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Liu W, Qu A, Yuan J, Wang L, Chen J, Zhang X, Wang H, Han Z, Li Y. Colorectal cancer histopathology image analysis: A comparative study of prognostic values of automatically extracted morphometric nuclear features in multispectral and red-blue-green imagery. Histol Histopathol 2024; 39:1303-1316. [PMID: 38343355 DOI: 10.14670/hh-18-715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
OBJECTIVES Multispectral imaging (MSI) has been utilized to predict the prognosis of colorectal cancer (CRC) patients, however, our understanding of the prognostic value of nuclear morphological parameters of bright-field MSI in CRC is still limited. This study was designed to compare the efficiency of MSI and standard red-green-blue (RGB) images in predicting the prognosis of CRC. METHODS We compared the efficiency of MS and conventional RGB images on the quantitative assessment of hematoxylin-eosin (HE) stained histopathology images. A pipeline was developed using a pixel-wise support vector machine (SVM) classifier for gland-stroma segmentation, and a marker-controlled watershed algorithm was used for nuclei segmentation. The correlation between extracted morphological parameters and the five-year disease-free survival (5-DFS) was analyzed. RESULTS Forty-seven nuclear morphological parameters were extracted in total. Based on Kaplan-Meier analysis, eight features derived from MS images and seven featured derived from RGB images were significantly associated with 5-DFS, respectively. Compared with RGB images, MSI showed higher accuracy, precision, and Dice index in nuclei segmentation. Multivariate analysis indicated that both integrated parameters 1 (factors negatively correlated with CRC prognosis including nuclear number, circularity, eccentricity, major axis length) and 2 (factors positively correlated with CRC prognosis including nuclear average area, area perimeter, total area/total perimeter ratio, average area/perimeter ratio) in MS images were independent prognostic factors of 5-DFS, in contrast with only integrated parameter 1 (P<0.001) in RGB images. More importantly, the quantification of HE-stained MS images displayed higher accuracy in predicting 5-DFS compared with RGB images (76.9% vs 70.9%). CONCLUSIONS Quantitative evaluation of HE-stained MS images could yield more information and better predictive performance for CRC prognosis than conventional RGB images, thereby contributing to precision oncology.
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Affiliation(s)
- Wenlou Liu
- Department of Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Aiping Qu
- School of Computer, University of South China, Hengyang, China
| | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Linwei Wang
- Department of Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jiamei Chen
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiuli Zhang
- Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Hongmei Wang
- Department of Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Zhengxiang Han
- Department of Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
| | - Yan Li
- Department of Cancer Surgery, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China.
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Zhao Z, Hu B, Xu K, Jiang Y, Xu X, Liu Y. A quantitative analysis of artificial intelligence research in cervical cancer: a bibliometric approach utilizing CiteSpace and VOSviewer. Front Oncol 2024; 14:1431142. [PMID: 39296978 PMCID: PMC11408476 DOI: 10.3389/fonc.2024.1431142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Accepted: 08/16/2024] [Indexed: 09/21/2024] Open
Abstract
Background Cervical cancer, a severe threat to women's health, is experiencing a global increase in incidence, notably among younger demographics. With artificial intelligence (AI) making strides, its integration into medical research is expanding, particularly in cervical cancer studies. This bibliometric study aims to evaluate AI's role, highlighting research trends and potential future directions in the field. Methods This study systematically retrieved literature from the Web of Science Core Collection (WoSCC), employing VOSviewer and CiteSpace for analysis. This included examining collaborations and keyword co-occurrences, with a focus on the relationship between citing and cited journals and authors. A burst ranking analysis identified research hotspots based on citation frequency. Results The study analyzed 927 articles from 2008 to 2024 by 5,299 authors across 81 regions. China, the U.S., and India were the top contributors, with key institutions like the Chinese Academy of Sciences and the NIH leading in publications. Schiffman, Mark, featured among the top authors, while Jemal, A, was the most cited. 'Diagnostics' and 'IEEE Access' stood out for publication volume and citation impact, respectively. Keywords such as 'cervical cancer,' 'deep learning,' 'classification,' and 'machine learning' were dominant. The most cited article was by Berner, ES; et al., published in 2008. Conclusions AI's application in cervical cancer research is expanding, with a growing scholarly community. The study suggests that AI, especially deep learning and machine learning, will remain a key research area, focusing on improving diagnostics and treatment. There is a need for increased international collaboration to maximize AI's potential in advancing cervical cancer research and patient care.
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Affiliation(s)
- Ziqi Zhao
- School of Basic Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Boqian Hu
- Hebei Provincial Hospital of Traditional Chinese Medicine, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
| | - Kun Xu
- School of Basic Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yizhuo Jiang
- School of Basic Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xisheng Xu
- School of Basic Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yuliang Liu
- School of Basic Medicine, Zhejiang Chinese Medical University, Hangzhou, China
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Jaiswal M, Mukhtar U, Shakya KS, Laddi A, Singha LA. Computerised assessment-a novel approach for calculation of percentage of hypomineralized lesion on incisors and its correlation with aesthetic concern. J Oral Biol Craniofac Res 2024; 14:570-577. [PMID: 39139516 PMCID: PMC11320481 DOI: 10.1016/j.jobcr.2024.07.004] [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: 04/15/2024] [Revised: 07/09/2024] [Accepted: 07/16/2024] [Indexed: 08/15/2024] Open
Abstract
Introduction Molar-incisor hypomineralization (MIH) is a localized, qualitative, demarcated enamel defect that affects first permanent molars (FPMs) and/or permanent incisors. The aim of present study was to introduce a novel computerised assessment process to detect and quantify the percentage opacity associated with MIH affected maxillary central incisors. Methodology Children (8-16 years) enrolled in the primary study having mild (white/cream or yellow/brown) MIH lesion on fully erupted maxillary permanent central incisor. 50 standardised images of MIH lesions were captured in an artificially lit room with fixed parameters and were anonymized and securely stored. Images were analysed by AI-driven computerised software and generates output classifications via a sophisticated algorithm crafted using a meticulously annotated image dataset as reference through supervised machine learning (SML). For the validation of computerised assessment of MIH lesions, the percentage of demarked opacity was calculated using ADOBE PHOTOSHOP CS7. Results The percentage of MIH lesion was calculated through histogram plotting with the maxima ranging from 7.29 % to 71.21 % with the mean value of 34.51 %. The validation score ranged from 10.29 % to 67.27 % with the mean value of 35.32 %. The difference between the two was statistically not significant. Out of 50 patients; 11 patients had 1-30 % of surface affected with MIH and 2 had aesthetic concern; 24 had 30-60 % of surface affected and 13 had aesthetic concern; 15 had >60 % of surface affected and 12 had aesthetic concerns. Conclusions The proposed approach exhibit sufficient quality to be integrated into a dental software addressing practical challenges encountered in daily clinical settings.
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Affiliation(s)
- Manojkumar Jaiswal
- A Unit of Pediatric and Preventive Dentistry, Oral Health Sciences Center, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Umer Mukhtar
- A Unit of Pediatric and Preventive Dentistry, Oral Health Sciences Center, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | | | - Amit Laddi
- CSIR-Central Scientific Instruments Organisation, Chandigarh, India
| | - L Akash Singha
- A Unit of Pediatric and Preventive Dentistry, Oral Health Sciences Center, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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Ram TB, Krishnan S, Jeevanandam J, Danquah MK, Thomas S. Emerging Biohybrids of Aptamer-Based Nano-Biosensing Technologies for Effective Early Cancer Detection. Mol Diagn Ther 2024; 28:425-453. [PMID: 38775897 DOI: 10.1007/s40291-024-00717-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/01/2024] [Indexed: 06/28/2024]
Abstract
Cancer is a leading global cause of mortality, which underscores the imperative of early detection for improved patient outcomes. Biorecognition molecules, especially aptamers, have emerged as highly effective tools for early and accurate cancer cell identification. Aptamers, with superior versatility in synthesis and modification, offer enhanced binding specificity and stability compared with conventional antibodies. Hence, this article reviews diagnostic strategies employing aptamer-based biohybrid nano-biosensing technologies, focusing on their utility in detecting cancer biomarkers and abnormal cells. Recent developments include the synthesis of nano-aptamers using diverse nanomaterials, such as metallic nanoparticles, metal oxide nanoparticles, carbon-derived substances, and biohybrid nanostructures. The integration of these nanomaterials with aptamers significantly enhances sensitivity and specificity, promising innovative and efficient approaches for cancer diagnosis. This convergence of nanotechnology with aptamer research holds the potential to revolutionize cancer treatment through rapid, accurate, and non-invasive diagnostic methods.
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Affiliation(s)
| | | | - Jaison Jeevanandam
- CQM-Centro de Química da Madeira, Universidade da Madeira, Campus da Penteada, 9020-105, Funchal, Madeira, Portugal.
| | - Michael K Danquah
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN, USA
| | - Sabu Thomas
- School of Polymer Science and Technology and School of Chemical Sciences, Mahatma Gandhi University, Kottayam, Kerala, India
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Kumar A, Vishwakarma A, Bajaj V. ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 251:108207. [PMID: 38723437 DOI: 10.1016/j.cmpb.2024.108207] [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: 01/04/2024] [Revised: 03/20/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND AND OBJECTIVE Lung cancer (LC) has a high fatality rate that continuously affects human lives all over the world. Early detection of LC prolongs human life and helps to prevent the disease. Histopathological inspection is a common method to diagnose LC. Visual inspection of histopathological diagnosis necessitates more inspection time, and the decision depends on the subjective perception of clinicians. Usually, machine learning techniques mostly depend on traditional feature extraction which is labor-intensive and may not be appropriate for enormous data. In this work, a convolutional neural network (CNN)-based architecture is proposed for the more effective classification of lung tissue subtypes using histopathological images. METHODS Authors have utilized the first-time nonlocal mean (NLM) filter to suppress the effect of noise from histopathological images. NLM filter efficiently eliminated noise while preserving the edges of images. Then, the obtained denoised images are given as input to the proposed multi-headed lung cancer classification convolutional neural network (ML3CNet). Furthermore, the model quantization technique is utilized to reduce the size of the proposed model for the storage of the data. Reduction in model size requires less memory and speeds up data processing. RESULTS The effectiveness of the proposed model is compared with the other existing state-of-the-art methods. The proposed ML3CNet achieved an average classification accuracy of 99.72%, sensitivity of 99.66%, precision of 99.64%, specificity of 99.84%, F-1 score of 0.9965, and area under the curve of 0.9978. The quantized accuracy of 98.92% is attained by the proposed model. To validate the applicability of the proposed ML3CNet, it has also been tested on the colon cancer dataset. CONCLUSION The findings reveal that the proposed approach can be beneficial to automatically classify LC subtypes that might assist healthcare workers in making decisions more precisely. The proposed model can be implemented on the hardware using Raspberry Pi for practical realization.
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Affiliation(s)
- Anurodh Kumar
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India.
| | - Amit Vishwakarma
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India.
| | - Varun Bajaj
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India; Maulana Azad National Institute of Technology Bhopal, Bhopal, 462003, India.
