1
|
Li S, Xie J, Liu J, Wu Y, Wang Z, Cao Z, Wen D, Zhang X, Wang B, Yang Y, Lu L, Dong X. Prognostic Value of a Combined Nomogram Model Integrating 3-Dimensional Deep Learning and Radiomics for Head and Neck Cancer. J Comput Assist Tomogr 2024; 48:498-507. [PMID: 38438336 DOI: 10.1097/rct.0000000000001584] [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: 03/06/2024]
Abstract
OBJECTIVE The preoperative prediction of the overall survival (OS) status of patients with head and neck cancer (HNC) is significant value for their individualized treatment and prognosis. This study aims to evaluate the impact of adding 3D deep learning features to radiomics models for predicting 5-year OS status. METHODS Two hundred twenty cases from The Cancer Imaging Archive public dataset were included in this study; 2212 radiomics features and 304 deep features were extracted from each case. The features were selected by univariate analysis and the least absolute shrinkage and selection operator, and then grouped into a radiomics model containing Positron Emission Tomography /Computed Tomography (PET/CT) radiomics features score, a deep model containing deep features score, and a combined model containing PET/CT radiomics features score +3D deep features score. TumorStage model was also constructed using initial patient tumor node metastasis stage to compare the performance of the combined model. A nomogram was constructed to analyze the influence of deep features on the performance of the model. The 10-fold cross-validation of the average area under the receiver operating characteristic curve and calibration curve were used to evaluate performance, and Shapley Additive exPlanations (SHAP) was developed for interpretation. RESULTS The TumorStage model, radiomics model, deep model, and the combined model achieved areas under the receiver operating characteristic curve of 0.604, 0.851, 0.840, and 0.895 on the train set and 0.571, 0.849, 0.832, and 0.900 on the test set. The combined model showed better performance of predicting the 5-year OS status of HNC patients than the radiomics model and deep model. The combined model was shown to provide a favorable fit in calibration curves and be clinically useful in decision curve analysis. SHAP summary plot and SHAP The SHAP summary plot and SHAP force plot visually interpreted the influence of deep features and radiomics features on the model results. CONCLUSIONS In predicting 5-year OS status in patients with HNC, 3D deep features could provide richer features for combined model, which showed outperformance compared with the radiomics model and deep model.
Collapse
Affiliation(s)
| | - Jiayi Xie
- Department of automation, Tsinghua University, Beijing, China
| | | | | | - Zhongxiao Wang
- From the Hebei International Research Center for Medical-Engineering
| | - Zhendong Cao
- Department of Radiology, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei
| | - Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing
| | - Xiaolei Zhang
- From the Hebei International Research Center for Medical-Engineering
| | | | - Yifan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou
| | | |
Collapse
|
2
|
Zou XC, Luo CW, Yuan RM, Jin MN, Zeng T, Chao HC. Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy. Urolithiasis 2024; 52:64. [PMID: 38613668 DOI: 10.1007/s00240-024-01562-7] [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: 01/15/2024] [Accepted: 03/21/2024] [Indexed: 04/15/2024]
Abstract
Radiomics and machine learning have been extensively utilized in the realm of urinary stones, particularly in forecasting stone treatment outcomes. The objective of this study was to integrate clinical variables and radiomic features to develop a machine learning model for predicting the stone-free rate (SFR) following percutaneous nephrolithotomy (PCNL). A total of 212 eligible patients who underwent PCNL surgery at the Second Affiliated Hospital of Nanchang University were included in a retrospective analysis. Preoperative clinical variables and non-contrast-enhanced CT images of all patients were collected, and radiomic features were extracted after delineating the stone ROI. Univariate analysis was conducted to identify clinical variables strongly correlated with the stone-free rate after PCNL, and the least absolute shrinkage and selection operator algorithm (lasso regression) was utilized to screen radiomic features. Four supervised machine learning algorithms, including Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Decision Tree (GBDT), were employed. The clinical variables with strong correlation and screened radiomic features were integrated into the four machine learning algorithms to construct a prediction model, and the receiver operating curve was plotted. The area under the receiver operating curve (AUC), the accuracy rate, the specificity, etc., were used to evaluate the predictive performance of the four models. After analyzing postoperative statistics, the stone-free rate following the procedure was found to be 70.3% (n = 149). Among the various clinical variables examined, factors, such as stone number, stone diameter, stone CT value, stone location, and history of stone surgery, were identified as statistically significant in relation to the stone-free rate after PCNL. A total of 121 radiomic features were extracted, and through lasso regression, 7 features most closely associated with the stone-free rate post-PCNL were identified. The predictive accuracy of different models (Logistic Regression, RF, XGBoost, and GBDT) for determining the stone-free rate after PCNL was evaluated, yielding accuracies of 78.1%, 76.6%, 75.0%, and 73.4%, respectively. The corresponding area under the curve AUC (95%CI) were 0.85 (0.83-0.89), 0.81 (0.76-0.85), 0.82 (0.78-0.85), and 0.77 (0.73-0.81), positioning these models among the top performers in logistic regression prediction. In terms of predictive importance scores, the key factors identified by the logistic regression model were number of stone, zone percentage, stone diameter, and surface area. Similarly, the RF model highlighted number of stone, stone CT value, stone diameter, and surface area as the top predictors. Among the four machine learning models, the logistic regression model demonstrated the highest accuracy and discrimination ability in predicting the stone-free rate following PCNL. In comparison to XGBoost and GBDT, RF also exhibited superior accuracy and a certain level of discrimination ability. However, based on the performance of all four models, logistic regression is more likely to aid in clinical decision-making by assisting clinicians in diagnosing PCNL in patients. This enables us to effectively predict the presence of residual stones post-surgery and ultimately select patients who are suitable candidates for PCNL.
Collapse
Affiliation(s)
- Xin Chang Zou
- Department of Urology, Second Affiliated Hospital of Nanchang University, Nanchang, 330008, China
| | - Cheng Wei Luo
- Department of Urology, Second Affiliated Hospital of Nanchang University, Nanchang, 330008, China
| | | | - Meng Ni Jin
- Department of Imaging, Second Affiliated Hospital of Nanchang University, Nanchang, 330008, China
| | - Tao Zeng
- Department of Urology, Second Affiliated Hospital of Nanchang University, Nanchang, 330008, China.
| | - Hai Chao Chao
- Department of Urology, Second Affiliated Hospital of Nanchang University, Nanchang, 330008, China
| |
Collapse
|
3
|
Zhao J, Jiao Y, Wang H, Song P, Gao Z, Bing X, Zhang C, Ouyang A, Yao J, Wang S, Jiang H. Radiomic features of the hippocampal based on magnetic resonance imaging in the menopausal mouse model linked to neuronal damage and cognitive deficits. Brain Imaging Behav 2024; 18:368-377. [PMID: 38102441 PMCID: PMC11156756 DOI: 10.1007/s11682-023-00808-z] [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] [Accepted: 10/01/2023] [Indexed: 12/17/2023]
Abstract
Estrogen deficiency in the early postmenopausal phase is associated with an increased long-term risk of cognitive decline or dementia. Non-invasive characterization of the pathological features of the pathological hallmarks in the brain associated with postmenopausal women (PMW) could enhance patient management and the development of therapeutic strategies. Radiomics is a means to quantify the radiographic phenotype of a diseased tissue via the high-throughput extraction and mining of quantitative features from images acquired from modalities such as CT and magnetic resonance imaging (MRI). This study set out to explore the correlation between radiomics features based on MRI and pathological features of the hippocampus and cognitive function in the PMW mouse model. Ovariectomized (OVX) mice were used as PWM models. MRI scans were performed two months after surgery. The brain's hippocampal region was manually annotated, and the radiomic features were extracted with PyRadiomics. Chemiluminescence was used to evaluate the peripheral blood estrogen level of mice, and the Morris water maze test was used to evaluate the cognitive ability of mice. Nissl staining and immunofluorescence were used to quantify neuronal damage and COX1 expression in brain sections of mice. The OVX mice exhibited marked cognitive decline, brain neuronal damage, and increased expression of mitochondrial complex IV subunit COX1, which are pathological phenomena commonly observed in the brains of AD patients, and these phenotypes were significantly correlated with radiomics features (p < 0.05, |r|>0.5), including Original_firstorder_Interquartile Range, Original_glcm_Difference Average, Original_glcm_Difference Average and Wavelet-LHH_glszm_Small Area Emphasis. Meanwhile, the above radiomics features were significantly different between the sham-operated and OVX groups (p < 0.01) and were associated with decreased serum estrogen levels (p < 0.05, |r|>0.5). This initial study indicates that the above radiomics features may have a role in the assessment of the pathology of brain damage caused by estrogen deficiency using routinely acquired structural MR images.
Collapse
Affiliation(s)
- Jie Zhao
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yan Jiao
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Hui Wang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Peiji Song
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhen Gao
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Xue Bing
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Chunling Zhang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Aimei Ouyang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Jian Yao
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Song Wang
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, No.725, South Wanping Road, Shanghai, 200032, China.
