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Wong ANN, He Z, Leung KL, To CCK, Wong CY, Wong SCC, Yoo JS, Chan CKR, Chan AZ, Lacambra MD, Yeung MHY. Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 14:3780. [PMID: 35954443 PMCID: PMC9367360 DOI: 10.3390/cancers14153780] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/27/2022] [Accepted: 08/01/2022] [Indexed: 02/05/2023] Open
Abstract
The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.
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Affiliation(s)
- Alex Ngai Nick Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Zebang He
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Ka Long Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Curtis Chun Kit To
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Chun Yin Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Sze Chuen Cesar Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Jung Sun Yoo
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Cheong Kin Ronald Chan
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Angela Zaneta Chan
- Department of Anatomical and Cellular Pathology, Prince of Wales Hospital, Shatin, Hong Kong SAR, China;
| | - Maribel D. Lacambra
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Martin Ho Yin Yeung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
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Prabhu S, Prasad K, Robels-Kelly A, Lu X. AI-based carcinoma detection and classification using histopathological images: A systematic review. Comput Biol Med 2022; 142:105209. [DOI: 10.1016/j.compbiomed.2022.105209] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/01/2022] [Accepted: 01/01/2022] [Indexed: 02/07/2023]
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Wang M, Aung PP, Prieto VG. Standardized Method for Defining a 1-mm2 Region of Interest for Calculation of Mitotic Rate on Melanoma Whole Slide Images. Arch Pathol Lab Med 2021; 145:1255-1263. [PMID: 33417687 DOI: 10.5858/arpa.2020-0137-oa] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2020] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Mitotic rate counting is essential in pathologic evaluations in melanoma. The American Joint Committee on Cancer recommends reporting the number of mitotic figures (MFs) in a 1-mm2 area encompassing the "hot spot." There is currently no standard procedure for delineating a 1-mm2 region of interest for MF counting on a digital whole slide image (WSI) of melanoma. OBJECTIVE.— To establish a standardized method to enclose a 1-mm2 region of interest for MF counting in melanoma based on WSIs and assess the method's effectiveness. DESIGN.— Whole slide images were visualized using the ImageScope viewer (Aperio). Different monitors and viewing magnifications were explored and the annotation tools provided by ImageScope were evaluated. For validation, we compared mitotic rates obtained from WSIs with our method and those from glass slides with traditional microscopy with 30 melanoma cases. RESULTS.— Of the monitors we examined, a 32-inch monitor with 3840 × 2160 resolution was optimal for counting MFs within a 1-mm2 region of interest in melanoma. When WSIs were viewed in the ImageScope viewer, ×10 to ×20 magnification during screening could efficiently locate a hot spot and ×20 to ×40 magnification during counting could accurately identify MFs. Fixed-shape annotations with 500 × 500-μm squares or circles can precisely and efficiently enclose a 1-mm2 region of interest. Our method on WSIs was able to produce a higher mitotic rate than with glass slides. CONCLUSIONS.— Whole slide images may be used to efficiently count MFs. We recommend fixed-shape annotation with 500 × 500-μm squares or circles for routine practice in counting MFs for melanoma.
