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Machado P, Tahmasebi A, Fallon S, Liu JB, Dogan BE, Needleman L, Lazar M, Willis AI, Brill K, Nazarian S, Berger A, Forsberg F. Characterizing Sentinel Lymph Node Status in Breast Cancer Patients Using a Deep-Learning Model Compared With Radiologists' Analysis of Grayscale Ultrasound and Lymphosonography. Ultrasound Q 2024; 40:e00683. [PMID: 38958999 DOI: 10.1097/ruq.0000000000000683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
ABSTRACT The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists' assessments.Seventy-nine women with breast cancer were enrolled and underwent lymphosonography and contrast-enhanced ultrasound (CEUS) examination after subcutaneous injection of ultrasound contrast agent around their tumor to identify SLNs. Google AutoML was used to develop image classification model. Grayscale and CEUS images acquired during the ultrasound examination were uploaded with a data distribution of 80% for training/20% for testing. The performance metric used was area under precision/recall curve (AuPRC). In addition, 3 radiologists assessed SLNs as normal or abnormal based on a clinical established classification. Two-hundred seventeen SLNs were divided in 2 for model development; model 1 included all SLNs and model 2 had an equal number of benign and malignant SLNs. Validation results model 1 AuPRC 0.84 (grayscale)/0.91 (CEUS) and model 2 AuPRC 0.91 (grayscale)/0.87 (CEUS). The comparison between artificial intelligence (AI) and readers' showed statistical significant differences between all models and ultrasound modes; model 1 grayscale AI versus readers, P = 0.047, and model 1 CEUS AI versus readers, P < 0.001. Model 2 r grayscale AI versus readers, P = 0.032, and model 2 CEUS AI versus readers, P = 0.041.The interreader agreement overall result showed κ values of 0.20 for grayscale and 0.17 for CEUS.In conclusion, AutoML showed improved diagnostic performance in balance volume datasets. Radiologist performance was not influenced by the dataset's distribution.
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Affiliation(s)
- Priscilla Machado
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA
| | - Aylin Tahmasebi
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA
| | - Samuel Fallon
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA
| | - Ji-Bin Liu
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA
| | - Basak E Dogan
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX
| | | | - Melissa Lazar
- Department of Surgery, Thomas Jefferson University, Philadelphia, PA
| | - Alliric I Willis
- Department of Surgery, Thomas Jefferson University, Philadelphia, PA
| | - Kristin Brill
- Department of Surgery, Thomas Jefferson University, Philadelphia, PA
| | - Susanna Nazarian
- Department of Surgery, Thomas Jefferson University, Philadelphia, PA
| | - Adam Berger
- Chief, Department of Melanoma and Soft Tissue Surgical Oncology, Rutgers University, New Brunswick, NJ
| | - Flemming Forsberg
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA
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Song Y, Liu J, Jin C, Zheng Y, Zhao Y, Zhang K, Zhou M, Zhao D, Hou L, Dong F. Value of Contrast-Enhanced Ultrasound Combined with Immune-Inflammatory Markers in Predicting Axillary Lymph Node Metastasis of Breast Cancer. Acad Radiol 2024:S1076-6332(24)00371-4. [PMID: 38918153 DOI: 10.1016/j.acra.2024.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/16/2024] [Accepted: 06/06/2024] [Indexed: 06/27/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with immune-inflammatory markers in predicting axillary lymph node metastasis (ALNM) in breast cancer patients. METHODS From January 2020 to June 2023, the clinicopathological data and ultrasound features of 401 breast cancer patients who underwent biopsy or surgery were recorded. Patients were randomly divided into a training set (321 patients) and a validation set (80 patients). The risk factors for ALNM were determined using univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, and prediction models were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess their diagnostic performance. RESULTS Logistic regression analysis demonstrated that systemic immunoinflammatory index (SII), CA125, Ki67, pathological type, lesion size, enhancement pattern and Breast Imaging Reporting and Data System (BI-RADS) category were significant risk factors for ALNM. Three different models were constructed, and the combined model yielded an AUC of 0.903, which was superior to the clinical model (AUC=0.790) and ultrasound model (AUC=0.781). A nomogram was constructed based on the combined model, calibration curves and DCA demonstrated its satisfactory performance in predicting ALNM. CONCLUSION The nomogram combining ultrasound features and immune-inflammatory markers could serve as a valuable instrument for predicting ALNM in breast cancer patients. DATA AVAILABILITY STATEMENT The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.
