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Mustapha A, Ishak I, Zaki NNM, Ismail-Fitry MR, Arshad S, Sazili AQ. Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review. Heliyon 2024; 10:e32189. [PMID: 38975107 PMCID: PMC11225673 DOI: 10.1016/j.heliyon.2024.e32189] [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: 01/29/2024] [Revised: 05/20/2024] [Accepted: 05/29/2024] [Indexed: 07/09/2024] Open
Abstract
Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat.
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Affiliation(s)
- Abdul Mustapha
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Iskandar Ishak
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, 43400, Malaysia
| | - Nor Nadiha Mohd Zaki
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Animal Science, Faculty of Agriculture, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Mohammad Rashedi Ismail-Fitry
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Food Technology, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Syariena Arshad
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Awis Qurni Sazili
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Animal Science, Faculty of Agriculture, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
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Lu S, Yang J, Gu Y, He D, Wu H, Sun W, Xu D, Li C, Guo C. Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors. ACS Sens 2024; 9:1134-1148. [PMID: 38363978 DOI: 10.1021/acssensors.3c02670] [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] [Indexed: 02/18/2024]
Abstract
Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.
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Affiliation(s)
- Shasha Lu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Jianyu Yang
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Yu Gu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Dongyuan He
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Haocheng Wu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Wei Sun
- College of Chemistry and Chemical Engineering, Hainan Normal University, Haikou 571158, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Changming Li
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Chunxian Guo
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
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Karamti H, Alharthi R, Umer M, Shaiba H, Ishaq A, Abuzinadah N, Alsubai S, Ashraf I. Breast cancer detection employing stacked ensemble model with convolutional features. Cancer Biomark 2024; 40:155-170. [PMID: 38160347 PMCID: PMC11322706 DOI: 10.3233/cbm-230294] [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: 07/11/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024]
Abstract
Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine learning approaches tend to show better results than traditional methods, however, accuracy lacks the desired level. This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.
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Affiliation(s)
- Hanen Karamti
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Raed Alharthi
- Department of Computer Science and Engineering, University of Hafr Al-Batin, Hafar, Saudi Arabia
| | - Muhammad Umer
- Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Hadil Shaiba
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Abid Ishaq
- Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Nihal Abuzinadah
- Faculty of Computer Science and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Korea
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Gallos IK, Tryfonopoulos D, Shani G, Amditis A, Haick H, Dionysiou DD. Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions. Diagnostics (Basel) 2023; 13:3673. [PMID: 38132257 PMCID: PMC10743128 DOI: 10.3390/diagnostics13243673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.
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Affiliation(s)
- Ioannis K. Gallos
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Dimitrios Tryfonopoulos
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Gidi Shani
- Laboratory for Nanomaterial-Based Devices, Technion—Israel Institute of Technology, Haifa 3200003, Israel; (G.S.); (H.H.)
| | - Angelos Amditis
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Hossam Haick
- Laboratory for Nanomaterial-Based Devices, Technion—Israel Institute of Technology, Haifa 3200003, Israel; (G.S.); (H.H.)
| | - Dimitra D. Dionysiou
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
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Zhang J, Mai Q, Di D, Zhou H, Zhang R, Wang Q. Potential roles of gut microbiota in metal mixture and bone mineral density and osteoporosis risk association: an epidemiologic study in Wuhan. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:117201-117213. [PMID: 37864687 DOI: 10.1007/s11356-023-30388-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/06/2023] [Indexed: 10/23/2023]
Abstract
Few studies have focused on the effects of multiple metal mixtures on bone health and the underlying mechanisms related to alterations in the gut microbiota. This study aimed to examine the potential roles of gut microbiota alterations in metal mixtures and their association with osteoporosis traits. Adults aged ≥ 55 years were recruited from two community healthcare centers in Wuhan City during 2016-2019. The plasma concentrations of six metals (zinc, iron, selenium, lead, cadmium, and arsenic) were measured using an inductively coupled plasma mass spectrometer. The k-means clustering method was employed to explore the exposure profiles of metal mixtures for all participants. 16S rRNA gene sequencing was used to profile the gut microbiota of participants. Combining these results with those of our previous study, we identified overlapping taxa and evaluated their potential roles. A total of 806 participants (516 females), with an average age of 67.36 years were included. The participants were grouped into three clusters using k-means clustering: Cluster 1 (n = 458), Cluster 2 (n = 199), and Cluster 3 (n = 149). The high-exposure group for iron, zinc, lead, and cadmium (Cluster 3) showed a negative association with lumbar spine 1-4 bone mineral density (BMD). A total of 201 individuals (121 females) underwent sequencing of the gut microbiota. Both alpha and beta diversities were statistically different among the three groups. Bacteroidaceae, Lachnospiraceae, Bifidobacteriaceae, Bacteroides, and Lachnospiraceae_incertae_sedis were identified as overlapping taxa associated with the metal mixtures and BMD. Interaction analysis revealed that Cluster 3 interacted with Bacteroidaceae/Bacteroides, resulting in a positive effect on LS1-4 BMD (β = 0.358 g/cm2, 95% CI: 0.047 to 0.669, P = 0.025). Our findings indicate associations between multiple metal mixtures and BMD as well as gut microbiota alterations. Exploring the interaction between metal mixtures and the gut microbiota provides new perspectives for the precise prevention and treatment of osteoporosis.
