1
|
Nissar I, Alam S, Masood S, Kashif M. MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108121. [PMID: 38531147 DOI: 10.1016/j.cmpb.2024.108121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 02/15/2024] [Accepted: 03/06/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND AND OBJECTIVE Deep Learning models have emerged as a significant tool in generating efficient solutions for complex problems including cancer detection, as they can analyze large amounts of data with high efficiency and performance. Recent medical studies highlight the significance of molecular subtype detection in breast cancer, aiding the development of personalized treatment plans as different subtypes of cancer respond better to different therapies. METHODS In this work, we propose a novel lightweight dual-channel attention-based deep learning model MOB-CBAM that utilizes the backbone of MobileNet-V3 architecture with a Convolutional Block Attention Module to make highly accurate and precise predictions about breast cancer. We used the CMMD mammogram dataset to evaluate the proposed model in our study. Nine distinct data subsets were created from the original dataset to perform coarse and fine-grained predictions, enabling it to identify masses, calcifications, benign, malignant tumors and molecular subtypes of cancer, including Luminal A, Luminal B, HER-2 Positive, and Triple Negative. The pipeline incorporates several image pre-processing techniques, including filtering, enhancement, and normalization, for enhancing the model's generalization ability. RESULTS While identifying benign versus malignant tumors, i.e., coarse-grained classification, the MOB-CBAM model produced exceptional results with 99 % accuracy, precision, recall, and F1-score values of 0.99 and MCC of 0.98. In terms of fine-grained classification, the MOB-CBAM model has proven to be highly efficient in accurately identifying mass with (benign/malignant) and calcification with (benign/malignant) classification tasks with an impressive accuracy rate of 98 %. We have also cross-validated the efficiency of the proposed MOB-CBAM deep learning architecture on two datasets: MIAS and CBIS-DDSM. On the MIAS dataset, an accuracy of 97 % was reported for the task of classifying benign, malignant, and normal images, while on the CBIS-DDSM dataset, an accuracy of 98 % was achieved for the classification of mass with either benign or malignant, and calcification with benign and malignant tumors. CONCLUSION This study presents lightweight MOB-CBAM, a novel deep learning framework, to address breast cancer diagnosis and subtype prediction. The model's innovative incorporation of the CBAM enhances precise predictions. The extensive evaluation of the CMMD dataset and cross-validation on other datasets affirm the model's efficacy.
Collapse
Affiliation(s)
- Iqra Nissar
- Department of Computer Engineering, Jamia Millia Islamia (A Central University), New Delhi, 110025, India.
| | - Shahzad Alam
- Department of Computer Engineering, Jamia Millia Islamia (A Central University), New Delhi, 110025, India
| | - Sarfaraz Masood
- Department of Computer Engineering, Jamia Millia Islamia (A Central University), New Delhi, 110025, India
| | - Mohammad Kashif
- Department of Computer Engineering, Jamia Millia Islamia (A Central University), New Delhi, 110025, India
| |
Collapse
|
2
|
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.
Collapse
Affiliation(s)
| | - Léon Groenhoff
- Radiology Department, Maggiore della Carità Hospital, 28100 Novara, Italy; (A.C.); (E.V.); (P.B.); (M.A.)
| | | | | | | |
Collapse
|
3
|
Seth I, Lim B, Joseph K, Gracias D, Xie Y, Ross RJ, Rozen WM. Use of artificial intelligence in breast surgery: a narrative review. Gland Surg 2024; 13:395-411. [PMID: 38601286 PMCID: PMC11002485 DOI: 10.21037/gs-23-414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/21/2024] [Indexed: 04/12/2024]
Abstract
Background and Objective We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application. Methods Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded. Key Content and Findings AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence. Conclusions Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.
Collapse
Affiliation(s)
- Ishith Seth
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Bryan Lim
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Konrad Joseph
- Department of Surgery, Port Macquarie Base Hospital, New South Wales, Australia
| | - Dylan Gracias
- Department of Surgery, Townsville Hospital, Queensland, Australia
| | - Yi Xie
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
| | - Richard J. Ross
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Warren M. Rozen
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| |
Collapse
|
4
|
Lin FY, Lee CE, Chen CM, Chang YC, Huang CS. Automated marker-free longitudinal infrared breast image registration by GA-PSO. Phys Med Biol 2023; 68:245026. [PMID: 37832565 DOI: 10.1088/1361-6560/ad0357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 10/13/2023] [Indexed: 10/15/2023]
Abstract
The automated marker-free longitudinal Infrared (IR) breast image registration overcomes several challenges like no anatomic fiducial markers on the body surface, blurry boundaries, heat pattern variation by environmental and physiological factors, nonrigid deformation, etc, has the ability of quantitative pixel-wise analysis with the heat energy and patterns change in a time course study. To achieve the goal, scale-invariant feature transform, Harris corner, and Hessian matrix were employed to generate the feature points as anatomic fiducial markers, and hybrid genetic algorithm and particle swarm optimization minimizing the matching errors was used to find the appropriate corresponding pairs between the 1st IR image and thenth IR image. Moreover, the mechanism of the IR spectrogram hardware system has a high level of reproducibility. The performance of the proposed longitudinal image registration system was evaluated by the simulated experiments and the clinical trial. In the simulated experiments, the mean difference of our system is 1.64 mm, which increases 57.58% accuracy than manual determination and makes a 17.4% improvement than the previous study. In the clinical trial, 80 patients were captured several times of IR breast images during chemotherapy. Most of them were well aligned in the spatiotemporal domain. In the few cases with evident heat pattern dissipation and spatial deviation, it still provided a reliable comparison of vascular variation. Therefore, the proposed system is accurate and robust, which could be considered as a reliable tool for longitudinal approaches to breast cancer diagnosis.
Collapse
Affiliation(s)
- Fan-Ya Lin
- The Department of Biomedical Engineering, National Taiwan University, No. 1, Section 1, Jen-Ai Rd., Taipei 100, Taiwan
| | - Chi-En Lee
- The Department of Biomedical Engineering, National Taiwan University, No. 1, Section 1, Jen-Ai Rd., Taipei 100, Taiwan
| | - Chung-Ming Chen
- The Department of Biomedical Engineering, National Taiwan University, No. 1, Section 1, Jen-Ai Rd., Taipei 100, Taiwan
| | - Yeun-Chung Chang
- The Department of Medical Image, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Changde Street, Zhongzheng District, Taipei City, 100, Taiwan
| | - Chiun-Sheng Huang
- The Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Changde Street, Zhongzheng District, Taipei City, 100, Taiwan
| |
Collapse
|
5
|
Trovato B, Roggio F, Sortino M, Rapisarda L, Petrigna L, Musumeci G. Thermal profile classification of the back of sportive and sedentary healthy individuals. J Therm Biol 2023; 118:103751. [PMID: 38000144 DOI: 10.1016/j.jtherbio.2023.103751] [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: 04/17/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND Infrared thermography (IRT) is a non-harmful, risk-free imaging technique and it has application for healthy and pathological population. OBJECTIVE The aim of this study is to evaluate the thermographic profiles of the back of sport practitioners from different disciplines and compare it with those of sedentary healthy individuals. METHOD The back of 160 healthy subjects were evaluated, and participants were grouped considering their sport practice: team sport (TS), individual sport (IS), weight training (WT), inactive (I). Three regions of interest were identified to analyze the cervical, thoracic and lumbar temperatures of the back. RESULTS The Multivariate analysis of variance (MANOVA) resulted significant showing statistical differences for the cervical (p < 0.001), dorsal (p = 0.0011), and lumbar areas (p = 0.0366). The Tukey post-hoc test for pairwise comparison showed statistically significant differences between groups. For the cervical area significance was found between the IN and WT group (p = 0.002), the IN and IS group (p < 0.001), IN and TS group (p = 0.020). The dorsal area resulted significant between the IN and WT group (p = 0.007), the IN and IS group (p < 0.001), IN and TS group. The lumbar area showed significant differences only between the IN and WT group and the IN and IS group (p = 0.043). CONCLUSION This study demonstrated that inactive individuals manifest a statistically significant higher temperature in the cervical, dorsal and lumbar area of the back compared to sportive individuals.
Collapse
Affiliation(s)
- Bruno Trovato
- Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia n°97, 95123, Catania, Italy
| | - Federico Roggio
- Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia n°97, 95123, Catania, Italy; Sport and Exercise Sciences Research Unit, Department of Psychology, Educational Science and Human Movement, University of Palermo, Via Giovanni Pascoli 6, Palermo, 90144, Italy
| | - Martina Sortino
- Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia n°97, 95123, Catania, Italy
| | | | - Luca Petrigna
- Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia n°97, 95123, Catania, Italy.
| | - Giuseppe Musumeci
- Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia n°97, 95123, Catania, Italy; Research Center on Motor Activities (CRAM), University of Catania, Via S. Sofia n°97, 95123, Catania, Italy; Department of Biology, Sbarro Institute for Cancer Research and Molecular Medicine, College of Science and Technology, Temple University, Philadelphia, 19122, PA, United States
| |
Collapse
|
6
|
Yang L, Peng S, Yahya RO, Qian L. Cancer detection in breast cells using a hybrid method based on deep complex neural network and data mining. J Cancer Res Clin Oncol 2023; 149:13331-13344. [PMID: 37486394 DOI: 10.1007/s00432-023-05191-2] [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: 07/01/2023] [Accepted: 07/16/2023] [Indexed: 07/25/2023]
Abstract
INTRODUCTION Diagnosis of cancer in breast cells is an important and vital issue in the field of medicine. In this context, the use of advanced methods such as deep complex neural networks and data mining can significantly improve the accuracy and speed of diagnosis. A hybrid approach that can be effective in breast cancer diagnosis is the use of deep complex neural networks and data mining. Due to their powerful nonlinear capabilities in extracting complex features from data, deep neural networks have a very good ability to detect patterns related to cancer. By analyzing millions of data related to breast cells and recognizing common and unusual patterns in them, these networks are able to diagnose cancer with high accuracy. Also, the use of data mining method plays an important role in this process. METHODOLOGY Using data mining algorithms and techniques, useful information can be extracted from the available data and the characteristics of healthy and cancerous cells can be separated. This information can be given as input to the deep neural network to achieve more accurate diagnosis. Another method to diagnose breast cancer is the use of thermography, which we use in this research along with data mining and deep learning. RESULTS Thermography uses an infrared camera to record the temperature of the target area. This method of breast cancer imaging is less expensive and completely safe compared to other methods. A total of 187 volunteers including 152 healthy people and 35 cancer patients were evaluated. Each person had ten thermographic images, resulting in a total of 1870 thermographic images. Four alternative deep complex neural network models, namely ResNet18, ResNet50, VGG19, and Xception, were used to identify thermal images, including benign and malignant images. CONCULSION The evaluation results showed that the use of a combined method based on deep complex neural network and data mining in the diagnosis of cancer in breast cells can bring a significant improvement in the accuracy and speed of diagnosis of this important disease.
