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Sadique FL, Subramaiam H, Krishnappa P, Chellappan DK, Ma JH. Recent advances in breast cancer metastasis with special emphasis on metastasis to the brain. Pathol Res Pract 2024; 260:155378. [PMID: 38850880 DOI: 10.1016/j.prp.2024.155378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/28/2024] [Accepted: 05/28/2024] [Indexed: 06/10/2024]
Abstract
Understanding the underlying mechanisms of breast cancer metastasis is of vital importance for developing treatment approaches. This review emphasizes contemporary breakthrough studies with special focus on breast cancer brain metastasis. Acquired mutational changes in metastatic lesions are often distinct from the primary tumor, suggesting altered mutagenesis pathways. The concept of micrometastases and heterogeneity within the tumors unravels novel therapeutic targets at genomic and molecular levels through epigenetic and proteomic profiling. Several pre-clinical studies have identified mechanisms involving the immune system, where tumor associated macrophages are key players. Expression of cell proteins like Syndecan1, fatty acid-binding protein 7 and tropomyosin kinase receptor B have been implicated in aiding the transmigration of breast cancer cells to the brain. Changes in the proteomic landscape of the blood-brain-barrier show altered permeability characteristics, supporting entry of cancer cells. Findings from laboratory studies pave the path for the emergence of new biomarkers, especially blood-based miRNA and circulating tumor cell markers for prognostic staging. The constantly evolving therapeutics call for clinical trials backing supportive evidence of efficacies of both novel and existing approaches. The challenge lying ahead is discovering innovative techniques to replace use of human samples and optimize small-scale patient recruitment in trials.
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Affiliation(s)
- Fairooz Labiba Sadique
- Department of Biomedical Science, School of Health Sciences, International Medical University, Kuala Lumpur 57000, Malaysia
| | - Hemavathy Subramaiam
- Division of Pathology, School of Medicine, International Medical University, Kuala Lumpur 57000, Malaysia.
| | - Purushotham Krishnappa
- Division of Pathology, School of Medicine, International Medical University, Kuala Lumpur 57000, Malaysia
| | - Dinesh Kumar Chellappan
- Department of Life Sciences, School of Pharmacy, International Medical University, Kuala Lumpur 57000, Malaysia
| | - Jin Hao Ma
- School of Medicine, International Medical University, Kuala Lumpur 57000, Malaysia
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2
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Lococo F, Ghaly G, Chiappetta M, Flamini S, Evangelista J, Bria E, Stefani A, Vita E, Martino A, Boldrini L, Sassorossi C, Campanella A, Margaritora S, Mohammed A. Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review. Cancers (Basel) 2024; 16:1832. [PMID: 38791910 PMCID: PMC11119930 DOI: 10.3390/cancers16101832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/02/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.
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Affiliation(s)
- Filippo Lococo
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Galal Ghaly
- Faculty of Medicine and Surgery, Thoracic Surgery Unit, Cairo University, Giza 12613, Egypt; (G.G.); (A.M.)
| | - Marco Chiappetta
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Sara Flamini
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Jessica Evangelista
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Emilio Bria
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Alessio Stefani
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Emanuele Vita
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Antonella Martino
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Radiotherapy Unit, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Luca Boldrini
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Radiotherapy Unit, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Carolina Sassorossi
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Annalisa Campanella
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Stefano Margaritora
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Abdelrahman Mohammed
- Faculty of Medicine and Surgery, Thoracic Surgery Unit, Cairo University, Giza 12613, Egypt; (G.G.); (A.M.)
