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Abedi S, Behmanesh A, Mazhar FN, Bagherifard A, Sami SH, Heidari N, Hossein-Khannazer N, Namazifard S, Kazem Arki M, Shams R, Zarrabi A, Vosough M. Machine learning and experimental analyses identified miRNA expression models associated with metastatic osteosarcoma. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167357. [PMID: 39033966 DOI: 10.1016/j.bbadis.2024.167357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 07/09/2024] [Accepted: 07/11/2024] [Indexed: 07/23/2024]
Abstract
Osteosarcoma (OS), as the most common primary bone cancer, has a high invasiveness and metastatic potential, therefore, it has a poor prognosis. This study identified early diagnostic biomarkers using miRNA expression profiles associated with osteosarcoma metastasis. In the first step, we used RNA-seq and online microarray data from osteosarcoma tissues and cell lines to identify differentially expressed miRNAs. Then, using seven feature selection algorithms for ranking, the first-ranked miRNAs were selected as input for five machine learning systems. Using network analysis and machine learning algorithms, we developed new diagnostic models that successfully differentiated metastatic osteosarcoma from non-metastatic samples based on newly discovered miRNA signatures. The results showed that miR-34c-3p and miR-154-3p act as the most promising models in the diagnosis of metastatic osteosarcoma. Validation for this model by RT-qPCR in benign tissue and osteosarcoma biopsies confirmed the lower expression of miR-34c-3p and miR-154-3p in OS samples. In addition, a direct correlation between miR-34c-3p expression, miR-154-3p expression and tumor grade was discovered. The combined values of miR-34c-3p and miR-154-3p showed 90 % diagnostic power (AUC = 0.90) for osteosarcoma samples and 85 % (AUC = 0.85) for metastatic osteosarcoma. Adhesion junction and focal adhesion pathways, as well as epithelial-to-mesenchymal transition (EMT) GO terms, were identified as the most significant KEGG and GO terms for the top miRNAs. The findings of this study highlight the potential use of novel miRNA expression signatures for early detection of metastatic osteosarcoma. These findings may help in determining therapeutic approaches with a quantitative and faster method of metastasis detection and also be used in the development of targeted molecular therapy for this aggressive cancer. Further research is needed to confirm the clinical utility of miR-34c-3p and miR-154-3p as diagnostic biomarkers for metastatic osteosarcoma.
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Affiliation(s)
- Samira Abedi
- Department of Cellular and Molecular Biology, Faculty of Sciences and Advanced Technology in Biology, University of Science and Culture, Tehran, Iran; Department of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Ali Behmanesh
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Farid Najd Mazhar
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Abolfazl Bagherifard
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Sam Hajialiloo Sami
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Negar Heidari
- Department of Cellular and Molecular Biology, Faculty of Sciences and Advanced Technology in Biology, University of Science and Culture, Tehran, Iran; Department of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Nikoo Hossein-Khannazer
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Saina Namazifard
- University of Texas at Arlington, Department of Mechanical and Aerospace Engineering, USA
| | - Mandana Kazem Arki
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Roshanak Shams
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Ali Zarrabi
- Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Turkiye; Graduate School of Biotechnology and Bioengineering, Yuan Ze University, Taoyuan 320315, Taiwan; Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600 077, India.
| | - Massoud Vosough
- Department of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran; Experimental Cancer Medicine, Institution for Laboratory Medicine, Karolinska Institute, Stockholm, Sweden.
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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] [MESH Headings] [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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Pati A, Panigrahi A, Parhi M, Giri J, Qin H, Mallik S, Pattanayak SR, Agrawal UK. Performance assessment of hybrid machine learning approaches for breast cancer and recurrence prediction. PLoS One 2024; 19:e0304768. [PMID: 39088457 PMCID: PMC11293701 DOI: 10.1371/journal.pone.0304768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 05/19/2024] [Indexed: 08/03/2024] Open
Abstract
Breast cancer is a major health concern for women everywhere and a major killer of women. Malignant tumors may be distinguished from benign ones, allowing for early diagnosis of this disease. Therefore, doctors need an accurate method of diagnosing tumors as either malignant or benign. Even if therapy begins immediately after diagnosis, some cancer cells may persist in the body, increasing the risk of a recurrence. Metastasis and recurrence are the leading causes of death from breast cancer. Therefore, detecting a return of breast cancer early has become a pressing medical issue. Evaluating and contrasting various Machine Learning (ML) techniques for breast cancer and recurrence prediction is crucial to choosing the best successful method. Inaccurate forecasts are common when using datasets with a large number of attributes. This study addresses the need for effective feature selection and optimization methods by introducing Recursive Feature Elimination (RFE) and Grey Wolf Optimizer (GWO), in response to the limitations observed in existing approaches. In this research, the performance evaluation of methods is enhanced by employing the RFE and GWO, considering the Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Prognostic Breast Cancer (WPBC) datasets taken from the UCI-ML repository. Various preprocessing techniques are applied to raw data, including imputation, scaling, and others. In the second step, relevant feature correlations are used with RFE to narrow down candidate discriminative features. The GWO chooses the best possible combination of attributes for the most accurate result in the next step. We use seven ML classifiers in both datasets to make a binary decision. On the WDBC and WPBC datasets, several experiments have shown accuracies of 98.25% and 93.27%, precisions of 98.13% and 95.56%, sensitivities of 99.06% and 96.63%, specificities of 96.92% and 73.33%, F1-scores of 98.59% and 96.09% and AUCs of 0.982 and 0.936, respectively. The hybrid approach's superior feature selection improved the accuracy of breast cancer performance indicators and recurrence classification.
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Affiliation(s)
- Abhilash Pati
- Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Amrutanshu Panigrahi
- Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Manoranjan Parhi
- Centre for Data Science, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Jayant Giri
- Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India
- Department of VLSI Microelectronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
| | - Hong Qin
- Department of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, Tamil Nadu, United States of America
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Sambit Ranjan Pattanayak
- Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Umang Kumar Agrawal
- School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India
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Alelyani T, Alshammari MM, Almuhanna A, Asan O. Explainable Artificial Intelligence in Quantifying Breast Cancer Factors: Saudi Arabia Context. Healthcare (Basel) 2024; 12:1025. [PMID: 38786433 PMCID: PMC11120946 DOI: 10.3390/healthcare12101025] [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: 03/30/2024] [Revised: 05/11/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
Breast cancer represents a significant health concern, particularly in Saudi Arabia, where it ranks as the most prevalent cancer type among women. This study focuses on leveraging eXplainable Artificial Intelligence (XAI) techniques to predict benign and malignant breast cancer cases using various clinical and pathological features specific to Saudi Arabian patients. Six distinct models were trained and evaluated based on common performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC score. To enhance interpretability, Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) were applied. The analysis identified the Random Forest model as the top performer, achieving an accuracy of 0.72, along with robust precision, recall, F1 score, and AUC-ROC score values. Conversely, the Support Vector Machine model exhibited the poorest performance metrics, indicating its limited predictive capability. Notably, the XAI approaches unveiled variations in the feature importance rankings across models, underscoring the need for further investigation. These findings offer valuable insights into breast cancer diagnosis and machine learning interpretation, aiding healthcare providers in understanding and potentially integrating such technologies into clinical practices.
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Affiliation(s)
- Turki Alelyani
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 1988, Saudi Arabia
| | - Maha M. Alshammari
- Department of Environmental Health, Institute for Research and Medical Consultations, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia;
| | - Afnan Almuhanna
- Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia;
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ 07030, USA;
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Nunez JJ, Leung B, Ho C, Ng RT, Bates AT. Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing. COMMUNICATIONS MEDICINE 2024; 4:69. [PMID: 38589545 PMCID: PMC11001970 DOI: 10.1038/s43856-024-00495-x] [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: 12/23/2023] [Accepted: 03/28/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Patients with cancer often have unmet psychosocial needs. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This work used natural language processing to predict which patients will see a counsellor or psychiatrist from a patient's initial oncology consultation document. We believe this is the first use of artificial intelligence to predict psychiatric outcomes from non-psychiatric medical documents. METHODS This retrospective prognostic study used data from 47,625 patients at BC Cancer. We analyzed initial oncology consultation documents using traditional and neural language models to predict whether patients would see a counsellor or psychiatrist in the 12 months following their initial oncology consultation. RESULTS Here, we show our best models achieved a balanced accuracy (receiver-operating-characteristic area-under-curve) of 73.1% (0.824) for predicting seeing a psychiatrist, and 71.0% (0.784) for seeing a counsellor. Different words and phrases are important for predicting each outcome. CONCLUSION These results suggest natural language processing can be used to predict psychosocial needs of patients with cancer from their initial oncology consultation document. Future research could extend this work to predict the psychosocial needs of medical patients in other settings.
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Affiliation(s)
- John-Jose Nunez
- BC Cancer, Vancouver, BC, Canada.
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.
| | | | | | - Raymond T Ng
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Alan T Bates
- BC Cancer, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
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Sajithkumar A, Thomas J, Saji AM, Ali F, E K HH, Adampulan HAG, Sarathchand S. Artificial Intelligence in pathology: current applications, limitations, and future directions. Ir J Med Sci 2024; 193:1117-1121. [PMID: 37542634 DOI: 10.1007/s11845-023-03479-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 07/26/2023] [Indexed: 08/07/2023]
Abstract
PURPOSE Given AI's recent success in computer vision applications, majority of pathologists anticipate that it will be able to assist them with a variety of digital pathology activities. Massive improvements in deep learning have enabled a synergy between Artificial Intelligence (AI) and deep learning, enabling image-based diagnosis against the backdrop of digital pathology. AI-based solutions are being developed to eliminate errors and save pathologists time. AIMS In this paper, we will discuss the components that went into the use of Artificial Intelligence in Pathology, its use in the medical profession, the obstacles and constraints that it encounters, and the future possibilities of AI in the medical field. CONCLUSIONS Based on these factors, we elaborate upon the use of AI in medical pathology and provide future recommendations for its successful implementation in this field.