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Liang M, Jiang X, Cao J, Zhang S, Liu H, Li B, Wang L, Zhang C, Jia X. HSG-MGAF Net: Heterogeneous subgraph-guided multiscale graph attention fusion network for interpretable prediction of whole-slide image. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108099. [PMID: 38442623 DOI: 10.1016/j.cmpb.2024.108099] [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: 12/17/2023] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND AND OBJECTIVE Pathological whole slide image (WSI) prediction and region of interest (ROI) localization are important issues in computer-aided diagnosis and postoperative analysis in clinical applications. Existing computer-aided methods for predicting WSI are mainly based on multiple instance learning (MIL) and its variants. However, most of the methods are based on instance independence and identical distribution assumption and performed at a single scale, which not fully exploit the hierarchical multiscale heterogeneous information contained in WSI. METHODS Heterogeneous Subgraph-Guided Multiscale Graph Attention Fusion Network (HSG-MGAF Net) is proposed to build the topology of critical image patches at two scales for adaptive WSI prediction and lesion localization. The HSG-MGAF Net simulates the hierarchical heterogeneous information of WSI through graph and hypergraph at two scales, respectively. This framework not only fully exploits the low-order and potential high-order correlations of image patches at each scale, but also leverages the heterogeneous information of the two scales for adaptive WSI prediction. RESULTS We validate the superiority of the proposed method on the CAMELYON16 and the TCGA- NSCLC, and the results show that HSG-MGAF Net outperforms the state-of-the-art method on both datasets. The average ACC, AUC and F1 score of HSG-MGAF Net can reach 92.7 %/0.951/0.892 and 92.2 %/0.957/0.919, respectively. The obtained heatmaps can also localize the positive regions more accurately, which have great consistency with the pixel-level labels. CONCLUSIONS The results demonstrate that HSG-MGAF Net outperforms existing weakly supervised learning methods by introducing critical heterogeneous information between the two scales. This approach paves the way for further research on light weighted heterogeneous graph-based WSI prediction and ROI localization.
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Affiliation(s)
- Meiyan Liang
- School of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China.
| | - Xing Jiang
- School of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China
| | - Jie Cao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
| | - Shupeng Zhang
- School of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China
| | - Haishun Liu
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Bo Li
- Department of Rehabilitation Treatment, Shanxi Rongjun Hospital, Taiyuan 030000, China
| | - Lin Wang
- Department of Pathology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan 030032, China
| | - Cunlin Zhang
- Department of physics, Capital Normal University, Beijing 100048, China
| | - Xiaojun Jia
- School of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China
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Aalam SW, Ahanger AB, Masoodi TA, Bhat AA, Akil ASAS, Khan MA, Assad A, Macha MA, Bhat MR. Deep learning-based identification of esophageal cancer subtypes through analysis of high-resolution histopathology images. Front Mol Biosci 2024; 11:1346242. [PMID: 38567100 PMCID: PMC10985197 DOI: 10.3389/fmolb.2024.1346242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/23/2024] [Indexed: 04/04/2024] Open
Abstract
Esophageal cancer (EC) remains a significant health challenge globally, with increasing incidence and high mortality rates. Despite advances in treatment, there remains a need for improved diagnostic methods and understanding of disease progression. This study addresses the significant challenges in the automatic classification of EC, particularly in distinguishing its primary subtypes: adenocarcinoma and squamous cell carcinoma, using histopathology images. Traditional histopathological diagnosis, while being the gold standard, is subject to subjectivity and human error and imposes a substantial burden on pathologists. This study proposes a binary class classification system for detecting EC subtypes in response to these challenges. The system leverages deep learning techniques and tissue-level labels for enhanced accuracy. We utilized 59 high-resolution histopathological images from The Cancer Genome Atlas (TCGA) Esophageal Carcinoma dataset (TCGA-ESCA). These images were preprocessed, segmented into patches, and analyzed using a pre-trained ResNet101 model for feature extraction. For classification, we employed five machine learning classifiers: Support Vector Classifier (SVC), Logistic Regression (LR), Decision Tree (DT), AdaBoost (AD), Random Forest (RF), and a Feed-Forward Neural Network (FFNN). The classifiers were evaluated based on their prediction accuracy on the test dataset, yielding results of 0.88 (SVC and LR), 0.64 (DT and AD), 0.82 (RF), and 0.94 (FFNN). Notably, the FFNN classifier achieved the highest Area Under the Curve (AUC) score of 0.92, indicating its superior performance, followed closely by SVC and LR, with a score of 0.87. This suggested approach holds promising potential as a decision-support tool for pathologists, particularly in regions with limited resources and expertise. The timely and precise detection of EC subtypes through this system can substantially enhance the likelihood of successful treatment, ultimately leading to reduced mortality rates in patients with this aggressive cancer.
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Affiliation(s)
- Syed Wajid Aalam
- Department of Computer Science, Islamic University of Science and Technology, Awantipora, India
| | - Abdul Basit Ahanger
- Department of Computer Science, Islamic University of Science and Technology, Awantipora, India
| | - Tariq A. Masoodi
- Human Immunology Department, Research Branch, Sidra Medicine, Doha, Qatar
| | - Ajaz A. Bhat
- Department of Human Genetics-Precision Medicine in Diabetes, Obesity and Cancer Program, Sidra Medicine, Doha, Qatar
| | - Ammira S. Al-Shabeeb Akil
- Department of Human Genetics-Precision Medicine in Diabetes, Obesity and Cancer Program, Sidra Medicine, Doha, Qatar
| | | | - Assif Assad
- Department of Computer Science and Engineering, Islamic University of Science and Technology, Awantipora, India
| | - Muzafar A. Macha
- Watson-Crick Centre for Molecular Medicine, Islamic University of Science and Technology, Awantipora, India
| | - Muzafar Rasool Bhat
- Department of Computer Science, Islamic University of Science and Technology, Awantipora, India
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Hussain Y, Singh J, Meena A, Sinha RA, Luqman S. Escin-sorafenib synergy up-regulates LC3-II and p62 to induce apoptosis in hepatocellular carcinoma cells. ENVIRONMENTAL TOXICOLOGY 2024; 39:840-856. [PMID: 37853854 DOI: 10.1002/tox.23988] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 09/19/2023] [Accepted: 09/21/2023] [Indexed: 10/20/2023]
Abstract
INTRODUCTION Hepatocellular carcinoma (HCC) is a common solid cancer and the leading cause of cancer deaths worldwide. Sorafenib is the first drug used to treat HCC but its effectiveness needs to be improved, and it is important to find ways to treat cancer that combine sorafenib with other drugs. Synergistic therapies lower effective drug doses and side effects while enhancing the anticancer effect. PURPOSE In the present study, the therapeutic potential of sorafenib in combination with escin and its underlying mechanism in targeting liver cancer has been established. STUDY DESIGN/METHODS The IC50 of sorafenib and escin against HepG2, PLC/PRF5 and Huh7 cell lines were determined using MTT assay. The combination index, dose reduction index, isobologram and concentrations producing synergy were evaluated using the Chou-Talaly algorithm. The sub-effective concentration of sorafenib and escin was selected to analyze cytotoxic synergistic potential. Cellular ROS, mitochondrial membrane potential, annexin V and cell cycle were evaluated using a flow-cytometer, and autophagy biomarkers were determined using western blotting. Moreover, autophagy was knocked down using ATG5 siRNA to confirm its role. A DEN-induced liver cancer rat model was developed to check the synergy of sorafenib and escin. RESULTS Different concentrations of escin reduced the IC50 of sorafenib in HepG2, PLC/PRF5 and Huh7 cell lines. Chou-Talaly algorithm determined cytotoxic synergistic concentrations of sorafenib and escin in these cell lines. Mechanistically, this combination over-expressed p62 and LC-II, reflecting autophagy block and induced late apoptosis, further reconfirmed by ATG5 knockdown. Sorafenib and escin combination reduced HCC serum biomarker α-feto protein (α-FP) by 1.5 folds. This combination restricted liver weight, tumor number and size, also, conserved morphological features of liver cells. The combination selectively targeted the G0 /G1 phase of cancer cells. CONCLUSION Escin and sorafenib combination potentially up-regulates p62 to block autophagy to induce late apoptosis in liver cancer cells.
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Affiliation(s)
- Yusuf Hussain
- Bioprospection and Product Development Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Jyoti Singh
- Bioprospection and Product Development Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, India
- Jawaharlal Nehru University, New Delhi, India
| | - Abha Meena
- Bioprospection and Product Development Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Rohit Anthony Sinha
- Department of Endocrinology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India
| | - Suaib Luqman
- Bioprospection and Product Development Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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Ashraf FB, Alam SM, Sakib SM. Enhancing breast cancer classification via histopathological image analysis: Leveraging self-supervised contrastive learning and transfer learning. Heliyon 2024; 10:e24094. [PMID: 38293493 PMCID: PMC10827455 DOI: 10.1016/j.heliyon.2024.e24094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 12/06/2023] [Accepted: 01/03/2024] [Indexed: 02/01/2024] Open
Abstract
Breast cancer, a significant threat to women's health, demands early detection. Automating histopathological image analysis offers a promising solution to enhance efficiency and accuracy in diagnosis. This study addresses the challenge of breast cancer histopathological image classification by leveraging the ResNet architecture, known for its depth and skip connections. In this work, two distinct approaches were pursued, each driven by unique motivations. The first approach aimed to improve the learning process through self-supervised contrastive learning. It utilizes a small subset of the training data for initial model training and progressively expands the training set by incorporating confidently labeled data from the unlabeled pool, ultimately achieving a reliable model with limited training data. The second approach focused on optimizing the architecture by combining ResNet50 and Inception module to get a lightweight and efficient classifier. The dataset utilized in this work comprises histopathological images categorized into benign and malignant classes at varying magnification levels (40X, 100X, 200X, 400X), all originating from the same source image. The results demonstrate state-of-the-art performance, achieving 98% accuracy for images magnified at 40X and 200X, and 94% for 100X and 400X. Notably, the proposed architecture boasts a substantially reduced parameter count of approximately 3.6 million, contrasting with existing leading architectures, which possess parameter sizes at least twice as large.
