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Ali SM, Adnan Y, Ahmad Z, Chawla T, Ali SMA. Significant Association of PD-L1 With CD44 Expression and Patient Survival: Avenues for Immunotherapy and Cancer Stem Cells Downregulation in Pancreatic Cancers. J Cancer Epidemiol 2024; 2024:3448648. [PMID: 39148690 PMCID: PMC11325009 DOI: 10.1155/2024/3448648] [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: 04/04/2024] [Revised: 06/19/2024] [Accepted: 06/24/2024] [Indexed: 08/17/2024] Open
Abstract
Background: Pancreatic cancers are known for their aggressive nature. This aggressiveness may be attributed to the presence of cancer stem cells (CSCs), which promote relapse, metastasis, and resistance to chemotherapy. Targeting CSCs is essential to reverse this aggressiveness in pancreatic malignancies. Literature highlights the association of PD-L1 expression with CSCs in various cancers, suggesting immunotherapy as a promising therapeutic approach. This study is aimed at investigating the potential of immunotherapy in pancreatic cancers by examining its association with selected CSC marker expression. Method: A retrospective cohort study was conducted involving 56 patients with confirmed diagnoses of pancreatic cancers at Aga Khan University Hospital from January 2015 to October 2022. After exclusions, based on refusal to provide consent or incomplete follow-up data, 38 patients were enrolled in the study. Immunohistochemistry was performed on formalin-fixed paraffin-embedded (FFPE) tumor tissue samples to assess the expression of CSC markers (CD133, CD44, and L1CAM) and immune checkpoint inhibitor marker (PD-L1). Statistical analysis was employed to determine associations between marker expression, clinical factors, and overall survival. Results: The study revealed that 86.8% of pancreatic cancer cases exhibited positive PD-L1 expression. Moreover, a significant association of PD-L1 expression was observed with the presence of CD44 protein (p = 0.030), as well as with the overall survival of patients (p = 0.023). Conclusion: Our findings show a significant association of PD-L1 with CD44 marker expression as well as patient survival. This research shows the potential to serve as the foundation for investigating the efficacy of immunotherapy in reducing CD44-expressing CSCs in pancreatic cancer, potentially enhancing patient outcomes.
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Affiliation(s)
| | - Yumna Adnan
- Department of Surgery Aga Khan University Hospital, Karachi, Pakistan
| | - Zubair Ahmad
- Consultant Histopathologist Sultan Qaboos Comprehensive Cancer Care and Research Centre, Seeb, Oman
- Department of Pathology and Laboratory Medicine Aga Khan University Hospital, Karachi, Pakistan
| | - Tabish Chawla
- Department of Surgery Aga Khan University Hospital, Karachi, Pakistan
| | - S M Adnan Ali
- Department of Surgery Aga Khan University Hospital, Karachi, Pakistan
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Tang Y, Su YX, Zheng JM, Zhuo ML, Qian QF, Shen QL, Lin P, Chen ZK. Radiogenomic analysis for predicting lymph node metastasis and molecular annotation of radiomic features in pancreatic cancer. J Transl Med 2024; 22:690. [PMID: 39075486 PMCID: PMC11288107 DOI: 10.1186/s12967-024-05479-y] [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: 01/27/2024] [Accepted: 07/03/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND To provide a preoperative prediction model for lymph node metastasis in pancreatic cancer patients and provide molecular information of key radiomic features. METHODS Two cohorts comprising 151 and 54 pancreatic cancer patients were included in the analysis. Radiomic features from the tumor region of interests were extracted by using PyRadiomics software. We used a framework that incorporated 10 machine learning algorithms and generated 77 combinations to construct radiomics-based models for lymph node metastasis prediction. Weighted gene coexpression network analysis (WGCNA) was subsequently performed to determine the relationships between gene expression levels and radiomic features. Molecular pathways enrichment analysis was performed to uncover the underlying molecular features. RESULTS Patients in the in-house cohort (mean age, 61.3 years ± 9.6 [SD]; 91 men [60%]) were separated into training (n = 105, 70%) and validation (n = 46, 30%) cohorts. A total of 1,239 features were extracted and subjected to machine learning algorithms. The 77 radiomic models showed moderate performance for predicting lymph node metastasis, and the combination of the StepGBM and Enet algorithms had the best performance in the training (AUC = 0.84, 95% CI = 0.77-0.91) and validation (AUC = 0.85, 95% CI = 0.73-0.98) cohorts. We determined that 15 features were core variables for lymph node metastasis. Proliferation-related processes may respond to the main molecular alterations underlying these features. CONCLUSIONS Machine learning-based radiomics could predict the status of lymph node metastasis in pancreatic cancer, which is associated with proliferation-related alterations.
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Affiliation(s)
- Yi Tang
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Yi-Xi Su
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Jin-Mei Zheng
- Department of Radiology, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Min-Ling Zhuo
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Qing-Fu Qian
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Qing-Ling Shen
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Peng Lin
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China.
| | - Zhi-Kui Chen
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China.
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3
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Paverd H, Zormpas-Petridis K, Clayton H, Burge S, Crispin-Ortuzar M. Radiology and multi-scale data integration for precision oncology. NPJ Precis Oncol 2024; 8:158. [PMID: 39060351 PMCID: PMC11282284 DOI: 10.1038/s41698-024-00656-0] [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: 01/19/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
In this Perspective paper we explore the potential of integrating radiological imaging with other data types, a critical yet underdeveloped area in comparison to the fusion of other multi-omic data. Radiological images provide a comprehensive, three-dimensional view of cancer, capturing features that would be missed by biopsies or other data modalities. This paper explores the complexities and challenges of incorporating medical imaging into data integration models, in the context of precision oncology. We present the different categories of imaging-omics integration and discuss recent progress, highlighting the opportunities that arise from bringing together spatial data on different scales.
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Affiliation(s)
- Hania Paverd
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | | | - Hannah Clayton
- Department of Oncology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Sarah Burge
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Mireia Crispin-Ortuzar
- Department of Oncology, University of Cambridge, Cambridge, UK.
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
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4
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Nicoletti A, Paratore M, Vitale F, Negri M, Quero G, Esposto G, Mignini I, Alfieri S, Gasbarrini A, Zocco MA, Zileri Dal Verme L. Understanding the Conundrum of Pancreatic Cancer in the Omics Sciences Era. Int J Mol Sci 2024; 25:7623. [PMID: 39062863 PMCID: PMC11276793 DOI: 10.3390/ijms25147623] [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: 05/01/2024] [Revised: 07/03/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
Pancreatic cancer (PC) is an increasing cause of cancer-related death, with a dismal prognosis caused by its aggressive biology, the lack of clinical symptoms in the early phases of the disease, and the inefficacy of treatments. PC is characterized by a complex tumor microenvironment. The interaction of its cellular components plays a crucial role in tumor development and progression, contributing to the alteration of metabolism and cellular hyperproliferation, as well as to metastatic evolution and abnormal tumor-associated immunity. Furthermore, in response to intrinsic oncogenic alterations and the influence of the tumor microenvironment, cancer cells undergo a complex oncogene-directed metabolic reprogramming that includes changes in glucose utilization, lipid and amino acid metabolism, redox balance, and activation of recycling and scavenging pathways. The advent of omics sciences is revolutionizing the comprehension of the pathogenetic conundrum of pancreatic carcinogenesis. In particular, metabolomics and genomics has led to a more precise classification of PC into subtypes that show different biological behaviors and responses to treatments. The identification of molecular targets through the pharmacogenomic approach may help to personalize treatments. Novel specific biomarkers have been discovered using proteomics and metabolomics analyses. Radiomics allows for an earlier diagnosis through the computational analysis of imaging. However, the complexity, high expertise required, and costs of the omics approach are the main limitations for its use in clinical practice at present. In addition, the studies of extracellular vesicles (EVs), the use of organoids, the understanding of host-microbiota interactions, and more recently the advent of artificial intelligence are helping to make further steps towards precision and personalized medicine. This present review summarizes the main evidence for the application of omics sciences to the study of PC and the identification of future perspectives.
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Affiliation(s)
- Alberto Nicoletti
- CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
| | - Mattia Paratore
- CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
| | - Federica Vitale
- CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
| | - Marcantonio Negri
- CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
| | - Giuseppe Quero
- Centro Pancreas, Chirurgia Digestiva, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.Q.); (S.A.)
| | - Giorgio Esposto
- CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
| | - Irene Mignini
- CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
| | - Sergio Alfieri
- Centro Pancreas, Chirurgia Digestiva, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.Q.); (S.A.)
| | - Antonio Gasbarrini
- CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
| | - Maria Assunta Zocco
- CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
| | - Lorenzo Zileri Dal Verme
- CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
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Holder AM, Dedeilia A, Sierra-Davidson K, Cohen S, Liu D, Parikh A, Boland GM. Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours. Nat Rev Cancer 2024; 24:498-512. [PMID: 38867074 DOI: 10.1038/s41568-024-00705-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/14/2024]
Abstract
Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.
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Affiliation(s)
- Ashley M Holder
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Sonia Cohen
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - David Liu
- Dana Farber Cancer Institute, Boston, MA, USA
| | - Aparna Parikh
- Cancer Center, Massachusetts General Hospital, Boston, MA, USA
| | - Genevieve M Boland
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
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6
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Sun Y, Hu S, Li X, Wu Y. Development and Application of a Novel Machine Learning Model Predicting Pancreatic Cancer-Specific Mortality. Cureus 2024; 16:e57161. [PMID: 38681451 PMCID: PMC11056009 DOI: 10.7759/cureus.57161] [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] [Accepted: 03/28/2024] [Indexed: 05/01/2024] Open
Abstract
Precise prognostication is vital for guiding treatment decisions in people diagnosed with pancreatic cancer. Existing models depend on predetermined variables, constraining their effectiveness. Our objective was to explore a novel machine learning approach to enhance a prognostic model for predicting pancreatic cancer-specific mortality and, subsequently, to assess its performance against Cox regression models. Datasets were retrospectively collected and analyzed for 9,752 patients diagnosed with pancreatic cancer and with surgery performed. The primary outcomes were the mortality of patients with pancreatic carcinoma at one year, three years, and five years. Model discrimination was assessed using the concordance index (C-index), and calibration was assessed using Brier scores. The Survival Quilts model was compared with Cox regression models in clinical use, and decision curve analysis was done. The Survival Quilts model demonstrated robust discrimination for one-year (C-index 0.729), three-year (C-index 0.693), and five-year (C-index 0.672) pancreatic cancer-specific mortality. In comparison to Cox models, the Survival Quilts models exhibited a higher C-index up to 32 months but displayed inferior performance after 33 months. A subgroup analysis was conducted, revealing that within the subset of individuals without metastasis, the Survival Quilts models showcased a significant advantage over the Cox models. In the cohort with metastatic pancreatic cancer, Survival Quilts outperformed the Cox model before 24 months but exhibited a weaker performance after 25 months. This study has developed and validated a novel machine learning-based Survival Quilts model to predict pancreatic cancer-specific mortality that outperforms the Cox regression model.