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Affiliation(s)
- Akhil Sajithkumar
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India.
| | - Jubin Thomas
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Ajish Meprathumalil Saji
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Fousiya Ali
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Haneena Hasin E K
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Hannan Abdul Gafoor Adampulan
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Swathy Sarathchand
- Sree Narayana Institute of Medical Sciences, Chalakka - Kuthiathode Rd, North Kuthiathode, Kunnukara, Kerala, 683594, India
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7
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Hussain S, Ali M, Naseem U, Nezhadmoghadam F, Jatoi MA, Gulliver TA, Tamez-Peña JG. Breast cancer risk prediction using machine learning: a systematic review. Front Oncol 2024; 14:1343627. [PMID: 38571502 PMCID: PMC10987819 DOI: 10.3389/fonc.2024.1343627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/26/2024] [Indexed: 04/05/2024] Open
Abstract
Background Breast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies. Objective and methods This study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers. Results A total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI. Conclusion This study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.
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Affiliation(s)
- Sadam Hussain
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada
| | - Mansoor Ali
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico
| | - Usman Naseem
- College of Science and Engineering, James Cook University, Cairns, QLD, Australia
| | | | - Munsif Ali Jatoi
- Department of Biomedical Engineering, Salim Habib University, Karachi, Pakistan
| | - T. Aaron Gulliver
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada
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Rakhshaninejad M, Fathian M, Shirkoohi R, Barzinpour F, Gandomi AH. Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach. BMC Bioinformatics 2024; 25:33. [PMID: 38253993 PMCID: PMC10810249 DOI: 10.1186/s12859-024-05657-1] [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: 12/03/2023] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.
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Affiliation(s)
- Morteza Rakhshaninejad
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran
| | - Mohammad Fathian
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran.
| | - Reza Shirkoohi
- Cancer Biology Research Center, Cancer Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Keshavarz Boulevard, Tehran, 1419733141, Tehran, Iran
| | - Farnaz Barzinpour
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran
| | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, 2007, NSW, Australia
- University Research and Innovation Center (EKIK), Óbuda University, Budapest, 1034, Hungary
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Nazari E, Naderi H, Tabadkani M, ArefNezhad R, Farzin AH, Dashtiahangar M, Khazaei M, Ferns GA, Mehrabian A, Tabesh H, Avan A. Breast cancer prediction using different machine learning methods applying multi factors. J Cancer Res Clin Oncol 2023; 149:17133-17146. [PMID: 37773467 DOI: 10.1007/s00432-023-05388-5] [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: 07/08/2023] [Accepted: 09/01/2023] [Indexed: 10/01/2023]
Abstract
OBJECTIVE Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors. METHODS The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated. RESULT Compared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54). CONCLUSION Breast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.
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Affiliation(s)
- Elham Nazari
- Faculty of Medicine, Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Naderi
- Faculty of Medicine, Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahla Tabadkani
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Reza ArefNezhad
- Halal Research Center of IRI, FDA, Tehran, Iran
- Department of Anatomy, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | | | - Majid Khazaei
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Division of Medical Education, Brighton & Sussex Medical School, Falmer, Brighton, BN1 9PH, Sussex, UK
| | - Amin Mehrabian
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Hamed Tabesh
- Faculty of Medicine, Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
- College of Medicine, University of Warith Al-Anbiyaa, Karbala, Iraq.
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10
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Rao A, Manley L, Smith S. Use of artificial intelligence to identify patients to be assessed in a breast clinic on 2-week wait: a retrospective cohort study. Ann Med Surg (Lond) 2023; 85:5459-5463. [PMID: 37915669 PMCID: PMC10617910 DOI: 10.1097/ms9.0000000000001293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 09/03/2023] [Indexed: 11/03/2023] Open
Abstract
Background The number of urgent referrals from primary care to specialist one stop breast clinics continues to rise beyond the capacity of the 2-week wait service. This study aims to use artificial intelligence (AI) to identify patients with new breast symptoms requiring a biopsy to identify those who should be prioritised for urgent breast clinic assessment. Methods Data were collected retrospectively for patients attending one stop triple assessment breast clinic at Broomfield hospital between 1 June and 1 October 2021. PHP machine learning software was used to run AI on the data to identify patients who had a core biopsy in clinic. Results A total of 794 cases were referred to one stop breast clinic for new breast symptoms-37 male (4.6%) and 757 female (95.3%). The average age of the patients included was 43.2 years. Five hundred thirty-six patients (67.5%) presented with a breast lump, 180 (22.7%) with breast pain, 61 (7.7%) with changes to shape or skin and 13 (1.6%) with a lump identified by their general practitioner. The patients who had a biopsy were of increased age [52.8 (SD 17.9) vs. 44.1 (SD 16.8), P<0.001], and had previous mammogram [n=21, (31.8%) vs. n=148 (20.3%), P 0.03], previous benign breast disease [n=9 (13.6%) vs. n=23 (3.1%), P<0.001], and increased use of HRT [n=13 (19.7%) vs. n=53 (6.4%), P<0.001]. The sensitivity and specificity of AI with neural network algorithms were 84% and 90%, respectively. Conclusion AI was very effective at predicting the presenting symptoms that are likely to result in biopsy and can therefore be used to identify patients who need to be seen urgently in breast clinic.
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Affiliation(s)
- Ahsan Rao
- Department of Public Health, Imperial College London, London
| | - Lara Manley
- Breast Unit, Broomfield Hospital, Chelmsford, Essex, UK
| | - Simon Smith
- Breast Unit, Broomfield Hospital, Chelmsford, Essex, UK
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11
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Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol 2023; 18:109. [PMID: 37784122 PMCID: PMC10546747 DOI: 10.1186/s13000-023-01375-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 07/21/2023] [Indexed: 10/04/2023] Open
Abstract
Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided diagnostic techniques are being used for assisting, augmenting and empowering the computational histopathology and AI-enabled diagnostics. This is paving the way for advancement in precision medicine in cancer. Automated whole slide imaging (WSI) scanners are now rendering diagnostic quality, high-resolution images of entire glass slides and combining these images with innovative digital pathology tools is making it possible to integrate imaging into all aspects of pathology reporting including anatomical, clinical, and molecular pathology. The recent approvals of WSI scanners for primary diagnosis by the FDA as well as the approval of prostate AI algorithm has paved the way for starting to incorporate this exciting technology for use in primary diagnosis. AI tools can provide a unique platform for innovations and advances in anatomical and clinical pathology workflows. In this review, we describe the milestones and landmark trials in the use of AI in clinical pathology with emphasis on future directions.
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Affiliation(s)
- Saba Shafi
- Department of Pathology, The Ohio State University Wexner Medical Center, E409 Doan Hall, 410 West 10th Ave, Columbus, OH, 43210, USA
| | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, E409 Doan Hall, 410 West 10th Ave, Columbus, OH, 43210, USA.
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12
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Sufyan M, Shokat Z, Ashfaq UA. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective. Comput Biol Med 2023; 165:107356. [PMID: 37688994 DOI: 10.1016/j.compbiomed.2023.107356] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/21/2023] [Accepted: 08/12/2023] [Indexed: 09/11/2023]
Abstract
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.
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Affiliation(s)
- Muhammad Sufyan
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Zeeshan Shokat
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
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13
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Liu Y, Han D, Parwani AV, Li Z. Applications of Artificial Intelligence in Breast Pathology. Arch Pathol Lab Med 2023; 147:1003-1013. [PMID: 36800539 DOI: 10.5858/arpa.2022-0457-ra] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/15/2022] [Indexed: 02/19/2023]
Abstract
CONTEXT.— Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology. OBJECTIVE.— To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes. DATA SOURCES.— We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience. CONCLUSIONS.— With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.
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Affiliation(s)
- Yueping Liu
- From the Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China (Liu, Han)
| | - Dandan Han
- From the Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China (Liu, Han)
| | - Anil V Parwani
- The Department of Pathology, The Ohio State University, Columbus (Parwani, Li)
| | - Zaibo Li
- From the Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China (Liu, Han)
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14
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Yosofvand M, Khan SY, Dhakal R, Nejat A, Moustaid-Moussa N, Rahman RL, Moussa H. Automated Detection and Scoring of Tumor-Infiltrating Lymphocytes in Breast Cancer Histopathology Slides. Cancers (Basel) 2023; 15:3635. [PMID: 37509295 PMCID: PMC10377197 DOI: 10.3390/cancers15143635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
Detection of tumor-infiltrating lymphocytes (TILs) in cancer images has gained significant importance as these lymphocytes can be used as a biomarker in cancer detection and treatment procedures. Our goal was to develop and apply a TILs detection tool that utilizes deep learning models, following two sequential steps. First, based on the guidelines from the International Immuno-Oncology Biomarker Working Group (IIOBWG) on Breast Cancer, we labeled 63 large pathology imaging slides and annotated the TILs in the stroma area to create the dataset required for model development. In the second step, various machine learning models were employed and trained to detect the stroma where U-Net deep learning structure was able to achieve 98% accuracy. After detecting the stroma area, a Mask R-CNN model was employed for the TILs detection task. The R-CNN model detected the TILs in various images and was used as the backbone analysis network for the GUI development of the TILs detection tool. This is the first study to combine two deep learning models for TILs detection at the cellular level in breast tumor histopathology slides. Our novel approach can be applied to scoring TILs in large cancer slides. Statistical analysis showed that the output of the implemented approach had 95% concordance with the scores assigned by the pathologists, with a p-value of 0.045 (n = 63). This demonstrated that the results from the developed software were statistically meaningful and highly accurate. The implemented approach in analyzing whole tumor histology slides and the newly developed TILs detection tool can be used for research purposes in biomedical and pathology applications and it can provide researchers and clinicians with the TIL score for various input images. Future research using additional breast cancer slides from various sources for further training and validation of the developed models is necessary for more inclusive, rigorous, and robust clinical applications.