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Affiliation(s)
- Faisal Bin Ashraf
- Department of Computer Science and Engineering, University of California, Riverside, 92521, CA, USA
| | - S.M. Maksudul Alam
- Department of Computer Science and Engineering, University of California, Riverside, 92521, CA, USA
| | - Shahriar M. Sakib
- Marlan and Rosemary Bourns College of Engineering, University of California, Riverside, 92521, CA, USA
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Aloor LJ, Skariyachan S, Raghavamenon AC, Kumar KM, Narayanappa R, Uttarkar A, Niranjan V, Cherian T. BRCA1/TP53 tumor proteins inhibited by novel analogues of curcumin - Insight from computational modelling, dynamic simulation and experimental validation. Int J Biol Macromol 2023; 253:126989. [PMID: 37739292 DOI: 10.1016/j.ijbiomac.2023.126989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/06/2023] [Accepted: 09/16/2023] [Indexed: 09/24/2023]
Abstract
The current study aimed to design novel curcumin analogue inhibitors with antiproliferative and antitumor activity towards BRCA1 and TP53 tumor proteins and to study their therapeutic potential by computer-aided molecular designing and experimental investigations. Four curcumin analogues were computationally designed and their drug-likeness and pharmacokinetic properties were predicted. The binding of these analogues against six protein targets belonging to BRCA1 and TP53 tumor proteins were modelled by molecular docking and their binding energies were compared with that of curcumin and the standard drug cyclophosphamide and its validated target. The stabilities of selected docked complexes were confirmed by molecular dynamic simulation (MDS) and MMGBSA calculations. The best-docked analogue was chemically synthesized, characterized, and used for in vitro cytotoxic screening using DLA, EAC, and C127I cell lines. In vivo antitumor studies were carried out in Swiss Albino Mice. The study revealed that the designed analogues satisfied drug-likeness and pharmacokinetic properties and demonstrated better binding affinity to the selected targets than curcumin. Among the analogues, NLH demonstrated significant interaction with the BRCA1-BRCT-c domain (TG3; binding energy -8.3 kcal/mol) when compared to the interaction of curcumin (binding energy -6.19 kcal) and cyclophosphamide (binding energy -3.8 kcal/mol) and its usual substrate (TG7). The MDS and MM/GBSA studies revealed that the binding free energy of the NLH-TG3 complex (-61.24 kcal/mol) was better when compared to that of the cyclophosphamide-TG7 complex (-21.67 kcal/mol). In vitro, cytotoxic studies showed that NLH demonstrated significant antiproliferative activities against tumor cell lines. The in vivo study depicted NLH possesses the potential for tumor inhibition. Thus, the newly synthesized curcumin analogue is probably used to develop novel therapeutic agents against breast cancer.
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Affiliation(s)
- Lovely Jacob Aloor
- Department of Chemistry, Little Flower College, Guruvayoor, Kerala, India; Post Graduate & Research Department of Chemistry, Christ College (Autonomous), Irinjalakuda, Kerala, India
| | - Sinosh Skariyachan
- Department of Microbiology, St. Pius X College, Rajapuram, Kerala, India.
| | | | - Kalavathi Murugan Kumar
- Department of Bioinformatics, Pondicherry University, Chinna Kalapet, Kalapet, Puducherry, Tamil Nadu, India
| | - Rajeswari Narayanappa
- Department of Biotechnology, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India
| | - Akshay Uttarkar
- Department of Biotechnology, RV College of Engineering, Bengaluru, Karnataka, India
| | - Vidya Niranjan
- Department of Biotechnology, RV College of Engineering, Bengaluru, Karnataka, India
| | - Tom Cherian
- Post Graduate & Research Department of Chemistry, Christ College (Autonomous), Irinjalakuda, Kerala, India.
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Longo LHDC, Roberto GF, Tosta TAA, de Faria PR, Loyola AM, Cardoso SV, Silva AB, do Nascimento MZ, Neves LA. Classification of Multiple H&E Images via an Ensemble Computational Scheme. ENTROPY (BASEL, SWITZERLAND) 2023; 26:34. [PMID: 38248160 PMCID: PMC10814107 DOI: 10.3390/e26010034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/23/2023] [Accepted: 12/25/2023] [Indexed: 01/23/2024]
Abstract
In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of 94.83% to 100%, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images.
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Affiliation(s)
- Leonardo H. da Costa Longo
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, São José do Rio Preto 15054-000, SP, Brazil
| | - Guilherme F. Roberto
- Department of Informatics Engineering, Faculty of Engineering, University of Porto, Dr. Roberto Frias, sn, 4200-465 Porto, Portugal;
| | - Thaína A. A. Tosta
- Science and Technology Institute, Federal University of São Paulo (UNIFESP), Avenida Cesare Mansueto Giulio Lattes, 1201, São José dos Campos 12247-014, SP, Brazil;
| | - Paulo R. de Faria
- Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia (UFU), Av. Amazonas, S/N, Uberlândia 38405-320, MG, Brazil;
| | - Adriano M. Loyola
- Area of Oral Pathology, School of Dentistry, Federal University of Uberlândia (UFU), R. Ceará—Umuarama, Uberlândia 38402-018, MG, Brazil; (A.M.L.)
| | - Sérgio V. Cardoso
- Area of Oral Pathology, School of Dentistry, Federal University of Uberlândia (UFU), R. Ceará—Umuarama, Uberlândia 38402-018, MG, Brazil; (A.M.L.)
| | - Adriano B. Silva
- Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, Uberlândia 38400-902, MG, Brazil
| | - Marcelo Z. do Nascimento
- Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, Uberlândia 38400-902, MG, Brazil
| | - Leandro A. Neves
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, São José do Rio Preto 15054-000, SP, Brazil
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Sun X, Li W, Fu B, Peng Y, He J, Wang L, Yang T, Meng X, Li J, Wang J, Huang P, Wang R. TGMIL: A hybrid multi-instance learning model based on the Transformer and the Graph Attention Network for whole-slide images classification of renal cell carcinoma. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107789. [PMID: 37722310 DOI: 10.1016/j.cmpb.2023.107789] [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: 03/22/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 09/20/2023]
Abstract
BACKGROUND AND OBJECTIVES The pathological diagnosis of renal cell carcinoma is crucial for treatment. Currently, the multi-instance learning method is commonly used for whole-slide image classification of renal cell carcinoma, which is mainly based on the assumption of independent identical distribution. But this is inconsistent with the need to consider the correlation between different instances in the diagnosis process. Furthermore, the problem of high resource consumption of pathology images is still urgent to be solved. Therefore, we propose a new multi-instance learning method to solve this problem. METHODS In this study, we proposed a hybrid multi-instance learning model based on the Transformer and the Graph Attention Network, called TGMIL, to achieve whole-slide image of renal cell carcinoma classification without pixel-level annotation or region of interest extraction. Our approach is divided into three steps. First, we designed a feature pyramid with the multiple low magnifications of whole-slide image named MMFP. It makes the model incorporates richer information, and reduces memory consumption as well as training time compared to the highest magnification. Second, TGMIL amalgamates the Transformer and the Graph Attention's capabilities, adeptly addressing the loss of instance contextual and spatial. Within the Graph Attention network stream, an easy and efficient approach employing max pooling and mean pooling yields the graph adjacency matrix, devoid of extra memory consumption. Finally, the outputs of two streams of TGMIL are aggregated to achieve the classification of renal cell carcinoma. RESULTS On the TCGA-RCC validation set, a public dataset for renal cell carcinoma, the area under a receiver operating characteristic (ROC) curve (AUC) and accuracy of TGMIL were 0.98±0.0015,0.9191±0.0062, respectively. It showcased remarkable proficiency on the private validation set of renal cell carcinoma pathology images, attaining AUC of 0.9386±0.0162 and ACC of 0.9197±0.0124. Furthermore, on the public breast cancer whole-slide image test dataset, CAMELYON 16, our model showed good classification performance with an accuracy of 0.8792. CONCLUSIONS TGMIL models the diagnostic process of pathologists and shows good classification performance on multiple datasets. Concurrently, the MMFP module efficiently diminishes resource requirements, offering a novel angle for exploring computational pathology images.
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Affiliation(s)
- Xinhuan Sun
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China; Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Wuchao Li
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Bangkang Fu
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Yunsong Peng
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Junjie He
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China; Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Lihui Wang
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China
| | - Tongyin Yang
- Department of Pathology, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Xue Meng
- Department of Pathology, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, China
| | - Jin Li
- Department of Pathology, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, China
| | - Jinjing Wang
- Department of Pathology, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, China
| | - Ping Huang
- Department of Pathology, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Rongpin Wang
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang, 550002, China.
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Zhang F, Geng J, Zhang DG, Gui J, Su R. Prediction of cancer recurrence based on compact graphs of whole slide images. Comput Biol Med 2023; 167:107663. [PMID: 37931526 DOI: 10.1016/j.compbiomed.2023.107663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 10/19/2023] [Accepted: 10/31/2023] [Indexed: 11/08/2023]
Abstract
Cancer recurrence is one of the primary causes of patient mortality following treatment, indicating increased aggressiveness of cancer cells and difficulties in achieving a cure. A critical step to improve patients' survival is accurately predicting recurrence status and giving appropriate treatment. Whole Slide Images (WSIs) are a common type of image data in the field of digital pathology, containing high-resolution tissue information. Furthermore, WSIs of primary tumors contain microenvironmental information directly associated with the growth of tumor cells. To effectively utilize this microenvironmental information. Firstly, we represented microenvironmental features of histopathological images as compact graphs. Secondly, this work aims to develop an enhanced lightweight graph neural network called the Adaptive Graph Clustering Network (AGCNet) for predicting cancer recurrence. Experiments are conducted on three cancer datasets from The Cancer Genome Atlas (TCGA), and AGCNet achieved an accuracy of 81.81% in BLCA, 69.66% in PAAD, and 81.96% in STAD. These results indicated that AGCNet is an effective model for predicting cancer recurrence and is expected to be applied in clinical applications.
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Affiliation(s)
- Fengyun Zhang
- School of Computer Software, College of Intelligence and Computing, Tianjin University, China
| | - Jie Geng
- TianJin Chest Hospital, Tianjin University, TianJin, China
| | - De-Gan Zhang
- Tianjin Key Lab of Intelligent Computing and Novel Software Technology, Tianjin University of Technology, TianJin, China
| | - Jinglong Gui
- School of Computer Software, College of Intelligence and Computing, Tianjin University, China
| | - Ran Su
- School of Computer Software, College of Intelligence and Computing, Tianjin University, China.
