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Zhou X, Lu Y, Wu Y, Yu Y, Liu Y, Wang C, Zhao Z, Wang C, Gao Z, Li Z, Zhao Y, Cao W. Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108369. [PMID: 38703632 DOI: 10.1016/j.ejso.2024.108369] [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: 11/18/2023] [Revised: 03/09/2024] [Accepted: 04/23/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND TNM staging is the main reference standard for prognostic prediction of colorectal cancer (CRC), but the prognosis heterogeneity of patients with the same stage is still large. This study aimed to classify the tumor microenvironment of patients with stage III CRC and quantify the classified tumor tissues based on deep learning to explore the prognostic value of the developed tumor risk signature (TRS). METHODS A tissue classification model was developed to identify nine tissues (adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, and tumor) in whole-slide images (WSIs) of stage III CRC patients. This model was used to extract tumor tissues from WSIs of 265 stage III CRC patients from The Cancer Genome Atlas and 70 stage III CRC patients from the Sixth Affiliated Hospital of Sun Yat-sen University. We used three different deep learning models for tumor feature extraction and applied a Cox model to establish the TRS. Survival analysis was conducted to explore the prognostic performance of TRS. RESULTS The tissue classification model achieved 94.4 % accuracy in identifying nine tissue types. The TRS showed a Harrell's concordance index of 0.736, 0.716, and 0.711 in the internal training, internal validation, and external validation sets. Survival analysis showed that TRS had significant predictive ability (hazard ratio: 3.632, p = 0.03) for prognostic prediction. CONCLUSION The TRS is an independent and significant prognostic factor for PFS of stage III CRC patients and it contributes to risk stratification of patients with different clinical stages.
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Affiliation(s)
- Xuezhi Zhou
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yizhan Lu
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yue Wu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yi Yu
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yong Liu
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Chang Wang
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Zongya Zhao
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Chong Wang
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Zhixian Gao
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Zhenxin Li
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China.
| | - Yandong Zhao
- Department of Pathology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
| | - Wuteng Cao
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
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Yin Y, Ahmadianfar I, Karim FK, Elmannai H. Advanced forecasting of COVID-19 epidemic: Leveraging ensemble models, advanced optimization, and decomposition techniques. Comput Biol Med 2024; 175:108442. [PMID: 38678939 DOI: 10.1016/j.compbiomed.2024.108442] [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: 01/06/2024] [Revised: 03/25/2024] [Accepted: 04/07/2024] [Indexed: 05/01/2024]
Abstract
In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-KRidge-dRVFL-Ridge). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy.
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Affiliation(s)
- Yingyu Yin
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China.
| | - Iman Ahmadianfar
- Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.
| | - Faten Khalid Karim
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.BOX 84428, Riyadh 11671, Saudi Arabia.
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.BOX 84428, Riyadh 11671, Saudi Arabia.
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Bing P, Liu W, Zhai Z, Li J, Guo Z, Xiang Y, He B, Zhu L. A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme. Front Cardiovasc Med 2024; 11:1277123. [PMID: 38699582 PMCID: PMC11064874 DOI: 10.3389/fcvm.2024.1277123] [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: 08/14/2023] [Accepted: 03/25/2024] [Indexed: 05/05/2024] Open
Abstract
Background Electrocardiogram (ECG) signals are inevitably contaminated with various kinds of noises during acquisition and transmission. The presence of noises may produce the inappropriate information on cardiac health, thereby preventing specialists from making correct analysis. Methods In this paper, an efficient strategy is proposed to denoise ECG signals, which employs a time-frequency framework based on S-transform (ST) and combines bi-dimensional empirical mode decomposition (BEMD) and non-local means (NLM). In the method, the ST maps an ECG signal into a subspace in the time frequency domain, then the BEMD decomposes the ST-based time-frequency representation (TFR) into a series of sub-TFRs at different scales, finally the NLM removes noise and restores ECG signal characteristics based on structural self-similarity. Results The proposed method is validated using numerous ECG signals from the MIT-BIH arrhythmia database, and several different types of noises with varying signal-to-noise (SNR) are taken into account. The experimental results show that the proposed technique is superior to the existing wavelet based approach and NLM filtering, with the higher SNR and structure similarity index measure (SSIM), the lower root mean squared error (RMSE) and percent root mean square difference (PRD). Conclusions The proposed method not only significantly suppresses the noise presented in ECG signals, but also preserves the characteristics of ECG signals better, thus, it is more suitable for ECG signals processing.