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Affiliation(s)
- Mohammad Yosofvand
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Sonia Y Khan
- Breast Center of Excellence, Department of Surgery, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Rabin Dhakal
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Ali Nejat
- Department of Civil, Environmental, & Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Naima Moustaid-Moussa
- Department of Nutritional Sciences, Texas Tech University, Lubbock, TX 79409, USA
- Obesity Research Institute, Texas Tech University, Lubbock, TX 79409, USA
| | - Rakhshanda Layeequr Rahman
- Breast Center of Excellence, Department of Surgery, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Hanna Moussa
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA
- Obesity Research Institute, Texas Tech University, Lubbock, TX 79409, USA
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15
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Plisson M, Moll A, Sarrazin V, Charles D, Antoine T, Ionescu R, Koehren O, Raymond E. Methods for Inclusive Underwriting of Breast Cancer Risk with Machine Learning and Innovative Algorithms. J Insur Med 2023; 50:36-48. [PMID: 37725502 DOI: 10.17849/insm-50-1-36-48.1] [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: 07/15/2022] [Accepted: 03/21/2023] [Indexed: 09/21/2023]
Abstract
INTRODUCTION -Due to early detection and improved therapies, the prevalence of long-term breast cancer survivors is increasing. This has increased the need for more inclusive underwriting in individuals with a history of breast cancer. Herein, we developed a method using algorithm aiming facilitating the underwriting of multiple parameters in breast cancer survivors. METHODS -Variables and data were extracted from the SEER database and analyzed using 4 different machine learning based algorithms (Logistic Regression, GA2M, Random Forest, and XGBoost) that were compared with Kaplan Meier survival estimates. The performances of these algorithms have been compared with multiple metrics (Log Loss, AUC, and SMR). In situ (non-invasive) and metastatic breast cancer were excluded from this analysis. RESULTS -Parameters included the pathological subtype, pTNM staging (T: tumor size, N; number of nodes; M presence or absence of metastases), Scarff-Bloom-Richardson grading, the expression of estrogen and progesterone hormone receptors were selected to predict the individual outcome at any time point from diagnosis. While all models had identical performance in terms of statistical metrics (AUC, Log Loss, and SMR), the logistic regression was the one and only model that respects all business constraints and was intelligible for medical and underwriting users. CONCLUSION -This study provides insight to develop algorithms to set underwriter-friendly calculators for more accurate risk estimations that can be used to rationalize insurance pricing for breast cancer survivors. This study supports the development of a more inclusive underwriting based on models that can encompass the heterogeneity of several malignancies such as breast cancer.
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Affiliation(s)
- Manuel Plisson
- SCOR Global Life, Knowledge Team, 5 Avenue Kléber, 75795 Paris Cedex 16, France
| | - Antoine Moll
- SCOR Global Life, Knowledge Team, 5 Avenue Kléber, 75795 Paris Cedex 16, France
| | - Valentine Sarrazin
- SCOR Global Life, Knowledge Team, 5 Avenue Kléber, 75795 Paris Cedex 16, France
| | - Denis Charles
- SCOR Global Life, Knowledge Team, 5 Avenue Kléber, 75795 Paris Cedex 16, France
- Université de Poitiers, CRIEF
| | - Thibault Antoine
- SCOR Global Life, Knowledge Team, 5 Avenue Kléber, 75795 Paris Cedex 16, France
| | - Razvan Ionescu
- SCOR Global Life, Knowledge Team, 5 Avenue Kléber, 75795 Paris Cedex 16, France
| | - Odile Koehren
- SCOR Global Life, Knowledge Team, 5 Avenue Kléber, 75795 Paris Cedex 16, France
| | - Eric Raymond
- SCOR Global Life, Knowledge Team, 5 Avenue Kléber, 75795 Paris Cedex 16, France
- Université de Poitiers, CRIEF
- Department of Oncology, Groupe Hospitalier Paris Saint Joseph, 185 Rue Raymond Losserand, 75014 Paris, France
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16
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Zarean Shahraki S, Azizmohammad Looha M, Mohammadi kazaj P, Aria M, Akbari A, Emami H, Asadi F, Akbari ME. Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models. Front Oncol 2023; 13:1147604. [PMID: 37342184 PMCID: PMC10277681 DOI: 10.3389/fonc.2023.1147604] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 05/19/2023] [Indexed: 06/22/2023] Open
Abstract
Background Breast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up. Materials and methods This study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype. Results The random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals. Conclusion This study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments.
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Affiliation(s)
- Saba Zarean Shahraki
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehdi Azizmohammad Looha
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Pooya Mohammadi kazaj
- Geographic Information Systems Department, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Mehrad Aria
- Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tehran, Iran
| | - Atieh Akbari
- Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hassan Emami
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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17
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Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer. DATA 2023. [DOI: 10.3390/data8020035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Machine learning (ML) was used to develop classification models to predict individual tumor patients’ outcomes. Binary classification defined whether the tumor was malignant or benign. This paper presents a comparative analysis of machine learning algorithms used for breast cancer prediction. This study used a dataset obtained from the National Cancer Institute (NIH), USA, which contains 1.7 million data records. Classical and deep learning methods were included in the accuracy assessment. Classical decision tree (DT), linear discriminant (LD), logistic regression (LR), support vector machine (SVM), and ensemble techniques (ET) algorithms were used. Probabilistic neural network (PNN), deep neural network (DNN), and recurrent neural network (RNN) methods were used for comparison. Feature selection and its effect on accuracy were also investigated. The results showed that decision trees and ensemble techniques outperformed the other techniques, as they both achieved a 98.7% accuracy.
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18
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Li C, Weng Y, Zhang Y, Wang B. A Systematic Review of Application Progress on Machine Learning-Based Natural Language Processing in Breast Cancer over the Past 5 Years. Diagnostics (Basel) 2023; 13:diagnostics13030537. [PMID: 36766641 PMCID: PMC9913934 DOI: 10.3390/diagnostics13030537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 02/04/2023] Open
Abstract
Artificial intelligence (AI) has been steadily developing in the medical field in the past few years, and AI-based applications have advanced cancer diagnosis. Breast cancer has a massive amount of data in oncology. There has been a high level of research enthusiasm to apply AI techniques to assist in breast cancer diagnosis and improve doctors' efficiency. However, the wise utilization of tedious breast cancer-related medical care is still challenging. Over the past few years, AI-based NLP applications have been increasingly proposed in breast cancer. In this systematic review, we conduct the review using preferred reporting items for systematic reviews and meta-analyses (PRISMA) and investigate the recent five years of literature in natural language processing (NLP)-based AI applications. This systematic review aims to uncover the recent trends in this area, close the research gap, and help doctors better understand the NLP application pipeline. We first conduct an initial literature search of 202 publications from Scopus, Web of Science, PubMed, Google Scholar, and the Association for Computational Linguistics (ACL) Anthology. Then, we screen the literature based on inclusion and exclusion criteria. Next, we categorize and analyze the advantages and disadvantages of the different machine learning models. We also discuss the current challenges, such as the lack of a public dataset. Furthermore, we suggest some promising future directions, including semi-supervised learning, active learning, and transfer learning.
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Affiliation(s)
- Chengtai Li
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
| | - Ying Weng
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
- Correspondence:
| | - Yiming Zhang
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
| | - Boding Wang
- Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, China
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19
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Nunez JJ, Leung B, Ho C, Bates AT, Ng RT. Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing. JAMA Netw Open 2023; 6:e230813. [PMID: 36848085 PMCID: PMC9972192 DOI: 10.1001/jamanetworkopen.2023.0813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
IMPORTANCE Predicting short- and long-term survival of patients with cancer may improve their care. Prior predictive models either use data with limited availability or predict the outcome of only 1 type of cancer. OBJECTIVE To investigate whether natural language processing can predict survival of patients with general cancer from a patient's initial oncologist consultation document. DESIGN, SETTING, AND PARTICIPANTS This retrospective prognostic study used data from 47 625 of 59 800 patients who started cancer care at any of the 6 BC Cancer sites located in the province of British Columbia between April 1, 2011, and December 31, 2016. Mortality data were updated until April 6, 2022, and data were analyzed from update until September 30, 2022. All patients with a medical or radiation oncologist consultation document generated within 180 days of diagnosis were included; patients seen for multiple cancers were excluded. EXPOSURES Initial oncologist consultation documents were analyzed using traditional and neural language models. MAIN OUTCOMES AND MEASURES The primary outcome was the performance of the predictive models, including balanced accuracy and receiver operating characteristics area under the curve (AUC). The secondary outcome was investigating what words the models used. RESULTS Of the 47 625 patients in the sample, 25 428 (53.4%) were female and 22 197 (46.6%) were male, with a mean (SD) age of 64.9 (13.7) years. A total of 41 447 patients (87.0%) survived 6 months, 31 143 (65.4%) survived 36 months, and 27 880 (58.5%) survived 60 months, calculated from their initial oncologist consultation. The best models achieved a balanced accuracy of 0.856 (AUC, 0.928) for predicting 6-month survival, 0.842 (AUC, 0.918) for 36-month survival, and 0.837 (AUC, 0.918) for 60-month survival, on a holdout test set. Differences in what words were important for predicting 6- vs 60-month survival were found. CONCLUSIONS AND RELEVANCE These findings suggest that models performed comparably with or better than previous models predicting cancer survival and that they may be able to predict survival using readily available data without focusing on 1 cancer type.