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Affiliation(s)
- Jianli Zhang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Mai
- Wuhan Center for Disease Control and Prevention, Wuhan, China
| | - Dongsheng Di
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haolong Zhou
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruyi Zhang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wang
- MOE Key Lab of Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Sahu A, Das PK, Meher S. Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms. Phys Med 2023; 114:103138. [PMID: 37914431 DOI: 10.1016/j.ejmp.2023.103138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 07/22/2023] [Accepted: 09/14/2023] [Indexed: 11/03/2023] Open
Abstract
OBJECTIVE Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensive surveys have been presented to briefly analyze methods for detecting breast cancer with mammograms. In this article, our objective is to give an overview of recent advancements in machine learning (ML) and deep learning (DL)-based breast cancer detection systems. METHODS We give a structured framework to categorize mammogram-based breast cancer detection techniques. Several publicly available mammogram databases and different performance measures are also mentioned. RESULTS After deliberate investigation, we find most of the works classify breast tumors either as normal-abnormal or malignant-benign rather than classifying them into three classes. Furthermore, DL-based features are more significant than hand-crafted features. However, transfer learning is preferred over others as it yields better performance in small datasets, unlike classical DL techniques. SIGNIFICANCE AND CONCLUSION In this article, we have made an attempt to give recent advancements in artificial intelligence (AI)-based breast cancer detection systems. Furthermore, a number of challenging issues and possible research directions are mentioned, which will help researchers in further scopes of research in this field.
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Affiliation(s)
- Adyasha Sahu
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India.
| | - Pradeep Kumar Das
- School of Electronics Engineering (SENSE), VIT Vellore, Tamil Nadu, 632014, India.
| | - Sukadev Meher
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India.
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Vakharia VN, Toescu S, Copp AJ, Thompson DNP. A topographical analysis of encephalocele locations: generation of a standardised atlas and cluster analysis. Childs Nerv Syst 2023; 39:1911-1920. [PMID: 36897404 PMCID: PMC7614697 DOI: 10.1007/s00381-023-05883-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 02/14/2023] [Indexed: 03/11/2023]
Abstract
OBJECTIVE Encephaloceles are considered to result from defects in the developing skull through which meninges, and potentially brain tissue, herniate. The pathological mechanism underlying this process is incompletely understood. We aimed to describe the location of encephaloceles through the generation of a group atlas to determine whether they occur at random sites or clusters within distinct anatomical regions. METHODS Patients diagnosed with cranial encephaloceles or meningoceles were identified from a prospectively maintained database between 1984 and 2021. Images were transformed to atlas space using non-linear registration. The bone defect, encephalocele and herniated brain contents were manually segmented allowing for a 3-dimensional heat map of encephalocele locations to be generated. The centroids of the bone defects were clustered utilising a K-mean clustering machine learning algorithm in which the elbow method was used to identify the optimal number of clusters. RESULTS Of the 124 patients identified, 55 had volumetric imaging in the form of MRI (48/55) or CT (7/55) that could be used for atlas generation. Median encephalocele volume was 14,704 (IQR 3655-86,746) mm3 and the median surface area of the skull defect was 679 (IQR 374-765) mm2. Brain herniation into the encephalocele was found in 45% (25/55) with a median volume of 7433 (IQR 3123-14,237) mm3. Application of the elbow method revealed 3 discrete clusters: (1) anterior skull base (22%; 12/55), (2) parieto-occipital junction (45%; 25/55) and (3) peri-torcular (33%; 18/55). Cluster analysis revealed no correlation between the location of the encephalocele with gender (χ2 (2, n = 91) = 3.86, p = 0.15). Compared to expected population frequencies, encephaloceles were relatively more common in Black, Asian and Other compared to White ethnicities. A falcine sinus was identified in 51% (28/55) of cases. Falcine sinuses were more common (χ2 (2, n = 55) = 6.09, p = 0.05) whilst brain herniation was less common (χ2 (2, n = 55) = .16.24, p < 0.0003) in the parieto-occipital location. CONCLUSION This analysis revealed three predominant clusters for the location of encephaloceles, with the parieto-occipital junction being the most common. The stereotypic location of encephaloceles into anatomically distinct clusters and the coexistence of distinct venous malformations at certain sites suggests that their location is not random and raises the possibility of distinct pathogenic mechanisms unique to each of these regions.
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Affiliation(s)
| | - Sebastien Toescu
- Department of Neurosurgery, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK
| | - Andrew J Copp
- Department of Neurosurgery, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK
- Developmental Biology & Cancer Department, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Dominic N P Thompson
- Department of Neurosurgery, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK
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Albadr MAA, Ayob M, Tiun S, AL-Dhief FT, Arram A, Khalaf S. Breast cancer diagnosis using the fast learning network algorithm. Front Oncol 2023; 13:1150840. [PMID: 37434975 PMCID: PMC10332166 DOI: 10.3389/fonc.2023.1150840] [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: 02/02/2023] [Accepted: 04/10/2023] [Indexed: 07/13/2023] Open
Abstract
The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector.
