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Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024:S0030-6665(24)00072-0. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
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Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
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Chen Z, Wang Z, Liu S, Zhang S, Zhou Y, Zhang R, Yang W. Nomograms based on multiparametric MRI radiomics integrated with clinical-radiological features for predicting the response to induction chemotherapy in nasopharyngeal carcinoma. Eur J Radiol 2024; 175:111438. [PMID: 38613869 DOI: 10.1016/j.ejrad.2024.111438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 01/27/2024] [Accepted: 03/20/2024] [Indexed: 04/15/2024]
Abstract
OBJECTIVE To establish nomograms integrating multiparametric MRI radiomics with clinical-radiological features to identify the responders and non-responders to induction chemotherapy (ICT) in nasopharyngeal carcinoma (NPC). METHODS We retrospectively analyzed the clinical and MRI data of 168 NPC patients between December 2015 and April 2022. We used 3D-Slicer to segment the regions of interest (ROIs) and the "Pyradiomic" package to extract radiomics features. We applied the least absolute shrinkage and selection operator regression to select radiomics features. We developed clinical-only, radiomics-only, and the combined clinical-radiomics nomograms using logistic regression analysis. The receiver operating characteristic curves, DeLong test, calibration, and decision curves were used to assess the discriminative performance of the models. The model was internally validated using 10-fold cross-validation. RESULTS A total of 14 optimal features were finally selected to develop a radiomic signature, with an AUC of 0.891 (95 % CI, 0.825-0.946) in the training cohort and 0.837 (95 % CI, 0.723-0.932) in the testing cohort. The nomogram based on the Rad-Score and clinical-radiological factors for evaluating tumor response to ICT yielded an AUC of 0.926 (95 % CI, 0.875-0.965) and 0.901 (95 % CI, 0.815-0.979) in the two cohorts, respectively. Decision curves demonstrated that the combined clinical-radiomics nomograms were clinically useful. CONCLUSION Nomograms integrating multiparametric MRI-based radiomics and clinical-radiological features could non-invasively discriminate ICT responders from non-responders in NPC patients.
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Affiliation(s)
- Zhiqiang Chen
- Department of Radiology, the First Affiliated Hospital of Hainan Medical University, Haikou, Hainan 570102, China; Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China.
| | - Zhuo Wang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Shili Liu
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Shaoru Zhang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Yunshu Zhou
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Ruodi Zhang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Wenjun Yang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medicine and Life Sciences, Hainan Medical University, Haikou, Hainan 571199, China.
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Eisazadeh R, Shahbazi-Akbari M, Mirshahvalad SA, Pirich C, Beheshti M. Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [ 18F]F-FDG Tracers Part II. [ 18F]F-FLT, [ 18F]F-FET, [ 11C]C-MET and Other Less-Commonly Used Radiotracers. Semin Nucl Med 2024; 54:293-301. [PMID: 38331629 DOI: 10.1053/j.semnuclmed.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 02/10/2024]
Abstract
Following the previous part of the narrative review on artificial intelligence (AI) applications in positron emission tomography (PET) using tracers rather than 18F-fluorodeoxyglucose ([18F]F-FDG), in this part we review the impact of PET-derived radiomics data on the diagnostic performance of other PET radiotracers, 18F-O-(2-fluoroethyl)-L-tyrosine ([18F]F-FET), 18F-Fluorothymidine ([18F]F-FLT) and 11C-Methionine ([11C]C-MET). [18F]F-FET-PET, using an artificial amino acid taken up into upregulated tumoral cells, showed potential in lesion detection and tumor characterization, especially with its ability to reflect glioma heterogeneity. [18F]F-FET-PET-derived textural features appeared to have the potential to reveal considerable information for accurate delineation for guiding biopsy and treatment, differentiate between low-grade and high-grade glioma and related wild-type genotypes, and distinguish pseudoprogression from true progression. In addition, models built using clinical parameters and [18F]F-FET-PET-derived radiomics features showed acceptable results for survival stratification of glioblastoma patients. [18F]F-FLT-PET-based characteristics also showed potential in evaluating glioma patients, correlating with Ki-67 and patient prognosis. AI-based PET-volumetry using this radiotracer as a proliferation marker also revealed promising preliminary results in terms of guide-targeting bone marrow-preserving adaptive radiation therapy. Similar to [18F]F-FET, the other amino acid tracer which reflects cellular proliferation, [11C]C-MET, has also shown acceptable performance in predicting tumor grade, distinguishing brain tumor recurrence from radiation necrosis, and treatment monitoring by PET-derived radiomics models. In addition, PET-derived radiomics features of various radiotracers such as [18F]F-DOPA, [18F]F-FACBC, [18F]F-NaF, [68Ga]Ga-CXCR-4 and [18F]F-FMISO may also provide useful information for tumor characterization and predict of disease outcome. In conclusion, AI using tracers beyond [18F]F-FDG could improve the diagnostic performance of PET-imaging for specific indications and help clinicians in their daily routine by providing features that are often not detectable by the naked eye.