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Affiliation(s)
- Minhua Wang
- From the Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
| | - Phyu P Aung
- From the Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
| | - Victor G Prieto
- From the Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
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Xing F, Zhang X, Cornish TC. Artificial intelligence for pathology. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00011-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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5
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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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Ternes L, Huang G, Lanciault C, Thibault G, Riggers R, Gray JW, Muschler J, Chang YH. VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts. Sci Rep 2020; 10:20904. [PMID: 33262400 PMCID: PMC7708430 DOI: 10.1038/s41598-020-78061-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 11/18/2020] [Indexed: 12/14/2022] Open
Abstract
Mechanistic disease progression studies using animal models require objective and quantifiable assessment of tissue pathology. Currently quantification relies heavily on staining methods which can be expensive, labor/time-intensive, inconsistent across laboratories and batch, and produce uneven staining that is prone to misinterpretation and investigator bias. We developed an automated semantic segmentation tool utilizing deep learning for rapid and objective quantification of histologic features relying solely on hematoxylin and eosin stained pancreatic tissue sections. The tool segments normal acinar structures, the ductal phenotype of acinar-to-ductal metaplasia (ADM), and dysplasia with Dice coefficients of 0.79, 0.70, and 0.79, respectively. To deal with inaccurate pixelwise manual annotations, prediction accuracy was also evaluated against biological truth using immunostaining mean structural similarity indexes (SSIM) of 0.925 and 0.920 for amylase and pan-keratin respectively. Our tool's disease area quantifications were correlated to the quantifications of immunostaining markers (DAPI, amylase, and cytokeratins; Spearman correlation score = 0.86, 0.97, and 0.92) in unseen dataset (n = 25). Moreover, our tool distinguishes ADM from dysplasia, which are not reliably distinguished with immunostaining, and demonstrates generalizability across murine cohorts with pancreatic disease. We quantified the changes in histologic feature abundance for murine cohorts with oncogenic Kras-driven disease, and the predictions fit biological expectations, showing stromal expansion, a reduction of normal acinar tissue, and an increase in both ADM and dysplasia as disease progresses. Our tool promises to accelerate and improve the quantification of pancreatic disease in animal studies and become a unifying quantification tool across laboratories.
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Affiliation(s)
- Luke Ternes
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Portland, OR, USA
- Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
| | - Ge Huang
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Portland, OR, USA
| | - Christian Lanciault
- Department of Pathology, Oregon Health & Science University, Portland, OR, USA
| | - Guillaume Thibault
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Portland, OR, USA
| | - Rachelle Riggers
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Portland, OR, USA
| | - Joe W Gray
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Portland, OR, USA
- Knight Cancer Institute, Portland, OR, USA
| | - John Muschler
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Portland, OR, USA.
- Brenden-Colson Center for Pancreatic Care, Portland, OR, USA.
| | - Young Hwan Chang
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Portland, OR, USA.
- Computational Biology Program, Oregon Health & Science University, Portland, OR, USA.
- Knight Cancer Institute, Portland, OR, USA.
- Brenden-Colson Center for Pancreatic Care, Portland, OR, USA.
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Burlingame EA, McDonnell M, Schau GF, Thibault G, Lanciault C, Morgan T, Johnson BE, Corless C, Gray JW, Chang YH. SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning. Sci Rep 2020; 10:17507. [PMID: 33060677 PMCID: PMC7566625 DOI: 10.1038/s41598-020-74500-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 09/28/2020] [Indexed: 02/07/2023] Open
Abstract
Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin (H&E)-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that estimate the underlying distribution of the tumor cell marker pan-cytokeratin (panCK). To build a dataset suitable for learning this task, we developed a serial staining protocol which allows IF and H&E images from the same tissue to be spatially registered. We show that deep learning-extracted morphological feature representations of histological images can guide representative sample selection, which improved SHIFT generalizability in a small but heterogenous set of human pancreatic cancer samples. With validation in larger cohorts, SHIFT could serve as an efficient preliminary, auxiliary, or substitute for panCK IF by delivering virtual panCK IF images for a fraction of the cost and in a fraction of the time required by traditional IF.
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Affiliation(s)
- Erik A Burlingame
- Computational Biology Program, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
- OHSU Center for Spatial Systems Biomedicine, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Mary McDonnell
- OHSU Center for Spatial Systems Biomedicine, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Geoffrey F Schau
- Computational Biology Program, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
- OHSU Center for Spatial Systems Biomedicine, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Guillaume Thibault
- OHSU Center for Spatial Systems Biomedicine, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Christian Lanciault
- Department of Pathology, Oregon Health and Science University, Portland, OR, USA
| | - Terry Morgan
- Department of Pathology, Oregon Health and Science University, Portland, OR, USA
| | - Brett E Johnson
- OHSU Center for Spatial Systems Biomedicine, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Christopher Corless
- Knight Diagnostic Laboratories, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Joe W Gray
- OHSU Center for Spatial Systems Biomedicine, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Brenden-Colson Center for Pancreatic Care, Oregon Health and Science University, Portland, OR, USA
| | - Young Hwan Chang
- Computational Biology Program, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.
- OHSU Center for Spatial Systems Biomedicine, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.
