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Lasalvia M, Capozzi V, Perna G. Classification of healthy and cancerous colon cells by Fourier transform infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 321:124683. [PMID: 38908360 DOI: 10.1016/j.saa.2024.124683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 06/04/2024] [Accepted: 06/18/2024] [Indexed: 06/24/2024]
Abstract
Colorectal cancer is one of the most diagnosed types of cancer in developed countries. Current diagnostic methods are partly dependent on pathologist experience and laboratories instrumentation. In this study, we used Fourier Transform Infrared (FTIR) spectroscopy in transflection mode, combined with Principal Components Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares - Discriminant Analysis (PLS-DA), to build a classification algorithm to diagnose colon cancer in cell samples, based on absorption spectra measured in two spectral ranges of the mid-infrared spectrum. In particular, PCA technique highlights small biochemical differences between healthy and cancerous cells: these are related to the larger lipid content in the former compared with the latter and to the larger relative amount of protein and nucleic acid components in the cancerous cells compared with the healthy ones. Comparison of the classification accuracy of PCA-LDA and PLS-DA methods applied to FTIR spectra measured in the 1000-1800 cm-1 (low wavenumber range, LWR) and 2700-3700 cm-1 (high wavenumber range, HWR) remarks that both algorithms are able to classify hidden class FTIR spectra with excellent accuracy (100 %) in both spectral regions. This is a hopeful result for clinical translation of infrared spectroscopy: in fact, it makes reliable the predictions obtained using FTIR measurements carried out only in the HWR, in which the glass slides used in clinical laboratories are transparent to IR radiation.
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Affiliation(s)
- Maria Lasalvia
- Dipartimento di Medicina Clinica e Sperimentale, Università di Foggia, 71122 Foggia, Italy
| | - Vito Capozzi
- Dipartimento di Medicina Clinica e Sperimentale, Università di Foggia, 71122 Foggia, Italy
| | - Giuseppe Perna
- Dipartimento di Medicina Clinica e Sperimentale, Università di Foggia, 71122 Foggia, Italy.
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Tabatabaei Z, Wang Y, Colomer A, Oliver Moll J, Zhao Z, Naranjo V. WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval. Bioengineering (Basel) 2023; 10:1144. [PMID: 37892874 PMCID: PMC10604333 DOI: 10.3390/bioengineering10101144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/24/2023] [Accepted: 09/25/2023] [Indexed: 10/29/2023] Open
Abstract
The paper proposes a federated content-based medical image retrieval (FedCBMIR) tool that utilizes federated learning (FL) to address the challenges of acquiring a diverse medical data set for training CBMIR models. CBMIR is a tool to find the most similar cases in the data set to assist pathologists. Training such a tool necessitates a pool of whole-slide images (WSIs) to train the feature extractor (FE) to extract an optimal embedding vector. The strict regulations surrounding data sharing in hospitals makes it difficult to collect a rich data set. FedCBMIR distributes an unsupervised FE to collaborative centers for training without sharing the data set, resulting in shorter training times and higher performance. FedCBMIR was evaluated by mimicking two experiments, including two clients with two different breast cancer data sets, namely BreaKHis and Camelyon17 (CAM17), and four clients with the BreaKHis data set at four different magnifications. FedCBMIR increases the F1 score (F1S) of each client from 96% to 98.1% in CAM17 and from 95% to 98.4% in BreaKHis, with 11.44 fewer hours in training time. FedCBMIR provides 98%, 96%, 94%, and 97% F1S in the BreaKHis experiment with a generalized model and accomplishes this in 25.53 fewer hours of training.