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China.
| |
Collapse
|
4
|
Huang BT, Wang Y, Lin PX. Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy. J Cancer Res Clin Oncol 2024; 150:34. [PMID: 38277078 PMCID: PMC10817845 DOI: 10.1007/s00432-023-05536-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/03/2023] [Indexed: 01/27/2024]
Abstract
PURPOSE The study aims to develop and validate a combined model for predicting 3-year cancer-specific survival (CSS) in lung cancer patients treated with stereotactic body radiation therapy (SBRT) by integrating clinical and radiomic parameters. METHODS Clinical data and pre-treatment CT images were collected from 102 patients treated with lung SBRT. Multivariate logistic regression and the least absolute shrinkage and selection operator were used to determine the clinical and radiomic factors associated with 3-year CSS. Three prediction models were developed using clinical factors, radiomic factors, and a combination of both. The performance of the models was assessed using receiver operating characteristic curve and calibration curve. A nomogram was also created to visualize the 3-year CSS prediction. RESULTS With a 36-month follow-up, 40 patients (39.2%) died of lung cancer and 62 patients (60.8%) survived. Three clinical factors, including gender, clinical stage, and lymphocyte ratio, along with three radiomic features, were found to be independent factors correlated with 3-year CSS. The area under the curve values for the clinical, radiomic, and combined model were 0.839 (95% CI 0.735-0.914), 0.886 (95% CI 0.790-0.948), and 0.914 (95% CI 0.825-0.966) in the training cohort, and 0.757 (95% CI 0.580-0.887), 0.818 (95% CI 0.648-0.929), and 0.843 (95% CI 0.677-0.944) in the validation cohort, respectively. Additionally, the calibration curve demonstrated good calibration performance and the nomogram created from the combined model showed potential for clinical utility. CONCLUSION A clinical-radiomic model was developed to predict the 3-year CSS for lung cancer patients treated with SBRT.
Collapse
Affiliation(s)
- Bao-Tian Huang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, 515000, China.
| | - Ying Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Pei-Xian Lin
- Department of Nosocomial Infection Management, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| |
Collapse
|
5
|
Yazdani E, Geramifar P, Karamzade-Ziarati N, Sadeghi M, Amini P, Rahmim A. Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens. Diagnostics (Basel) 2024; 14:181. [PMID: 38248059 PMCID: PMC10814892 DOI: 10.3390/diagnostics14020181] [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: 11/23/2023] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.
Collapse
Affiliation(s)
- Elmira Yazdani
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran; (P.G.); (N.K.-Z.)
| | - Najme Karamzade-Ziarati
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran; (P.G.); (N.K.-Z.)
| | - Mahdi Sadeghi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Payam Amini
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| |
Collapse
|
6
|
Williams TL, Gonen M, Wray R, Do RKG, Simpson AL. Quantitation of Oncologic Image Features for Radiomic Analyses in PET. Methods Mol Biol 2024; 2729:409-421. [PMID: 38006509 DOI: 10.1007/978-1-0716-3499-8_23] [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] [Indexed: 11/27/2023]
Abstract
Radiomics is an emerging and exciting field of study involving the extraction of many quantitative features from radiographic images. Positron emission tomography (PET) images are used in cancer diagnosis and staging. Utilizing radiomics on PET images can better quantify the spatial relationships between image voxels and generate more consistent and accurate results for diagnosis, prognosis, treatment, etc. This chapter gives the general steps a researcher would take to extract PET radiomic features from medical images and properly develop models to implement.
Collapse
Affiliation(s)
- Travis L Williams
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mithat Gonen
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rick Wray
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber L Simpson
- School of Computing and Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada.
| |
Collapse
|
7
|
O’Sullivan NJ, Temperley HC, Horan MT, Corr A, Mehigan BJ, Larkin JO, McCormick PH, Kavanagh DO, Meaney JFM, Kelly ME. Radiogenomics: Contemporary Applications in the Management of Rectal Cancer. Cancers (Basel) 2023; 15:5816. [PMID: 38136361 PMCID: PMC10741704 DOI: 10.3390/cancers15245816] [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: 11/08/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
Radiogenomics, a sub-domain of radiomics, refers to the prediction of underlying tumour biology using non-invasive imaging markers. This novel technology intends to reduce the high costs, workload and invasiveness associated with traditional genetic testing via the development of 'imaging biomarkers' that have the potential to serve as an alternative 'liquid-biopsy' in the determination of tumour biological characteristics. Radiogenomics also harnesses the potential to unlock aspects of tumour biology which are not possible to assess by conventional biopsy-based methods, such as full tumour burden, intra-/inter-lesion heterogeneity and the possibility of providing the information of tumour biology longitudinally. Several studies have shown the feasibility of developing a radiogenomic-based signature to predict treatment outcomes and tumour characteristics; however, many lack prospective, external validation. We performed a systematic review of the current literature surrounding the use of radiogenomics in rectal cancer to predict underlying tumour biology.
Collapse
Affiliation(s)
- Niall J. O’Sullivan
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James’s Hospital, D08 NHY1 Dublin, Ireland
| | - Hugo C. Temperley
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - Michelle T. Horan
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James’s Hospital, D08 NHY1 Dublin, Ireland
| | - Alison Corr
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
| | - Brian J. Mehigan
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - John O. Larkin
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - Paul H. McCormick
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - Dara O. Kavanagh
- Department of Surgery, Tallaght University Hospital, D24 NR0A Dublin, Ireland
- Department of Surgery, Royal College of Surgeons, D02 YN77 Dublin, Ireland
| | - James F. M. Meaney
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
- The National Centre for Advanced Medical Imaging (CAMI), St. James’s Hospital, D08 NHY1 Dublin, Ireland
| | - Michael E. Kelly
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
- Trinity St. James’s Cancer Institute (TSJCI), D08 NHY1 Dublin, Ireland
| |
Collapse
|
8
|
Huang W, Tao Z, Younis MH, Cai W, Kang L. Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives. VIEW 2023; 4:20230032. [PMID: 38179181 PMCID: PMC10766416 DOI: 10.1002/viw.20230032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/20/2023] [Indexed: 01/06/2024] Open
Abstract
Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.
Collapse
Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Zihao Tao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Muhsin H. Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| |
Collapse
|
9
|
O'Sullivan NJ, Kelly ME. Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions. Curr Oncol 2023; 30:4936-4945. [PMID: 37232830 DOI: 10.3390/curroncol30050372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/19/2023] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Radiomics refers to the conversion of medical imaging into high-throughput, quantifiable data in order to analyse disease patterns, guide prognosis and aid decision making. Radiogenomics is an extension of radiomics that combines conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data, serving as an alternative to costly, labour-intensive genetic testing. Data on radiomics and radiogenomics in the field of pelvic oncology remain novel concepts in the literature. We aim to perform an up-to-date analysis of current applications of radiomics and radiogenomics in the field of pelvic oncology, particularly focusing on the prediction of survival, recurrence and treatment response. Several studies have applied these concepts to colorectal, urological, gynaecological and sarcomatous diseases, with individual efficacy yet poor reproducibility. This article highlights the current applications of radiomics and radiogenomics in pelvic oncology, as well as the current limitations and future directions. Despite a rapid increase in publications investigating the use of radiomics and radiogenomics in pelvic oncology, the current evidence is limited by poor reproducibility and small datasets. In the era of personalised medicine, this novel field of research has significant potential, particularly for predicting prognosis and guiding therapeutic decisions. Future research may provide fundamental data on how we treat this cohort of patients, with the aim of reducing the exposure of high-risk patients to highly morbid procedures.
Collapse
Affiliation(s)
- Niall J O'Sullivan
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Michael E Kelly
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
| |
Collapse
|
10
|
Amrane K, Thuillier P, Bourhis D, Le Meur C, Quere C, Leclere JC, Ferec M, Jestin-Le Tallec V, Doucet L, Alemany P, Salaun PY, Metges JP, Schick U, Abgral R. Prognostic value of pre-therapeutic FDG-PET radiomic analysis in gastro-esophageal junction cancer. Sci Rep 2023; 13:5789. [PMID: 37031233 PMCID: PMC10082755 DOI: 10.1038/s41598-023-31587-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/14/2023] [Indexed: 04/10/2023] Open
Abstract
The main aim of this study was to evaluate the prognostic value of radiomic approach in pre-therapeutic 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET/CT) in a large cohort of patients with gastro-esophageal junction cancer (GEJC). This was a retrospective monocenter study including 97 consecutive patients with GEJC who underwent a pre-therapeutic FDG-PET and were followed up for 3 years. Standard first-order radiomic PET indices including SUVmax, SUVmean, SUVpeak, MTV and TLG and 32 textural features (TFs) were calculated using LIFEx software on PET imaging. Prognostic significance of these parameters was assessed in univariate and multivariate analysis. Relapse-free survival (RFS) and overall survival (OS) were respectively chosen as primary and secondary endpoints. An internal validation cohort was used by randomly drawing one-third of included patients. The main characteristics of this cohort were: median age of 65 years [41-88], sex ratio H/F = 83/14, 81.5% of patients with a histopathology of adenocarcinoma and 43.3% with a stage IV disease. The median follow-up was 28.5 months [4.2-108.5]. Seventy-seven (79.4%) patients had locoregional or distant progression or recurrence and 71 (73.2%) died. In univariate analysis, SUVmean, Histogram-Entropy and 2 TFs (GLCM-Homogeneity and GLCM-Energy) were significantly correlated with RFS and OS, as well as 2 others TFs (GLRLM-LRE and GLRLM-GLNU) with OS only. In multivariate analysis, Histogram-Entropy remained an independent prognostic factor of both RFS and OS whereas SUVmean was an independent prognostic factor of OS only. These results were partially confirmed in our internal validation cohort of 33 patients. Our results suggest that radiomic approach reveals independent prognostic factors for survival in patients with GEJC.