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Muacevic A, Adler JR. Role of Artificial Intelligence and Machine Learning in Prediction, Diagnosis, and Prognosis of Cancer. Cureus 2022; 14:e31008. [PMID: 36475188 PMCID: PMC9717523 DOI: 10.7759/cureus.31008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/02/2022] [Indexed: 01/25/2023] Open
Abstract
Cancer is one of the most devastating, fatal, dangerous, and unpredictable ailments. To reduce the risk of fatality in this disease, we need some ways to predict the disease, diagnose it faster and precisely, and predict the prognosis accurately. The incorporation of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms into the healthcare system has already proven to work wonders for patients. Artificial intelligence is a simulation of intelligence that uses data, rules, and information programmed in it to make predictions. The science of machine learning (ML) uses data to enhance performance in a variety of activities and tasks. A bigger family of machine learning techniques built on artificial neural networks and representation learning is deep learning (DL). To clarify, we require AI, ML, and DL to predict cancer risk, survival chances, cancer recurrence, cancer diagnosis, and cancer prognosis. All of these are required to improve patient's quality of life, increase their survival rates, decrease anxiety and fear to some extent, and make a proper personalized treatment plan for the suffering patient. The survival rates of people with diffuse large B-cell lymphoma (DLBCL) can be forecasted. Both solid and non-solid tumors can be diagnosed precisely with the help of AI and ML algorithms. The prognosis of the disease can also be forecasted with AI and its approaches like deep learning. This improvement in cancer care is a turning point in advanced healthcare and will deeply impact patient's life for good.
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Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning. Bioengineering (Basel) 2022; 9:bioengineering9070268. [PMID: 35877319 PMCID: PMC9312290 DOI: 10.3390/bioengineering9070268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 11/16/2022] Open
Abstract
There are three primary challenges in the automatic diagnosis of arrhythmias by electrocardiogram (ECG): the significant variation among individual patients, the multiple pathologies in the ECG signal and the high cost in annotating clinical ECG with the corresponding labels. Traditional ECG processing approaches rely heavily on prior knowledge, such as those from feature extraction and waveform analysis. The preprocessing for prior knowledge incurs computational overhead. Furthermore, standard deep learning methods do not fully consider the dynamic temporal, spatial and multi-labeling characteristics of ECG data. In clinical ECG waveforms, it is common to see multi-labeling in which a patient is labeled with multiple classes of arrhythmias. However, multiclass approaches in current research mainly solve the multi-label machine learning problem, ignoring the correlation between diseases, resulting in information loss. In this paper, an arrhythmia detection and classification scheme called multi-label fusion deep learning is proposed. The objective is to build a unified system with automatic feature learning which supports effective multi-label classification. First, a multi-label ECG-based feature selection method is combined with a matrix decomposition and sparse learning theory. The optimal feature subset is selected as a preprocessing algorithm for ECG data. A multi-label classifier is then constructed by fusing CNN and RNN networks to fully exploit the interactions and features of the time and space dimensions. The experimental result demonstrates that the proposed method can achieve a state-of-the-art performance compared to other algorithms in multi-label database experiments.
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Han J, Xiao N, Yang W, Luo S, Zhao J, Qiang Y, Chaudhary S, Zhao J. MS-ResNet: disease-specific survival prediction using longitudinal CT images and clinical data. Int J Comput Assist Radiol Surg 2022; 17:1049-1057. [PMID: 35445285 PMCID: PMC9020752 DOI: 10.1007/s11548-022-02625-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 03/24/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Medical imaging data of lung cancer in different stages contain a large amount of time information related to its evolution (emergence, development, or extinction). We try to explore the evolution process of lung images in time dimension to improve the prediction of lung cancer survival by using longitudinal CT images and clinical data jointly. METHODS In this paper, we propose an innovative multi-branch spatiotemporal residual network (MS-ResNet) for disease-specific survival (DSS) prediction by integrating the longitudinal computed tomography (CT) images at different times and clinical data. Specifically, we first extract the deep features from the multi-period CT images by an improved residual network. Then, the feature selection algorithm is used to select the most relevant feature subset from the clinical data. Finally, we integrate the deep features and feature subsets to take full advantage of the complementarity between the two types of data to generate the final prediction results. RESULTS The experimental results demonstrate that our MS-ResNet model is superior to other methods, achieving a promising 86.78% accuracy in the classification of short-survivor, med-survivor, and long-survivor. CONCLUSION In computer-aided prognostic analysis of cancer, the time dimension features of the course of disease and the integration of patient clinical data and CT data can effectively improve the prediction accuracy.