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Affiliation(s)
- Ying Song
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, China
| | - Jinjin Liu
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, China
| | - Chenyang Jin
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, China
| | - Yan Zheng
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, China
| | - Yingying Zhao
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, China
| | - Kairen Zhang
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, China
| | - Mengqi Zhou
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, China
| | - Dan Zhao
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, China
| | - Lizhu Hou
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, China
| | - Fenglin Dong
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, China.
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Carriero A, Groenhoff L, Vologina E, Basile P, Albera M. Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024. Diagnostics (Basel) 2024; 14:848. [PMID: 38667493 PMCID: PMC11048882 DOI: 10.3390/diagnostics14080848] [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: 02/29/2024] [Revised: 04/07/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.
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Affiliation(s)
| | - Léon Groenhoff
- Radiology Department, Maggiore della Carità Hospital, 28100 Novara, Italy; (A.C.); (E.V.); (P.B.); (M.A.)
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Park TY, Kwon LM, Hyeon J, Cho BJ, Kim BJ. Deep Learning Prediction of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Clinical Implication-Applied Preprocessed CT Images. Curr Oncol 2024; 31:2278-2288. [PMID: 38668072 PMCID: PMC11049657 DOI: 10.3390/curroncol31040169] [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: 03/26/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Background: Accurate detection of axillary lymph node (ALN) metastases in breast cancer is crucial for clinical staging and treatment planning. This study aims to develop a deep learning model using clinical implication-applied preprocessed computed tomography (CT) images to enhance the prediction of ALN metastasis in breast cancer patients. Methods: A total of 1128 axial CT images of ALN (538 malignant and 590 benign lymph nodes) were collected from 523 breast cancer patients who underwent preoperative CT scans between January 2012 and July 2022 at Hallym University Medical Center. To develop an optimal deep learning model for distinguishing metastatic ALN from benign ALN, a CT image preprocessing protocol with clinical implications and two different cropping methods (fixed size crop [FSC] method and adjustable square crop [ASC] method) were employed. The images were analyzed using three different convolutional neural network (CNN) architectures (ResNet, DenseNet, and EfficientNet). Ensemble methods involving and combining the selection of the two best-performing CNN architectures from each cropping method were applied to generate the final result. Results: For the two different cropping methods, DenseNet consistently outperformed ResNet and EfficientNet. The area under the receiver operating characteristic curve (AUROC) for DenseNet, using the FSC and ASC methods, was 0.934 and 0.939, respectively. The ensemble model, which combines the performance of the DenseNet121 architecture for both cropping methods, delivered outstanding results with an AUROC of 0.968, an accuracy of 0.938, a sensitivity of 0.980, and a specificity of 0.903. Furthermore, distinct trends observed in gradient-weighted class activation mapping images with the two cropping methods suggest that our deep learning model not only evaluates the lymph node itself, but also distinguishes subtler changes in lymph node margin and adjacent soft tissue, which often elude human interpretation. Conclusions: This research demonstrates the promising performance of a deep learning model in accurately detecting malignant ALNs in breast cancer patients using CT images. The integration of clinical considerations into image processing and the utilization of ensemble methods further improved diagnostic precision.