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Affiliation(s)
- Pingping Bing
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Wei Liu
- College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Zhixing Zhai
- College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Jianghao Li
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Zhiqun Guo
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Yanrui Xiang
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Binsheng He
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Lemei Zhu
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
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Jia S, Yu L, Wang L, Peng L. The functional significance of circRNA/miRNA/mRNA interactions as a regulatory network in lung cancer biology. Int J Biochem Cell Biol 2024; 169:106548. [PMID: 38360264 DOI: 10.1016/j.biocel.2024.106548] [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: 09/15/2023] [Revised: 01/22/2024] [Accepted: 02/02/2024] [Indexed: 02/17/2024]
Abstract
Lung cancer, the leading cause of cancer-related deaths, presents significant challenges to patients due to its poor prognosis. Recent research has increasingly implicated circular RNAs in the development and progression of lung cancer. These circular RNAs have been found to impact various aspects of tumor behavior, including proliferation, metastasis, cell cycle regulation, apoptosis, cancer stem cells, therapy response, and the tumor microenvironment. One of the key mechanisms by which circular RNAs exert their influence is through their ability to act as miRNA sponges, sequestering microRNAs and preventing them from targeting other RNA molecules. Accumulating evidence suggests that circular RNAs can function as competing endogenous RNAs, affecting the expression of target mRNAs by sequestering microRNAs. Dysregulation of competing endogenous RNAs networks involving circular RNAs, microRNAs, and mRNAs leads to the aberrant expression of oncogenes and tumor suppressors involved in lung cancer pathogenesis. Understanding the dynamic interplay and molecular mechanisms among circular RNAs, microRNAs, and mRNAs holds great promise for advancing early diagnosis, personalized therapeutic interventions, and improved patient outcomes in lung cancer. Therefore, this study aims to provide an in-depth exploration of the executive roles of circular RNAs/microRNAs/ mRNAs interactions in lung cancer pathogenesis and their potential utility for diagnosing lung cancer, predicting patient prognosis, and guiding targeted therapies. By offering a comprehensive overview of the dysregulation of the axes as driving factors in lung cancer, we aim to pave the way for their translation into clinical practice in the future.
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Affiliation(s)
- Shengnan Jia
- Department of Respiratory Medicine, The First Hospital of Jilin University, 71 Xinmin Street, Changchun, Jilin 130021, China; Department of Hepatopancreatobiliary Medicine, The Second Hospital of Jilin University, Changchun, Jilin 130041, China
| | - Ling Yu
- Department of Pharmacy, The Second Hospital of Jilin University, Changchun 130041, China
| | - Lihui Wang
- Department of Respiratory Medicine, The First Hospital of Jilin University, 71 Xinmin Street, Changchun, Jilin 130021, China.
| | - Liping Peng
- Department of Respiratory Medicine, The First Hospital of Jilin University, 71 Xinmin Street, Changchun, Jilin 130021, China.