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Affiliation(s)
- Roya Eisazadeh
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Malihe Shahbazi-Akbari
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research center for Nuclear Medicine, Department of Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Ali Mirshahvalad
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research center for Nuclear Medicine, Department of Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran; Joint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, Ontario, Canada
| | - Christian Pirich
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Mohsen Beheshti
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
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Lin Y, Yang Z, Chen J, Li M, Cai Z, Wang X, Zhai T, Lin Z. A contrast-enhanced CT radiomics-based model to identify candidates for deintensified chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma patients. Eur Radiol 2024; 34:1302-1313. [PMID: 37594526 DOI: 10.1007/s00330-023-09987-1] [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: 11/16/2022] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 08/19/2023]
Abstract
OBJECTIVES To develop a contrast-enhanced CT (CECT) radiomics-based model to identify locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients who would benefit from deintensified chemoradiotherapy. METHODS LA-NPC patients who received low-dose concurrent cisplatin therapy (cumulative: 150 mg/m2), were randomly divided into training and validation groups. 107 radiomics features based on the primary nasopharyngeal tumor were extracted from each pre-treatment CECT scan. Through Cox regression analysis, a radiomics model and patients' corresponding radiomics scores were created with predictive independent radiomics features. T stage (T) and radiomics score (R) were compared as predictive factors. Combining the N stage (N), a clinical model (T + N), and a substitution model (R + N) were constructed. RESULTS Training and validation groups consisted of 66 and 33 patients, respectively. Three significant independent radiomics features (flatness, mean, and gray level non-uniformity in gray level dependence matrix (GLDM-GLN)) were found. The radiomics score showed better predictive ability than the T stage (concordance index (C-index): 0.67 vs. 0.61, AUC: 0.75 vs. 0.60). The R + N model had better predictive performance and more effective risk stratification than the T + N model (C-index: 0.77 vs. 0.68, AUC: 0.80 vs. 0.70). The R + N model identified a low-risk group as deintensified chemoradiotherapy candidates in which no patient developed progression within 3 years, with 5-year progression-free survival (PFS) and overall survival (OS) both 90.7% (hazard ratio (HR) = 4.132, p = 0.018). CONCLUSION Our radiomics-based model combining radiomics score and N stage can identify specific LA-NPC candidates for whom de-escalation therapy can be performed without compromising therapeutic efficacy. CLINICAL RELEVANCE STATEMENT Our study shows that the radiomics-based model (R + N) can accurately stratify patients into different risk groups, with satisfactory prognosis in the low-risk group when treated with low-dose concurrent chemotherapy, providing new options for individualized de-escalation strategies. KEY POINTS • A radiomics score, consisting of 3 predictive radiomics features (flatness, mean, and GLDM-GLN) integrated with the N stage, can identify specific LA-NPC populations for deintensified treatment. • In the selection of LA-NPC candidates for de-intensified treatment, radiomics score extracted from primary nasopharyngeal tumors based on CECT can be superior to traditional T stage classification as a predictor.
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Affiliation(s)
- Yinbing Lin
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Zhining Yang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Jiechen Chen
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Mei Li
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Zeman Cai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Xiao Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Tiantian Zhai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China.
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China.
| | - Zhixiong Lin
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China.
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China.
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Rogasch JMM, Shi K, Kersting D, Seifert R. Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET). Nuklearmedizin 2023; 62:361-369. [PMID: 37995708 PMCID: PMC10667066 DOI: 10.1055/a-2198-0545] [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: 09/15/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023]
Abstract
AIM Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction. METHODS A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into "adequate" or "inadequate". The association between the number of "adequate" criteria per article and the date of publication was examined. RESULTS One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated "adequate" was 65% (range: 23-98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an "adequate" rating per article was 12.5 out of 17 (range, 4-17), and this did not increase with later dates of publication (Spearman's rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated "adequate". Only 8% of articles published the source code, and 10% made the dataset openly available. CONCLUSION Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.
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Affiliation(s)
- Julian Manuel Michael Rogasch
- Department of Nuclear Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital University Hospital Bern, Bern, Switzerland
| | - David Kersting
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Robert Seifert
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
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Dai J, Wang H, Xu Y, Chen X, Tian R. Clinical application of AI-based PET images in oncological patients. Semin Cancer Biol 2023; 91:124-142. [PMID: 36906112 DOI: 10.1016/j.semcancer.2023.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/28/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.