Collapse
Affiliation(s)
- Ling Yang
- School of Informatics, Harbin Guangsha College, Harbin, 150025, Heilongjiang, China
| | - Shengguang Peng
- School of Engineering and Management, Pingxiang University, Pingxiang, 337055, Jiangxi, China.
| | - Rebaz Othman Yahya
- Department of Computer Science, College of Science, Cihan University-Erbil, Erbil, Iraq
| | - Leren Qian
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, 85281, USA
| |
Collapse
|
7
|
Mazdeyasna S, Ghassemi P, Wang Q. Best Practices for Body Temperature Measurement with Infrared Thermography: External Factors Affecting Accuracy. SENSORS (BASEL, SWITZERLAND) 2023; 23:8011. [PMID: 37766064 PMCID: PMC10536210 DOI: 10.3390/s23188011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/12/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023]
Abstract
Infrared thermographs (IRTs) are commonly used during disease pandemics to screen individuals with elevated body temperature (EBT). To address the limited research on external factors affecting IRT accuracy, we conducted benchtop measurements and computer simulations with two IRTs, with or without an external temperature reference source (ETRS) for temperature compensation. The combination of an IRT and an ETRS forms a screening thermograph (ST). We investigated the effects of viewing angle (θ, 0-75°), ETRS set temperature (TETRS, 30-40 °C), ambient temperature (Tatm, 18-32 °C), relative humidity (RH, 15-80%), and working distance (d, 0.4-2.8 m). We discovered that STs exhibited higher accuracy compared to IRTs alone. Across the tested ranges of Tatm and RH, both IRTs exhibited absolute measurement errors of less than 0.97 °C, while both STs maintained absolute measurement errors of less than 0.12 °C. The optimal TETRS for EBT detection was 36-37 °C. When θ was below 30°, the two STs underestimated calibration source (CS) temperature (TCS) of less than 0.05 °C. The computer simulations showed absolute temperature differences of up to 0.28 °C and 0.04 °C between estimated and theoretical temperatures for IRTs and STs, respectively, considering d of 0.2-3.0 m, Tatm of 15-35 °C, and RH of 5-95%. The results highlight the importance of precise calibration and environmental control for reliable temperature readings and suggest proper ranges for these factors, aiming to enhance current standard documents and best practice guidelines. These insights enhance our understanding of IRT performance and their sensitivity to various factors, thereby facilitating the development of best practices for accurate EBT measurement.
Collapse
Affiliation(s)
| | | | - Quanzeng Wang
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA; (S.M.); (P.G.)
| |
Collapse
|
8
|
Majji R, G OPP, Rajeswari R, R C. Smart IoT in Breast Cancer Detection Using Optimal Deep Learning. J Digit Imaging 2023; 36:1489-1506. [PMID: 37221422 PMCID: PMC10406774 DOI: 10.1007/s10278-023-00834-9] [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: 04/02/2022] [Revised: 04/04/2023] [Accepted: 04/13/2023] [Indexed: 05/25/2023] Open
Abstract
IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)-based smart healthcare system, a trustworthy breast cancer classification method called Feedback Artificial Crow Search (FACS)-based Shepherd Convolutional Neural Network (ShCNN) is developed in this research. To choose the best routes, the secure routing operation is first carried out using the recommended FACS while taking fitness measures such as distance, energy, link quality, and latency into account. Then, by merging the Crow Search Algorithm (CSA) and Feedback Artificial Tree, the produced FACS is put into practice (FAT). After the completion of routing phase, the breast cancer categorization process is started at the base station. The feature extraction step is then introduced to the pre-processed input mammography image. As a result, it is possible to successfully get features including area, mean, variance, energy, contrast, correlation, skewness, homogeneity, Gray Level Co-occurrence Matrix (GLCM), and Local Gabor Binary Pattern (LGBP). The quality of the image is next enhanced through data augmentation, and finally, the developed FACS algorithm's ShCNN is used to classify breast cancer. The performance of FACS-based ShCNN is examined using six metrics, including energy, delay, accuracy, sensitivity, specificity, and True Positive Rate (TPR), with the maximum energy of 0.562 J, the least delay of 0.452 s, the highest accuracy of 91.56%, the higher sensitivity of 96.10%, the highest specificity of 91.80%, and the maximum TPR of 99.45%.
Collapse
Affiliation(s)
- Ramachandro Majji
- Department of Information Technology, Vardhaman College of Engineering, Kacharam, Hyderabad, Telangana, India.
| | - Om Prakash P G
- Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattangalathur, Chennai, Tamil Nadu, India
| | - R Rajeswari
- Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India
| | - Cristin R
- Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India
| |
Collapse
|
9
|
Avilés-Mendoza K, Gaibor-León NG, Asanza V, Lorente-Leyva LL, Peluffo-Ordóñez DH. A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network. Biomimetics (Basel) 2023; 8:255. [PMID: 37366850 DOI: 10.3390/biomimetics8020255] [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: 05/18/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/28/2023] Open
Abstract
About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.
Collapse
Affiliation(s)
- Karla Avilés-Mendoza
- Neuroimaging and Bioengineering Laboratory (LNB), Facultad de Ingeniería en Mecánica y Ciencias de la Producción, Escuela Superior Politécnica del Litoral (ESPOL), Campus Gustavo Galindo km 30.5 Vía Perimetral, Guayaquil 090903, Ecuador
| | - Neil George Gaibor-León
- Neuroimaging and Bioengineering Laboratory (LNB), Facultad de Ingeniería en Mecánica y Ciencias de la Producción, Escuela Superior Politécnica del Litoral (ESPOL), Campus Gustavo Galindo km 30.5 Vía Perimetral, Guayaquil 090903, Ecuador
| | | | - Leandro L Lorente-Leyva
- SDAS Research Group, Ben Guerir 43150, Morocco
- Faculty of Law, Administrative and Social Sciences, Universidad UTE, Quito 170147, Ecuador
| | - Diego H Peluffo-Ordóñez
- SDAS Research Group, Ben Guerir 43150, Morocco
- College of Computing, Mohammed VI Polytechnic University, Ben Guerir 47963, Morocco
| |
Collapse
|
10
|
González-Castro L, Chávez M, Duflot P, Bleret V, Martin AG, Zobel M, Nateqi J, Lin S, Pazos-Arias JJ, Del Fiol G, López-Nores M. Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records. Cancers (Basel) 2023; 15:2741. [PMID: 37345078 DOI: 10.3390/cancers15102741] [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: 03/28/2023] [Revised: 04/26/2023] [Accepted: 05/06/2023] [Indexed: 06/23/2023] Open
Abstract
Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recurrence. We hypothesized that combining features from structured and unstructured sources would provide better prediction results for 5-year cancer recurrence than either source alone. We collected and preprocessed clinical data from a cohort of BC patients, resulting in 823 valid subjects for analysis. We derived three sets of features: structured information, features from free text, and a combination of both. We evaluated the performance of five ML algorithms to predict 5-year cancer recurrence and selected the best-performing to test our hypothesis. The XGB (eXtreme Gradient Boosting) model yielded the best performance among the five evaluated algorithms, with precision = 0.900, recall = 0.907, F1-score = 0.897, and area under the receiver operating characteristic AUROC = 0.807. The best prediction results were achieved with the structured dataset, followed by the unstructured dataset, while the combined dataset achieved the poorest performance. ML algorithms for BC recurrence prediction are valuable tools to improve patient risk stratification, help with post-cancer monitoring, and plan more effective follow-up. Structured data provides the best results when fed to ML algorithms. However, an approach based on natural language processing offers comparable results while potentially requiring less mapping effort.
Collapse
Affiliation(s)
| | - Marcela Chávez
- Department of Information System Management, Centre Hospitalier Universitaire de Liège, 4000 Liège, Belgium
| | - Patrick Duflot
- Department of Information System Management, Centre Hospitalier Universitaire de Liège, 4000 Liège, Belgium
| | - Valérie Bleret
- Senology Department, Centre Hospitalier Universitaire de Liège, 4000 Liège, Belgium
| | | | - Marc Zobel
- Science Department, Symptoma GmbH, 1030 Vienna, Austria
| | - Jama Nateqi
- Science Department, Symptoma GmbH, 1030 Vienna, Austria
- Department of Internal Medicine, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Simon Lin
- Science Department, Symptoma GmbH, 1030 Vienna, Austria
- Department of Internal Medicine, Paracelsus Medical University, 5020 Salzburg, Austria
| | - José J Pazos-Arias
- atlanTTic Research Center, Department of Telematics Engineering, University of Vigo, 36310 Vigo, Spain
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84108, USA
| | - Martín López-Nores
- atlanTTic Research Center, Department of Telematics Engineering, University of Vigo, 36310 Vigo, Spain
| |
Collapse
|
11
|
Rehman SU, Khan MA, Masood A, Almujally NA, Baili J, Alhaisoni M, Tariq U, Zhang YD. BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images. Diagnostics (Basel) 2023; 13:diagnostics13091618. [PMID: 37175009 PMCID: PMC10178634 DOI: 10.3390/diagnostics13091618] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/16/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
The early detection of breast cancer using mammogram images is critical for lowering women's mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features do not perform well in several cases due to redundant and irrelevant information. We created a new framework for diagnosing breast cancer using entropy-controlled deep learning and flower pollination optimization from the mammogram images. In the proposed framework, a filter fusion-based method for contrast enhancement is developed. The pre-trained ResNet-50 model is then improved and trained using transfer learning on both the original and enhanced datasets. Deep features are extracted and combined into a single vector in the following phase using a serial technique known as serial mid-value features. The top features are then classified using neural networks and machine learning classifiers in the following stage. To accomplish this, a technique for flower pollination optimization with entropy control has been developed. The exercise used three publicly available datasets: CBIS-DDSM, INbreast, and MIAS. On these selected datasets, the proposed framework achieved 93.8, 99.5, and 99.8% accuracy, respectively. Compared to the current methods, the increase in accuracy and decrease in computational time are explained.
