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Mukund A, Afridi MA, Karolak A, Park MA, Permuth JB, Rasool G. Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence. Cancers (Basel) 2024; 16:2240. [PMID: 38927945 PMCID: PMC11201559 DOI: 10.3390/cancers16122240] [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: 05/07/2024] [Revised: 06/03/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools in revolutionizing PDAC care across various dimensions. Consequently, many studies have focused on using AI to improve the standard of PDAC care. This review article attempts to consolidate the literature from the past five years to identify high-impact, novel, and meaningful studies focusing on their transformative potential in PDAC management. Our analysis spans a broad spectrum of applications, including but not limited to patient risk stratification, early detection, and prediction of treatment outcomes, thereby highlighting AI's potential role in enhancing the quality and precision of PDAC care. By categorizing the literature into discrete sections reflective of a patient's journey from screening and diagnosis through treatment and survivorship, this review offers a comprehensive examination of AI-driven methodologies in addressing the multifaceted challenges of PDAC. Each study is summarized by explaining the dataset, ML model, evaluation metrics, and impact the study has on improving PDAC-related outcomes. We also discuss prevailing obstacles and limitations inherent in the application of AI within the PDAC context, offering insightful perspectives on potential future directions and innovations.
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Affiliation(s)
- Ashwin Mukund
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
| | - Muhammad Ali Afridi
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan;
| | - Aleksandra Karolak
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
| | - Margaret A. Park
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (M.A.P.); (J.B.P.)
| | - Jennifer B. Permuth
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (M.A.P.); (J.B.P.)
| | - Ghulam Rasool
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
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Viriyasaranon T, Chun JW, Koh YH, Cho JH, Jung MK, Kim SH, Kim HJ, Lee WJ, Choi JH, Woo SM. Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospective Diagnostic Study. Cancers (Basel) 2023; 15:3392. [PMID: 37444502 DOI: 10.3390/cancers15133392] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The aim of this study was to develop a novel deep learning (DL) model without requiring large-annotated training datasets for detecting pancreatic cancer (PC) using computed tomography (CT) images. This retrospective diagnostic study was conducted using CT images collected from 2004 and 2019 from 4287 patients diagnosed with PC. We proposed a self-supervised learning algorithm (pseudo-lesion segmentation (PS)) for PC classification, which was trained with and without PS and validated on randomly divided training and validation sets. We further performed cross-racial external validation using open-access CT images from 361 patients. For internal validation, the accuracy and sensitivity for PC classification were 94.3% (92.8-95.4%) and 92.5% (90.0-94.4%), and 95.7% (94.5-96.7%) and 99.3 (98.4-99.7%) for the convolutional neural network (CNN) and transformer-based DL models (both with PS), respectively. Implementing PS on a small-sized training dataset (randomly sampled 10%) increased accuracy by 20.5% and sensitivity by 37.0%. For external validation, the accuracy and sensitivity were 82.5% (78.3-86.1%) and 81.7% (77.3-85.4%) and 87.8% (84.0-90.8%) and 86.5% (82.3-89.8%) for the CNN and transformer-based DL models (both with PS), respectively. PS self-supervised learning can increase DL-based PC classification performance, reliability, and robustness of the model for unseen, and even small, datasets. The proposed DL model is potentially useful for PC diagnosis.
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Affiliation(s)
- Thanaporn Viriyasaranon
- Graduate Program in System Health Science and Engineering, Division of Mechanical and Biomedical Engineering, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Jung Won Chun
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang 10408, Republic of Korea
| | - Young Hwan Koh
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang 10408, Republic of Korea
| | - Jae Hee Cho
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Min Kyu Jung
- Department of Internal Medicine, Kyungpook National University Hospital, Daegu 41944, Republic of Korea
| | - Seong-Hun Kim
- Department of Internal Medicine, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju 54907, Republic of Korea
| | - Hyo Jung Kim
- Department of Gastroenterology, Korea University Guro Hospital, Seoul 10408, Republic of Korea
| | - Woo Jin Lee
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang 10408, Republic of Korea
| | - Jang-Hwan Choi
- Graduate Program in System Health Science and Engineering, Division of Mechanical and Biomedical Engineering, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Sang Myung Woo
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang 10408, Republic of Korea
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Alessandrino F, Gozlan EC, Hosein PJ, Merchant NB, McKinney A, Datta J. Texture Analysis Identifies Distinct Radiomic Signals Associated with Survival in Hispanic Patients with Pancreatic Cancer. Ann Surg Oncol 2023; 30:1953-1955. [PMID: 36581727 DOI: 10.1245/s10434-022-13013-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 12/12/2022] [Indexed: 12/31/2022]
Affiliation(s)
- Francesco Alessandrino
- Division of Abdominal Imaging, Department of Radiology, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA.