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Aung TN, Bates KM, Rimm DL. High-Plex Assessment of Biomarkers in Tumors. Mod Pathol 2024; 37:100425. [PMID: 38219953 DOI: 10.1016/j.modpat.2024.100425] [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: 08/30/2023] [Revised: 01/02/2024] [Accepted: 01/08/2024] [Indexed: 01/16/2024]
Abstract
The assessment of biomarkers plays a critical role in the diagnosis and treatment of many cancers. Biomarkers not only provide diagnostic, prognostic, or predictive information but also can act as effective targets for new pharmaceutical therapies. As the utility of biomarkers increases, it becomes more important to utilize accurate and efficient methods for biomarker discovery and, ultimately, clinical assessment. High-plex imaging studies, defined here as assessment of 8 or more biomarkers on a single slide, have become the method of choice for biomarker discovery and assessment of biomarker spatial context. In this review, we discuss methods of measuring biomarkers in slide-mounted tissue samples, detail the various high-plex methods that allow for the simultaneous assessment of multiple biomarkers in situ, and describe the impact of high-plex biomarker assessment on the future of anatomic pathology.
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Affiliation(s)
- Thazin N Aung
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Katherine M Bates
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut; Department of Internal Medicine (Medical Oncology), Yale University School of Medicine, New Haven, Connecticut.
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6
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Jakkaladiki SP, Maly F. Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis. PeerJ Comput Sci 2024; 10:e1850. [PMID: 38435578 PMCID: PMC10909230 DOI: 10.7717/peerj-cs.1850] [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: 08/02/2023] [Accepted: 01/10/2024] [Indexed: 03/05/2024]
Abstract
Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep-learning model for the identification and categorization of breast tumors. The system's performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study's findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1-score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1-score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1-score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.
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Affiliation(s)
- Sudha Prathyusha Jakkaladiki
- Faculty of Informatics and Management, University of Hradec Králové, Hradec Kralove, Hradec Kralove, Czech Republic
| | - Filip Maly
- Faculty of Informatics and Management, University of Hradec Králové, Hradec Kralove, Hradec Kralove, Czech Republic
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Li Y, Zhao D, Ma C, Escorcia-Gutierrez J, Aljehane NO, Ye X. CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images. Comput Biol Med 2024; 169:107838. [PMID: 38171259 DOI: 10.1016/j.compbiomed.2023.107838] [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: 11/28/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024]
Abstract
To improve the detection of COVID-19, this paper researches and proposes an effective swarm intelligence algorithm-driven multi-threshold image segmentation (MTIS) method. First, this paper proposes a novel RIME structure integrating the Co-adaptive hunting and dispersed foraging strategies, called CDRIME. Specifically, the Co-adaptive hunting strategy works in coordination with the basic search rules of RIME at the individual level, which not only facilitates the algorithm to explore the global optimal solution but also enriches the population diversity to a certain extent. The dispersed foraging strategy further enriches the population diversity to help the algorithm break the limitation of local search and thus obtain better convergence. Then, on this basis, a new multi-threshold image segmentation method is proposed by combining the 2D non-local histogram with 2D Kapur entropy, called CDRIME-MTIS. Finally, the results of experiments based on IEEE CEC2017, IEEE CEC2019, and IEEE CEC2022 demonstrate that CDRIME has superior performance than some other basic, advanced, and state-of-the-art algorithms in terms of global search, convergence performance, and escape from local optimality. Meanwhile, the segmentation experiments on COVID-19 X-ray images demonstrate that CDRIME is more advantageous than RIME and other peers in terms of segmentation effect and adaptability to different threshold levels. In conclusion, the proposed CDRIME significantly enhances the global optimization performance and image segmentation of RIME and has great potential to improve COVID-19 diagnosis.
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Affiliation(s)
- Yupeng Li
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.
| | - Chao Ma
- School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen, 518172, China.
| | - José Escorcia-Gutierrez
- Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia.
| | - Nojood O Aljehane
- Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Kingdom of Saudi Arabia.
| | - Xia Ye
- School of the 1st Clinical Medical Sciences (School of Information and Engineering), Wenzhou Medical University, Wenzhou, 325000, China.