Collapse
Affiliation(s)
- Shams Ur Rehman
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan
| | | | - Anum Masood
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
| | - Nouf Abdullah Almujally
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Jamel Baili
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
| | - Majed Alhaisoni
- College of Computer Science and Engineering, University of Ha'il, Ha'il 81451, Saudi Arabia
| | - Usman Tariq
- Management Information System Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
| | - Yu-Dong Zhang
- Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
| |
Collapse
|
12
|
Iqbal S, N. Qureshi A, Li J, Mahmood T. On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:3173-3233. [PMID: 37260910 PMCID: PMC10071480 DOI: 10.1007/s11831-023-09899-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/19/2023] [Indexed: 06/02/2023]
Abstract
Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.
Collapse
Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
- Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Tariq Mahmood
- Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer & Information Sciences (CCIS), Prince Sultan University, Riyadh, 11586 Kingdom of Saudi Arabia
| |
Collapse
|
13
|
Mokoatle M, Marivate V, Mapiye D, Bornman R, Hayes VM. A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application. BMC Bioinformatics 2023; 24:112. [PMID: 36959534 PMCID: PMC10037872 DOI: 10.1186/s12859-023-05235-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/17/2023] [Indexed: 03/25/2023] Open
Abstract
BACKGROUND Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various malignancies. Initially, a series of preprocessing steps and image segmentation steps are performed to extract region of interest features from noisy features. Then, the extracted features are applied to several machine learning and deep learning methods for the detection of cancer. METHODS In this work, a review of all the methods that have been applied to develop machine learning algorithms that detect cancer is provided. With more than 100 types of cancer, this study only examines research on the four most common and prevalent cancers worldwide: lung, breast, prostate, and colorectal cancer. Next, by using state-of-the-art sentence transformers namely: SBERT (2019) and the unsupervised SimCSE (2021), this study proposes a new methodology for detecting cancer. This method requires raw DNA sequences of matched tumor/normal pair as the only input. The learnt DNA representations retrieved from SBERT and SimCSE will then be sent to machine learning algorithms (XGBoost, Random Forest, LightGBM, and CNNs) for classification. As far as we are aware, SBERT and SimCSE transformers have not been applied to represent DNA sequences in cancer detection settings. RESULTS The XGBoost model, which had the highest overall accuracy of 73 ± 0.13 % using SBERT embeddings and 75 ± 0.12 % using SimCSE embeddings, was the best performing classifier. In light of these findings, it can be concluded that incorporating sentence representations from SimCSE's sentence transformer only marginally improved the performance of machine learning models.
Collapse
Affiliation(s)
- Mpho Mokoatle
- Department of Computer Science, University of Pretoria, Pretoria, South Africa.
| | - Vukosi Marivate
- Department of Computer Science, University of Pretoria, Pretoria, South Africa
| | | | - Riana Bornman
- School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa
| | - Vanessa M Hayes
- School of Medical Sciences, The University of Sydney, Sydney, Australia
- School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa
| |
Collapse
|
14
|
Cardone D, Trevisi G, Perpetuini D, Filippini C, Merla A, Mangiola A. Intraoperative thermal infrared imaging in neurosurgery: machine learning approaches for advanced segmentation of tumors. Phys Eng Sci Med 2023; 46:325-337. [PMID: 36715852 PMCID: PMC10030394 DOI: 10.1007/s13246-023-01222-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 01/17/2023] [Indexed: 01/31/2023]
Abstract
Surgical resection is one of the most relevant practices in neurosurgery. Finding the correct surgical extent of the tumor is a key question and so far several techniques have been employed to assist the neurosurgeon in preserving the maximum amount of healthy tissue. Some of these methods are invasive for patients, not always allowing high precision in the detection of the tumor area. The aim of this study is to overcome these limitations, developing machine learning based models, relying on features obtained from a contactless and non-invasive technique, the thermal infrared (IR) imaging. The thermal IR videos of thirteen patients with heterogeneous tumors were recorded in the intraoperative context. Time (TD)- and frequency (FD)-domain features were extracted and fed different machine learning models. Models relying on FD features have proven to be the best solutions for the optimal detection of the tumor area (Average Accuracy = 90.45%; Average Sensitivity = 84.64%; Average Specificity = 93,74%). The obtained results highlight the possibility to accurately detect the tumor lesion boundary with a completely non-invasive, contactless, and portable technology, revealing thermal IR imaging as a very promising tool for the neurosurgeon.
Collapse
Affiliation(s)
- Daniela Cardone
- Department of Engineering and Geology, University G. d'Annunzio Chieti-Pescara, Pescara, Italy.
| | - Gianluca Trevisi
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio Chieti-Pescara, Chieti, Italy
| | - David Perpetuini
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio Chieti-Pescara, Chieti, Italy
| | - Chiara Filippini
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio Chieti-Pescara, Chieti, Italy
| | - Arcangelo Merla
- Department of Engineering and Geology, University G. d'Annunzio Chieti-Pescara, Pescara, Italy
| | - Annunziato Mangiola
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio Chieti-Pescara, Chieti, Italy
| |
Collapse
|
15
|
Breast Cancer Diagnosis in Thermography Using Pre-Trained VGG16 with Deep Attention Mechanisms. Symmetry (Basel) 2023. [DOI: 10.3390/sym15030582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
One of the most prevalent cancers in women is breast cancer. The mortality rate related to this disease can be decreased by early, accurate diagnosis to increase the chance of survival. Infrared thermal imaging is one of the breast imaging modalities in which the temperature of the breast tissue is measured using a screening tool. The previous studies did not use pre-trained deep learning (DL) with deep attention mechanisms (AMs) on thermographic images for breast cancer diagnosis. Using thermal images from the Database for Research Mastology with Infrared Image (DMR-IR), the study investigates the use of a pre-trained Visual Geometry Group with 16 layers (VGG16) with AMs that can produce good diagnosis performance utilizing the thermal images of breast cancer. The symmetry of the three models resulting from the combination of VGG16 with three types of AMs is evident in all its stages in methodology. The models were compared to state-of-art breast cancer diagnosis approaches and tested for accuracy, sensitivity, specificity, precision, F1-score, AUC score, and Cohen’s kappa. The test accuracy rates for the AMs using the VGG16 model on the breast thermal dataset were encouraging, at 99.80%, 99.49%, and 99.32%. Test accuracy for VGG16 without AMs was 99.18%, whereas test accuracy for VGG16 with AMs improved by 0.62%. The proposed approaches also performed better than previous approaches examined in the related studies.
Collapse
|
16
|
Pakarinen T, Joutsen A, Oksala N, Vehkaoja A. Assessment of chronic limb threatening ischemia using thermal imaging. J Therm Biol 2023; 112:103467. [PMID: 36796912 DOI: 10.1016/j.jtherbio.2023.103467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023]
Abstract
OBJECTIVES Current chronic limb threatening ischemia (CLTI) diagnostics require expensive equipment, using ionizing radiation or contrast agents, or summative surrogate methods lacking in spatial information. Our aim is to develop and improve contactless, non-ionizing and cost-effective diagnostic methods for CLTI assessment with high spatial accuracy by utilizing dynamic thermal imaging and the angiosome concept. APPROACH Dynamic thermal imaging test protocol was suggested and implemented with a number of computational parameters. Pilot data was measured from 3 healthy young subjects, 4 peripheral artery disease (PAD) patients and 4 CLTI patients. The protocol consists of clinical reference measurements, including ankle- and toe-brachial indices (ABI, TBI), and a modified patient bed for hydrostatic and thermal modulation tests. The data was analyzed using bivariate correlation. RESULTS The thermal recovery time constant was on average higher for the PAD (88%) and CLTI (83%) groups with respect to the healthy young subjects. The contralateral symmetry was high for the healthy young group and low for the CLTI group. The recovery time constants showed high negative correlation to TBI (ρ = -0.73) and ABI (ρ = -0.60). The relation of these clinical parameters to the hydrostatic response and absolute temperatures (|ρ|<0.3) remained unclear. CONCLUSION The lack of correlation for absolute temperatures or their contralateral differences with the clinical status, ABI and TBI disputes their use in CLTI diagnostics. Thermal modulation tests tend to augment the signs of thermoregulation deficiencies and accordingly high correlations were found with all reference metrics. The method is promising for establishing the connection between impaired perfusion and thermography. The hydrostatic modulation test requires more research with stricter test conditions.
Collapse
Affiliation(s)
- Tomppa Pakarinen
- Faculty of Medicine and Health Technology, Tampere University, Postal: Tampereen Yliopisto, Korkeakoulunkatu 3, 33720, Tampere, Finland.
| | - Atte Joutsen
- Department of Medical Physics, Medical Imaging Center, Tampere University Hospital, Postal: Sädetie 6, 33520, Tampere, Finland.
| | - Niku Oksala
- Faculty of Medicine and Health Technology, Tampere University, Postal: Tampereen Yliopisto, Korkeakoulunkatu 3, 33720, Tampere, Finland; Vascular Surgery and Procedural Radiology, Tampere University Hospital, Postal: Elämänaukio 2, 33520, Tampere, Finland.
| | - Antti Vehkaoja
- Faculty of Medicine and Health Technology, Tampere University, Postal: Tampereen Yliopisto, Korkeakoulunkatu 3, 33720, Tampere, Finland.
| |
Collapse
|
17
|
Nasser M, Yusof UK. Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction. Diagnostics (Basel) 2023; 13:diagnostics13010161. [PMID: 36611453 PMCID: PMC9818155 DOI: 10.3390/diagnostics13010161] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/19/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023] Open
Abstract
Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients' survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.
