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Sohrabei S, Moghaddasi H, Hosseini A, Ehsanzadeh SJ. Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study. BMC Cancer 2024; 24:852. [PMID: 39026174 PMCID: PMC11256548 DOI: 10.1186/s12885-024-12575-1] [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: 10/26/2023] [Accepted: 06/27/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Providing appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients. METHOD A systematic review was conducted using the PubMed, Embase, Scopus, and Web of Science databases in September 2023. We performed the search strategy with keywords, namely: Breast Cancer, Artificial intelligence, and precision Oncology along with their synonyms in the article titles. Descriptive, qualitative, review, and non-English studies were excluded. The quality assessment of the articles and evaluation of bias were determined based on the SJR journal and JBI indices, as well as the PRISMA2020 guideline. RESULTS Forty-six studies were selected that focused on personalized breast cancer management using artificial intelligence models. Seventeen studies using various deep learning methods achieved a satisfactory outcome in predicting treatment response and prognosis, contributing to personalized breast cancer management. Two studies utilizing neural networks and clustering provided acceptable indicators for predicting patient survival and categorizing breast tumors. One study employed transfer learning to predict treatment response. Twenty-six studies utilizing machine-learning methods demonstrated that these techniques can improve breast cancer classification, screening, diagnosis, and prognosis. The most frequent modeling techniques used were NB, SVM, RF, XGBoost, and Reinforcement Learning. The average area under the curve (AUC) for the models was 0.91. Moreover, the average values for accuracy, sensitivity, specificity, and precision were reported to be in the range of 90-96% for the models. CONCLUSION Artificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data. Intelligent processing of omics data through protein and gene pattern classification and the utilization of deep neural patterns has the potential to significantly transform the field of complex disease management.
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Affiliation(s)
- Solmaz Sohrabei
- Department of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Moghaddasi
- Department of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Azamossadat Hosseini
- Department of Health Information Technology and Management, Health Information Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Seyed Jafar Ehsanzadeh
- Department of English Language, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
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Ensenyat-Mendez M, Orozco JIJ, Llinàs-Arias P, Íñiguez-Muñoz S, Baker JL, Salomon MP, Martí M, DiNome ML, Cortés J, Marzese DM. Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer. COMMUNICATIONS MEDICINE 2023; 3:93. [PMID: 37430006 DOI: 10.1038/s43856-023-00311-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/31/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND Immune checkpoint inhibitors (ICI) improve clinical outcomes in triple-negative breast cancer (TNBC) patients. However, a subset of patients does not respond to treatment. Biomarkers that show ICI predictive potential in other solid tumors, such as levels of PD-L1 and the tumor mutational burden, among others, show a modest predictive performance in patients with TNBC. METHODS We built machine learning models based on pre-ICI treatment gene expression profiles to construct gene expression classifiers to identify primary TNBC ICI-responder patients. This study involved 188 ICI-naïve and 721 specimens treated with ICI plus chemotherapy, including TNBC tumors, HR+/HER2- breast tumors, and other solid non-breast tumors. RESULTS The 37-gene TNBC ICI predictive (TNBC-ICI) classifier performs well in predicting pathological complete response (pCR) to ICI plus chemotherapy on an independent TNBC validation cohort (AUC = 0.86). The TNBC-ICI classifier shows better performance than other molecular signatures, including PD-1 (PDCD1) and PD-L1 (CD274) gene expression (AUC = 0.67). Integrating TNBC-ICI with molecular signatures does not improve the efficiency of the classifier (AUC = 0.75). TNBC-ICI displays a modest accuracy in predicting ICI response in two different cohorts of patients with HR + /HER2- breast cancer (AUC = 0.72 to pembrolizumab and AUC = 0.75 to durvalumab). Evaluation of six cohorts of patients with non-breast solid tumors treated with ICI plus chemotherapy shows overall poor performance (median AUC = 0.67). CONCLUSION TNBC-ICI predicts pCR to ICI plus chemotherapy in patients with primary TNBC. The study provides a guide to implementing the TNBC-ICI classifier in clinical studies. Further validations will consolidate a novel predictive panel to improve the treatment decision-making for patients with TNBC.
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Affiliation(s)
- Miquel Ensenyat-Mendez
- Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Health Research Institute of the Balearic Islands (IdISBa), Palma, Spain
| | - Javier I J Orozco
- Saint John's Cancer Institute, Providence Saint John's Health Center, Santa Monica, CA, USA
| | - Pere Llinàs-Arias
- Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Health Research Institute of the Balearic Islands (IdISBa), Palma, Spain
| | - Sandra Íñiguez-Muñoz
- Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Health Research Institute of the Balearic Islands (IdISBa), Palma, Spain
| | - Jennifer L Baker
- Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Matthew P Salomon
- Department of Medicine, University of Southern California (USC), Los Angeles, CA, USA
| | - Mercè Martí
- Immunology Unit, Department of Cell Biology, Physiology, and Immunology, Institut de Biotecnologia I Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Bellaterra, Barcelona, Spain
- Biosensing and Bioanalysis Group, Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain
| | - Maggie L DiNome
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Javier Cortés
- International Breast Cancer Center (IBCC), Pangaea Oncology, Quironsalud Group, Barcelona, Spain
- Medical Scientia Innovation Research (MedSIR), Barcelona, Spain
- Faculty of Biomedical and Health Sciences, Department of Medicine, Universidad Europea de Madrid, Madrid, Spain
| | - Diego M Marzese
- Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Health Research Institute of the Balearic Islands (IdISBa), Palma, Spain.