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Albalawi E, Thakur A, Ramakrishna MT, Bhatia Khan S, SankaraNarayanan S, Almarri B, Hadi TH. Oral squamous cell carcinoma detection using EfficientNet on histopathological images. Front Med (Lausanne) 2024; 10:1349336. [PMID: 38348235 PMCID: PMC10859441 DOI: 10.3389/fmed.2023.1349336] [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: 12/04/2023] [Accepted: 12/28/2023] [Indexed: 02/15/2024] Open
Abstract
Introduction Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model's objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization. Methods The research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies. Results The customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model's efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC. Discussion This research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model's ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC.
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Affiliation(s)
- Eid Albalawi
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Arastu Thakur
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - Mahesh Thyluru Ramakrishna
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science, Engineering and Environment, University of Salford, Salford, United Kingdom
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Suresh SankaraNarayanan
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Badar Almarri
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Theyazn Hassn Hadi
- Applied College in Abqaiq, King Faisal University, Al-Ahsa, Saudi Arabia
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Munshi RM. Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction. PLoS One 2024; 19:e0296107. [PMID: 38198475 PMCID: PMC10781159 DOI: 10.1371/journal.pone.0296107] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/06/2023] [Indexed: 01/12/2024] Open
Abstract
Cervical cancer remains a leading cause of female mortality, particularly in developing regions, underscoring the critical need for early detection and intervention guided by skilled medical professionals. While Pap smear images serve as valuable diagnostic tools, many available datasets for automated cervical cancer detection contain missing data, posing challenges for machine learning models' efficacy. To address these hurdles, this study presents an automated system adept at managing missing information using ADASYN characteristics, resulting in exceptional accuracy. The proposed methodology integrates a voting classifier model harnessing the predictive capacity of three distinct machine learning models. It further incorporates SVM Imputer and ADASYN up-sampled features to mitigate missing value concerns, while leveraging CNN-generated features to augment the model's capabilities. Notably, this model achieves remarkable performance metrics, boasting a 99.99% accuracy, precision, recall, and F1 score. A comprehensive comparative analysis evaluates the proposed model against various machine learning algorithms across four scenarios: original dataset usage, SVM imputation, ADASYN feature utilization, and CNN-generated features. Results indicate the superior efficacy of the proposed model over existing state-of-the-art techniques. This research not only introduces a novel approach but also offers actionable suggestions for refining automated cervical cancer detection systems. Its impact extends to benefiting medical practitioners by enabling earlier detection and improved patient care. Furthermore, the study's findings have substantial societal implications, potentially reducing the burden of cervical cancer through enhanced diagnostic accuracy and timely intervention.
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Affiliation(s)
- Raafat M. Munshi
- Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia
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10
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Wang R, Li L, Chen M, Li X, Liu Y, Xue Z, Ma Q, Chen J. Gene expression insights: Chronic stress and bipolar disorder: A bioinformatics investigation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:392-414. [PMID: 38303428 DOI: 10.3934/mbe.2024018] [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: 02/03/2024]
Abstract
Bipolar disorder (BD) is a psychiatric disorder that affects an increasing number of people worldwide. The mechanisms of BD are unclear, but some studies have suggested that it may be related to genetic factors with high heritability. Moreover, research has shown that chronic stress can contribute to the development of major illnesses. In this paper, we used bioinformatics methods to analyze the possible mechanisms of chronic stress affecting BD through various aspects. We obtained gene expression data from postmortem brains of BD patients and healthy controls in datasets GSE12649 and GSE53987, and we identified 11 chronic stress-related genes (CSRGs) that were differentially expressed in BD. Then, we screened five biomarkers (IGFBP6, ALOX5AP, MAOA, AIF1 and TRPM3) using machine learning models. We further validated the expression and diagnostic value of the biomarkers in other datasets (GSE5388 and GSE78936) and performed functional enrichment analysis, regulatory network analysis and drug prediction based on the biomarkers. Our bioinformatics analysis revealed that chronic stress can affect the occurrence and development of BD through many aspects, including monoamine oxidase production and decomposition, neuroinflammation, ion permeability, pain perception and others. In this paper, we confirm the importance of studying the genetic influences of chronic stress on BD and other psychiatric disorders and suggested that biomarkers related to chronic stress may be potential diagnostic tools and therapeutic targets for BD.