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Affiliation(s)
- John-Jose Nunez
- BC Cancer, Vancouver, British Columbia, Canada
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Cheryl Ho
- BC Cancer, Vancouver, British Columbia, Canada
| | - Alan T. Bates
- BC Cancer, Vancouver, British Columbia, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Raymond T. Ng
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada
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Chandrashekar K, Setlur AS, Sabhapathi C A, Raiker SS, Singh S, Niranjan V. Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications. Cancer Inform 2023; 22:11769351221147244. [PMID: 36714384 PMCID: PMC9880585 DOI: 10.1177/11769351221147244] [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: 10/29/2022] [Accepted: 12/06/2022] [Indexed: 01/24/2023] Open
Abstract
Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.
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Affiliation(s)
| | | | | | | | | | - Vidya Niranjan
- Vidya Niranjan, Department of
Biotechnology, R V College of Engineering, Bengaluru, Karnataka 560059, India.
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21
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Sarkar S, Mali K. Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters. Digit Health 2023; 9:20552076231207203. [PMID: 37860702 PMCID: PMC10583530 DOI: 10.1177/20552076231207203] [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: 06/18/2023] [Accepted: 09/26/2023] [Indexed: 10/21/2023] Open
Abstract
Background Breast cancer is a highly predominant destructive disease among women characterised with varied tumour biology, molecular subgroups and diverse clinicopathological specifications. The potentiality of machine learning to transform complex medical data into meaningful knowledge has led to its application in breast cancer detection and prognostic evaluation. Objective The emergence of data-driven diagnostic model for assisting clinicians in diagnostic decision making has gained an increasing curiosity in breast cancer identification and analysis. This motivated us to develop a breast cancer data-driven model for subtype classification more accurately. Method In this article, we proposed a firefly-support vector machine (SVM) breast cancer predictive model that uses clinicopathological and demographic data gathered from various tertiary care cancer hospitals or oncological centres to distinguish between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC). Results The results of the firefly-support vector machine (firefly-SVM) predictive model were distinguished from the traditional grid search-support vector machine (Grid-SVM) model, particle swarm optimisation-support vector machine (PSO-SVM) and genetic algorithm-support vector machine (GA-SVM) hybrid models through hyperparameter tuning. The findings show that the recommended firefly-SVM classification model outperformed other existing models in terms of prediction accuracy (93.4%, 86.6%, 69.6%) for automated SVM parameter selection. The effectiveness of the prediction model was also evaluated using well-known metrics, such as the F1-score, mean square error, area under the ROC curve, logarithmic loss and precision-recall curve. Conclusion Firefly-SVM predictive model may be treated as an alternate tool for breast cancer subgroup classification that would benefit the clinicians for managing the patient with proper treatment and diagnostic outcome.
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Affiliation(s)
- Suvobrata Sarkar
- Department of Computer Science and Engineering, Dr. B.C. Roy Engineering College, Durgapur, West Bengal, India
| | - Kalyani Mali
- Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India
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22
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Yang Y, Zhang Y, Li Y. Artificial intelligence applications in pediatric oncology diagnosis. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:157-169. [PMID: 36937318 PMCID: PMC10017189 DOI: 10.37349/etat.2023.00127] [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: 10/17/2022] [Accepted: 12/30/2022] [Indexed: 03/04/2023] Open
Abstract
Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.
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Affiliation(s)
- Yuhan Yang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yimao Zhang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yuan Li
- Laboratory of Digestive Surgery, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
- Correspondence: Yuan Li, Laboratory of Digestive Surgery, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
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Data augmentation guided breast cancer diagnosis and prognosis using an integrated deep-generative framework based on breast tumor’s morphological information. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
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A Machine Learning Applied Diagnosis Method for Subcutaneous Cyst by Ultrasonography. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:1526540. [PMID: 36299601 PMCID: PMC9592196 DOI: 10.1155/2022/1526540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/19/2022] [Accepted: 09/28/2022] [Indexed: 11/18/2022]
Abstract
For decades, ultrasound images have been widely used in the detection of various diseases due to their high security and efficiency. However, reading ultrasound images requires years of experience and training. In order to support the diagnosis of clinicians and reduce the workload of doctors, many ultrasonic computer aided diagnostic systems have been proposed. In recent years, the success of deep learning in image classification and segmentation has made more and more scholars realize the potential performance improvement brought by the application of deep learning in ultrasonic computer-aided diagnosis systems. This study is aimed at applying several machine learning algorithms and develop a machine learning method to diagnose subcutaneous cyst. Clinical features are extracted from datasets and images of ultrasonography of 132 patients from Hunan Provincial People's Hospital in China. All datasets are separated into 70% training and 30% testing. Four kinds of machine learning algorithms including decision tree (DT), support vector machine (SVM), K-nearest neighbors (KNN), and neural networks (NN) had been approached to determine the best performance. Compared with all the results from each feature, SVM achieved the best performance from 91.7% to 100%. Results show that SVM performed the highest accuracy in the diagnosis of subcutaneous cyst by ultrasonography, which provide a good reference in further application to clinical practice of ultrasonography of subcutaneous cyst.
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25
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P D, C G. A systematic review on machine learning and deep learning techniques in cancer survival prediction. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 174:62-71. [PMID: 35933043 DOI: 10.1016/j.pbiomolbio.2022.07.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/13/2022] [Accepted: 07/19/2022] [Indexed: 06/15/2023]
Abstract
Cancer is a disease which is characterised by the unusual and uncontrollable growth of body cells. This usually happens asymptomatically and gets spread to other parts of the body. The major problem in treating cancer is that its progress is not monitored once it is diagnosed. The progress or the prognosis can be done through survival analysis. The survival analysis is the branch of statistics that deals in predicting the time of event of occurrence. In the case of cancer prognosis the event is the survival time of the patient from the onset of the disease or it can be the recurrence of the disease after undergoing a treatment. This study aims to bring out the machine learning and deep learning models involved in providing the prognosis to the cancer patients.
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Affiliation(s)
- Deepa P
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Gunavathi C
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
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26
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Sarkar S, Mali K. Breast Cancer Subtypes Classification with Hybrid Machine Learning Model. Methods Inf Med 2022; 61:68-83. [PMID: 36096144 DOI: 10.1055/s-0042-1751043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
BACKGROUND Breast cancer is the most prevailing heterogeneous disease among females characterized with distinct molecular subtypes and varied clinicopathological features. With the emergence of various artificial intelligence techniques especially machine learning, the breast cancer research has attained new heights in cancer detection and prognosis. OBJECTIVE Recent development in computer driven diagnostic system has enabled the clinicians to improve the accuracy in detecting various types of breast tumors. Our study is to develop a computer driven diagnostic system which will enable the clinicians to improve the accuracy in detecting various types of breast tumors. METHODS In this article, we proposed a breast cancer classification model based on the hybridization of machine learning approaches for classifying triple-negative breast cancer and non-triple negative breast cancer patients with clinicopathological features collected from multiple tertiary care hospitals/centers. RESULTS The results of genetic algorithm and support vector machine (GA-SVM) hybrid model was compared with classics feature selection SVM hybrid models like support vector machine-recursive feature elimination (SVM-RFE), LASSO-SVM, Grid-SVM, and linear SVM. The classification results obtained from GA-SVM hybrid model outperformed the other compared models when applied on two distinct hospital-based datasets of patients investigated with breast cancer in North West of African subcontinent. To validate the predictive model accuracy, 10-fold cross-validation method was applied on all models with the same multicentered datasets. The model performance was evaluated with well-known metrics like mean squared error, logarithmic loss, F1-score, area under the ROC curve, and the precision-recall curve. CONCLUSION The hybrid machine learning model can be employed for breast cancer subtypes classification that could help the medical practitioners in better treatment planning and disease outcome.