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Chen H, Lan X, Yu T, Li L, Tang S, Liu S, Jiang F, Wang L, Huang Y, Cao Y, Wang W, Wang X, Zhang J. Development and validation of a radiogenomics model to predict axillary lymph node metastasis in breast cancer integrating MRI with transcriptome data: A multicohort study. Front Oncol 2022; 12:1076267. [PMID: 36644636 PMCID: PMC9837803 DOI: 10.3389/fonc.2022.1076267] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/01/2022] [Indexed: 12/31/2022] Open
Abstract
Introduction To develop and validate a radiogenomics model for predicting axillary lymph node metastasis (ALNM) in breast cancer compared to a genomics and radiomics model. Methods This retrospective study integrated transcriptomic data from The Cancer Genome Atlas with matched MRI data from The Cancer Imaging Archive for the same set of 111 patients with breast cancer, which were used as the training and testing groups. Fifteen patients from one hospital were enrolled as the external validation group. Radiomics features were extracted from dynamic contrast-enhanced (DCE)-MRI of breast cancer, and genomics features were derived from differentially expressed gene analysis of transcriptome data. Boruta was used for genomics and radiomics data dimension reduction and feature selection. Logistic regression was applied to develop genomics, radiomics, and radiogenomics models to predict ALNM. The performance of the three models was assessed by receiver operating characteristic curves and compared by the Delong test. Results The genomics model was established by nine genomics features, and the radiomics model was established by three radiomics features. The two models showed good discrimination performance in predicting ALNM in breast cancer, with areas under the curves (AUCs) of 0.80, 0.67, and 0.52 for the genomics model and 0.72, 0.68, and 0.71 for the radiomics model in the training, testing and external validation groups, respectively. The radiogenomics model integrated with five genomics features and three radiomics features had a better performance, with AUCs of 0.84, 0.75, and 0.82 in the three groups, respectively, which was higher than the AUC of the radiomics model in the training group and the genomics model in the external validation group (both P < 0.05). Conclusion The radiogenomics model combining radiomics features and genomics features improved the performance to predict ALNM in breast cancer.
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Fonseca-Montaño MA, Blancas S, Herrera-Montalvo LA, Hidalgo-Miranda A. Cancer Genomics. Arch Med Res 2022; 53:723-731. [PMID: 36460546 DOI: 10.1016/j.arcmed.2022.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 12/04/2022]
Abstract
In the past decade, genomics has fundamentally changed our view of cancer biology, allowing comprehensive analyses of mutations, copy number alterations, structural variants, gene expression and DNA methylation profiles in large-scale studies across different cancer types. Efforts like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have fostered international collaborations for cancer genomic analyses and have generated public databases that give scientists around the world access to thoroughly curated data, which have been extensively used as a tool for further hypothesis driven research on several aspects of cancer biology. In parallel, some of these findings are being translated into specific clinical benefits for cancer patients. In this review, we provide a brief historical description of the evolution of international public cancer genome projects and related databases, as well as we discuss about their impact on general cancer research.
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Affiliation(s)
- Marco A Fonseca-Montaño
- Instituto Nacional de Medicina Genómica, Ciudad de México, México; Laboratorio de Genómica del Cáncer, Instituto Nacional de Medicina Genómica, Ciudad de México, México
| | - Susana Blancas
- Instituto Nacional de Medicina Genómica, Ciudad de México, México; Cátedras Consejo Nacional de Ciencia y Tecnología, Ciudad de México, México; Laboratorio de Genómica del Cáncer, Instituto Nacional de Medicina Genómica, Ciudad de México, México
| | | | - Alfredo Hidalgo-Miranda
- Instituto Nacional de Medicina Genómica, Ciudad de México, México; Laboratorio de Genómica del Cáncer, Instituto Nacional de Medicina Genómica, Ciudad de México, México.
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Fisher CS, Teshome M, Blair SL. 23rd Annual Meeting of the American Society of Breast Surgeons: Back to In-Person Scientific Exploration. Ann Surg Oncol 2022; 29:6087-6089. [PMID: 35902494 PMCID: PMC9333076 DOI: 10.1245/s10434-022-12263-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 07/11/2022] [Indexed: 11/22/2022]
Affiliation(s)
- Carla S Fisher
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Mediget Teshome
- Department of Breast Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sarah L Blair
- Department of Surgery, University of California San Diego, San Diego, CA, USA.
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Orozco JIJ, Le J, Baker JL, Marzese DM, DiNome ML. ASO Author Reflections: Molecular Signatures May Render Surgical Staging of the Axilla Obsolete. Ann Surg Oncol 2022; 29:6415-6416. [PMID: 35918577 DOI: 10.1245/s10434-022-12327-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 07/12/2022] [Indexed: 11/18/2022]
Affiliation(s)
- Javier I J Orozco
- Saint John's Cancer Institute, Providence Saint John's Health Center, Santa Monica, CA, USA
| | - Julie Le
- Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jennifer L Baker
- Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Diego M Marzese
- Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d'Investigació Sanitària Illes Balears (IdISBa), Palma, Illes Balears, Spain
| | - Maggie L DiNome
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA.
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