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Affiliation(s)
- Musatafa Abbas Abbood Albadr
- Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Masri Ayob
- Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Sabrina Tiun
- Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Fahad Taha AL-Dhief
- Department of Communication Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia, (UTM), Johor Bahru, Johor, Malaysia
| | - Anas Arram
- Department of Computer Science, Birzeit University, Birzeit, Palestine
| | - Sura Khalaf
- Department of Communication Technology Engineering, College of Information Technology, Imam Ja’afer Al-Sadiq University, Baghdad, Iraq
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An ensemble algorithm using quantum evolutionary optimization of weighted type-II fuzzy system and staged Pegasos Quantum Support Vector Classifier with multi-criteria decision making system for diagnosis and grading of breast cancer. Soft comput 2023. [DOI: 10.1007/s00500-023-07939-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2023]
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10
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Xu H, Mohamed M, Flannery M, Peppone L, Ramsdale E, Loh KP, Wells M, Jamieson L, Vogel VG, Hall BA, Mustian K, Mohile S, Culakova E. An Unsupervised Machine Learning Approach to Evaluating the Association of Symptom Clusters With Adverse Outcomes Among Older Adults With Advanced Cancer: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open 2023; 6:e234198. [PMID: 36947036 PMCID: PMC10034574 DOI: 10.1001/jamanetworkopen.2023.4198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 12/20/2022] [Indexed: 03/23/2023] Open
Abstract
Importance Older adults with advanced cancer who have high pretreatment symptom severity often experience adverse events during cancer treatments. Unsupervised machine learning may help stratify patients into different risk groups. Objective To evaluate whether clusters identified from baseline patient-reported symptom severity were associated with adverse outcomes. Design, Setting, and Participants This secondary analysis of the Geriatric Assessment Intervention for Reducing Toxicity in Older Patients With Advanced Cancer (GAP70+) Trial (2014-2019) included patients who completed the National Cancer Institute Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) before starting a new cancer treatment regimen and received care at community oncology sites across the United States. An unsupervised machine learning algorithm (k-means with Euclidean distance) clustered patients based on similarities of baseline symptom severities. Clustering variables included severity items of 24 PRO-CTCAE symptoms (range, 0-4; corresponding to none, mild, moderate, severe, and very severe). Total severity score was calculated as the sum of 24 items (range, 0-96). Whether the clusters were associated with unplanned hospitalization, death, and toxic effects was then examined. Analyses were conducted in January and February 2022. Exposures Symptom severity. Main Outcomes and Measures Unplanned hospitalization over 3 months (primary), all-cause mortality over 1 year, and any clinician-rated grade 3 to 5 toxic effect over 3 months. Results Of 718 enrolled patients, 706 completed baseline PRO-CTCAE and were included (mean [SD] age, 77.2 [5.5] years, 401 [56.8%] male patients; 51 [7.2%] Black and 619 [87.8%] non-Hispanic White patients; 245 [34.7%] with gastrointestinal cancer; 175 [24.8%] with lung cancer; mean [SD] impaired Geriatric Assessment domains, 4.5 [1.6]). The algorithm classified 310 (43.9%), 295 (41.8%), and 101 (14.3%) into low-, medium-, and high-severity clusters (within-cluster mean [SD] severity scores: low, 6.3 [3.4]; moderate, 16.6 [4.3]; high, 29.8 [7.8]; P < .001). Controlling for sociodemographic variables, clinical factors, study group, and practice site, compared with patients in the low-severity cluster, those in the moderate-severity cluster were more likely to experience hospitalization (risk ratio, 1.36; 95% CI, 1.01-1.84; P = .046). Moderate- and high-severity clusters were associated with a higher risk of death (moderate: hazard ratio, 1.31; 95% CI, 1.01-1.69; P = .04; high: hazard ratio, 2.00; 95% CI, 1.43-2.78; P < .001), but not toxic effects. Conclusions and Relevance In this study, unsupervised machine learning partitioned patients into distinct symptom severity clusters; patients with higher pretreatment severity were more likely to experience hospitalization and death. Trial Registration ClinicalTrials.gov Identifier: NCT02054741.
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Affiliation(s)
- Huiwen Xu
- School of Public and Population Health, University of Texas Medical Branch, Galveston
- Sealy Center on Aging, University of Texas Medical Branch, Galveston
| | - Mostafa Mohamed
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, New York
- James P. Wilmot Cancer Institute, Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, New York
| | - Marie Flannery
- School of Nursing, University of Rochester Medical Center, Rochester, New York
| | - Luke Peppone
- Department of Surgery, Supportive Care in Cancer, University of Rochester Medical Center, Rochester, New York
| | - Erika Ramsdale
- James P. Wilmot Cancer Institute, Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, New York
| | - Kah Poh Loh
- James P. Wilmot Cancer Institute, Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, New York
| | - Megan Wells
- James P. Wilmot Cancer Institute, Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, New York
| | - Leah Jamieson
- Metro Minnesota Community Oncology Research Program, St Louis Park, Minnesota
| | - Victor G. Vogel
- Geisinger Cancer Institute National Cancer Institute Community Oncology Research Program, Danville, Pennsylvania
| | - Bianca Alexandra Hall
- James P. Wilmot Cancer Institute, Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, New York
| | - Karen Mustian
- Department of Surgery, Supportive Care in Cancer, University of Rochester Medical Center, Rochester, New York
| | - Supriya Mohile
- James P. Wilmot Cancer Institute, Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, New York
| | - Eva Culakova
- Department of Surgery, Supportive Care in Cancer, University of Rochester Medical Center, Rochester, New York
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Ranjbarzadeh R, Dorosti S, Jafarzadeh Ghoushchi S, Caputo A, Tirkolaee EB, Ali SS, Arshadi Z, Bendechache M. Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods. Comput Biol Med 2023; 152:106443. [PMID: 36563539 DOI: 10.1016/j.compbiomed.2022.106443] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/24/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
The Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case.
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Affiliation(s)
- Ramin Ranjbarzadeh
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | - Shadi Dorosti
- Department of Industrial Engineering, Urmia University of Technology, Urmia, Iran.
| | | | - Annalina Caputo
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | | | - Sadia Samar Ali
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Zahra Arshadi
- Faculty of Electronics, Telecommunications and Physics Engineering, Polytechnic University, Turin, Italy.
| | - Malika Bendechache
- Lero & ADAPT Research Centres, School of Computer Science, University of Galway, Ireland.