Collapse
Affiliation(s)
- Karim Amrane
- Department of Oncology, Regional Hospital of Morlaix, Morlaix, France.
| | - Philippe Thuillier
- Department of Endocrinology, University Hospital of Brest, Brest, France
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
| | - David Bourhis
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | - Coline Le Meur
- Department of Oncology, University Hospital of Brest, Brest, France
| | - Chloe Quere
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | | | - Marc Ferec
- Department of Gastroenterology, Regional Hospital of Morlaix, Morlaix, France
| | | | - Laurent Doucet
- Department of Pathology, University Hospital of Brest, Brest, France
| | - Pierre Alemany
- Department of Pathology, Ouestpathology Brest, Brest, France
| | - Pierre-Yves Salaun
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | | | - Ulrike Schick
- Department of Radiotherapy, University Hospital of Brest, Brest, France
| | - Ronan Abgral
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France.
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France.
| |
Collapse
|
11
|
Prognostic analysis of curatively resected pancreatic cancer using harmonized positron emission tomography radiomic features. Eur J Hybrid Imaging 2023; 7:5. [PMID: 36872413 PMCID: PMC9986192 DOI: 10.1186/s41824-023-00163-8] [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: 12/05/2022] [Accepted: 01/18/2023] [Indexed: 03/07/2023] Open
Abstract
BACKGROUND Texture features reflecting tumour heterogeneity enable us to investigate prognostic factors. The R package ComBat can harmonize the quantitative texture features among several positron emission tomography (PET) scanners. We aimed to identify prognostic factors among harmonized PET radiomic features and clinical information from pancreatic cancer patients who underwent curative surgery. METHODS Fifty-eight patients underwent preoperative enhanced dynamic computed tomography (CT) scanning and fluorodeoxyglucose PET/CT using four PET scanners. Using LIFEx software, we measured PET radiomic parameters including texture features with higher order and harmonized these PET parameters. For progression-free survival (PFS) and overall survival (OS), we evaluated clinical information, including age, TNM stage, and neural invasion, and the harmonized PET radiomic features based on univariate Cox proportional hazard regression. Next, we analysed the prognostic indices by multivariate Cox proportional hazard regression (1) by using either significant (p < 0.05) or borderline significant (p = 0.05-0.10) indices in the univariate analysis (first multivariate analysis) or (2) by using the selected features with random forest algorithms (second multivariate analysis). Finally, we checked these multivariate results by log-rank test. RESULTS Regarding the first multivariate analysis for PFS after univariate analysis, age was the significant prognostic factor (p = 0.020), and MTV and GLCM contrast were borderline significant (p = 0.051 and 0.075, respectively). Regarding the first multivariate analysis of OS, neural invasion, Shape sphericity and GLZLM LZLGE were significant (p = 0.019, 0.042 and 0.0076). In the second multivariate analysis, only MTV was significant (p = 0.046) for PFS, whereas GLZLM LZLGE was significant (p = 0.047), and Shape sphericity was borderline significant (p = 0.088) for OS. In the log-rank test, age, MTV and GLCM contrast were borderline significant for PFS (p = 0.08, 0.06 and 0.07, respectively), whereas neural invasion and Shape sphericity were significant (p = 0.03 and 0.04, respectively), and GLZLM LZLGE was borderline significant for OS (p = 0.08). CONCLUSIONS Other than the clinical factors, MTV and GLCM contrast for PFS and Shape sphericity and GLZLM LZLGE for OS may be prognostic PET parameters. A prospective multicentre study with a larger sample size may be warranted.
Collapse
|
12
|
Heterogeneidad del tumor primario en la18F-FDG PET/TC pretratamiento para predecir el pronóstico en pacientes con cáncer de recto sometidos a cirugía tras terapia neoadyuvante. Rev Esp Med Nucl Imagen Mol 2023. [DOI: 10.1016/j.remn.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
|
13
|
Gülbahar Ateş S, Bilir Dilek G, Uçmak G. Primary tumor heterogeneity on pretreatment 18F-FDG PET/CT to predict outcome in patients with rectal cancer who underwent surgery after neoadjuvant therapy. Rev Esp Med Nucl Imagen Mol 2023:S2253-8089(23)00001-0. [PMID: 36690032 DOI: 10.1016/j.remnie.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 01/21/2023]
Abstract
PURPOSE This retrospective study aimed to investigate the value of texture features of primary tumors in pretreatment 18F-FDG PET/CT in the prediction of response to treatment, progression, and overall survival in patients with rectal cancer who underwent surgery after neoadjuvant therapy(NAT). METHODS Patients with rectal cancer who had pretreatment 18F-FDG PET/CT, and underwent surgery after NAT were included in this study. Clinicopathologic features, date of last follow-up, progression, and death were recorded. Textural and conventional PET parameters(maximum standardized uptake value-SUVmax, metabolic tumor volume-MTV, total lesion glycolysis-TLG) were obtained from PET/CT images using LifeX program. Parameters were grouped using Youden index in ROC analysis. Factors predicting the pathological response to treatment, progression, and overall survival were determined using logistic regression and Cox regression analyses. RESULTS Forty-four patients (26(59%) male, 18(41%) female; 60.1±11.4 years) with rectal cancer were included in this study. The numbers of patients with responders and non-responders to NAT were 15(34.9%) and 28(65.1%), respectively. One patient' pathology report did not contain the response status to NAT. The median of follow-up duration was 29.9 months. 9(20.5%) showed disease progression, and 8(18.2%) died during the follow-up period. Difference entropyGLCM and correlationGLCM parameters were found as independent predictors for response to NAT. The positivity of surgical margin, intensity interquartile rangeCONV and AUC-CSHDISC texture parameters were independent predictors of progression, while normalized inverse differenceGLCM and LZLGEGLZLM parameters were independent predictors of mortality. CONCLUSION The texture parameters obtained from pretreatment 18F-FDG PET/CT have presented a more robust predictive value than conventional parameters in patients with rectal cancer who underwent surgery after NAT.
Collapse
Affiliation(s)
- Seda Gülbahar Ateş
- Department of Nuclear Medicine, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey.
| | - Gülay Bilir Dilek
- Department of Pathology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Gülin Uçmak
- Department of Nuclear Medicine, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| |
Collapse
|
14
|
Radiomics in Cardiac Computed Tomography. Diagnostics (Basel) 2023; 13:diagnostics13020307. [PMID: 36673115 PMCID: PMC9857691 DOI: 10.3390/diagnostics13020307] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/09/2023] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
In recent years, there has been an increasing recognition of coronary computed tomographic angiography (CCTA) and gated non-contrast cardiac CT in the workup of coronary artery disease in patients with low and intermediate pretest probability, through the readjustment guidelines by medical societies. However, in routine clinical practice, these CT data sets are usually evaluated dominantly regarding relevant coronary artery stenosis and calcification. The implementation of radiomics analysis, which provides visually elusive quantitative information from digital images, has the potential to open a new era for cardiac CT that goes far beyond mere stenosis or calcification grade estimation. This review offers an overview of the results obtained from radiomics analyses in cardiac CT, including the evaluation of coronary plaques, pericoronary adipose tissue, and the myocardium itself. It also highlights the advantages and disadvantages of use in routine clinical practice.