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Affiliation(s)
- Jiahao Han
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ning Xiao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Wanting Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shichao Luo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jun Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Suman Chaudhary
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
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Guan X, Qin T, Qi T. Precision Medicine in Lung Cancer Theranostics: Paving the Way from Traditional Technology to Advance Era. Cancer Control 2022. [PMCID: PMC8862127 DOI: 10.1177/10732748221077351] [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] [Indexed: 11/18/2022] Open
Abstract
Precision medicine for lung cancer theranostics is an advanced model combining prevention, diagnosis, and treatment for individual or specific population diseases to match individual patient differences. It involves collection and integration of genome, transcriptome, proteome, and metabolome features of lung cancer patients, combined with clinical characteristics. Subsequently, large data and artificial intelligence (AI) analysis have emerged to identify the most suitable therapeutic targets and personal treatment strategies for treatment of patients with lung cancer. We review the development and challenges associated with diagnosis and therapy of lung cancer from traditional technology, including immunotherapy prediction markers, liquid biopsy, surgery, and tumor immune microenvironment and patient-derived xenograft models, to AI in the era of precision medicine. AI has improved precision medicine and the predictive ability and accuracy of patient outcomes. Finally, we discuss some opportunities and challenges for lung cancer theranostics. Precision medicine in lung cancer can help us find the optimum treatment dose and time for a specific patient, which can advance the development of lung cancer therapeutics.
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Affiliation(s)
- Xiaoyong Guan
- Department of Laboratory Medicine, The First Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou, China
| | - Tian Qin
- Department of Oncology, The First Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou, China
| | - Tao Qi
- Oncology Hematology Department, Xijing 986 Hospital, Fourth Military Medical University, Xi’an, China
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Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction. ENTROPY 2021; 23:e23101252. [PMID: 34681977 PMCID: PMC8534606 DOI: 10.3390/e23101252] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/04/2021] [Accepted: 09/14/2021] [Indexed: 11/17/2022]
Abstract
The university curriculum is a systematic and organic study complex with some immediate associated steps; the initial learning of each semester’s course is crucial, and significantly impacts the learning process of subsequent courses and further studies. However, the low teacher–student ratio makes it difficult for teachers to consistently follow up on the detail-oriented learning situation of individual students. The extant learning early warning system is committed to automatically detecting whether students have potential difficulties—or even the risk of failing, or non-pass reports—before starting the course. Previous related research has the following three problems: first of all, it mainly focused on e-learning platforms and relied on online activity data, which was not suitable for traditional teaching scenarios; secondly, most current methods can only proffer predictions when the course is in progress, or even approaching the end; thirdly, few studies have focused on the feature redundancy in these learning data. Aiming at the traditional classroom teaching scenario, this paper transforms the pre-class student performance prediction problem into a multi-label learning model, and uses the attribute reduction method to scientifically streamline the characteristic information of the courses taken and explore the important relationship between the characteristics of the previously learned courses and the attributes of the courses to be taken, in order to detect high-risk students in each course before the course begins. Extensive experiments were conducted on 10 real-world datasets, and the results proved that the proposed approach achieves better performance than most other advanced methods in multi-label classification evaluation metrics.
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Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives. Int J Biol Sci 2021; 17:1581-1587. [PMID: 33907522 PMCID: PMC8071762 DOI: 10.7150/ijbs.58855] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 03/06/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis and treatment, and socioeconomics. The association of AI and COVID-19 can accelerate to rapidly diagnose positive patients. To learn the dynamics of a pandemic with relevance to AI, we search the literature using the different academic databases (PubMed, PubMed Central, Scopus, Google Scholar) and preprint servers (bioRxiv, medRxiv, arXiv). In the present review, we address the clinical applications of machine learning and deep learning, including clinical characteristics, electronic medical records, medical images (CT, X-ray, ultrasound images, etc.) in the COVID-19 diagnosis. The current challenges and future perspectives provided in this review can be used to direct an ideal deployment of AI technology in a pandemic.