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12
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Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm. Cancers (Basel) 2022; 14:cancers14236015. [PMID: 36497497 PMCID: PMC9737339 DOI: 10.3390/cancers14236015] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/21/2022] [Accepted: 12/04/2022] [Indexed: 12/12/2022] Open
Abstract
Breast cancer is a common cause of female mortality in developing countries. Screening and early diagnosis can play an important role in the prevention and treatment of these cancers. This study proposes an ensemble learning-based voting classifier that combines the logistic regression and stochastic gradient descent classifier with deep convoluted features for the accurate detection of cancerous patients. Deep convoluted features are extracted from the microscopic features and fed to the ensemble voting classifier. This idea provides an optimized framework that accurately classifies malignant and benign tumors with improved accuracy. Results obtained using the voting classifier with convoluted features demonstrate that the highest classification accuracy of 100% is achieved. The proposed approach revealed the accuracy enhancement in comparison with the state-of-the-art approaches.
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13
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Zaheer MZ, Lee JH, Mahmood A, Astrid M, Lee SI. Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2022; 31:5963-5975. [PMID: 36094978 DOI: 10.1109/tip.2022.3204217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Recently, anomaly scores have been formulated using reconstruction loss of the adversarially learned generators and/or classification loss of discriminators. Unavailability of anomaly examples in the training data makes optimization of such networks challenging. Attributed to the adversarial training, performance of such models fluctuates drastically with each training step, making it difficult to halt the training at an optimal point. In the current study, we propose a robust anomaly detection framework that overcomes such instability by transforming the fundamental role of the discriminator from identifying real vs. fake data to distinguishing good vs. bad quality reconstructions. For this purpose, we propose a method that utilizes the current state as well as an old state of the same generator to create good and bad quality reconstruction examples. The discriminator is trained on these examples to detect the subtle distortions that are often present in the reconstructions of anomalous data. In addition, we propose an efficient generic criterion to stop the training of our model, ensuring elevated performance. Extensive experiments performed on six datasets across multiple domains including image and video based anomaly detection, medical diagnosis, and network security, have demonstrated excellent performance of our approach.
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14
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Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5869529. [PMID: 36017156 PMCID: PMC9398810 DOI: 10.1155/2022/5869529] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 07/28/2022] [Indexed: 02/08/2023]
Abstract
Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data.
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15
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Basurto-Hurtado JA, Cruz-Albarran IA, Toledano-Ayala M, Ibarra-Manzano MA, Morales-Hernandez LA, Perez-Ramirez CA. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms. Cancers (Basel) 2022; 14:3442. [PMID: 35884503 PMCID: PMC9322973 DOI: 10.3390/cancers14143442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/02/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
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Affiliation(s)
- Jesus A. Basurto-Hurtado
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Irving A. Cruz-Albarran
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Manuel Toledano-Ayala
- División de Investigación y Posgrado de la Facultad de Ingeniería (DIPFI), Universidad Autónoma de Querétaro, Cerro de las Campanas S/N Las Campanas, Santiago de Querétaro 76010, Mexico;
| | - Mario Alberto Ibarra-Manzano
- Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, Division de Ingenierias Campus Irapuato-Salamanca (DICIS), Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico;
| | - Luis A. Morales-Hernandez
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
| | - Carlos A. Perez-Ramirez
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
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16
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Fordellone M, Chiodini P. Unsupervised Hierarchical Classification Approach for Imprecise Data in the Breast Cancer Detection. ENTROPY 2022; 24:e24070926. [PMID: 35885149 PMCID: PMC9316630 DOI: 10.3390/e24070926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/27/2022] [Accepted: 07/01/2022] [Indexed: 11/16/2022]
Abstract
(1) Background: in recent years, a lot of the research of statistical methods focused on the classification problem in presence of imprecise data. A particular case of imprecise data is the interval-valued data. Following this research line, in this work a new hierarchical classification technique for multivariate interval-valued data is suggested for diagnosis of the breast cancer; (2) Methods: an unsupervised hierarchical classification method for imprecise multivariate data (called HC-ID) is performed for diagnosis of breast cancer (i.e., to discriminate between benign or malignant masses) and the results have been compared with the conventional (unsupervised) hierarchical classification approach (HC); (3) Results: the application on real data shows that the HC-ID procedure performs better HC procedure in terms of accuracy (HC-ID = 0.80, HC = 0.66) and sensitivity (HC-ID = 0.61, HC = 0.08). In the results obtained by the usual procedure, there is a high degree of false-negative (i.e., benign cancer diagnosis in malignant status) affected by the high degree of variability (i.e., uncertainty) characterizing the worst data.
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17
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Huang Y, Yang J, Sun Q, Ma S, Yuan Y, Tan W, Cao P, Feng C. Vessel filtering and segmentation of coronary CT angiographic images. Int J Comput Assist Radiol Surg 2022; 17:1879-1890. [PMID: 35764765 DOI: 10.1007/s11548-022-02655-7] [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: 10/01/2021] [Accepted: 04/22/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Coronary artery segmentation in coronary computed tomography angiography (CTA) images plays a crucial role in diagnosing cardiovascular diseases. However, due to the complexity of coronary CTA images and coronary structure, it is difficult to automatically segment coronary arteries accurately and efficiently from numerous coronary CTA images. METHOD In this study, an automatic method based on symmetrical radiation filter (SRF) and D-means is presented. The SRF, which is applied to the three orthogonal planes, is designed to filter the suspicious vessel tissue according to the features of gradient changes on vascular boundaries to segment coronary arteries accurately and reduce computational cost. Additionally, the D-means local clustering is proposed to be embedded into vessel segmentation to eliminate noise impact in coronary CTA images. RESULTS The results of the proposed method were compared against the manual delineations in 210 coronary CTA data sets. The average values of true positive, false positive, Jaccard measure, and Dice coefficient were [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Moreover, comparing the delineated data sets and public data sets showed that the proposed method is better than the related methods. CONCLUSION The experimental results indicate that the proposed method can perform complete, robust, and accurate segmentation of coronary arteries with low computational cost. Therefore, the proposed method is proven effective in vessel segmentation of coronary CTA images without extensive training data and can meet clinical applications.