Collapse
|
15
|
Onozato Y, Iwata T, Uematsu Y, Shimizu D, Yamamoto T, Matsui Y, Ogawa K, Kuyama J, Sakairi Y, Kawakami E, Iizasa T, Yoshino I. Predicting pathological highly invasive lung cancer from preoperative [ 18F]FDG PET/CT with multiple machine learning models. Eur J Nucl Med Mol Imaging 2023; 50:715-726. [PMID: 36385219 PMCID: PMC9852187 DOI: 10.1007/s00259-022-06038-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/08/2022] [Indexed: 11/18/2022]
Abstract
PURPOSE The efficacy of sublobar resection of primary lung cancer have been proven in recent years. However, sublobar resection for highly invasive lung cancer increases local recurrence. We developed and validated multiple machine learning models predicting pathological invasiveness of lung cancer based on preoperative [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomic features. METHODS Overall, 873 patients who underwent lobectomy or segmentectomy for primary lung cancer were enrolled. Radiomics features were extracted from preoperative PET/CT images with the PyRadiomics package. Seven machine learning models and an ensemble of all models (ENS) were evaluated after 100 iterations. In addition, the probability of highly invasive lung cancer was calculated in a nested cross-validation to assess the calibration plot and clinical usefulness and to compare to consolidation tumour ratio (CTR) on CT images, one of the generally used diagnostic criteria. RESULTS In the training set, when PET and CT features were combined, all models achieved an area under the curve (AUC) of ≥ 0.880. In the test set, ENS showed the highest mean AUC of 0.880 and smallest standard deviation of 0.0165, and when the cutoff was 0.5, accuracy of 0.804, F1 of 0.851, precision of 0.821, and recall of 0.885. In the nested cross-validation, the AUC of 0.882 (95% CI: 0.860-0.905) showed a high discriminative ability, and the calibration plot indicated consistency with a Brier score of 0.131. A decision curve analysis showed that the ENS was valid with a threshold probability ranging from 3 to 98%. Accuracy showed an improvement of more than 8% over the CTR. CONCLUSION The machine learning model based on preoperative [18F]FDG PET/CT images was able to predict pathological highly invasive lung cancer with high discriminative ability and stability. The calibration plot showed good consistency, suggesting its usefulness in quantitative risk assessment.
Collapse
Affiliation(s)
- Yuki Onozato
- Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717 Japan
| | - Takekazu Iwata
- Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717 Japan
| | - Yasufumi Uematsu
- Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717 Japan
| | - Daiki Shimizu
- Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717 Japan
| | - Takayoshi Yamamoto
- Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717 Japan
| | - Yukiko Matsui
- Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717 Japan
| | - Kazuyuki Ogawa
- Division of Nuclear Medicine, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717 Japan
| | - Junpei Kuyama
- Division of Nuclear Medicine, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717 Japan
| | - Yuichi Sakairi
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Eiryo Kawakami
- Department of Artificial Intelligence Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Toshihiko Iizasa
- Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717 Japan
| | - Ichiro Yoshino
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| |
Collapse
|
16
|
A Review of Radiomics and Artificial Intelligence and Their Application in Veterinary Diagnostic Imaging. Vet Sci 2022; 9:vetsci9110620. [PMID: 36356097 PMCID: PMC9693121 DOI: 10.3390/vetsci9110620] [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: 10/01/2022] [Revised: 10/31/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022] Open
Abstract
Simple Summary The goal of this paper is to provide an overview of current radiomic and AI applications in veterinary diagnostic imaging. We discuss the essential elements of AI for veterinary practitioners with the aim of helping them make informed decisions in applying AI technologies to their practices and that veterinarians will play an integral role in ensuring the appropriate uses and suitable curation of data. The expertise of veterinary professionals will be vital to ensuring suitable data and, subsequently, AI that meets the needs of the profession. Abstract Great advances have been made in human health care in the application of radiomics and artificial intelligence (AI) in a variety of areas, ranging from hospital management and virtual assistants to remote patient monitoring and medical diagnostics and imaging. To improve accuracy and reproducibility, there has been a recent move to integrate radiomics and AI as tools to assist clinical decision making and to incorporate it into routine clinical workflows and diagnosis. Although lagging behind human medicine, the use of radiomics and AI in veterinary diagnostic imaging is becoming more frequent with an increasing number of reported applications. The goal of this paper is to provide an overview of current radiomic and AI applications in veterinary diagnostic imaging.
Collapse
|
17
|
Measures of Corticalization. J Clin Med 2022; 11:jcm11185463. [PMID: 36143109 PMCID: PMC9500652 DOI: 10.3390/jcm11185463] [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: 07/09/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
After the insertion of dental implants into living bone, the condition of the peri-implant bone changes with time. Implant-loading phenomena can induce bone remodeling in the form of the corticalization of the trabecular bone. The aim of this study was to see how bone index (BI) values behave in areas of bone loss (radiographically translucent non-trabecular areas) and to propose other indices specifically dedicated to detecting corticalization in living bone. Eight measures of corticalization in clinical standardized intraoral radiographs were studied: mean optical density, entropy, differential entropy, long-run emphasis moment, BI, corticalization index ver. 1 and ver. 2 (CI v.1, CI v.2) and corticalization factor (CF). The analysis was conducted on 40 cortical bone image samples, 40 cancellous bone samples and 40 soft tissue samples. It was found that each measure distinguishes corticalization significantly (p < 0.001), but only CI v.1 and CI v.2 do so selectively. CF or the inverse of BI can serve as a measure of peri-implant bone corticalization. However, better measures are CIs as they are dedicated to detecting this phenomenon and allowing clear clinical deduction.
Collapse
|
18
|
Production Review of Accelerator-Based Medical Isotopes. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27165294. [PMID: 36014532 PMCID: PMC9415084 DOI: 10.3390/molecules27165294] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 08/15/2022] [Accepted: 08/16/2022] [Indexed: 11/17/2022]
Abstract
The production of reactor-based medical isotopes is fragile, which has meant supply shortages from time to time. This paper reviews alternative production methods in the form of cyclotrons, linear accelerators and neutron generators. Finally, the status of the production of medical isotopes in China is described.
Collapse
|
19
|
Sun R, Henry T, Laville A, Carré A, Hamaoui A, Bockel S, Chaffai I, Levy A, Chargari C, Robert C, Deutsch E. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer 2022; 10:jitc-2022-004848. [PMID: 35793875 PMCID: PMC9260846 DOI: 10.1136/jitc-2022-004848] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/17/2022] Open
Abstract
Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient’s outcomes and to assess response to these promising combinations.
Collapse
Affiliation(s)
- Roger Sun
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Théophraste Henry
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,Department of Nuclear Medicine, Gustave Roussy, Villejuif, France
| | - Adrien Laville
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Alexandre Carré
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Anthony Hamaoui
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Sophie Bockel
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Ines Chaffai
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Antonin Levy
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Cyrus Chargari
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,Department of Radiation Oncology, Brachytherapy Unit, Gustave Roussy, Villejuif, France
| | - Charlotte Robert
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Eric Deutsch
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France .,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,INSERM U1030, Gustave Roussy, Villejuif, France
| |
Collapse
|
20
|
Langen Stokmo H, Aly M, Bowitz Lothe IM, Borja AJ, Mehdizadeh Seraj S, Ghorpade R, Miao X, Hjortland GO, Malinen E, Sorbye H, Werner TJ, Alavi A, Revheim M. Volumetric parameters from [ 18 F]FDG PET/CT predicts survival in patients with high-grade gastroenteropancreatic neuroendocrine neoplasms. J Neuroendocrinol 2022; 34:e13170. [PMID: 35729738 PMCID: PMC9539477 DOI: 10.1111/jne.13170] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/12/2022] [Accepted: 05/23/2022] [Indexed: 11/30/2022]
Abstract
A positive fluorine-18 labelled 2-deoxy-2-fluoroglucose ([18 F]FDG) positron emission tomography/computed tomography (PET/CT) has been associated with more aggressive disease and less differentiated neuroendocrine neoplasms (NEN). Although a high maximum standardized uptake value (SUVmax ) predicts poor outcome in NEN, volumetric parameters from [18 F]FDG PET have not been evaluated for prognostication in a pure high-grade gastroenteropancreatic (GEP) NEN cohort. In this retrospective observational study, we evaluated the volumetric PET parameters total metabolic tumour volume (tMTV) and total total lesion glycolysis (tTLG) for independent prognostication of overall survival (OS). High-grade GEP NEN patients with [18 F]FDG PET/CT examination and biopsy within 90 days were included. Total MTV and tTLG were calculated using an adaptive thresholding software. Patients were dichotomised into low and high metabolic groups based on median tMTV and tTLG. OS was compared using Kaplan-Meier estimator and log-rank test. Uni and multivariable Cox regression was used to estimate effect sizes and adjust for tumour differentiation and SUVmax . Sixty-six patients (median age 64 years) were included with 14 NET G3 and 52 NEC cases after histological re-evaluation. Median tMTV was 208 cm3 and median tTLG 1899 g. Median OS in the low versus high tMTV-group was 21.2 versus 5.7 months (HR 2.53, p = 0.0007) and 22.8 versus 5.7 months (HR 2.42, p = 0.0012) in the tTLG-group. Adjusted for tumour differentiation and SUVmax , tMTV and tTLG still predicted for poor OS, and both tMTV and tTLG were stronger prognostic parameters than SUVmax . Both regression models showed a strong association between volumetric parameters and OS for both neuroendocrine tumours (NET) G3 and neuroendocrine carcinomas (NEC). OS for the tTLG low metabolic NEC was much higher than for the tTLG high metabolic NET G3 (18.3 vs. 5.7 months). High-grade GEP NEN patients with high tMTV or tTLG had a worse OS regardless of tumour differentiation (NET G3 or NEC). Volumetric PET parameters were stronger prognostic parameters than SUVmax .