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Affiliation(s)
- Jiaona Dai
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hui Wang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang City 421001, China
| | - Xiyang Chen
- Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
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Current Role of Delta Radiomics in Head and Neck Oncology. Int J Mol Sci 2023; 24:ijms24032214. [PMID: 36768535 PMCID: PMC9916410 DOI: 10.3390/ijms24032214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or the extraction of data from various imaging modalities, is a tool often used to evaluate specific features related to the tumour or normal tissue that are not identifiable by the naked eye and which can add value to existing clinical data. Furthermore, the assessment of feature variations from one time point to another based on subsequent images, known as delta radiomics, was shown to have even higher value for treatment-outcome prediction or patient stratification into risk categories. The information gathered from delta radiomics can, further, be used for decision making regarding treatment adaptation or other interventions found to be beneficial to the patient. The aim of this work is to collate the existing studies on delta radiomics in head and neck cancer and evaluate its role in tumour response and normal-tissue toxicity predictions alike. Moreover, this work also highlights the role of holomics, which brings under the same umbrella clinical and radiomic features, for a more complex patient characterization and treatment optimisation.
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Sminia P, Guipaud O, Viktorsson K, Ahire V, Baatout S, Boterberg T, Cizkova J, Dostál M, Fernandez-Palomo C, Filipova A, François A, Geiger M, Hunter A, Jassim H, Edin NFJ, Jordan K, Koniarová I, Selvaraj VK, Meade AD, Milliat F, Montoro A, Politis C, Savu D, Sémont A, Tichy A, Válek V, Vogin G. Clinical Radiobiology for Radiation Oncology. RADIOBIOLOGY TEXTBOOK 2023:237-309. [DOI: 10.1007/978-3-031-18810-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
AbstractThis chapter is focused on radiobiological aspects at the molecular, cellular, and tissue level which are relevant for the clinical use of ionizing radiation (IR) in cancer therapy. For radiation oncology, it is critical to find a balance, i.e., the therapeutic window, between the probability of tumor control and the probability of side effects caused by radiation injury to the healthy tissues and organs. An overview is given about modern precision radiotherapy (RT) techniques, which allow optimal sparing of healthy tissues. Biological factors determining the width of the therapeutic window are explained. The role of the six typical radiobiological phenomena determining the response of both malignant and normal tissues in the clinic, the 6R’s, which are Reoxygenation, Redistribution, Repopulation, Repair, Radiosensitivity, and Reactivation of the immune system, is discussed. Information is provided on tumor characteristics, for example, tumor type, growth kinetics, hypoxia, aberrant molecular signaling pathways, cancer stem cells and their impact on the response to RT. The role of the tumor microenvironment and microbiota is described and the effects of radiation on the immune system including the abscopal effect phenomenon are outlined. A summary is given on tumor diagnosis, response prediction via biomarkers, genetics, and radiomics, and ways to selectively enhance the RT response in tumors. Furthermore, we describe acute and late normal tissue reactions following exposure to radiation: cellular aspects, tissue kinetics, latency periods, permanent or transient injury, and histopathology. Details are also given on the differential effect on tumor and late responding healthy tissues following fractionated and low dose rate irradiation as well as the effect of whole-body exposure.
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Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Prospective clinical research of radiomics and deep learning in oncology: A translational review. Crit Rev Oncol Hematol 2022; 179:103823. [PMID: 36152912 DOI: 10.1016/j.critrevonc.2022.103823] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/13/2022] [Accepted: 09/20/2022] [Indexed: 10/31/2022] Open
Abstract
Radiomics and deep learning (DL) hold transformative promise and substantial and significant advances in oncology; however, most methods have been tested in retrospective or simulated settings. There is considerable interest in the biomarker validation, clinical utility, and methodological robustness of these studies and their deployment in real-world settings. This review summarizes the characteristics of studies, the level of prospective validation, and the overview of research on different clinical endpoints. The discussion of methodological robustness shows the potential for independent external replication of prospectively reported results. These in-depth analyses further describe the barriers limiting the translation of radiomics and DL into primary care options and provide specific recommendations regarding clinical deployment. Finally, we propose solutions for integrating novel approaches into the treatment environment to unravel the critical process of translating AI models into the clinical routine and explore strategies to improve personalized medicine.