Collapse
|
18
|
Jiang Y, Hu H, He X, Li X, Zhang Y, Lou J, Wu Y, Fang J, Shao X, Fang J. Specificity for the correlation between the body surface and viscera in the pathological state of COPD: A prospective, controlled, and assessor-blinded trial. Front Physiol 2023; 14:1051190. [PMID: 37153229 PMCID: PMC10159081 DOI: 10.3389/fphys.2023.1051190] [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: 09/23/2022] [Accepted: 04/10/2023] [Indexed: 05/09/2023] Open
Abstract
Background: The association between the body surface and viscera remains obscure, but a better understanding of the body surface-viscera correlation will maximize its diagnostic and therapeutic values in clinical practice. Therefore, this study aimed to investigate the specificity of body surface-viscera correlation in the pathological state. Methods: The study subjects included 40 participants with chronic obstructive pulmonary disease (COPD) in the COPD group and 40 age-matched healthy participants in the healthy control group. Laser Doppler flowmetry, infrared thermography, and functional near-infrared spectroscopy were respectively adopted to measure 1) the perfusion unit (PU), 2) temperature, and 3) regional oxygen saturation (rSO2) of four specific sites distributed in the heart and lung meridians. These three outcome measures reflected the microcirculatory, thermal, and metabolic characteristics, respectively. Results: Regarding the microcirculatory and thermal characteristics of the body surface, the PU and temperature of specific sites on the body surface [i.e., Taiyuan (LU9) and Chize (LU5) in the lung meridian] in the COPD group were significantly increased compared with healthy controls (p < 0.05), whereas PU and temperature of other sites in the heart meridian [i.e., Shenmen (HT7) and Shaohai (HT3)] did not change significantly (p > 0.05). Regarding the metabolic characteristics, rSO2 of specific sites in the lung meridian [i.e., Taiyuan (LU9) and Chize (LU5)] and Shaohai (HT3) of the heart meridian in the COPD group was significantly decreased compared with healthy controls (p < 0.01), whereas rSO2 of Shenmen (HT7) in the heart meridian did not change significantly (p > 0.05). Conclusion: In the disease state of COPD, the microcirculatory, thermal, and metabolic characteristics of specific sites on the body surface in the lung meridian generally manifest more significant changes than those in the heart meridian, thereby supporting relative specificity for the body surface-viscera correlation in the pathological state.
Collapse
Affiliation(s)
- Yongliang Jiang
- Key Laboratory of Acupuncture and Neurology of Zhejiang Province, Department of Neurobiology and Acupuncture Research, The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Hantong Hu
- Key Laboratory of Acupuncture and Neurology of Zhejiang Province, Department of Neurobiology and Acupuncture Research, The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Acupuncture and Moxibustion, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Xiaofen He
- Key Laboratory of Acupuncture and Neurology of Zhejiang Province, Department of Neurobiology and Acupuncture Research, The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaoyu Li
- Key Laboratory of Acupuncture and Neurology of Zhejiang Province, Department of Neurobiology and Acupuncture Research, The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yajun Zhang
- Key Laboratory of Acupuncture and Neurology of Zhejiang Province, Department of Neurobiology and Acupuncture Research, The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jiali Lou
- Key Laboratory of Acupuncture and Neurology of Zhejiang Province, Department of Neurobiology and Acupuncture Research, The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yuanyuan Wu
- Key Laboratory of Acupuncture and Neurology of Zhejiang Province, Department of Neurobiology and Acupuncture Research, The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Acupuncture and Moxibustion, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Junfan Fang
- Key Laboratory of Acupuncture and Neurology of Zhejiang Province, Department of Neurobiology and Acupuncture Research, The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaomei Shao
- Key Laboratory of Acupuncture and Neurology of Zhejiang Province, Department of Neurobiology and Acupuncture Research, The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jianqiao Fang
- Key Laboratory of Acupuncture and Neurology of Zhejiang Province, Department of Neurobiology and Acupuncture Research, The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
- *Correspondence: Jianqiao Fang,
| |
Collapse
|
19
|
Čepas A, Fomkinas M, Gindriūnas S, Budreckis K, Pilipaitytė L, Rainys D. The Use of Thermal Imaging in Free Perforator Flap Planning. LIETUVOS CHIRURGIJA 2022. [DOI: 10.15388/lietchirur.2022.21.68] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Background. Preoperative planning and design of microsurgical perforator flaps are the main steps for successful operation. The aim of this study was to determine the concordance between thermographic images obtained with smartphone thermal imaging camera and hand-held Doppler in the anterolateral thigh flap (ALT) model. Methods. A concordance study of diagnostic tests was carried out in Hospital of Lithuanian University of Health Sciences Kaunas Clinics, Plastic and Reconstructive Surgery Department during 2020–2022. Patients’ who were scheduled to undergo reconstruction with ALT flap and healthy volunteers were included in the study. Dynamic thermal images were performed with smartphone thermal camera FLIR One PRO in the typical ALT flap territory. The number and distance of hotspots in the thermogram with respect to anterior superior iliac spine were recorded in the study protocol. Later, the examination was repeated with hand-held Doppler and the control of hotspot was performed. Sensitivity, specificity and concordance index calculations were performed. Statistical analysis was performed using IMB SPSS 23.0. Results. A total of 100 ALT flap territories were examined. 266 hotspots were detected with thermal imaging and 275 perforators with hand-held Doppler. In 96.6% of cases, hotspots detected by a thermal camera were confirmed by hand-held Doppler as perforators. The sensitivity and specificity of thermography for the detection of perforators with respect to the hand-held Doppler were 93.5% and 96.9% respectively. The measure of concordance kappa index was 0.095 (p = 0.001). Conclusion. Smartphone thermal imaging have a high concordance with hand-held Doppler in perforator mapping, thus could be considered a useful adjunct to conventional methods.
Collapse
|
20
|
Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review. Diagnostics (Basel) 2022; 12:diagnostics12123111. [PMID: 36553119 PMCID: PMC9777253 DOI: 10.3390/diagnostics12123111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
Collapse
|
21
|
Tamang T, Baral S, Paing MP. Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12122903. [PMID: 36552910 PMCID: PMC9777002 DOI: 10.3390/diagnostics12122903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022] Open
Abstract
White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.
Collapse
Affiliation(s)
- Thinam Tamang
- Madan Bhandari Memorial College, New Baneshwor, Kathmandu 44600, Nepal
| | - Sushish Baral
- Department of Robotics and AI, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
- Correspondence: (S.B.); (M.P.P.)
| | - May Phu Paing
- Department of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
- Correspondence: (S.B.); (M.P.P.)
| |
Collapse
|
22
|
A. Mohamed E, Gaber T, Karam O, Rashed EA. A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms. PLoS One 2022; 17:e0276523. [PMID: 36269756 PMCID: PMC9586394 DOI: 10.1371/journal.pone.0276523] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Breast cancer is the second most frequent cancer worldwide, following lung cancer and the fifth leading cause of cancer death and a major cause of cancer death among women. In recent years, convolutional neural networks (CNNs) have been successfully applied for the diagnosis of breast cancer using different imaging modalities. Pooling is a main data processing step in CNN that decreases the feature maps’ dimensionality without losing major patterns. However, the effect of pooling layer was not studied efficiently in literature. In this paper, we propose a novel design for the pooling layer called vector pooling block (VPB) for the CCN algorithm. The proposed VPB consists of two data pathways, which focus on extracting features along horizontal and vertical orientations. The VPB makes the CNNs able to collect both global and local features by including long and narrow pooling kernels, which is different from the traditional pooling layer, that gathers features from a fixed square kernel. Based on the novel VPB, we proposed a new pooling module called AVG-MAX VPB. It can collect informative features by using two types of pooling techniques, maximum and average pooling. The VPB and the AVG-MAX VPB are plugged into the backbone CNNs networks, such as U-Net, AlexNet, ResNet18 and GoogleNet, to show the advantages in segmentation and classification tasks associated with breast cancer diagnosis from thermograms. The proposed pooling layer was evaluated using a benchmark thermogram database (DMR-IR) and its results compared with U-Net results which was used as base results. The U-Net results were as follows: global accuracy = 96.6%, mean accuracy = 96.5%, mean IoU = 92.07%, and mean BF score = 78.34%. The VBP-based results were as follows: global accuracy = 98.3%, mean accuracy = 97.9%, mean IoU = 95.87%, and mean BF score = 88.68% while the AVG-MAX VPB-based results were as follows: global accuracy = 99.2%, mean accuracy = 98.97%, mean IoU = 98.03%, and mean BF score = 94.29%. Other network architectures also demonstrate superior improvement considering the use of VPB and AVG-MAX VPB.
Collapse
Affiliation(s)
- Esraa A. Mohamed
- Faculty of Science, Department of Mathematics, Suez Canal University, Ismailia, Egypt
| | - Tarek Gaber
- Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt
- School of Science, Engineering and Environment University of Salford, Manchester, United Kingdom
- * E-mail:
| | - Omar Karam
- Faculty of Informatics and Computer Science, British University in Egypt (BUE), Cairo, Egypt
| | - Essam A. Rashed
- Faculty of Science, Department of Mathematics, Suez Canal University, Ismailia, Egypt
- Graduate School of Information Science, University of Hyogo, Kobe, Japan
| |
Collapse
|
23
|
Wang D, Hu Y, Zhan C, Zhang Q, Wu Y, Ai T. A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer. Front Oncol 2022; 12:940655. [PMID: 36338691 PMCID: PMC9633001 DOI: 10.3389/fonc.2022.940655] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 10/07/2022] [Indexed: 10/03/2023] Open
Abstract
PURPOSE To develop a nomogram based on radiomics signature and deep-learning signature for predicting the axillary lymph node (ALN) metastasis in breast cancer. METHODS A total of 151 patients were assigned to a training cohort (n = 106) and a test cohort (n = 45) in this study. Radiomics features were extracted from DCE-MRI images, and deep-learning features were extracted by VGG-16 algorithm. Seven machine learning models were built using the selected features to evaluate the predictive value of radiomics or deep-learning features for the ALN metastasis in breast cancer. A nomogram was then constructed based on the multivariate logistic regression model incorporating radiomics signature, deep-learning signature, and clinical risk factors. RESULTS Five radiomics features and two deep-learning features were selected for machine learning model construction. In the test cohort, the AUC was above 0.80 for most of the radiomics models except DecisionTree and ExtraTrees. In addition, the K-nearest neighbor (KNN), XGBoost, and LightGBM models using deep-learning features had AUCs above 0.80 in the test cohort. The nomogram, which incorporated the radiomics signature, deep-learning signature, and MRI-reported LN status, showed good calibration and performance with the AUC of 0.90 (0.85-0.96) in the training cohort and 0.90 (0.80-0.99) in the test cohort. The DCA showed that the nomogram could offer more net benefit than radiomics signature or deep-learning signature. CONCLUSIONS Both radiomics and deep-learning features are diagnostic for predicting ALN metastasis in breast cancer. The nomogram incorporating radiomics and deep-learning signatures can achieve better prediction performance than every signature used alone.