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14
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Dai Z, Jambor I, Taimen P, Pantelic M, Elshaikh M, Dabaja A, Rogers C, Ettala O, Boström PJ, Aronen HJ, Merisaari H, Wen N. Prostate cancer detection and segmentation on MRI using non-local mask R-CNN with histopathological ground truth. Med Phys 2023; 50:7748-7763. [PMID: 37358061 DOI: 10.1002/mp.16557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 05/04/2023] [Accepted: 05/29/2023] [Indexed: 06/27/2023] Open
Abstract
BACKGROUND Automatic detection and segmentation of intraprostatic lesions (ILs) on preoperative multiparametric-magnetic resonance images (mp-MRI) can improve clinical workflow efficiency and enhance the diagnostic accuracy of prostate cancer and is an essential step in dominant intraprostatic lesion boost. PURPOSE The goal is to improve the detection and segmentation accuracy of 3D ILs in MRI by a proposed a deep learning (DL)-based algorithm with histopathological ground truth. METHODS This retrospective study included 262 patients with in vivo prostate biparametric MRI (bp-MRI) scans and were divided into three cohorts based on their data analysis and annotation. Histopathological ground truth was established by using histopathology images as delineation reference standard on cohort 1, which consisted of 64 patients and was randomly split into 20 training, 12 validation, and 32 testing patients. Cohort 2 consisted of 158 patients with bp-MRI based lesion delineation, and was randomly split into 104 training, 15 validation, and 39 testing patients. Cohort 3 consisted of 40 unannotated patients, used in semi-supervised learning. We proposed a non-local Mask R-CNN and boosted its performance by applying different training techniques. The performance of non-local Mask R-CNN was compared with baseline Mask R-CNN, 3D U-Net and an experienced radiologist's delineation and was evaluated by detection rate, dice similarity coefficient (DSC), sensitivity, and Hausdorff Distance (HD). RESULTS The independent testing set consists of 32 patients with histopathological ground truth. With the training technique maximizing detection rate, the non-local Mask R-CNN achieved 80.5% and 94.7% detection rate; 0.548 and 0.604 DSC; 5.72 and 6.36 95 HD (mm); 0.613 and 0.580 sensitivity for ILs of all Gleason Grade groups (GGGs) and clinically significant ILs (GGG > 2), which outperformed baseline Mask R-CNN and 3D U-Net. For clinically significant ILs, the model segmentation accuracy was significantly higher than that of the experienced radiologist involved in the study, who achieved 0.512 DSC (p = 0.04), 8.21 (p = 0.041) 95 HD (mm), and 0.398 (p = 0.001) sensitivity. CONCLUSION The proposed DL model achieved state-of-art performance and has the potential to help improve radiotherapy treatment planning and noninvasive prostate cancer diagnosis.
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Affiliation(s)
- Zhenzhen Dai
- Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan, USA
| | - Ivan Jambor
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
| | - Pekka Taimen
- Institute of Biomedicine and FICAN West Cancer Centre, University of Turku, Turku, Finland
- Department of Pathology, Turku University Hospital, Turku, Finland
| | - Milan Pantelic
- Department of Radiology, Henry Ford Health System, Detroit, Michigan, USA
| | - Mohamed Elshaikh
- Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan, USA
| | - Ali Dabaja
- Vattikuti Urology Institute, Henry Ford Health System, Detroit, Michigan, USA
| | - Craig Rogers
- Vattikuti Urology Institute, Henry Ford Health System, Detroit, Michigan, USA
| | - Otto Ettala
- Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Peter J Boström
- Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Hannu J Aronen
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
| | - Harri Merisaari
- Institute of Biomedicine and FICAN West Cancer Centre, University of Turku, Turku, Finland
| | - Ning Wen
- Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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15
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Voon W, Hum YC, Tee YK, Yap WS, Nisar H, Mokayed H, Gupta N, Lai KW. Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images. Sci Rep 2023; 13:20518. [PMID: 37993544 PMCID: PMC10665422 DOI: 10.1038/s41598-023-46619-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 11/02/2023] [Indexed: 11/24/2023] Open
Abstract
Debates persist regarding the impact of Stain Normalization (SN) on recent breast cancer histopathological studies. While some studies propose no influence on classification outcomes, others argue for improvement. This study aims to assess the efficacy of SN in breast cancer histopathological classification, specifically focusing on Invasive Ductal Carcinoma (IDC) grading using Convolutional Neural Networks (CNNs). The null hypothesis asserts that SN has no effect on the accuracy of CNN-based IDC grading, while the alternative hypothesis suggests the contrary. We evaluated six SN techniques, with five templates selected as target images for the conventional SN techniques. We also utilized seven ImageNet pre-trained CNNs for IDC grading. The performance of models trained with and without SN was compared to discern the influence of SN on classification outcomes. The analysis unveiled a p-value of 0.11, indicating no statistically significant difference in Balanced Accuracy Scores between models trained with StainGAN-normalized images, achieving a score of 0.9196 (the best-performing SN technique), and models trained with non-normalized images, which scored 0.9308. As a result, we did not reject the null hypothesis, indicating that we found no evidence to support a significant discrepancy in effectiveness between stain-normalized and non-normalized datasets for IDC grading tasks. This study demonstrates that SN has a limited impact on IDC grading, challenging the assumption of performance enhancement through SN.
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Affiliation(s)
- Wingates Voon
- Department of Mechatronics and Biomedical Engineering, Faculty of Engineering and Science, Lee Kong Chian, Universiti Tunku Abdul Rahman, Kampar, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Faculty of Engineering and Science, Lee Kong Chian, Universiti Tunku Abdul Rahman, Kampar, Malaysia.
| | - Yee Kai Tee
- Department of Mechatronics and Biomedical Engineering, Faculty of Engineering and Science, Lee Kong Chian, Universiti Tunku Abdul Rahman, Kampar, Malaysia
| | - Wun-She Yap
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Science, Lee Kong Chian, Universiti Tunku Abdul Rahman, Kampar, Malaysia
| | - Humaira Nisar
- Department of Electronic Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, 31900, Kampar, Malaysia
| | - Hamam Mokayed
- Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Lulea, Sweden
| | - Neha Gupta
- School of Electronics Engineering, Vellore Institute of Technology, Amaravati, AP, India
| | - Khin Wee Lai
- Department of Biomedical Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
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16
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Hanna MG, Brogi E. Future Practices of Breast Pathology Using Digital and Computational Pathology. Adv Anat Pathol 2023; 30:421-433. [PMID: 37737690 DOI: 10.1097/pap.0000000000000414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
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Aftab R, Qiang Y, Zhao J, Urrehman Z, Zhao Z. Graph Neural Network for representation learning of lung cancer. BMC Cancer 2023; 23:1037. [PMID: 37884929 PMCID: PMC10601264 DOI: 10.1186/s12885-023-11516-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023] Open
Abstract
The emergence of image-based systems to improve diagnostic pathology precision, involving the intent to label sets or bags of instances, greatly hinges on Multiple Instance Learning for Whole Slide Images(WSIs). Contemporary works have shown excellent performance for a neural network in MIL settings. Here, we examine a graph-based model to facilitate end-to-end learning and sample suitable patches using a tile-based approach. We propose MIL-GNN to employ a graph-based Variational Auto-encoder with a Gaussian mixture model to discover relations between sample patches for the purposes to aggregate patch details into an individual vector representation. Using the classical MIL dataset MUSK and distinguishing two lung cancer sub-types, lung cancer called adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we exhibit the efficacy of our technique. We achieved a 97.42% accuracy on the MUSK dataset and a 94.3% AUC on the classification of lung cancer sub-types utilizing features.
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Affiliation(s)
- Rukhma Aftab
- College of Information and Computer, Taiyuan University of Technology, No. 79 Yingze West Street, Taiyuan, 030024 China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, No. 79 Yingze West Street, Taiyuan, 030024 China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, No. 79 Yingze West Street, Taiyuan, 030024 China
| | - Zia Urrehman
- College of Information and Computer, Taiyuan University of Technology, No. 79 Yingze West Street, Taiyuan, 030024 China
| | - Zijuan Zhao
- College of Information and Computer, Taiyuan University of Technology, No. 79 Yingze West Street, Taiyuan, 030024 China
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Shanmugam K, Rajaguru H. Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images. Diagnostics (Basel) 2023; 13:3289. [PMID: 37892110 PMCID: PMC10606104 DOI: 10.3390/diagnostics13203289] [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: 10/12/2023] [Revised: 10/20/2023] [Accepted: 10/21/2023] [Indexed: 10/29/2023] Open
Abstract
Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often diagnosed late due to delayed symptoms. To catch it early, researchers are developing algorithms to study lung cancer images. The primary objective of this work is to propose a novel approach for the detection of lung cancer using histopathological images. In this work, the histopathological images underwent preprocessing, followed by segmentation using a modified approach of KFCM-based segmentation and the segmented image intensity values were dimensionally reduced using Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Algorithms such as KL Divergence and Invasive Weed Optimization (IWO) are used for feature selection. Seven different classifiers such as SVM, KNN, Random Forest, Decision Tree, Softmax Discriminant, Multilayer Perceptron, and BLDC were used to analyze and classify the images as benign or malignant. Results were compared using standard metrics, and kappa analysis assessed classifier agreement. The Decision Tree Classifier with GWO feature extraction achieved good accuracy of 85.01% without feature selection and hyperparameter tuning approaches. Furthermore, we present a methodology to enhance the accuracy of the classifiers by employing hyperparameter tuning algorithms based on Adam and RAdam. By combining features from GWO and IWO, and using the RAdam algorithm, the Decision Tree classifier achieves the commendable accuracy of 91.57%.
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Affiliation(s)
| | - Harikumar Rajaguru
- Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638401, India;
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Hussain Y, Singh J, Meena A, Sinha RA, Luqman S. Escin enhanced the efficacy of sorafenib by autophagy-mediated apoptosis in lung cancer cells. Phytother Res 2023; 37:4819-4837. [PMID: 37468281 DOI: 10.1002/ptr.7948] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 06/25/2023] [Accepted: 06/27/2023] [Indexed: 07/21/2023]
Abstract
Combining anti-cancer drugs has been exploited as promising treatment strategy to target lung cancer. Synergistic chemotherapies increase anti-cancer effect and reduce effective drug doses and side effects. In this study, therapeutic potential of escin in combination with sorafenib has been explored. 3-(4,5-Dimethylthiazol-2-yl)-2 5-diphenyltetrazolium bromide assay was used to calculate IC50 values. The synergy was evaluated using Chou-Talaly algorithm. Cellular reactive oxygen species, mitochondrial membrane potential, annexin V, and cell-cycle studies were done by flow-cytometer, and autophagy biomarkers expression were determined using western blotting. Moreover, autophagy was knocked down using ATG5 siRNA to confirm its role, diethylnitrosamine-induced lung cancer model was used to check the synergy of sorafenib/escin. Escin significantly reduced the IC50 of sorafenib in A549 and NCIH460 cells. The combination of sorafenib/escin produced a 2.95 and 5.45 dose reduction index for sorafenib in A549 and NCI-H460 cells. The combination of over-expressed p62 and LC3-II reflects autophagy block-mediated late apoptosis. This phenomenon was reconfirmed by ATG5 knockdown. This combination also selectively targeted G0/G1 phase of cancer cells. In in vivo study, the combination reduced tumour load and lower elevated serum biochemical parameters. The combination of sorafenib/escin synergistically inhibits autophagy to induce late apoptosis in lung cancer cells' G0/G1 phase.