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Affiliation(s)
- Xingping Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China; Department of New Networks, Peng Cheng Laboratory, Shenzhen 518000, China
| | - Yanchun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China; Department of New Networks, Peng Cheng Laboratory, Shenzhen 518000, China; Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, VIC 8001, Australia.
| | - Guijuan Zhang
- Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110189, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
| | - Liefa Liao
- School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330000, China; School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
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Gallez B. The Role of Imaging Biomarkers to Guide Pharmacological Interventions Targeting Tumor Hypoxia. Front Pharmacol 2022; 13:853568. [PMID: 35910347 PMCID: PMC9335493 DOI: 10.3389/fphar.2022.853568] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 06/23/2022] [Indexed: 12/12/2022] Open
Abstract
Hypoxia is a common feature of solid tumors that contributes to angiogenesis, invasiveness, metastasis, altered metabolism and genomic instability. As hypoxia is a major actor in tumor progression and resistance to radiotherapy, chemotherapy and immunotherapy, multiple approaches have emerged to target tumor hypoxia. It includes among others pharmacological interventions designed to alleviate tumor hypoxia at the time of radiation therapy, prodrugs that are selectively activated in hypoxic cells or inhibitors of molecular targets involved in hypoxic cell survival (i.e., hypoxia inducible factors HIFs, PI3K/AKT/mTOR pathway, unfolded protein response). While numerous strategies were successful in pre-clinical models, their translation in the clinical practice has been disappointing so far. This therapeutic failure often results from the absence of appropriate stratification of patients that could benefit from targeted interventions. Companion diagnostics may help at different levels of the research and development, and in matching a patient to a specific intervention targeting hypoxia. In this review, we discuss the relative merits of the existing hypoxia biomarkers, their current status and the challenges for their future validation as companion diagnostics adapted to the nature of the intervention.
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Marcu LG, Marcu DC. Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy. J Pers Med 2021; 11:jpm11111094. [PMID: 34834445 PMCID: PMC8625829 DOI: 10.3390/jpm11111094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 12/12/2022] Open
Abstract
Chemoradiotherapy remains the most common management of locally advanced head and neck cancer. While both treatment components have greatly developed over the years, the quality of life and long-term survival of patients undergoing treatment for head and neck malignancies are still poor. Research in head and neck oncology is equally focused on the improvement of tumour response to treatment and on the limitation of normal tissue toxicity. In this regard, personalised therapy through a multi-omics approach targeting patient management from diagnosis to treatment shows promising results. The aim of this paper is to discuss the latest results regarding the personalised approach to chemoradiotherapy of head and neck cancer by gathering the findings of the newest omics, involving radiotherapy (dosiomics), chemotherapy (pharmacomics), and medical imaging for treatment monitoring (radiomics). The incorporation of these omics into head and neck cancer management offers multiple viewpoints to treatment that represent the foundation of personalised therapy.
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Affiliation(s)
- Loredana G. Marcu
- Faculty of Informatics & Science, University of Oradea, 410087 Oradea, Romania
- Cancer Research Institute, University of South Australia, Adelaide, SA 5001, Australia
- Correspondence:
| | - David C. Marcu
- Faculty of Electrical Engineering & Information Technology, University of Oradea, 410087 Oradea, Romania;
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Castorina P, Castorina L, Ferini G. Non-Homogeneous Tumor Growth and Its Implications for Radiotherapy: A Phenomenological Approach. J Pers Med 2021; 11:jpm11060527. [PMID: 34207503 PMCID: PMC8229245 DOI: 10.3390/jpm11060527] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/31/2021] [Accepted: 06/06/2021] [Indexed: 12/22/2022] Open
Abstract
Tumor regrowth and heterogeneity are important clinical parameters during radiotherapy, and the probability of treatment benefit critically depends on the tumor progression pattern in the interval between the fractional irradiation treatments. We propose an analytic, easy-to-use method to take into account clonal subpopulations with different specific growth rates and radiation resistances. The different strain regrowth effects, as described by Gompertz law, require a dose-boost to reproduce the survival probability of the corresponding homogeneous system and for uniform irradiation. However, the estimate of the survival fraction for a tumor with a hypoxic subpopulation is more reliable when there is a slow specific regrowth rate and when the dependence on the oxygen enhancement ratio of radiotherapy is consistently taken into account. The approach is discussed for non-linear two-population dynamics for breast cancer and can be easily generalized to a larger number of components and different tumor phenotypes.
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Affiliation(s)
- Paolo Castorina
- Istituto Nazionale Fisica Nucleare, 95100 Catania, Italy
- Istituto Oncologico del Mediterraneo, 95029 Viagrande, Italy
- Faculty of Mathematics and Physics, Charles University, 18000 Prague, Czech Republic
- Correspondence:
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Song LL, Chen SJ, Chen W, Shi Z, Wang XD, Song LN, Chen DS. Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images. BMC Med Imaging 2021; 21:54. [PMID: 33743615 PMCID: PMC7981906 DOI: 10.1186/s12880-021-00581-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 03/07/2021] [Indexed: 01/04/2023] Open
Abstract
Background Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared. Results The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity = 0.90 and 0.88, specificity = 0.82 and 0.80, positive predictive value = 0.86 and 0.84, negative predictive value = 0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p > 0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p < 0.001) and validation (0.90 vs. 0.68, p = 0.001) cohorts. Conclusions The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00581-9.