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Affiliation(s)
- Zahra Tabatabaei
- Department of Artificial Intelligence, Tyris Tech S.L., 46021 Valencia, Spain
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-Tech, Universitat Politècnica de València, 46021 Valencia, Spain
| | - Yuandou Wang
- Multiscale Networked Systems, Universiteit van Amsterdam, 1098XH Amsterdam, The Netherlands
| | - Adrián Colomer
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-Tech, Universitat Politècnica de València, 46021 Valencia, Spain
- ValgrAI—Valencian Graduate School and Research Network for Artificial Intelligence, 46022 Valencia, Spain
| | - Javier Oliver Moll
- Department of Artificial Intelligence, Tyris Tech S.L., 46021 Valencia, Spain
| | - Zhiming Zhao
- Multiscale Networked Systems, Universiteit van Amsterdam, 1098XH Amsterdam, The Netherlands
| | - Valery Naranjo
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-Tech, Universitat Politècnica de València, 46021 Valencia, Spain
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EOSA-GAN: Feature enriched latent space optimized adversarial networks for synthesization of histopathology images using Ebola optimization search algorithm. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
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Amin MS, Ahn H. FabNet: A Features Agglomeration-Based Convolutional Neural Network for Multiscale Breast Cancer Histopathology Images Classification. Cancers (Basel) 2023; 15:cancers15041013. [PMID: 36831359 PMCID: PMC9954749 DOI: 10.3390/cancers15041013] [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: 01/12/2023] [Revised: 01/31/2023] [Accepted: 01/31/2023] [Indexed: 02/08/2023] Open
Abstract
The definitive diagnosis of histology specimen images is largely based on the radiologist's comprehensive experience; however, due to the fine to the coarse visual appearance of such images, experts often disagree with their assessments. Sophisticated deep learning approaches can help to automate the diagnosis process of the images and reduce the analysis duration. More efficient and accurate automated systems can also increase the diagnostic impartiality by reducing the difference between the operators. We propose a FabNet model that can learn the fine-to-coarse structural and textural features of multi-scale histopathological images by using accretive network architecture that agglomerate hierarchical feature maps to acquire significant classification accuracy. We expand on a contemporary design by incorporating deep and close integration to finely combine features across layers. Our deep layer accretive model structure combines the feature hierarchy in an iterative and hierarchically manner that infers higher accuracy and fewer parameters. The FabNet can identify malignant tumors from images and patches from histopathology images. We assessed the efficiency of our suggested model standard cancer datasets, which included breast cancer as well as colon cancer histopathology images. Our proposed avant garde model significantly outperforms existing state-of-the-art models in respect of the accuracy, F1 score, precision, and sensitivity, with fewer parameters.
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Chen J, Yang Y, Luo B, Wen Y, Chen Q, Ma R, Huang Z, Zhu H, Li Y, Chen Y, Qian D. Further predictive value of lymphovascular invasion explored via supervised deep learning for lymph node metastases in breast cancer. Hum Pathol 2023; 131:26-37. [PMID: 36481204 DOI: 10.1016/j.humpath.2022.11.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/22/2022] [Accepted: 11/27/2022] [Indexed: 12/12/2022]
Abstract
Lymphovascular invasion, specifically lymph-blood vessel invasion (LBVI), is a risk factor for metastases in breast invasive ductal carcinoma (IDC) and is routinely screened using hematoxylin-eosin histopathological images. However, routine reports only describe whether LBVI is present and does not provide other potential prognostic information of LBVI. This study aims to evaluate the clinical significance of LBVI in 685 IDC cases and explore the added predictive value of LBVI on lymph node metastases (LNM) via supervised deep learning (DL), an expert-experience embedded knowledge transfer learning (EEKT) model in 40 LBVI-positive cases signed by the routine report. Multivariate logistic regression and propensity score matching analysis demonstrated that LBVI (OR 4.203, 95% CI 2.809-6.290, P < 0.001) was a significant risk factor for LNM. Then, the EEKT model trained on 5780 image patches automatically segmented LBVI with a patch-wise Dice similarity coefficient of 0.930 in the test set and output counts, location, and morphometric features of the LBVIs. Some morphometric features were beneficial for further stratification within the 40 LBVI-positive cases. The results showed that LBVI in cases with LNM had a higher short-to-long side ratio of the minimum rectangle (MR) (0.686 vs. 0.480, P = 0.001), LBVI-to-MR area ratio (0.774 vs. 0.702, P = 0.002), and solidity (0.983 vs. 0.934, P = 0.029) compared to LBVI in cases without LNM. The results highlight the potential of DL to assist pathologists in quantifying LBVI and, more importantly, in exploring added prognostic information from LBVI.
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Affiliation(s)
- Jiamei Chen
- Center of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Yang Yang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200000, China
| | - Bo Luo
- Department of Pathology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Yaofeng Wen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200000, China
| | - Qingzhong Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200000, China
| | - Ru Ma
- Department of Peritoneal Cancer Surgery, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, 100038, China
| | - Zhen Huang
- Center of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Hangjia Zhu
- Center of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Yan Li
- Department of Peritoneal Cancer Surgery, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, 100038, China; Department of Pathology, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, 100038, China.
| | - Yongshun Chen
- Center of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
| | - Dahong Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200000, China.
