). The observers had average accuracy of 0.80, and most clinically relevant discordances occurred in distinguishing benign from CIS (7.1% of classifications). With the assistance of algorithm A, the observers significantly increased their average accuracy to 0.88. In test B, algorithm B had accuracy of 0.49, with the lowest accuracy in the CIS class (0.06). The observers had average accuracy of 0.86, and most clinically relevant discordances occurred in distinguishing benign from CIS (6.3% of classifications). With the assistance of algorithm B, the observers maintained their average accuracy. CONCLUSIONS AI tools can increase the classification accuracy of pathologists in the setting of breast lesions.
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Affiliation(s)
- António Polónia
- Department of Pathology, Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal
- I3S – Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
| | - Sofia Campelos
- Department of Pathology, Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal
- I3S – Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
| | - Ana Ribeiro
- Department of Pathology, Centro Hospitalar de Vila Nova de Gaia / Espinho, EPE, Vila Nova de Gaia, Portugal
| | - Ierece Aymore
- Department of Pathology, Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal
- I3S – Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
| | - Daniel Pinto
- Department of Pathology, Centro Hospitalar de Lisboa Ocidental, EPE, Lisboa, Portugal
| | - Magdalena Biskup-Fruzynska
- Department of Tumor Pathology, Maria Sklodowska-Curie National Research Institute of Oncology (MSCNRIO), Gliwice, Poland
| | | | | | - Guilherme Aresta
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Engineering, University of Porto, Porto, Portugal
| | - Teresa Araújo
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Engineering, University of Porto, Porto, Portugal
| | - Aurélio Campilho
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Engineering, University of Porto, Porto, Portugal
| | | | - Paulo Aguiar
- I3S – Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
- Instituto Nacional de Engenharia Biomédica (INEB), Universidade do Porto, Porto, Portugal
| | - Catarina Eloy
- Department of Pathology, Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal
- I3S – Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
- Faculty of Medicine, University of Porto, Porto, Portugal
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158
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Derscheid RJ, Rahe MC, Burrough ER, Schwartz KJ, Arruda B. Disease diagnostic coding to facilitate evidence-based medicine: current and future perspectives. J Vet Diagn Invest 2021; 33:419-427. [PMID: 33719780 DOI: 10.1177/1040638721999373] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Technologic advances in information management have rapidly changed laboratory testing and the practice of veterinary medicine. Timely and strategic sampling, same-day assays, and 24-h access to laboratory results allow for rapid implementation of intervention and treatment protocols. Although agent detection and monitoring systems have progressed, and wider tracking of diseases across veterinary diagnostic laboratories exists, such as by the National Animal Health Laboratory Network (NAHLN), the distinction between detection of agent and manifestation of disease is critical to improved disease management. The implementation of a consistent, intuitive, and useful disease diagnosis coding system, specific for veterinary medicine and applicable to multiple animal species within and between veterinary diagnostic laboratories, is the first phase of disease data aggregation. Feedback loops for continuous improvement that could aggregate existing clinical and laboratory databases to improve the value and applications of diagnostic processes and clinical interventions, with interactive capabilities between clinicians and diagnosticians, and that differentiate disease causation from mere agent detection, remain incomplete. Creating an interface that allows aggregation of existing data from clinicians, including final diagnosis, interventions, or treatments applied, and measures of outcomes, is the second phase. Prototypes for stakeholder cooperation, collaboration, and beta testing of this vision are in development and becoming a reality. We focus here on how such a system is being developed and utilized at the Iowa State University Veterinary Diagnostic Laboratory to facilitate evidence-based medicine and utilize diagnostic coding for continuous improvement of animal health and welfare.
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Affiliation(s)
- Rachel J Derscheid
- Veterinary Diagnostic Laboratory, Iowa State University College of Veterinary Medicine, Ames, IA
| | - Michael C Rahe
- Veterinary Diagnostic Laboratory, Iowa State University College of Veterinary Medicine, Ames, IA
| | - Eric R Burrough
- Veterinary Diagnostic Laboratory, Iowa State University College of Veterinary Medicine, Ames, IA
| | - Kent J Schwartz
- Veterinary Diagnostic Laboratory, Iowa State University College of Veterinary Medicine, Ames, IA
| | - Bailey Arruda
- Veterinary Diagnostic Laboratory, Iowa State University College of Veterinary Medicine, Ames, IA
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159
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Narayanan PL, Raza SEA, Hall AH, Marks JR, King L, West RB, Hernandez L, Guppy N, Dowsett M, Gusterson B, Maley C, Hwang ES, Yuan Y. Unmasking the immune microecology of ductal carcinoma in situ with deep learning. NPJ Breast Cancer 2021; 7:19. [PMID: 33649333 PMCID: PMC7921670 DOI: 10.1038/s41523-020-00205-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 10/21/2020] [Indexed: 02/07/2023] Open
Abstract
Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its association with progression are not well understood. To characterise tissue spatial architecture and the microenvironment of DCIS, we designed and validated a new deep learning pipeline, UNMaSk. Following automated detection of individual DCIS ducts using a new method IM-Net, we applied spatial tessellation to create virtual boundaries for each duct. To study local TIL infiltration for each duct, DRDIN was developed for mapping the distribution of TILs. In a dataset comprising grade 2-3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, the colocalisation of TILs with DCIS ducts was significantly lower in pure DCIS compared to adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts in adjacent DCIS cases. Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable an automated morphological and microenvironmental analysis of DCIS, providing a new way to study differential immune ecology for individual ducts and identify new markers of progression.
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Affiliation(s)
- Priya Lakshmi Narayanan
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK.
- Division of Molecular Pathology, Institute of Cancer Research, London, UK.
| | - Shan E Ahmed Raza
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK
- Division of Molecular Pathology, Institute of Cancer Research, London, UK
| | - Allison H Hall
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA
| | - Jeffrey R Marks
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Lorraine King
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Robert B West
- Department of Pathology, Surgical Pathology, Stanford, CA, USA
| | - Lucia Hernandez
- Department of Anatomic Pathology, Hospital Universitario, 12 de Octubre, Madrid, Spain
| | - Naomi Guppy
- Breast Cancer Now Histopathology Core, Institute of Cancer Research, London, UK
- UCL Advanced Diagnostics, University College London, London, UK
| | - Mitch Dowsett
- The Breast Cancer Now Toby Robins Research Centre, Institute of Cancer Research, London, UK
- Academic Department of Biochemistry, Royal Marsden Hospital, London, UK
| | - Barry Gusterson
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK
| | - Carlo Maley
- Biodesign Center for Personalized Diagnostics and School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - E Shelley Hwang
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Yinyin Yuan
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK.
- Division of Molecular Pathology, Institute of Cancer Research, London, UK.
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160
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Murtaza G, Abdul Wahab AW, Raza G, Shuib L. A tree-based multiclassification of breast tumor histopathology images through deep learning. Comput Med Imaging Graph 2021; 89:101870. [PMID: 33545489 DOI: 10.1016/j.compmedimag.2021.101870] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 12/28/2020] [Accepted: 01/21/2021] [Indexed: 10/22/2022]
Abstract
Worldwide, the burden of cancer is drastically increasing over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of unnatural deaths. For early diagnosis, histopathology (Hp) imaging is a gold standard for positive and detailed (at tissue level) diagnosis of breast tumor (BrT) compared to mammogram images. A large number of studies used BrT Hp images to solve binary or multiclassification problems using high computational resources. However, classification models' performance may be compromised due to the high correlation among various types of BrT in Hp images, which raises the misclassification rate. Thus, this paper aims to develop a tree-based BrT multiclassification model via deep learning (DL) to extract discriminative features to solve the multiclassification problem with better performance using less computational resources. The main contributions of this work are to create an ensemble, tree-based DL model that is pre-trained on the BreakHis dataset, and implementation of a misclassification reduction algorithm. The ensemble, tree-based DL model, extracts discriminative BrT features from Hp images. The target dataset (i.e., Bioimaging challenge 2015 breast histology) is small in size; thus, to avoid overfitting of the proposed model, pretraining is performed on the BreakHis dataset. Whereas, misclassification reduction algorithm is implemented to enhance the performance of the classification model. The experimental results show that the proposed model outperformed the existing state-of-the-art baseline studies. The achieved classification accuracy is ranging from 87.50 % to 100 % for four subtypes of BrT. Thus, the proposed model can assist doctors as the second opinion in any healthcare centre.
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Affiliation(s)
- Ghulam Murtaza
- Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia; Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan.
| | - Ainuddin Wahid Abdul Wahab
- Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Ghulam Raza
- Our Lady of Lourdes Hospital Drogheda Ireland, Ireland.
| | - Liyana Shuib
- Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia.
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161
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Li R, An Y, Jin T, Zhang F, He P. Detection of MUC1 protein on tumor cells and their derived exosomes for breast cancer surveillance with an electrochemiluminescence aptasensor. J Electroanal Chem (Lausanne) 2021. [DOI: 10.1016/j.jelechem.2021.115011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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162
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Harmon SA, Patel PG, Sanford TH, Caven I, Iseman R, Vidotto T, Picanço C, Squire JA, Masoudi S, Mehralivand S, Choyke PL, Berman DM, Turkbey B, Jamaspishvili T. High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts. Mod Pathol 2021; 34:478-489. [PMID: 32884130 PMCID: PMC9152638 DOI: 10.1038/s41379-020-00674-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 08/21/2020] [Accepted: 08/21/2020] [Indexed: 02/06/2023]
Abstract
Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker. The objective of this work was to develop an artificial intelligence (AI) system for automated detection and localization of PTEN loss on immunohistochemically (IHC) stained sections. PTEN loss was assessed using IHC in two prostate tissue microarrays (TMA) (internal cohort, n = 272 and external cohort, n = 129 patients). TMA cores were visually scored for PTEN loss by pathologists and, if present, spatially annotated. Cores from each patient within the internal TMA cohort were split into 90% cross-validation (N = 2048) and 10% hold-out testing (N = 224) sets. ResNet-101 architecture was used to train core-based classification using a multi-resolution ensemble approach (×5, ×10, and ×20). For spatial annotations, single resolution pixel-based classification was trained from patches extracted at ×20 resolution, interpolated to ×40 resolution, and applied in a sliding-window fashion. A final AI-based prediction model was created from combining multi-resolution and pixel-based models. Performance was evaluated in 428 cores of external cohort. From both cohorts, a total of 2700 cores were studied, with a frequency of PTEN loss of 14.5% in internal (180/1239) and external 13.5% (43/319) cancer cores. The final AI-based prediction of PTEN status demonstrated 98.1% accuracy (95.0% sensitivity, 98.4% specificity; median dice score = 0.811) in internal cohort cross-validation set and 99.1% accuracy (100% sensitivity, 99.0% specificity; median dice score = 0.804) in internal cohort test set. Overall core-based classification in the external cohort was significantly improved in the external cohort (area under the curve = 0.964, 90.6% sensitivity, 95.7% specificity) when further trained (fine-tuned) using 15% of cohort data (19/124 patients). These results demonstrate a robust and fully automated method for detection and localization of PTEN loss in prostate cancer tissue samples. AI-based algorithms have potential to streamline sample assessment in research and clinical laboratories.