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Affiliation(s)
- Yongji Sun
- Department of Surgery, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, CHN
| | - Sien Hu
- Department of Surgery, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, CHN
| | - Xiawei Li
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, CHN
| | - Yulian Wu
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, CHN
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7
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Anghel C, Grasu MC, Anghel DA, Rusu-Munteanu GI, Dumitru RL, Lupescu IG. Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images. Diagnostics (Basel) 2024; 14:438. [PMID: 38396476 PMCID: PMC10887967 DOI: 10.3390/diagnostics14040438] [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: 01/10/2024] [Revised: 02/10/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.
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Affiliation(s)
- Cristian Anghel
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Mugur Cristian Grasu
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Denisa Andreea Anghel
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Gina-Ionela Rusu-Munteanu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Radu Lucian Dumitru
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Ioana Gabriela Lupescu
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
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Tong L, Shi W, Isgut M, Zhong Y, Lais P, Gloster L, Sun J, Swain A, Giuste F, Wang MD. Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence. IEEE Rev Biomed Eng 2024; 17:80-97. [PMID: 37824325 DOI: 10.1109/rbme.2023.3324264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.
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Cellina M, De Padova G, Caldarelli N, Libri D, Cè M, Martinenghi C, Alì M, Papa S, Carrafiello G. Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy. Crit Rev Oncog 2024; 29:1-13. [PMID: 38505877 DOI: 10.1615/critrevoncog.2023050439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and virtual biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
| | - Giuseppe De Padova
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Nazarena Caldarelli
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Dario Libri
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Carlo Martinenghi
- Radiology Department, Ospedale San Raffaele, Via Olgettina, 60 - 20132 Milan, Italy
| | - Marco Alì
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Sergio Papa
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
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10
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Hashemi M, Nazdari N, Gholamiyan G, Paskeh MDA, Jafari AM, Nemati F, Khodaei E, Abyari G, Behdadfar N, Raei B, Raesi R, Nabavi N, Hu P, Rashidi M, Taheriazam A, Entezari M. EZH2 as a potential therapeutic target for gastrointestinal cancers. Pathol Res Pract 2024; 253:154988. [PMID: 38118215 DOI: 10.1016/j.prp.2023.154988] [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: 08/15/2023] [Revised: 11/18/2023] [Accepted: 11/27/2023] [Indexed: 12/22/2023]
Abstract
Gastrointestinal (GI) cancers continue to be a major cause of mortality and morbidity globally. Understanding the molecular pathways associated with cancer progression and severity is essential for creating effective cancer treatments. In cancer research, there is a notable emphasis on Enhancer of zeste homolog 2 (EZH2), a key player in gene expression influenced by its irregular expression and capacity to attach to promoters and alter methylation status. This review explores the impact of EZH2 signaling on various GI cancers, such as colorectal, gastric, pancreatic, hepatocellular, esophageal, and cholangiocarcinoma. The primary function of EZH2 signaling is to facilitate the accelerated progression of cancer cells. Additionally, EZH2 has the capacity to modulate the reaction of GI cancers to chemotherapy and radiotherapy. Numerous pathways, including long non-coding RNAs and microRNAs, serve as upstream regulators of EZH2 in these types of cancer. EZH2's enzymatic activity enables it to attach to target gene promoters, resulting in methylation that modifies their expression. EZH2 could be considered as an independent prognostic factor, with increased expression correlating with a worse disease prognosis. Additionally, a range of gene therapies including small interfering RNA, and anti-tumor agents are being explored to target EZH2 for cancer treatment. This comprehensive review underscores the current insights into EZH2 signaling in gastrointestinal cancers and examines the prospect of therapies targeting EZH2 to enhance patient outcomes.
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Affiliation(s)
- Mehrdad Hashemi
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Naghmeh Nazdari
- Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Ghazaleh Gholamiyan
- Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Mahshid Deldar Abad Paskeh
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Ali Moghadas Jafari
- Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Fateme Nemati
- Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Elaheh Khodaei
- Department of Dermatology, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Ghazal Abyari
- Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Nazanin Behdadfar
- Young Researchers and Elite Club, Buinzahra Branch, Islamic Azad University, Buinzahra, Iran
| | - Behnaz Raei
- Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Rasoul Raesi
- Department of Health Services Management, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Medical-Surgical Nursing, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Noushin Nabavi
- Department of Urologic Sciences and Vancouver Prostate Centre, University of British Columbia, V6H3Z6 Vancouver, BC, Canada
| | - Peng Hu
- Department of Emergency, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China
| | - Mohsen Rashidi
- Department Pharmacology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran; The Health of Plant and Livestock Products Research Center, Mazandaran University of Medical Sciences, Sari, Iran.
| | - Afshin Taheriazam
- Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Department of Orthopedics, Faculty of medicine, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
| | - Maliheh Entezari
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
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11
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Al Taher RS, Abbas MA, Halahleh K, Sughayer MA. Correlation Between ImageJ and Conventional Manual Scoring Methods for Programmed Death-Ligand 1 Immuno-Histochemically Stained Sections. Technol Cancer Res Treat 2024; 23:15330338241242635. [PMID: 38562094 PMCID: PMC10989033 DOI: 10.1177/15330338241242635] [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: 09/21/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Background: One of the most frequently used methods for quantifying PD-L1 (programmed cell death-ligand 1) expression in tumor tissue is IHC (immunohistochemistry). This may predict the patient's response to anti-PD1/PD-L1 therapy in cancer. Methods: ImageJ software was used to score IHC-stained sections for PD-L1 and compare the results with the conventional manual method. Results: In diffuse large B cell lymphoma, no significant difference between the scores obtained by the conventional method and ImageJ scores obtained using the option "RGB" or "Brightness/Contrast." On the other hand, a significant difference was found between the conventional and HSB scoring methods. ImageJ faced some challenges in analyzing head and neck squamous cell carcinoma tissues because of tissue heterogenicity. A significant difference was found between the conventional and ImageJ scores using HSB or RGB but not with the "Brightness/Contrast" option. Scores obtained by ImageJ analysis after taking images using 20 × objective lens gave significantly higher readings compared to 40 × magnification. A significant difference between camera-captured images' scores and scanner whole slide images' scores was observed. Conclusion: ImageJ can be used to score homogeneous tissues. In the case of highly heterogeneous tissues, it is advised to use the conventional method rather than ImageJ scoring.
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Affiliation(s)
- Rand Suleiman Al Taher
- Department of Medical Laboratory Sciences, Faculty of Allied Medical Sciences, Al-Ahliyya Amman University, Amman, Jordan
- Department of Pathology and Laboratory Medicine, King Hussein Cancer Center, Amman, Jordan
| | - Manal A. Abbas
- Department of Medical Laboratory Sciences, Faculty of Allied Medical Sciences, Al-Ahliyya Amman University, Amman, Jordan
- Pharmacological and Diagnostic Research Laboratory, Al-Ahliyya Amman University, Amman, Jordan
| | - Khalid Halahleh
- Department of Medical Oncology, King Hussein Cancer Center, Amman, Jordan
| | - Maher A. Sughayer
- Department of Pathology and Laboratory Medicine, King Hussein Cancer Center, Amman, Jordan
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12
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Li Q, Huang Y, Xia Y, Li M, Tang W, Zhang M, Zhao Z. Radiogenomics for predicting microsatellite instability status and PD-L1 expression with machine learning in endometrial cancers: A multicenter study. Heliyon 2023; 9:e23166. [PMID: 38149198 PMCID: PMC10750045 DOI: 10.1016/j.heliyon.2023.e23166] [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: 05/19/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 12/28/2023] Open
Abstract
Purpose To evaluate the effectiveness of machine learning model based on magnetic resonance imaging (MRI) in identifying microsatellite instability (MSI) status and PD-L1 expression in endometrial cancer (EC). Methods This retrospective study included 82 EC patients from 2 independent centers. Radiomics features from the intratumoral and peritumoral regions, obtained from four conventional MRI sequences (T2-weighted images; contrast-enhanced T1-weighted images; diffusion-weighted images; apparent diffusion coefficient), were combined with clinicopathologic characteristics to develop machine learning model for predicting MSI status and PD-L1 expression. 60 patients from center 1 were used as the training set for model construction, while 22 patients from center 2 were used as an external validation set for model evaluation. Results For predicting MSI status, the clinicopathologic model, radscore model, and combination model achieved area under the curves (AUCs) of 0.728, 0.833, and 0.889 in the training set, respectively, and 0.595, 0.790, and 0.848 in the validation set, respectively. For predicting PD-L1 expression, the clinicopathologic model, radscore model, and combination model achieved AUCs of 0.648, 0.814, and 0.834 in the training set, respectively, and 0.660, 0.708, and 0.764 in the validation set, respectively. Calibration curve analysis and decision curve analysis demonstrated good calibration and clinical utility of the combination model. Conclusion The machine learning model incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for predicting MSI status and PD-L1 expression in EC. This approach may contribute to precision medicine for EC patients.
