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van Timmeren JE, Bussink J, Koopmans P, Smeenk RJ, Monshouwer R. Longitudinal Image Data for Outcome Modeling. Clin Oncol (R Coll Radiol) 2024:S0936-6555(24)00277-2. [PMID: 39003124 DOI: 10.1016/j.clon.2024.06.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 04/15/2024] [Accepted: 06/24/2024] [Indexed: 07/15/2024]
Abstract
In oncology, medical imaging is crucial for diagnosis, treatment planning and therapy execution. Treatment responses can be complex and varied and are known to involve factors of treatment, patient characteristics and tumor microenvironment. Longitudinal image analysis is able to track temporal changes, aiding in disease monitoring, treatment evaluation, and outcome prediction. This allows for the enhancement of personalized medicine. However, analyzing longitudinal 2D and 3D images presents unique challenges, including image registration, reliable segmentation, dealing with variable imaging intervals, and sparse data. This review presents an overview of techniques and methodologies in longitudinal image analysis, with a primary focus on outcome modeling in radiation oncology.
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Affiliation(s)
- J E van Timmeren
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - J Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - P Koopmans
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - R J Smeenk
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - R Monshouwer
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
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Liu J, Li X, Wang G, Zeng W, Zeng H, Wen C, Xu W, He Z, Qin G, Chen W. Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach. J Magn Reson Imaging 2024. [PMID: 38850180 DOI: 10.1002/jmri.29405] [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: 01/04/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-series dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pCR identification in BC is unclear. PURPOSE To identify pCR to neoadjuvant chemotherapy (NAC) using deep learning (DL) models based on the VGG-LSTM network. STUDY TYPE Retrospective. POPULATION Center A: 235 patients (47.7 ± 10.0 years) were divided 7:3 into training (n = 164) and validation set (n = 71). Center B: 150 patients (48.5 ± 10.4 years) were used as test set. FIELD STRENGTH/SEQUENCE 3-T, T2-weighted spin-echo sequence imaging, and gradient echo DCE sequence imaging. ASSESSMENT Patients underwent MRI examinations at three sequential time points: pretreatment, after three cycles of treatment, and prior to surgery, with tumor regions of interest manually delineated. Histopathology was the gold standard. We used VGG-LSTM network to establish seven DL models using time-series DCE-MR images: pre-NAC images (t0 model), early NAC images (t1 model), post-NAC images (t2 model), pre-NAC and early NAC images (t0 + t1 model), pre-NAC and post-NAC images (t0 + t2 model), pre-NAC, early NAC and post-NAC images (t0 + t1 + t2 model), and the optimal model combined with the clinical features and imaging features (combined model). The models were trained and optimized on the training and validation set, and tested on the test set. STATISTICAL TESTS The DeLong, Student's t-test, Mann-Whitney U, Chi-squared, Fisher's exact, Hosmer-Lemeshow tests, decision curve analysis, and receiver operating characteristics analysis were performed. P < 0.05 was considered significant. RESULTS Compared with the other six models, the combined model achieved the best performance in the test set yielding an AUC of 0.927. DATA CONCLUSION The combined model that used time-series DCE-MR images, clinical features and imaging features shows promise for identifying pCR in BC. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY Stage 4.
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Affiliation(s)
- Jialing Liu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xu Li
- Department of Radiotherapy, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Gang Wang
- Department of Radiology, The Tenth Affiliated Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong Province, China
| | - Weixiong Zeng
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Hui Zeng
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Chanjuan Wen
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Weimin Xu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Zilong He
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Genggeng Qin
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Weiguo Chen
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
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Nicolis O, De Los Angeles D, Taramasco C. A contemporary review of breast cancer risk factors and the role of artificial intelligence. Front Oncol 2024; 14:1356014. [PMID: 38699635 PMCID: PMC11063273 DOI: 10.3389/fonc.2024.1356014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 03/25/2024] [Indexed: 05/05/2024] Open
Abstract
Background Breast cancer continues to be a significant global health issue, necessitating advancements in prevention and early detection strategies. This review aims to assess and synthesize research conducted from 2020 to the present, focusing on breast cancer risk factors, including genetic, lifestyle, and environmental aspects, as well as the innovative role of artificial intelligence (AI) in prediction and diagnostics. Methods A comprehensive literature search, covering studies from 2020 to the present, was conducted to evaluate the diversity of breast cancer risk factors and the latest advances in Artificial Intelligence (AI) in this field. The review prioritized high-quality peer-reviewed research articles and meta-analyses. Results Our analysis reveals a complex interplay of genetic, lifestyle, and environmental risk factors for breast cancer, with significant variability across different populations. Furthermore, AI has emerged as a promising tool in enhancing the accuracy of breast cancer risk prediction and the personalization of prevention strategies. Conclusion The review highlights the necessity for personalized breast cancer prevention and detection approaches that account for individual risk factor profiles. It underscores the potential of AI to revolutionize these strategies, offering clear recommendations for future research directions and clinical practice improvements.