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Affiliation(s)
- Le-le Song
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Shun-Jun Chen
- The Department of Ultrasound, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Wang Chen
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Zhan Shi
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Xiao-Dong Wang
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China
| | - Li-Na Song
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dian-Sen Chen
- The Department of Radiology, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China.
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Tomaszewski MR, Gillies RJ. The Biological Meaning of Radiomic Features. Radiology 2021; 298:505-516. [PMID: 33399513 PMCID: PMC7924519 DOI: 10.1148/radiol.2021202553] [Citation(s) in RCA: 221] [Impact Index Per Article: 73.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 07/30/2020] [Accepted: 08/17/2020] [Indexed: 02/06/2023]
Abstract
An earlier incorrect version appeared online. This article was corrected on February 10, 2021.
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Affiliation(s)
- Michal R. Tomaszewski
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
| | - Robert J. Gillies
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
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Flaus A, Nevesny S, Guy JB, Sotton S, Magné N, Prévot N. Positron emission tomography for radiotherapy planning in head and neck cancer: What impact? Nucl Med Commun 2021; 42:234-243. [PMID: 33252513 DOI: 10.1097/mnm.0000000000001329] [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: 11/25/2022]
Abstract
PET-computed tomography (CT) plays a growing role to guide target volume delineation for head and neck cancer in radiation oncology. Pretherapeutic [18F]FDG PET-CT adds information to morphological imaging. First, as a whole-body imaging modality, it reveals regional or distant metastases that induce major therapeutic changes in more than 10% of the cases. Moreover, it allows better pathological lymph node selection which improves overall regional control and overall survival. Second, locally, it allows us to define the metabolic tumoral volume, which is a reliable prognostic feature for survival outcome. [18F]FDG PET-CT-based gross tumor volume (GTV) is on average significantly smaller than GTV based on CT. Nevertheless, the overlap is incomplete and more evaluation of composite GTV based on PET and GTV based on CT are needed. However, in clinical practice, the study showed that using GTV PET alone for treatment planning was similar to using GTVCT for local control and dose distribution was better as a dose to organs at risk significantly decreased. In addition to FDG, pretherapeutic PET could give access to different biological tumoral volumes - thanks to different tracers - guiding heterogeneous dose delivery (dose painting concept) to resistant subvolumes. During radiotherapy treatment, follow-up [18F]FDG PET-CT revealed an earlier and more important diminution of GTV than other imaging modality. It may be a valuable support for adaptative radiotherapy as a new treatment plan with a significant impact on dose distribution became possible. Finally, additional studies are required to prospectively validate long-term outcomes and lower toxicity resulting from the use of PET-CT in treatment planning.
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Affiliation(s)
- Anthime Flaus
- Service de Médecine Nucléaire, Centre Hospitalier Universitaire de Saint-Etienne, St Etienne
| | - Stéphane Nevesny
- Département de Radiothérapie, Institut de Cancérologie de la Loire-Lucien Neuwirth, St Priest en Jarez
| | - Jean-Baptiste Guy
- Département de Radiothérapie, Institut de Cancérologie de la Loire-Lucien Neuwirth, St Priest en Jarez
- UMR CNRS 5822/IN2P3, IPNL, PRISME, Laboratoire de Radiobiologie Cellulaire et Moléculaire, Faculté de Médecine Lyon-Sud, Université Lyon 1, Oullins Cedex
| | - Sandrine Sotton
- Department of Research and Teaching, Lucien Neuwirth Cancer Institute, Saint-Priest-en-Jarez, University Departement of Research and Teaching
| | - Nicolas Magné
- Département de Radiothérapie, Institut de Cancérologie de la Loire-Lucien Neuwirth, St Priest en Jarez
- UMR CNRS 5822/IN2P3, IPNL, PRISME, Laboratoire de Radiobiologie Cellulaire et Moléculaire, Faculté de Médecine Lyon-Sud, Université Lyon 1, Oullins Cedex
| | - Nathalie Prévot
- Service de Médecine Nucléaire, Centre Hospitalier Universitaire de Saint-Etienne, St Etienne
- INSERM U 1059 Sainbiose, Université Jean Monnet, Saint-Etienne, France
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16
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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Lazzeroni M, Ureba A, Wiedenmann N, Nicolay NH, Mix M, Thomann B, Baltas D, Toma-Dasu I, Grosu AL. Evolution of the hypoxic compartment on sequential oxygen partial pressure maps during radiochemotherapy in advanced head and neck cancer. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 17:100-105. [PMID: 33898787 PMCID: PMC8058025 DOI: 10.1016/j.phro.2021.01.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 01/22/2021] [Accepted: 01/28/2021] [Indexed: 12/01/2022]
Abstract
Repeated PET imaging of hypoxia may be pivotal in radiotherapy outcome prediction. Oxygen partial pressure maps can be non-linearly derived from radiotracer uptake. The hypoxic target volume evolution in extension and severity can be determined. The first two treatment week parameters have potential for outcome prediction. Information may be used for treatment adaptation personalised strategies.