Collapse
Affiliation(s)
- Henning Langen Stokmo
- Division of Radiology and Nuclear MedicineOslo University HospitalOsloNorway
- Institute of Clinical Medicine, Faculty of MedicineUniversity of OsloOsloNorway
| | - Mahmoud Aly
- Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of RadiologyAsyut University HospitalAsyutEgypt
| | | | - Austin J. Borja
- Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Perelman School of Medicine at the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Rina Ghorpade
- Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Xuan Miao
- Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Eirik Malinen
- Department of Medical PhysicsOslo University HospitalOsloNorway
- Department of PhysicsUniversity of OsloOsloNorway
| | - Halfdan Sorbye
- Department of OncologyHaukeland University HospitalBergenNorway
- Department of Clinical ScienceUniversity of BergenBergenNorway
| | - Thomas J. Werner
- Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Abass Alavi
- Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Mona‐Elisabeth Revheim
- Division of Radiology and Nuclear MedicineOslo University HospitalOsloNorway
- Institute of Clinical Medicine, Faculty of MedicineUniversity of OsloOsloNorway
- Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| |
Collapse
|
21
|
Shiiba T, Takano K, Takaki A, Suwazono S. Dopamine transporter single-photon emission computed tomography-derived radiomics signature for detecting Parkinson's disease. EJNMMI Res 2022; 12:39. [PMID: 35759054 PMCID: PMC9237203 DOI: 10.1186/s13550-022-00910-1] [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: 02/03/2022] [Accepted: 06/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We hypothesised that the radiomics signature, which includes texture information of dopamine transporter single-photon emission computed tomography (DAT-SPECT) images for Parkinson's disease (PD), may assist semi-quantitative indices. Herein, we constructed a radiomics signature using DAT-SPECT-derived radiomics features that effectively discriminated PD from healthy individuals and evaluated its classification performance. RESULTS We analysed 413 cases of both normal control (NC, n = 101) and PD (n = 312) groups from the Parkinson's Progression Markers Initiative database. Data were divided into the training and two test datasets with different SPECT manufacturers. DAT-SPECT images were spatially normalised to the Montreal Neurologic Institute space. We calculated 930 radiomics features, including intensity- and texture-based features in the caudate, putamen, and pallidum volumes of interest. The striatum uptake ratios (SURs) of the caudate, putamen, and pallidum were also calculated as conventional semi-quantification indices. The least absolute shrinkage and selection operator was used for feature selection and construction of the radiomics signature. The four classification models were constructed using a radiomics signature and/or semi-quantitative indicator. Furthermore, we compared the classification performance of the semi-quantitative indicator alone and the combination with the radiomics signature for the classification models. The receiver operating characteristics (ROC) analysis was used to evaluate the classification performance. The classification performance of SURputamen was higher than that of other semi-quantitative indicators. The radiomics signature resulted in a slightly increased area under the ROC curve (AUC) compared to SURputamen in each test dataset. When combined with SURputamen and radiomics signature, all classification models showed slightly higher AUCs than that of SURputamen alone. CONCLUSION We constructed a DAT-SPECT image-derived radiomics signature. Performance analysis showed that the current radiomics signature would be helpful for the diagnosis of PD and has the potential to provide robust diagnostic performance.
Collapse
Affiliation(s)
- Takuro Shiiba
- Department of Molecular Imaging, School of Medical Sciences, Fujita Health University, 1-98, Dengakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
| | - Kazuki Takano
- Department of Molecular Imaging, School of Medical Sciences, Fujita Health University, 1-98, Dengakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Akihiro Takaki
- Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, 6-22 Misakimachi, Omuta-shi, Fukuoka, 836-8505, Japan
| | - Shugo Suwazono
- Department of Neurology and Center for Clinical Neuroscience, National Hospital Organization Okinawa National Hospital, 3-20-14 Ganeko, Ginowan, 901-2214, Okinawa, Japan
| |
Collapse
|
22
|
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.
Collapse
|
23
|
Falahatpour Z, Geramifar P, Mahdavi SR, Abdollahi H, Salimi Y, Nikoofar A, Ay MR. Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy. J Appl Clin Med Phys 2022; 23:e13696. [PMID: 35699200 PMCID: PMC9512354 DOI: 10.1002/acm2.13696] [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: 11/13/2021] [Revised: 04/20/2022] [Accepted: 05/27/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy. METHODS Thirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTVCT , CTVCT ), PET (GTVPET40 , CTVPET40 ), and RFMs (GTVRFM , CTVRFM ,). Intratumoral heterogeneity areas were segmented as GTVPET50-Boost and radiomic boost target volume (RTVBoost ) on PET and RFMs, respectively. GTVCT in homogenous tumors and GTVPET40 in heterogeneous tumors were considered as GTVgold standard (GTVGS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTVRFM with GTVGS . Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM-derived target volumes. RESULTS Entropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTVRFM-entropy , GTVRFM-contrast , and GTVRFM-H-correlation with GTVGS , respectively. The linear regression results showed a positive correlation between GTVGS and GTVRFM-entropy (r = 0.98, p < 0.001), between GTVGS and GTVRFM-contrast (r = 0.93, p < 0.001), and between GTVGS and GTVRFM-H-correlation (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTVRFM-entropy and CTVRFM-contrast with CTVGS , respectively. Moreover, we used RFM to determine RTVBoost in the heterogeneous tumors. Comparison of RTVBoost with GTVPET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTVBoost-entropy , RTVBoost-contrast , and RTVBoost-H-correlation , respectively. CONCLUSIONS FDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.
Collapse
Affiliation(s)
- Zahra Falahatpour
- Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Rabie Mahdavi
- Department of Medical Physics, Faculty of Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiology Technology, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Yazdan Salimi
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Nikoofar
- Department of Radiation Oncology, Faculty of Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Ay
- Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
24
|
Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study. Tomography 2022; 8:1113-1128. [PMID: 35448725 PMCID: PMC9025788 DOI: 10.3390/tomography8020091] [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] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/04/2022] [Accepted: 04/08/2022] [Indexed: 01/12/2023] Open
Abstract
For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strategy on feature robustness and agreement. A purpose-built texture pattern phantom was scanned on 10 different PET scanners in 7 institutions with various different image acquisition and reconstruction protocols. An image harmonization technique based on equalizing a contrast-to-noise ratio was employed to generate a "harmonized" alongside a "standard" dataset for a reproducibility study. In addition, a repeatability study was performed with images from a single PET scanner of variable image noise, varying the binning time of the reconstruction. Feature agreement was measured using the intraclass correlation coefficient (ICC). In the repeatability study, 81/93 features had a lower ICC on the images with the highest image noise as compared to the images with the lowest image noise. Using the harmonized dataset significantly improved the feature agreement for five of the six investigated feature classes over the standard dataset. For three feature classes, high feature agreement corresponded with higher sensitivity to the different patterns, suggesting a way to select suitable features for predictive models.
Collapse
|
25
|
Lv W, Xu H, Han X, Zhang H, Ma J, Rahmim A, Lu L. Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer. Cancers (Basel) 2022; 14:cancers14071674. [PMID: 35406449 PMCID: PMC8996849 DOI: 10.3390/cancers14071674] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/19/2022] [Accepted: 03/21/2022] [Indexed: 12/15/2022] Open
Abstract
Simple Summary This study investigated the ability of context-aware saliency-guided PET/CT radiomics in the prediction of outcome and HPV status for head and neck cancer. In total, 806 HNC patients (training vs. validation vs. external testing: 500 vs. 97 vs. 209) from 9 centers were collected from The Cancer Imaging Archive (TCIA). Saliency-guided radiomics showed enhanced performance for both outcome and HPV-status predictions relative to conventional radiomics. The radiomics-predicted HPV status also showed complementary prognostic value. This multi-center study highlights the feasibility of saliency-guided PET/CT radiomics in outcome predictions of head and neck cancer, confirming that certain regions are more relevant to tumor aggressiveness and prognosis. Abstract Purpose: This multi-center study aims to investigate the prognostic value of context-aware saliency-guided radiomics in 18F-FDG PET/CT images of head and neck cancer (HNC). Methods: 806 HNC patients (training vs. validation vs. external testing: 500 vs. 97 vs. 209) from 9 centers were collected from The Cancer Imaging Archive (TCIA). There were 100/384 and 60/123 oropharyngeal carcinoma (OPC) patients with human papillomavirus (HPV) status in training and testing cohorts, respectively. Six types of images were used for radiomics feature extraction and further model construction, namely (i) the original image (Origin), (ii) a context-aware saliency map (SalMap), (iii, iv) high- or low-saliency regions in the original image (highSal or lowSal), (v) a saliency-weighted image (SalxImg), and finally, (vi) a fused PET-CT image (FusedImg). Four outcomes were evaluated, i.e., recurrence-free survival (RFS), metastasis-free survival (MFS), overall survival (OS), and disease-free survival (DFS), respectively. Multivariate Cox analysis and logistic regression were adopted to construct radiomics scores for the prediction of outcome (Rad_Ocm) and HPV-status (Rad_HPV), respectively. Besides, the prognostic value of their integration (Rad_Ocm_HPV) was also investigated. Results: In the external testing cohort, compared with the Origin model, SalMap and SalxImg achieved the highest C-indices for RFS (0.621 vs. 0.559) and MFS (0.785 vs. 0.739) predictions, respectively, while FusedImg performed the best for both OS (0.685 vs. 0.659) and DFS (0.641 vs. 0.582) predictions. In the OPC HPV testing cohort, FusedImg showed higher AUC for HPV-status prediction compared with the Origin model (0.653 vs. 0.484). In the OPC testing cohort, compared with Rad_Ocm or Rad_HPV alone, Rad_Ocm_HPV performed the best for OS and DFS predictions with C-indices of 0.702 (p = 0.002) and 0.684 (p = 0.006), respectively. Conclusion: Saliency-guided radiomics showed enhanced performance for both outcome and HPV-status predictions relative to conventional radiomics. The radiomics-predicted HPV status also showed complementary prognostic value.