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Affiliation(s)
- Orietta Nicolis
- Engineering Faculty, Universidad Andres Bello, Viña del Mar, Chile
- Centro para la Prevención y Control del Cáncer (CECAN), Santiago, Chile
| | - Denisse De Los Angeles
- Engineering Faculty, Universidad Andres Bello, Viña del Mar, Chile
- Centro para la Prevención y Control del Cáncer (CECAN), Santiago, Chile
| | - Carla Taramasco
- Engineering Faculty, Universidad Andres Bello, Viña del Mar, Chile
- Centro para la Prevención y Control del Cáncer (CECAN), Santiago, Chile
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Al Moteri M, Mahesh TR, Thakur A, Vinoth Kumar V, Khan SB, Alojail M. Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer. Front Med (Lausanne) 2024; 11:1373244. [PMID: 38515985 PMCID: PMC10954891 DOI: 10.3389/fmed.2024.1373244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 02/27/2024] [Indexed: 03/23/2024] Open
Abstract
Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen's Kappa value. These indicators highlight the model's proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information.
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Affiliation(s)
- Moteeb Al Moteri
- Department of Management Information Systems, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
| | - T. R. Mahesh
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - Arastu Thakur
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - V. Vinoth Kumar
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science Engineering and Environment, University of Salford, Manchester, United Kingdom
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Mohammed Alojail
- Department of Management Information Systems, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
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Lo Gullo R, Marcus E, Huayanay J, Eskreis-Winkler S, Thakur S, Teuwen J, Pinker K. Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction. Invest Radiol 2024; 59:230-242. [PMID: 37493391 PMCID: PMC10818006 DOI: 10.1097/rli.0000000000001010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
ABSTRACT Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Jorge Huayanay
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Radiology, National Institute of Neoplastic Diseases, Lima, Peru
| | - Sarah Eskreis-Winkler
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Sunitha Thakur
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jonas Teuwen
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
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Dell'Aquila K, Vadlamani A, Maldjian T, Fineberg S, Eligulashvili A, Chung J, Adam R, Hodges L, Hou W, Makower D, Duong TQ. Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population. Breast Cancer Res 2024; 26:7. [PMID: 38200586 PMCID: PMC10782738 DOI: 10.1186/s13058-023-01762-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Generalizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population. METHODS Demographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy in an inner-city health system (01/01/2012 to 12/31/2021). Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with fivefold cross validation. RESULTS pCR was not associated with age, race, ethnicity, tumor staging, Nottingham grade, income, and insurance status (p > 0.05). ER-/HER2+ showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2- (all p < 0.05), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.01). Machine learning models ranked ER+/HER2-, ER-/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74-0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine learning models ranked tumor stage, pCR, nodal stage, and triple-negative subtype as top predictors of OS (AUC = 0.83-0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05). CONCLUSION Tumor subtypes and imaging characteristics were top predictors of pCR in our inner-city population. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine learning models accurately predicted pCR and OS.
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Affiliation(s)
- Kevin Dell'Aquila
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Abhinav Vadlamani
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Takouhie Maldjian
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Susan Fineberg
- Department of Pathology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA
| | - Anna Eligulashvili
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Julie Chung
- Department of Oncology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA
| | - Richard Adam
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Laura Hodges
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Wei Hou
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Della Makower
- Department of Oncology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA
| | - Tim Q Duong
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA.
- Center for Health Data Innovation, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA.
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van Staalduinen EK, Matthews R, Khan A, Punn I, Cattell RF, Li H, Franceschi A, Samara GJ, Czerwonka L, Bangiyev L, Duong TQ. Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone. Diagnostics (Basel) 2023; 14:71. [PMID: 38201380 PMCID: PMC10802850 DOI: 10.3390/diagnostics14010071] [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: 11/28/2023] [Revised: 12/24/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
Accurate differentiation of benign and malignant cervical lymph nodes is important for prognosis and treatment planning in patients with head and neck squamous cell carcinoma. We evaluated the diagnostic performance of magnetic resonance image (MRI) texture analysis and traditional 18F-deoxyglucose positron emission tomography (FDG-PET) features. This retrospective study included 21 patients with head and neck squamous cell carcinoma. We used texture analysis of MRI and FDG-PET features to evaluate 109 histologically confirmed cervical lymph nodes (41 metastatic, 68 benign). Predictive models were evaluated using area under the curve (AUC). Significant differences were observed between benign and malignant cervical lymph nodes for 36 of 41 texture features (p < 0.05). A combination of 22 MRI texture features discriminated benign and malignant nodal disease with AUC, sensitivity, and specificity of 0.952, 92.7%, and 86.7%, which was comparable to maximum short-axis diameter, lymph node morphology, and maximum standard uptake value (SUVmax). The addition of MRI texture features to traditional FDG-PET features differentiated these groups with the greatest AUC, sensitivity, and specificity (0.989, 97.5%, and 94.1%). The addition of the MRI texture feature to lymph node morphology improved nodal assessment specificity from 70.6% to 88.2% among FDG-PET indeterminate lymph nodes. Texture features are useful for differentiating benign and malignant cervical lymph nodes in patients with head and neck squamous cell carcinoma. Lymph node morphology and SUVmax remain accurate tools. Specificity is improved by the addition of MRI texture features among FDG-PET indeterminate lymph nodes. This approach is useful for differentiating benign and malignant cervical lymph nodes.