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Affiliation(s)
- Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Palak G Patel
- Division of Cancer Biology & Genetics, Cancer Research Institute, Queen's University, Kingston, ON, Canada
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
- Department of Cell Biology at The Arthur and Sonia Labatt Brain Tumour Research Centre at the Hospital for Sick Children, Toronto, ON, Canada
| | - Thomas H Sanford
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Urology, Upstate Medical University, Syracuse, NY, USA
| | - Isabelle Caven
- Division of Cancer Biology & Genetics, Cancer Research Institute, Queen's University, Kingston, ON, Canada
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
| | - Rachael Iseman
- Division of Cancer Biology & Genetics, Cancer Research Institute, Queen's University, Kingston, ON, Canada
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
| | - Thiago Vidotto
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Clarissa Picanço
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Jeremy A Squire
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Samira Masoudi
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sherif Mehralivand
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - David M Berman
- Division of Cancer Biology & Genetics, Cancer Research Institute, Queen's University, Kingston, ON, Canada
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tamara Jamaspishvili
- Division of Cancer Biology & Genetics, Cancer Research Institute, Queen's University, Kingston, ON, Canada.
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada.
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163
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Rohan TE, Arthur R, Wang Y, Weinmann S, Ginsberg M, Loi S, Salgado R. Infiltrating immune cells in benign breast disease and risk of subsequent invasive breast cancer. Breast Cancer Res 2021; 23:15. [PMID: 33516237 PMCID: PMC7846992 DOI: 10.1186/s13058-021-01395-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 01/19/2021] [Indexed: 11/23/2022] Open
Abstract
Background It is well established that tumors are antigenic and can induce an immune response by the host, entailing lymphocytic infiltration of the tumor and surrounding stroma. The extent and composition of the immune response to the tumor, assessed through evaluation of tumor-infiltrating lymphocyte counts, has been shown in many studies to have prognostic and predictive value for invasive breast cancer, but currently, there is little evidence regarding the association between infiltrating immune cell counts (IICCs) in women with benign breast disease (BBD) and risk of subsequent invasive breast cancer. Methods Using a cohort of 15,395 women biopsied for BBD at Kaiser Permanente Northwest, we conducted a nested case-control study in which cases were women who developed a subsequent invasive breast cancer during follow-up and controls were individually matched to cases on age at BBD diagnosis. We assessed IICCs in normal tissue and in the BBD lesions, and we used unconditional logistic regression to estimate the multivariable odds ratios (OR) and 95% confidence intervals (CI) for the associations between IICCs and breast cancer risk. Results There was no association between the IICC in normal tissue (multivariable OR per 5% increase in IICC = 1.05, 95% CI = 0.96–1.16) or in the BBD lesion (OR per 5% increase in IICC = 1.06, 95% CI = 0.96–1.18) and risk of subsequent invasive breast cancer. Also, there were no associations within subgroups defined by menopausal status, BBD histology, BMI, and history of smoking. Conclusion The results of this study suggest that IICCs in BBD tissue are not associated with altered risk of subsequent invasive breast cancer.
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Affiliation(s)
- Thomas E Rohan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Ave., Bronx, NY, 10461, USA.
| | - Rhonda Arthur
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Ave., Bronx, NY, 10461, USA
| | - Yihong Wang
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Sheila Weinmann
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA
| | - Mindy Ginsberg
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Ave., Bronx, NY, 10461, USA
| | - Sherene Loi
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia.,Division of Research, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Roberto Salgado
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Australia.,Department of Pathology, GZA-ZNA, Antwerp, Belgium
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164
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Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, Liu Y, Topol E, Dean J, Socher R. Deep learning-enabled medical computer vision. NPJ Digit Med 2021; 4:5. [PMID: 33420381 PMCID: PMC7794558 DOI: 10.1038/s41746-020-00376-2] [Citation(s) in RCA: 256] [Impact Index Per Article: 85.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 12/01/2020] [Indexed: 02/07/2023] Open
Abstract
A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields-including medicine-to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques-powered by deep learning-for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit-including cardiology, pathology, dermatology, ophthalmology-and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.
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Affiliation(s)
| | | | | | - Nikhil Naik
- Salesforce AI Research, San Francisco, CA, USA
| | - Ali Madani
- Salesforce AI Research, San Francisco, CA, USA
| | | | - Yun Liu
- Google Research, Mountain View, CA, USA
| | - Eric Topol
- Scripps Research Translational Institute, La Jolla, CA, USA
| | - Jeff Dean
- Google Research, Mountain View, CA, USA
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165
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Shin SJ, You SC, Jeon H, Jung JW, An MH, Park RW, Roh J. Style transfer strategy for developing a generalizable deep learning application in digital pathology. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 198:105815. [PMID: 33160111 DOI: 10.1016/j.cmpb.2020.105815] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 10/20/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVES Despite recent advances in artificial intelligence for medical images, the development of a robust deep learning model for identifying malignancy on pathology slides has been limited by problems related to substantial inter- and intra-institutional heterogeneity attributable to tissue preparation. The paucity of available data aggravates this limitation for relatively rare cancers. Here, using ovarian cancer pathology images, we explored the effect of image-to-image style transfer approaches on diagnostic performance. METHODS We leveraged a relatively large public image set for 142 patients with ovarian cancer from The Cancer Image Archive (TCIA) to fine-tune the renowned deep learning model Inception V3 for identifying malignancy on tissue slides. As an external validation, the performance of the developed classifier was tested using a relatively small institutional pathology image set for 32 patients. To reduce deterioration of the performance associated with the inter-institutional heterogeneity of pathology slides, we translated the style of the small image set of the local institution into the large image set style of the TCIA using cycle-consistent generative adversarial networks. RESULTS Without style transfer, the performance of the classifier was as follows: area under the receiver operating characteristic curve (AUROC) = 0.737 and area under the precision recall curve (AUPRC) = 0.710. After style transfer, AUROC and AUPRC improved to 0.916 and 0.898, respectively. CONCLUSIONS This study provides a case of the successful application of style transfer technology to generalize a deep learning model into small image sets in the field of digital pathology. Researchers at local institutions can select this collaborative system to make their small image sets acceptable to the deep learning model.
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Affiliation(s)
- Seo Jeong Shin
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Hokyun Jeon
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | - Ji Won Jung
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea; Asan Institute for Life Science, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Min Ho An
- So Ahn Public Health Center, Wando-gun, Jeollanam-do, Republic of Korea
| | - Rae Woong Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea; Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.
| | - Jin Roh
- Department of Pathology, Ajou University Hospital, Suwon, Republic of Korea.
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166
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Salvi M, Acharya UR, Molinari F, Meiburger KM. The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Comput Biol Med 2021; 128:104129. [DOI: 10.1016/j.compbiomed.2020.104129] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 11/13/2020] [Indexed: 12/12/2022]
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167
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Nofallah S, Mehta S, Mercan E, Knezevich S, May CJ, Weaver D, Witten D, Elmore JG, Shapiro L. Machine learning techniques for mitoses classification. Comput Med Imaging Graph 2021; 87:101832. [PMID: 33302246 PMCID: PMC7855641 DOI: 10.1016/j.compmedimag.2020.101832] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 10/09/2020] [Accepted: 11/17/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND Pathologists analyze biopsy material at both the cellular and structural level to determine diagnosis and cancer stage. Mitotic figures are surrogate biomarkers of cellular proliferation that can provide prognostic information; thus, their precise detection is an important factor for clinical care. Convolutional Neural Networks (CNNs) have shown remarkable performance on several recognition tasks. Utilizing CNNs for mitosis classification may aid pathologists to improve the detection accuracy. METHODS We studied two state-of-the-art CNN-based models, ESPNet and DenseNet, for mitosis classification on six whole slide images of skin biopsies and compared their quantitative performance in terms of sensitivity, specificity, and F-score. We used raw RGB images of mitosis and non-mitosis samples with their corresponding labels as training input. In order to compare with other work, we studied the performance of these classifiers and two other architectures, ResNet and ShuffleNet, on the publicly available MITOS breast biopsy dataset and compared the performance of all four in terms of precision, recall, and F-score (which are standard for this data set), architecture, training time and inference time. RESULTS The ESPNet and DenseNet results on our primary melanoma dataset had a sensitivity of 0.976 and 0.968, and a specificity of 0.987 and 0.995, respectively, with F-scores of .968 and .976, respectively. On the MITOS dataset, ESPNet and DenseNet showed a sensitivity of 0.866 and 0.916, and a specificity of 0.973 and 0.980, respectively. The MITOS results using DenseNet had a precision of 0.939, recall of 0.916, and F-score of 0.927. The best published result on MITOS (Saha et al. 2018) reported precision of 0.92, recall of 0.88, and F-score of 0.90. In our architecture comparisons on MITOS, we found that DenseNet beats the others in terms of F-Score (DenseNet 0.927, ESPNet 0.890, ResNet 0.865, ShuffleNet 0.847) and especially Recall (DenseNet 0.916, ESPNet 0.866, ResNet 0.807, ShuffleNet 0.753), while ResNet and ESPNet have much faster inference times (ResNet 6 s, ESPNet 8 s, DenseNet 31 s). ResNet is faster than ESPNet, but ESPNet has a higher F-Score and Recall than ResNet, making it a good compromise solution. CONCLUSION We studied several state-of-the-art CNNs for detecting mitotic figures in whole slide biopsy images. We evaluated two CNNs on a melanoma cancer dataset and then compared four CNNs on a public breast cancer data set, using the same methodology on both. Our methodology and architecture for mitosis finding in both melanoma and breast cancer whole slide images has been thoroughly tested and is likely to be useful for finding mitoses in any whole slide biopsy images.