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Affiliation(s)
- Rongyanqi Wang
- School of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Lan Li
- College of Basic Medicine, Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Man Chen
- College of Basic Medicine, Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Xiaojuan Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China
| | - Yueyun Liu
- School of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Zhe Xue
- School of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Qingyu Ma
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China
| | - Jiaxu Chen
- School of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
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Venkatesan VK, Kuppusamy Murugesan KR, Chandrasekaran KA, Thyluru Ramakrishna M, Khan SB, Almusharraf A, Albuali A. Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques. Diagnostics (Basel) 2023; 13:3452. [PMID: 37998588 PMCID: PMC10670706 DOI: 10.3390/diagnostics13223452] [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: 09/29/2023] [Revised: 10/30/2023] [Accepted: 11/04/2023] [Indexed: 11/25/2023] Open
Abstract
Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen-Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model's decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies.
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Affiliation(s)
- Vinoth Kumar Venkatesan
- School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore 632014, India;
| | - Karthick Raghunath Kuppusamy Murugesan
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore 562112, India; (K.R.K.M.); (M.T.R.)
| | | | - Mahesh Thyluru Ramakrishna
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore 562112, India; (K.R.K.M.); (M.T.R.)
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science Engineering and Environment, University of Salford, Manchester M5 4WT, UK
- Department of Engineering and Environment, University of Religions and Denominations, Qom 37491-13357, Iran
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
| | - Ahlam Almusharraf
- Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Abdullah Albuali
- Department of Computer Science, School of Computer Science and Information Technology, King Faisal University, Hofuf 11671, Saudi Arabia;
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Zhou B, Li M, Chen T, She J. Case Report: Acute Myocarditis Due to PD-L1 Inhibitor Durvalumab Monotherapy in a Patient With Lung Squamous Cell Carcinoma. Front Med (Lausanne) 2022; 9:866068. [PMID: 35801208 PMCID: PMC9253414 DOI: 10.3389/fmed.2022.866068] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 06/01/2022] [Indexed: 12/19/2022] Open
Abstract
BackgroundDurvalumab, as a PD-L1 inhibitor, is commonly used for the treatment of various cancers. Adverse events associated with the therapy include hepatitis, nephritis, dermatitis, and myocarditis. Especially, myocarditis as an adverse event after PD-L1 inhibitor therapy is characterized for its low incidence and high mortality.Case SummaryHere we present a rare case of a 67-year-old male with lung squamous cell carcinoma complicated with empyema who experienced myocarditis after only PD-L1 inhibitor durvalumab monotherapy. He presented with markedly decrease left ventricular ejection fraction, elevated Natriuretic peptide BNP, Troponin T, Troponin I, ESR, CRP and interleukin-6. The electrocardiogram showed sinus tachycardia, low voltage of limb leads, T wave inversion in anterior waves and V1-V3 QS type. Myocardial injury occurred in a short period and quickly returned to normal after glucocorticoids therapy.ConclusionThis case report is of clinical value for the treatment of PD-L1 related myocarditis.