Background and purpose Longitudinal Positron Emission Tomography (PET) with hypoxia-specific radiotracers allows monitoring the time evolution of regions of increased radioresistance and may become fundamental in determining the radiochemotherapy outcome in Head-and-Neck Squamous Cell Carcinoma (HNSCC). The aim of this study was to investigate the evolution of the hypoxic target volume on oxygen partial pressure maps (pO2-HTV) derived from 18FMISO-PET images acquired before and during radiochemotherapy and to uncover correlations between extent and severity of hypoxia and treatment outcome. Material and methods 18FMISO-PET/CT images were acquired at three time points (before treatment start, in weeks two and five) for twenty-eight HNSCC patients treated with radiochemotherapy. The images were converted into pO2 maps and corresponding pO2-HTVs (pO2-HTV1, pO2-HTV2, pO2-HTV3) were contoured at 10 mmHg. Different parameters describing the pO2-HTV time evolution were considered, such as the percent and absolute difference between the pO2-HTVs (%HTVi,j and HTVi-HTVj with i,j = 1, 2, 3, respectively) and the slope of the linear regression curve fitting the pO2-HTVs in time. Correlations were sought between the pO2-HTV evolution parameters and loco-regional recurrence (LRR) using the Receiver Operating Characteristic method. Results The Area Under the Curve values for %HTV1,2, HTV1-HTV2, HTV1-HTV3 and the slope of the pO2-HTV linear regression curve were 0.75 (p = 0.04), 0.73 (p = 0.02), 0.73 (p = 0.02) and 0.75 (p = 0.007), respectively. Other parameter combinations were not statistically significant. Conclusions The pO2-HTV evolution during radiochemotherapy showed predictive value for LRR. The changes in the tumour hypoxia during the first two treatment weeks may be used for adaptive personalized treatment approaches.
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Affiliation(s)
- Marta Lazzeroni
- Department of Physics, Stockholm, Sweden.,Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Ana Ureba
- Skandion Clinic, Uppsala, Sweden.,Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Nicole Wiedenmann
- Department of Radiation Oncology, Medical Center, Medical Faculty Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, Freiburg, Germany
| | - Nils H Nicolay
- Department of Radiation Oncology, Medical Center, Medical Faculty Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, Freiburg, Germany
| | - Michael Mix
- Department of Nuclear Medicine, University Medical Center, Freiburg, Germany
| | - Benedikt Thomann
- Department of Radiation Oncology, Medical Center, Medical Faculty Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, Freiburg, Germany
| | - Dimos Baltas
- Department of Radiation Oncology, Medical Center, Medical Faculty Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, Freiburg, Germany
| | - Iuliana Toma-Dasu
- Department of Physics, Stockholm, Sweden.,Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Anca L Grosu
- Department of Radiation Oncology, Medical Center, Medical Faculty Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, Freiburg, Germany
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Peng Z, Wang Y, Wang Y, Jiang S, Fan R, Zhang H, Jiang W. Application of radiomics and machine learning in head and neck cancers. Int J Biol Sci 2021; 17:475-486. [PMID: 33613106 PMCID: PMC7893590 DOI: 10.7150/ijbs.55716] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/10/2020] [Indexed: 02/07/2023] Open
Abstract
With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.
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Affiliation(s)
- Zhouying Peng
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Yaxuan Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Sijie Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Ruohao Fan
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Hua Zhang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
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Li W, Liu H, Cheng F, Li Y, Li S, Yan J. Artificial intelligence applications for oncological positron emission tomography imaging. Eur J Radiol 2020; 134:109448. [PMID: 33307463 DOI: 10.1016/j.ejrad.2020.109448] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 10/07/2020] [Accepted: 11/26/2020] [Indexed: 12/16/2022]
Abstract
Positron emission tomography (PET), a functional and dynamic molecular imaging technique, is generally used to reveal tumors' biological behavior. Radiomics allows a high-throughput extraction of multiple features from images with artificial intelligence (AI) approaches and develops rapidly worldwide. Quantitative and objective features of medical images have been explored to recognize reliable biomarkers, with the development of PET radiomics. This paper will review the current clinical exploration of PET-based classical machine learning and deep learning methods, including disease diagnosis, the prediction of histological subtype, gene mutation status, tumor metastasis, tumor relapse, therapeutic side effects, therapeutic intervention and evaluation of prognosis. The applications of AI in oncology will be mainly discussed. The image-guided biopsy or surgery assisted by PET-based AI will be introduced as well. This paper aims to present the applications and methods of AI for PET imaging, which may offer important details for further clinical studies. Relevant precautions are put forward and future research directions are suggested.