Collapse
Affiliation(s)
- Dawei Wang
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiqi Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chenao Zhan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Zhang
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiping Wu
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
24
|
Towards an Accurate MRI Acute Ischemic Stroke Lesion Segmentation Based on Bioheat Equation and U-Net Model. Int J Biomed Imaging 2022; 2022:5529726. [PMID: 35880140 PMCID: PMC9308529 DOI: 10.1155/2022/5529726] [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: 05/03/2021] [Revised: 08/11/2021] [Accepted: 07/04/2022] [Indexed: 11/29/2022] Open
Abstract
Acute ischemic stroke represents a cerebrovascular disease, for which it is practical, albeit challenging to segment and differentiate infarct core from salvageable penumbra brain tissue. Ischemic stroke causes the variation of cerebral blood flow and heat generation due to metabolism. Therefore, the temperature is modified in the ischemic stroke region. In this paper, we incorporate acute ischemic stroke temperature profile to reinforce segmentation accuracy in MRI. Pennes bioheat equation was used to generate brain thermal images that may provide rich information regarding the temperature change in acute ischemic stroke lesions. The thermal images were generated by calculating the temperature of the brain with acute ischemic stroke. Then, U-Net was used in this paper for the segmentation of acute ischemic stroke. A dataset of 3192 images was created to train U-Net using k-fold crossvalidation. The training time was about 10 hours and 35 minutes in NVIDIA GPU. Next, the obtained trained model was compared with recent methods to analyze the effect of the ischemic stroke temperature profile in segmentation. The obtained results show that significant parts of acute ischemic stroke and background areas are segmented only in thermal images, which proves the importance of using thermal information to improve the segmentation outcomes in MRI diagnosis.
Collapse
|
25
|
Vats V, Nagori A, Singh P, Dutt R, Bandhey H, Wason M, Lodha R, Sethi T. Early Prediction of Hemodynamic Shock in Pediatric Intensive Care Units With Deep Learning on Thermal Videos. Front Physiol 2022; 13:862411. [PMID: 35923238 PMCID: PMC9340772 DOI: 10.3389/fphys.2022.862411] [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: 01/25/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Shock is one of the major killers in intensive care units, and early interventions can potentially reverse it. In this study, we advance a noncontact thermal imaging modality for continuous monitoring of hemodynamic shock working on 1,03,936 frames from 406 videos recorded longitudinally upon 22 pediatric patients. Deep learning was used to preprocess and extract the Center-to-Peripheral Difference (CPD) in temperature values from the videos. This time-series data along with the heart rate was finally analyzed using Long-Short Term Memory models to predict the shock status up to the next 6 h. Our models achieved the best area under the receiver operating characteristic curve of 0.81 ± 0.06 and area under the precision-recall curve of 0.78 ± 0.05 at 5 h, providing sufficient time to stabilize the patient. Our approach, thus, provides a reliable shock prediction using an automated decision pipeline that can provide better care and save lives.
Collapse
Affiliation(s)
- Vanshika Vats
- Indraprastha Institute of Information Technology, Delhi, India
| | - Aditya Nagori
- Indraprastha Institute of Information Technology, Delhi, India
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Pradeep Singh
- Indraprastha Institute of Information Technology, Delhi, India
| | - Raman Dutt
- Computer Science and Engineering, Shiv Nadar University, Greater Noida, India
| | - Harsh Bandhey
- Indraprastha Institute of Information Technology, Delhi, India
| | - Mahika Wason
- Indraprastha Institute of Information Technology, Delhi, India
| | - Rakesh Lodha
- Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, India
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, Delhi, India
- Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, India
- *Correspondence: Tavpritesh Sethi,
| |
Collapse
|
26
|
Yang E, Lu W, Muñoz-Vergara D, Goldfinger E, Kaptchuk TJ, Napadow V, Ahn AC, Wayne PM. Skin Temperature of Acupoints in Health and Disease: A Systematic Review. JOURNAL OF INTEGRATIVE AND COMPLEMENTARY MEDICINE 2022; 28:552-568. [PMID: 35475679 DOI: 10.1089/jicm.2021.0437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Introduction: Despite substantial progress made in the field of acupuncture research, the existence and specificity of acupoints remain controversial. In recent years, the concept of acupoint sensitization has emerged as a theoretical framework for understanding acupoints as dynamic functional entities that are sensitized in pathological conditions. Based on this premise, some have claimed that specific acupoints are thermally distinct between healthy and clinical populations, but no systematic review has been conducted to synthesize and evaluate the quality of studies supporting such claims. In this review, we provide a summary and quality assessment of the existing literature addressing the question of whether changes in skin temperature at specific acupoints are indicative of pathological conditions. Methods: A systematic literature search was performed in PubMed, EMBASE, and AltHealthWatch (EBSCO Host), by combining variations of search terms relevant to acupoints and temperature. The search was limited to the English language, and publication dates ranged from database inception to December 2020. Two authors independently screened all resulting abstracts and subsequently read full-text articles for eligibility. Information on study design, sample, acupoints, parameters of skin temperature assessments, and main findings were extracted from included studies. Quality of the thermal sensing methodology was evaluated using a thermal assessment checklist, adapted from the Thermographic Imaging in Sports and Exercise Medicine (TISEM) consensus checklist, and a modified Newcastle-Ottawa Scale (NOS) for case-control studies. Results: The search strategy yielded a total of 1771 studies, of which 10 articles met the eligibility criteria. Eight studies compared skin temperature at acupoints in healthy versus clinical populations, and two studies assessed within-subject changes in temperature of acupoints in relation to changes in health status. There were seven clinical conditions examined in the included studies: chronic bronchial asthma, chronic hepatitis, hyperplasia of mammary glands, infertility, intracranial hypertension, obesity, and primary dysmenorrhea. There were numerous methodological quality issues related to skin temperature measurements. Eight studies with case-control designs reported significant differences between healthy and clinical populations in temperature at certain acupoints. Two studies with pre-post designs reported that changes in health-disease status could be associated with changes in temperature at specific acupoints. Conclusion: A review of the available literature suggests that certain acupoints may be thermally distinct between healthy and unhealthy states. However, given the methodological limitations and heterogeneity across included studies, no definitive conclusion could be drawn as to whether changes in skin temperature at specific acupoints are indicative of pathological conditions.
Collapse
Affiliation(s)
- EunMee Yang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Osher Center for Integrative Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Weidong Lu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dennis Muñoz-Vergara
- Osher Center for Integrative Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Esme Goldfinger
- Osher Center for Integrative Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Ted J Kaptchuk
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Vitaly Napadow
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Andrew C Ahn
- PhysioQ Organization, Boston, MA, USA
- Department of Medicine, Veteran Affairs Boston Healthcare System, West Roxbury, MA, USA
| | - Peter M Wayne
- Osher Center for Integrative Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| |
Collapse
|
27
|
Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection. APPLIED COMPUTING AND INFORMATICS 2022. [DOI: 10.1108/aci-03-2022-0075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeIn time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.Design/methodology/approachIn this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.FindingsThe proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.Research limitations/implicationsResearch in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.Originality/valueThe proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.
Collapse
|
28
|
Kim GN, Zhang HY, Cho YE, Ryu SJ. Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images. Healthcare (Basel) 2022; 10:healthcare10061094. [PMID: 35742145 PMCID: PMC9222567 DOI: 10.3390/healthcare10061094] [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: 04/12/2022] [Revised: 05/29/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Doctors in primary hospitals can obtain the impression of lumbosacral radiculopathy with a physical exam and need to acquire medical images, such as an expensive MRI, for diagnosis. Then, doctors will perform a foraminal root block to the target root for pain control. However, there was insufficient screening medical image examination for precise L5 and S1 lumbosacral radiculopathy, which is most prevalent in the clinical field. Therefore, to perform differential screening of L5 and S1 lumbosacral radiculopathy, the authors applied digital infrared thermographic images (DITI) to the machine learning (ML) algorithm, which is the bag of visual words method. DITI dataset included data from the healthy population and radiculopathy patients with herniated lumbar discs (HLDs) L4/5 and L5/S1. A total of 842 patients were enrolled and the dataset was split into a 7:3 ratio as the training algorithm and test dataset to evaluate model performance. The average accuracy was 0.72 and 0.67, the average precision was 0.71 and 0.77, the average recall was 0.69 and 0.74, and the F1 score was 0.70 and 0.75 for the training and test datasets. Application of the bag of visual words algorithm to DITI classification will aid in the differential screening of lumbosacral radiculopathy and increase the therapeutic effect of primary pain interventions with economical cost.
Collapse
Affiliation(s)
- Gi Nam Kim
- Department of Spinal Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea; (G.N.K.); (Y.E.C.)
| | - Ho Yeol Zhang
- Department of Neurosurgery, National Health Insurance Service Ilsan Hospital, Yonsei University College of Medicine, Goyang 10444, Korea;
| | - Yong Eun Cho
- Department of Spinal Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea; (G.N.K.); (Y.E.C.)
| | - Seung Jun Ryu
- Department of Neurosurgery, National Health Insurance Service Ilsan Hospital, Yonsei University College of Medicine, Goyang 10444, Korea;
- Correspondence: ; Tel.: +82-10-2367-9263
| |
Collapse
|
29
|
Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head. Diagnostics (Basel) 2022; 12:diagnostics12051152. [PMID: 35626307 PMCID: PMC9139754 DOI: 10.3390/diagnostics12051152] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/23/2022] [Accepted: 04/28/2022] [Indexed: 11/16/2022] Open
Abstract
Introduction and Background: Despite fast developments in the medical field, histological diagnosis is still regarded as the benchmark in cancer diagnosis. However, the input image feature extraction that is used to determine the severity of cancer at various magnifications is harrowing since manual procedures are biased, time consuming, labor intensive, and error-prone. Current state-of-the-art deep learning approaches for breast histopathology image classification take features from entire images (generic features). Thus, they are likely to overlook the essential image features for the unnecessary features, resulting in an incorrect diagnosis of breast histopathology imaging and leading to mortality. Methods: This discrepancy prompted us to develop DEEP_Pachi for classifying breast histopathology images at various magnifications. The suggested DEEP_Pachi collects global and regional features that are essential for effective breast histopathology image classification. The proposed model backbone is an ensemble of DenseNet201 and VGG16 architecture. The ensemble model extracts global features (generic image information), whereas DEEP_Pachi extracts spatial information (regions of interest). Statistically, the evaluation of the proposed model was performed on publicly available dataset: BreakHis and ICIAR 2018 Challenge datasets. Result: A detailed evaluation of the proposed model’s accuracy, sensitivity, precision, specificity, and f1-score metrics revealed the usefulness of the backbone model and the DEEP_Pachi model for image classifying. The suggested technique outperformed state-of-the-art classifiers, achieving an accuracy of 1.0 for the benign class and 0.99 for the malignant class in all magnifications of BreakHis datasets and an accuracy of 1.0 on the ICIAR 2018 Challenge dataset. Conclusion: The acquired findings were significantly resilient and proved helpful for the suggested system to assist experts at big medical institutions, resulting in early breast cancer diagnosis and a reduction in the death rate.