Collapse
Affiliation(s)
- Wenbing Lv
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Hui Xu
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Xu Han
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Hao Zhang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China;
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada;
- Department of Radiology, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC V6T 1Z1, Canada
- Department of Physics, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1, Canada
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
- Correspondence: ; Tel.: +86-020-62789116
| |
Collapse
|
26
|
The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria. J Clin Med 2022; 11:jcm11061740. [PMID: 35330068 PMCID: PMC8948743 DOI: 10.3390/jcm11061740] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/08/2022] [Accepted: 03/18/2022] [Indexed: 12/15/2022] Open
Abstract
Simple Summary The introduction of immune checkpoint inhibitors has represented a milestone in cancer treatment. Despite PD-L1 expression being the standard biomarker used before the start of therapy, there is still a strict need to identify complementary non-invasive biomarkers in order to better select patients. In this context, radiomics is an emerging approach for examining medical images and clinical data by capturing multiple features hidden from human eye and is potentially able to predict response assessment and survival in the course of immunotherapy. We reviewed the available studies investigating the role of radiomics in cancer patients, focusing on non-small cell lung cancer treated with immune checkpoint inhibitors. Although preliminary research shows encouraging results, different issues need to be solved before radiomics can enter into clinical practice. Abstract Immune checkpoint inhibitors (ICI) have demonstrated encouraging results in terms of durable clinical benefit and survival in several malignancies. Nevertheless, the search to identify an “ideal” biomarker for predicting response to ICI is still far from over. Radiomics is a new translational field of study aiming to extract, by dedicated software, several features from a given medical image, ranging from intensity distribution and spatial heterogeneity to higher-order statistical parameters. Based on these premises, our review aims to summarize the current status of radiomics as a potential predictor of clinical response following immunotherapy treatment. A comprehensive search of PubMed results was conducted. All studies published in English up to and including December 2021 were selected, comprising those that explored computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for radiomic analyses in the setting of ICI. Several studies have demonstrated the potential applicability of radiomic features in the monitoring of the therapeutic response beyond the traditional morphologic and metabolic criteria, as well as in the prediction of survival or non-invasive assessment of the tumor microenvironment. Nevertheless, important limitations emerge from our review in terms of standardization in feature selection, data sharing, and methods, as well as in external validation. Additionally, there is still need for prospective clinical trials to confirm the potential significant role of radiomics during immunotherapy.
Collapse
|
27
|
Escobar T, Vauclin S, Orlhac F, Nioche C, Pineau P, Champion L, Brisse H, Buvat I. Voxel-wise supervised analysis of tumors with multimodal engineered features to highlight interpretable biological patterns. Med Phys 2022; 49:3816-3829. [PMID: 35302238 PMCID: PMC9325536 DOI: 10.1002/mp.15603] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/31/2022] [Accepted: 02/27/2022] [Indexed: 11/15/2022] Open
Abstract
Background Translation of predictive and prognostic image‐based learning models to clinical applications is challenging due in part to their lack of interpretability. Some deep‐learning‐based methods provide information about the regions driving the model output. Yet, due to the high‐level abstraction of deep features, these methods do not completely solve the interpretation challenge. In addition, low sample size cohorts can lead to instabilities and suboptimal convergence for models involving a large number of parameters such as convolutional neural networks. Purpose Here, we propose a method for designing radiomic models that combines the interpretability of handcrafted radiomics with a sub‐regional analysis. Materials and methods Our approach relies on voxel‐wise engineered radiomic features with average global aggregation and logistic regression. The method is illustrated using a small dataset of 51 soft tissue sarcoma (STS) patients where the task is to predict the risk of lung metastasis occurrence during the follow‐up period. Results Using positron emission tomography/computed tomography and two magnetic resonance imaging sequences separately to build two radiomic models, we show that our approach produces quantitative maps that highlight the signal that contributes to the decision within the tumor region of interest. In our STS example, the analysis of these maps identified two biological patterns that are consistent with STS grading systems and knowledge: necrosis development and glucose metabolism of the tumor. Conclusions We demonstrate how that method makes it possible to spatially and quantitatively interpret radiomic models amenable to sub‐regions identification and biological interpretation for patient stratification.
Collapse
Affiliation(s)
- Thibault Escobar
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Université Paris Saclay, U1288 Inserm, Institut Curie, Orsay, France.,DOSIsoft SA, Cachan, France
| | | | - Fanny Orlhac
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Université Paris Saclay, U1288 Inserm, Institut Curie, Orsay, France
| | - Christophe Nioche
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Université Paris Saclay, U1288 Inserm, Institut Curie, Orsay, France
| | | | - Laurence Champion
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Université Paris Saclay, U1288 Inserm, Institut Curie, Orsay, France.,Department of Nuclear Medicine and Endocrine Oncology, Institut Curie, Saint-Cloud, France
| | - Hervé Brisse
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Université Paris Saclay, U1288 Inserm, Institut Curie, Orsay, France.,Department of Medical Imaging, Institut Curie, Paris, France
| | - Irène Buvat
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Université Paris Saclay, U1288 Inserm, Institut Curie, Orsay, France
| |
Collapse
|
28
|
Qi TH, Hian OH, Kumaran AM, Tan TJ, Cong TRY, Su-Xin GL, Lim EH, Ng R, Yeo MCR, Tching FLLW, Zewen Z, Hui CYS, Xin WR, Ooi SKG, Leong LCH, Tan SM, Preetha M, Sim Y, Tan VKM, Yeong J, Yong WF, Cai Y, Nei WL. Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer. Breast Cancer Res Treat 2022; 193:121-138. [PMID: 35262831 DOI: 10.1007/s10549-022-06521-7] [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: 09/30/2021] [Accepted: 12/31/2021] [Indexed: 01/06/2023]
Abstract
BACKGROUND Neoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumors, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumors' response to chemotherapy and provides important prognostic information. There are currently no clearly defined clinical models that incorporate imaging with clinical data to predict response to NAC. Thus, the aim of this work is to develop a predictive AI model based on routine CT imaging and clinical parameters to predict response to NAC. METHODS The CT scans of 324 patients with NAC from multiple centers in Singapore were used in this study. Four different radiomics models were built for predicting pathological complete response (pCR): first two were based on textural features extracted from peri-tumoral and tumoral regions, the third model based on novel space-resolved radiomics which extract feature maps using voxel-based radiomics and the fourth model based on deep learning (DL). Clinical parameters were included to build a final prognostic model. RESULTS The best performing models were based on space-resolved and DL approaches. Space-resolved radiomics improves the clinical AUCs of pCR prediction from 0.743 (0.650 to 0.831) to 0.775 (0.685 to 0.860) and our DL model improved it from 0.743 (0.650 to 0.831) to 0.772 (0.685 to 0.853). The tumoral radiomics model performs the worst with no improvement of the AUC from the clinical model. The peri-tumoral combined model gives moderate performance with an AUC of 0.765 (0.671 to 0.855). CONCLUSIONS Radiomics features extracted from diagnostic CT augment the predictive ability of pCR when combined with clinical features. The novel space-resolved radiomics and DL radiomics approaches outperformed conventional radiomics techniques.