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Affiliation(s)
- Tae Yong Park
- Medical Artificial Intelligence Center, Doheon Institute for Digital Innovation in Medicine, Hallym Univesity Medical Center, Anyang-si 14068, Republic of Korea;
| | - Lyo Min Kwon
- Department of Radiology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang-si 14068, Republic of Korea;
| | - Jini Hyeon
- School of Medicine, Hallym University College of Medicine, Chuncheon 24252, Republic of Korea;
| | - Bum-Joo Cho
- Medical Artificial Intelligence Center, Doheon Institute for Digital Innovation in Medicine, Hallym Univesity Medical Center, Anyang-si 14068, Republic of Korea;
- Department of Ophthalmology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang-si 14068, Republic of Korea
| | - Bum Jun Kim
- Division of Hematology-Oncology, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang-si 14068, Republic of Korea
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Eida S, Fukuda M, Katayama I, Takagi Y, Sasaki M, Mori H, Kawakami M, Nishino T, Ariji Y, Sumi M. Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma. Cancers (Basel) 2024; 16:274. [PMID: 38254765 PMCID: PMC10813890 DOI: 10.3390/cancers16020274] [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: 11/23/2023] [Revised: 12/28/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Ultrasonography is the preferred modality for detailed evaluation of enlarged lymph nodes (LNs) identified on computed tomography and/or magnetic resonance imaging, owing to its high spatial resolution. However, the diagnostic performance of ultrasonography depends on the examiner's expertise. To support the ultrasonographic diagnosis, we developed YOLOv7-based deep learning models for metastatic LN detection on ultrasonography and compared their detection performance with that of highly experienced radiologists and less experienced residents. We enrolled 462 B- and D-mode ultrasound images of 261 metastatic and 279 non-metastatic histopathologically confirmed LNs from 126 patients with head and neck squamous cell carcinoma. The YOLOv7-based B- and D-mode models were optimized using B- and D-mode training and validation images and their detection performance for metastatic LNs was evaluated using B- and D-mode testing images, respectively. The D-mode model's performance was comparable to that of radiologists and superior to that of residents' reading of D-mode images, whereas the B-mode model's performance was higher than that of residents but lower than that of radiologists on B-mode images. Thus, YOLOv7-based B- and D-mode models can assist less experienced residents in ultrasonographic diagnoses. The D-mode model could raise the diagnostic performance of residents to the same level as experienced radiologists.
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Affiliation(s)
- Sato Eida
- Department of Radiology and Biomedical Informatics, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki 852-8588, Japan; (S.E.); (I.K.); (Y.T.); (M.S.); (H.M.); (M.K.); (T.N.)
| | - Motoki Fukuda
- Department of Oral Radiology, Osaka Dental University, 1-5-17 Otemae, Chuo-ku, Osaka 540-0008, Japan; (M.F.); (Y.A.)
| | - Ikuo Katayama
- Department of Radiology and Biomedical Informatics, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki 852-8588, Japan; (S.E.); (I.K.); (Y.T.); (M.S.); (H.M.); (M.K.); (T.N.)
| | - Yukinori Takagi
- Department of Radiology and Biomedical Informatics, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki 852-8588, Japan; (S.E.); (I.K.); (Y.T.); (M.S.); (H.M.); (M.K.); (T.N.)
| | - Miho Sasaki
- Department of Radiology and Biomedical Informatics, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki 852-8588, Japan; (S.E.); (I.K.); (Y.T.); (M.S.); (H.M.); (M.K.); (T.N.)
| | - Hiroki Mori
- Department of Radiology and Biomedical Informatics, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki 852-8588, Japan; (S.E.); (I.K.); (Y.T.); (M.S.); (H.M.); (M.K.); (T.N.)
| | - Maki Kawakami
- Department of Radiology and Biomedical Informatics, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki 852-8588, Japan; (S.E.); (I.K.); (Y.T.); (M.S.); (H.M.); (M.K.); (T.N.)
| | - Tatsuyoshi Nishino
- Department of Radiology and Biomedical Informatics, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki 852-8588, Japan; (S.E.); (I.K.); (Y.T.); (M.S.); (H.M.); (M.K.); (T.N.)
| | - Yoshiko Ariji
- Department of Oral Radiology, Osaka Dental University, 1-5-17 Otemae, Chuo-ku, Osaka 540-0008, Japan; (M.F.); (Y.A.)
| | - Misa Sumi
- Department of Radiology and Biomedical Informatics, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki 852-8588, Japan; (S.E.); (I.K.); (Y.T.); (M.S.); (H.M.); (M.K.); (T.N.)