- Brenden-Colson Center for Pancreatic Care, Oregon Health and Science University, Portland, OR, USA.
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Shamai G, Binenbaum Y, Slossberg R, Duek I, Gil Z, Kimmel R. Artificial Intelligence Algorithms to Assess Hormonal Status From Tissue Microarrays in Patients With Breast Cancer. JAMA Netw Open 2019; 2:e197700. [PMID: 31348505 PMCID: PMC6661721 DOI: 10.1001/jamanetworkopen.2019.7700] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
IMPORTANCE Immunohistochemistry (IHC) is the most widely used assay for identification of molecular biomarkers. However, IHC is time consuming and costly, depends on tissue-handling protocols, and relies on pathologists' subjective interpretation. Image analysis by machine learning is gaining ground for various applications in pathology but has not been proposed to replace chemical-based assays for molecular detection. OBJECTIVE To assess the prediction feasibility of molecular expression of biomarkers in cancer tissues, relying only on tissue architecture as seen in digitized hematoxylin-eosin (H&E)-stained specimens. DESIGN, SETTING, AND PARTICIPANTS This single-institution retrospective diagnostic study assessed the breast cancer tissue microarrays library of patients from Vancouver General Hospital, British Columbia, Canada. The study and analysis were conducted from July 1, 2015, through July 1, 2018. A machine learning method, termed morphological-based molecular profiling (MBMP), was developed. Logistic regression was used to explore correlations between histomorphology and biomarker expression, and a deep convolutional neural network was used to predict the biomarker expression in examined tissues. MAIN OUTCOMES AND MEASURES Positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristics curve measures of MBMP for assessment of molecular biomarkers. RESULTS The database consisted of 20 600 digitized, publicly available H&E-stained sections of 5356 patients with breast cancer from 2 cohorts. The median age at diagnosis was 61 years for cohort 1 (412 patients) and 62 years for cohort 2 (4944 patients), and the median follow-up was 12.0 years and 12.4 years, respectively. Tissue histomorphology was significantly correlated with the molecular expression of all 19 biomarkers assayed, including estrogen receptor (ER), progesterone receptor (PR), and ERBB2 (formerly HER2). Expression of ER was predicted for 105 of 207 validation patients in cohort 1 (50.7%) and 1059 of 2046 validation patients in cohort 2 (51.8%), with PPVs of 97% and 98%, respectively, NPVs of 68% and 76%, respectively, and accuracy of 91% and 92%, respectively, which were noninferior to traditional IHC (PPV, 91%-98%; NPV, 51%-78%; and accuracy, 81%-90%). Diagnostic accuracy improved given more data. Morphological analysis of patients with ER-negative/PR-positive status by IHC revealed resemblance to patients with ER-positive status (Bhattacharyya distance, 0.03) and not those with ER-negative/PR-negative status (Bhattacharyya distance, 0.25). This suggests a false-negative IHC finding and warrants antihormonal therapy for these patients. CONCLUSIONS AND RELEVANCE For at least half of the patients in this study, MBMP appeared to predict biomarker expression with noninferiority to IHC. Results suggest that prediction accuracy is likely to improve as data used for training expand. Morphological-based molecular profiling could be used as a general approach for mass-scale molecular profiling based on digitized H&E-stained images, allowing quick, accurate, and inexpensive methods for simultaneous profiling of multiple biomarkers in cancer tissues.