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Affiliation(s)
- Suvobrata Sarkar
- Department of Computer Science and Engineering, Dr. B.C. Roy Engineering College, Durgapur, West Bengal, India
| | - Kalyani Mali
- Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India
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Iliadou E, Su Q, Kikidis D, Bibas T, Kloukinas C. Profiling hearing aid users through big data explainable artificial intelligence techniques. Front Neurol 2022; 13:933940. [PMID: 36090867 PMCID: PMC9459083 DOI: 10.3389/fneur.2022.933940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Debilitating hearing loss (HL) affects ~6% of the human population. Only 20% of the people in need of a hearing assistive device will eventually seek and acquire one. The number of people that are satisfied with their Hearing Aids (HAids) and continue using them in the long term is even lower. Understanding the personal, behavioral, environmental, or other factors that correlate with the optimal HAid fitting and with users' experience of HAids is a significant step in improving patient satisfaction and quality of life, while reducing societal and financial burden. In SMART BEAR we are addressing this need by making use of the capacity of modern HAids to provide dynamic logging of their operation and by combining this information with a big amount of information about the medical, environmental, and social context of each HAid user. We are studying hearing rehabilitation through a 12-month continuous monitoring of HL patients, collecting data, such as participants' demographics, audiometric and medical data, their cognitive and mental status, their habits, and preferences, through a set of medical devices and wearables, as well as through face-to-face and remote clinical assessments and fitting/fine-tuning sessions. Descriptive, AI-based analysis and assessment of the relationships between heterogeneous data and HL-related parameters will help clinical researchers to better understand the overall health profiles of HL patients, and to identify patterns or relations that may be proven essential for future clinical trials. In addition, the future state and behavioral (e.g., HAids Satisfiability and HAids usage) of the patients will be predicted with time-dependent machine learning models to assist the clinical researchers to decide on the nature of the interventions. Explainable Artificial Intelligence (XAI) techniques will be leveraged to better understand the factors that play a significant role in the success of a hearing rehabilitation program, constructing patient profiles. This paper is a conceptual one aiming to describe the upcoming data collection process and proposed framework for providing a comprehensive profile for patients with HL in the context of EU-funded SMART BEAR project. Such patient profiles can be invaluable in HL treatment as they can help to identify the characteristics making patients more prone to drop out and stop using their HAids, using their HAids sufficiently long during the day, and being more satisfied by their HAids experience. They can also help decrease the number of needed remote sessions with their Audiologist for counseling, and/or HAids fine tuning, or the number of manual changes of HAids program (as indication of poor sound quality and bad adaptation of HAids configuration to patients' real needs and daily challenges), leading to reduced healthcare cost.
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Affiliation(s)
- Eleftheria Iliadou
- 1st Department of Otorhinolaryngology-Head and Neck Surgery, National and Kapodistrian University of Athens Medical School, Athens, Greece
| | - Qiqi Su
- Department of Computer Science, University of London, London, United Kingdom
| | - Dimitrios Kikidis
- 1st Department of Otorhinolaryngology-Head and Neck Surgery, National and Kapodistrian University of Athens Medical School, Athens, Greece
| | - Thanos Bibas
- 1st Department of Otorhinolaryngology-Head and Neck Surgery, National and Kapodistrian University of Athens Medical School, Athens, Greece
| | - Christos Kloukinas
- Department of Computer Science, University of London, London, United Kingdom
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Russo V, Lallo E, Munnia A, Spedicato M, Messerini L, D’Aurizio R, Ceroni EG, Brunelli G, Galvano A, Russo A, Landini I, Nobili S, Ceppi M, Bruzzone M, Cianchi F, Staderini F, Roselli M, Riondino S, Ferroni P, Guadagni F, Mini E, Peluso M. Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022; 14:4012. [PMID: 36011003 PMCID: PMC9406544 DOI: 10.3390/cancers14164012] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/26/2022] [Accepted: 08/12/2022] [Indexed: 12/24/2022] Open
Abstract
Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set.
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Affiliation(s)
- Valentina Russo
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Eleonora Lallo
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Armelle Munnia
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Miriana Spedicato
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Luca Messerini
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy
| | - Romina D’Aurizio
- Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy
| | - Elia Giuseppe Ceroni
- Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy
| | - Giulia Brunelli
- Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy
| | - Antonio Galvano
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Antonio Russo
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Ida Landini
- Department of Health Sciences, University of Florence, 50139 Florence, Italy
| | - Stefania Nobili
- Department of Neurosciences, Imaging and Clinical Sciences, “G. D’Annunzio” Chieti-Pescara, 66100 Chieti, Italy
| | - Marcello Ceppi
- Clinical Epidemiology Unit, IRCCS-Ospedale Policlinico San Martino, 16131 Genova, Italy
| | - Marco Bruzzone
- Clinical Epidemiology Unit, IRCCS-Ospedale Policlinico San Martino, 16131 Genova, Italy
| | - Fabio Cianchi
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy
| | - Fabio Staderini
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy
| | - Mario Roselli
- Medical Oncology Unit, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
| | - Silvia Riondino
- Medical Oncology Unit, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
| | - Patrizia Ferroni
- BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy
| | - Fiorella Guadagni
- BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy
| | - Enrico Mini
- Department of Health Sciences, University of Florence, 50139 Florence, Italy
| | - Marco Peluso
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
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Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis. Cancers (Basel) 2022; 14:cancers14133215. [PMID: 35804988 PMCID: PMC9265023 DOI: 10.3390/cancers14133215] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary The rise of Big Data, the widespread use of Machine Learning, and the cheapening of omics techniques have allowed for the creation of more sophisticated and accurate models in biomedical research. This article presents the state-of-the-art predictive models of cancer prognosis that use multimodal data, considering clinical, molecular (omics and non-omics), and image data. The subject of study, the data modalities used, the data processing and modelling methods applied, the validation strategies involved, the integration strategies encompassed, and the evolution of prognostic predictive models are discussed. Finally, we discuss challenges and opportunities in this field of cancer research, with great potential impact on the clinical management of patients and, by extension, on the implementation of personalised and precision medicine. Abstract Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression.
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Kazama H, Kawaguchi O, Seto T, Suzuki K, Matsuyama H, Matsubara N, Tajima Y, Fukao T. Comprehensive analysis of the associations between clinical factors and outcomes by machine learning, using post marketing surveillance data of cabazitaxel in patients with castration-resistant prostate cancer. BMC Cancer 2022; 22:470. [PMID: 35484517 PMCID: PMC9052565 DOI: 10.1186/s12885-022-09509-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 04/04/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We aimed to evaluate relationships between clinical outcomes and explanatory variables by network clustering analysis using data from a post marketing surveillance (PMS) study of castration-resistant prostate cancer (CRPC) patients. METHODS The PMS was a prospective, multicenter, observational study of patients with metastatic, docetaxel-refractory CRPC treated with cabazitaxel in Japan after its launch in 2014. Graphical Markov (GM) model-based simulations and network clustering in 'R' package were conducted to identify correlations between clinical factors and outcomes. Factors shown to be associated with overall survival (OS) in the machine learning analysis were confirmed according to the clinical outcomes observed in the PMS. RESULTS Among the 660 patients analyzed, median patient age was 70.0 years, and median OS and time-to-treatment failure (TTF) were 319 and 116 days, respectively. In GM-based simulations, factors associated with OS were liver metastases, performance status (PS), TTF, and neutropenia (threshold 0.05), and liver metastases, PS, and TTF (threshold 0.01). Factors associated with TTF were OS and relative dose intensity (threshold 0.05), and OS (threshold 0.01). In network clustering in 'R' package, factors associated with OS were number of treatment cycles, discontinuation due to disease progression, and TTF (threshold 0.05), and liver and lung metastases, PS, discontinuation due to adverse events, and febrile neutropenia (threshold 0.01). Kaplan-Meier analysis of patient subgroups demonstrated that visceral metastases and poor PS at baseline were associated with worse OS, while neutropenia or febrile neutropenia and higher number of cabazitaxel cycles were associated with better OS. CONCLUSIONS Neutropenia may be a predictive factor for treatment efficacy in terms of survival. Poor PS and distant metastases to the liver and lungs were shown to be associated with worse outcomes, while factors related to treatment duration were shown to positively correlate with better OS.
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Affiliation(s)
- Hirotaka Kazama
- Sanofi Specialty Care Medical Oncology, 3-20-2 Nishi-Shinjuku, Shinjuku, Tokyo, 163-1488, Japan
| | - Osamu Kawaguchi
- Sanofi Research and Development, 3-20-2 Nishi-Shinjuku, Shinjuku, Tokyo, 163-1488, Japan
| | - Takeshi Seto
- Sanofi Medical Affairs, 3-20-2 Nishi-Shinjuku, Shinjuku, Tokyo, 163-1488, Japan
| | - Kazuhiro Suzuki
- Department of Urology, Gunma University Graduate School of Medicine, 3-39-22 Showa-machi, Maebashi, Gunma, 371-8511, Japan
| | - Hideyasu Matsuyama
- Department of Urology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan
| | - Nobuaki Matsubara
- Department of Medical Oncology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Yuki Tajima
- Sanofi Medical Affairs, 3-20-2 Nishi-Shinjuku, Shinjuku, Tokyo, 163-1488, Japan
| | - Taro Fukao
- Sanofi Global Oncology, 450 Water Street, Cambridge, MA, 02141, USA.
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Johnson H, El-Schich Z, Ali A, Zhang X, Simoulis A, Wingren AG, Persson JL. Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients. Cancers (Basel) 2022; 14:cancers14082045. [PMID: 35454952 PMCID: PMC9030299 DOI: 10.3390/cancers14082045] [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: 03/23/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 12/13/2022] Open
Abstract
Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.