Collapse
|
30
|
Ragab M, Albukhari A, Alyami J, Mansour RF. Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images. BIOLOGY 2022; 11:biology11030439. [PMID: 35336813 PMCID: PMC8945718 DOI: 10.3390/biology11030439] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/25/2022] [Accepted: 03/11/2022] [Indexed: 01/02/2023]
Abstract
Simple Summary In the literature, there exist plenty of research works focused on the detection and classification of breast cancer. However, only a few works have focused on the classification of breast cancer using ultrasound scan images. Although deep transfer learning models are useful in breast cancer classification, owing to their outstanding performance in a number of applications, image pre-processing and segmentation techniques are essential. In this context, the current study developed a new Ensemble Deep-Learning-Enabled Clinical Decision Support System for the diagnosis and classification of breast cancer using ultrasound images. In the study, an optimal multi-level thresholding-based image segmentation technique was designed to identify the tumor-affected regions. The study also developed an ensemble of three deep learning models for feature extraction and an optimal machine learning classifier for breast cancer detection. The study offers a means of assisting radiologists and healthcare professionals in the breast cancer classification process. Abstract Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist’s experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur’s entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods.
Collapse
Affiliation(s)
- Mahmoud Ragab
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Mathematics Department, Faculty of Science, Al-Azhar University, Cairo 11884, Egypt
- Correspondence:
| | - Ashwag Albukhari
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Jaber Alyami
- Diagnostic Radiology Department, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Imaging Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Romany F. Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt;
| |
Collapse
|
31
|
Detection of Breast Cancer from Five-View Thermal Images Using Convolutional Neural Networks. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4295221. [PMID: 35265301 PMCID: PMC8901325 DOI: 10.1155/2022/4295221] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 01/06/2022] [Accepted: 01/19/2022] [Indexed: 11/18/2022]
Abstract
Breast cancer is one of the most common forms of cancer. Its aggressive nature coupled with high mortality rates makes this cancer life-threatening; hence early detection gives the patient a greater chance of survival. Currently, the preferred diagnosis method is mammography. However, mammography is expensive and exposes the patient to radiation. A cost-effective and less invasive method known as thermography is gaining popularity. Bearing this in mind, the work aims to initially create machine learning models based on convolutional neural networks using multiple thermal views of the breast to detect breast cancer using the Visual DMR dataset. The performances of these models are then verified with the clinical data. Findings indicate that the addition of clinical data decisions to the model helped increase its performance. After building and testing two models with different architectures, the model used the same architecture for all three views performed best. It performed with an accuracy of 85.4%, which increased to 93.8% after the clinical data decision was added. After the addition of clinical data decisions, the model was able to classify more patients correctly with a specificity of 96.7% and sensitivity of 88.9% when considering sick patients as the positive class. Currently, thermography is among the lesser-known diagnosis methods with only one public dataset. We hope our work will divert more attention to this area.
Collapse
|
32
|
Gonçalves CB, Souza JR, Fernandes H. CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images. Comput Biol Med 2022; 142:105205. [DOI: 10.1016/j.compbiomed.2021.105205] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/28/2021] [Accepted: 12/29/2021] [Indexed: 11/03/2022]
|
33
|
Deep learning model for fully automated breast cancer detection system from thermograms. PLoS One 2022; 17:e0262349. [PMID: 35030211 PMCID: PMC8759675 DOI: 10.1371/journal.pone.0262349] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/22/2021] [Indexed: 11/19/2022] Open
Abstract
Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.
Collapse
|
34
|
Diniz de Lima E, Souza Paulino JA, Lira de Farias Freitas AP, Viana Ferreira JE, Barbosa JDS, Bezerra Silva DF, Bento PM, Araújo Maia Amorim AM, Melo DP. Artificial intelligence and infrared thermography as auxiliary tools in the diagnosis of temporomandibular disorder. Dentomaxillofac Radiol 2022; 51:20210318. [PMID: 34613829 PMCID: PMC8802706 DOI: 10.1259/dmfr.20210318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVE To assess three machine learning (ML) attribute extraction methods: radiomic, semantic and radiomic-semantic association on temporomandibular disorder (TMD) detection using infrared thermography (IT); and to determine which ML classifier, KNN, SVM and MLP, is the most efficient for this purpose. METHODS AND MATERIALS 78 patients were selected by applying the Fonseca questionnaire and RDC/TMD to categorize control patients (37) and TMD patients (41). IT lateral projections of each patient were acquired. The masseter and temporal muscles were selected as regions of interest (ROI) for attribute extraction. Three methods of extracting attributes were assessed: radiomic, semantic and radiomic-semantic association. For radiomic attribute extraction, 20 texture attributes were assessed using co-occurrence matrix in a standardized angulation of 0°. The semantic features were the ROI mean temperature and pain intensity data. For radiomic-semantic association, a single dataset composed of 28 features was assessed. The classification algorithms assessed were KNN, SVM and MLP. Hopkins's statistic, Shapiro-Wilk, ANOVA and Tukey tests were used to assess data. The significance level was set at 5% (p < 0.05). RESULTS Training and testing accuracy values differed statistically for the radiomic-semantic association (p = 0.003). MLP differed from the other classifiers for the radiomic-semantic association (p = 0.004). Accuracy, precision and sensitivity values of semantic and radiomic-semantic association differed statistically from radiomic features (p = 0.008, p = 0.016 and p = 0.013). CONCLUSION Semantic and radiomic-semantic-associated ML feature extraction methods and MLP classifier should be chosen for TMD detection using IT images and pain scale data. IT associated with ML presents promising results for TMD detection.
Collapse
Affiliation(s)
- Elisa Diniz de Lima
- Department of Dentistry, State University of Paraíba, Campina Grande, Paraíba, Brazil
| | | | | | | | | | | | - Patrícia Meira Bento
- Department of Dentistry, State University of Paraíba, Campina Grande, Paraíba, Brazil
| | | | - Daniela Pita Melo
- Department of Dentistry, State University of Paraíba, Campina Grande, Paraíba, Brazil
| |
Collapse
|
35
|
Dey S, Roychoudhury R, Malakar S, Sarkar R. Screening of breast cancer from thermogram images by edge detection aided deep transfer learning model. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:9331-9349. [PMID: 35035264 PMCID: PMC8742669 DOI: 10.1007/s11042-021-11477-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 07/12/2021] [Accepted: 08/19/2021] [Indexed: 06/14/2023]
Abstract
Breast cancer, the most common invasive cancer, causes deaths of thousands of women in the world every year. Early detection of the same is a remedy to lessen the death rate. Hence, screening of breast cancer in its early stage is utmost required. However, in the developing nations not many can afford the screening and detection procedures owing to its cost. Hence, an effective and less expensive way of detecting breast cancer is performed using thermography which, unlike other methods, can be used on women of various ages. To this end, we propose a computer aided breast cancer detection system that accepts thermal breast images to detect the same. Here, we use the pre-trained DenseNet121 model as a feature extractor to build a classifier for the said purpose. Before extracting features, we work on the original thermal breast images to get outputs using two edge detectors - Prewitt and Roberts. These two edge-maps along with the original image make the input to the DenseNet121 model as a 3-channel image. The thermal breast image dataset namely, Database for Mastology Research (DMR-IR) is used to evaluate performance of our model. We achieve the highest classification accuracy of 98.80% on the said database, which outperforms many state-of-the-art methods, thereby confirming the superiority of the proposed model. Source code of this work is available here: https://github.com/subro608/thermogram_breast_cancer.
Collapse
Affiliation(s)
- Subhrajit Dey
- Department of Electrical Engineering, Jadavpur University, Kolkata, India
| | | | - Samir Malakar
- Department of Computer Science, Asutosh College, Kolkata, India
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| |
Collapse
|
36
|
Shah SM, Khan RA, Arif S, Sajid U. Artificial intelligence for breast cancer analysis: Trends & directions. Comput Biol Med 2022; 142:105221. [PMID: 35016100 DOI: 10.1016/j.compbiomed.2022.105221] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 01/03/2022] [Accepted: 01/03/2022] [Indexed: 12/18/2022]
Abstract
Breast cancer is one of the leading causes of death among women. Early detection of breast cancer can significantly improve the lives of millions of women across the globe. Given importance of finding solution/framework for early detection and diagnosis, recently many AI researchers are focusing to automate this task. The other reasons for surge in research activities in this direction are advent of robust AI algorithms (deep learning), availability of hardware that can run/train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths and limitations. It also enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade to detect breast cancer using various imaging modalities. Primarily, in this article we have focused on reviewing frameworks that have reported results using mammograms as it is the most widely used breast imaging modality that serves as the first test that medical practitioners usually prescribe for the detection of breast cancer. Another reason for focusing on mammogram imaging modalities is the availability of its labelled datasets. Datasets availability is one of the most important aspects for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.
Collapse
Affiliation(s)
- Shahid Munir Shah
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan
| | - Rizwan Ahmed Khan
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan.
| | - Sheeraz Arif
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan
| | - Unaiza Sajid
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan
| |
Collapse
|
37
|
Deep hybrid architectures for binary classification of medical breast cancer images. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103226] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
38
|
Mridha MF, Hamid MA, Monowar MM, Keya AJ, Ohi AQ, Islam MR, Kim JM. A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis. Cancers (Basel) 2021; 13:6116. [PMID: 34885225 PMCID: PMC8656730 DOI: 10.3390/cancers13236116] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/25/2021] [Accepted: 12/01/2021] [Indexed: 12/11/2022] Open
Abstract
Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.