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Affiliation(s)
- Shigao Huang
- Cancer Centre, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau 999078, Macau SAR, China
| | - Jie Yang
- Department of Computer and Information Science, University of Macau 999078, Macau SAR, China
- Chongqing Industry & Trade Polytechnic 408000, Chongqing, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau 999078, Macau SAR, China
| | - Qi Zhao
- Cancer Centre, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau 999078, Macau SAR, China
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Mousli A, Bihin B, Gustin T, Koerts G, Mouchamps M, Daisne JF. Application of a multi-institutional nomogram predicting salvage whole brain radiation-free survival to patients treated with postoperative stereotactic radiotherapy for brain metastases. Cancer Radiother 2021; 25:141-146. [PMID: 33422416 DOI: 10.1016/j.canrad.2020.06.036] [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: 06/05/2020] [Revised: 06/16/2020] [Accepted: 06/18/2020] [Indexed: 10/22/2022]
Abstract
PURPOSE The ultimate goal of stereotactic radiotherapy (SRT) of brain metastases (BM) is to avoid or postpone whole brain radiotherapy (WBRT). A nomogram based on multi-institutional data was developed by Gorovets, et al. to estimate the 6 and 12-months WBRT-free survival (WFS). The aim of the current retrospective study was to validate the nomogram in a cohort of postoperative BM patients treated with adjuvant SRT. MATERIAL AND METHODS We reviewed the data of 68 patients treated between 2008-2017 with postoperative SRT for BM. The primary endpoint was the WFS. The receiver operating characteristic curve and area under the curve (AUC) were calculated for both 6- and 12-months time points. RESULTS After a median follow-up of 64 months, the 1-year cumulative incidence of local and distant brain relapse rates were 15% [95% CI=8-26%] and 34% [95% CI=24-48%], respectively. At recurrence, repeated SRT or salvage WBRT were applied in 33% and 57% cases, respectively. The WFS rates at 6 and 12 months were 88% [95% CI=81-97%] and 67% [95% CI=56-81%], respectively. Using the Gorovets nomogram, the 6 months rates were overestimated while they were accurate at 12 months. AUC values were 0.47 and 0.62 for the 6- and 12-months respectively. Overall, Harrell's concordance index was 0.54. CONCLUSION This nomogram-predicted well the 12 months WFS but its discriminative power was quite low. This underlines the limits of this kind of predictive tool and leads us to consider the use of big data analysis in the future.
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Affiliation(s)
- A Mousli
- Radiation oncology department, CHU-UCL-Namur, site Sainte-Elisabeth, université catholique de Louvain, Namur, Belgium; Radiation oncology department, Tunis El Manar university, Salah Azaiez Institute, Tunis, Tunisia.
| | - B Bihin
- Biostatistics unit, CHU-UCL-Namur, site Godinne, université catholique de Louvain, Yvoir, Belgium.
| | - T Gustin
- Neurosurgery department, CHU-UCL-Namur, site Godinne, université catholique de Louvain, Yvoir, Belgium
| | - G Koerts
- Neurosurgery department, centre hospitalier régional Sambre et Meuse, Namur, Belgium
| | - M Mouchamps
- Neurosurgery department, centre hospitalier régional de Liège, Liège, Belgium; Neurosurgery department, centre hospitalier chrétien Saint-Joseph, Liège, Belgium
| | - J-F Daisne
- Radiation oncology department, CHU-UCL-Namur, site Sainte-Elisabeth, université catholique de Louvain, Namur, Belgium; Radiation oncology department, katholieke universiteit Leuven, university hospitals Leuven, Leuven, Belgium
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Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA. Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging 2020; 4:24. [PMID: 34191197 PMCID: PMC8218106 DOI: 10.1186/s41824-020-00094-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/26/2020] [Indexed: 12/20/2022] Open
Abstract
Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.