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Affiliation(s)
| | - Sachin Mehta
- University of Washington, Seattle WA 98195, USA.
| | - Ezgi Mercan
- University of Washington, Seattle WA 98195, USA.
| | | | | | | | | | - Joann G Elmore
- David Geffen School of Medicine, UCLA, Los Angeles CA 90024, USA.
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Reducing annotation effort in digital pathology: A Co-Representation learning framework for classification tasks. Med Image Anal 2021; 67:101859. [DOI: 10.1016/j.media.2020.101859] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 09/14/2020] [Accepted: 09/25/2020] [Indexed: 01/07/2023]
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169
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Fu H, Mi W, Pan B, Guo Y, Li J, Xu R, Zheng J, Zou C, Zhang T, Liang Z, Zou J, Zou H. Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks. Front Oncol 2021; 11:665929. [PMID: 34249702 PMCID: PMC8267174 DOI: 10.3389/fonc.2021.665929] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/10/2021] [Indexed: 01/11/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancer types worldwide, with the lowest 5-year survival rate among all kinds of cancers. Histopathology image analysis is considered a gold standard for PDAC detection and diagnosis. However, the manual diagnosis used in current clinical practice is a tedious and time-consuming task and diagnosis concordance can be low. With the development of digital imaging and machine learning, several scholars have proposed PDAC analysis approaches based on feature extraction methods that rely on field knowledge. However, feature-based classification methods are applicable only to a specific problem and lack versatility, so that the deep-learning method is becoming a vital alternative to feature extraction. This paper proposes the first deep convolutional neural network architecture for classifying and segmenting pancreatic histopathological images on a relatively large WSI dataset. Our automatic patch-level approach achieved 95.3% classification accuracy and the WSI-level approach achieved 100%. Additionally, we visualized the classification and segmentation outcomes of histopathological images to determine which areas of an image are more important for PDAC identification. Experimental results demonstrate that our proposed model can effectively diagnose PDAC using histopathological images, which illustrates the potential of this practical application.
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Affiliation(s)
- Hao Fu
- Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Weiming Mi
- Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China
| | - Boju Pan
- Molecular Pathology Research Center, Department of Pathology, Peking Union Medical College Hospital (PUMCH), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yucheng Guo
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
| | - Junjie Li
- Molecular Pathology Research Center, Department of Pathology, Peking Union Medical College Hospital (PUMCH), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Rongyan Xu
- Shanghai Chenshan Plant Science Research Center, Chinese Academy of Sciences, Shanghai, China
| | - Jie Zheng
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
| | - Chunli Zou
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
| | - Tao Zhang
- Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China
| | - Zhiyong Liang
- Molecular Pathology Research Center, Department of Pathology, Peking Union Medical College Hospital (PUMCH), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- *Correspondence: Zhiyong Liang, ; Hao Zou, ; Junzhong Zou,
| | - Junzhong Zou
- Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
- *Correspondence: Zhiyong Liang, ; Hao Zou, ; Junzhong Zou,
| | - Hao Zou
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
- *Correspondence: Zhiyong Liang, ; Hao Zou, ; Junzhong Zou,
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Li B, Mercan E, Mehta S, Knezevich S, Arnold CW, Weaver DL, Elmore JG, Shapiro LG. Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline. PROCEEDINGS OF THE ... IAPR INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION. INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION 2021; 2020:8727-8734. [PMID: 36745147 PMCID: PMC9893896 DOI: 10.1109/icpr48806.2021.9412824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification. Based on recent advancements in instance segmentation and the Mask RCNN model, our duct-level segmenter tries to identify each ductal individual inside a microscopic image; then, it extracts tissue-level information from the identified ductal instances. Leveraging three levels of information obtained from these ductal instances and also the histopathology image, the proposed DIOP outperforms previous approaches (both feature-based and CNN-based) in all diagnostic tasks; for the four-way classification task, the DIOP achieves comparable performance to general pathologists in this unique dataset. The proposed DIOP only takes a few seconds to run in the inference time, which could be used interactively on most modern computers. More clinical explorations are needed to study the robustness and generalizability of this system in the future.
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Affiliation(s)
- Beibin Li
- University of Washington, Seattle, WA,Seattle Children’s Hospital, Seattle, WA
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171
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Wu W, Mehta S, Nofallah S, Knezevich S, May CJ, Chang OH, Elmore JG, Shapiro LG. Scale-Aware Transformers for Diagnosing Melanocytic Lesions. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:163526-163541. [PMID: 35211363 PMCID: PMC8865389 DOI: 10.1109/access.2021.3132958] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Diagnosing melanocytic lesions is one of the most challenging areas of pathology with extensive intra- and inter-observer variability. The gold standard for a diagnosis of invasive melanoma is the examination of histopathological whole slide skin biopsy images by an experienced dermatopathologist. Digitized whole slide images offer novel opportunities for computer programs to improve the diagnostic performance of pathologists. In order to automatically classify such images, representations that reflect the content and context of the input images are needed. In this paper, we introduce a novel self-attention-based network to learn representations from digital whole slide images of melanocytic skin lesions at multiple scales. Our model softly weighs representations from multiple scales, allowing it to discriminate between diagnosis-relevant and -irrelevant information automatically. Our experiments show that our method outperforms five other state-of-the-art whole slide image classification methods by a significant margin. Our method also achieves comparable performance to 187 practicing U.S. pathologists who interpreted the same cases in an independent study. To facilitate relevant research, full training and inference code is made publicly available at https://github.com/meredith-wenjunwu/ScATNet.
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Affiliation(s)
- Wenjun Wu
- Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA 98195, USA
| | - Sachin Mehta
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA
| | - Shima Nofallah
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA
| | | | | | - Oliver H Chang
- Department of Pathology, University of Washington, Seattle, WA 98195, USA
| | - Joann G Elmore
- David Geffen School of Medicine, UCLA, Los Angeles, CA 90024, USA
| | - Linda G Shapiro
- Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA 98195, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
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172
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De Vera Mudry MC, Martin J, Schumacher V, Venugopal R. Deep Learning in Toxicologic Pathology: A New Approach to Evaluate Rodent Retinal Atrophy. Toxicol Pathol 2020; 49:851-861. [PMID: 33371793 DOI: 10.1177/0192623320980674] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Quantification of retinal atrophy, caused by therapeutics and/or light, by manual measurement of retinal layers is labor intensive and time-consuming. In this study, we explored the role of deep learning (DL) in automating the assessment of retinal atrophy, particularly of the outer and inner nuclear layers, in rats. Herein, we report our experience creating and employing a hybrid approach, which combines conventional image processing and DL to quantify rodent retinal atrophy. Utilizing a DL approach based upon the VGG16 model architecture, models were trained, tested, and validated using 10,746 image patches scanned from whole slide images (WSIs) of hematoxylin-eosin stained rodent retina. The accuracy of this computational method was validated using pathologist annotated WSIs throughout and used to separately quantify the thickness of the outer and inner nuclear layers of the retina. Our results show that DL can facilitate the evaluation of therapeutic and/or light-induced atrophy, particularly of the outer retina, efficiently in rodents. In addition, this study provides a template which can be used to train, validate, and analyze the results of toxicologic pathology DL models across different animal species used in preclinical efficacy and safety studies.
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Affiliation(s)
- Maria Cristina De Vera Mudry
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, 1529F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Jim Martin
- 1529Roche Tissue Diagnostics, Santa Clara, CA, USA
| | - Vanessa Schumacher
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, 1529F. Hoffmann-La Roche Ltd, Basel, Switzerland
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Radakovich N, Nagy M, Nazha A. Artificial Intelligence in Hematology: Current Challenges and Opportunities. Curr Hematol Malig Rep 2020; 15:203-210. [PMID: 32239350 DOI: 10.1007/s11899-020-00575-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI), and in particular its subcategory machine learning, is finding an increasing number of applications in medicine, driven in large part by an abundance of data and powerful, accessible tools that have made AI accessible to a larger circle of investigators. RECENT FINDINGS AI has been employed in the analysis of hematopathological, radiographic, laboratory, genomic, pharmacological, and chemical data to better inform diagnosis, prognosis, treatment planning, and foundational knowledge related to benign and malignant hematology. As more widespread implementation of clinical AI nears, attention has also turned to the effects this will have on other areas in medicine. AI offers many promising tools to clinicians broadly, and specifically in the practice of hematology. Ongoing research into its various applications will likely result in an increasing utilization of AI by a broader swath of clinicians.
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Affiliation(s)
- Nathan Radakovich
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Matthew Nagy
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Aziz Nazha
- Center for Clinical Artificial Intelligence, Cleveland Clinic, Cleveland, OH, USA.
- Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Desk R35 9500 Euclid Ave., Cleveland, OH, 44195, USA.
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A Semisupervised Learning Scheme with Self-Paced Learning for Classifying Breast Cancer Histopathological Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8826568. [PMID: 33376479 PMCID: PMC7738795 DOI: 10.1155/2020/8826568] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 11/02/2020] [Accepted: 11/05/2020] [Indexed: 12/18/2022]
Abstract
The unavailability of large amounts of well-labeled data poses a significant challenge in many medical imaging tasks. Even in the likelihood of having access to sufficient data, the process of accurately labeling the data is an arduous and time-consuming one, requiring expertise skills. Again, the issue of unbalanced data further compounds the abovementioned problems and presents a considerable challenge for many machine learning algorithms. In lieu of this, the ability to develop algorithms that can exploit large amounts of unlabeled data together with a small amount of labeled data, while demonstrating robustness to data imbalance, can offer promising prospects in building highly efficient classifiers. This work proposes a semisupervised learning method that integrates self-training and self-paced learning to generate and select pseudolabeled samples for classifying breast cancer histopathological images. A novel pseudolabel generation and selection algorithm is introduced in the learning scheme to generate and select highly confident pseudolabeled samples from both well-represented classes to less-represented classes. Such a learning approach improves the performance by jointly learning a model and optimizing the generation of pseudolabels on unlabeled-target data to augment the training data and retraining the model with the generated labels. A class balancing framework that normalizes the class-wise confidence scores is also proposed to prevent the model from ignoring samples from less represented classes (hard-to-learn samples), hence effectively handling the issue of data imbalance. Extensive experimental evaluation of the proposed method on the BreakHis dataset demonstrates the effectiveness of the proposed method.