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Affiliation(s)
- Yusuf Hussain
- Bioprospection and Product Development Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, India
| | - Jyoti Singh
- Bioprospection and Product Development Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, India
- Jawaharlal Nehru University, New Delhi, India
| | - Abha Meena
- Bioprospection and Product Development Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, India
| | - Rohit Anthony Sinha
- Department of Endocrinology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India
| | - Suaib Luqman
- Bioprospection and Product Development Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, India
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20
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Deng C, Li D, Feng M, Han D, Huang Q. The value of deep neural networks in the pathological classification of thyroid tumors. Diagn Pathol 2023; 18:95. [PMID: 37598149 PMCID: PMC10439627 DOI: 10.1186/s13000-023-01380-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/08/2023] [Indexed: 08/21/2023] Open
Abstract
BACKGROUND To explore the distinguishing diagnostic value and clinical application potential of deep neural networks (DNN) for pathological images of thyroid tumors. METHODS A total of 799 pathological thyroid images of 559 patients with thyroid tumors were retrospectively analyzed. The pathological types included papillary thyroid carcinoma (PTC), medullary thyroid carcinoma (MTC), follicular thyroid carcinoma (FTC), adenomatous goiter, adenoma, and normal thyroid gland. The dataset was divided into a training set and a test set. Resnet50, Resnext50, EfficientNet, and Densenet121 were trained using the training set data and tested with the test set data to determine the diagnostic efficiency of different pathology types and to further analyze the causes of misdiagnosis. RESULTS The recall, precision, negative predictive value (NPV), accuracy, specificity, and F1 scores of the four models ranged from 33.33% to 100.00%. The area under curve (AUC) ranged from 0.822 to 0.994, and the Kappa coefficient ranged from 0.7508 to 0.7713. However, the performance of diagnosing FTC, adenoma, and adenomatous goiter was slightly inferior to other types of pathological tissues. CONCLUSION The DNN model achieved satisfactory results in the task of classifying thyroid tumors by learning thyroid pathology images. These results indicate the potential of the DNN model for the efficient diagnosis of thyroid tumor histopathology.
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Affiliation(s)
- Chengwen Deng
- Department of Nuclear Medicine, Chaohu Hospital of Anhui Medical University, Heifei, 238000, China
| | - Dan Li
- Department of Nuclear Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510289, China
| | - Ming Feng
- Tongji University, Shanghai, 200082, China
| | - Dongyan Han
- Shanghai Tenth People's Hospital Tongji University, Shanghai, 200072, China
| | - Qingqing Huang
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China.
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21
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Predicting EGFR gene mutation status in lung adenocarcinoma based on multifeature fusion. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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22
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Peterson T, Mann S, Sun BL, Peng L, Cai H, Liang R. Motionless volumetric structured light sheet microscopy. BIOMEDICAL OPTICS EXPRESS 2023; 14:2209-2224. [PMID: 37206125 PMCID: PMC10191636 DOI: 10.1364/boe.489280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/11/2023] [Accepted: 04/11/2023] [Indexed: 05/21/2023]
Abstract
To meet the increasing need for low-cost, compact imaging technology with cellular resolution, we have developed a microLED-based structured light sheet microscope for three-dimensional ex vivo and in vivo imaging of biological tissue in multiple modalities. All the illumination structure is generated directly at the microLED panel-which serves as the source-so light sheet scanning and modulation is completely digital, yielding a system that is simpler and less prone to error than previously reported methods. Volumetric images with optical sectioning are thus achieved in an inexpensive, compact form factor without any moving parts. We demonstrate the unique properties and general applicability of our technique by ex vivo imaging of porcine and murine tissue from the gastrointestinal tract, kidney, and brain.
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Affiliation(s)
- Tyler Peterson
- Wyant College of Optical Sciences,
The University of Arizona, Tucson, Arizona 85721, USA
| | - Shivani Mann
- Department of Neuroscience, The University of Arizona, Tucson, Arizona 85721, USA
| | - Belinda L. Sun
- Department of Pathology, College of Medicine, The University of Arizona, Tucson, Arizona 85721, USA
| | - Leilei Peng
- Wyant College of Optical Sciences,
The University of Arizona, Tucson, Arizona 85721, USA
| | - Haijiang Cai
- Department of Neuroscience, The University of Arizona, Tucson, Arizona 85721, USA
| | - Rongguang Liang
- Wyant College of Optical Sciences,
The University of Arizona, Tucson, Arizona 85721, USA
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23
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Moncayo R, Martel AL, Romero E. Removing non-nuclei information from histopathological images: A preprocessing step towards improving nuclei segmentation methods. J Pathol Inform 2023; 14:100315. [PMID: 37811335 PMCID: PMC10550762 DOI: 10.1016/j.jpi.2023.100315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 04/10/2023] [Accepted: 04/12/2023] [Indexed: 10/10/2023] Open
Abstract
Disease interpretation by computer-aided diagnosis systems in digital pathology depends on reliable detection and segmentation of nuclei in hematoxylin and eosin (HE) images. These 2 tasks are challenging since appearance of both cell nuclei and background structures are very variable. This paper presents a method to improve nuclei detection and segmentation in HE images by removing tiles that only contain background information. The method divides each image into smaller patches and uses their projection to the noiselet space to capture different spatial features from non-nuclei background and nuclei structures. The noiselet features are clustered by a K-means algorithm and the resultant partition, defined by the cluster centroids, is herein named the noiselet code-book. A part of an image, a tile, is divided into patches and represented by the histogram of occurrences of the projected patches in the noiselet code-book. Finally, with these histograms, a classifier learns to differentiate between nuclei and non-nuclei tiles. By applying a conventional watershed-marked method to detect and segment nuclei, evaluation consisted in comparing pure watershed method against denoising-plus-watershed in an open database with 8 different types of tissues. The averaged F-score of nuclei detection improved from 0.830 to 0.86 and the dice score after segmentation increased from 0.701 to 0.723.
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Affiliation(s)
- Ricardo Moncayo
- Computer Imaging and Medical Applications Laboratory (CIM@LAB), Universidad Nacional de Colombia, Bogotá, Colombia
| | - Anne L. Martel
- Department of Medical Biophysics, University of Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory (CIM@LAB), Universidad Nacional de Colombia, Bogotá, Colombia
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24
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Quintana-Quintana L, Ortega S, Fabelo H, Balea-Fernández FJ, Callico GM. Blur-specific image quality assessment of microscopic hyperspectral images. OPTICS EXPRESS 2023; 31:12261-12279. [PMID: 37157389 DOI: 10.1364/oe.476949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Hyperspectral (HS) imaging (HSI) expands the number of channels captured within the electromagnetic spectrum with respect to regular imaging. Thus, microscopic HSI can improve cancer diagnosis by automatic classification of cells. However, homogeneous focus is difficult to achieve in such images, being the aim of this work to automatically quantify their focus for further image correction. A HS image database for focus assessment was captured. Subjective scores of image focus were obtained from 24 subjects and then correlated to state-of-the-art methods. Maximum Local Variation, Fast Image Sharpness block-based Method and Local Phase Coherence algorithms provided the best correlation results. With respect to execution time, LPC was the fastest.
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25
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Kang H, Yang M, Zhang F, Xu H, Ren S, Li J, Chen D, Wang F, Li D, Chen X. Identification lymph node metastasis in esophageal squamous cell carcinoma using whole slide images and a hybrid network of multiple instance and transfer learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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26
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Butte S, Wang H, Vakanski A, Xian M. ENHANCED SHARP-GAN FOR HISTOPATHOLOGY IMAGE SYNTHESIS. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2023; 2023:10.1109/isbi53787.2023.10230516. [PMID: 38572451 PMCID: PMC10989243 DOI: 10.1109/isbi53787.2023.10230516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Histopathology image synthesis aims to address the data shortage issue in training deep learning approaches for accurate cancer detection. However, existing methods struggle to produce realistic images that have accurate nuclei boundaries and less artifacts, which limits the application in downstream tasks. To address the challenges, we propose a novel approach that enhances the quality of synthetic images by using nuclei topology and contour regularization. The proposed approach uses the skeleton map of nuclei to integrate nuclei topology and separate touching nuclei. In the loss function, we propose two new contour regularization terms that enhance the contrast between contour and non-contour pixels and increase the similarity between contour pixels. We evaluate the proposed approach on the two datasets using image quality metrics and a downstream task (nuclei segmentation). The proposed approach outperforms Sharp-GAN in all four image quality metrics on two datasets. By integrating 6k synthetic images from the proposed approach into training, a nuclei segmentation model achieves the state-of-the-art segmentation performance on TNBC dataset and its detection quality (DQ), segmentation quality (SQ), panoptic quality (PQ), and aggregated Jaccard index (AJI) is 0.855, 0.863, 0.691, and 0.683, respectively.
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27
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Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis. J Pers Med 2023; 13:jpm13030388. [PMID: 36983570 PMCID: PMC10051974 DOI: 10.3390/jpm13030388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/16/2023] [Accepted: 02/16/2023] [Indexed: 02/25/2023] Open
Abstract
Background: Lung cancer is a fatal disease that kills approximately 85% of those diagnosed with it. In recent years, advances in medical imaging have greatly improved the acquisition, storage, and visualization of various pathologies, making it a necessary component in medicine today. Objective: Develop a computer-aided diagnostic system to detect lung cancer early by segmenting tumor and non-tumor tissue on Tissue Micro Array Analysis (TMA) histopathological images. Method: The prototype computer-aided diagnostic system was developed to segment tumor areas, non-tumor areas, and fundus on TMA histopathological images. Results: The system achieved an average accuracy of 83.4% and an F-measurement of 84.4% in segmenting tumor and non-tumor tissue. Conclusion: The computer-aided diagnostic system provides a second diagnostic opinion to specialists, allowing for more precise diagnoses and more appropriate treatments for lung cancer.
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28
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Nanoscale Prognosis of Colorectal Cancer Metastasis from AFM Image Processing of Histological Sections. Cancers (Basel) 2023; 15:cancers15041220. [PMID: 36831563 PMCID: PMC9953928 DOI: 10.3390/cancers15041220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 02/05/2023] [Accepted: 02/08/2023] [Indexed: 02/17/2023] Open
Abstract
Early ascertainment of metastatic tumour phases is crucial to improve cancer survival, formulate an accurate prognostic report of disease advancement, and, most importantly, quantify the metastatic progression and malignancy state of primary cancer cells with a universal numerical indexing system. This work proposes an early improvement to metastatic cancer detection with 97.7 nm spatial resolution by indexing the metastatic cancer phases from the analysis of atomic force microscopy images of human colorectal cancer histological sections. The procedure applies variograms of residuals of Gaussian filtering and theta statistics of colorectal cancer tissue image settings. This methodology elucidates the early metastatic progression at the nanoscale level by setting metastatic indexes and critical thresholds based on relatively large histological sections and categorising the malignancy state of a few suspicious cells not identified with optical image analysis. In addition, we sought to detect early tiny morphological differentiations indicating potential cell transition from epithelial cell phenotypes of low metastatic potential to those of high metastatic potential. This metastatic differentiation, which is also identified in higher moments of variograms, sets different hierarchical levels for metastatic progression dynamics.
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29
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Chan RC, To CKC, Cheng KCT, Yoshikazu T, Yan LLA, Tse GM. Artificial intelligence in breast cancer histopathology. Histopathology 2023; 82:198-210. [PMID: 36482271 DOI: 10.1111/his.14820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/22/2022] [Accepted: 09/28/2022] [Indexed: 12/13/2022]
Abstract
This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists-namely, molecular and hormonal analysis, grading, mitotic figure counting, ki-67 indexing, tumour-infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open-source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.