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Affiliation(s)
- Qianling Li
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Zhejiang University School of Medicine, Shaoxing, 312000, China
| | - Ya'nan Huang
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
| | - Yang Xia
- Department of Radiology, Shaoxing Maternity and Child Health Care Hospital, Shaoxing, 312000, China
| | - Meiping Li
- Department of Pathology, Shaoxing Maternity and Child Health Care Hospital, Shaoxing, Zhejiang, 312000, China
| | - Wei Tang
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University, Hangzhou, 310000, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
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Gu J, Bao S, Akemuhan R, Jia Z, Zhang Y, Huang C. Radiomics Based on Contrast-Enhanced CT for Recognizing c-Met-Positive Hepatocellular Carcinoma: a Noninvasive Approach to Predict the Outcome of Sorafenib Resistance. Mol Imaging Biol 2023; 25:1073-1083. [PMID: 37932610 DOI: 10.1007/s11307-023-01870-1] [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: 07/30/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVES The purpose of our project was to investigate the effectiveness of radiomic features based on contrast-enhanced computed tomography (CT) that can detect the expression of c-Met in hepatocellular carcinoma (HCC) and to validate its efficacy in predicting the outcome of sorafenib resistance. MATERIALS AND METHODS In total, 130 patients (median age, 60 years) with pathologically confirmed HCC who underwent contrast material-enhanced CT from October 2012 to July 2020 were randomly divided into a training set (n = 91) and a test set (n = 39). Radiomic features were extracted from arterial phase (AP), portal venous phase (VP) and delayed phase (DP) images of every participant's enhanced CT images. RESULTS The entire group comprised 39 Met-positive and 91 Met-negative patients. The combined model, which included the clinical factors and the radiomic features, performed well in the training (area under the curve [AUC] = 0.878) and validation (AUC = 0.851) cohorts. The nomogram, which relied on the combined model, fits well in the calibration curves. Decision curve analysis (DCA) further confirmed that the clinical valuation of the nomogram achieved comparable accuracy in c-Met prediction. Among another 20 patients with HCC who had received sorafenib, the predicted high-risk group had shorter overall survival (OS) than the predicted low-risk group (p < 0.05). CONCLUSION A multivariate model acquired from three phases (AP, VP and DP) of enhanced CT, HBV-DNA and γ glutamyl transpeptidase isoenzyme II (GGT-II) could be considered a satisfactory preoperative marker of the expression of c-Met in patients with HCC. This approach may help in overcoming sorafenib resistance in advanced HCC.
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Affiliation(s)
- Jingxiao Gu
- Department of Vascular Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, the, People's Republic of China
- Department of Radiology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China
| | - Shanlei Bao
- Department of Nuclear Medicine, Affiliated Hospital of Nantong University, Nantong, China
| | | | - Zhongzheng Jia
- Department of Radiology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China.
| | - Yu Zhang
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Nantong University, Nantong, China.
| | - Chen Huang
- Department of Vascular Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, the, People's Republic of China.
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Blise KE, Sivagnanam S, Betts CB, Betre K, Kirchberger N, Tate B, Furth EE, Dias Costa A, Nowak JA, Wolpin BM, Vonderheide RH, Goecks J, Coussens LM, Byrne KT. Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563335. [PMID: 37961410 PMCID: PMC10634700 DOI: 10.1101/2023.10.20.563335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Tumor molecular datasets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning to analyze a single-cell, spatial, and highly multiplexed proteomic dataset from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcome. A novel multiplex immunohistochemistry antibody panel was used to audit T cell functionality and spatial localization in resected tumors from treatment-naive patients with localized pancreatic ductal adenocarcinoma (PDAC) compared to a second cohort of patients treated with neoadjuvant agonistic CD40 (αCD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both treatment cohorts were assayed, and more than 1,000 tumor microenvironment (TME) features were quantified. We then trained machine learning models to accurately predict αCD40 treatment status and disease-free survival (DFS) following αCD40 therapy based upon TME features. Through downstream interpretation of the machine learning models' predictions, we found αCD40 therapy to reduce canonical aspects of T cell exhaustion within the TME, as compared to treatment-naive TMEs. Using automated clustering approaches, we found improved DFS following αCD40 therapy to correlate with the increased presence of CD44+ CD4+ Th1 cells located specifically within cellular spatial neighborhoods characterized by increased T cell proliferation, antigen-experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of machine learning in molecular cancer immunology applications, highlight the impact of αCD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for αCD40-treated patients with PDAC.
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Affiliation(s)
- Katie E. Blise
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR USA
- The Knight Cancer Institute, Oregon Health & Science University, Portland, OR USA
| | - Shamilene Sivagnanam
- The Knight Cancer Institute, Oregon Health & Science University, Portland, OR USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR USA
| | - Courtney B. Betts
- The Knight Cancer Institute, Oregon Health & Science University, Portland, OR USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR USA
- Current affiliation: Akoya Biosciences, 100 Campus Drive, 6th Floor, Marlborough, MA USA
| | - Konjit Betre
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR USA
| | - Nell Kirchberger
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR USA
| | - Benjamin Tate
- The Knight Cancer Institute, Oregon Health & Science University, Portland, OR USA
- Immune Monitoring and Cancer Omics Services, Oregon Health & Science University, Portland, OR USA
| | - Emma E. Furth
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Andressa Dias Costa
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
| | - Jonathan A. Nowak
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
| | - Brian M. Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
| | - Robert H. Vonderheide
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- Parker Institute for Cancer Immunotherapy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Jeremy Goecks
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR USA
- The Knight Cancer Institute, Oregon Health & Science University, Portland, OR USA
- Current affiliation: Department of Machine Learning, H. Lee Moffitt Cancer Center, Tampa, FL USA
- Current affiliation: Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center, Tampa, FL USA
| | - Lisa M. Coussens
- The Knight Cancer Institute, Oregon Health & Science University, Portland, OR USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR USA
| | - Katelyn T. Byrne
- The Knight Cancer Institute, Oregon Health & Science University, Portland, OR USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR USA
- Lead contact
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Wei L, Niraula D, Gates EDH, Fu J, Luo Y, Nyflot MJ, Bowen SR, El Naqa IM, Cui S. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. Br J Radiol 2023; 96:20230211. [PMID: 37660402 PMCID: PMC10546458 DOI: 10.1259/bjr.20230211] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/15/2023] [Accepted: 06/27/2023] [Indexed: 09/05/2023] Open
Abstract
Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.
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Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, University of Michigan, Michigan, United States
| | - Dipesh Niraula
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Evan D. H. Gates
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Jie Fu
- Department of Radiation Oncology, Stanford University, Stanford, California, United States
| | - Yi Luo
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Matthew J. Nyflot
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Stephen R. Bowen
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Issam M El Naqa
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Sunan Cui
- Department of Radiation Oncology, University of Washington, Washington, United States
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Zhong X, Peng J, Shu Z, Song Q, Li D. Prediction of p53 mutation status in rectal cancer patients based on magnetic resonance imaging-based nomogram: a study of machine learning. Cancer Imaging 2023; 23:88. [PMID: 37723592 PMCID: PMC10507842 DOI: 10.1186/s40644-023-00607-1] [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: 03/27/2023] [Accepted: 09/05/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND The current study aimed to construct and validate a magnetic resonance imaging (MRI)-based radiomics nomogram to predict tumor protein p53 gene status in rectal cancer patients using machine learning. METHODS Clinical and imaging data from 300 rectal cancer patients who underwent radical resections were included in this study, and a total of 166 patients with p53 mutations according to pathology reports were included in these patients. These patients were allocated to the training (n = 210) or validation (n = 90) cohorts (7:3 ratio) according to the examination time. Using the training data set, the radiomic features of primary tumor lesions from T2-weighted images (T2WI) of each patient were analyzed by dimensionality reduction. Multivariate logistic regression was used to screen predictive features, which were combined with a radiomics model to construct a nomogram to predict p53 gene status. The accuracy and reliability of the nomograms were assessed in both training and validation data sets using receiver operating characteristic (ROC) curves. RESULTS Using the radiomics model with the training and validation cohorts, the diagnostic efficacies were 0.828 and 0.795, the sensitivities were 0.825 and 0.891, and the specificities were 0.722 and 0.659, respectively. Using the nomogram with the training and validation data sets, the diagnostic efficacies were 0.86 and 0.847, the sensitivities were 0.758 and 0.869, and the specificities were 0.833 and 0.75, respectively. CONCLUSIONS The radiomics nomogram based on machine learning was able to predict p53 gene status and facilitate preoperative molecular-based pathological diagnoses.
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Affiliation(s)
- Xia Zhong
- The First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhenyu Shu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qiaowei Song
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Dongxue Li
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
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Taha A, Taha-Mehlitz S, Ortlieb N, Ochs V, Honaker MD, Rosenberg R, Lock JF, Bolli M, Cattin PC. Machine learning in pancreas surgery, what is new? literature review. Front Surg 2023; 10:1142585. [PMID: 37383385 PMCID: PMC10293756 DOI: 10.3389/fsurg.2023.1142585] [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: 01/11/2023] [Accepted: 05/19/2023] [Indexed: 06/30/2023] Open
Abstract
Background Machine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery. Methods We integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included. Results A search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022. Conclusion The integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes.
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Affiliation(s)
- Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Stephanie Taha-Mehlitz
- Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Niklas Ortlieb
- Goethe University Frankfurt, Faculty of Business and Economics, Frankfurt am Main, Germany
| | - Vincent Ochs
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Michael Drew Honaker
- Department of Surgery, East Carolina University, Brody School of Medicine, Greenville, NC, United States
| | - Robert Rosenberg
- Cantonal Hospital Basel-Landschaft, Centre for Gastrointestinal and Liver Diseases, Liestal, Switzerland
| | - Johan F. Lock
- Department of General, Visceral, Transplantation, Vascular and Pediatric Surgery, University Hospital Würzburg, Würzburg, Germany
| | - Martin Bolli
- Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Philippe C. Cattin
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
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Ali SMA, Adnan Y, Ali SM, Ahmad Z, Chawla T, Farooqui HA. Immunohistochemical analysis of a panel of cancer stem cell markers and potential therapeutic markers in pancreatic ductal adenocarcinoma. J Cancer Res Clin Oncol 2023; 149:2279-2292. [PMID: 36066622 DOI: 10.1007/s00432-022-04315-4] [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: 02/28/2022] [Accepted: 08/19/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Pancreatic Ductal Adenocarcinoma (PDAC) is the most common type of pancreatic malignancies. It is known for its aggressive nature and high mortality rate. This calls for an urgent need of new prognostic and therapeutic markers that can be targeted for personalized treatment of the patient. METHODS Among 142 patients diagnosed with pancreatic cancers at Aga Khan University Hospital, a total of 62 patients were selected based on their confirmed diagnosis of PDAC. Immunohistochemistry was performed on Formalin-Fixed Paraffin-Embedded (FFPE) sections using selected antibodies (CD44, CD133, L1CAM, HER2, PD-L1, EGFR, COX2 and cyclin D1). All the slides were scored independently by two pathologists as per the set criteria. RESULTS Expression of all cancer stem cell markers was found to be significantly associated with one or more potential therapeutic markers. CD44 expression was significantly associated with HER2 (p = 0.032), COX2 (p = 0.005) and EGFR expression (p = 0.008). CD133 expression also showed significant association with HER2 (p = 0.036), COX2 (p = 0.004) and EGFR expression (p = 0.018). L1CAM expression was found to be associated with expression of COX2 (p = 0.017). None of the proteins markers showed association with overall survival of the patient. On the other hand, among the clinicopathological characteristics, histological differentiation (p = 0.047), lymphovascular invasion (p = 0.021) and perineural invasion (p = 0.014) were found to be significantly associated with patient's overall survival. CONCLUSION Internationally, this is the first report that assesses the selected panel of cancer stem cell markers and potential therapeutic targets in a single study and evaluates its combined expression. The study clearly demonstrates association between expression of cancer stem cell markers and therapeutic targets hence paves a way for precision medicine for pancreatic cancer patients.