| | - Etienne C Gozlan
- University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
| | - Peter J Hosein
- Division of Medical Oncology, Department of Medicine, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, Miami, FL, USA
| | - Nipun B Merchant
- Sylvester Comprehensive Cancer Center, Miami, FL, USA
- Division of Surgical Oncology, Department of Surgery, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
| | - Alexander McKinney
- Department of Radiology, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
| | - Jashodeep Datta
- Sylvester Comprehensive Cancer Center, Miami, FL, USA
- Division of Surgical Oncology, Department of Surgery, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
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Telisnor G, DeRemer DL, Frimpong E, Agyare E, Allen J, Ricks-Santi L, Han B, George T, Rogers SC. Review of genetic and pharmacogenetic differences in cytotoxic and targeted therapies for pancreatic cancer in African Americans. J Natl Med Assoc 2023; 115:164-174. [PMID: 36801148 PMCID: PMC10639003 DOI: 10.1016/j.jnma.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/16/2022] [Accepted: 01/24/2023] [Indexed: 02/19/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is currently the third leading cause of cancer mortality and the incidence is projected to increase by 2030. Despite recent advances in its treatment, African Americans have a 50-60% higher incidence and 30% higher mortality rate when compared to European Americans possibly resulting from differences in socioeconomic status, access to healthcare, and genetics. Genetics plays a role in cancer predisposition, response to cancer therapeutics (pharmacogenetics), and in tumor behavior, making some genes targets for oncologic therapeutics. We hypothesize that the germline genetic differences in predisposition, drug response, and targeted therapies also impact PDAC disparities. To demonstrate the impact of genetics and pharmacogenetics on PDAC disparities, a review of the literature was performed using PubMed with variations of the following keywords: pharmacogenetics, pancreatic cancer, race, ethnicity, African, Black, toxicity, and the FDA-approved drug names: Fluoropyrimidines, Topoisomerase inhibitors, Gemcitabine, Nab-Paclitaxel, Platinum agents, Pembrolizumab, PARP-inhibitors, and NTRK fusion inhibitors. Our findings suggest that the genetic profiles of African Americans may contribute to disparities related to FDA approved chemotherapeutic response for patients with PDAC. We recommend a strong focus on improving genetic testing and participation in biobank sample donations for African Americans. In this way, we can improve our current understanding of genes that influence drug response for patients with PDAC.
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Affiliation(s)
- Guettchina Telisnor
- College of Pharmacy, CaRE(2) Health Equity Center, University of Florida, Gainesville, FL, USA
| | - David L DeRemer
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Esther Frimpong
- Department of Pharmaceutical Sciences, College of Pharmacy, Florida Agricultural and Mechanical University, Tallahassee, FL, USA
| | - Edward Agyare
- Department of Pharmaceutical Sciences, College of Pharmacy, Florida Agricultural and Mechanical University, Tallahassee, FL, USA
| | - John Allen
- College of Pharmacy, CaRE(2) Health Equity Center, University of Florida, Gainesville, FL, USA
| | - Luisel Ricks-Santi
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Bo Han
- Department of Surgery, College of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Thomas George
- Division of Hematology and Oncology, College of Medicine, University of Florida, 600 SW Archer Road, PO BOX 100278, Gainesville, FL 32610- 0278, USA
| | - Sherise C Rogers
- Division of Hematology and Oncology, College of Medicine, University of Florida, 600 SW Archer Road, PO BOX 100278, Gainesville, FL 32610- 0278, USA.
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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Chaunzwa TL, del Rey MQ, Bitterman DS. Clinical Informatics Approaches to Understand and Address Cancer Disparities. Yearb Med Inform 2022; 31:121-130. [PMID: 36463869 PMCID: PMC9719762 DOI: 10.1055/s-0042-1742511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
Abstract
OBJECTIVES Disparities in cancer incidence and outcomes across race, ethnicity, gender, socioeconomic status, and geography are well-documented, but their etiologies are often poorly understood and multifactorial. Clinical informatics can provide tools to better understand and address these disparities by enabling high-throughput analysis of multiple types of data. Here, we review recent efforts in clinical informatics to study and measure disparities in cancer. METHODS We carried out a narrative review of clinical informatics studies related to cancer disparities and bias published from 2018-2021, with a focus on domains such as real-world data (RWD) analysis, natural language processing (NLP), radiomics, genomics, proteomics, metabolomics, and metagenomics. RESULTS Clinical informatics studies that investigated cancer disparities across race, ethnicity, gender, and age were identified. Most cancer disparities work within clinical informatics used RWD analysis, NLP, radiomics, and genomics. Emerging applications of clinical informatics to understand cancer disparities, including proteomics, metabolomics, and metagenomics, were less well represented in the literature but are promising future research avenues. Algorithmic bias was identified as an important consideration when developing and implementing cancer clinical informatics techniques, and efforts to address this bias were reviewed. CONCLUSIONS In recent years, clinical informatics has been used to probe a range of data sources to understand cancer disparities across different populations. As informatics tools become integrated into clinical decision-making, attention will need to be paid to ensure that algorithmic bias does not amplify existing disparities. In our increasingly interconnected medical systems, clinical informatics is poised to untap the full potential of multi-platform health data to address cancer disparities.
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Affiliation(s)
- Tafadzwa L. Chaunzwa
- Department of Radiation Oncology, Dana-Farber Brigham Cancer Center, Harvard Medical School, Boston, MA, USA,Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
| | - Maria Quiles del Rey
- Department of Radiation Oncology, Dana-Farber Brigham Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Danielle S. Bitterman
- Department of Radiation Oncology, Dana-Farber Brigham Cancer Center, Harvard Medical School, Boston, MA, USA,Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA,Correspondence to: Dr. Danielle S. Bitterman Department of Radiation Oncology, Dana-Farber Cancer Institute/Brigham and Women's Hospital75 Francis Street, Boston, MA 02115USA+1 857 215 1489+1 617 975 0985
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Bednar F. Clinical Staging Uncertainty and Treatment Sequencing in Pancreatic Cancer. Ann Surg 2022; 275:422-423. [PMID: 34793357 DOI: 10.1097/sla.0000000000005317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Filip Bednar
- Department of Surgery, University of Michigan, Ann Arbor, MI
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