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Affiliation(s)
- Bo Zhou
- Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- *Correspondence: Bo Zhou
| | - Manxiang Li
- Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Tianjun Chen
- Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jianqing She
- Cardiology Department, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Jianqing She
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13
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She J, Liu H, Wu H, Tuerhongjiang G, Zheng T, Bai L. Cardiotoxicity Related to Immune Checkpoint Inhibitors: A Real-World Retrospective Analysis. Front Cardiovasc Med 2022; 9:838488. [PMID: 35711379 PMCID: PMC9193585 DOI: 10.3389/fcvm.2022.838488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 04/21/2022] [Indexed: 12/02/2022] Open
Abstract
Background Cardiotoxicity related to immune checkpoint inhibitors (ICIs) is a rare but potentially lethal. In ICI-associated adverse events, evidence of cardiotoxicity and clinical predictive factors related to ICI is lacking. Here, we aim to assess the incidence and predictive factors of cardiotoxicity related to ICIs in real-world practice. Objective We retrospectively analyzed consecutive patients who received PD-1 or PD-L1 at the First Affiliated Hospital of Xi'an Jiaotong University. Clinical characteristics and cardiac lesion markers were collected both at baseline and during longitudinal follow-up from the Biobank database. Follow-up CKMB and NT-proBNP levels and ratios were then evaluated. Results A total of 2,304 patients with either PD-1 or PDL-1 utilization between August 2018 and April 2021 were collected. The average age was 59.44 ± 11.45 among PD-1 inhibitor utilizer and 58.97 ± 12.16 among PDL-1 inhibitor utilizer. The baseline creatine kinase isoenzyme MB (CKMB) levels were 17 ± 19 U/L in PD-1 inhibitor users and 17 ± 23 U/L in PDL-1 inhibitor users. Majority of patients were male, with advanced stage cancer, and received ICIs as second-line therapy. The longitudinal change of cardiac enzymes and NT-pro BNP were collected. Cardiac lesion as defined by three times increase of CKMB happens in only minority of patients receiving ICIs therapy. It is also identified that increased CKMB happened in PD-1 inhibitor groups, but not PDL-1 inhibitor groups. Conclusion We evaluated the profile of cardiotoxicities caused by ICIs based on real-world experience. The cardiac lesion markers are generally unaltered, but it appears that the increased CKMB happened in PD-1 inhibitor groups, but not PDL-1 inhibitor groups.
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Affiliation(s)
- Jianqing She
- Cardiovascular Department, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi'an, China
- Jianqing She
| | - Hui Liu
- Biobank, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Haoyu Wu
- Cardiovascular Department, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi'an, China
| | - Gulinigaer Tuerhongjiang
- Cardiovascular Department, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi'an, China
| | - Tao Zheng
- Cardiovascular Department, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi'an, China
| | - Ling Bai
- Cardiovascular Department, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi'an, China
- *Correspondence: Ling Bai
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Solary E, Abou-Zeid N, Calvo F. Ageing and cancer: a research gap to fill. Mol Oncol 2022; 16:3220-3237. [PMID: 35503718 PMCID: PMC9490141 DOI: 10.1002/1878-0261.13222] [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: 03/01/2022] [Revised: 04/01/2022] [Accepted: 05/02/2022] [Indexed: 12/03/2022] Open
Abstract
The complex mechanisms of ageing biology are increasingly understood. Interventions to reduce or delay ageing‐associated diseases are emerging. Cancer is one of the diseases promoted by tissue ageing. A clockwise mutational signature is identified in many tumours. Ageing might be a modifiable cancer risk factor. To reduce the incidence of ageing‐related cancer and to detect the disease at earlier stages, we need to understand better the links between ageing and tumours. When a cancer is established, geriatric assessment and measures of biological age might help to generate evidence‐based therapeutic recommendations. In this approach, patients and caregivers would include the respective weight to give to the quality of life and survival in the therapeutic choices. The increasing burden of cancer in older patients requires new generations of researchers and geriatric oncologists to be trained, to properly address disease complexity in a multidisciplinary manner, and to reduce health inequities in this population of patients. In this review, we propose a series of research challenges to tackle in the next few years to better prevent, detect and treat cancer in older patients while preserving their quality of life.
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Affiliation(s)
- Eric Solary
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France.,Université Paris Saclay, Faculté de Médecine, Le Kremlin-Bicêtre, France.,Gustave Roussy Cancer Center, INSERM U1287, Villejuif, France
| | - Nancy Abou-Zeid
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France
| | - Fabien Calvo
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France.,Université de Paris, Paris, France
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