Collapse
Affiliation(s)
- Tan Hong Qi
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Ong Hiok Hian
- School of Computer Science and Engineering, Nanyang Technological University Singapore, Singapore, Singapore
| | - Arjunan Muthu Kumaran
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Tira J Tan
- Division of Medical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Tan Ryan Ying Cong
- Division of Medical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Ghislaine Lee Su-Xin
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Elaine Hsuen Lim
- Division of Medical Oncology, National Cancer Center Singapore, Singapore, Singapore
| | - Raymond Ng
- Division of Medical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Ming Chert Richard Yeo
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Faye Lynette Lim Wei Tching
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Zhang Zewen
- Division of Medical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Christina Yang Shi Hui
- Division of Surgery and Surgical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
| | - Wong Ru Xin
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Su Kai Gideon Ooi
- Division of Oncologic Imaging, National Cancer Center Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Lester Chee Hao Leong
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
| | - Su Ming Tan
- Division of Breast Surgery, Changi General Hospital, Singapore, Singapore
| | - Madhukumar Preetha
- Division of Surgery and Surgical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
| | - Yirong Sim
- Division of Surgery and Surgical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
| | - Veronique Kiak Mien Tan
- Division of Surgery and Surgical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
| | - Joe Yeong
- Division of Pathology, Singapore General Hospital, Singapore, Singapore.,Institute of Molecular and Cell Biology, Agency for Science Technology and Research, Singapore, Singapore
| | - Wong Fuh Yong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore. .,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore.
| | - Yiyu Cai
- School of Mechanical & Aerospace Engineering, Nanyang Technological University Singapore, Singapore, Singapore.
| | - Wen Long Nei
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore. .,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore.
| | | | | |
Collapse
|
29
|
Vaz SC, Adam JA, Bolton RCD, Vera P, van Elmpt W, Herrmann K, Hicks RJ, Lievens Y, Santos A, Schöder H, Dubray B, Visvikis D, Troost EGC, de Geus-Oei LF. Joint EANM/SNMMI/ESTRO practice recommendations for the use of 2-[ 18F]FDG PET/CT external beam radiation treatment planning in lung cancer V1.0. Eur J Nucl Med Mol Imaging 2022; 49:1386-1406. [PMID: 35022844 PMCID: PMC8921015 DOI: 10.1007/s00259-021-05624-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 11/15/2021] [Indexed: 12/16/2022]
Abstract
Purpose 2-[18F]FDG
PET/CT is of utmost importance for radiation treatment (RT) planning and response monitoring in lung cancer patients, in both non-small and small cell lung cancer (NSCLC and SCLC). This topic has been addressed in guidelines composed by experts within the field of radiation oncology. However, up to present, there is no procedural guideline on this subject, with involvement of the nuclear medicine societies. Methods A literature review was performed, followed by a discussion between a multidisciplinary team of experts in the different fields involved in the RT planning of lung cancer, in order to guide clinical management. The project was led by experts of the two nuclear medicine societies (EANM and SNMMI) and radiation oncology (ESTRO). Results and conclusion This guideline results from a joint and dynamic collaboration between the relevant disciplines for this topic. It provides a worldwide, state of the art, and multidisciplinary guide to 2-[18F]FDG PET/CT RT planning in NSCLC and SCLC. These practical recommendations describe applicable updates for existing clinical practices, highlight potential flaws, and provide solutions to overcome these as well. Finally, the recent developments considered for future application are also reviewed.
Collapse
Affiliation(s)
- Sofia C Vaz
- Nuclear Medicine Radiopharmacology, Champalimaud Centre for the Unkown, Champalimaud Foundation, Lisbon, Portugal.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Judit A Adam
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Roberto C Delgado Bolton
- Department of Diagnostic Imaging (Radiology) and Nuclear Medicine, University Hospital San Pedro and Centre for Biomedical Research of La Rioja (CIBIR), Logroño (La Rioja), Spain
| | - Pierre Vera
- Henri Becquerel Cancer Center, QuantIF-LITIS EA 4108, Université de Rouen, Rouen, France
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Ken Herrmann
- Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
| | - Rodney J Hicks
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Yolande Lievens
- Radiation Oncology Department, Ghent University Hospital and Ghent University, Ghent, Belgium
| | - Andrea Santos
- Nuclear Medicine Department, CUF Descobertas Hospital, Lisbon, Portugal
| | - Heiko Schöder
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Bernard Dubray
- Department of Radiotherapy and Medical Physics, Centre Henri Becquerel, Rouen, France.,QuantIF-LITIS EA4108, University of Rouen, Rouen, France
| | | | - Esther G C Troost
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,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.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany.,German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| |
Collapse
|
30
|
Edalat-Javid M, Shiri I, Hajianfar G, Abdollahi H, Arabi H, Oveisi N, Javadian M, Shamsaei Zafarghandi M, Malek H, Bitarafan-Rajabi A, Oveisi M, Zaidi H. Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study. J Nucl Cardiol 2021; 28:2730-2744. [PMID: 32333282 DOI: 10.1007/s12350-020-02109-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 03/12/2020] [Indexed: 11/30/2022]
Abstract
BACKGROUND The aim of this work was to assess the robustness of cardiac SPECT radiomic features against changes in imaging settings, including acquisition, and reconstruction parameters. METHODS Four commercial SPECT and SPECT/CT cameras were used to acquire images of a static cardiac phantom mimicking typical myorcardial perfusion imaging using 185 MBq of 99mTc. The effects of different image acquisition and reconstruction parameters, including number of views, view matrix size, attenuation correction, as well as image reconstruction related parameters (algorithm, number of iterations, number of subsets, type of post-reconstruction filter, and its associated parameters, including filter order and cut-off frequency) were studied. In total, 5,063 transverse views were reconstructed by varying the aforementioned factors. Eighty-seven radiomic features including first-, second-, and high-order textures were extracted from these images. To assess reproducibility and repeatability, the coefficient of variation (COV), as a widely adopted metric, was measured for each of the radiomic features over the different imaging settings. RESULTS The Inverse Difference Moment Normalized (IDMN) and Inverse Difference Normalized (IDN) features from the Gray Level Co-occurrence Matrix (GLCM), Run Percentage (RP) from the Gray Level Co-occurrence Matrix (GLRLM), Zone Entropy (ZE) from the Gray Level Size Zone Matrix (GLSZM), and Dependence Entropy (DE) from the Gray Level Dependence Matrix (GLDM) feature sets were the only features that exhibited high reproducibility (COV ≤ 5%) against changes in all imaging settings. In addition, Large Area Low Gray Level Emphasis (LALGLE), Small Area Low Gray Level Emphasis (SALGLE) and Low Gray Level Zone Emphasis (LGLZE) from GLSZM, and Small Dependence Low Gray Level Emphasis (SDLGLE) from GLDM feature sets turned out to be less reproducible (COV > 20%) against changes in imaging settings. The GLRLM (31.88%) and GLDM feature set (54.2%) had the highest (COV < 5%) and lowest (COV > 20%) number of the reproducible features, respectively. Matrix size had the largest impact on feature variability as most of the features were not repeatable when matrix size was modified with 82.8% of them having a COV > 20%. CONCLUSION The repeatability and reproducibility of SPECT/CT cardiac radiomic features under different imaging settings is feature-dependent. Different image acquisition and reconstruction protocols have variable effects on radiomic features. The radiomic features exhibiting low COV are potential candidates for future clinical studies.
Collapse
Affiliation(s)
- Mohammad Edalat-Javid
- Department of Energy Engineering and Physics, Amir Kabir University of Technology, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Niki Oveisi
- School of Population and Public Health, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Mohammad Javadian
- Department of Computer Engineering, Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
| | | | - Hadi Malek
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ahmad Bitarafan-Rajabi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, Geneva University, 1205, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
| |
Collapse
|
31
|
Sun R, Deutsch E, Fournier L. [Artificial intelligence and medical imaging]. Bull Cancer 2021; 109:83-88. [PMID: 34782120 DOI: 10.1016/j.bulcan.2021.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/31/2021] [Accepted: 09/02/2021] [Indexed: 01/06/2023]
Abstract
The use of artificial intelligence methods for image recognition is one of the most developed branches of the AI field and these technologies are now commonly used in our daily lives. In the field of medical imaging, approaches based on artificial intelligence are particularly promising, with numerous applications and a strong interest in the search for new biomarkers. Here, we will present the general methods used in these approaches as well as the potential areas of application.
Collapse
Affiliation(s)
- Roger Sun
- Gustave Roussy Cancer Campus, université Paris-Saclay, département de Radiothérapie, Inserm U1030, 94805 Villejuif, France.
| | - Eric Deutsch
- Gustave Roussy Cancer Campus, université Paris-Saclay, département de Radiothérapie, Inserm U1030, 94805 Villejuif, France
| | - Laure Fournier
- Hôpital Européen Georges-Pompidou, département de radiologie, 20, rue Leblanc, 75015 Paris, France
| |
Collapse
|
32
|
Wang L, Xu N, Song J. Decoding intra-tumoral spatial heterogeneity on radiological images using the Hilbert curve. Insights Imaging 2021; 12:154. [PMID: 34716809 PMCID: PMC8557226 DOI: 10.1186/s13244-021-01100-8] [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: 07/01/2021] [Accepted: 09/26/2021] [Indexed: 12/17/2022] Open
Abstract
Background Current intra-tumoral heterogeneous feature extraction in radiology is limited to the use of a single slice or the region of interest within a few context-associated slices, and the decoding of intra-tumoral spatial heterogeneity using whole tumor samples is rare. We aim to propose a mathematical model of space-filling curve-based spatial correspondence mapping to interpret intra-tumoral spatial locality and heterogeneity. Methods A Hilbert curve-based approach was employed to decode and visualize intra-tumoral spatial heterogeneity by expanding the tumor volume to a two-dimensional (2D) matrix in voxels while preserving the spatial locality of the neighboring voxels. The proposed method was validated using three-dimensional (3D) volumes constructed from lung nodules from the LIDC-IDRI dataset, regular axial plane images, and 3D blocks. Results Dimensionality reduction of the Hilbert volume with a single regular axial plane image showed a sparse and scattered pixel distribution on the corresponding 2D matrix. However, for 3D blocks and lung tumor inside the volume, the dimensionality reduction to the 2D matrix indicated regular and concentrated squares and rectangles. For classification into benign and malignant masses using lung nodules from the LIDC-IDRI dataset, the Inception-V4 indicated that the Hilbert matrix images improved accuracy (85.54% vs. 73.22%, p < 0.001) compared to the original CT images of the test dataset. Conclusions Our study indicates that Hilbert curve-based spatial correspondence mapping is promising for decoding intra-tumoral spatial heterogeneity of partial or whole tumor samples on radiological images. This spatial-locality-preserving approach for voxel expansion enables existing radiomics and convolution neural networks to filter structured and spatially correlated high-dimensional intra-tumoral heterogeneity. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-021-01100-8.