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Affiliation(s)
- Eric K. van Staalduinen
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Radiology, Bronx, NY 10467, USA
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Robert Matthews
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Adam Khan
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Isha Punn
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Renee F. Cattell
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Haifang Li
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Ana Franceschi
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Ghassan J. Samara
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Lukasz Czerwonka
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Lev Bangiyev
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Tim Q. Duong
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Radiology, Bronx, NY 10467, USA
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Khan MJ, Singh AK, Sultana R, Singh PP, Khan A, Saxena S. Breast cancer: A comparative review for breast cancer detection using machine learning techniques. Cell Biochem Funct 2023; 41:996-1007. [PMID: 37812062 DOI: 10.1002/cbf.3868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 09/05/2023] [Accepted: 09/24/2023] [Indexed: 10/10/2023]
Abstract
Breast cancer is the most common cancer among women globally and presents a significant challenge due to its rising incidence and fatality rates. Factors such as cultural, socioeconomic, and educational barriers contribute to inadequate awareness and access to healthcare services, often leading to delayed diagnoses and poor patient outcomes. Furthermore, fostering a collaborative approach among healthcare providers, policymakers, and community leaders is crucial in addressing this critical women's health issue, reducing mortality rates, alleviating, and the overall burden of breast cancer. The main goal of this review is to explore various techniques of machine learning algorithms to examine high accuracy and early detection of breast cancer for the safe health of women.
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Affiliation(s)
- Mohd Jawed Khan
- Department of Computer Science & Engineering, Central Institute of Technology, Kokrajhar, Assam, India
| | - Arun Kumar Singh
- Department of Computer Science and Engineering, Greater Noida Institute of Technology, Greater Noida, India
| | - Razia Sultana
- Department of Biotechnology, School of Biotechnology, Gautam Buddha University, Greater Noida, India
| | - Pankaj Pratap Singh
- Department of Computer Science & Engineering, Central Institute of Technology, Kokrajhar, Assam, India
| | - Asif Khan
- Department of Computer Science and Engineering, Greater Noida Institute of Technology, Greater Noida, India
| | - Sandeep Saxena
- Department of Computer Science and Engineering, Greater Noida Institute of Technology, Greater Noida, India
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Rangel G, Cuevas-Tello JC, Rivera M, Renteria O. A Deep Learning Model Based on Capsule Networks for COVID Diagnostics through X-ray Images. Diagnostics (Basel) 2023; 13:2858. [PMID: 37685396 PMCID: PMC10486517 DOI: 10.3390/diagnostics13172858] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
X-ray diagnostics are widely used to detect various diseases, such as bone fracture, pneumonia, or intracranial hemorrhage. This method is simple and accessible in most hospitals, but requires an expert who is sometimes unavailable. Today, some diagnoses are made with the help of deep learning algorithms based on Convolutional Neural Networks (CNN), but these algorithms show limitations. Recently, Capsule Networks (CapsNet) have been proposed to overcome these problems. In our work, CapsNet is used to detect whether a chest X-ray image has disease (COVID or pneumonia) or is healthy. An improved model called DRCaps is proposed, which combines the advantage of CapsNet and the dilation rate (dr) parameter to manage images with 226 × 226 resolution. We performed experiments with 16,669 chest images, in which our model achieved an accuracy of 90%. Furthermore, the model size is 11M with a reconstruction stage, which helps to avoid overfitting. Experiments show how the reconstruction stage works and how we can avoid the max-pooling operation for networks with a stride and dilation rate to downsampling the convolution layers. In this paper, DRCaps is superior to other comparable models in terms of accuracy, parameters, and image size handling. The main idea is to keep the model as simple as possible without using data augmentation or a complex preprocessing stage.
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Affiliation(s)
- Gabriela Rangel
- Facultad de Ingeniería, Universidad Autonoma de San Luis Potosi, San Luis Potosi 78290, Mexico;
- Tecnologico Nacional de Mexico/ITSSLPC, San Luis Potosi 78421, Mexico
| | - Juan C. Cuevas-Tello
- Facultad de Ingeniería, Universidad Autonoma de San Luis Potosi, San Luis Potosi 78290, Mexico;
| | - Mariano Rivera
- Centro de Investigacion en Matematicas, Guanajuato 36000, Mexico; (M.R.); (O.R.)
| | - Octavio Renteria
- Centro de Investigacion en Matematicas, Guanajuato 36000, Mexico; (M.R.); (O.R.)
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