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Affiliation(s)
- Gil Shamai
- Department of Electrical Engineering, Technion Israel Institute of Technology, Haifa, Israel
| | - Yoav Binenbaum
- Laboratory of Pediatric Oncology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Laboratory for Applied Cancer Research, Rambam Healthcare Campus, Rappaport Institute of Medicine and Research, Haifa, Israel
| | - Ron Slossberg
- Departmemt of Computer Science, Technion Israel Institute of Technology, Haifa, Israel
| | - Irit Duek
- Department of Otolaryngology-Head and Neck Surgery, Rambam Health Care Campus, Haifa, Israel
| | - Ziv Gil
- Laboratory for Applied Cancer Research, Rambam Healthcare Campus, Rappaport Institute of Medicine and Research, Haifa, Israel
- Department of Otolaryngology-Head and Neck Surgery, Rambam Health Care Campus, Haifa, Israel
| | - Ron Kimmel
- Departmemt of Computer Science, Technion Israel Institute of Technology, Haifa, Israel
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Saikia AR, Bora K, Mahanta LB, Das AK. Comparative assessment of CNN architectures for classification of breast FNAC images. Tissue Cell 2019; 57:8-14. [PMID: 30947968 DOI: 10.1016/j.tice.2019.02.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 01/30/2019] [Accepted: 02/02/2019] [Indexed: 01/27/2023]
Abstract
Fine needle aspiration cytology (FNAC) entails using a narrow gauge (25-22 G) needle to collect a sample of a lesion for microscopic examination. It allows a minimally invasive, rapid diagnosis of tissue but does not preserve its histological architecture. FNAC is commonly used for diagnosis of breast cancer, with traditional practice being based on the subjective visual assessment of the breast cytopathology cell samples under a microscope to evaluate the state of various cytological features. Therefore, there are many challenges in maintaining consistency and reproducibility of findings. However, the advent of digital imaging and computational aid in diagnosis can improve the diagnostic accuracy and reduce the effective workload of pathologists. This paper presents a comparison of various deep convolutional neural network (CNN) based fine-tuned transfer learned classification approach for the diagnosis of the cell samples. The proposed approach has been tested using VGG16, VGG19, ResNet-50 and GoogLeNet-V3 (aka Inception V3) architectures of CNN on an image dataset of 212 images (99 benign and 113 malignant), later augmented and cleansed to 2120 images (990 benign and 1130 malignant), where the network was trained using images of 80% cell samples and tested on the rest. This paper presents a comparative assessment of the models giving a new dimension to FNAC study where GoogLeNet-V3 (fine-tuned) achieved an accuracy of 96.25% which is highly satisfactory.
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Affiliation(s)
- Amartya Ranjan Saikia
- The Department of Computer Science and Engineering, Assam Engineering College, Guwahati 781013, Assam, India.
| | - Kangkana Bora
- The Department of Centre for Computational and Numerical Sciences, Institute of Advanced Study in Science and Technology, Guwahati 781035, Assam, India.
| | - Lipi B Mahanta
- The Department of Centre for Computational and Numerical Sciences, Institute of Advanced Study in Science and Technology, Guwahati 781035, Assam, India
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Moravec R, Divi R, Verma M. Detecting circulating tumor material and digital pathology imaging during pancreatic cancer progression. World J Gastrointest Oncol 2017; 9:235-250. [PMID: 28656074 PMCID: PMC5472554 DOI: 10.4251/wjgo.v9.i6.235] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 03/04/2017] [Accepted: 03/24/2017] [Indexed: 02/05/2023] Open
Abstract
Pancreatic cancer (PC) is a leading cause of cancer-related death worldwide. Clinical symptoms typically present late when treatment options are limited and survival expectancy is very short. Metastatic mutations are heterogeneous and can accumulate up to twenty years before PC diagnosis. Given such genetic diversity, detecting and managing the complex states of disease progression may be limited to imaging modalities and markers present in circulation. Recent developments in digital pathology imaging show potential for early PC detection, making a differential diagnosis, and predicting treatment sensitivity leading to long-term survival in advanced stage patients. Despite large research efforts, the only serum marker currently approved for clinical use is CA 19-9. Utility of CA 19-9 has been shown to improve when it is used in combination with PC-specific markers. Efforts are being made to develop early-screening assays that can detect tumor-derived material, present in circulation, before metastasis takes a significant course. Detection of markers that identify circulating tumor cells and tumor-derived extracellular vesicles (EVs) in biofluid samples offers a promising non-invasive method for this purpose. Circulating tumor cells exhibit varying expression of epithelial and mesenchymal markers depending on the state of tumor differentiation. This offers a possibility for monitoring disease progression using minimally invasive procedures. EVs also offer the benefit of detecting molecular cargo of tumor origin and add the potential to detect circulating vesicle markers from tumors that lack invasive properties. This review integrates recent genetic insights of PC progression with developments in digital pathology and early detection of tumor-derived circulating material.
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