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175
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Shi Z, Miao C, Schoepf UJ, Savage RH, Dargis DM, Pan C, Chai X, Li XL, Xia S, Zhang X, Gu Y, Zhang Y, Hu B, Xu W, Zhou C, Luo S, Wang H, Mao L, Liang K, Wen L, Zhou L, Yu Y, Lu GM, Zhang LJ. A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images. Nat Commun 2020; 11:6090. [PMID: 33257700 PMCID: PMC7705757 DOI: 10.1038/s41467-020-19527-w] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 10/13/2020] [Indexed: 01/17/2023] Open
Abstract
Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to clinicians' assessment.
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Affiliation(s)
- Zhao Shi
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China
| | - Chongchang Miao
- Department of Radiology, Lianyungang First People's Hospital, Lianyungang, Jiangsu, 222002, P.R. China
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Rock H Savage
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Danielle M Dargis
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Chengwei Pan
- Computer Science Department, School of EECS, Peking University, Beijing, 100089, P.R. China
| | - Xue Chai
- Department of Radiology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, 210002, P.R. China
| | - Xiu Li Li
- DeepWise AI lab., Beijing, 100089, P.R. China
| | - Shuang Xia
- Department of Radiology, Tianjin First Central Hospital, Tianjin, 300192, P.R. China
| | - Xin Zhang
- Department of Neurosurgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China
| | - Yan Gu
- Department of Radiology, Lianyungang First People's Hospital, Lianyungang, Jiangsu, 222002, P.R. China
| | - Yonggang Zhang
- Department of Radiology, Lianyungang First People's Hospital, Lianyungang, Jiangsu, 222002, P.R. China
| | - Bin Hu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China
| | - Wenda Xu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China
| | - Changsheng Zhou
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China
| | - Song Luo
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China
| | - Hao Wang
- DeepWise AI lab., Beijing, 100089, P.R. China
| | - Li Mao
- DeepWise AI lab., Beijing, 100089, P.R. China
| | | | - Lili Wen
- Department of Neurosurgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China
| | - Longjiang Zhou
- Department of Neurosurgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China
| | - Yizhou Yu
- DeepWise AI lab., Beijing, 100089, P.R. China
| | - Guang Ming Lu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China.
| | - Long Jiang Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China. .,Department of Diagnostic Radiology, Jinling Hospital, Sothern Medical University, Nanjing, Jiangsu, 210002, P.R. China.
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176
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Barba D, León-Sosa A, Lugo P, Suquillo D, Torres F, Surre F, Trojman L, Caicedo A. Breast cancer, screening and diagnostic tools: All you need to know. Crit Rev Oncol Hematol 2020; 157:103174. [PMID: 33249359 DOI: 10.1016/j.critrevonc.2020.103174] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 09/18/2020] [Accepted: 11/05/2020] [Indexed: 02/06/2023] Open
Abstract
Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.
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Affiliation(s)
- Diego Barba
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador
| | - Ariana León-Sosa
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador
| | - Paulina Lugo
- Hospital de los Valles HDLV, Quito, Ecuador; Fundación Ayuda Familiar y Comunitaria AFAC, Quito, Ecuador
| | - Daniela Suquillo
- Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador; Ingeniería en Procesos Biotecnológicos, Colegio de Ciencias Biológicas y Ambientales COCIBA, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | - Fernando Torres
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Hospital de los Valles HDLV, Quito, Ecuador
| | - Frederic Surre
- University of Glasgow, James Watt School of Engineering, Glasgow, G12 8QQ, United Kingdom
| | - Lionel Trojman
- LISITE, Isep, 75006, Paris, France; Universidad San Francisco de Quito USFQ, Colegio de Ciencias e Ingenierías Politécnico - USFQ, Instituto de Micro y Nanoelectrónica, IMNE, USFQ, Quito, Ecuador
| | - Andrés Caicedo
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador; Sistemas Médicos SIME, Universidad San Francisco de Quito USFQ, Quito, Ecuador.
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177
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Amendola LM, Muenzen K, Biesecker LG, Bowling KM, Cooper GM, Dorschner MO, Driscoll C, Foreman AKM, Golden-Grant K, Greally JM, Hindorff L, Kanavy D, Jobanputra V, Johnston JJ, Kenny EE, McNulty S, Murali P, Ou J, Powell BC, Rehm HL, Rolf B, Roman TS, Van Ziffle J, Guha S, Abhyankar A, Crosslin D, Venner E, Yuan B, Zouk H, Jarvik GP, Jarvik GP. Variant Classification Concordance using the ACMG-AMP Variant Interpretation Guidelines across Nine Genomic Implementation Research Studies. Am J Hum Genet 2020; 107:932-941. [PMID: 33108757 DOI: 10.1016/j.ajhg.2020.09.011] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 09/29/2020] [Indexed: 12/31/2022] Open
Abstract
Harmonization of variant pathogenicity classification across laboratories is important for advancing clinical genomics. The two CLIA-accredited Electronic Medical Record and Genomics Network sequencing centers and the six CLIA-accredited laboratories and one research laboratory performing genome or exome sequencing in the Clinical Sequencing Evidence-Generating Research Consortium collaborated to explore current sources of discordance in classification. Eight laboratories each submitted 20 classified variants in the ACMG secondary finding v.2.0 genes. After removing duplicates, each of the 158 variants was annotated and independently classified by two additional laboratories using the ACMG-AMP guidelines. Overall concordance across three laboratories was assessed and discordant variants were reviewed via teleconference and email. The submitted variant set included 28 P/LP variants, 96 VUS, and 34 LB/B variants, mostly in cancer (40%) and cardiac (27%) risk genes. Eighty-six (54%) variants reached complete five-category (i.e., P, LP, VUS, LB, B) concordance, and 17 (11%) had a discordance that could affect clinical recommendations (P/LP versus VUS/LB/B). 21% and 63% of variants submitted as P and LP, respectively, were discordant with VUS. Of the 54 originally discordant variants that underwent further review, 32 reached agreement, for a post-review concordance rate of 84% (118/140 variants). This project provides an updated estimate of variant concordance, identifies considerations for LP classified variants, and highlights ongoing sources of discordance. Continued and increased sharing of variant classifications and evidence across laboratories, and the ongoing work of ClinGen to provide general as well as gene- and disease-specific guidance, will lead to continued increases in concordance.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Gail P Jarvik
- Department of Medicine, Division of Medical Genetics, University of Washington Medical Center, Seattle, WA 98195, USA
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178
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Van Bockstal MR, Berlière M, Duhoux FP, Galant C. Interobserver Variability in Ductal Carcinoma In Situ of the Breast. Am J Clin Pathol 2020; 154:596-609. [PMID: 32566938 DOI: 10.1093/ajcp/aqaa077] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES Since most patients with ductal carcinoma in situ (DCIS) of the breast are treated upon diagnosis, evidence on its natural progression to invasive carcinoma is limited. It is estimated that around half of the screen-detected DCIS lesions would have remained indolent if they had never been detected. Many patients with DCIS are therefore probably overtreated. Four ongoing randomized noninferiority trials explore active surveillance as a treatment option. Eligibility for these trials is mainly based on histopathologic features. Hence, the call for reproducible histopathologic assessment has never sounded louder. METHODS Here, the available classification systems for DCIS are discussed in depth. RESULTS This comprehensive review illustrates that histopathologic evaluation of DCIS is characterized by significant interobserver variability. Future digitalization of pathology, combined with development of deep learning algorithms or so-called artificial intelligence, may be an innovative solution to tackle this problem. However, implementation of digital pathology is not within reach for each laboratory worldwide. An alternative classification system could reduce the disagreement among histopathologists who use "conventional" light microscopy: the introduction of dichotomous histopathologic assessment is likely to increase interobserver concordance. CONCLUSIONS Reproducible histopathologic assessment is a prerequisite for robust risk stratification and adequate clinical decision-making. Two-tier histopathologic assessment might enhance the quality of care.
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Affiliation(s)
- Mieke R Van Bockstal
- Department of Pathology, Brussels, Belgium
- Breast Clinic, Brussels, Belgium
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Martine Berlière
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques universitaires Saint-Luc, Brussels, Belgium
- Laboratory of Experimental Cancer Research, Department of Radiation Oncology and Experimental Cancer Research, Ghent University, Ghent, Belgium
| | - Francois P Duhoux
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques universitaires Saint-Luc, Brussels, Belgium
- Laboratory of Experimental Cancer Research, Department of Radiation Oncology and Experimental Cancer Research, Ghent University, Ghent, Belgium
- Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
| | - Christine Galant
- Department of Pathology, Brussels, Belgium
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques universitaires Saint-Luc, Brussels, Belgium
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179
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Li S, Shi H, Sui D, Hao A, Qin H. A Novel Pathological Images and Genomic Data Fusion Framework for Breast Cancer Survival Prediction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1384-1387. [PMID: 33018247 DOI: 10.1109/embc44109.2020.9176360] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Survival analysis is a valid solution for cancer treatments and outcome evaluations. Due to the wide application of medical imaging and genome technology, computer-aided survival analysis has become a popular and promising area, from which we can get relatively satisfactory results. Although there are already some impressive technologies in this field, most of them make some recommendations using single-source medical data and have not combined multi-level and multi-source data efficiently. In this paper, we propose a novel pathological images and gene expression data fusion framework to perform the survival prediction. Different from previous methods, our framework can extract correlated multi-scale deep features from whole slide images (WSIs) and dimensionality reduced gene expression data respectively for jointly survival analysis. The experiment results demonstrate that the integrated multi-level image and genome features can achieve higher prediction accuracy compared with single-source features.
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180
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Ma B, Guo Y, Hu W, Yuan F, Zhu Z, Yu Y, Zou H. Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach. Front Pharmacol 2020; 11:572372. [PMID: 33132910 PMCID: PMC7562716 DOI: 10.3389/fphar.2020.572372] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 09/08/2020] [Indexed: 12/23/2022] Open
Abstract
Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. It takes some time from chronic gastritis to develop in GC. Early detection of GC will help patients obtain timely treatment. Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. We identified normal mucosa, chronic gastritis, and intestinal-type GC: this is the most common route of gastric carcinogenesis. We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. The staining variability of WSIs was reduced significantly through stain normalization, and saved the cost and time of preparing new slides. Stain normalization improved the effect of the CNN model. The accuracy rate at the patch-level reached 98.4%, and 94.5% for discriminating normal → chronic gastritis → GC. The accuracy rate at the WSIs-level for discriminating normal tissue and cancerous tissue reached 96.0%, which is a state-of-the-art result. Survival analyses indicated that the features extracted from the CNN exerted a significant impact on predicting the survival of cancer patients. Our CNN model disclosed significant potential for adjuvant diagnosis of gastric diseases, especially GC, and usefulness for predicting the prognosis.