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Affiliation(s)
- S M Adnan Ali
- Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Pakistan.
| | - Yumna Adnan
- Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Pakistan
| | - Saleema Mehboob Ali
- Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Pakistan
| | - Zubair Ahmad
- Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Pakistan
| | - Tabish Chawla
- Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Pakistan
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Jiang J, Chao WL, Culp S, Krishna SG. Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma. Cancers (Basel) 2023; 15:2410. [PMID: 37173876 PMCID: PMC10177524 DOI: 10.3390/cancers15092410] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65-75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.
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Affiliation(s)
- Joanna Jiang
- Department of Internal Medicine, Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Wei-Lun Chao
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Stacey Culp
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Somashekar G. Krishna
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, Ohio State University Wexner Medical Ceter, Columbus, OH 43210, USA
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Tanaka T, Masuda A, Inoue J, Hamada T, Ikegawa T, Toyama H, Sofue K, Shiomi H, Sakai A, Kobayashi T, Tanaka S, Nakano R, Yamada Y, Ashina S, Tsujimae M, Yamakawa K, Abe S, Gonda M, Masuda S, Inomata N, Uemura H, Kohashi S, Nagao K, Kanzawa M, Itoh T, Ueda Y, Fukumoto T, Kodama Y. Integrated analysis of tertiary lymphoid structures in relation to tumor-infiltrating lymphocytes and patient survival in pancreatic ductal adenocarcinoma. J Gastroenterol 2023; 58:277-291. [PMID: 36705749 DOI: 10.1007/s00535-022-01939-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 12/04/2022] [Indexed: 01/28/2023]
Abstract
BACKGROUND Tertiary lymphoid structure (TLS) reflects an intense immune response against cancer, which correlates with favorable patient survival. However, the association of TLS with tumor-infiltrating lymphocytes (TILs) and clinical outcomes has not been investigated comprehensively in pancreatic ductal adenocarcinoma (PDAC). METHODS We utilized an integrative molecular pathological epidemiology database on 162 cases with resected PDAC, and examined TLS in relation to levels of TILs, patient survival, and treatment response. In whole-section slides, we assessed the formation of TLS and conducted immunohistochemistry for tumor-infiltrating T cells (CD4, CD8, CD45RO, and FOXP3). As confounding factors, we assessed alterations of four main driver genes (KRAS, TP53, CDKN2A [p16], and SMAD4) using next-generation sequencing and immunohistochemistry, and tumor CD274 (PD-L1) expression assessed by immunohistochemistry. RESULTS TLSs were found in 112 patients with PDAC (69.1%). TLS was associated with high levels of CD4+ TILs (multivariable odds ratio [OR], 3.50; 95% confidence interval [CI] 1.65-7.80; P = 0.0002), CD8+ TILs (multivariable OR, 11.0; 95% CI 4.57-29.7, P < 0.0001) and CD45RO+ TILs (multivariable OR, 2.65; 95% CI 1.25-5.80, P = 0.01), but not with levels of FOXP3+ TILs. TLS was associated with longer pancreatic cancer-specific survival (multivariable hazard ratio, 0.37; 95% CI 0.25-0.56, P < 0.0001) and favorable outcomes of adjuvant S-1-treatment. TLS was not associated with driver gene alterations but tumor CD274 negative expression. CONCLUSIONS Our comprehensive data supports the surrogacy of TLS for vigorous anti-tumor immune response characterized by high levels of helper and cytotoxic T cells and their prognostic role.
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Affiliation(s)
- Takeshi Tanaka
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Atsuhiro Masuda
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan.
| | - Jun Inoue
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Tsuyoshi Hamada
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, 7-5-1 Kusunoki-Cho, Chuo-Ku, Tokyo, 113-8655, Japan
| | - Takuya Ikegawa
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Hirochika Toyama
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Keitaro Sofue
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Hideyuki Shiomi
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Arata Sakai
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Takashi Kobayashi
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Shunta Tanaka
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Ryota Nakano
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Yasutaka Yamada
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Shigeto Ashina
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Masahiro Tsujimae
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Kohei Yamakawa
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Shohei Abe
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Masanori Gonda
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Shigeto Masuda
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Noriko Inomata
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Hisahiro Uemura
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Shinya Kohashi
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Kae Nagao
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Maki Kanzawa
- Division of Diagnostic Pathology, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Tomoo Itoh
- Division of Diagnostic Pathology, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Yoshihide Ueda
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Takumi Fukumoto
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
| | - Yuzo Kodama
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-Cho, Chuo-Ku, Kobe, Hyogo, 650-0017, Japan
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Xue C, Zhou Q, Xi H, Zhou J. Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment. Diagn Interv Imaging 2023; 104:113-122. [PMID: 36283933 DOI: 10.1016/j.diii.2022.10.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 12/24/2022]
Abstract
With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.
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Affiliation(s)
- Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Huaze Xi
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China.
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22
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Cui H, Sun Y, Zhao D, Zhang X, Kong H, Hu N, Wang P, Zuo X, Fan W, Yao Y, Fu B, Tian J, Wu M, Gao Y, Ning S, Zhang L. Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions. J Transl Med 2023; 21:44. [PMID: 36694240 PMCID: PMC9875533 DOI: 10.1186/s12967-022-03840-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 12/19/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Human epidermal growth factor receptor 2 (HER2) overexpressed associated with poor prognosis in breast cancer and HER2 has been defined as a therapeutic target for breast cancer treatment. We aimed to explore the molecular biological information in ultrasound radiomic features (URFs) of HER2-positive breast cancer using radiogenomic analysis. Moreover, a radiomics model was developed to predict the status of HER2 in breast cancer. METHODS This retrospective study included 489 patients who were diagnosed with breast cancer. URFs were extracted from a radiomics analysis set using PyRadiomics. The correlations between differential URFs and HER2-related genes were calculated using Pearson correlation analysis. Functional enrichment of the identified URFs-correlated HER2 positive-specific genes was performed. Lastly, the radiomics model was developed based on the URF-module mined from auxiliary differential URFs to assess the HER2 status of breast cancer. RESULTS Eight differential URFs (p < 0.05) were identified among the 86 URFs extracted by Pyradiomics. 25 genes that were found to be the most closely associated with URFs. Then, the relevant biological functions of each differential URF were obtained through functional enrichment analysis. Among them, Zone Entropy is related to immune cell activity, which regulate the generation of calcification in breast cancer. The radiomics model based on the Logistic classifier and URF-module showed good discriminative ability (AUC = 0.80, 95% CI). CONCLUSION We searched for the URFs of HER2-positive breast cancer, and explored the underlying genes and biological functions of these URFs. Furthermore, the radiomics model based on the Logistic classifier and URF-module relatively accurately predicted the HER2 status in breast cancer.
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Affiliation(s)
- Hao Cui
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Yue Sun
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 China
| | - Dantong Zhao
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Xudong Zhang
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Hanqing Kong
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Nana Hu
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Panting Wang
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Xiaoxuan Zuo
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Wei Fan
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Yuan Yao
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Baiyang Fu
- grid.412463.60000 0004 1762 6325Department of Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Jiawei Tian
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Meixin Wu
- grid.412463.60000 0004 1762 6325Department of Clinical Medicine, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang, 150086 China
| | - Yue Gao
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 China
| | - Shangwei Ning
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 China
| | - Lei Zhang
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
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23
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Ghaffari Laleh N, Ligero M, Perez-Lopez R, Kather JN. Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer. Clin Cancer Res 2023; 29:316-323. [PMID: 36083132 DOI: 10.1158/1078-0432.ccr-22-0390] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/26/2022] [Accepted: 08/29/2022] [Indexed: 01/19/2023]
Abstract
Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy for many types of solid tumors. However, the majority of patients with cancer will not respond, and predicting response to this therapy is still a challenge. Artificial intelligence (AI) methods can extract meaningful information from complex data, such as image data. In clinical routine, radiology or histopathology images are ubiquitously available. AI has been used to predict the response to immunotherapy from radiology or histopathology images, either directly or indirectly via surrogate markers. While none of these methods are currently used in clinical routine, academic and commercial developments are pointing toward potential clinical adoption in the near future. Here, we summarize the state of the art in AI-based image biomarkers for immunotherapy response based on radiology and histopathology images. We point out limitations, caveats, and pitfalls, including biases, generalizability, and explainability, which are relevant for researchers and health care providers alike, and outline key clinical use cases of this new class of predictive biomarkers.