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Ding X, Liu Y, Zhao J, Wang R, Li C, Luo Q, Shen C. A novel wavelet-transform-based convolution classification network for cervical lymph node metastasis of papillary thyroid carcinoma in ultrasound images. Comput Med Imaging Graph 2023; 109:102298. [PMID: 37769402 DOI: 10.1016/j.compmedimag.2023.102298] [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: 05/05/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/30/2023]
Abstract
Preoperative assessment of cervical lymph nodes metastasis (CLNM) for accurate qualitative and locating diagnosis is important for choosing the best treatment option for patients with papillary thyroid cancer. Non-destructive, non-invasive ultrasound is currently the imaging method of choice for lymph node metastatic assessment. For lymph node characteristics and ultrasound images, this paper proposes a multitasking network framework for diagnosing metastatic lymph nodes in ultrasound images, in which localization module not only provides information on the location of lymph nodes to focus on the peripheral and self regions of lymph nodes, but also provides structural features of lymph nodes for subsequent classification module. In the classification module, we design a novel wavelet-transform-based convolution network. Wavelet transform is introduced into the deep learning convolution module to analyze ultrasound images in both spatial and frequency domains, which effectively enriches the feature information and improves the classification performance of the model without increasing the model parameters. We collected 510 patient data (N = 1376) from Shanghai Sixth People's Hospital regarding ultrasound lymph nodes in the neck, as well as used three publicly available ultrasound datasets, including SCUI2020 (N = 2914), DDTI (N = 480), and BUSI (N = 780). Compared to the optimal two-stage model, our model has improved its accuracy and AUC indexes by 5.83% and 4%, which outperforms the two-stage architectures and also surpasses the latest classification networks.
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Affiliation(s)
- Xuehai Ding
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Yanting Liu
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Junjuan Zhao
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China.
| | - Ren Wang
- Department of Ultrasound Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Yishan Rd, Shanghai, 200233, China
| | - Chengfan Li
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Quanyong Luo
- Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Yishan Rd, Shanghai, 200233, China
| | - Chentian Shen
- Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Yishan Rd, Shanghai, 200233, China.
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Li WB, Du ZC, Liu YJ, Gao JX, Wang JG, Dai Q, Huang WH. Prediction of axillary lymph node metastasis in early breast cancer patients with ultrasonic videos based deep learning. Front Oncol 2023; 13:1219838. [PMID: 37719009 PMCID: PMC10503049 DOI: 10.3389/fonc.2023.1219838] [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: 05/09/2023] [Accepted: 07/06/2023] [Indexed: 09/19/2023] Open
Abstract
Objective To develop a deep learning (DL) model for predicting axillary lymph node (ALN) metastasis using dynamic ultrasound (US) videos in breast cancer patients. Methods A total of 271 US videos from 271 early breast cancer patients collected from Xiang'an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultra-sonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models. Results Among the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P<0.05). The Grad-CAM technology confirmed the heatmap of the model, which highlighted important subregions of the keyframe for ultra-sonographers' review. Conclusion A feasible and improved DL model to predict ALN metastasis from breast cancer US video images was developed. The DL model in this study with reliable interpretability would provide an early diagnostic strategy for the appropriate management of axillary in the early breast cancer patients.
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Affiliation(s)
- Wei-Bin Li
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Department of Ultrasonic Medicine Affiliated Hospital of Xizang Minzu University, Xianyang, China
| | - Zhi-Cheng Du
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yue-Jie Liu
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Department of Ultrasonic Medicine, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Jun-Xue Gao
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Department of Ultrasonic Medicine, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Jia-Gang Wang
- Department of Ultrasonic Medicine of Shantou Central Hospital, Shantou, China
| | - Qian Dai
- School of Informatics, Xiamen University, Xiamen, China
| | - Wen-He Huang
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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Zakareya S, Izadkhah H, Karimpour J. A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images. Diagnostics (Basel) 2023; 13:diagnostics13111944. [PMID: 37296796 DOI: 10.3390/diagnostics13111944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/15/2023] [Accepted: 05/28/2023] [Indexed: 06/12/2023] Open
Abstract
Breast cancer is one of the most prevalent cancers among women worldwide, and early detection of the disease can be lifesaving. Detecting breast cancer early allows for treatment to begin faster, increasing the chances of a successful outcome. Machine learning helps in the early detection of breast cancer even in places where there is no access to a specialist doctor. The rapid advancement of machine learning, and particularly deep learning, leads to an increase in the medical imaging community's interest in applying these techniques to improve the accuracy of cancer screening. Most of the data related to diseases is scarce. On the other hand, deep-learning models need much data to learn well. For this reason, the existing deep-learning models on medical images cannot work as well as other images. To overcome this limitation and improve breast cancer classification detection, inspired by two state-of-the-art deep networks, GoogLeNet and residual block, and developing several new features, this paper proposes a new deep model to classify breast cancer. Utilizing adopted granular computing, shortcut connection, two learnable activation functions instead of traditional activation functions, and an attention mechanism is expected to improve the accuracy of diagnosis and consequently decrease the load on doctors. Granular computing can improve diagnosis accuracy by capturing more detailed and fine-grained information about cancer images. The proposed model's superiority is demonstrated by comparing it to several state-of-the-art deep models and existing works using two case studies. The proposed model achieved an accuracy of 93% and 95% on ultrasound images and breast histopathology images, respectively.