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Affiliation(s)
- Bowei Ma
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Yucheng Guo
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Weian Hu
- Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Fei Yuan
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhenggang Zhu
- Department of General Surgery, Ruijin Hospital, Shanghai Institute of Digestive Surgery, Shanghai Key Lab for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingyan Yu
- Department of General Surgery, Ruijin Hospital, Shanghai Institute of Digestive Surgery, Shanghai Key Lab for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Zou
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
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181
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Armaroli P, Riggi E, Basu P, Anttila A, Ponti A, Carvalho AL, Dillner J, Elfström MK, Giordano L, Lönnberg S, Ronco G, Senore C, Soerjomataram I, Tomatis M, Vale DB, Jarm K, Sankaranarayanan R, Segnan N. Performance indicators in breast cancer screening in the European Union: A comparison across countries of screen positivity and detection rates. Int J Cancer 2020; 147:1855-1863. [PMID: 32159224 DOI: 10.1002/ijc.32968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 02/05/2020] [Accepted: 02/24/2020] [Indexed: 01/19/2023]
Abstract
Comparable performance indicators for breast cancer screening in the European Union (EU) have not been previously reported. We estimated adjusted breast cancer screening positivity rate (PR) and detection rates (DR) to investigate variation across EU countries. For the age 50-69 years, the adjusted EU-pooled PR for initial screening was 8.9% (cross-programme variation range 3.2-19.5%) while DR of invasive cancers was 5.3/1,000 (range 3.8-7.4/1,000) and DR of ductal carcinoma in situ (DCIS) was 1.3/1,000 (range 0.7-2.7/1,000). For subsequent screening, the adjusted EU-pooled PR was 3.6% (range 1.4-8.4%), the DR was 4.0/1,000 (range 2.2-5.8/1,000) and 0.8/1,000 (range 0.5-1.3/1,000) for invasive and DCIS, respectively. Adjusted performance indicators showed remarkable heterogeneity, likely due to different background breast cancer risk and awareness between target populations, and also different screening protocols and organisation. Periodic reporting of the screening indicators permits comparison and evaluation of the screening activities between and within countries aiming to improve the quality and the outcomes of screening programmes. Cancer Screening Registries would be a milestone in this direction and EU Screening Reports provide a fundamental contribution to building them.
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Affiliation(s)
- Paola Armaroli
- 'AOU Città della Salute e della Scienza' University Hospital, CPO Piemonte, Turin, Italy
| | - Emilia Riggi
- 'AOU Città della Salute e della Scienza' University Hospital, CPO Piemonte, Turin, Italy
| | - Partha Basu
- Screening Group, International Agency for Research on Cancer, Lyon, France
| | - Ahti Anttila
- Mass Screening Registry, Finish Cancer Registry, Helsinki, Finland
| | - Antonio Ponti
- 'AOU Città della Salute e della Scienza' University Hospital, CPO Piemonte, Turin, Italy
| | - Andre L Carvalho
- Screening Group, International Agency for Research on Cancer, Lyon, France
| | - Joakim Dillner
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Miriam K Elfström
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Livia Giordano
- 'AOU Città della Salute e della Scienza' University Hospital, CPO Piemonte, Turin, Italy
| | - Stefan Lönnberg
- Mass Screening Registry, Finish Cancer Registry, Helsinki, Finland
| | - Gugliemo Ronco
- 'AOU Città della Salute e della Scienza' University Hospital, CPO Piemonte, Turin, Italy
- International Agency for Research on Cancer, Lyon, France
| | - Carlo Senore
- 'AOU Città della Salute e della Scienza' University Hospital, CPO Piemonte, Turin, Italy
| | - Isabelle Soerjomataram
- Section of Cancer Surveillance, International Agency for Research on Cancer, Lyon, France
| | - Mariano Tomatis
- 'AOU Città della Salute e della Scienza' University Hospital, CPO Piemonte, Turin, Italy
| | - Diama B Vale
- Department of Obstetrics and Gynecology, State University of Campinas (Unicamp), Campinas, Brazil
| | - Katja Jarm
- Epidemiology and Cancer Registry, Institute of Oncology Ljubljana, Ljubljana, Slovenia
| | | | - Nereo Segnan
- 'AOU Città della Salute e della Scienza' University Hospital, CPO Piemonte, Turin, Italy
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182
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Groen EJ, Hudecek J, Mulder L, van Seijen M, Almekinders MM, Alexov S, Kovács A, Ryska A, Varga Z, Andreu Navarro FJ, Bianchi S, Vreuls W, Balslev E, Boot MV, Kulka J, Chmielik E, Barbé E, de Rooij MJ, Vos W, Farkas A, Leeuwis-Fedorovich NE, Regitnig P, Westenend PJ, Kooreman LFS, Quinn C, Floris G, Cserni G, van Diest PJ, Lips EH, Schaapveld M, Wesseling J. Prognostic value of histopathological DCIS features in a large-scale international interrater reliability study. Breast Cancer Res Treat 2020; 183:759-770. [PMID: 32734520 PMCID: PMC7497690 DOI: 10.1007/s10549-020-05816-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 07/17/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE For optimal management of ductal carcinoma in situ (DCIS), reproducible histopathological assessment is essential to distinguish low-risk from high-risk DCIS. Therefore, we analyzed interrater reliability of histopathological DCIS features and assessed their associations with subsequent ipsilateral invasive breast cancer (iIBC) risk. METHODS Using a case-cohort design, reliability was assessed in a population-based, nationwide cohort of 2767 women with screen-detected DCIS diagnosed between 1993 and 2004, treated by breast-conserving surgery with/without radiotherapy (BCS ± RT) using Krippendorff's alpha (KA) and Gwet's AC2 (GAC2). Thirty-eight raters scored histopathological DCIS features including grade (2-tiered and 3-tiered), growth pattern, mitotic activity, periductal fibrosis, and lymphocytic infiltrate in 342 women. Using majority opinion-based scores for each feature, their association with subsequent iIBC risk was assessed using Cox regression. RESULTS Interrater reliability of grade using various classifications was fair to moderate, and only substantial for grade 1 versus 2 + 3 when using GAC2 (0.78). Reliability for growth pattern (KA 0.44, GAC2 0.78), calcifications (KA 0.49, GAC2 0.70) and necrosis (KA 0.47, GAC2 0.70) was moderate using KA and substantial using GAC2; for (type of) periductal fibrosis and lymphocytic infiltrate fair to moderate estimates were found and for mitotic activity reliability was substantial using GAC2 (0.70). Only in patients treated with BCS-RT, high mitotic activity was associated with a higher iIBC risk in univariable analysis (Hazard Ratio (HR) 2.53, 95% Confidence Interval (95% CI) 1.05-6.11); grade 3 versus 1 + 2 (HR 2.64, 95% CI 1.35-5.14) and a cribriform/solid versus flat epithelial atypia/clinging/(micro)papillary growth pattern (HR 3.70, 95% CI 1.34-10.23) were independently associated with a higher iIBC risk. CONCLUSIONS Using majority opinion-based scores, DCIS grade, growth pattern, and mitotic activity are associated with iIBC risk in patients treated with BCS-RT, but interrater variability is substantial. Semi-quantitative grading, incorporating and separately evaluating nuclear pleomorphism, growth pattern, and mitotic activity, may improve the reliability and prognostic value of these features.