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Affiliation(s)
| | - Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.,Department of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.,Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.,Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.,Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
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24
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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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25
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Hinzpeter R, Kulanthaivelu R, Kohan A, Avery L, Pham NA, Ortega C, Metser U, Haider M, Veit-Haibach P. CT Radiomics and Whole Genome Sequencing in Patients with Pancreatic Ductal Adenocarcinoma: Predictive Radiogenomics Modeling. Cancers (Basel) 2022; 14:cancers14246224. [PMID: 36551709 PMCID: PMC9776865 DOI: 10.3390/cancers14246224] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/02/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
We investigate whether computed tomography (CT) derived radiomics may correlate with driver gene mutations in patients with pancreatic ductal adenocarcinoma (PDAC). In this retrospective study, 47 patients (mean age 64 ± 11 years; range: 42-86 years) with PDAC, who were treated surgically and who underwent preoperative CT imaging at our institution were included in the study. Image segmentation and feature extraction was performed semi-automatically with a commonly used open-source software platform. Genomic data from whole genome sequencing (WGS) were collected from our institution's web-based resource. Two statistical models were then built, in order to evaluate the predictive ability of CT-derived radiomics feature for driver gene mutations in PDAC. 30/47 of all tumor samples harbored 2 or more gene mutations. Overall, 81% of tumor samples demonstrated mutations in KRAS, 68% of samples had alterations in TP53, 26% in SMAD4 and 19% in CDKN2A. Extended statistical analysis revealed acceptable predictive ability for KRAS and TP53 (Youden Index 0.56 and 0.67, respectively) and mild to acceptable predictive signal for SMAD4 and CDKN2A (Youden Index 0.5, respectively). Our study establishes acceptable correlation of radiomics features and driver gene mutations in PDAC, indicating an acceptable prognostication of genomic profiles using CT-derived radiomics. A larger and more homogenous cohort may further enhance the predictive ability.
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Affiliation(s)
- Ricarda Hinzpeter
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
- Correspondence: ; Tel.: +1-416-340-4800
| | - Roshini Kulanthaivelu
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Andres Kohan
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Lisa Avery
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Nhu-An Pham
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Claudia Ortega
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Ur Metser
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Masoom Haider
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
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26
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Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J. Artificial intelligence in pancreatic cancer. Theranostics 2022; 12:6931-6954. [PMID: 36276650 PMCID: PMC9576619 DOI: 10.7150/thno.77949] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.
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Affiliation(s)
- Bowen Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Haoran Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Shuting Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Dingyue Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Qingya Shi
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jianzhou Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Junchao Guo
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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27
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Sahu M, Gupta R, Ambasta RK, Kumar P. Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 190:57-100. [PMID: 36008002 DOI: 10.1016/bs.pmbts.2022.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India.
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Jena B, Saxena S, Nayak GK, Balestrieri A, Gupta N, Khanna NN, Laird JR, Kalra MK, Fouda MM, Saba L, Suri JS. Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework. Cancers (Basel) 2022; 14:4052. [PMID: 36011048 PMCID: PMC9406706 DOI: 10.3390/cancers14164052] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/17/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.
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Affiliation(s)
- Biswajit Jena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | - Gopal Krishna Nayak
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | | | - Neha Gupta
- Department of IT, Bharati Vidyapeeth’s College of Engineering, New Delhi 110056, India
| | - Narinder N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Luca Saba
- Department of Radiology, AOU, University of Cagliari, 09124 Cagliari, Italy
| | - Jasjit S. Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
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The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization. Cancers (Basel) 2022; 14:cancers14143349. [PMID: 35884409 PMCID: PMC9321521 DOI: 10.3390/cancers14143349] [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: 06/05/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications. Abstract Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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Bai Y, Wang H, Wu X, Weng M, Han Q, Xu L, Zhang H, Chang C, Jin C, Chen M, Luo K, Teng X. Study on Molecular Information Intelligent Diagnosis and Treatment of Bladder Cancer on Pathological Tissue Image. Front Med (Lausanne) 2022; 9:838182. [PMID: 35755066 PMCID: PMC9215327 DOI: 10.3389/fmed.2022.838182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Background Molecular information about bladder cancer is significant for treatment and prognosis. The immunohistochemistry (IHC) method is widely used to analyze the specific biomarkers to determine molecular subtypes. However, procedures in IHC and plenty of reagents are time and labor-consuming and expensive. This study established a computer-aid diagnosis system for predicting molecular subtypes, p53 status, and programmed death-ligand 1 (PD-L1) status of bladder cancer with pathological images. Materials and Methods We collected 119 muscle-invasive bladder cancer (MIBC) patients who underwent radical cystectomy from January 2016 to September 2018. All the pathological sections are scanned into digital whole slide images (WSIs), and the IHC results of adjacent sections were recorded as the label of the corresponding slide. The tumor areas are first segmented, then molecular subtypes, p53 status, and PD-L1 status of those tumor-positive areas would be identified by three independent convolutional neural networks (CNNs). We measured the performance of this system for predicting molecular subtypes, p53 status, and PD-L1 status of bladder cancer with accuracy, sensitivity, and specificity. Results For the recognition of molecular subtypes, the accuracy is 0.94, the sensitivity is 1.00, and the specificity is 0.909. For PD-L1 status recognition, the accuracy is 0.897, the sensitivity is 0.875, and the specificity is 0.913. For p53 status recognition, the accuracy is 0.846, the sensitivity is 0.857, and the specificity is 0.750. Conclusion Our computer-aided diagnosis system can provide a novel and simple assistant tool to obtain the molecular subtype, PD-L1 status, and p53 status. It can reduce the workload of pathologists and the medical cost.
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Affiliation(s)
- Yanfeng Bai
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Huogen Wang
- Hithink RoyalFlush Information Network Co., Ltd., Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Xuesong Wu
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Menghan Weng
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qingmei Han
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Liming Xu
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Han Zhang
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengdong Chang
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chaohui Jin
- Hithink RoyalFlush Information Network Co., Ltd., Hangzhou, China
| | - Ming Chen
- Hithink RoyalFlush Information Network Co., Ltd., Hangzhou, China
| | - Kunfeng Luo
- Hithink RoyalFlush Information Network Co., Ltd., Hangzhou, China
| | - Xiaodong Teng
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Xiaodong Teng,
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Kothari G. Role of radiomics in predicting immunotherapy response. J Med Imaging Radiat Oncol 2022; 66:575-591. [PMID: 35581928 PMCID: PMC9323544 DOI: 10.1111/1754-9485.13426] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/02/2022] [Indexed: 12/13/2022]
Abstract
Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue-based biomarkers. Quantitative image analysis or radiomics, which involves the high-throughput extraction of imaging features, has the potential to non-invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune-related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high-quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well-defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune-related adverse effects and less well-studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models.
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Affiliation(s)
- Gargi Kothari
- Department of Radiation OncologyPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of Oncology, University of MelbournePeter MacCallum Cancer CentreMelbourneVictoriaAustralia
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Zhang Y, Chen X, Mo S, Ma H, Lu Z, Yu S, Chen J. PD-L1 and PD-L2 expression in pancreatic ductal adenocarcinoma and their correlation with immune infiltrates and DNA damage response molecules. JOURNAL OF PATHOLOGY CLINICAL RESEARCH 2022; 8:257-267. [PMID: 35037417 PMCID: PMC8977274 DOI: 10.1002/cjp2.259] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 11/19/2021] [Accepted: 01/03/2022] [Indexed: 01/04/2023]
Abstract
Immunotherapy targeting programmed cell death‐1 (PD‐1) has considerably improved the prognosis of patients with advanced cancers; however, its efficacy in the treatment of pancreatic ductal adenocarcinoma (PDAC) is unfavourable. To address the issue of PDAC immunotherapy, we investigated the expression of two PD‐1 ligands, PD‐L1 and PD‐L2, in PDAC, analysed their role in survival, and explored their correlation with clinicopathological features, immune infiltration, and DNA damage response molecules. Immunohistochemistry was performed on 291 surgically resected PDAC samples. In tumour cells (TCs) and immune cells (ICs), the positivity of PD‐L1 expression was 30 and 20% and that of PD‐L2 expression was 40 and 20%, respectively. Moreover, PD‐L1 expression on TCs correlated with its expression on ICs (p < 0.0001); a similar result was observed for PD‐L2 (p < 0.0001). Nonetheless, no correlation was observed between PD‐L1 and PD‐L2 expression. Positive PD‐L1 expression on TCs was related to N1 stage (p = 0.011) and AJCC II stage (p = 0.002), whereas positive PD‐L2 expression on TCs was associated with high FOXP3+ cell infiltration (p = 0.001) and high BRCA2 expression (p < 0.0001). Survival analysis revealed that positive PD‐L1 (p = 0.046) and PD‐L2 (p = 0.028) expression on TCs was an independent risk factor for unfavourable disease‐specific survival (DSS). Furthermore, positive PD‐L2 expression on TCs was an independent risk factor for lower DSS in the pN0 (p = 0.023), moderate and well tumour differentiation (p = 0.004), low BRCA1 (p = 0.017), wild‐type p53 (p = 0.034), and proficient mismatch repair (p = 0.004) subgroups. Moreover, post‐operative adjuvant chemotherapy could significantly affect DSS, regardless of PD‐L1/PD‐L2 expression status (positive or negative) on TCs, while it only prolonged DSS in PDL1‐ICs(−) (p < 0.0001) and PDL2‐ICs(−) (p < 0.0001) subgroups. This study provides a comprehensive understanding of the roles of PD‐L1 and PD‐L2 in PDAC, supporting anti‐PD‐1 axis immunotherapy for PDAC.
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Affiliation(s)
- Yue Zhang
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Xianlong Chen
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Shengwei Mo
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Heng Ma
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Zhaohui Lu
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Shuangni Yu
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Jie Chen
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
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Iwatate Y, Yokota H, Hoshino I, Ishige F, Kuwayama N, Itami M, Mori Y, Chiba S, Arimitsu H, Yanagibashi H, Takayama W, Uno T, Lin J, Nakamura Y, Tatsumi Y, Shimozato O, Nagase H. Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived from transcriptome analysis in pancreatic cancer. Int J Oncol 2022; 60:60. [PMID: 35419611 PMCID: PMC8997334 DOI: 10.3892/ijo.2022.5350] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/09/2022] [Indexed: 11/07/2022] Open
Abstract
Radiogenomics has attracted attention for predicting the molecular biological characteristics of tumors from clinical images, which are originally a collection of numerical values, such as computed tomography (CT) scans. A prediction model using genetic information is constructed using thousands of image features extracted and calculated from these numerical values. In the present study, RNA sequencing of pancreatic ductal adenocarcinoma (PDAC) tissues from 12 patients was performed to identify genes useful in evaluating clinical pathology, and 107 PDAC samples were immunostained to verify the obtained findings. In addition, radiogenomics analysis of gene expression was performed by machine learning using CT images and constructed prediction models. Bioinformatics analysis of RNA sequencing data identified integrin αV (ITGAV) as being important for clinicopathological factors, such as metastasis and prognosis, and the results of sequencing and immunostaining demonstrated a significant correlation (r=0.625, P=0.039). Notably, the ITGAV high‑expression group was associated with a significantly worse prognosis (P=0.005) and recurrence rate (P=0.003) compared with the low‑expression group. The ITGAV prediction model showed some detectability (AUC=0.697), and the predicted ITGAV high‑expression group was also associated with a worse prognosis (P=0.048). In conclusion, radiogenomics predicted the expression of ITGAV in pancreatic cancer, as well as the prognosis.