Collapse
Affiliation(s)
- Muhammad Firoz Mridha
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (A.J.K.); (A.Q.O.)
| | - Md. Abdul Hamid
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (M.A.H.); (M.M.M.)
| | - Muhammad Mostafa Monowar
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (M.A.H.); (M.M.M.)
| | - Ashfia Jannat Keya
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (A.J.K.); (A.Q.O.)
| | - Abu Quwsar Ohi
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (A.J.K.); (A.Q.O.)
| | - Md. Rashedul Islam
- Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh;
| | - Jong-Myon Kim
- Department of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan 680-749, Korea
| |
Collapse
|
39
|
Ali S, Li J, Pei Y, Khurram R, Rehman KU, Rasool AB. State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods. Cancers (Basel) 2021; 13:5546. [PMID: 34771708 PMCID: PMC8583666 DOI: 10.3390/cancers13215546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 11/16/2022] Open
Abstract
Thus far, the most common cause of death in the world is cancer. It consists of abnormally expanding areas that are threatening to human survival. Hence, the timely detection of cancer is important to expanding the survival rate of patients. In this survey, we analyze the state-of-the-art approaches for multi-organ cancer detection, segmentation, and classification. This article promptly reviews the present-day works in the breast, brain, lung, and skin cancer domain. Afterwards, we analytically compared the existing approaches to provide insight into the ongoing trends and future challenges. This review also provides an objective description of widely employed imaging techniques, imaging modality, gold standard database, and related literature on each cancer in 2016-2021. The main goal is to systematically examine the cancer diagnosis systems for multi-organs of the human body as mentioned. Our critical survey analysis reveals that greater than 70% of deep learning researchers attain promising results with CNN-based approaches for the early diagnosis of multi-organ cancer. This survey includes the extensive discussion part along with current research challenges, possible solutions, and prospects. This research will endow novice researchers with valuable information to deepen their knowledge and also provide the room to develop new robust computer-aid diagnosis systems, which assist health professionals in bridging the gap between rapid diagnosis and treatment planning for cancer patients.
Collapse
Affiliation(s)
- Saqib Ali
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (S.A.); (J.L.); (K.u.R.)
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (S.A.); (J.L.); (K.u.R.)
| | - Yan Pei
- Computer Science Division, University of Aizu, Aizuwakamatsu 965-8580, Japan
| | - Rooha Khurram
- Beijing Key Laboratory for Green Catalysis and Separation, Department of Chemistry and Chemical Engineering, Beijing University of Technology, Beijing 100124, China;
| | - Khalil ur Rehman
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (S.A.); (J.L.); (K.u.R.)
| | - Abdul Basit Rasool
- Research Institute for Microwave and Millimeter-Wave (RIMMS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan;
| |
Collapse
|
40
|
Tashakori M, Nahvi A, Ebrahimian Hadi Kiashari S. Driver drowsiness detection using facial thermal imaging in a driving simulator. Proc Inst Mech Eng H 2021; 236:43-55. [PMID: 34477030 DOI: 10.1177/09544119211044232] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Driver drowsiness causes fatal driving accidents. Thermal imaging is a suitable drowsiness detection method as it is non-invasive and robust against changes in the ambient light. In this paper, driver drowsiness is detected by measuring the forehead temperature at the region covering the supratrochlear artery and also the cheek temperature. About 30 subjects drove on a highway in a driving simulator in two sessions. A thermal camera was used to monitor the facial temperature pattern. The subjects' drowsiness levels were estimated by three human observers. The forehead and the cheek regions were located and tracked in each frame. The forehead and the cheek skin temperatures were obtained at three levels of drowsiness. The Support Vector Machine, the K-Nearest Neighbor, and the regression tree classifiers were used. From wakefulness to extreme drowsiness, the forehead skin temperature and the absolute cheek-forehead skin temperature gradient decreased by 0.46°C and 0.81°C, respectively. But the cheek skin temperature increased by 0.35°C in two sessions. The gradient difference is on average 50% higher than the forehead or the cheek temperature change alone. The results indicate that drowsiness can be detected with an accuracy of 82%, sensitivity of 85%, specificity of 90%, and precision of 84%. Driver drowsiness can be detected by monitoring changes in the forehead and the cheek temperature signal. Also, the temperature gradient can be used as a more robust and sensitive indicator of drowsiness.
Collapse
Affiliation(s)
- Masoumeh Tashakori
- Virtual Reality Laboratory, K.N. Toosi University of Technology, Tehran, Iran
| | - Ali Nahvi
- Virtual Reality Laboratory, K.N. Toosi University of Technology, Tehran, Iran
| | | |
Collapse
|
41
|
Sharma R, Sharma JB, Maheshwari R, Baleanu D. Early anomaly prediction in breast thermogram by hybrid model consisting of superpixel segmentation, sparse feature descriptors and extreme learning machine classifier. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
42
|
Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4. Neural Comput Appl 2021; 34:333-348. [PMID: 34393379 PMCID: PMC8349135 DOI: 10.1007/s00521-021-06372-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 07/26/2021] [Indexed: 11/17/2022]
Abstract
Breast cancer is one of the most significant causes of death for women around the world. Breast thermography supported by deep convolutional neural networks is expected to contribute significantly to early detection and facilitate treatment at an early stage. The goal of this study is to investigate the behavior of different recent deep learning methods for identifying breast disorders. To evaluate our proposal, we built classifiers based on deep convolutional neural networks modelling inception V3, inception V4, and a modified version of the latter called inception MV4. MV4 was introduced to maintain the computational cost across all layers by making the resultant number of features and the number of pixel positions equal. DMR database was used for these deep learning models in classifying thermal images of healthy and sick patients. A set of epochs 3–30 were used in conjunction with learning rates 1 × 10–3, 1 × 10–4 and 1 × 10–5, Minibatch 10 and different optimization methods. The training results showed that inception V4 and MV4 with color images, a learning rate of 1 × 10–4, and SGDM optimization method, reached very high accuracy, verified through several experimental repetitions. With grayscale images, inception V3 outperforms V4 and MV4 by a considerable accuracy margin, for any optimization methods. In fact, the inception V3 (grayscale) performance is almost comparable to inception V4 and MV4 (color) performance but only after 20–30 epochs. inception MV4 achieved 7% faster classification response time compared to V4. The use of MV4 model is found to contribute to saving energy consumed and fluidity in arithmetic operations for the graphic processor. The results also indicate that increasing the number of layers may not necessarily be useful in improving the performance.
Collapse
|
43
|
Lin X, Li Z, Qiu J, Wang Q, Wang J, Zhang H, Chen T. Fascinating MXene nanomaterials: emerging opportunities in the biomedical field. Biomater Sci 2021; 9:5437-5471. [PMID: 34296233 DOI: 10.1039/d1bm00526j] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In recent years, there has been rapid progress in MXene research due to its distinctive two-dimensional structure and outstanding properties. Especially in biomedical applications, MXenes have attracted widespread favor with numerous studies on biosafety, bioimaging, therapy, and biosensing, although their development is still in the experimental stage. A comprehensive understanding of the current status of MXenes in biomedicine will promote their use in clinical applications. Here, we review advances in MXene research. First, we introduce the methods of synthesis, surface modification and functionalization of MXenes. Then, we summarize the biosafety and biocompatibility, paving the way for specific biomedical applications. On this basis, MXene nanostructures are described with respect to their use in antibacterial, bioimaging, cancer therapy, tissue regeneration and biosensor applications. Finally, we discuss MXene as a promising candidate material for further applications in biomedicine.
Collapse
Affiliation(s)
- Xiangping Lin
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.
| | - Zhongjun Li
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Institute of Microscale Optoelectronics, and Otolaryngology Department and Biobank of the First Affiliated Hospital, Shenzhen Second People's Hospital, Health Science Center, Shenzhen University, Shenzhen 518060, China.
| | - Jinmei Qiu
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.
| | - Qi Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.
| | - Jianxin Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China. and Department of Pharmaceutics, School of Pharmacy, Fudan University and Key Laboratory of Smart Drug Delivery, Ministry of Education, Shanghai 201203, China
| | - Han Zhang
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Institute of Microscale Optoelectronics, and Otolaryngology Department and Biobank of the First Affiliated Hospital, Shenzhen Second People's Hospital, Health Science Center, Shenzhen University, Shenzhen 518060, China.
| | - Tongkai Chen
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.
| |
Collapse
|
44
|
Thapa A, Alsadoon A, Prasad PWC, Bajaj S, Alsadoon OH, Rashid TA, Ali RS, Jerew OD. Deep learning for breast cancer classification: Enhanced tangent function. Comput Intell 2021. [DOI: 10.1111/coin.12476] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Ashu Thapa
- School of Computing and Mathematics Charles Sturt University (CSU) Wagga Wagga Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics Charles Sturt University (CSU) Wagga Wagga Australia
- School of Computer Data and Mathematical Sciences University of Western Sydney (UWS) Sydney Australia
- Kent Institute Australia Sydney Australia
- Asia Pacific International College (APIC) Sydney Australia
| | - P. W. C. Prasad
- School of Computing and Mathematics Charles Sturt University (CSU) Wagga Wagga Australia
| | - Simi Bajaj
- School of Computer Data and Mathematical Sciences University of Western Sydney (UWS) Sydney Australia
| | | | - Tarik A. Rashid
- Computer Science and Engineering University of Kurdistan Hewler Erbil KRG IRAQ
| | - Rasha S. Ali
- Department of Computer Techniques Engineering AL Nisour University College Baghdad Iraq
| | - Oday D. Jerew
- Asia Pacific International College (APIC) Sydney Australia
| |
Collapse
|
45
|
Leite ML, de Loiola Costa LS, Cunha VA, Kreniski V, de Oliveira Braga Filho M, da Cunha NB, Costa FF. Artificial intelligence and the future of life sciences. Drug Discov Today 2021; 26:2515-2526. [PMID: 34245910 DOI: 10.1016/j.drudis.2021.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 05/12/2021] [Accepted: 07/01/2021] [Indexed: 12/23/2022]
Abstract
Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials; mobile devices can also be used to improve aspects of both the diagnosis and treatment of diseases. In addition, AI can be used in the development of new drugs or for drug repurposing, in faster diagnosis and more efficient treatment for various diseases, as well as to identify data-driven hypotheses for scientists. In this review, we discuss how AI is starting to revolutionize the life sciences sector.