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Affiliation(s)
- Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
- Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Francesco Bartoli
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Andrea Marciano
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberta Zanca
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Riemer H J A Slart
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands
- Faculty of Science and Technology, Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands
| | - Paola A Erba
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands.
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Gao H, Dang Y, Qi T, Huang S, Zhang X. Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model. Medicine (Baltimore) 2020; 99:e21798. [PMID: 32872080 PMCID: PMC7437828 DOI: 10.1097/md.0000000000021798] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
This study is to establish the nomogram model and provide clinical therapy decision-making for extensive-stage small-cell lung cancer (ES-SCLC) patients with different metastatic sites using the Surveillance, Epidemiology, and End Results (SEER) Program.A total of 10,025 patients of ES-SCLC with metastasis from January 2010 to December 2016 were enrolled from the SEER database. All samples were randomly divided into a derivation cohort and a validation cohort, and the derivation cohort was divided into 6 groups by different metastatic sites: bone, liver, lung, brain, multiple organs, and other organs. Using Cox proportional hazards models to analyze candidate prognostic factors, screening out the independent prognostic factors to establish the nomogram. Compare the different models by Net reclassification improvement and integrated discrimination improvement. Concordance index (C-index) and the calibration curve were used to verify the prediction efficiency of the nomogram in the derivation cohort and validation cohort.In the derivation cohort, the median overall survival was 7 months. The overall survival rates at 6-month, 1-year, and 2-year were 55.07%, 24.61%, and 7.56%, respectively. The median survival time was 10, 8, 7, 9, 7, and 6 months for the 6 groups of different metastatic sites: other, bone, liver, lung, brain, and multiple organs, respectively. Age, sex, race, T, N, distant metastatic site, and chemotherapy were contained in the final nomogram prognostic model. The C-index was 0.6569777 in the derivation cohort and 0.8386301 in the validation cohort.The survival time of ES-SCLC patients with different metastatic sites was significantly different. The nomogram can effectively predict the prognosis of individuals and provide a basis for clinical decision-making.
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Affiliation(s)
- Hongxiang Gao
- Radiotherapy Department, The First Affiliated Hospital of Xi’an Jiaotong University
- Department of Oncology, Chang An Hospital
| | - Yazheng Dang
- Radiotherapy Department, 986 Hospital affiliated to The Fourth Military Medical University, Xi’an, Shaan Xi
| | - Tao Qi
- Radiotherapy Department, 986 Hospital affiliated to The Fourth Military Medical University, Xi’an, Shaan Xi
| | - Shigao Huang
- Faculty of Health Sciences, University of Macau, Taipa, Macao SAR, China
| | - Xiaozhi Zhang
- Radiotherapy Department, The First Affiliated Hospital of Xi’an Jiaotong University
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12
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Jamshidi MB, Lalbakhsh A, Talla J, Peroutka Z, Hadjilooei F, Lalbakhsh P, Jamshidi M, Spada LL, Mirmozafari M, Dehghani M, Sabet A, Roshani S, Roshani S, Bayat-Makou N, Mohamadzade B, Malek Z, Jamshidi A, Kiani S, Hashemi-Dezaki H, Mohyuddin W. Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:109581-109595. [PMID: 34192103 PMCID: PMC8043506 DOI: 10.1109/access.2020.3001973] [Citation(s) in RCA: 180] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 06/02/2020] [Indexed: 05/15/2023]
Abstract
COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.