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Affiliation(s)
- Salman Zakareya
- Department of Computer Science, University of Tabriz, Tabriz 5166616471, Iran
| | - Habib Izadkhah
- Department of Computer Science, University of Tabriz, Tabriz 5166616471, Iran
- Research Department of Computational Algorithms and Mathematical Models, University of Tabriz, Tabriz 5166616471, Iran
| | - Jaber Karimpour
- Department of Computer Science, University of Tabriz, Tabriz 5166616471, Iran
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Vrdoljak J, Krešo A, Kumrić M, Martinović D, Cvitković I, Grahovac M, Vickov J, Bukić J, Božic J. The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review. Cancers (Basel) 2023; 15:cancers15082400. [PMID: 37190328 DOI: 10.3390/cancers15082400] [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: 03/28/2023] [Revised: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 05/17/2023] Open
Abstract
Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71-0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74-0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82-0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.
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Affiliation(s)
- Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
| | - Ante Krešo
- Department of Surgery, University Hospital of Split, 21000 Split, Croatia
| | - Marko Kumrić
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
| | - Dinko Martinović
- Department of Surgery, University Hospital of Split, 21000 Split, Croatia
| | - Ivan Cvitković
- Department of Surgery, University Hospital of Split, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, 21000 Split, Croatia
| | - Josip Vickov
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
| | - Josipa Bukić
- Department of Pharmacy, University of Split School of Medicine, 21000 Split, Croatia
| | - Joško Božic
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
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Windsor GO, Bai H, Lourenco AP, Jiao Z. Application of artificial intelligence in predicting lymph node metastasis in breast cancer. FRONTIERS IN RADIOLOGY 2023; 3:928639. [PMID: 37492388 PMCID: PMC10364981 DOI: 10.3389/fradi.2023.928639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 01/31/2023] [Indexed: 07/27/2023]
Abstract
Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.
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Affiliation(s)
- Gabrielle O. Windsor
- Department of Diagnostic Imaging, Brown University, Providence, RI, United States
| | - Harrison Bai
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Ana P. Lourenco
- Department of Diagnostic Imaging, Brown University, Providence, RI, United States
| | - Zhicheng Jiao
- Department of Diagnostic Imaging, Brown University, Providence, RI, United States
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Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis-A Rapid Review. Diagnostics (Basel) 2022; 13:diagnostics13010058. [PMID: 36611350 PMCID: PMC9818181 DOI: 10.3390/diagnostics13010058] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Ultrasound (US) is a fundamental diagnostic tool in breast imaging. However, US remains an operator-dependent examination. Research into and the application of artificial intelligence (AI) in breast US are increasing. The aim of this rapid review was to assess the current development of US-based artificial intelligence in the field of breast cancer. METHODS Two investigators with experience in medical research performed literature searching and data extraction on PubMed. The studies included in this rapid review evaluated the role of artificial intelligence concerning BC diagnosis, prognosis, molecular subtypes of breast cancer, axillary lymph node status, and the response to neoadjuvant chemotherapy. The mean values of sensitivity, specificity, and AUC were calculated for the main study categories with a meta-analytical approach. RESULTS A total of 58 main studies, all published after 2017, were included. Only 9/58 studies were prospective (15.5%); 13/58 studies (22.4%) used an ML approach. The vast majority (77.6%) used DL systems. Most studies were conducted for the diagnosis or classification of BC (55.1%). At present, all the included studies showed that AI has excellent performance in breast cancer diagnosis, prognosis, and treatment strategy. CONCLUSIONS US-based AI has great potential and research value in the field of breast cancer diagnosis, treatment, and prognosis. More prospective and multicenter studies are needed to assess the potential impact of AI in breast ultrasound.