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Affiliation(s)
- Emma J. Groen
- Department of Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Jan Hudecek
- Department of Research IT, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Lennart Mulder
- Department of Molecular Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Maartje van Seijen
- Department of Molecular Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Mathilde M. Almekinders
- Department of Molecular Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Stoyan Alexov
- Department of Pathology, Oncology Hospital, Sofia, Bulgaria
| | - Anikó Kovács
- Department of Clinical Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Ales Ryska
- The Fingerland Department of Pathology, Charles University Medical Faculty and University Hospital Hradec Kralove, Hradec Kralove, Czech Republic
| | - Zsuzsanna Varga
- Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | | | - Simonetta Bianchi
- Division of Pathological Anatomy, Department of Health Sciences, University of Florence, Florence, Italy
| | - Willem Vreuls
- Department of Pathology, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands
| | - Eva Balslev
- Department of Pathology, Herlev University Hospital, Herlev, Denmark
| | - Max V. Boot
- Department of Pathology, Amsterdam University Medical Center, Location VUmc, Amsterdam, The Netherlands
| | - Janina Kulka
- 2nd Department of Pathology, Semmelweis University, Budapest, Hungary
| | - Ewa Chmielik
- Tumor Pathology Department, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Ellis Barbé
- Department of Pathology, Amsterdam University Medical Center, Location VUmc, Amsterdam, The Netherlands
| | | | - Winand Vos
- Department of Pathology, Zuyderland Medical Center, Location Sittard-Geleen, Sittard-Geleen, The Netherlands
| | - Andrea Farkas
- Department of Pathology, Gävle Hospital, Gävle, Sweden
| | | | - Peter Regitnig
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | | | - Loes F. S. Kooreman
- Department of Pathology and GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Cecily Quinn
- Department of Pathology and Laboratory Medicine, St. Vincent’s University Hospital, Dublin, Ireland
| | - Giuseppe Floris
- Laboratory of Translational Cell & Tissue Research, Department of Imaging and Pathology, KU Leuven - University of Leuven, Leuven, Belgium
- Department of Pathology, University Hospitals Leuven, Leuven, Belgium
| | - Gábor Cserni
- Department of Pathology, Bács-Kiskun County Teaching Hospital, Kecskemét, Hungary
- Department of Pathology, University of Szeged, Szeged, Hungary
| | - Paul J. van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Esther H. Lips
- Department of Molecular Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Michael Schaapveld
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Jelle Wesseling
- Department of Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Molecular Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Grand Challenge PRECISION consortium
- Department of Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Research IT, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, The Netherlands
- Department of Molecular Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, The Netherlands
- Department of Pathology, Oncology Hospital, Sofia, Bulgaria
- Department of Clinical Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden
- The Fingerland Department of Pathology, Charles University Medical Faculty and University Hospital Hradec Kralove, Hradec Kralove, Czech Republic
- Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
- Atryshealth Co, S.L., Barcelona, Spain
- Division of Pathological Anatomy, Department of Health Sciences, University of Florence, Florence, Italy
- Department of Pathology, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands
- Department of Pathology, Herlev University Hospital, Herlev, Denmark
- Department of Pathology, Amsterdam University Medical Center, Location VUmc, Amsterdam, The Netherlands
- 2nd Department of Pathology, Semmelweis University, Budapest, Hungary
- Tumor Pathology Department, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
- Symbiant Pathology Expert Centre, Location ZMC, Zaandam, The Netherlands
- Department of Pathology, Zuyderland Medical Center, Location Sittard-Geleen, Sittard-Geleen, The Netherlands
- Department of Pathology, Gävle Hospital, Gävle, Sweden
- Department of Pathology, Deventer Hospital, Deventer, The Netherlands
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
- Laboratory for Pathology Dordrecht, Dordrecht, The Netherlands
- Department of Pathology and GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Pathology and Laboratory Medicine, St. Vincent’s University Hospital, Dublin, Ireland
- Laboratory of Translational Cell & Tissue Research, Department of Imaging and Pathology, KU Leuven - University of Leuven, Leuven, Belgium
- Department of Pathology, University Hospitals Leuven, Leuven, Belgium
- Department of Pathology, Bács-Kiskun County Teaching Hospital, Kecskemét, Hungary
- Department of Pathology, University of Szeged, Szeged, Hungary
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, The Netherlands
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Uzan C, Mazouni C, Rossoni C, De Korvin B, de Lara CT, Cohen M, Chabbert N, Zilberman S, Boussion V, Vincent Salomon A, Espie M, Coutant C, Marchal F, Salviat F, Boulanger L, Doutriaux-Dumoulin I, Jouve E, Mathelin C, de Saint Hilaire P, Mollard J, Balleyguier C, Joyon N, Triki ML, Delaloge S, Michiels S. Prospective Multicenter Study Validate a Prediction Model for Surgery Uptake Among Women with Atypical Breast Lesions. Ann Surg Oncol 2020; 28:2138-2145. [PMID: 32920723 DOI: 10.1245/s10434-020-09107-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 08/18/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND Diagnosis of atypical breast lesions (ABLs) leads to unnecessary surgery in 75-90% of women. We have previously developed a model including age, complete radiological target excision after biopsy, and focus size that predicts the probability of cancer at surgery. The present study aimed to validate this model in a prospective multicenter setting. - METHODS Women with a recently diagnosed ABL on image-guided biopsy were recruited in 18 centers, before wire-guided localized excisional lumpectomy. Primary outcome was the negative predictive value (NPV) of the model. RESULTS The NOMAT model could be used in 287 of the 300 patients included (195 with ADH). At surgery, 12 invasive (all grade 1), and 43 in situ carcinomas were identified (all ABL: 55/287, 19%; ADH only: 49/195, 25%). The area under the receiving operating characteristics curve of the model was 0.64 (95% CI 0.58-0.69) for all ABL, and 0.63 for ADH only (95% CI 0.56-0.70). For the pre-specified threshold of 20% predicted probability of cancer, NPV was 82% (77-87%) for all ABL, and 77% (95% CI 71-83%) for patients with ADH. At a 10% threshold, NPV was 89% (84-94%) for all ABL, and 85% (95% CI 78--92%) for the ADH. At this threshold, 58% of the whole ABL population (and 54% of ADH patients) could have avoided surgery with only 2 missed invasive cancers. CONCLUSION The NOMAT model could be useful to avoid unnecessary surgery among women with ABL, including for patients with ADH. CLINICAL TRIAL REGISTRATION NCT02523612.
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Affiliation(s)
- Catherine Uzan
- AP-HP (Assistance Publique des Hôpitaux de Paris), Department of Gynecological and Breast Surgery and Oncology, Pitié-Salpêtrière University Hospital, Paris, France. .,Sorbonne University, INSERM UMR_S_938, "Cancer Biology and Therapeutics", Centre de Recherche Saint-Antoine (CRSA), Paris, France. .,Institut Universitaire de Cancérologie (IUC), Paris, France.
| | | | | | | | | | | | | | | | | | - Anne Vincent Salomon
- Institut Curie, Université Paris-Sciences Lettres, INSERM U934, Département de Médecine Diagnostique et Théranostique, Paris, France
| | - Marc Espie
- University of Paris, Hôpital Saint Louis, APHP, Paris, France
| | | | - Frederic Marchal
- Institut de Cancérologie de Lorraine, Vandœuvre-lès-Nancy, France
| | - Flore Salviat
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Villejuif, France.,CESP INSERM U1018, Université Paris-Sud, Université Paris-Saclay, Villejuif, France
| | | | | | - Eva Jouve
- Institut Claudius Regaud-Oncopole, Toulouse, France
| | - Carole Mathelin
- Les Hôpitaux universitaires de Strasbourg, Strasbourg, France
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184
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Jain R, Katz DR, Kapoor AD. The Clinical Utility of a Negative Result at Molecular Breast Imaging: Initial Proof of Concept. Radiol Imaging Cancer 2020; 2:e190096. [PMID: 33778735 PMCID: PMC7983715 DOI: 10.1148/rycan.2020190096] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 04/28/2020] [Accepted: 05/27/2020] [Indexed: 11/11/2022]
Abstract
Purpose To calculate the negative predictive value (NPV) and false-negative rate (FNR) of molecular breast imaging (MBI) performed in patients who had low-suspicion index findings on mammograms and US images. Materials and Methods This retrospective study included patients who had undergone MBI between January 2015 and July 2017, who had index findings on screening mammograms and/or US images, and for whom either histopathologic results or a minimum of 1-year imaging follow-up results were available. A drawn dose of 8 mCi (296 MBq) of technetium 99m sestamibi was administered to all patients for MBI. The NPV and FNR of MBI was calculated for the cohort of 381 findings among 338 women (median age, 56 years; age range, 28-89 years) included in this study. Results Overall, 292 of the 381 (76.6%) MBI results were interpreted as negative. Of the 292, 27 patients underwent subsequent biopsies, results of which were negative for cancer; one patient underwent biopsy, and the result was positive for cancer; and 264 patients had true-negative findings based on follow-up imaging for a minimum of 1 year. Of the 89 MBI acquisitions interpreted as positive, there were 36 cancers. The NPV was calculated to be 99.7% (291 of 292, 95% confidence interval [CI]: 99.1%, 100%), and the FNR was 2.7% (one of 37, 95% CI: 0%, 7.9%). Interposing MBI reduced the number of biopsies by 67.5%. Conclusion The concept of the clinical utility of a negative MBI result may be valid but requires further testing.Keywords: Breast, Molecular Imaging-Cancer© RSNA, 2020.
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Affiliation(s)
- Ravi Jain
- From Middlesex Health, 28 Crescent St, Middletown, CT 06457
| | - Deanna R. Katz
- From Middlesex Health, 28 Crescent St, Middletown, CT 06457
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185
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Gordon PB, Branch E. Upgrade Rate of Flat Epithelial Atypia Diagnosed at Stereotactic Core Needle Biopsy of Microcalcifications: Is Excisional Biopsy Indicated? JOURNAL OF BREAST IMAGING 2020; 2:336-342. [PMID: 38424960 DOI: 10.1093/jbi/wbaa037] [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: 04/04/2019] [Indexed: 03/02/2024]
Abstract
OBJECTIVE Whether the optimal management of pure flat epithelial atypia (FEA) found on core needle biopsy (CNB) specimens is surgical excision or imaging follow-up remains controversial. This study aimed to determine the upgrade rate to ductal carcinoma in situ (DCIS), invasive carcinoma or a high-risk lesion (atypical ductal hyperplasia, atypical lobular hyperplasia, or lobular carcinoma in situ), and it explored the relationship between a family history of breast cancer and the risk of upgrade. METHODS Cases with pure FEA found on stereotactic CNB of microcalcifications between March 2011 to December 2017 were followed by excisional biopsy or periodic imaging. The proportion of cases upgraded to a high-risk lesion and the odds of upgrade as related to a family history of breast cancer were determined with 95% confidence intervals (CIs). RESULTS We identified 622 cases of pure FEA; 101 (16.2%) underwent surgical excision and 269 (43.2%) had imaging follow-up of ≥ 24 months. There were no upgrades to DCIS or invasive cancer in any of these 370 individuals (0%), and 4.6% (17/370; 95% CI: 2.9%-7.2%) were upgraded to a high-risk lesion. There was a nonstatistically significant trend between family history and upgrade to high-risk lesion (odds ratio 1.72 [95% CI: 0.65%-4.57%]). CONCLUSION In our study, the upgrade rate of pure FEA to malignancy was 0%. We suggest that regular imaging follow-up is an appropriate alternative to surgery. Because of potential differences in biopsy techniques and pathologist interpretation of the primary biopsy, individual institutions should audit their own results prior to altering their management of FEA.
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Affiliation(s)
- Paula B Gordon
- BC Women's Hospital and Health Care Centre, Sadie Diamond Breast Program, Vancouver, BC, Canada
| | - Emma Branch
- BC Women's Health Research Institute, Vancouver, BC, Canada
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186
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Hameed Z, Zahia S, Garcia-Zapirain B, Javier Aguirre J, María Vanegas A. Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models. SENSORS 2020; 20:s20164373. [PMID: 32764398 PMCID: PMC7472736 DOI: 10.3390/s20164373] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 08/01/2020] [Accepted: 08/03/2020] [Indexed: 12/13/2022]
Abstract
Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.
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Affiliation(s)
- Zabit Hameed
- eVida Research Group, University of Deusto, 48007 Bilbao, Spain; (S.Z.); (B.G.-Z.)