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Affiliation(s)
| | - Zahra El-Schich
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
| | - Amjad Ali
- Department of Molecular Biology, Umeå University, SE-901 87 Umeå, Sweden;
| | - Xuhui Zhang
- Department of Bio-Diagnosis, Institute of Basic Medical Sciences, Beijing 100005, China;
| | - Athanasios Simoulis
- Department of Clinical Pathology and Cytology, Skåne University Hospital, SE-205 02 Malmö, Sweden;
| | - Anette Gjörloff Wingren
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
| | - Jenny L. Persson
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
- Department of Molecular Biology, Umeå University, SE-901 87 Umeå, Sweden;
- Correspondence: ; Tel.: +46-0706391199
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Jin S, Qin D, Liang BS, Zhang LC, Wei XX, Wang YJ, Zhuang B, Zhang T, Yang ZP, Cao YW, Jin SL, Yang P, Jiang B, Rao BQ, Shi HP, Lu Q. Machine learning predicts cancer-associated deep vein thrombosis using clinically available variables. Int J Med Inform 2022; 161:104733. [DOI: 10.1016/j.ijmedinf.2022.104733] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 02/23/2022] [Accepted: 03/02/2022] [Indexed: 12/17/2022]
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Andjelkovic J, Ljubic B, Hai AA, Stanojevic M, Pavlovski M, Diaz W, Obradovic Z. Sequential machine learning in prediction of common cancers. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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Zhou L, Rueda M, Alkhateeb A. Classification of Breast Cancer Nottingham Prognostic Index Using High-Dimensional Embedding and Residual Neural Network. Cancers (Basel) 2022. [PMID: 35205681 DOI: 10.3390/cancers14040934.pmid:35205681;pmcid:pmc8870306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
The Nottingham Prognostics Index (NPI) is a prognostics measure that predicts operable primary breast cancer survival. The NPI value is calculated based on the size of the tumor, the number of lymph nodes, and the tumor grade. Next-generation sequencing advancements have led to measuring different biological indicators called multi-omics data. The availability of multi-omics data triggered the challenge of integrating and analyzing these various biological measures to understand the progression of the diseases. High-dimensional embedding techniques are incorporated to present the features in the lower dimension, i.e., in a 2-dimensional map. The dataset consists of three -omics: gene expression, copy number alteration (CNA), and mRNA from 1885 female patients. The model creates a gene similarity network (GSN) map for each omic using t-distributed stochastic neighbor embedding (t-SNE) before being merged into the residual neural network (ResNet) classification model. The aim of this work was to (i) extract multi-omics biomarkers that are associated with the prognosis and prediction of breast cancer survival; and (ii) build a prediction model for multi-class breast cancer NPI classes. We evaluated this model and compared it to different high-dimensional embedding techniques and neural network combinations. The proposed model outperformed the other methods with an accuracy of 98.48%, and the area under the curve (AUC) equals 0.9999. The findings in the literature confirm associations between some of the extracted omics and breast cancer prognosis and survival including CDCA5, IL17RB, MUC2, NOD2 and NXPH4 from the gene expression dataset; MED30, RAD21, EIF3H and EIF3E from the CNA dataset; and CENPA, MACF1, UGT2B7 and SEMA3B from the mRNA dataset.
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Affiliation(s)
- Li Zhou
- School of Computer Science, University of Windsor, Windsor, ON N9B 3P4, Canada
| | - Maria Rueda
- Department of Chemistry and Biochemistry, University of Windsor, Windsor, ON N9B 3P4, Canada
| | - Abedalrhman Alkhateeb
- School of Computer Science, University of Windsor, Windsor, ON N9B 3P4, Canada
- King Hussein School of Computing Science, Princess Sumaya University for Technology, Al-Jubaiha, Amman P.O. Box 1438, Jordan
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Classification of Breast Cancer Nottingham Prognostic Index Using High-Dimensional Embedding and Residual Neural Network. Cancers (Basel) 2022; 14:cancers14040934. [PMID: 35205681 PMCID: PMC8870306 DOI: 10.3390/cancers14040934] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/29/2022] [Accepted: 02/10/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary A deep learning model based on multi-omics data to classify Nottingham prognostic Index score levels. The model represents each omic dataset using 2-dimensional map before integrating all omics maps into the prediction model. The literature confirms the relationship between the extracted omics features with the progression and survival of breast cancer. Abstract The Nottingham Prognostics Index (NPI) is a prognostics measure that predicts operable primary breast cancer survival. The NPI value is calculated based on the size of the tumor, the number of lymph nodes, and the tumor grade. Next-generation sequencing advancements have led to measuring different biological indicators called multi-omics data. The availability of multi-omics data triggered the challenge of integrating and analyzing these various biological measures to understand the progression of the diseases. High-dimensional embedding techniques are incorporated to present the features in the lower dimension, i.e., in a 2-dimensional map. The dataset consists of three -omics: gene expression, copy number alteration (CNA), and mRNA from 1885 female patients. The model creates a gene similarity network (GSN) map for each omic using t-distributed stochastic neighbor embedding (t-SNE) before being merged into the residual neural network (ResNet) classification model. The aim of this work was to (i) extract multi-omics biomarkers that are associated with the prognosis and prediction of breast cancer survival; and (ii) build a prediction model for multi-class breast cancer NPI classes. We evaluated this model and compared it to different high-dimensional embedding techniques and neural network combinations. The proposed model outperformed the other methods with an accuracy of 98.48%, and the area under the curve (AUC) equals 0.9999. The findings in the literature confirm associations between some of the extracted omics and breast cancer prognosis and survival including CDCA5, IL17RB, MUC2, NOD2 and NXPH4 from the gene expression dataset; MED30, RAD21, EIF3H and EIF3E from the CNA dataset; and CENPA, MACF1, UGT2B7 and SEMA3B from the mRNA dataset.
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Pourasad Y, Zarouri E, Salemizadeh Parizi M, Salih Mohammed A. Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning. Diagnostics (Basel) 2021; 11:1870. [PMID: 34679568 PMCID: PMC8534593 DOI: 10.3390/diagnostics11101870] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/02/2021] [Accepted: 10/03/2021] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor's location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.
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Affiliation(s)
- Yaghoub Pourasad
- Department of Electrical Engineering, Urmia University of Technology (UUT), Urmia 57166-93188, Iran
| | - Esmaeil Zarouri
- School of Electrical Engineering, Electronic Engineering, Iran University of Science and Technology—IUST, Tehran 16846-13114, Iran;
| | | | - Amin Salih Mohammed
- Department of Computer Engineering, College of Engineering and Computer Science, Lebanese French University, Erbil 44001, Iraq;
- Department of Software and Informatics Engineering, Salahaddin University, Erbil 44002, Iraq
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Yang X, Mu D, Peng H, Li H, Wang Y, Wang P, Wang Y, Han S. Research and Application of Artificial Intelligence (AI) based on Electronic Health Records from Patients with Cancer: a Systematic Review (Preprint). JMIR Med Inform 2021; 10:e33799. [PMID: 35442195 PMCID: PMC9069295 DOI: 10.2196/33799] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/24/2022] [Accepted: 03/14/2022] [Indexed: 01/12/2023] Open
Abstract
Background With the accumulation of electronic health records and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of artificial intelligence based on electronic health records in cancer care. Objective The aim of this study was to conduct a review to introduce the current state and limitations of artificial intelligence based on electronic health records of patients with cancer and to summarize the performance of artificial intelligence in mining electronic health records and its impact on cancer care. Methods Three databases were systematically searched to retrieve potentially relevant papers published from January 2009 to October 2020. Four principal reviewers assessed the quality of the papers and reviewed them for eligibility based on the inclusion criteria in the extracted data. The summary measures used in this analysis were the number and frequency of occurrence of the themes. Results Of the 1034 papers considered, 148 papers met the inclusion criteria. Cancer care, especially cancers of female organs and digestive organs, could benefit from artificial intelligence based on electronic health records through cancer emergencies and prognostic estimates, cancer diagnosis and prediction, tumor stage detection, cancer case detection, and treatment pattern recognition. The models can always achieve an area under the curve of 0.7. Ensemble methods and deep learning are on the rise. In addition, electronic medical records in the existing studies are mainly in English and from private institutional databases. Conclusions Artificial intelligence based on electronic health records performed well and could be useful for cancer care. Improving the performance of artificial intelligence can help patients receive more scientific-based and accurate treatments. There is a need for the development of new methods and electronic health record data sharing and for increased passion and support from cancer specialists.
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Affiliation(s)
- Xinyu Yang
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Dongmei Mu
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Hao Peng
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Hua Li
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Ying Wang
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Ping Wang
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Yue Wang
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Siqi Han
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
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Chakraborty D, Ivan C, Amero P, Khan M, Rodriguez-Aguayo C, Başağaoğlu H, Lopez-Berestein G. Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer. Cancers (Basel) 2021; 13:3450. [PMID: 34298668 PMCID: PMC8303703 DOI: 10.3390/cancers13143450] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 12/29/2022] Open
Abstract
We investigated the data-driven relationship between immune cell composition in the tumor microenvironment (TME) and the ≥5-year survival rates of breast cancer patients using explainable artificial intelligence (XAI) models. We acquired TCGA breast invasive carcinoma data from the cbioPortal and retrieved immune cell composition estimates from bulk RNA sequencing data from TIMER2.0 based on EPIC, CIBERSORT, TIMER, and xCell computational methods. Novel insights derived from our XAI model showed that B cells, CD8+ T cells, M0 macrophages, and NK T cells are the most critical TME features for enhanced prognosis of breast cancer patients. Our XAI model also revealed the inflection points of these critical TME features, above or below which ≥5-year survival rates improve. Subsequently, we ascertained the conditional probabilities of ≥5-year survival under specific conditions inferred from the inflection points. In particular, the XAI models revealed that the B cell fraction (relative to all cells in a sample) exceeding 0.025, M0 macrophage fraction (relative to the total immune cell content) below 0.05, and NK T cell and CD8+ T cell fractions (based on cancer type-specific arbitrary units) above 0.075 and 0.25, respectively, in the TME could enhance the ≥5-year survival in breast cancer patients. The findings could lead to accurate clinical predictions and enhanced immunotherapies, and to the design of innovative strategies to reprogram the breast TME.