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Affiliation(s)
- Yan Huang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China. .,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
| | - Qi Sun
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Shuang Ma
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Yuliang Yuan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Peng Cao
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Chaolu Feng
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
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18
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ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data. JOURNAL OF THE INSTITUTION OF ENGINEERS (INDIA): SERIES B 2022. [PMCID: PMC8724598 DOI: 10.1007/s40031-021-00696-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.
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19
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Medulla oblongata volume as a promising predictor of survival in amyotrophic lateral sclerosis. Neuroimage Clin 2022; 34:103015. [PMID: 35561555 PMCID: PMC9111981 DOI: 10.1016/j.nicl.2022.103015] [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: 10/11/2021] [Revised: 03/29/2022] [Accepted: 04/19/2022] [Indexed: 11/21/2022]
Abstract
Brainstem volumes reflect the disease severity expressed as ALSFRS-r (total score and its bulbar and spinal subscores). Medulla oblongata volume demonstrated a significant accuracy to discriminate long and short survivors ALS patients. Brainstem volumes may reflect the impairment of corticospinal and corticobulbar tracts as well as lower bulbar motor neurons. Furthermore, medulla oblongata could be used as an early predictor of survival in ALS patients.
Background Unconventional magnetic resonance imaging studies of the brainstem have recently acquired a growing interest in amyotrophic lateral sclerosis (ALS) pathology since they provide a unique opportunity to evaluate motor tract degeneration and bulbar lower motor neuron involvement. The aim of this study was to investigate the role of brainstem structures as accurate biomarkers of disease severity and predictors of survival. Materials and Methods A total of 60 ALS patients and 30 healthy controls subjects (CS) were recruited in this study. Patients were divided in two subgroups according to the onset of the disease: 42 spinal (S-ALS) and 18 bulbar (B-ALS). All subjects underwent 3D-structural MRI. Brainstem volume both of the entire cohort of ALS patients and S-ALS and B-ALS onset were compared with those of CS. In addition the two ALS subgroups were tested for differences in brainstem volumes. Volumetric, vertex-wise, and voxel-based approaches were implemented to assess correlations between MR structural features and clinical characteristics expressed as ALSFRS-r and its bulbar (ALSFSR-r-B) and spinal subscores (ALSFSR-r-S). ROC curves were performed to test the accuracy of midbrain, pons, and medulla oblongata volumes able to discriminate patients dichotomized into long and short survivors by using Two-Steps cluster analysis. Univariate and multivariate survival analyses were carried out to test the prognostic role of brainstem structures’ volume, trichotomized by applying a k-means clustering algorithm. Results Both the entire cohort of ALS patients and B-ALS and S-ALS showed significant lower volumes of both medulla oblongata and pons compared to CS. Furthermore, B-ALS showed a significant lower volume of medulla oblongata, compared to S-ALS. Lower score of ALSFRS-r correlated to atrophy in the anterior compartment of midbrain, pons, and medulla oblongata, as well as in the posterior portion of only this latter region. ALSFSR-r-S positively correlated with shape deformation and density reduction of the anterior portion of the entire brainstem, along the corticospinal tracts. ALSFSR-r-B instead showed a positive correlation with shape deformation of the floor of the fourth ventricle in the medulla oblongata and the crus cerebri in the midbrain. Only medulla oblongata volume demonstrated a significant accuracy to discriminate long and short survivors ALS patients (ROC AUC 0.76, p < 0.001). Univariate and multivariate analysis confirmed the survival predictive role of the medulla oblongata (log rank test p: 0.003). Discussions Our findings suggest that brainstem volume may reflect the impairment of corticospinal and corticobulbar tracts as well as lower bulbar motor neurons. Furthermore, medulla oblongata could be used as an early predictor of survival in ALS patients.
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20
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Mohammad WT, Teete R, Al-Aaraj H, Rubbai YSY, Arabyat MM. Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques. Appl Bionics Biomech 2022; 2022:6187275. [PMID: 35401789 PMCID: PMC8993572 DOI: 10.1155/2022/6187275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 11/17/2022] Open
Abstract
Breast cancer must be addressed by a multidisciplinary team aiming at the patient's comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.
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Affiliation(s)
- Walid Theib Mohammad
- Al-Hussein Bin Talal University, Princess Aisha Bint Al Hussein College for Nursing and Health Sciences, Jordan
| | - Ronza Teete
- Al-Hussein Bin Talal University, Princess Aisha College for Nursing and Allied Science, Nursing Department, Jordan
| | - Heyam Al-Aaraj
- Al-Hussein Bin Talal University, Princess Aisha Bint Al-Hussein College of Nursing and Health Sciences, Ma'an, Jordan
| | - Yousef Saleh Yousef Rubbai
- Al-Hussein Bin Talal University, Princess Aisha Bint Al Hussein College for Nursing and Health Sciences, Jordan
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21
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Floricel C, Nipu N, Biggs M, Wentzel A, Canahuate G, Van Dijk L, Mohamed A, Fuller CD, Marai GE. THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:151-161. [PMID: 34591766 PMCID: PMC8785360 DOI: 10.1109/tvcg.2021.3114810] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.