- Correspondence:
| | - Sofia Zahia
- eVida Research Group, University of Deusto, 48007 Bilbao, Spain; (S.Z.); (B.G.-Z.)
| | | | - José Javier Aguirre
- Biokeralty Reseach Institute, 01510 Vitoria, Spain;
- Department of Pathological Anatomy, University Hospital of Araba, 01009 Vitoria, Spain
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187
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Ayhan MS, Kühlewein L, Aliyeva G, Inhoffen W, Ziemssen F, Berens P. Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection. Med Image Anal 2020; 64:101724. [DOI: 10.1016/j.media.2020.101724] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 05/05/2020] [Accepted: 05/11/2020] [Indexed: 12/14/2022]
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Amin AL, Fan F, Winblad OD, Larson KE, Wagner JL. Ipsilateral and Concurrent Breast Cancer and Atypical Ductal Hyperplasia: Does Atypia Also Need Surgical Excision? Ann Surg Oncol 2020; 27:4786-4794. [PMID: 32705514 DOI: 10.1245/s10434-020-08896-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 06/27/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND Standard-of-care management of atypical ductal hyperplasia (ADH) is surgical excision. Multiple studies have identified features of ADH in patients at low risk for upgrade who may benefit from omission of surgical excision. Patients with an ipsilateral breast cancer have been excluded from studies investigating observation for the management of ADH. METHODS This was a retrospective review of women with both a breast cancer and an ipsilateral separate site of ADH diagnosed on percutaneous biopsy, who underwent excision of both sites from 2008 to 2018. Radiographic and pathologic features of ADH and cancer were analyzed, including imaging size, biopsy modality, distance between sites, cancer subtype, grade, prognostic markers, ADH foci, and presence of necrosis or micropapillary features. Final pathology at the ADH site was used to determine upgrade. Multivariable logistic regression was performed to identify variables significantly associated with ADH upgrade to malignancy. RESULTS Among 62 women meeting the inclusion criteria, 11 (17.7%) upgraded to malignancy [9 ductal carcinoma in situ (DCIS), 2 invasive cancer] at the site of ADH. Upgrade was significantly higher with ipsilateral DCIS (p = 0.03), ultrasound biopsy at the ADH site (p = 0.01), and ADH with necrosis (p = 0.04). The group at lowest risk for upgrade had stereotactic biopsy and ADH without necrosis (0% upgrade). CONCLUSION The presence of breast cancer does not significantly increase the likelihood for upgrade at a separate site of ipsilateral concurrent ADH above contemporary reported upgrade rates of ADH alone (10-30%). When considering breast conservation for breast cancer, omitting excision of the site of ADH can be considered when low-risk features are present.
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Affiliation(s)
- Amanda L Amin
- Department of Surgery, The University of Kansas Health System, Kansas City, KS, USA.
| | - Fang Fan
- Department of Pathology, The University of Kansas Health System, Kansas City, KS, USA
| | - Onalisa D Winblad
- Department of Radiology, The University of Kansas Health System, Kansas City, KS, USA
| | - Kelsey E Larson
- Department of Surgery, The University of Kansas Health System, Kansas City, KS, USA
| | - Jamie L Wagner
- Department of Surgery, The University of Kansas Health System, Kansas City, KS, USA
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189
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Fenstermaker M, Tomlins SA, Singh K, Wiens J, Morgan TM. Development and Validation of a Deep-learning Model to Assist With Renal Cell Carcinoma Histopathologic Interpretation. Urology 2020; 144:152-157. [PMID: 32711010 DOI: 10.1016/j.urology.2020.05.094] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 04/22/2020] [Accepted: 05/17/2020] [Indexed: 01/19/2023]
Abstract
OBJECTIVE To develop and test the ability of a convolutional neural network (CNN) to accurately identify the presence of renal cell carcinoma (RCC) on histopathology specimens, as well as differentiate RCC histologic subtype and grade. MATERIALS AND METHODS Digital hematoxylin and eosin stained biopsy images were downloaded from The Cancer Genome Atlas. A CNN model was trained on 100 um2 samples of either normal (3000 samples) or RCC (12,168 samples) tissue samples from 42 patients. RCC specimens included clear cell, chromophobe, and papillary histiotypes, as well as tissue of Fuhrman grades 1 through 4. Model testing was performed on an additional held-out cohort of benign and RCC specimens. Model performance was assessed on the basis of diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS The CNN model achieved an overall accuracy of 99.1% in the testing cohort for distinguishing normal parenchyma from RCC (sensitivity 100%, specificity 97.1%). Accuracy for distinguishing between clear cell, papillary, and chromophobehistiotypes was 97.5%. Accuracy for predicting Fuhrman grade was 98.4%. CONCLUSION CNNs are able to rapidly and accurately identify the presence of RCC, distinguish RCC histologic subtypes, and identify tumor grade by analyzing histopathology specimens.
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Affiliation(s)
| | - Scott A Tomlins
- Department of Pathology, University of Michigan, Ann Arbor, MI; University of Michigan Rogel Cancer Center, Ann Arbor, MI
| | - Karandeep Singh
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Jenna Wiens
- Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI
| | - Todd M Morgan
- Department of Urology, University of Michigan, Ann Arbor, MI; University of Michigan Rogel Cancer Center, Ann Arbor, MI
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190
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Sweeny K, Christianson D, McNeill J. The Psychological Experience of Awaiting Breast Diagnosis. Ann Behav Med 2020; 53:630-641. [PMID: 30239562 DOI: 10.1093/abm/kay072] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Each year, over 1 million women in the USA undergo diagnostic breast biopsies, many of which culminate in a benign outcome. However, for many patients, the experience of awaiting biopsy results is far from benign, instead provoking high levels of distress. PURPOSE To take a multifaceted approach to understanding the psychological experience of patients undergoing a breast biopsy. METHOD Female patients (N = 214) were interviewed at an appointment for a breast biopsy, just prior to undergoing the biopsy procedure. Pertinent to the current investigation, the interview assessed various patient characteristics, subjective health and cancer history, support availability, outcome expectations, distress, and coping strategies. RESULTS The findings revealed a complex set of interrelationships among patient characteristics, markers of distress, and use of coping strategies. Patients who were more distressed engaged in more avoidant coping strategies. Regarding the correlates of distress and coping, subjective health was more strongly associated with distress and coping than was cancer history; perceptions of support availability were also reliably associated with distress. CONCLUSION Taken together, the results suggest that patients focus on their immediate experience (e.g., subjective health, feelings of risk, perceptions of support) in the face of the acute moment of uncertainty prompted by a biopsy procedure, relative to more distal considerations such as cancer history and demographic characteristics. These findings can guide clinicians' interactions with patients at the biopsy appointment and can serve as a foundation for interventions designed to reduce distress in this context.
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Affiliation(s)
- Kate Sweeny
- Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Deborah Christianson
- Radiology Department, Riverside University Health System-Medical Center, Moreno Valley, CA, USA
| | - Jeanine McNeill
- Radiology Department, Riverside University Health System-Medical Center, Moreno Valley, CA, USA
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191
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Marco Molina V, García Hernández F. [Histological lesions of risk of breast carcinoma. Survival guide for the general pathologist]. REVISTA ESPANOLA DE PATOLOGIA : PUBLICACION OFICIAL DE LA SOCIEDAD ESPANOLA DE ANATOMIA PATOLOGICA Y DE LA SOCIEDAD ESPANOLA DE CITOLOGIA 2020; 53:158-166. [PMID: 32650967 DOI: 10.1016/j.patol.2020.02.002] [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: 11/05/2019] [Revised: 12/24/2019] [Accepted: 02/12/2020] [Indexed: 06/11/2023]
Abstract
Proliferative epithelial lesions are risk factors for breast cancer. They are a heterogeneous group of lesions in which the presence of atypia is related to varying degrees of risk. They should be considered in the differential diagnosis with benign lesions, in situ ductal carcinoma and infiltrating carcinoma. An accurate histopathological diagnosis is important in choosing the best therapeutic option, including vacuum assisted biopsy and surgery. We revise diagnostic criteria and the differential diagnosis of usual ductal hyperplasia, radial scar and complex sclerosing lesions, distinct types of adenosis, papillary lesions, atypical ductal hyperplasia, flat epithelial atypia and lobular neoplasia in situ. Furthermore, we summarize the degree of risk associated with the different conditions and management possibilities.
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MESH Headings
- Biopsy, Needle
- Breast/pathology
- Breast/surgery
- Breast Carcinoma In Situ/diagnosis
- Breast Carcinoma In Situ/pathology
- Breast Carcinoma In Situ/surgery
- Breast Diseases/diagnosis
- Breast Diseases/pathology
- Breast Diseases/surgery
- Carcinoma, Ductal, Breast/diagnosis
- Carcinoma, Ductal, Breast/pathology
- Carcinoma, Ductal, Breast/surgery
- Carcinoma, Intraductal, Noninfiltrating/diagnosis
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Cicatrix/diagnosis
- Cicatrix/pathology
- Diagnosis, Differential
- Female
- Fibrocystic Breast Disease/diagnosis
- Fibrocystic Breast Disease/pathology
- Humans
- Hyperplasia/diagnosis
- Hyperplasia/pathology
- Pathologists
- Risk Factors
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Affiliation(s)
- Vicente Marco Molina
- Servicio de Anatomía Patológica, Hospital Quirónsalud Barcelona, Barcelona, España.
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192
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Abstract
Pathologists are adopting whole slide images (WSIs) for diagnosis, thanks to recent FDA approval of WSI systems as class II medical devices. In response to new market forces and recent technology advances outside of pathology, a new field of computational pathology has emerged that applies artificial intelligence (AI) and machine learning algorithms to WSIs. Computational pathology has great potential for augmenting pathologists' accuracy and efficiency, but there are important concerns regarding trust of AI due to the opaque, black-box nature of most AI algorithms. In addition, there is a lack of consensus on how pathologists should incorporate computational pathology systems into their workflow. To address these concerns, building computational pathology systems with explainable AI (xAI) mechanisms is a powerful and transparent alternative to black-box AI models. xAI can reveal underlying causes for its decisions; this is intended to promote safety and reliability of AI for critical tasks such as pathology diagnosis. This article outlines xAI enabled applications in anatomic pathology workflow that improves efficiency and accuracy of the practice. In addition, we describe HistoMapr-Breast, an initial xAI enabled software application for breast core biopsies. HistoMapr-Breast automatically previews breast core WSIs and recognizes the regions of interest to rapidly present the key diagnostic areas in an interactive and explainable manner. We anticipate xAI will ultimately serve pathologists as an interactive computational guide for computer-assisted primary diagnosis.
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193
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Bendau E, Smith J, Zhang L, Ackerstaff E, Kruchevsky N, Wu B, Koutcher JA, Alfano R, Shi L. Distinguishing metastatic triple-negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy. JOURNAL OF BIOPHOTONICS 2020; 13:e202000005. [PMID: 32219996 PMCID: PMC7433748 DOI: 10.1002/jbio.202000005] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 03/17/2020] [Accepted: 03/18/2020] [Indexed: 05/10/2023]
Abstract
Triple-negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African-American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time-consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label-free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole-tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.