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Li X, Yang L, Jiao X. Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer. Acad Radiol 2022:S1076-6332(22)00571-2. [DOI: 10.1016/j.acra.2022.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 10/09/2022] [Accepted: 10/15/2022] [Indexed: 11/13/2022]
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Coronado-Gutiérrez D, Ganau S, Bargalló X, Úbeda B, Porta M, Sanfeliu E, Burgos-Artizzu XP. Quantitative ultrasound image analysis of axillary lymph nodes to differentiate malignancy from reactive benign changes due to COVID-19 vaccination. Eur J Radiol 2022; 154:110438. [PMID: 35820268 PMCID: PMC9259511 DOI: 10.1016/j.ejrad.2022.110438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/23/2022] [Accepted: 07/05/2022] [Indexed: 11/27/2022]
Abstract
Purpose The aim of this study is to assess the potential of quantitative image analysis and machine learning techniques to differentiate between malignant lymph nodes and benign lymph nodes affected by reactive changes due to COVID-19 vaccination. Method In this institutional review board–approved retrospective study, we improved our previously published artificial intelligence model, by retraining it with newly collected images and testing its performance on images containing benign lymph nodes affected by COVID-19 vaccination. All the images were acquired and selected by specialized breast-imaging radiologists and the nature of each node (benign or malignant) was assessed through a strict clinical protocol using ultrasound-guided biopsies. Results A total of 180 new images from 154 different patients were recruited: 71 images (10 cases and 61 controls) were used to retrain the old model and 109 images (36 cases and 73 controls) were used to evaluate its performance. The achieved accuracy of the proposed method was 92.7% with 77.8% sensitivity and 100% specificity at the optimal cut-off point. In comparison, the visual node inspection made by radiologists from ultrasound images reached 69.7% accuracy with 41.7% sensitivity and 83.6% specificity. Conclusions The results obtained in this study show the potential of the proposed techniques to differentiate between malignant lymph nodes and benign nodes affected by reactive changes due to COVID-19 vaccination. These techniques could be useful to non-invasively diagnose lymph node status in patients with suspicious reactive nodes, although larger multicenter studies are needed to confirm and validate the results.
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Affiliation(s)
- David Coronado-Gutiérrez
- Transmural Biotech S. L., Barcelona, Spain; BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Clínic de Barcelona (University of Barcelona) and Hospital Sant Joan de Deu, Barcelona, Spain.
| | - Sergi Ganau
- Radiology Department, Hospital Clinic de Barcelona (University of Barcelona), Barcelona, Spain
| | - Xavier Bargalló
- Radiology Department, Hospital Clinic de Barcelona (University of Barcelona), Barcelona, Spain
| | - Belén Úbeda
- Radiology Department, Hospital Clinic de Barcelona (University of Barcelona), Barcelona, Spain
| | - Marta Porta
- Radiology Department, Hospital Clinic de Barcelona (University of Barcelona), Barcelona, Spain
| | - Esther Sanfeliu
- Radiology Department, Hospital Clinic de Barcelona (University of Barcelona), Barcelona, Spain
| | - Xavier P Burgos-Artizzu
- Transmural Biotech S. L., Barcelona, Spain; BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Clínic de Barcelona (University of Barcelona) and Hospital Sant Joan de Deu, Barcelona, Spain
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Abel F, Landsmann A, Hejduk P, Ruppert C, Borkowski K, Ciritsis A, Rossi C, Boss A. Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network. Diagnostics (Basel) 2022; 12:diagnostics12061347. [PMID: 35741157 PMCID: PMC9221636 DOI: 10.3390/diagnostics12061347] [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: 04/10/2022] [Revised: 05/26/2022] [Accepted: 05/26/2022] [Indexed: 11/16/2022] Open
Abstract
The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) “breast tissue”, (2) “benign lymph nodes”, and (3) “suspicious lymph nodes”. Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a “real-world” dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the “real-world” dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93–0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms.