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22
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Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis. J Pers Med 2021; 11:jpm11040272. [PMID: 33916398 PMCID: PMC8067194 DOI: 10.3390/jpm11040272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 03/23/2021] [Accepted: 04/01/2021] [Indexed: 12/15/2022] Open
Abstract
Primary breast cancer (PBC) is a heterogeneous disease at the clinical, histopathological, and molecular levels. The improved classification of PBC might be important to identify subgroups of the disease, relevant to patient management. Machine learning algorithms may allow a better understanding of the relationships within heterogeneous clinical syndromes. This work aims to show the potential of unsupervised learning techniques for improving classification in PBC. A dataset of 712 women with PBC is used as a motivating example. A set of variables containing biological prognostic parameters is considered to define groups of individuals. Four different clustering methods are used: K-means, self-organising maps, hierarchical agglomerative (HAC), and Gaussian mixture models clustering. HAC outperforms the other clustering methods. With an optimal partitioning parameter, the methods identify two clusters with different clinical profiles. Patients in the first cluster are younger and have lower values of the oestrogen receptor (ER) and progesterone receptor (PgR) than patients in the second cluster. Moreover, cathepsin D values are lower in the first cluster. The three most important variables identified by the HAC are: age, ER, and PgR. Unsupervised learning seems a suitable alternative for the analysis of PBC data, opening up new perspectives in the particularly active domain of dissecting clinical heterogeneity.
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23
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Zarei M, Rezai A, Falahieh Hamidpour SS. Breast cancer segmentation based on modified Gaussian mean shift algorithm for infrared thermal images. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2021. [DOI: 10.1080/21681163.2021.1897884] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Mahnoosh Zarei
- Electrical and Biomedical Engineering Department, ACECR Institute of Higher Education, Isfahan Branch, Isfahan, Iran
| | - Abdalhossein Rezai
- Electrical and Biomedical Engineering Department, ACECR Institute of Higher Education, Isfahan Branch, Isfahan, Iran
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Abbas S, Jalil Z, Javed AR, Batool I, Khan MZ, Noorwali A, Gadekallu TR, Akbar A. BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm. PeerJ Comput Sci 2021; 7:e390. [PMID: 33817036 PMCID: PMC7959601 DOI: 10.7717/peerj-cs.390] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 01/20/2021] [Indexed: 06/12/2023]
Abstract
Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.
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Affiliation(s)
- Shafaq Abbas
- Department of Computer Science, Air University, Islamabad, Pakistan
| | - Zunera Jalil
- Department of Cyber Security, Air University, Islamabad, Pakistan
| | | | - Iqra Batool
- Department of Computer Science, Air University, Islamabad, Pakistan
| | - Mohammad Zubair Khan
- Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia
| | | | - Thippa Reddy Gadekallu
- School of Information Technology and Engineering, Vellore Institute of Technology University, Tamil Nadu, India
| | - Aqsa Akbar
- Department of Computer Science, Air University, Islamabad, Pakistan
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Zhu X, Tian X, Ji L, Zhang X, Cao Y, Shen C, Hu Y, Wong JWH, Fang JY, Hong J, Chen H. A tumor microenvironment-specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients. NPJ Precis Oncol 2021; 5:7. [PMID: 33580207 PMCID: PMC7881244 DOI: 10.1038/s41698-021-00142-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 10/21/2020] [Indexed: 12/14/2022] Open
Abstract
Studies have shown that tumor microenvironment (TME) might affect drug sensitivity and the classification of colorectal cancer (CRC). Using TME-specific gene signature to identify CRC subtypes with distinctive clinical relevance has not yet been tested. A total of 18 "bulk" RNA-seq datasets (total n = 2269) and four single-cell RNA-seq datasets were included in this study. We constructed a "Signature associated with FOLFIRI resistant and Microenvironment" (SFM) that could discriminate both TME and drug sensitivity. Further, SFM subtypes were identified using K-means clustering and verified in three independent cohorts. Nearest template prediction algorithm was used to predict drug response. TME estimation was performed by CIBERSORT and microenvironment cell populations-counter (MCP-counter) methods. We identified six SFM subtypes based on SFM signature that discriminated both TME and drug sensitivity. The SFM subtypes were associated with distinct clinicopathological, molecular and phenotypic characteristics, specific enrichments of gene signatures, signaling pathways, prognosis, gut microbiome patterns, and tumor lymphocytes infiltration. Among them, SFM-C and -F were immune suppressive. SFM-F had higher stromal fraction with epithelial-to-mesenchymal transition phenotype, while SFM-C was characterized as microsatellite instability phenotype which was responsive to immunotherapy. SFM-D, -E, and -F were sensitive to FOLFIRI and FOLFOX, while SFM-A, -B, and -C were responsive to EGFR inhibitors. Finally, SFM subtypes had strong prognostic value in which SFM-E and -F had worse survival than other subtypes. SFM subtypes enable the stratification of CRC with potential chemotherapy response thereby providing more precise therapeutic options for these patients.
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Affiliation(s)
- Xiaoqiang Zhu
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xianglong Tian
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Gastroenterology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linhua Ji
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xinyu Zhang
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yingying Cao
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chaoqin Shen
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ye Hu
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason W H Wong
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Jing-Yuan Fang
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Jie Hong
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Haoyan Chen
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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26
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Cao D, Chen Y, Chen J, Zhang H, Yuan Z. An improved algorithm for the maximal information coefficient and its application. ROYAL SOCIETY OPEN SCIENCE 2021; 8:201424. [PMID: 33972855 PMCID: PMC8074658 DOI: 10.1098/rsos.201424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 01/18/2021] [Indexed: 06/12/2023]
Abstract
The maximal information coefficient (MIC) captures both linear and nonlinear correlations between variable pairs. In this paper, we proposed the BackMIC algorithm for MIC estimation. The BackMIC algorithm adds a searching back process on the equipartitioned axis to obtain a better grid partition than the original implementation algorithm ApproxMaxMI. And similar to the ChiMIC algorithm, it terminates the grid search process by the χ 2-test instead of the maximum number of bins B(n, α). Results on simulated data show that the BackMIC algorithm maintains the generality of MIC, and gives more reasonable grid partition and MIC values for independent and dependent variable pairs under comparable running times. Moreover, it is robust under different α in B(n, α). MIC calculated by the BackMIC algorithm reveals an improvement in statistical power and equitability. We applied (1-MIC) as the distance measurement in the K-means algorithm to perform a clustering of the cancer/normal samples. The results on four cancer datasets demonstrated that the MIC values calculated by the BackMIC algorithm can obtain better clustering results, indicating the correlations between samples measured by the BackMIC algorithm were more credible than those measured by other algorithms.