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Affiliation(s)
- Ronald Ck Chan
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Chun Kit Curtis To
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Ka Chuen Tom Cheng
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Tada Yoshikazu
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Lai Ling Amy Yan
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Gary M Tse
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
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30
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Wang X, Yu G, Yan Z, Wan L, Wang W, Cui L. Lung Cancer Subtype Diagnosis by Fusing Image-Genomics Data and Hybrid Deep Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:512-523. [PMID: 34855599 DOI: 10.1109/tcbb.2021.3132292] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Accurate diagnosis of cancer subtypes is crucial for precise treatment, because different cancer subtypes are involved with different pathology and require different therapies. Although deep learning techniques have made great success in computer vision and other fields, they do not work well on Lung cancer subtype diagnosis, due to the distinction of slide images between different cancer subtypes is ambiguous. Furthermore, they often over-fit to high-dimensional genomics data with limited samples, and do not fuse the image and genomics data in a sensible way. In this paper, we propose a hybrid deep network based approach LungDIG for Lung cancer subtype Diagnosis by fusing Image-Genomics data. LungDIG first tiles the tissue slide image into small patches and extracts the patch-level features by fine-tuning an Inception-V3 model. Since the patches may contain some false positives in non-diagnostic regions, it further designs a patch-level feature combination strategy to integrate the extracted patch features and maintain the diversity between different cancer subtypes. At the same time, it extracts the genomics features from Copy Number Variation data by an attention based nonlinear extractor. Next, it fuses the image and genomics features by an attention based multilayer perceptron (MLP) to diagnose cancer subtype. Experiments on TCGA lung cancer data show that LungDIG can not only achieve higher accuracy for cancer subtype diagnosis than state-of-the-art methods, but also have a high authenticity and good interpretability.
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31
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Lee JS, Wu WK. Breast Tumor Tissue Image Classification Using DIU-Net. SENSORS (BASEL, SWITZERLAND) 2022; 22:9838. [PMID: 36560207 PMCID: PMC9786106 DOI: 10.3390/s22249838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/10/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Inspired by the observation that pathologists pay more attention to the nuclei regions when analyzing pathological images, this study utilized soft segmentation to imitate the visual focus mechanism and proposed a new segmentation-classification joint model to achieve superior classification performance for breast cancer pathology images. Aiming at the characteristics of different sizes of nuclei in pathological images, this study developed a new segmentation network with excellent cross-scale description ability called DIU-Net. To enhance the generalization ability of the segmentation network, that is, to avoid the segmentation network from learning low-level features, we proposed the Complementary Color Conversion Scheme in the training phase. In addition, due to the disparity between the area of the nucleus and the background in the pathology image, there is an inherent data imbalance phenomenon, dice loss and focal loss were used to overcome this problem. In order to further strengthen the classification performance of the model, this study adopted a joint training scheme, so that the output of the classification network can not only be used to optimize the classification network itself, but also optimize the segmentation network. In addition, this model can also provide the pathologist model's attention area, increasing the model's interpretability. The classification performance verification of the proposed method was carried out with the BreaKHis dataset. Our method obtains binary/multi-class classification accuracy 97.24/93.75 and 98.19/94.43 for 200× and 400× images, outperforming existing methods.
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32
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Mostafa FA, Elrefaei LA, Fouda MM, Hossam A. A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images. Diagnostics (Basel) 2022; 12:3034. [PMID: 36553041 PMCID: PMC9777249 DOI: 10.3390/diagnostics12123034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
Thoracic diseases refer to disorders that affect the lungs, heart, and other parts of the rib cage, such as pneumonia, novel coronavirus disease (COVID-19), tuberculosis, cardiomegaly, and fracture. Millions of people die every year from thoracic diseases. Therefore, early detection of these diseases is essential and can save many lives. Earlier, only highly experienced radiologists examined thoracic diseases, but recent developments in image processing and deep learning techniques are opening the door for the automated detection of these diseases. In this paper, we present a comprehensive review including: types of thoracic diseases; examination types of thoracic images; image pre-processing; models of deep learning applied to the detection of thoracic diseases (e.g., pneumonia, COVID-19, edema, fibrosis, tuberculosis, chronic obstructive pulmonary disease (COPD), and lung cancer); transfer learning background knowledge; ensemble learning; and future initiatives for improving the efficacy of deep learning models in applications that detect thoracic diseases. Through this survey paper, researchers may be able to gain an overall and systematic knowledge of deep learning applications in medical thoracic images. The review investigates a performance comparison of various models and a comparison of various datasets.
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Affiliation(s)
- Fatma A. Mostafa
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
| | - Lamiaa A. Elrefaei
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Aya Hossam
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
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33
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Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images. Sci Rep 2022; 12:19200. [PMID: 36357456 PMCID: PMC9649772 DOI: 10.1038/s41598-022-21848-3] [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: 02/24/2022] [Accepted: 10/04/2022] [Indexed: 11/11/2022] Open
Abstract
Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set and 0.9308 ± 0.0211on the test set.
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34
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Breast cancer image analysis using deep learning techniques – a survey. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00703-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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35
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Bacteriocins as Potential Therapeutic Approaches in the Treatment of Various Cancers: A Review of In Vitro Studies. Cancers (Basel) 2022; 14:cancers14194758. [PMID: 36230679 PMCID: PMC9563265 DOI: 10.3390/cancers14194758] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/23/2022] [Accepted: 09/25/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Current cancer treatment strategies such as surgery, chemotherapy, and radiotherapy, have significant drawbacks. There is a need for a breakthrough approach to cancer treatment. Bacteriocin, an antimicrobial peptide, has shown several anticancer properties in vitro. Therefore, this article reviews the effect of bacteriocin on cancer cells and how bacteriocins affect cancer cells in vitro. This article aims to promote additional bacteriocin research, particularly in vivo studies, to fully understand the potential of bacteriocin as a cancer treatment agent. Abstract Cancer is regarded as one of the most common and leading causes of death. Despite the availability of conventional treatments against cancer cells, current treatments are not the optimal treatment for cancer as they possess the possibility of causing various unwanted side effects to the body. As a result, this prompts a search for an alternative treatment without exhibiting any additional side effects. One of the promising novel therapeutic candidates against cancer is an antimicrobial peptide produced by bacteria called bacteriocin. It is a non-toxic peptide that is reported to exhibit potency against cancer cell lines. Experimental studies have outlined the therapeutic potential of bacteriocin against various cancer cell lines. In this review article, the paper focuses on the various bacteriocins and their cytotoxic effects, mode of action and efficacies as therapeutic agents against various cancer cell lines.
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36
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Zaalouk AM, Ebrahim GA, Mohamed HK, Hassan HM, Zaalouk MMA. A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer. Bioengineering (Basel) 2022; 9:bioengineering9080391. [PMID: 36004916 PMCID: PMC9405040 DOI: 10.3390/bioengineering9080391] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 07/19/2022] [Accepted: 08/12/2022] [Indexed: 11/16/2022] Open
Abstract
Breast cancer is a gigantic burden on humanity, causing the loss of enormous numbers of lives and amounts of money. It is the world’s leading type of cancer among women and a leading cause of mortality and morbidity. The histopathological examination of breast tissue biopsies is the gold standard for diagnosis. In this paper, a computer-aided diagnosis (CAD) system based on deep learning is developed to ease the pathologist’s mission. For this target, five pre-trained convolutional neural network (CNN) models are analyzed and tested—Xception, DenseNet201, InceptionResNetV2, VGG19, and ResNet152—with the help of data augmentation techniques, and a new approach is introduced for transfer learning. These models are trained and tested with histopathological images obtained from the BreakHis dataset. Multiple experiments are performed to analyze the performance of these models through carrying out magnification-dependent and magnification-independent binary and eight-class classifications. The Xception model has shown promising performance through achieving the highest classification accuracies for all the experiments. It has achieved a range of classification accuracies from 93.32% to 98.99% for magnification-independent experiments and from 90.22% to 100% for magnification-dependent experiments.
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Affiliation(s)
- Ahmed M. Zaalouk
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
- School of Computing, Coventry University—Egypt Branch, Hosted at the Knowledge Hub Universities, Cairo, Egypt
- Correspondence: (A.M.Z.); (G.A.E.)
| | - Gamal A. Ebrahim
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
- Correspondence: (A.M.Z.); (G.A.E.)
| | - Hoda K. Mohamed
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
| | - Hoda Mamdouh Hassan
- Department of Information Sciences and Technology, College of Engineering and Computing, George Mason University, Fairfax, VA 22030, USA
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37
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Lu X, Zhu X. Automatic segmentation of breast cancer histological images based on dual-path feature extraction network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:11137-11153. [PMID: 36124584 DOI: 10.3934/mbe.2022519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The traditional manual breast cancer diagnosis method of pathological images is time-consuming and labor-intensive, and it is easy to be misdiagnosed. Computer-aided diagnosis of WSIs gradually comes into people*s sight. However, the complexity of high-resolution breast cancer pathological images poses a great challenge to automatic diagnosis, and the existing algorithms are often difficult to balance the accuracy and efficiency. In order to solve these problems, this paper proposes an automatic image segmentation method based on dual-path feature extraction network for breast pathological WSIs, which has a good segmentation accuracy. Specifically, inspired by the concept of receptive fields in the human visual system, dilated convolutional networks are introduced to encode rich contextual information. Based on the channel attention mechanism, a feature attention module and a feature fusion module are proposed to effectively filter and combine the features. In addition, this method uses a light-weight backbone network and performs pre-processing on the data, which greatly reduces the computational complexity of the algorithm. Compared with the classic models, it has improved accuracy and efficiency and is highly competitive.
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Affiliation(s)
- Xi Lu
- School of Mechanical Engineering, Southeast University, Nanjing 211189, China
| | - Xuedong Zhu
- School of Mechanical Engineering, Southeast University, Nanjing 211189, China
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38
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Mahadevan S, Kwong K, Lu M, Kelly E, Chami B, Romin Y, Fujisawa S, Manova K, Moore MAS, Zoellner H. A Novel Cartesian Plot Analysis for Fixed Monolayers That Relates Cell Phenotype to Transfer of Contents between Fibroblasts and Cancer Cells by Cell-Projection Pumping. Int J Mol Sci 2022; 23:ijms23147949. [PMID: 35887295 PMCID: PMC9316567 DOI: 10.3390/ijms23147949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/15/2022] [Accepted: 07/16/2022] [Indexed: 01/27/2023] Open
Abstract
We recently described cell-projection pumping as a mechanism transferring cytoplasm between cells. The uptake of fibroblast cytoplasm by co-cultured SAOS-2 osteosarcoma cells changes SAOS-2 morphology and increases cell migration and proliferation, as seen by single-cell tracking and in FACS separated SAOS-2 from co-cultures. Morphological changes in SAOS-2 seen by single cell tracking are consistent with previous observations in fixed monolayers of SAOS-2 co-cultures. Notably, earlier studies with fixed co-cultures were limited by the absence of a quantitative method for identifying sub-populations of co-cultured cells, or for quantitating transfer relative to control populations of SAOS-2 or fibroblasts cultured alone. We now overcome that limitation by a novel Cartesian plot analysis that identifies individual co-cultured cells as belonging to one of five distinct cell populations, and also gives numerical measure of similarity to control cell populations. We verified the utility of the method by first confirming the previously established relationship between SAOS-2 morphology and uptake of fibroblast contents, and also demonstrated similar effects in other cancer cell lines including from melanomas, and cancers of the ovary and colon. The method was extended to examine global DNA methylation, and while there was no clear effect on SAOS-2 DNA methylation, co-cultured fibroblasts had greatly reduced DNA methylation, similar to cancer associated fibroblasts.