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Liu Y, Ye F, Wang Y, Zheng X, Huang Y, Zhou J. Elaboration and Validation of a Nomogram Based on Axillary Ultrasound and Tumor Clinicopathological Features to Predict Axillary Lymph Node Metastasis in Patients With Breast Cancer. Front Oncol 2022; 12:845334. [PMID: 35651796 PMCID: PMC9148964 DOI: 10.3389/fonc.2022.845334] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 04/12/2022] [Indexed: 01/02/2023] Open
Abstract
Background This study aimed at constructing a nomogram to predict axillary lymph node metastasis (ALNM) based on axillary ultrasound and tumor clinicopathological features. Methods A retrospective analysis of 281 patients with pathologically confirmed breast cancer was performed between January 2015 and March 2018. All patients were randomly divided into a training cohort (n = 197) and a validation cohort (n = 84). Univariate and multivariable logistic regression analyses were performed to identify the clinically important predictors of ALNM when developin1 g the nomogram. The area under the curve (AUC), calibration plots, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical utility of the nomogram. Results In univariate and multivariate analyses, lymphovascular invasion (LVI), axillary lymph node (ALN) cortex thickness, and an obliterated ALN fatty hilum were identified as independent predictors and integrated to develop a nomogram for predicting ALNM. The nomogram showed favorable sensitivity for ALNM with AUCs of 0.87 (95% confidence interval (CI), 0.81–0.92) and 0.84 (95% CI, 0.73–0.92) in the training and validation cohorts, respectively. The calibration plots of the nomogram showed good agreement between the nomogram prediction and actual ALNM diagnosis (P > 0.05). Decision curve analysis (DCA) revealed the net benefit of the nomogram. Conclusions This study developed a nomogram based on three daily available clinical parameters, with good accuracy and clinical utility, which may help the radiologist in decision-making for ultrasound-guided fine needle aspiration cytology/biopsy (US-FNAC/B) according to the nomogram score.
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Affiliation(s)
- Yubo Liu
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Ultrasound, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Feng Ye
- Department of Breast Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Breast Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yun Wang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Ultrasound, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xueyi Zheng
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Ultrasound, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yini Huang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Ultrasound, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Ultrasound, Sun Yat-Sen University Cancer Center, Guangzhou, China
- *Correspondence: Jianhua Zhou,
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Althobaiti MM, Ashour AA, Alhindi NA, Althobaiti A, Mansour RF, Gupta D, Khanna A. Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3714422. [PMID: 35572730 PMCID: PMC9098312 DOI: 10.1155/2022/3714422] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/29/2022] [Accepted: 04/07/2022] [Indexed: 01/28/2023]
Abstract
The rapid development of technologies in biomedical research has enriched and broadened the range of medical equipment. Magnetic resonance imaging, ultrasonic imaging, and optical imaging have been discovered by diverse research communities to design multimodal systems, which is essential for biomedical applications. One of the important tools is photoacoustic multimodal imaging (PAMI) which combines the concepts of optics and ultrasonic systems. At the same time, earlier detection of breast cancer becomes essential to reduce mortality. The recent advancements of deep learning (DL) models enable detection and classification the breast cancer using biomedical images. This article introduces a novel social engineering optimization with deep transfer learning-based breast cancer detection and classification (SEODTL-BDC) model using PAI. The intention of the SEODTL-BDC technique is to detect and categorize the presence of breast cancer using ultrasound images. Primarily, bilateral filtering (BF) is applied as an image preprocessing technique to remove noise. Besides, a lightweight LEDNet model is employed for the segmentation of biomedical images. In addition, residual network (ResNet-18) model can be utilized as a feature extractor. Finally, SEO with recurrent neural network (RNN) model, named SEO-RNN classifier, is applied to allot proper class labels to the biomedical images. The performance validation of the SEODTL-BDC technique is carried out using benchmark dataset and the experimental outcomes pointed out the supremacy of the SEODTL-BDC approach over the existing methods.
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Affiliation(s)
- Maha M. Althobaiti
- Department of Computer Science, College of Computing and Information Technology, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia
| | - Amal Adnan Ashour
- Department of Oral & Maxillofacial Surgery and Diagnostic Sciences, Faculty of Dentistry, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Nada A. Alhindi
- Oral Diagnostic Sciences Department, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Asim Althobaiti
- Regional Laboratory and Blood Bank, Taif Health, Taif, Saudi Arabia
| | - Romany F. Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
| | - Deepak Gupta
- Department of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
| | - Ashish Khanna
- Department of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
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