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Affiliation(s)
- Yosuke Iwatate
- Division of Hepato-Biliary-Pancreatic Surgery, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Hajime Yokota
- Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Isamu Hoshino
- Division of Gastroenterological Surgery, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Fumitaka Ishige
- Division of Hepato-Biliary-Pancreatic Surgery, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Naoki Kuwayama
- Division of Gastroenterological Surgery, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Makiko Itami
- Division of Clinical Pathology, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Yasukuni Mori
- Graduate School of Engineering, Faculty of Engineering, Chiba University, Chiba 263-8522, Japan
| | - Satoshi Chiba
- Division of Hepato-Biliary-Pancreatic Surgery, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Hidehito Arimitsu
- Division of Hepato-Biliary-Pancreatic Surgery, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Hiroo Yanagibashi
- Division of Hepato-Biliary-Pancreatic Surgery, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Wataru Takayama
- Division of Hepato-Biliary-Pancreatic Surgery, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Takashi Uno
- Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Jason Lin
- Laboratory of Cancer Genetics, Chiba Cancer Center Research Institute, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Yuki Nakamura
- Laboratory of Cancer Genetics, Chiba Cancer Center Research Institute, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Yasutoshi Tatsumi
- Laboratory of Cancer Genetics, Chiba Cancer Center Research Institute, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Osamu Shimozato
- Laboratory of Cancer Genetics, Chiba Cancer Center Research Institute, Chiba Cancer Center, Chiba 260-8717, Japan
| | - Hiroki Nagase
- Laboratory of Cancer Genetics, Chiba Cancer Center Research Institute, Chiba Cancer Center, Chiba 260-8717, Japan
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Sun TG, Mao L, Chai ZK, Shen XM, Sun ZJ. Predicting the Proliferation of Tongue Cancer With Artificial Intelligence in Contrast-Enhanced CT. Front Oncol 2022; 12:841262. [PMID: 35463386 PMCID: PMC9026338 DOI: 10.3389/fonc.2022.841262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Tongue squamous cell carcinoma (TSCC) is the most common oral malignancy. The proliferation status of tumor cells as indicated with the Ki-67 index has great impact on tumor microenvironment, therapeutic strategy making, and patients’ prognosis. However, the most commonly used method to obtain the proliferation status is through biopsy or surgical immunohistochemical staining. Noninvasive method before operation remains a challenge. Hence, in this study, we aimed to validate a novel method to predict the proliferation status of TSCC using contrast-enhanced CT (CECT) based on artificial intelligence (AI). CECT images of the lesion area from 179 TSCC patients were analyzed using a convolutional neural network (CNN). Patients were divided into a high proliferation status group and a low proliferation status group according to the Ki-67 index of patients with the median 20% as cutoff. The model was trained and then the test set was automatically classified. Results of the test set showed an accuracy of 65.38% and an AUC of 0.7172, suggesting that the majority of samples were classified correctly and the model was stable. Our study provided a possibility of predicting the proliferation status of TSCC using AI in CECT noninvasively before operation.
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Affiliation(s)
- Ting-Guan Sun
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Liang Mao
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Oral Maxillofacial-Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Zi-Kang Chai
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Xue-Meng Shen
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Zhi-Jun Sun
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Oral Maxillofacial-Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, China
- *Correspondence: Zhi-Jun Sun,
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Wu K, Wu P, Yang K, Li Z, Kong S, Yu L, Zhang E, Liu H, Guo Q, Wu S. A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images. Eur Radiol 2022; 32:2255-2265. [PMID: 34800150 DOI: 10.1007/s00330-021-08353-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/16/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVES We tried to realize accurate pathological classification, assessment of prognosis, and genomic molecular typing of renal cell carcinoma by CT texture feature analysis. To determine whether CT texture features can perform accurate pathological classification and evaluation of prognosis and genomic characteristics in renal cell carcinoma. METHODS Patients with renal cell carcinoma from five open-source cohorts were analyzed retrospectively in this study. These data were randomly split to train and test machine learning algorithms to segment the lesion, predict the histological subtype, tumor stage, and pathological grade. Dice coefficient and performance metrics such as accuracy and AUC were calculated to evaluate the segmentation and classification model. Quantitative decomposition of the predictive model was conducted to explore the contribution of each feature. Besides, survival analysis and the statistical correlation between CT texture features, pathological, and genomic signatures were investigated. RESULTS A total of 569 enhanced CT images of 443 patients (mean age 59.4, 278 males) were included in the analysis. In the segmentation task, the mean dice coefficient was 0.96 for the kidney and 0.88 for the cancer region. For classification of histologic subtype, tumor stage, and pathological grade, the model was on a par with radiologists and the AUC was 0.83 [Formula: see text] 0.1, 0.80 [Formula: see text] 0.1, and 0.77 [Formula: see text] 0.1 at 95% confidence intervals, respectively. Moreover, specific quantitative CT features related to clinical prognosis were identified. A strong statistical correlation (R2 = 0.83) between the feature crosses and genomic characteristics was shown. The structural equation modeling confirmed significant associations between CT features, pathological (β = - 0.75), and molecular subtype (β = - 0.30). CONCLUSIONS The framework illustrates high performance in the pathological classification of renal cell carcinoma. Prognosis and genomic characteristics can be inferred by quantitative image analysis. KEY POINTS • The analytical framework exhibits high-performance pathological classification of renal cell carcinoma and is on a par with human radiologists. • Quantitative decomposition of the predictive model shows that specific texture features contribute to histologic subtype and tumor stage classification. • Structural equation modeling shows the associations of genomic characteristics to CT texture features. Overall survival and molecular characteristics can be inferred by quantitative CT texture analysis in renal cell carcinoma.
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Affiliation(s)
- Kai Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Peng Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Kai Yang
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, 518116, China
| | - Zhe Li
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Sijia Kong
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Lu Yu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Enpu Zhang
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Hanlin Liu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Qing Guo
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 518001, China
| | - Song Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518001, China
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, 518116, China
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515041, China
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Preuss K, Thach N, Liang X, Baine M, Chen J, Zhang C, Du H, Yu H, Lin C, Hollingsworth MA, Zheng D. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers (Basel) 2022; 14:cancers14071654. [PMID: 35406426 PMCID: PMC8997008 DOI: 10.3390/cancers14071654] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel quantitative imaging methods that treat medical images as minable data instead of just pictures, have shown promise in advancing personalized management of pancreatic cancer through diagnosing precursor diseases, early detection, accurate diagnosis, and treatment personalization. Radiomics and deep learning methods aim to collect hidden information in medical images that is missed by conventional radiology practices through expanding the data search and comparing information across different patients. Both methods have been studied and applied in pancreatic cancer. In this review, we focus on the current progress of these two methods in pancreatic cancer and provide a comprehensive narrative review on the topic. With better regulation, enhanced workflow, and larger prospective patient datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through personalized precision medicine. Abstract As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.
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Affiliation(s)
- Kiersten Preuss
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Nutrition and Health Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA
| | - Nate Thach
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Justin Chen
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Naperville North High School, Naperville, IL 60563, USA
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Huijing Du
- Department of Mathematics, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Hongfeng Yu
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Chi Lin
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Michael A. Hollingsworth
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Radiation Oncology, University of Rochester, Rochester, NY 14626, USA
- Correspondence: ; Tel.: +1-(585)-276-3255
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Janssen BV, Verhoef S, Wesdorp NJ, Huiskens J, de Boer OJ, Marquering H, Stoker J, Kazemier G, Besselink MG. Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer: A Scoping Review. Ann Surg 2022; 275:560-567. [PMID: 34954758 DOI: 10.1097/sla.0000000000005349] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To perform a scoping review of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC. SUMMARY OF BACKGROUND DATA Patients with PDAC could benefit from better selection for systemic and surgical therapy. Imaging-based machine-learning models may improve treatment selection. METHODS A scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses-scoping review guidelines in the PubMed and Embase databases (inception-October 2020). The review protocol was prospectively registered (open science framework registration: m4cyx). Included were studies on imaging-based machine-learning models for predicting clinical outcomes and identifying biomarkers for PDAC. The primary outcome was model performance. An area under the curve (AUC) of ≥0.75, or a P-value of ≤0.05, was considered adequate model performance. Methodological study quality was assessed using the modified radiomics quality score. RESULTS After screening 1619 studies, 25 studies with 2305 patients fulfilled the eligibility criteria. All but 1 study was published in 2019 and 2020. Overall, 23/25 studies created models using radiomics features, 1 study quantified vascular invasion on computed tomography, and one used histopathological data. Nine models predicted clinical outcomes with AUC measures of 0.78-0.95, and C-indices of 0.65-0.76. Seventeen models identified biomarkers with AUC measures of 0.68-0.95. Adequate model performance was reported in 23/25 studies. The methodological quality of the included studies was suboptimal, with a median modified radiomics quality score score of 7/36. CONCLUSIONS The use of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC is increasingly rapidly. Although these models mostly have good performance scores, their methodological quality should be improved.