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Affiliation(s)
- Swarna Mahadevan
- The Cellular and Molecular Pathology Research Unit, Oral Pathology and Oral Medicine, School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Westmead Hospital, Westmead, NSW 2145, Australia; (S.M.); (K.K.); (M.L.); (E.K.); (B.C.)
| | - Kenelm Kwong
- The Cellular and Molecular Pathology Research Unit, Oral Pathology and Oral Medicine, School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Westmead Hospital, Westmead, NSW 2145, Australia; (S.M.); (K.K.); (M.L.); (E.K.); (B.C.)
| | - Mingjie Lu
- The Cellular and Molecular Pathology Research Unit, Oral Pathology and Oral Medicine, School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Westmead Hospital, Westmead, NSW 2145, Australia; (S.M.); (K.K.); (M.L.); (E.K.); (B.C.)
| | - Elizabeth Kelly
- The Cellular and Molecular Pathology Research Unit, Oral Pathology and Oral Medicine, School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Westmead Hospital, Westmead, NSW 2145, Australia; (S.M.); (K.K.); (M.L.); (E.K.); (B.C.)
| | - Belal Chami
- The Cellular and Molecular Pathology Research Unit, Oral Pathology and Oral Medicine, School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Westmead Hospital, Westmead, NSW 2145, Australia; (S.M.); (K.K.); (M.L.); (E.K.); (B.C.)
- The School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Yevgeniy Romin
- Molecular Cytology, The Memorial Sloan Kettering Cancer Center, 415-417 E 68 Street, ZRC 1962, New York, NY 10065, USA; (Y.R.); (S.F.); (K.M.)
| | - Sho Fujisawa
- Molecular Cytology, The Memorial Sloan Kettering Cancer Center, 415-417 E 68 Street, ZRC 1962, New York, NY 10065, USA; (Y.R.); (S.F.); (K.M.)
| | - Katia Manova
- Molecular Cytology, The Memorial Sloan Kettering Cancer Center, 415-417 E 68 Street, ZRC 1962, New York, NY 10065, USA; (Y.R.); (S.F.); (K.M.)
| | - Malcolm A. S. Moore
- Cell Biology, The Memorial Sloan Kettering Cancer Center, 430 E 67th St, RRL 717, New York, NY 10065, USA;
| | - Hans Zoellner
- The Cellular and Molecular Pathology Research Unit, Oral Pathology and Oral Medicine, School of Dentistry, Faculty of Medicine and Health, The University of Sydney, Westmead Hospital, Westmead, NSW 2145, Australia; (S.M.); (K.K.); (M.L.); (E.K.); (B.C.)
- Cell Biology, The Memorial Sloan Kettering Cancer Center, 430 E 67th St, RRL 717, New York, NY 10065, USA;
- Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia
- Graduate School of Biomedical Engineering, University of NSW, Kensington, NSW 2052, Australia
- Strongarch Pty Ltd., Pennant Hills, NSW 2120, Australia
- Correspondence: ; Tel.: +61-466400028
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Classification of breast cancer from histopathology images using an ensemble of deep multiscale networks. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.006] [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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40
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He W, Liu T, Han Y, Ming W, Du J, Liu Y, Yang Y, Wang L, Jiang Z, Wang Y, Yuan J, Cao C. A review: The detection of cancer cells in histopathology based on machine vision. Comput Biol Med 2022; 146:105636. [PMID: 35751182 DOI: 10.1016/j.compbiomed.2022.105636] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/04/2022] [Accepted: 04/28/2022] [Indexed: 12/24/2022]
Abstract
Machine vision is being employed in defect detection, size measurement, pattern recognition, image fusion, target tracking and 3D reconstruction. Traditional cancer detection methods are dominated by manual detection, which wastes time and manpower, and heavily relies on the pathologists' skill and work experience. Therefore, these manual detection approaches are not convenient for the inheritance of domain knowledge, and are not suitable for the rapid development of medical care in the future. The emergence of machine vision can iteratively update and learn the domain knowledge of cancer cell pathology detection to achieve automated, high-precision, and consistent detection. Consequently, this paper reviews the use of machine vision to detect cancer cells in histopathology images, as well as the benefits and drawbacks of various detection approaches. First, we review the application of image preprocessing and image segmentation in histopathology for the detection of cancer cells, and compare the benefits and drawbacks of different algorithms. Secondly, for the characteristics of histopathological cancer cell images, the research progress of shape, color and texture features and other methods is mainly reviewed. Furthermore, for the classification methods of histopathological cancer cell images, the benefits and drawbacks of traditional machine vision approaches and deep learning methods are compared and analyzed. Finally, the above research is discussed and forecasted, with the expected future development tendency serving as a guide for future research.
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Affiliation(s)
- Wenbin He
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Ting Liu
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yongjie Han
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Wuyi Ming
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China; Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Dongguan, 523808, China.
| | - Jinguang Du
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yinxia Liu
- Laboratory Medicine of Dongguan Kanghua Hospital, Dongguan, 523808, China
| | - Yuan Yang
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China.
| | - Leijie Wang
- School of Mechanical Engineering, Dongguan University of Technology Dongguan, 523808, China
| | - Zhiwen Jiang
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yongqiang Wang
- Zhengzhou Coal Mining Machinery Group Co., Ltd, Zhengzhou, 450016, China
| | - Jie Yuan
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Chen Cao
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China; Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Dongguan, 523808, China
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41
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Isberg OG, Giunchiglia V, McKenzie JS, Takats Z, Jonasson JG, Bodvarsdottir SK, Thorsteinsdottir M, Xiang Y. Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning. Metabolites 2022; 12:455. [PMID: 35629959 PMCID: PMC9143055 DOI: 10.3390/metabo12050455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/10/2022] [Accepted: 05/13/2022] [Indexed: 02/04/2023] Open
Abstract
Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that both deep learning algorithms based on conventional optical images and MSI-based shallow learning can provide automated diagnostics with F1-scores higher than 90%, with the latter intrinsically built on biochemical information that can be used for further analysis.
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Affiliation(s)
- Olof Gerdur Isberg
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
- Faculty of Pharmaceutical Sciences, University of Iceland, Hofsvallagata 53, 107 Reykjavik, Iceland
- Biomedical Center, School of Health Sciences, University of Iceland, 101 Reykjavik, Iceland;
| | - Valentina Giunchiglia
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
| | - James S. McKenzie
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
| | - Jon Gunnlaugur Jonasson
- Department of Pathology, Landspitali the National University Hospital, Hringbraut, 101 Reykjavik, Iceland;
- Faculty of Medicine, University of Iceland, Vatnsmyrarvegur 16, 101 Reykjavik, Iceland
| | | | - Margret Thorsteinsdottir
- Faculty of Pharmaceutical Sciences, University of Iceland, Hofsvallagata 53, 107 Reykjavik, Iceland
- Biomedical Center, School of Health Sciences, University of Iceland, 101 Reykjavik, Iceland;
| | - Yuchen Xiang
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
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42
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Deng C, Han D, Feng M, Lv Z, Li D. Differential diagnostic value of the ResNet50, random forest, and DS ensemble models for papillary thyroid carcinoma and other thyroid nodules. J Int Med Res 2022; 50:3000605221094276. [PMID: 35469474 PMCID: PMC9087260 DOI: 10.1177/03000605221094276] [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] [Indexed: 11/16/2022] Open
Abstract
Objective To explore the differential diagnostic efficiency of the
residual network (ResNet)50, random forest (RF), and DS ensemble models for
papillary thyroid carcinoma (PTC) and other pathological types of thyroid
nodules. Methods This study retrospectively analyzed 559 patients with
thyroid nodules and collected thyroid pathological images and auxiliary
examination results (laboratory and ultrasound results) to construct datasets.
The pathological image dataset was used to train a ResNet50 model, the text
dataset was used to train a random forest (RF) model, and a DS ensemble model
was constructed from the results of the two models. The differential diagnostic
values of the three models for PTC and other types of thyroid nodules were then
compared. Results The DS ensemble model had the highest sensitivity,
specificity, accuracy, and area under the receiver operating characteristic
curve (85.87%, 97.18%, 93.77%, and 0.982, respectively). Conclusions Compared with Resnet50 and the RF models trained only on
imaging data or text information, respectively, the DS ensemble model showed
better diagnostic value for PTC.
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Affiliation(s)
- Chengwen Deng
- Shanghai Tenth People's Hospital Tongji University, Shanghai, China
| | - Dongyan Han
- Shanghai Tenth People's Hospital Tongji University, Shanghai, China
| | | | - Zhongwei Lv
- Shanghai Tenth People's Hospital Tongji University, Shanghai, China
| | - Dan Li
- Shanghai Tenth People's Hospital Tongji University, Shanghai, China
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43
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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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44
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Belharbi S, Rony J, Dolz J, Ayed IB, Mccaffrey L, Granger E. Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:702-714. [PMID: 34705638 DOI: 10.1109/tmi.2021.3123461] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Weakly-supervised learning (WSL) has recently triggered substantial interest as it mitigates the lack of pixel-wise annotations. Given global image labels, WSL methods yield pixel-level predictions (segmentations), which enable to interpret class predictions. Despite their recent success, mostly with natural images, such methods can face important challenges when the foreground and background regions have similar visual cues, yielding high false-positive rates in segmentations, as is the case in challenging histology images. WSL training is commonly driven by standard classification losses, which implicitly maximize model confidence, and locate the discriminative regions linked to classification decisions. Therefore, they lack mechanisms for modeling explicitly non-discriminative regions and reducing false-positive rates. We propose novel regularization terms, which enable the model to seek both non-discriminative and discriminative regions, while discouraging unbalanced segmentations. We introduce high uncertainty as a criterion to localize non-discriminative regions that do not affect classifier decision, and describe it with original Kullback-Leibler (KL) divergence losses evaluating the deviation of posterior predictions from the uniform distribution. Our KL terms encourage high uncertainty of the model when the latter inputs the latent non-discriminative regions. Our loss integrates: (i) a cross-entropy seeking a foreground, where model confidence about class prediction is high; (ii) a KL regularizer seeking a background, where model uncertainty is high; and (iii) log-barrier terms discouraging unbalanced segmentations. Comprehensive experiments and ablation studies over the public GlaS colon cancer data and a Camelyon16 patch-based benchmark for breast cancer show substantial improvements over state-of-the-art WSL methods, and confirm the effect of our new regularizers (our code is publicly available at https://github.com/sbelharbi/deep-wsl-histo-min-max-uncertainty).