Collapse
Affiliation(s)
- Michel L Leite
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil; Department of Molecular Biology, Biological Sciences Institute, University of Brasília, Campus Darcy Ribeiro, Block K, 70.790-900, Brasilia, Federal District, Brazil
| | - Lorena S de Loiola Costa
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Victor A Cunha
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Victor Kreniski
- Apple Developer Academy, Universidade Católica de Brasília, Brasilia, Brazil
| | | | - Nicolau B da Cunha
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Fabricio F Costa
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil; Apple Developer Academy, Universidade Católica de Brasília, Brasilia, Brazil; Cancer Biology and Epigenomics Program, Ann & Robert H Lurie Children's Hospital of Chicago Research Center and Northwestern University's Feinberg School of Medicine, 2430 N. Halsted St, Box 220, Chicago, IL 60614, USA; MATTER Chicago, 222 W. Merchandise Mart Plaza, Suite 12th Floor, Chicago, IL 60654, USA; Genomic Enterprise, San Diego, CA 92008, USA; Genomic Enterprise, New York, NY 11581, USA.
| |
Collapse
|
46
|
Javan AAK, Jafari M, Shoeibi A, Zare A, Khodatars M, Ghassemi N, Alizadehsani R, Gorriz JM. Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems. SENSORS (BASEL, SWITZERLAND) 2021; 21:3925. [PMID: 34200287 PMCID: PMC8200970 DOI: 10.3390/s21113925] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 01/03/2023]
Abstract
In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov's method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application.
Collapse
Affiliation(s)
- Ali Akbar Kekha Javan
- Faculty of Electrical Engineering, Zabol Branch, Islamic Azad University, Zabol 1939598616, Iran;
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan 3513119111, Iran;
| | - Afshin Shoeibi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran;
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran
| | - Marjane Khodatars
- Faculty of Engineering, Mashhad Branch, Islamic Azad University, Mashhad 91735413, Iran;
| | - Navid Ghassemi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia;
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, 52005 Granada, Spain;
| |
Collapse
|
47
|
[Connected bras for breast cancer detection in 2021: Analysis and perspectives]. ACTA ACUST UNITED AC 2021; 49:907-912. [PMID: 34091080 DOI: 10.1016/j.gofs.2021.05.008] [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: 03/04/2021] [Indexed: 11/21/2022]
Abstract
OBJECTIVES Breast cancer is the leading cancer in women worldwide with about 2 million new cases and 685,000 deaths each year. Mammography is the most widely used screening and diagnostic method. Currently, digital technologies advances facilitate the development of connected and portable devices. To overcome some of the disadvantages of mammography (breast compression, difficulty in analyzing dense breasts, radiation, limited accessibility in some countries, etc.), portable devices, conventionally known as connected bras (CB), have been created to offer an alternative method to mammography. The objective of our review was to list all the published CBs in order to know their main characteristics, their potential indications and their possible limitations. METHOD A bibliographical search in the PUBMED database selecting only articles written in French or English, between 2011 and 2020, found 7 CBs under development. RESULTS These CBs use thermal, ultrasonic and impedance sensors. Their advantages are an absence of irradiation, an absence of breast compression and a flexibility of use (outside an X-ray cabinet). Mammary gland analysis times vary, depending on the device, between 30min and 24h. They are all connected to data transmission systems and models that analyze the results. DISCUSSION AND CONCLUSION These CBs are mostly still undergoing clinical validation (only [iTBra] has been evaluated in a clinical trial) and require evaluation steps that will eventually allow their future use for breast cancer detection in high-risk women, particularly in women with dense breasts and in women between screening waves.
Collapse
|
48
|
Cherian Kurian N, Sethi A, Reddy Konduru A, Mahajan A, Rane SU. A 2021 update on cancer image analytics with deep learning. WIRES DATA MINING AND KNOWLEDGE DISCOVERY 2021. [DOI: 10.1002/widm.1410] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Nikhil Cherian Kurian
- Department of Electrical Engineering Indian Institute of Technology, Bombay Mumbai India
| | - Amit Sethi
- Department of Electrical Engineering Indian Institute of Technology, Bombay Mumbai India
| | - Anil Reddy Konduru
- Department of Pathology Tata Memorial Center‐ACTREC, HBNI Navi Mumbai India
| | - Abhishek Mahajan
- Department of Radiology Tata Memorial Hospital, HBNI Mumbai India
| | - Swapnil Ulhas Rane
- Department of Pathology Tata Memorial Center‐ACTREC, HBNI Navi Mumbai India
| |
Collapse
|
49
|
Tarakanov AV, Tarakanov AA, Vesnin S, Efremov VV, Goryanin I, Roberts N. Microwave Radiometry (MWR) temperature measurement is related to symptom severity in patients with Low Back Pain (LBP). J Bodyw Mov Ther 2021; 26:548-552. [PMID: 33992296 DOI: 10.1016/j.jbmt.2021.02.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 01/25/2021] [Accepted: 02/28/2021] [Indexed: 11/26/2022]
Abstract
Microwave Radiometry (MWR) has the advantage that measurements of internal (i.e. deep) tissue temperature may be obtained non-invasively by measuring naturally emitted radiation in GHz range. The goal of the present study is to further the development of MWR for clinical application in assessment of patients with Low Back Pain (LBP). In particular, a protocol was developed in which MWR was used to measure internal temperature at the level of the spinous processes of the L1 to L5 vertebral bodies along median and left and right para-vertebral lines. The protocol was used to study 48 patients with clinically confirmed acute or sub-acute LBP and 27 Controls. Analysis revealed there to be a significant increase in deep tissue temperature with increasing pain severity as measured by using a Visual Analogue Scale (VAS) in patients with LBP (p < 0.05). In conclusion, MWR potentially allows for objective assessment of the magnitude of clinical symptoms in patients with LBP and shows promise for measuring pain severity.
Collapse
Affiliation(s)
- A V Tarakanov
- Rostov State Medical University, Rostov-on-Don, Russian Federation
| | - A A Tarakanov
- Rostov State Medical University, Rostov-on-Don, Russian Federation
| | | | - V V Efremov
- Rostov State Medical University, Rostov-on-Don, Russian Federation
| | - I Goryanin
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - N Roberts
- Centre for Reproductive Health (CRH), School of Clinical Sciences, University of Edinburgh, UK.
| |
Collapse
|
50
|
Chang HW, Frey G, Liu H, Xing C, Steinman L, Boyle WJ, Short JM. Generating tumor-selective conditionally active biologic anti-CTLA4 antibodies via protein-associated chemical switches. Proc Natl Acad Sci U S A 2021; 118:e2020606118. [PMID: 33627407 PMCID: PMC7936328 DOI: 10.1073/pnas.2020606118] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Anticytotoxic T lymphocyte-associated protein 4 (CTLA4) antibodies have shown potent antitumor activity, but systemic immune activation leads to severe immune-related adverse events, limiting clinical usage. We developed novel, conditionally active biologic (CAB) anti-CTLA4 antibodies that are active only in the acidic tumor microenvironment. In healthy tissue, this binding is reversibly inhibited by a novel mechanism using physiological chemicals as protein-associated chemical switches (PaCS). No enzymes or potentially immunogenic covalent modifications to the antibody are required for activation in the tumor. The novel anti-CTLA4 antibodies show similar efficacy in animal models compared to an analog of a marketed anti-CTLA4 biologic, but have markedly reduced toxicity in nonhuman primates (in combination with an anti-PD1 checkpoint inhibitor), indicating a widened therapeutic index (TI). The PaCS encompass mechanisms that are applicable to a wide array of antibody formats (e.g., ADC, bispecifics) and antigens. Examples shown here include antibodies to EpCAM, Her2, Nectin4, CD73, and CD3. Existing antibodies can be engineered readily to be made sensitive to PaCS, and the inhibitory activity can be optimized for each antigen's varying expression level and tissue distribution. PaCS can modulate diverse physiological molecular interactions and are applicable to various pathologic conditions, enabling differential CAB antibody activities in normal versus disease microenvironments.
Collapse
MESH Headings
- 5'-Nucleotidase/antagonists & inhibitors
- 5'-Nucleotidase/genetics
- 5'-Nucleotidase/immunology
- Animals
- Antibodies, Monoclonal/chemistry
- Antibodies, Monoclonal/pharmacology
- Antibodies, Monoclonal, Humanized/chemistry
- Antibodies, Monoclonal, Humanized/pharmacology
- Antibodies, Neoplasm/chemistry
- Antibodies, Neoplasm/pharmacology
- B7-H1 Antigen/antagonists & inhibitors
- B7-H1 Antigen/genetics
- B7-H1 Antigen/immunology
- Bicarbonates/chemistry
- CD3 Complex/antagonists & inhibitors
- CD3 Complex/genetics
- CD3 Complex/immunology
- CTLA-4 Antigen/antagonists & inhibitors
- CTLA-4 Antigen/genetics
- CTLA-4 Antigen/immunology
- Cell Adhesion Molecules/antagonists & inhibitors
- Cell Adhesion Molecules/genetics
- Cell Adhesion Molecules/immunology
- Colonic Neoplasms/genetics
- Colonic Neoplasms/immunology
- Colonic Neoplasms/pathology
- Colonic Neoplasms/therapy
- Epithelial Cell Adhesion Molecule/antagonists & inhibitors
- Epithelial Cell Adhesion Molecule/genetics
- Epithelial Cell Adhesion Molecule/immunology
- GPI-Linked Proteins/antagonists & inhibitors
- GPI-Linked Proteins/genetics
- GPI-Linked Proteins/immunology
- Gene Expression
- Humans
- Hydrogen Sulfide/chemistry
- Hydrogen-Ion Concentration
- Immunotherapy/methods
- Macaca fascicularis
- Mice
- Neoplasm Proteins/antagonists & inhibitors
- Neoplasm Proteins/genetics
- Neoplasm Proteins/immunology
- Protein Engineering/methods
- Receptor, ErbB-2/antagonists & inhibitors
- Receptor, ErbB-2/genetics
- Receptor, ErbB-2/immunology
- T-Lymphocytes, Cytotoxic/drug effects
- T-Lymphocytes, Cytotoxic/immunology
- T-Lymphocytes, Cytotoxic/pathology
- Tumor Burden/drug effects
- Tumor Microenvironment/drug effects
- Xenograft Model Antitumor Assays
Collapse
Affiliation(s)
| | | | | | | | - Lawrence Steinman
- Stanford University School of Medicine, Stanford University, Stanford, CA 94305
| | | | | |
Collapse
|