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Affiliation(s)
- Debaditya Chakraborty
- Department of Construction Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Cristina Ivan
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (C.I.); (P.A.); (C.R.-A.); (G.L.-B.)
- Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Paola Amero
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (C.I.); (P.A.); (C.R.-A.); (G.L.-B.)
| | - Maliha Khan
- Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Cristian Rodriguez-Aguayo
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (C.I.); (P.A.); (C.R.-A.); (G.L.-B.)
- Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | | | - Gabriel Lopez-Berestein
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (C.I.); (P.A.); (C.R.-A.); (G.L.-B.)
- Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Mosallaie S, Rad M, Schiffauerova A, Ebadi A. Discovering the evolution of artificial intelligence in cancer research using dynamic topic modeling. COLLNET JOURNAL OF SCIENTOMETRICS AND INFORMATION MANAGEMENT 2021. [DOI: 10.1080/09737766.2021.1958659] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Shahab Mosallaie
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, H3G 1M8, Montreal, QC, Canada
| | - Mahdi Rad
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, H3G 1M8, Montreal, QC, Canada
| | - Andrea Schiffauerova
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, H3G 1M8, Montreal, QC, Canada
| | - Ashkan Ebadi
- Digital Technologies Research Centre, National Research Council Canada, H3T 2B2, Montreal, QC, Canada
- Concordia Institute for Information Systems Engineering, Concordia University, H3G 1M8, Montreal, QC, Canada
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Altaf MM. A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:5029-5046. [PMID: 34517476 DOI: 10.3934/mbe.2021256] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Radiology experts often face difficulties in mammography mass lesion labeling, which may lead to conclusive yet unnecessary and expensive breast biopsies. This paper focuses on building an automated diagnosis tool that supports radiologists in identifying and classifying mammography mass lesions. The paper's main contribution is to design a hybrid model based on Pulse-Coupled Neural Networks (PCNN) and Deep Convolutional Neural Networks (CNN). Due to the need for large datasets to train and tune CNNs, which are not available for medical images, Transfer Learning (TL) was exploited in this research. TL can be an effective approach when working with small-sized datasets. The paper's implementation was tested on three public benchmark datasets: DDMS, INbreast, and BCDR datasets for training and testing and MIAS for testing only. The results indicated the enhancement that PCNN provides when combined with CNN compared to other methods for the same public datasets. The hybrid model achieved 98.72% accuracy for DDMS, 97.5% for INbreast, and 96.94% for BCDR. To avoid overfitting, the proposed hybrid model was tested on an unseen MIAS dataset, achieving 98.77% accuracy. Other evaluation metrics are reported in the results section.
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Affiliation(s)
- Meteb M Altaf
- National Center for Robotics Technology and Internet of Things, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
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Hsiao YW, Tao CL, Chuang EY, Lu TP. A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models. J Adv Res 2021; 30:113-122. [PMID: 34026291 PMCID: PMC8132202 DOI: 10.1016/j.jare.2020.11.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 10/10/2020] [Accepted: 11/05/2020] [Indexed: 12/12/2022] Open
Abstract
Introduction Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients at different risk levels. Current risk prediction models used in OC have low sensitivity, and few of them are able to identify OC patients at high risk of mortality, which would both optimize the treatment of high-risk patients and prevent unnecessary medical intervention in those at low risk. Objectives To this end, we have developed a bagging-based algorithm with GA-XGBoost models that predicts the risk of death from OC using gene expression profiles. Methods Four gene expression datasets from public sources were used as training (n = 1) or validation (n = 3) sets. The performance of our proposed algorithm was compared with fine-tuning and other existing methods. Moreover, the biological function of selected genetic features was further interpreted, and the response to a panel of approved drugs was predicted for different risk levels. Results The proposed algorithm showed good sensitivity (74-100%) in the validation sets, compared with two simple models whose sensitivity only reached 47% and 60%. The prognostic gene signature used in this study was highly connected to AKT, a key component of the PI3K/AKT/mTOR signaling pathway, which influences the tumorigenesis, proliferation, and progression of OC. Conclusion These findings demonstrated an improvement in the sensitivity of risk classification of OC patients with our risk prediction models compared with other methods. Ongoing effort is needed to validate the outcomes of this approach for precise clinical treatment.
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Affiliation(s)
- Yi-Wen Hsiao
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chun-Liang Tao
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Eric Y. Chuang
- Bioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, Department of Electrical Engineering, National Taiwan University, Taiwan
| | - Tzu-Pin Lu
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Bioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
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Banegas-Luna AJ, Peña-García J, Iftene A, Guadagni F, Ferroni P, Scarpato N, Zanzotto FM, Bueno-Crespo A, Pérez-Sánchez H. Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey. Int J Mol Sci 2021; 22:4394. [PMID: 33922356 PMCID: PMC8122817 DOI: 10.3390/ijms22094394] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 12/18/2022] Open
Abstract
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine-specifically, to cancer research-and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors' predictive capacity and achieve individualised therapies in the near future.
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Affiliation(s)
- Antonio Jesús Banegas-Luna
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
| | - Jorge Peña-García
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
| | - Adrian Iftene
- Faculty of Computer Science, Universitatea Alexandru Ioan Cuza (UAIC), 700505 Jashi, Romania;
| | - Fiorella Guadagni
- Interinstitutional Multidisciplinary Biobank (BioBIM), IRCCS San Raffaele Roma, 00166 Rome, Italy; (F.G.); (P.F.)
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy;
| | - Patrizia Ferroni
- Interinstitutional Multidisciplinary Biobank (BioBIM), IRCCS San Raffaele Roma, 00166 Rome, Italy; (F.G.); (P.F.)
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy;
| | - Noemi Scarpato
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy;
| | - Fabio Massimo Zanzotto
- Dipartimento di Ingegneria dell’Impresa “Mario Lucertini”, University of Rome Tor Vergata, 00133 Rome, Italy;
| | - Andrés Bueno-Crespo
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
| | - Horacio Pérez-Sánchez
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
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Ye H, Li T, Wang H, Wu J, Yi C, Shi J, Wang P, Song C, Dai L, Jiang G, Huang Y, Yu Y, Li J. TSPAN1, TMPRSS4, SDR16C5, and CTSE as Novel Panel for Pancreatic Cancer: A Bioinformatics Analysis and Experiments Validation. Front Immunol 2021; 12:649551. [PMID: 33815409 PMCID: PMC8015801 DOI: 10.3389/fimmu.2021.649551] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 02/23/2021] [Indexed: 12/14/2022] Open
Abstract
Pancreatic cancer is a lethal malignancy with a poor prognosis. This study aims to identify pancreatic cancer-related genes and develop a robust diagnostic model to detect this disease. Weighted gene co-expression network analysis (WGCNA) was used to determine potential hub genes for pancreatic cancer. Their mRNA and protein expression levels were validated through reverse transcription PCR (RT-PCR) and immunohistochemical (IHC). Diagnostic models were developed by eight machine learning algorithms and ten-fold cross-validation. Four hub genes (TSPAN1, TMPRSS4, SDR16C5, and CTSE) were identified based on bioinformatics. RT-PCR showed that the four hub genes were expressed at medium to high levels, IHC revealed that their protein expression levels were higher in pancreatic cancer tissues. For the panel of these four genes, eight models performed with 0.87-0.92 area under the curve value (AUC), 0.91-0.94 sensitivity, and 0.84-0.86 specificity in the validation cohort. In the external validation set, these models also showed good performance (0.86-0.98 AUC, 0.84-1.00 sensitivity, and 0.86-1.00 specificity). In conclusion, this study has identified four hub genes that might be closely related to pancreatic cancer: TSPAN1, TMPRSS4, SDR16C5, and CTSE. Four-gene panels might provide a theoretical basis for the diagnosis of pancreatic cancer.
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Affiliation(s)
- Hua Ye
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Tiandong Li
- College of Public Health, Zhengzhou University, Zhengzhou, China
- Laboratory of Molecular Biology, Henan Luoyang Orthopedic Hospital (Henan Provincial Orthopedic Hospital), Zhengzhou, China
- Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
| | - Hua Wang
- College of Public Health, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
| | - Jinyu Wu
- College of Public Health, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
| | - Chuncheng Yi
- College of Public Health, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
| | - Jianxiang Shi
- Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Peng Wang
- College of Public Health, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
| | - Chunhua Song
- College of Public Health, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
| | - Liping Dai
- Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Guozhong Jiang
- Deparment of Pathology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuxin Huang
- Program in Public Health, University of California, Irvine, Irvine, CA, United States
| | - Yongwei Yu
- Department of Pathology, Second Military Medical University, Shanghai, China
| | - Jitian Li
- Laboratory of Molecular Biology, Henan Luoyang Orthopedic Hospital (Henan Provincial Orthopedic Hospital), Zhengzhou, China
- Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China
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Sun D, Wei E, Ma Z, Wu C, Xu S. Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO. SENSORS 2021; 21:s21061995. [PMID: 33808972 PMCID: PMC8000105 DOI: 10.3390/s21061995] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/21/2021] [Accepted: 02/24/2021] [Indexed: 01/08/2023]
Abstract
Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user's indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers' length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user's location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user's locations in a complex building without complex calibration as compared to other recent methods.