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Affiliation(s)
- Ethan Bendau
- Department of Biomedical Engineering, Columbia University, New York, New York
| | - Jason Smith
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York
| | - Lin Zhang
- Institute for Ultrafast Spectroscopy and Lasers, The City College of New York, New York, New York
| | - Ellen Ackerstaff
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Natalia Kruchevsky
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Binlin Wu
- Physics Department, CSCU Center for Nanotechnology, Southern Connecticut State University, New Haven, Connecticut
| | - Jason A. Koutcher
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Medical Physics and Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Weill Cornell Medical College, Cornell University, New York, New York
| | - Robert Alfano
- Institute for Ultrafast Spectroscopy and Lasers, The City College of New York, New York, New York
| | - Lingyan Shi
- Department of Bioengineering, University of California, San Diego, La Jolla, California
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Dif N, Elberrichi Z. A New Deep Learning Model Selection Method for Colorectal Cancer Classification. INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2020. [DOI: 10.4018/ijsir.2020070105] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Deep learning is one of the most commonly used techniques in computer-aided diagnosis systems. Their exploitation for histopathological image analysis is important because of the complex morphology of whole slide images. However, the main limitation of these methods is the restricted number of available medical images, which can lead to an overfitting problem. Many studies have suggested the use of static ensemble learning methods to address this issue. This article aims to propose a new dynamic ensemble deep learning method. First, it generates a set of models based on the transfer learning strategy from deep neural networks. Then, the relevant subset of models is selected by the particle swarm optimization algorithm and combined by voting or averaging methods. The proposed approach was tested on a histopathological dataset for colorectal cancer classification, based on seven types of CNNs. The method has achieved accurate results (94.52%) by the Resnet121 model and the voting strategy, which provides important insights into the efficiency of dynamic ensembling in deep learning.
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Affiliation(s)
- Nassima Dif
- EEDIS Laboraory, Djillali Liabes University, Sidi Bel Abbes, Algeria
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195
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He M, Li Z, Liu C, Shi D, Tan Z. Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge. Asia Pac J Ophthalmol (Phila) 2020; 9:299-307. [PMID: 32694344 DOI: 10.1097/apo.0000000000000301] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Artificial intelligence has rapidly evolved from the experimental phase to the implementation phase in many image-driven clinical disciplines, including ophthalmology. A combination of the increasing availability of large datasets and computing power with revolutionary progress in deep learning has created unprecedented opportunities for major breakthrough improvements in the performance and accuracy of automated diagnoses that primarily focus on image recognition and feature detection. Such an automated disease classification would significantly improve the accessibility, efficiency, and cost-effectiveness of eye care systems where it is less dependent on human input, potentially enabling diagnosis to be cheaper, quicker, and more consistent. Although this technology will have a profound impact on clinical flow and practice patterns sooner or later, translating such a technology into clinical practice is challenging and requires similar levels of accountability and effectiveness as any new medication or medical device due to the potential problems of bias, and ethical, medical, and legal issues that might arise. The objective of this review is to summarize the opportunities and challenges of this transition and to facilitate the integration of artificial intelligence (AI) into routine clinical practice based on our best understanding and experience in this area.
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Affiliation(s)
- Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Centre for Eye Research Australia, Royal Victorian Eye & Ear Hospital, Melbourne, Australia
| | - Zhixi Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Chi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- School of Computer Science, University of Technology Sydney, Ultimo NSW, Australia
| | - Danli Shi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zachary Tan
- Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Schwarzman College, Tsinghua University, Beijing, China
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196
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Ščupáková K, Balluff B, Tressler C, Adelaja T, Heeren RM, Glunde K, Ertaylan G. Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges. Clin Chem Lab Med 2020; 58:914-929. [PMID: 31665113 PMCID: PMC9867918 DOI: 10.1515/cclm-2019-0858] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 10/07/2019] [Indexed: 02/07/2023]
Abstract
Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained.
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Affiliation(s)
- Klára Ščupáková
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Benjamin Balluff
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Caitlin Tressler
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tobi Adelaja
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ron M.A. Heeren
- Corresponding author: Ron M.A. Heeren, Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands,
| | - Kristine Glunde
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; and The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Gökhan Ertaylan
- Unit Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
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197
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Nelson KP, Zhou TJ, Edwards D. Measuring intrarater association between correlated ordinal ratings. Biom J 2020; 62:1687-1701. [PMID: 32529683 DOI: 10.1002/bimj.201900177] [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: 06/18/2019] [Revised: 01/20/2020] [Accepted: 04/20/2020] [Indexed: 11/09/2022]
Abstract
Variability between raters' ordinal scores is commonly observed in imaging tests, leading to uncertainty in the diagnostic process. In breast cancer screening, a radiologist visually interprets mammograms and MRIs, while skin diseases, Alzheimer's disease, and psychiatric conditions are graded based on clinical judgment. Consequently, studies are often conducted in clinical settings to investigate whether a new training tool can improve the interpretive performance of raters. In such studies, a large group of experts each classify a set of patients' test results on two separate occasions, before and after some form of training with the goal of assessing the impact of training on experts' paired ratings. However, due to the correlated nature of the ordinal ratings, few statistical approaches are available to measure association between raters' paired scores. Existing measures are restricted to assessing association at just one time point for a single screening test. We propose here a novel paired kappa to provide a summary measure of association between many raters' paired ordinal assessments of patients' test results before versus after rater training. Intrarater association also provides valuable insight into the consistency of ratings when raters view a patient's test results on two occasions with no intervention undertaken between viewings. In contrast to existing correlated measures, the proposed kappa is a measure that provides an overall evaluation of the association among multiple raters' scores from two time points and is robust to the underlying disease prevalence. We implement our proposed approach in two recent breast-imaging studies and conduct extensive simulation studies to evaluate properties and performance of our summary measure of association.
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Affiliation(s)
- Kerrie P Nelson
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Thomas J Zhou
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Don Edwards
- Department of Statistics, University of South Carolina, Columbia, SC, USA
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198
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Ductal Carcinoma In Situ—Pathological Considerations. CURRENT BREAST CANCER REPORTS 2020. [DOI: 10.1007/s12609-020-00359-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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199
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WITHDRAWN: Intelligent microscopy to improve cataract surgery. Med Hypotheses 2020. [DOI: 10.1016/j.mehy.2020.110017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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200
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Hassan Z, Boulos F, Abbas J, El Charif MH, Assi H, Sbaity E. Intracystic papillary carcinoma: clinical presentation, patterns of practice, and oncological outcomes. Breast Cancer Res Treat 2020; 182:317-323. [PMID: 32462260 DOI: 10.1007/s10549-020-05680-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/09/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Intracystic/encapsulated papillary carcinoma remains a poorly understood disease of the breast with a little amount of reports that describe it. It shares features with DCIS and IDC and predominantly affects postmenopausal women. This study aims to evaluate the clinical presentation, treatment, and outcomes in IPC patients managed at our institution. METHODS We retrospectively pooled twenty-eight IPC patients' medical records at our institution. Descriptive analysis of clinicopathological characteristics, approach, and outcomes was done along with a quantitative statistical analysis. RESULTS Cases were divided into three groups: isolated IPC, IPC associated with DCIS, and IPC associated with Invasive Carcinoma. Treatment modalities varied according to the IPC type and its associated components. All patients presented with a palpable mass. Immunohistochemical staining revealed that all isolated IPCs were ER and PR positive and HER2 negative. Lymph node dissection proved necessary only in IPC associated invasive carcinoma. Irregular borders and lobulations, among others, were found on non-invasive core biopsies that turned out to be associated with invasion on surgical pathology. All patients were alive after a median follow-up time of 23 months when the study was over with no reports of recurrence. CONCLUSION IPC cases and treatment approaches at our institution appear similar to the available literature and confirm the excellent prognosis among IPC. Even more, further studies into the key features such as BMI, family history, and radiological findings are necessary for a potential algorithm that could assess for risk of finding invasion in surgical pathology and subsequently the need for axillary/sentinel lymph node biopsy.
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MESH Headings
- Adult
- Aged
- Aged, 80 and over
- Axilla
- Breast Neoplasms/diagnosis
- Breast Neoplasms/epidemiology
- Breast Neoplasms/pathology
- Breast Neoplasms/therapy
- Carcinoma, Intraductal, Noninfiltrating/diagnosis
- Carcinoma, Intraductal, Noninfiltrating/epidemiology
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Carcinoma, Intraductal, Noninfiltrating/therapy
- Carcinoma, Papillary/diagnosis
- Carcinoma, Papillary/epidemiology
- Carcinoma, Papillary/pathology
- Carcinoma, Papillary/therapy
- Chemotherapy, Adjuvant
- Female
- Follow-Up Studies
- Humans
- Mammary Glands, Human/pathology
- Mammary Glands, Human/surgery
- Mastectomy
- Medical History Taking
- Middle Aged
- Neoplasm Invasiveness/diagnosis
- Neoplasm Invasiveness/pathology
- Neoplasm Recurrence, Local/epidemiology
- Neoplasm Recurrence, Local/pathology
- Neoplasm Recurrence, Local/prevention & control
- Prognosis
- Retrospective Studies
- Sentinel Lymph Node/pathology
- Sentinel Lymph Node Biopsy
- Treatment Outcome
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Affiliation(s)
- Zeina Hassan
- Department of Surgery, American University of Beirut Medical Center (AUBMC), Phase 1 - Surgery Specialty Clinics - 4th Floor, Beirut, Lebanon
| | - Fouad Boulos
- Department of Pathology, American University of Beirut Medical Center (AUBMC), Beirut, Lebanon
| | - Jaber Abbas
- Department of Surgery, American University of Beirut Medical Center (AUBMC), Phase 1 - Surgery Specialty Clinics - 4th Floor, Beirut, Lebanon
| | - Mohamad Hadi El Charif
- Department of Surgery, American University of Beirut Medical Center (AUBMC), Phase 1 - Surgery Specialty Clinics - 4th Floor, Beirut, Lebanon
| | - Hazem Assi
- Department of Internal Medicine, American University of Beirut Medical Center (AUBMC), Beirut, Lebanon
| | - Eman Sbaity
- Department of Surgery, American University of Beirut Medical Center (AUBMC), Phase 1 - Surgery Specialty Clinics - 4th Floor, Beirut, Lebanon.
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