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Affiliation(s)
- Wanting Li
- Shanxi Medical University, Taiyuan 030009, PR China; Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Collaborative Innovation Center for Molecular Imaging, Taiyuan 030001, PR China
| | - Haiyan Liu
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Collaborative Innovation Center for Molecular Imaging, Taiyuan 030001, PR China; Cellular Physiology Key Laboratory of Ministry of Education, Translational Medicine Research Center, Shanxi Medical University, Taiyuan 030001, PR China
| | - Feng Cheng
- Shanxi Medical University, Taiyuan 030009, PR China
| | - Yanhua Li
- Shanxi Medical University, Taiyuan 030009, PR China
| | - Sijin Li
- Shanxi Medical University, Taiyuan 030009, PR China; Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Collaborative Innovation Center for Molecular Imaging, Taiyuan 030001, PR China.
| | - Jiangwei Yan
- Shanxi Medical University, Taiyuan 030009, PR China.
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20
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Abstract
Head and neck cancers are commonly encountered malignancies in the United States, of which the majority are attributed to squamous cell carcinoma. 18F-FDG-PET/CT has been well established in the evaluation, treatment planning, prognostic implications of these tumors and is routinely applied for the management of patients with these cancers. Many alternative investigational PET radiotracers have been extensively studied in the evaluation of these tumors. Although these radiotracers have not been able to replace 18F-FDG-PET/CT in routine clinical practice currently, they may provide important additional information about the biological mechanisms of these tumors, such as foci of tumor hypoxia as seen on hypoxia specific PET radiotracers such as 18F-Fluoromisonidazole (18F-FMISO), which could be useful in targeting radioresistant hypoxic tumor foci when treatment planning. There are multiple other hypoxia-specific PET radiotracers such as 18F-Fluoroazomycinarabinoside (FAZA), 18F-Flortanidazole (HX4), which have been evaluated similarly, of which 18F-Fluoromisonidazole (18F-FMISO) has been the most investigated. Other radiotracers frequently studied in the evaluation of these tumors include radiolabeled amino acid PET radiotracers, which show increased uptake in tumor cells with limited uptake in inflammatory tissue, which can be useful especially in differentiating postradiation inflammation from residual and/or recurrent disease. 18F-Fluorothymidine (FLT) is localized intracellularly by nucleoside transport and undergoes phosphorylation thereby being retained within tumor cells and can serve as an indicator of tumor proliferation. Decrease in radiotracer activity following treatment can be an early indicator of treatment response. This review aims at synthesizing the available literature on the most studied non-FDG-PET/CT in head and neck cancer.
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Affiliation(s)
- Charles Marcus
- Department of Radiology, West Virginia University, Morgantown, WV.
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21
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Socarrás Fernández JA, Mönnich D, Leibfarth S, Welz S, Zwanenburg A, Leger S, Löck S, Pfannenberg C, La Fougère C, Reischl G, Baumann M, Zips D, Thorwarth D. Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy. Phys Imaging Radiat Oncol 2020; 15:52-59. [PMID: 33043157 PMCID: PMC7536307 DOI: 10.1016/j.phro.2020.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 07/21/2020] [Accepted: 07/21/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND AND PURPOSE Hypoxia Positron-Emission-Tomography (PET) as well as Computed Tomography (CT) radiomics have been shown to be prognostic for radiotherapy outcome. Here, we investigate the stratification potential of CT-radiomics in head and neck cancer (HNC) patients and test if CT-radiomics is a surrogate predictor for hypoxia as identified by PET. MATERIALS AND METHODS Two independent cohorts of HNC patients were used for model development and validation, HN1 (n = 149) and HN2 (n = 47). The training set HN1 consisted of native planning CT data whereas for the validation cohort HN2 also hypoxia PET/CT data was acquired using [18F]-Fluoromisonidazole (FMISO). Machine learning algorithms including feature engineering and classifier selection were trained for two-year loco-regional control (LRC) to create optimal CT-radiomics signatures.Secondly, a pre-defined [18F]FMISO-PET tumour-to-muscle-ratio (TMRpeak ≥ 1.6) was used for LRC prediction. Comparison between risk groups identified by CT-radiomics or [18F]FMISO-PET was performed using area-under-the-curve (AUC) and Kaplan-Meier analysis including log-rank test. RESULTS The best performing CT-radiomics signature included two features with nearest-neighbour classification (AUC = 0.76 ± 0.09), whereas AUC was 0.59 for external validation. In contrast, [18F]FMISO TMRpeak reached an AUC of 0.66 in HN2. Kaplan-Meier analysis of the independent validation cohort HN2 did not confirm the prognostic value of CT-radiomics (p = 0.18), whereas for [18F]FMISO-PET significant differences were observed (p = 0.02). CONCLUSIONS No direct correlation of patient stratification using [18F]FMISO-PET or CT-radiomics was found in this study. Risk groups identified by CT-radiomics or hypoxia PET showed only poor overlap. Direct assessment of tumour hypoxia using PET seems to be more powerful to stratify HNC patients.