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Petay M, Cherfan M, Bouderlique E, Reguer S, Mathurin J, Dazzi A, L’Heronde M, Daudon M, Letavernier E, Deniset-Besseau A, Bazin D. Multiscale approach to provide a better physicochemical description of women breast microcalcifications. CR CHIM 2022. [DOI: 10.5802/crchim.210] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Lou P, Wang C, Guo R, Yao L, Zhang G, Yang J, Yuan Y, Dong Y, Gao Z, Gong T, Li C. HistoML, a markup language for representation and exchange of histopathological features in pathology images. Sci Data 2022; 9:387. [PMID: 35803960 PMCID: PMC9270329 DOI: 10.1038/s41597-022-01505-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/23/2022] [Indexed: 11/09/2022] Open
Abstract
The study of histopathological phenotypes is vital for cancer research and medicine as it links molecular mechanisms to disease prognosis. It typically involves integration of heterogenous histopathological features in whole-slide images (WSI) to objectively characterize a histopathological phenotype. However, the large-scale implementation of phenotype characterization has been hindered by the fragmentation of histopathological features, resulting from the lack of a standardized format and a controlled vocabulary for structured and unambiguous representation of semantics in WSIs. To fill this gap, we propose the Histopathology Markup Language (HistoML), a representation language along with a controlled vocabulary (Histopathology Ontology) based on Semantic Web technologies. Multiscale features within a WSI, from single-cell features to mesoscopic features, could be represented using HistoML which is a crucial step towards the goal of making WSIs findable, accessible, interoperable and reusable (FAIR). We pilot HistoML in representing WSIs of kidney cancer as well as thyroid carcinoma and exemplify the uses of HistoML representations in semantic queries to demonstrate the potential of HistoML-powered applications for phenotype characterization.
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Affiliation(s)
- Peiliang Lou
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Chunbao Wang
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shaanxi, China
| | - Ruifeng Guo
- Division of Anatomic Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Lixia Yao
- Department of Health Services Administration and Policy, Temple University, Philadelphia, PA, USA
| | - Guanjun Zhang
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shaanxi, China
| | - Jun Yang
- Department of Pathology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 3, Shang Qin Road, Xi'an, Shaanxi, China
| | - Yong Yuan
- Department of Pathology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, 309 Yanta West Road, Xi'an, Shaanxi, China
| | - Yuxin Dong
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Zeyu Gao
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Tieliang Gong
- Key Laboratory of Intelligent Networks and Network Security (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi, 710049, China
| | - Chen Li
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.
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Malakar S, Roy SD, Das S, Sen S, Velásquez JD, Sarkar R. Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:5525-5567. [PMID: 35729963 PMCID: PMC9199478 DOI: 10.1007/s11831-022-09776-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.
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Affiliation(s)
- Samir Malakar
- Department of Computer Science, Asutosh College, Kolkata, India
| | - Soumya Deep Roy
- Department of Metallurgical and Material Engineering, Jadavpur University, Kolkata, India
| | - Soham Das
- Department of Metallurgical and Material Engineering, Jadavpur University, Kolkata, India
| | - Swaraj Sen
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Juan D. Velásquez
- Departament of Industrial Engineering, University of Chile, Santiago, Chile
- Instituto Sistemas Complejos de Ingeniería (ISCI), Santiago, Chile
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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Murthy C, Balaji K. Histopathological analyses of breast cancer using deep learning. CARDIOMETRY 2022. [DOI: 10.18137/cardiometry.2022.22.456461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Deep Learning hosts a plethora of variants and models in Convolution Neural Networks (CNN), where the prudence of these methods is algorithmically proven when implemented with sturdy datasets. Much number of haphazard structures and textures are found in the histopathological images of breast cancer, where dealing with such multicolor and multi-structure components in the images is a challenging task. Working with such data in wet labs proves clinically consistent results, but added with the computational models will improvise them empirically. In this paper, we proposed a model to diagnose breast cancer using raw images of breast cancer with different resolutions, irrespective of the structures and textures. The floating image is mapped with the healthy reference image and examined using different statistics such as cross correlations and phase correlations. Experiments are carried out with the aim of establishing the optimal performance on histopathological images. The model attained satisfactory results and are proved good for decision making in cancer diagnosis.
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