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Affiliation(s)
- Boris V Janssen
- Department of Surgery, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Severano Verhoef
- Department of Surgery, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Nina J Wesdorp
- Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | | | - Onno J de Boer
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Henk Marquering
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jaap Stoker
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Geert Kazemier
- Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Marc G Besselink
- Department of Surgery, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
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Huang X, Sun Y, Tan M, Ma W, Gao P, Qi L, Lu J, Yang Y, Wang K, Chen W, Jin L, Kuang K, Duan S, Li M. Three-Dimensional Convolutional Neural Network-Based Prediction of Epidermal Growth Factor Receptor Expression Status in Patients With Non-Small Cell Lung Cancer. Front Oncol 2022; 12:772770. [PMID: 35186727 PMCID: PMC8848731 DOI: 10.3389/fonc.2022.772770] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 01/10/2022] [Indexed: 12/16/2022] Open
Abstract
Objectives EGFR testing is a mandatory step before targeted therapy for non-small cell lung cancer patients. Combining some quantifiable features to establish a predictive model of EGFR expression status, break the limitations of tissue biopsy. Materials and Methods We retrospectively analyzed 1074 patients of non-small cell lung cancer with complete reports of EGFR gene testing. Then manually segmented VOI, captured the clinicopathological features, analyzed traditional radiology features, and extracted radiomic, and deep learning features. The cases were randomly divided into training and test set. We carried out feature screening; then applied the light GBM algorithm, Resnet-101 algorithm, logistic regression to develop sole models, and fused models to predict EGFR mutation conditions. The efficiency of models was evaluated by ROC and PRC curves. Results We successfully established Modelclinical, Modelradiomic, ModelCNN (based on clinical-radiology, radiomic and deep learning features respectively), Modelradiomic+clinical (combining clinical-radiology and radiomic features), and ModelCNN+radiomic+clinical (combining clinical-radiology, radiomic, and deep learning features). Among the prediction models, ModelCNN+radiomic+clinical showed the highest performance, followed by ModelCNN, and then Modelradiomic+clinical. All three models were able to accurately predict EGFR mutation with AUC values of 0.751, 0.738, and 0.684, respectively. There was no significant difference in the AUC values between ModelCNN+radiomic+clinical and ModelCNN. Further analysis showed that ModelCNN+radiomic+clinical effectively improved the efficacy of Modelradiomic+clinical and showed better efficacy than ModelCNN. The inclusion of clinical-radiology features did not effectively improve the efficacy of Modelradiomic. Conclusions Either deep learning or radiomic signature-based models can provide a fairly accurate non-invasive prediction of EGFR expression status. The model combined both features effectively enhanced the performance of radiomic models and provided marginal enhancement to deep learning models. Collectively, fusion models offer a novel and more reliable way of providing the efficacy of currently developed prediction models, and have far-reaching potential for the optimization of noninvasive EGFR mutation status prediction methods.
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Affiliation(s)
- Xuemei Huang
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Mingyu Tan
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Weiling Ma
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Pan Gao
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Lin Qi
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Jinjuan Lu
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Yuling Yang
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Kun Wang
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Wufei Chen
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | - Liang Jin
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
| | | | - Shaofeng Duan
- Precision Health Institution, GE Healthcare, Shanghai, China
| | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China
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Golder W. [Forecasts from the retort. A Greek gift of artificial intelligence : Interdisciplinary data analysis in preoperative imaging diagnostics]. Chirurg 2022; 93:257-260. [PMID: 35129622 DOI: 10.1007/s00104-022-01591-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/14/2022] [Indexed: 11/03/2022]
Abstract
The growing influence of artificial intelligence on radiology not only leads to a fundamental change in the way diagnoses are made but also creates a wealth of additional information. Many programs correlate the parameters of image evaluation with the results of histological, molecular biological and genetic examinations and from these they derive therapeutic and prognostic statements that are intended to serve the planning of individual precision medicine. This information is included in the findings report and is therefore also fully available to the patient; however, the information takes no account of influencing factors, such as the time lag between diagnosis and start of treatment, comorbidities as well as the availability and tolerability of drugs. It is foreseeable that the supplementary statements of the expert systems will considerably influence the discourse between doctor and patient.
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Affiliation(s)
- Werner Golder
- , 23 rue de l'Oriflamme, 84000, Avignon, Frankreich.
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Arimura H, Kodama T, Urakami A, Kamezawa H, Hirose TA, Ninomiya K. [6. Imaging Biopsy for Assisting Cancer Precision Therapy -Information Extracted from Radiomics]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:219-224. [PMID: 35185102 DOI: 10.6009/jjrt.780213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Hidetaka Arimura
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University
| | - Takumi Kodama
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
| | - Akimasa Urakami
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
| | - Hidemi Kamezawa
- Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University
| | - Taka-Aki Hirose
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital
| | - Kenta Ninomiya
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
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Kang CY, Duarte SE, Kim HS, Kim E, Park J, Lee AD, Kim Y, Kim L, Cho S, Oh Y, Gim G, Park I, Lee D, Abazeed M, Velichko YS, Chae YK. OUP accepted manuscript. Oncologist 2022; 27:e471-e483. [PMID: 35348765 PMCID: PMC9177100 DOI: 10.1093/oncolo/oyac036] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/14/2022] [Indexed: 11/17/2022] Open
Abstract
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
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Affiliation(s)
| | | | - Hye Sung Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eugene Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Alice Daeun Lee
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yeseul Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Leeseul Kim
- Department of Internal Medicine, AMITA Health Saint Francis Hospital, Evanston, IL, USA
| | - Sukjoo Cho
- Department of Pediatrics, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Yoojin Oh
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gahyun Gim
- Department of Hematology and Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Inae Park
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Dongyup Lee
- Department of Physical Medicine and Rehabilitation, Geisinger Health System, Danville, PA, USA
| | - Mohamed Abazeed
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Yury S Velichko
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Young Kwang Chae
- Corresponding author: Young Kwang Chae, Department of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
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Casà C, Piras A, D’Aviero A, Preziosi F, Mariani S, Cusumano D, Romano A, Boskoski I, Lenkowicz J, Dinapoli N, Cellini F, Gambacorta MA, Valentini V, Mattiucci GC, Boldrini L. The impact of radiomics in diagnosis and staging of pancreatic cancer. Ther Adv Gastrointest Endosc 2022; 15:26317745221081596. [PMID: 35342883 PMCID: PMC8943316 DOI: 10.1177/26317745221081596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 02/02/2022] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION Pancreatic cancer (PC) is one of the most aggressive tumours, and better risk stratification among patients is required to provide tailored treatment. The meaning of radiomics and texture analysis as predictive techniques are not already systematically assessed. The aim of this study is to assess the role of radiomics in PC. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to assess the role of radiomics in PC. The search strategy was 'radiomics [All Fields] AND ("pancreas" [MeSH Terms] OR "pancreas" [All Fields] OR "pancreatic" [All Fields])' and only original articles referred to PC in humans in the English language were considered. RESULTS A total of 123 studies and 183 studies were obtained using the mentioned search strategy on PubMed and Embase, respectively. After the complete selection process, a total of 56 papers were considered eligible for the analysis of the results. Radiomics methods were applied in PC for assessment technical feasibility and reproducibility aspects analysis, risk stratification, biologic or genomic status prediction and treatment response prediction. DISCUSSION Radiomics seems to be a promising approach to evaluate PC from diagnosis to treatment response prediction. Further and larger studies are required to confirm the role and allowed to include radiomics parameter in a comprehensive decision support system.
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Affiliation(s)
- Calogero Casà
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | | | - Francesco Preziosi
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Silvia Mariani
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Davide Cusumano
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Angela Romano
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy
| | - Jacopo Lenkowicz
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Nicola Dinapoli
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Francesco Cellini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Vincenzo Valentini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Gian Carlo Mattiucci
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
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Lo Gullo R, Wen H, Reiner JS, Hoda R, Sevilimedu V, Martinez DF, Thakur SB, Jochelson MS, Gibbs P, Pinker K. Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results. Cancers (Basel) 2021; 13:cancers13246273. [PMID: 34944898 PMCID: PMC8699819 DOI: 10.3390/cancers13246273] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/03/2021] [Accepted: 12/09/2021] [Indexed: 12/20/2022] Open
Abstract
Simple Summary To our knowledge, this is the first study assessing radiomics coupled with machine learning from MRI-derived features to predict PD-L1 expression status in biopsy-proven triple negative breast cancers and comparing the performance of this approach with the performance of qualitative assessment by two radiologists. This pilot study shows that radiomics analysis coupled with machine learning of DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment. This technique could also be used to monitor PD-L1 expression, as it can vary over time and between different regions of the tumor, thus avoiding repeated biopsies. Abstract The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1− patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation. Separately, all lesions were qualitatively assessed by two radiologists independently according to the BI-RADS lexicon. Of 62 women (mean age 47, range 31–81), 27 had PD-L1− tumors and 35 had PD-L1+ tumors. The final radiomics model to predict PD-L1 status utilized three MRI parameters, i.e., variance (FO), run length variance (RLM), and large zone low grey level emphasis (LZLGLE), for a sensitivity of 90.7%, specificity of 85.1%, and diagnostic accuracy of 88.2%. There were no significant associations between qualitative assessed DCE-MRI imaging features and PD-L1 status. Thus, radiomics analysis coupled with ML based on standard-of-care DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment.
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Affiliation(s)
- Roberto Lo Gullo
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Hannah Wen
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (H.W.); (R.H.)
| | - Jeffrey S. Reiner
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Raza Hoda
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (H.W.); (R.H.)
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10017, USA;
| | - Danny F. Martinez
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Sunitha B. Thakur
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Maxine S. Jochelson
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Peter Gibbs
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Katja Pinker
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
- Correspondence: ; Tel.: +1-646-888-5200
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Gao Y, Liu J, Cai B, Chen Q, Wang G, Lu Z, Jiang K, Miao Y. Development of epithelial-mesenchymal transition-related lncRNA signature for predicting survival and immune microenvironment in pancreatic cancerwithexperiment validation. Bioengineered 2021; 12:10553-10567. [PMID: 34854360 PMCID: PMC8809919 DOI: 10.1080/21655979.2021.2000197] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Long non-coding RNAs (LncRNAs) have crucial function in epithelial–mesenchymal transition (EMT) in pancreatic cancer. It is necessary to comprehensively analyze the potential role of EMT-related lncRNA in pancreatic cancer. In the present study, genomic data of pancreatic cancer from the TCGA database were downloaded and we found 368 EMT-related lncRNAs. According to the expression characteristics of prognostic-related lncRNAs, all samples could be divided into two clusters with different clinical outcomes and different tumor microenvironments. Moreover, an eleven EMT-related lncRNAs signature was established and verified. Patients with pancreatic cancer in the high-risk group had a shorter overall survival than those in the low-risk group and the signature could act as an independent prognostic factor. Further analysis suggested that the EMT-related lncRNAs might affect the prognosis of patients through immune mechanisms. All findings indicated that the signature and eleven lncRNAs might serve as potential prognostic biomarkers and therapeutic targets in the treatment of pancreatic cancer.
