1
|
Kapila D, Panwar S, Raja MKMM, Mondal T, Rafi SM, Singh SP, Kumar B. Applications of Neural Network-Based Plan-Cancer Method for Primary Diagnosis of Mesothelioma Cancer. BIOMED RESEARCH INTERNATIONAL 2023; 2023:3164166. [PMID: 36785667 PMCID: PMC9922178 DOI: 10.1155/2023/3164166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/23/2022] [Accepted: 11/24/2022] [Indexed: 02/05/2023]
Abstract
"Malignant mesothelioma (MM)" is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is "malignant mesothelioma." Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination "epithelial/mesenchymal appearance of MM," however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful "Malignant Mesothelioma illness" identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support "Vector Machine, Neural Network, and Decision Tree" are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.
Collapse
Affiliation(s)
- Dhiraj Kapila
- Department of Computer Science & Engineering, Lovely Professional University, Phagwara, Punjab, India
| | - Sarika Panwar
- Department of Electronics and Telecommunication Engineering, AISSMS's Institute of Information Technology, Pune, India
| | | | - Tamal Mondal
- Department of Botany, Hiralal Mazumdar Memorial College for Women, Dakshineswar, Kolkata 700035, India
| | - Shaik Mohammad Rafi
- Department of Computer Science and Engineering, Sri Mittapalli College of Engineering, Guntur, Andhra Pradesh, India
| | - Suryabhan Pratap Singh
- Information Technology, Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur, India
| | | |
Collapse
|
2
|
Saxena K, Zamani AS, Bhavani R, Sagar KVD, Bangare PM, Ashwini S, Rahin SA. Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2318101. [PMID: 35845952 PMCID: PMC9283031 DOI: 10.1155/2022/2318101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/20/2022] [Indexed: 11/17/2022]
Abstract
Mesothelioma is a dangerous, violent cancer, which forms a protecting layer around inner tissues such as the lungs, stomach, and heart. We investigate numerous AI methodologies and consider the exact DM conclusion outcomes in this study, which focuses on DM determination. K-nearest neighborhood, linear-discriminant analysis, Naive Bayes, decision-tree, random forest, support vector machine, and logistic regression analyses have been used in clinical decision support systems in the detection of mesothelioma. To test the accuracy of the evaluated categorizers, the researchers used a dataset of 350 instances with 35 highlights and six execution measures. LDA, NB, KNN, SVM, DT, LogR, and RF have precisions of 65%, 70%, 92%, 100%, 100%, 100%, and 100%, correspondingly. In count, the calculated complication of individual approaches has been evaluated. Every process is chosen on the basis of its characterization, exactness, and calculated complications. SVM, DT, LogR, and RF outclass the others and, unexpectedly, earlier research.
Collapse
Affiliation(s)
- Komal Saxena
- Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India
| | - Abu Sarwar Zamani
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - R. Bhavani
- Institute of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 600124, India
| | - K. V. Daya Sagar
- Electronics and Computer Science, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
| | - Pushpa M. Bangare
- Department of E&TC, Sinhgad College of Engineering, Savitribai Phule Pune University, Pune, India
| | - S. Ashwini
- Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamilnadu, India
| | | |
Collapse
|
3
|
Massafra R, Catino A, Perrotti PMS, Pizzutilo P, Fanizzi A, Montrone M, Galetta D. Informative Power Evaluation of Clinical Parameters to Predict Initial Therapeutic Response in Patients with Advanced Pleural Mesothelioma: A Machine Learning Approach. J Clin Med 2022; 11:jcm11061659. [PMID: 35329985 PMCID: PMC8950691 DOI: 10.3390/jcm11061659] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 12/10/2022] Open
Abstract
Malignant pleural mesothelioma (MPM) is a rare neoplasm whose early diagnosis is challenging and systemic treatments are generally administered as first line in the advanced disease stage. The initial clinical response may represent a useful parameter in terms of identifying patients with a better long-term outcome. In this report, the initial therapeutical response in 46 patients affected with advanced/unresectable pleural mesothelioma was investigated. The initial therapeutic response was assessed by CT scan and clinical examination after 2–3 treatment cycles. Our preliminary evaluation shows that the group of patients treated with regimens including antiangiogenetics and/or immunotherapy had a significantly better initial response as compared to patients only treated with standard chemotherapy, exhibiting a disease control rate (DCR) of 100% (95% IC, 79.40–100%) and 80.0% (95% IC, 61.40–92.30%), respectively. Furthermore, the therapeutic response was correlated with the disease stage, blood leukocytes and neutrophils, high albumin serum levels, and basal body mass index (BMI). Specifically, the patients with disease stage III showed a DCR of 95.7% (95% IC, 78.1–99.9%), whereas for disease stage IV the DCR decreased to 66.7% (95% IC, 34.9–9.1%). Moreover, a better initial response was observed in patients with a higher BMI, who reached a DCR of 96.10% (95% IC, 80.36–99.90%). Furthermore, in order to evaluate in the predictive power of the collected features a multivariate way, we report the preliminary results of a machine learning model for predicting the initial therapeutic response. We trained a state-of-the-art algorithm combined to a sequential forward feature selection procedure. The model reached a median AUC value, accuracy, sensitivity, and specificity of 77.0%, 75%, 74.8%, and 83.3%, respectively. The features with greater informational power were gender, histotype, BMI, smoking habits, packs/year, and disease stage. Our preliminary data support the possible favorable correlation between innovative treatments and therapeutic response in patients with unresectable/advanced pleural mesothelioma. The small sample size does not allow concrete conclusions to be drawn; nevertheless, this work is the basis of an ongoing study that will also involve radiomics in a larger dataset.
Collapse
Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Annamaria Catino
- Struttura Semplice Dipartimentale di Oncologia Medica per la Patologia Toracica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (P.P.); (M.M.); (D.G.)
| | - Pia Maria Soccorsa Perrotti
- Struttura Semplice Dipartimentale di Radiologia, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pamela Pizzutilo
- Struttura Semplice Dipartimentale di Oncologia Medica per la Patologia Toracica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (P.P.); (M.M.); (D.G.)
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
- Correspondence: ; Tel.: +39-080-555-5111
| | - Michele Montrone
- Struttura Semplice Dipartimentale di Oncologia Medica per la Patologia Toracica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (P.P.); (M.M.); (D.G.)
| | - Domenico Galetta
- Struttura Semplice Dipartimentale di Oncologia Medica per la Patologia Toracica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (P.P.); (M.M.); (D.G.)
| |
Collapse
|
4
|
Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z, Wang L. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR Mhealth Uhealth 2019; 7:e11966. [PMID: 31376272 PMCID: PMC6696854 DOI: 10.2196/11966] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 04/14/2019] [Accepted: 06/12/2019] [Indexed: 01/10/2023] Open
Abstract
The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.
Collapse
Affiliation(s)
- Igbe Tobore
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China.,Graduate University, Chinese Academy of Sciences, Beijing, China
| | - Jingzhen Li
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liu Yuhang
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yousef Al-Handarish
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Abhishek Kandwal
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zedong Nie
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lei Wang
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| |
Collapse
|