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45
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Sharma AK, Nandal A, Dhaka A, Koundal D, Bogatinoska DC, Alyami H. Enhanced Watershed Segmentation Algorithm-Based Modified ResNet50 Model for Brain Tumor Detection. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7348344. [PMID: 35252454 PMCID: PMC8894002 DOI: 10.1155/2022/7348344] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/03/2021] [Accepted: 02/05/2022] [Indexed: 01/04/2023]
Abstract
This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture. It also involves stochastic approaches to help in developing enhanced watershed modeling. Cancer diseases, primarily the brain tumor, have been exponentially raised which has alarmed researchers from academia and industry. Nowadays, researchers need to attain a more effective, accurate, and trustworthy brain tumor tissue detection and classification approach. Different from traditional machine learning methods that are just targeting to enhance classification efficiency, this work highlights the process to extract several deep features to diagnose brain tumor effectively. This paper explains the modeling of a novel technique by integrating the modified ResNet50 with the Enhanced Watershed Segmentation (EWS) algorithm for brain tumor classification and deep feature extraction. The proposed model uses the ResNet50 model with a modified layer architecture including five convolutional layers and three fully connected layers. The proposed method can retain the optimal computational efficiency with high-dimensional deep features. This work obtains a comprised feature set by retrieving the diverse deep features from the ResNet50 deep learning model and feeds them as input to the classifier. The good performing capability of the proposed model is achieved by using hybrid features of ResNet50. The brain tumor tissue images were extracted by the suggested hybrid deep feature-based modified ResNet50 model and the EWS-based modified ResNet50 model with a high classification accuracy of 92% and 90%, respectively.
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Affiliation(s)
- Arpit Kumar Sharma
- Department of Computer and Communication Engineering, Manipal University Jaipur, India
| | - Amita Nandal
- Department of Computer and Communication Engineering, Manipal University Jaipur, India
| | - Arvind Dhaka
- Department of Computer and Communication Engineering, Manipal University Jaipur, India
| | - Deepika Koundal
- School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India
| | | | - Hashem Alyami
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia
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46
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Ruusuvuori P, Valkonen M, Kartasalo K, Valkonen M, Visakorpi T, Nykter M, Latonen L. Spatial analysis of histology in 3D: quantification and visualization of organ and tumor level tissue environment. Heliyon 2022; 8:e08762. [PMID: 35128089 PMCID: PMC8800033 DOI: 10.1016/j.heliyon.2022.e08762] [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: 10/27/2021] [Revised: 11/24/2021] [Accepted: 01/11/2022] [Indexed: 10/25/2022] Open
Abstract
Histological changes in tissue are of primary importance in pathological research and diagnosis. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue. Conventional histopathological assessments are performed from individual tissue sections, leading to the loss of three-dimensional context of the tissue. Yet, the tissue context and spatial determinants are critical in several pathologies, such as in understanding growth patterns of cancer in its local environment. Here, we develop computational methods for visualization and quantitative assessment of histopathological alterations in three dimensions. First, we reconstruct the 3D representation of the whole organ from serial sectioned tissue. Then, we proceed to analyze the histological characteristics and regions of interest in 3D. As our example cases, we use whole slide images representing hematoxylin-eosin stained whole mouse prostates in a Pten+/- mouse prostate tumor model. We show that quantitative assessment of tumor sizes, shapes, and separation between spatial locations within the organ enable characterizing and grouping tumors. Further, we show that 3D visualization of tissue with computationally quantified features provides an intuitive way to observe tissue pathology. Our results underline the heterogeneity in composition and cellular organization within individual tumors. As an example, we show how prostate tumors have nuclear density gradients indicating areas of tumor growth directions and reflecting varying pressure from the surrounding tissue. The methods presented here are applicable to any tissue and different types of pathologies. This work provides a proof-of-principle for gaining a comprehensive view from histology by studying it quantitatively in 3D.
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Affiliation(s)
- Pekka Ruusuvuori
- Institute of Biomedicine, University of Turku, Turku, Finland
- Faculty of Medicine and Health Technology, Tampere University, Finland
| | - Masi Valkonen
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Kimmo Kartasalo
- Faculty of Medicine and Health Technology, Tampere University, Finland
| | - Mira Valkonen
- Faculty of Medicine and Health Technology, Tampere University, Finland
| | - Tapio Visakorpi
- Faculty of Medicine and Health Technology, Tampere University, Finland
- Tays Cancer Center, Tampere University Hospital, Tampere, Finland
- Fimlab Laboratories Ltd, Tampere University Hospital, Tampere, Finland
| | - Matti Nykter
- Faculty of Medicine and Health Technology, Tampere University, Finland
- Tays Cancer Center, Tampere University Hospital, Tampere, Finland
| | - Leena Latonen
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
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47
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Rashmi R, Prasad K, Udupa CBK. Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review. J Med Syst 2021; 46:7. [PMID: 34860316 PMCID: PMC8642363 DOI: 10.1007/s10916-021-01786-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022]
Abstract
Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.
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Affiliation(s)
- R Rashmi
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
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Arunachalam P, Venkatakrishnan P, Janakiraman N, Sangeetha S. Detection of Structure Characteristics and Its Discontinuity Based Field Programmable Gate Array Processor in Cancer Cell by Wavelet Transform. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Digital clinical histopathology is one of the crucial techniques for precise cancer cell diagnosing in modern medicine. The Synovial Sarcoma (SS) cancer cell patterns seem to be a spindle shaped cell (SSC) structure and it is very difficult to identify the exact oval shaped cell
structure through pathologist’s eye perception. Meanwhile, there is necessitating for monitoring and securing the successful and effective image data processing in the the huge network data which is also a complex one. A field programmable Gate Array (FPGA) was regarded as a necessary
one for this. In this work, based on FPGA a Cancer Cell classification is made for the regulation and execution. Hence, mathematically the SSC regularity structures and its discontinuities are measured by the holder exponent (HE) function. In this research work, HE values have been
determined by Wavelet Transform Modulus Maxima (WTMM) and Wavelet Leader (WL) methods with basis function of Haar wavelet based on FPGA Processor. The quantitative parameters such as Mean of Asymptotic Discontinuity (MAD), Mean of Removable Discontinuity (MRD) and Number of Discontinuity Points
(NDPs) have been considered to determine the prediction of discontinuity detection between WTMM and WL methods. With the help of receiver operating characteristics (ROC) curve, the significant difference of discontinuity detection performance between both the methods has been analyzed. From
the experimental results, it is clear that the WL method is more practically feasible and it gives satisfactory performance, in terms of sensitivity and specificity percentage values, which are 80.56% and 59.46%, respectively in the blue color components of the SNR 20 dB noise image.
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Affiliation(s)
- P. Arunachalam
- Electronics and Communication Engineering Department, KLN College of Engineering-Madurai, Affiliated to Centre for Research Anna University-Chennai 630612, Tamilnadu, India
| | - P. Venkatakrishnan
- Electronics and Communication Engineering Department, CMR Technical Campus, Telangana 501401, India
| | - N. Janakiraman
- Electronics and Communication Engineering Department, KLN College of Engineering-Madurai, Affiliated to Centre for Research Anna University-Chennai 630612, Tamilnadu, India
| | - S. Sangeetha
- Electrical and Electronics Engineering Department, CMR College of Engineering & Technology, Telangana 501401, India
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49
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Diagnostic Benefit of High b-Value Computed Diffusion-Weighted Imaging in Patients with Hepatic Metastasis. J Clin Med 2021; 10:jcm10225289. [PMID: 34830572 PMCID: PMC8622173 DOI: 10.3390/jcm10225289] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/10/2021] [Accepted: 11/12/2021] [Indexed: 11/17/2022] Open
Abstract
Diffusion-weighted imaging (DWI) has rapidly become an essential tool for the detection of malignant liver lesions. The aim of this study was to investigate the usefulness of high b-value computed DWI (c-DWI) in comparison to standard DWI in patients with hepatic metastases. In total, 92 patients with histopathologic confirmed primary tumors with hepatic metastasis were retrospectively analyzed by two readers. DWI was obtained with b-values of 50, 400 and 800 or 1000 s/mm2 on a 1.5 T magnetic resonance imaging (MRI) scanner. C-DWI was calculated with a monoexponential model with high b-values of 1000, 2000, 3000, 4000 and 5000 s/mm2. All c-DWI images with high b-values were compared to the acquired DWI sequence at a b-value of 800 or 1000 s/mm2 in terms of volume, lesion detectability and image quality. In the group of a b-value of 800 from a b-value of 2000 s/mm2, hepatic lesion sizes were significantly smaller than on acquired DWI (metastases lesion sizes b = 800 vs. b 2000 s/mm2: mean 25 cm3 (range 10-60 cm3) vs. mean 17.5 cm3 (range 5-35 cm3), p < 0.01). In the second group at a high b-value of 1500 s/mm2, liver metastases were larger than on c-DWI at higher b-values (b = 1500 vs. b 2000 s/mm2, mean 10 cm3 (range 4-24 cm3) vs. mean 9 cm3 (range 5-19 cm3), p < 0.01). In both groups, there was a clear reduction in lesion detectability at b = 2000 s/mm2, with hepatic metastases being less visible compared to c-DWI images at b = 1500 s/mm2 in at least 80% of all patients. Image quality dropped significantly starting from c-DWI at b = 3000 s/mm2. In both groups, almost all high b-values images at b = 4000 s/mm2 and 5000 s/mm2 were not diagnostic due to poor image quality. High c-DWI b-values up to b = 1500 s/mm2 offer comparable detectability for hepatic metastases compared to standard DWI. Higher b-value images over 2000 s/mm2 lead to a noticeable reduction in imaging quality, which could hamper diagnosis.
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50
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Lu C, Shiradkar R, Liu Z. Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review. Chin J Cancer Res 2021; 33:563-573. [PMID: 34815630 PMCID: PMC8580801 DOI: 10.21147/j.issn.1000-9604.2021.05.03] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 10/22/2021] [Indexed: 11/18/2022] Open
Abstract
In the last decade, the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering "sub-visual" prognostic image cues from the histopathological image. While we are getting more knowledge and experience in digital pathology, the emerging goal is to integrate other-omics or modalities that will contribute for building a better prognostic assay. In this paper, we provide a brief review of representative works that focus on integrating pathomics with radiomics and genomics for cancer prognosis. It includes: correlation of pathomics and genomics; fusion of pathomics and genomics; fusion of pathomics and radiomics. We also present challenges, potential opportunities, and avenues for future work.
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Affiliation(s)
- Cheng Lu
- Biomedical Engineering Department, Case Western Reserve University, Cleveland 44106, OH, USA
| | - Rakesh Shiradkar
- Biomedical Engineering Department, Case Western Reserve University, Cleveland 44106, OH, USA
| | - Zaiyi Liu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510080, China
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