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Affiliation(s)
- Yong Gao
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China
| | - Jinhui Liu
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China
| | - Baobao Cai
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China
| | - Qun Chen
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China
| | - Guangfu Wang
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China
| | - Zipeng Lu
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China
| | - Kuirong Jiang
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China
| | - Yi Miao
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China.,Pancreas Center, the Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, People's Republic of China
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46
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Hoshino I, Yokota H, Iwatate Y, Mori Y, Kuwayama N, Ishige F, Itami M, Uno T, Nakamura Y, Tatsumi Y, Shimozato O, Nagase H. Prediction of the differences in tumor mutation burden between primary and metastatic lesions by radiogenomics. Cancer Sci 2021; 113:229-239. [PMID: 34689378 PMCID: PMC8748253 DOI: 10.1111/cas.15173] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 12/13/2022] Open
Abstract
Tumor mutational burden (TMB) is gaining attention as a biomarker for responses to immune checkpoint inhibitors in cancer patients. In this study, we evaluated the status of TMB in primary and liver metastatic lesions in patients with colorectal cancer (CRC). In addition, the status of TMB in primary and liver metastatic lesions was inferred by radiogenomics on the basis of computed tomography (CT) images. The study population included 24 CRC patients with liver metastases. DNA was extracted from primary and liver metastatic lesions obtained from the patients and TMB values were evaluated by next‐generation sequencing. The TMB value was considered high when it equaled to or exceeded 10/100 Mb. Radiogenomic analysis of TMB was performed by machine learning using CT images and the construction of prediction models. In 7 out of 24 patients (29.2%), the TMB status differed between the primary and liver metastatic lesions. Radiogenomic analysis was performed to predict whether TMB status was high or low. The maximum values for the area under the receiver operating characteristic curve were 0.732 and 0.812 for primary CRC and CRC with liver metastasis, respectively. The sensitivity, specificity, and accuracy of the constructed models for TMB status discordance were 0.857, 0.600, and 0.682, respectively. Our results suggested that accurate inference of the TMB status is possible using radiogenomics. Therefore, radiogenomics could facilitate the diagnosis, treatment, and prognosis of patients with CRC in the clinical setting.
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Affiliation(s)
- Isamu Hoshino
- Division of Gastroenterological Surgery, Chiba Cancer Center, Chiba, Japan
| | - Hajime Yokota
- Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yosuke Iwatate
- Division of Hepato-Biliary-Pancreatic Surgery, Chiba Cancer Center, Chiba, Japan
| | - Yasukuni Mori
- Faculty of Engineering, Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Naoki Kuwayama
- Division of Gastroenterological Surgery, Chiba Cancer Center, Chiba, Japan
| | - Fumitaka Ishige
- Division of Hepato-Biliary-Pancreatic Surgery, Chiba Cancer Center, Chiba, Japan
| | - Makiko Itami
- Division of Clinical Pathology, Chiba Cancer Center, Chiba, Japan
| | - Takashi Uno
- Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yuki Nakamura
- Laboratory of Cancer Genetics, Chiba Cancer Center Research Institute, Chiba Cancer Center, Chiba, Japan
| | - Yasutoshi Tatsumi
- Laboratory of Cancer Genetics, Chiba Cancer Center Research Institute, Chiba Cancer Center, Chiba, Japan
| | - Osamu Shimozato
- Laboratory of Cancer Genetics, Chiba Cancer Center Research Institute, Chiba Cancer Center, Chiba, Japan
| | - Hiroki Nagase
- Laboratory of Cancer Genetics, Chiba Cancer Center Research Institute, Chiba Cancer Center, Chiba, Japan
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47
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Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021; 11:1523. [PMID: 34573865 PMCID: PMC8465998 DOI: 10.3390/diagnostics11091523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/10/2021] [Accepted: 08/19/2021] [Indexed: 12/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.
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Affiliation(s)
- Song Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Yu-Qin Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Zhi-Ling Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Hong-Li Hua
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
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48
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Oei RW, Lyu Y, Ye L, Kong F, Du C, Zhai R, Xu T, Shen C, He X, Kong L, Hu C, Ying H. Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics. J Pers Med 2021; 11:jpm11080787. [PMID: 34442430 PMCID: PMC8398698 DOI: 10.3390/jpm11080787] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/08/2021] [Accepted: 08/10/2021] [Indexed: 12/24/2022] Open
Abstract
Background: The Cox proportional hazards (CPH) model is the most commonly used statistical method for nasopharyngeal carcinoma (NPC) prognostication. Recently, machine learning (ML) models are increasingly adopted for this purpose. However, only a few studies have compared the performances between CPH and ML models. This study aimed at comparing CPH with two state-of-the-art ML algorithms, namely, conditional survival forest (CSF) and DeepSurv for disease progression prediction in NPC. Methods: From January 2010 to March 2013, 412 eligible NPC patients were reviewed. The entire dataset was split into training cohort and testing cohort in a ratio of 90%:10%. Ten features from patient-related, disease-related, and treatment-related data were used to train the models for progression-free survival (PFS) prediction. The model performance was compared using the concordance index (c-index), Brier score, and log-rank test based on the risk stratification results. Results: DeepSurv (c-index = 0.68, Brier score = 0.13, log-rank test p = 0.02) achieved the best performance compared to CSF (c-index = 0.63, Brier score = 0.14, log-rank test p = 0.38) and CPH (c-index = 0.57, Brier score = 0.15, log-rank test p = 0.81). Conclusions: Both CSF and DeepSurv outperformed CPH in our relatively small dataset. ML-based survival prediction may guide physicians in choosing the most suitable treatment strategy for NPC patients.
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Affiliation(s)
- Ronald Wihal Oei
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yingchen Lyu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Lulu Ye
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Fangfang Kong
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chengrun Du
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ruiping Zhai
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Tingting Xu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chunying Shen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xiayun He
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Lin Kong
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chaosu Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Hongmei Ying
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Correspondence: ; Tel.: +86-21-64175590; Fax: +86-21-6417477
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49
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Hoshino I, Yokota H. Radiogenomics of gastroenterological cancer: The dawn of personalized medicine with artificial intelligence-based image analysis. Ann Gastroenterol Surg 2021; 5:427-435. [PMID: 34337291 PMCID: PMC8316732 DOI: 10.1002/ags3.12437] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 11/29/2020] [Accepted: 01/08/2021] [Indexed: 12/14/2022] Open
Abstract
Radiogenomics is a new field of medical science that integrates two omics, radiomics and genomics, and may bring a major paradigm shift in traditional personalized medicine strategies that require tumor tissue samples. In addition, the acquisition of the data does not require special imaging equipment or special imaging conditions, and it is possible to use image information from computed tomography, magnetic resonance imaging, positron emission tomography-computed tomography in clinical practice, so the versatility and cost-effectiveness of radiogenomics are expected. So far, the field of radiogenomics has developed, especially in the fields of brain tumors and breast cancer, but recently, reports of radiogenomic research on gastroenterological cancer are increasing. This review provides an overview of radiogenomic research methods and summarizes the current radiogenomic research in gastroenterological cancer. In addition, the application of artificial intelligence is considered to be indispensable for the integrated analysis of enormous omics information in the future, and the future direction of this research, including the latest technologies, will be discussed.
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Affiliation(s)
- Isamu Hoshino
- Division of Gastroenterological SurgeryChiba Cancer CenterChibaJapan
| | - Hajime Yokota
- Department of Diagnostic Radiology and Radiation OncologyGraduate School of MedicineChiba UniversityChibaJapan
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50
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Onozato Y, Nakajima T, Yokota H, Morimoto J, Nishiyama A, Toyoda T, Inage T, Tanaka K, Sakairi Y, Suzuki H, Uno T, Yoshino I. Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer. Sci Rep 2021; 11:13526. [PMID: 34188146 PMCID: PMC8241868 DOI: 10.1038/s41598-021-93002-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 06/18/2021] [Indexed: 01/15/2023] Open
Abstract
Tumor spread through air spaces (STAS) in non-small-cell lung cancer (NSCLC) is known to influence a poor patient outcome, even in patients presenting with early-stage disease. However, the pre-operative diagnosis of STAS remains challenging. With the progress of radiomics-based analyses several attempts have been made to predict STAS based on radiological findings. In the present study, patients with NSCLC which is located peripherally and tumors ≤ 2 cm in size on computed tomography (CT) that were potential candidates for sublobar resection were enrolled in this study. The radiologic features of the targeted tumors on thin-section CT were extracted using the PyRadiomics v3.0 software package, and a predictive model for STAS was built using the t-test and XGBoost. Thirty-five out of 226 patients had a STAS histology. The predictive model of STAS indicated an area under the receiver-operator characteristic curve (AUC) of 0.77. There was no significant difference in the overall survival (OS) for lobectomy between the predicted-STAS (+) and (−) groups (p = 0.19), but an unfavorable OS for sublobar resection was indicated in the predicted-STAS (+) group (p < 0.01). These results suggest that radiomics with machine-learning helped to develop a favorable model of STAS (+) NSCLC, which might be useful for the proper selection of candidates who should undergo sublobar resection.
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Affiliation(s)
- Yuki Onozato
- Departments of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Chuo-ku, Inohana, Chiba, 260-8670, Japan
| | - Takahiro Nakajima
- Departments of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Chuo-ku, Inohana, Chiba, 260-8670, Japan.
| | - Hajime Yokota
- Department of Diagnostic Radiology and Radiation Oncology, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Jyunichi Morimoto
- Departments of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Chuo-ku, Inohana, Chiba, 260-8670, Japan
| | - Akira Nishiyama
- Department of Radiology, Chiba University Hospital, Chiba, Japan
| | - Takahide Toyoda
- Departments of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Chuo-ku, Inohana, Chiba, 260-8670, Japan
| | - Terunaga Inage
- Departments of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Chuo-ku, Inohana, Chiba, 260-8670, Japan
| | - Kazuhisa Tanaka
- Departments of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Chuo-ku, Inohana, Chiba, 260-8670, Japan
| | - Yuichi Sakairi
- Departments of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Chuo-ku, Inohana, Chiba, 260-8670, Japan
| | - Hidemi Suzuki
- Departments of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Chuo-ku, Inohana, Chiba, 260-8670, Japan
| | - Takashi Uno
- Department of Diagnostic Radiology and Radiation Oncology, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Ichiro Yoshino
- Departments of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Chuo-ku, Inohana, Chiba, 260-8670, Japan
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