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Affiliation(s)
- Dan Cao
- Hunan Engineering and Technology Research Centre for Agricultural Big Data Analysis and Decision-making, Hunan Agricultural University, Changsha 410000, People's Republic of China
- Orient Science and Technology College of Hunan Agricultural University, Changsha 410000, Hunan, People's Republic of China
| | - Yuan Chen
- Hunan Engineering and Technology Research Centre for Agricultural Big Data Analysis and Decision-making, Hunan Agricultural University, Changsha 410000, People's Republic of China
| | - Jin Chen
- Hunan Engineering and Technology Research Centre for Agricultural Big Data Analysis and Decision-making, Hunan Agricultural University, Changsha 410000, People's Republic of China
| | - Hongyan Zhang
- Hunan Engineering and Technology Research Centre for Agricultural Big Data Analysis and Decision-making, Hunan Agricultural University, Changsha 410000, People's Republic of China
| | - Zheming Yuan
- Hunan Engineering and Technology Research Centre for Agricultural Big Data Analysis and Decision-making, Hunan Agricultural University, Changsha 410000, People's Republic of China
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27
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Omran NF, Abd-el Ghany SF, Saleh H, Nabil A. Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark. COMPLEXITY 2021; 2021:1-12. [DOI: 10.1155/2021/6653508] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Twitter integrates with streaming data technologies and machine learning to add new value to healthcare. This paper presented a real-time system to predict breast cancer based on streaming patient’s health data from Twitter. The proposed system consists of two major components: developing an offline building model and an online prediction pipeline. For the first component, we made a correlation between the features to determine the correlation between features and reduce the number of features from the Breast Cancer Wisconsin Diagnostic dataset. Two feature selection algorithms are recursive feature elimination and univariate feature selection algorithms which are applied to features after correlation to select the essential features. Four decision trees, logistic regression, support vector machine, and random forest classifier have been used on features after correlation and feature selection. Also, hyperparameter tuning and cross-validation have been applied with machine learning to optimize models and enhance accuracy. Apache Spark, Apache Kafka, and Twitter Streaming API are used to develop the second component. The best model with the highest accuracy obtained from the first component predicts breast cancer in real time from tweets’ streaming. The results showed that the best model is the random forest classifier which achieved the best accuracy.
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Affiliation(s)
- Nahla F. Omran
- Computer Science Department, Faculty of Science, South Valley University, Qena, Egypt
| | - Sara F. Abd-el Ghany
- Computer Science Department, Faculty of Science, South Valley University, Qena, Egypt
| | - Hager Saleh
- Faculty of Computers and Information, South Valley University, Hurghada, Egypt
| | - Ayman Nabil
- Faculty of Computer Science, Misr International University, Cairo, Egypt
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28
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Trojsi F, Di Nardo F, Siciliano M, Caiazzo G, Passaniti C, D'Alvano G, Ricciardi D, Russo A, Bisecco A, Lavorgna L, Bonavita S, Cirillo M, Esposito F, Tedeschi G. Resting state functional MRI brain signatures of fast disease progression in amyotrophic lateral sclerosis: a retrospective study. Amyotroph Lateral Scler Frontotemporal Degener 2020; 22:117-126. [PMID: 32885698 DOI: 10.1080/21678421.2020.1813306] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE Advanced neuroimaging techniques may offer the potential to monitor disease spreading in amyotrophic lateral sclerosis (ALS). We aim to investigate brain functional and structural magnetic resonance imaging (MRI) changes in a cohort of ALS patients, examined at diagnosis and clinically monitored over 18 months, in order to early discriminate fast progressors (FPs) from slow progressors (SPs). Methods: Resting state functional MRI (RS-fMRI), diffusion tensor imaging (DTI) and voxel-based morphometry (VBM) analyses were performed at baseline in 54 patients with ALS and 22 HCs. ALS patients were classified a posteriori into FPs (n = 25) and SPs (n = 29) based on changes in Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised score from baseline to the 18-month assessment (ΔALSFRS-R), applying a k-means clustering algorithm. Results: At diagnosis, when compared to HCs, ALS patients showed reduced functional connectivity in both motor and extra-motor networks. When compared to SPs, at baseline, FPs showed decreased function connectivity in paracentral lobule (sensorimotor network), precuneus (in the default mode network), middle frontal gyri (frontoparietal networks) and increased functional connectivity in insular cortices (salience network). Structural analyses did not reveal significant differences in gray and white matter damage by comparing FPs to SPs. Receiver operating characteristic (ROC) curve analysis showed that functional connectivity increase in the left insula at baseline best discriminated FPs and SPs (area under the curve 78%). Conclusions: Impairment of extra-motor networks may appear early in ALS patients with faster disease progression, suggesting that a more widespread functional connectivity damage may be an indicator of poorer prognosis.