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Affiliation(s)
- Danshi Sun
- School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
| | - Erhu Wei
- School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
- Correspondence:
| | - Zhuoxi Ma
- Xi’an Division of Surveying and Mapping, Xi’an 710054, China;
| | - Chenxi Wu
- BGI Engineering Consultants Ltd., Beijing 100038, China;
| | - Shiyi Xu
- Beijing Satellite Navigation Center, Beijing 100094, China;
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丁 妍, 韩 梦, 刘 月. [AI-assisted Prediction of Lymph Node Metastasis of Breast Cancer: Current and Prospective Research]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2021; 52:162-165. [PMID: 33829685 PMCID: PMC10408927 DOI: 10.12182/20210360102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Indexed: 11/23/2022]
Abstract
One of the most important application of artificial intelligence (AI) in pathology is prediction, using morphological features, of patient prognosis and response to specific treatments. As one of the most common kinds of malignancies in the world and the crucial important cause of death due to malignant tumor among women, breast cancer has become the center of attention in clinical services. Axillary lymph node metastasis is an important prognostic factor in breast cancer. The accuracy of the assessment of axillary lymph node metastasis bears heavily on clinical diagnosis and treatment. At present, based on the principle of non-invasive procedures, many studies have been done to develop models that can be used to predict sentinel lymph node metastasis of breast cancer. However, different clinical and pathological parameters are used in these predictive models. How to analyze the clinical and pathological data of breast cancer patients in a more comprehensive way and how to establish a prediction model with better precision have become the future direction of development. In this paper, we describe the research progress of AI in pathology and the current status of its use in breast cancer research. We have conducted in-depth reflection and looked into the future of ways to predict effectively breast cancer lymph node metastasis and to establish more accurate and effective deep-learning algorithm based on AI assistance so as to continuously improve the diagnosis and treatment of breast cancer.
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Affiliation(s)
- 妍 丁
- 河北医科大学第四医院 病理科 (石家庄 050011)Department of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - 梦雪 韩
- 河北医科大学第四医院 病理科 (石家庄 050011)Department of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - 月平 刘
- 河北医科大学第四医院 病理科 (石家庄 050011)Department of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
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Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18042197. [PMID: 33672300 PMCID: PMC7926954 DOI: 10.3390/ijerph18042197] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/15/2021] [Accepted: 02/19/2021] [Indexed: 11/30/2022]
Abstract
Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoietic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques. Furthermore, due to the overfitting problem of small samples, in case of a minor cancer, it does not have enough sample material for building a classification model. Therefore, we propose not only to build a classification model for five major subtypes using two kinds of losses, namely reconstruction loss and classification loss, but also to extract suitable features using a deep autoencoder. Furthermore, for considering the data imbalance problem, we apply an oversampling algorithm, the synthetic minority oversampling technique (SMOTE). For validation of our proposed autoencoder-based feature extraction approach for hematopoietic cancer subtype classification, we compared other traditional feature selection algorithms (principal component analysis, non-negative matrix factorization) and classification algorithms with the SMOTE oversampling approach. Additionally, we used the Shapley Additive exPlanations (SHAP) interpretation technique in our model to explain the important gene/protein for hematopoietic cancer subtype classification. Furthermore, we compared five widely used classification algorithms, including logistic regression, random forest, k-nearest neighbor, artificial neural network and support vector machine. The results of autoencoder-based feature extraction approaches showed good performance, and the best result was the SMOTE oversampling-applied support vector machine algorithm consider both focal loss and reconstruction loss as the loss function for autoencoder (AE) feature selection approach, which produced 97.01% accuracy, 92.60% recall, 99.52% specificity, 93.54% F1-measure, 97.87% G-mean and 95.46% index of balanced accuracy as subtype classification performance measures.
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Kaur B, Goyal B, Daniel E. A survey on Machine learning based Medical Assistive systems in Current Oncological Sciences. Curr Med Imaging 2021; 18:445-459. [PMID: 33596810 DOI: 10.2174/1573405617666210217154446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 12/04/2020] [Accepted: 01/15/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Cancer is one of the life threatening disease which is affecting a large number of population worldwide. The cancer cells multiply inside the body without showing much symptoms on the surface of the skin thereby making it difficult to predict and detect at the onset of disease. Many organizations are working towards automating the process of cancer detection with minimal false detection rates. INTRODUCTION The machine learning algorithms serve to be a promising alternative to support health care practitioners to rule out the disease and predict the growth with various imaging and statistical analysis tools. The medical practitioners are utilizing the output of these algorithms to diagnose and design the course of treatment. These algorithms are capable of finding out the risk level of the patient and can reduce the mortality rate concerning to cancer disease. METHOD This article presents the existing state of art techniques for identifying cancer affecting human organs based on machine learning models. The supported set of imaging operations are also elaborated for each type of Cancer. CONCLUSION The CAD tools are the aid for the diagnostic radiologists for preliminary investigations and detecting the nature of tumor cells.
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Affiliation(s)
| | | | - Ebenezer Daniel
- City of Hope, National Medical Centre, California. United States
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Bolourani S, Brenner M, Wang P, McGinn T, Hirsch JS, Barnaby D, Zanos TP. A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation. J Med Internet Res 2021; 23:e24246. [PMID: 33476281 PMCID: PMC7879728 DOI: 10.2196/24246] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/18/2020] [Accepted: 01/18/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. OBJECTIVE Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. METHODS Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. RESULTS The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. CONCLUSIONS The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.
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Affiliation(s)
- Siavash Bolourani
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Max Brenner
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Ping Wang
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Thomas McGinn
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Jamie S Hirsch
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Douglas Barnaby
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Theodoros P Zanos
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
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Fan Y, Li Y, Bao X, Zhu H, Lu L, Yao Y, Li Y, Su M, Feng F, Feng S, Feng M, Wang R. Development of Machine Learning Models for Predicting Postoperative Delayed Remission in Patients With Cushing's Disease. J Clin Endocrinol Metab 2021; 106:e217-e231. [PMID: 33000120 DOI: 10.1210/clinem/dgaa698] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 09/24/2020] [Indexed: 12/14/2022]
Abstract
CONTEXT Postoperative hypercortisolemia mandates further therapy in patients with Cushing's disease (CD). Delayed remission (DR) is defined as not achieving postoperative immediate remission (IR), but having spontaneous remission during long-term follow-up. OBJECTIVE We aimed to develop and validate machine learning (ML) models for predicting DR in non-IR patients with CD. METHODS We enrolled 201 CD patients, and randomly divided them into training and test datasets. We then used the recursive feature elimination (RFE) algorithm to select features and applied 5 ML algorithms to construct DR prediction models. We used permutation importance and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. RESULTS Eighty-eight (43.8%) of the 201 CD patients met the criteria for DR. Overall, patients who were younger, had a low body mass index, a Knosp grade of III-IV, and a tumor not found by pathological examination tended to achieve a lower rate of DR. After RFE feature selection, the Adaboost model, which comprised 18 features, had the greatest discriminatory ability, and its predictive ability was significantly better than using Knosp grading and postoperative immediate morning serum cortisol (PoC). The results obtained from permutation importance and LIME algorithms showed that preoperative 24-hour urine free cortisol, PoC, and age were the most important features, and showed the reliability and clinical practicability of the Adaboost model in DC prediction. CONCLUSIONS Machine learning-based models could serve as an effective noninvasive approach to predicting DR, and could aid in determining individual treatment and follow-up strategies for CD patients.
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Affiliation(s)
- Yanghua Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yichao Li
- DHC Software Co. Ltd, Beijing, China
| | - Xinjie Bao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huijuan Zhu
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lin Lu
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Yao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | | | | | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shanshan Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Hinojosa-Amaya JM, Cuevas-Ramos D. The definition of remission and recurrence of Cushing's disease. Best Pract Res Clin Endocrinol Metab 2021; 35:101485. [PMID: 33472761 DOI: 10.1016/j.beem.2021.101485] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Accurate classification of postsurgical remission, and early recognition of recurrence are crucial to timely treat and prevent excess mortality in Cushing's Disease, yet the criteria used to define remission are variable and there is no consensus to define recurrence. Remission is defined as postsurgical hypocortisolemia, but delayed remission may occur. Recurrence is the return of clinical manifestations with biochemical evidence of hypercortisolism. The proper combination of tests and their timing are controversial. Reliable predicting tools may lead to earlier diagnosis upon recurrence. Many factors have been studied independently for prediction with variable performance. Novel artificial intelligence approaches seek to integrate these variables into risk calculators and machine-learning algorithms with an acceptable short-term predictive performance but lack longer-term accuracy. Prospective studies using these approaches are needed. This review summarizes the evidence behind the definitions of remission and recurrence and provide an overview of the available tools to predict and/or diagnose them.
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Affiliation(s)
- José Miguel Hinojosa-Amaya
- Pituitary Clinic, Endocrinology Division and Department of Medicine, Hospital Universitario "Dr. José E. González", Universidad Autónoma de Nuevo León, Monterrey, Mexico.
| | - Daniel Cuevas-Ramos
- Neuroendocrinology Clinic, Department of Endocrinology and Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
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