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Affiliation(s)
- Mohammad Behdad Jamshidi
- Department of Electromechanical Engineering and Power Electronics (KEV)University of West Bohemia in Pilsen301 00PilsenCzech Republic
| | - Ali Lalbakhsh
- School of EngineeringMacquarie UniversitySydneyNSW2109Australia
| | - Jakub Talla
- Department of Electromechanical Engineering and Power Electronics (KEV)University of West Bohemia in Pilsen301 00PilsenCzech Republic
| | - Zdeněk Peroutka
- Regional Innovation Centre for Electrical engineering (RICE)University of West Bohemia in Pilsen301 00PilsenCzech Republic
| | - Farimah Hadjilooei
- Department of Radiation OncologyCancer Institute, Tehran University of Medical SciencesTehran1416753955Iran
| | - Pedram Lalbakhsh
- Department of English Language and LiteratureRazi UniversityKermanshah6714414971Iran
| | - Morteza Jamshidi
- Young Researchers and Elite Club, Kermanshah BranchIslamic Azad UniversityKermanshah1477893855Iran
| | - Luigi La Spada
- School of Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH11 4DYU.K.
| | - Mirhamed Mirmozafari
- Department of Electrical and Computer EngineeringUniversity of Wisconsin–MadisonMadisonWI53706USA
| | - Mojgan Dehghani
- Physics and Astronomy DepartmentLouisiana State UniversityBaton RougeLA70803USA
| | - Asal Sabet
- Irma Lerma Rangel College of PharmacyTexas A&M UniversityKingsvilleTX78363USA
| | - Saeed Roshani
- Department of Electrical EngineeringKermanshah Branch, Islamic Azad UniversityKermanshah1477893855Iran
| | - Sobhan Roshani
- Department of Electrical EngineeringKermanshah Branch, Islamic Azad UniversityKermanshah1477893855Iran
| | - Nima Bayat-Makou
- The Edward S. Rogers, Sr. Department of Electrical and Computer EngineeringUniversity of TorontoTorontoON M5SCanada
| | | | - Zahra Malek
- Medical Sciences Research Center, Faculty of Medicine, Tehran Medical Sciences BranchIslamic Azad UniversityTehran1477893855Iran
| | - Alireza Jamshidi
- Dentistry SchoolBabol University of Medical SciencesBabol4717647745Iran
| | - Sarah Kiani
- Medical Biology Research CenterHealth Technology Institute, Kermanshah University of Medical SciencesKermanshah6715847141Iran
| | - Hamed Hashemi-Dezaki
- Regional Innovation Centre for Electrical engineering (RICE)University of West Bohemia in Pilsen301 00PilsenCzech Republic
| | - Wahab Mohyuddin
- Research Institute for Microwave and Millimeter-Wave Studies, National University of Sciences and TechnologyIslamabad24090Pakistan
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Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach. Cancers (Basel) 2019; 11:cancers11122007. [PMID: 31842486 PMCID: PMC6966646 DOI: 10.3390/cancers11122007] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 12/01/2019] [Accepted: 12/09/2019] [Indexed: 12/11/2022] Open
Abstract
The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. From the colorectal cancer (CRC) registry of Chang Gung Memorial Hospital, Linkou, Taiwan, 4021 patients were selected for the analysis. Various ML algorithms were applied for the tumor stage prediction of the colon cancer by considering the Tumor Aggression Score (TAS) as a prognostic factor. Performances of different ML algorithms were evaluated using five-fold cross-validation, which is an effective way of the model validation. The accuracy achieved by the algorithms taking both cases of standard TNM staging and TNM staging with the Tumor Aggression Score was determined. It was observed that the Random Forest model achieved an F-measure of 0.89, when the Tumor Aggression Score was considered as an attribute along with the standard attributes normally used for the TNM stage prediction. We also found that the Random Forest algorithm outperformed all other algorithms, with an accuracy of approximately 84% and an area under the curve (AUC) of 0.82 ± 0.10 for predicting the five years DFS.
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Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett 2019; 471:61-71. [PMID: 31830558 DOI: 10.1016/j.canlet.2019.12.007] [Citation(s) in RCA: 204] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 12/04/2019] [Accepted: 12/06/2019] [Indexed: 02/06/2023]
Abstract
Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.
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Affiliation(s)
- Shigao Huang
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China
| | - Jie Yang
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China; Chongqing Industry&Trade Polytechnic, Chongqing, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China; Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai, China.
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China.
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