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Affiliation(s)
- Francesca Trojsi
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Federica Di Nardo
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Mattia Siciliano
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy.,Department of Psychology, Università degli Studi della Campania "Luigi Vanvitelli", Caserta, Italy, and
| | - Giuseppina Caiazzo
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Carla Passaniti
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy.,Department of Psychology, Università degli Studi della Campania "Luigi Vanvitelli", Caserta, Italy, and
| | - Giulia D'Alvano
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Dario Ricciardi
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Antonio Russo
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Alvino Bisecco
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Luigi Lavorgna
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Simona Bonavita
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Fabrizio Esposito
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy
| | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgical Sciences; MRI Research Center SUN-FISM, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
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29
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Jamali-Dinan SS, Soltanian-Zadeh H, Bowyer SM, Almohri H, Dehghani H, Elisevich K, Nazem-Zadeh MR. A Combination of Particle Swarm Optimization and Minkowski Weighted K-Means Clustering: Application in Lateralization of Temporal Lobe Epilepsy. Brain Topogr 2020; 33:519-532. [PMID: 32347472 DOI: 10.1007/s10548-020-00770-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 04/07/2020] [Indexed: 11/30/2022]
Abstract
K-Means is one of the most popular clustering algorithms that partitions observations into nonoverlapping subgroups based on a predefined similarity metric. Its drawbacks include a sensitivity to noisy features and a dependency of its resulting clusters upon the initial selection of cluster centroids resulting in the algorithm converging to local optima. Minkowski weighted K-Means (MWK-Means) addresses the issue of sensitivity to noisy features, but is sensitive to the initialization of clusters, and so the algorithm may similarly converge to local optima. Particle Swarm Optimization (PSO) uses a globalized search method to solve this issue. We present a hybrid Particle Swarm Optimization (PSO) + MWK-Means clustering algorithm to address all the above problems in a single framework, while maintaining benefits of PSO and MWK Means methods. This study investigated the utility of this approach in lateralizing the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Using MEG-CSI, we analyzed preoperative resting state MEG data from 17 adults TLE patients with Engel class I outcomes to determine coherence at 54 anatomical sites and compared the results with 17 age- and gender-matched controls. Fiber-tracking was performed through the same anatomical sites using DTI data. Indices of both MEG coherence and DTI nodal degree were calculated. A PSO + MWK-Means clustering algorithm was applied to identify the side of temporal lobe epileptogenicity and distinguish between normal and TLE cases. The PSO module was aimed at identifying initial cluster centroids and assigning initial feature weights to cluster centroids and, hence, transferring to the MWK-Means module for the final optimal clustering solution. We demonstrated improvements with the use of the PSO + MWK-Means clustering algorithm compared to that of K-Means and MWK-Means independently. PSO + MWK-Means was able to successfully distinguish between normal and TLE in 97.2% and 82.3% of cases for DTI and MEG data, respectively. It also lateralized left and right TLE in 82.3% and 93.6% of cases for DTI and MEG data, respectively. The proposed optimization and clustering methodology for MEG and DTI features, as they relate to focal epileptogenicity, would enhance the identification of the TLE laterality in cases of unilateral epileptogenicity.
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Affiliation(s)
| | - Hamid Soltanian-Zadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.,Research Administration, Radiology, Henry Ford Health System, Detroit, MI, 48202, USA
| | - Susan M Bowyer
- Neurology Departments, Henry Ford Health System, Detroit, MI, 48202, USA
| | - Haidar Almohri
- Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI, USA
| | - Hamed Dehghani
- Medical Physics, and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Kost Elisevich
- Department of Clinical Neurosciences, Spectrum Health, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA
| | - Mohammad-Reza Nazem-Zadeh
- Medical Physics, and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, Iran. .,Research Center for Molecular and Cellular Imaging, Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
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30
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Ak MF. A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications. Healthcare (Basel) 2020; 8:E111. [PMID: 32357391 PMCID: PMC7349542 DOI: 10.3390/healthcare8020111] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 04/13/2020] [Accepted: 04/14/2020] [Indexed: 12/26/2022] Open
Abstract
In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.
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Affiliation(s)
- Muhammet Fatih Ak
- Industrial Engineering Department, Antalya Bilim University, 07190 Antalya, Turkey
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31
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Li Y, Song T, Yang J, Zhang Y, Yang J. An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids. PLoS One 2016; 11:e0167430. [PMID: 27918587 PMCID: PMC5137889 DOI: 10.1371/journal.pone.0167430] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 11/14/2016] [Indexed: 11/30/2022] Open
Abstract
In this paper, we have proposed a novel alignment-free method for comparing the similarity of protein sequences. We first encode a protein sequence into a 440 dimensional feature vector consisting of a 400 dimensional Pseudo-Markov transition probability vector among the 20 amino acids, a 20 dimensional content ratio vector, and a 20 dimensional position ratio vector of the amino acids in the sequence. By evaluating the Euclidean distances among the representing vectors, we compare the similarity of protein sequences. We then apply this method into the ND5 dataset consisting of the ND5 protein sequences of 9 species, and the F10 and G11 datasets representing two of the xylanases containing glycoside hydrolase families, i.e., families 10 and 11. As a result, our method achieves a correlation coefficient of 0.962 with the canonical protein sequence aligner ClustalW in the ND5 dataset, much higher than those of other 5 popular alignment-free methods. In addition, we successfully separate the xylanases sequences in the F10 family and the G11 family and illustrate that the F10 family is more heat stable than the G11 family, consistent with a few previous studies. Moreover, we prove mathematically an identity equation involving the Pseudo-Markov transition probability vector and the amino acids content ratio vector.
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Affiliation(s)
- Yushuang Li
- School of Science, Yanshan University, Qinhuangdao, China
| | - Tian Song
- School of Science, Yanshan University, Qinhuangdao, China
| | - Jiasheng Yang
- Department of Civil and Environmental Engineering, National Universality of Singapore, Singapore
| | - Yi Zhang
- Department of Mathematics, Hebei University of Science and Technology, Shijiazhuang, Hebei, China
| | - Jialiang Yang
- School of Mathematics and Information Science, Henan Polytechnic University, Henan, China
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