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Affiliation(s)
| | - David Mönnich
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | - Sara Leibfarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | - Stefan Welz
- Department of Radiation Oncology, University of Tübingen, Germany
| | - Alex Zwanenburg
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefan Leger
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Steffen Löck
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
| | - Christina Pfannenberg
- Department of Diagnostic and Interventional Radiology, University of Tübingen, Germany
| | | | - Gerald Reischl
- Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Germany
| | - Michael Baumann
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Research Center DKFZ, Heidelberg, Germany
| | - Daniel Zips
- Department of Radiation Oncology, University of Tübingen, Germany
- German Cancer Consortium (DKTK), partner Site Tübingen, Tübingen, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
- German Cancer Consortium (DKTK), partner Site Tübingen, Tübingen, Germany
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22
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Busk M, Overgaard J, Horsman MR. Imaging of Tumor Hypoxia for Radiotherapy: Current Status and Future Directions. Semin Nucl Med 2020; 50:562-583. [PMID: 33059825 DOI: 10.1053/j.semnuclmed.2020.05.003] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Tumor regions that are transiently or chronically undersupplied with oxygen (hypoxia) and nutrients, and enriched with acidic waste products, are common due to an abnormal and inefficient tumor vasculature, and a deviant highly glycolytic energy metabolism. There is compelling evidence that tumor hypoxia is strongly linked to poor prognosis since oxygen-deprived cells are highly resistant to therapy including radio- and chemotherapy, and survival of such cells is a primary cause of disease relapse. Despite a general improvement in cancer survival rates, hypoxia remains a formidable challenge. Recent progress in radiation delivery systems with improved spatial accuracy that allows dose escalation to hypoxic tumors or even tumor subvolumes, and the development of hypoxia-selective drugs, including bioreductive prodrugs, holds great promise for overcoming this obstacle. However, apart from one notable exception, translation of promising preclinical therapies to the clinic have largely been disappointing. A major obstacle in clinical trials on hypoxia-targeting strategies has been the lack of reliable information on tumor hypoxia, which is crucial for patient stratification into groups of those that are likely to benefit from intervention and those who are not. Further, in many newer trials on hypoxia-selective drugs the choice of cancer disease and combination therapy has not always been ideal, especially not for clinical proof of principle trials. Clearly, there is a pending need for clinical applicable methodologies that may allow us to quantify, map and monitor hypoxia. Molecular imaging may provide the information required for narrowing the gap between potential and actual patient benefit of hypoxia-targeting strategies. The grand majority of preclinical and clinical work has focused on the usefulness of PET-based assessment of hypoxia-selective tracers. Since hypoxia PET has profound inherent weaknesses, the use of other methodologies, including more indirect methods that quantifies blood flow or oxygenation-dependent flux changes through ATP-generating pathways (eg, anaerobic glycolysis) is being extensively studied. In this review, we briefly discuss established and emerging hypoxia-targeting strategies, followed by a more thorough evaluation of strengths and weaknesses of clinical applicable imaging methodologies that may guide timely treatment intensification to overcome hypoxia-driven resistance. Historically, most evidence for the linkage between hypoxia and poor outcome is based on work in the field of radiotherapy. Therefore, main emphasis in this review is on targeting and imaging of hypoxia for improved radiotherapy.
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Affiliation(s)
- Morten Busk
- Experimental Clinical Oncology, Department of Oncology, Aarhus University Hospital (AUH), Aarhus, Denmark; Danish Centre for Particle Therapy, (AUH), Aarhus, Denmark.
| | - Jens Overgaard
- Experimental Clinical Oncology, Department of Oncology, Aarhus University Hospital (AUH), Aarhus, Denmark
| | - Michael R Horsman
- Experimental Clinical Oncology, Department of Oncology, Aarhus University Hospital (AUH), Aarhus, Denmark
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