Collapse
Affiliation(s)
- Lu Wang
- School of Health Management, China Medical University, No. 77 Puhe Rd, Shenbei District, Shenyang, 110122, Liaoning, China
| | - Nan Xu
- School of Health Management, China Medical University, No. 77 Puhe Rd, Shenbei District, Shenyang, 110122, Liaoning, China
| | - Jiangdian Song
- School of Health Management, China Medical University, No. 77 Puhe Rd, Shenbei District, Shenyang, 110122, Liaoning, China.
| |
Collapse
|
33
|
Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 2021; 11:4431-4460. [PMID: 34603997 DOI: 10.21037/qims-21-86] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/17/2021] [Indexed: 12/13/2022]
Abstract
Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.
Collapse
Affiliation(s)
- Cindy Xue
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China.,Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Gladys G Lo
- Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Amy T Y Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Darren M C Poon
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| |
Collapse
|
34
|
Orlhac F, Nioche C, Klyuzhin I, Rahmim A, Buvat I. Radiomics in PET Imaging:: A Practical Guide for Newcomers. PET Clin 2021; 16:597-612. [PMID: 34537132 DOI: 10.1016/j.cpet.2021.06.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Radiomics has undergone considerable development in recent years. In PET imaging, very promising results concerning the ability of handcrafted features to predict the biological characteristics of lesions and to assess patient prognosis or response to treatment have been reported in the literature. This article presents a checklist for designing a reliable radiomic study, gives an overview of the steps of the pipeline, and outlines approaches for data harmonization. Tips are provided for critical reading of the content of articles. The advantages and limitations of handcrafted radiomics compared with deep-learning approaches for the characterization of PET images are also discussed.
Collapse
Affiliation(s)
- Fanny Orlhac
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France.
| | - Christophe Nioche
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France
| | - Ivan Klyuzhin
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
| | - Irène Buvat
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France
| |
Collapse
|
35
|
Oikonomou EK, Siddique M, Antoniades C. Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease. Cardiovasc Res 2021; 116:2040-2054. [PMID: 32090243 DOI: 10.1093/cvr/cvaa021] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 11/29/2019] [Accepted: 01/23/2020] [Indexed: 12/23/2022] Open
Abstract
ABSTRACT Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.
Collapse
Affiliation(s)
- Evangelos K Oikonomou
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Department of Internal Medicine, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, USA
| | - Musib Siddique
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Caristo Diagnostics Ltd., Oxford, UK
| | - Charalambos Antoniades
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Oxford Centre of Research Excellence, British Heart Foundation, Oxford, UK.,Oxford Biomedical Research Centre, National Institute of Health Research, Oxford, UK
| |
Collapse
|
36
|
Fournier L, Costaridou L, Bidaut L, Michoux N, Lecouvet FE, de Geus-Oei LF, Boellaard R, Oprea-Lager DE, Obuchowski NA, Caroli A, Kunz WG, Oei EH, O'Connor JPB, Mayerhoefer ME, Franca M, Alberich-Bayarri A, Deroose CM, Loewe C, Manniesing R, Caramella C, Lopci E, Lassau N, Persson A, Achten R, Rosendahl K, Clement O, Kotter E, Golay X, Smits M, Dewey M, Sullivan DC, van der Lugt A, deSouza NM, European Society Of Radiology. Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers. Eur Radiol 2021; 31:6001-6012. [PMID: 33492473 PMCID: PMC8270834 DOI: 10.1007/s00330-020-07598-8] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/16/2020] [Accepted: 12/03/2020] [Indexed: 02/07/2023]
Abstract
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.
Collapse
Affiliation(s)
- Laure Fournier
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
| | - Lena Costaridou
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- School of Medicine, University of Patras, University Campus, Rio, 26 500, Patras, Greece
| | - Luc Bidaut
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- College of Science, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Nicolas Michoux
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Frederic E Lecouvet
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Lioe-Fee de Geus-Oei
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands
| | - Ronald Boellaard
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
| | - Daniela E Oprea-Lager
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
| | - Nancy A Obuchowski
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - Anna Caroli
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - Wolfgang G Kunz
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Edwin H Oei
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - James P B O'Connor
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Marius E Mayerhoefer
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Manuela Franca
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Centro Hospitalar Universitário do Porto, Instituto de Ciências Biomédicas de Abel Salazar, University of Porto, Porto, Portugal
| | - Angel Alberich-Bayarri
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers in Medicine (QUIBIM), Valencia, Spain
| | - Christophe M Deroose
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Christian Loewe
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cardiovascular and Interventional Radiology, Dept. for Bioimaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Rashindra Manniesing
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Caroline Caramella
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Radiology Department, Hôpital Marie Lannelongue, Institut d'Oncologie Thoracique, Université Paris-Saclay, Le Plessis-Robinson, France
| | - Egesta Lopci
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, Humanitas Clinical and Research Hospital - IRCCS, Rozzano, MI, Italy
| | - Nathalie Lassau
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Imaging Department, Gustave Roussy Cancer Campus Grand, Paris, UMR 1281, INSERM, CNRS, CEA, Universite Paris-Saclay, Saint-Aubin, France
| | - Anders Persson
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, and Department of Health, Medicine and Caring Sciences, Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Rik Achten
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Medical Imaging, Ghent University Hospital, Gent, Belgium
| | - Karen Rosendahl
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Hospital of North Norway, Tromsø, Norway
| | - Olivier Clement
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
| | - Elmar Kotter
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Xavier Golay
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Queen Square Institute of Neurology, University College London, London, UK
| | - Marion Smits
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Marc Dewey
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel C Sullivan
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Dept. of Radiology, Duke University, 311 Research Dr, Durham, NC, 27710, USA
| | - Aad van der Lugt
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Nandita M deSouza
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria.
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium.
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA.
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK.
| | | |
Collapse
|
37
|
Sun R, Lerousseau M, Henry T, Carré A, Leroy A, Estienne T, Niyoteka S, Bockel S, Rouyar A, Alvarez Andres É, Benzazon N, Battistella E, Classe M, Robert C, Scoazec JY, Deutsch É. [Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations]. Cancer Radiother 2021; 25:630-637. [PMID: 34284970 DOI: 10.1016/j.canrad.2021.06.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 06/19/2021] [Indexed: 12/24/2022]
Abstract
Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.
Collapse
Affiliation(s)
- R Sun
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France.
| | - M Lerousseau
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - T Henry
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de médecine nucléaire, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
| | - A Carré
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - A Leroy
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - T Estienne
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Niyoteka
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Bockel
- Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - A Rouyar
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - É Alvarez Andres
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - N Benzazon
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - E Battistella
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | | | - C Robert
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - J Y Scoazec
- Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France; Département de biologie et pathologie médicales, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
| | - É Deutsch
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| |
Collapse
|
38
|
Target Heterogeneity in Oncology: The Best Predictor for Differential Response to Radioligand Therapy in Neuroendocrine Tumors and Prostate Cancer. Cancers (Basel) 2021; 13:cancers13143607. [PMID: 34298822 PMCID: PMC8304541 DOI: 10.3390/cancers13143607] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/04/2021] [Accepted: 07/07/2021] [Indexed: 12/27/2022] Open
Abstract
Simple Summary In the era of precision medicine, novel targets have emerged on the surface of cancer cells, which have been exploited for the purpose of radioligand therapy. However, there have been variations in the way these receptors are expressed, especially in prostate cancers and neuroendocrine tumors. This variable expression of receptors across the grades of cancers led to the concept of ‘target heterogeneity’, which has not just impacted therapeutic decisions but also their outcomes. Radiopharmaceuticals targeting receptors need to be used when there are specific indicators—either clinical, radiological, or at molecular level—warranting their use. In addition, response to these radioligands can be assessed using different techniques, whereby we can prognosticate further outcomes. We shall also discuss, in this review, the conventional as well as novel approaches of detecting heterogeneity in prostate cancers and neuroendocrine tumors. Abstract Tumor or target heterogeneity (TH) implies presence of variable cellular populations having different genomic characteristics within the same tumor, or in different tumor sites of the same patient. The challenge is to identify this heterogeneity, as it has emerged as the most common cause of ‘treatment resistance’, to current therapeutic agents. We have focused our discussion on ‘Prostate Cancer’ and ‘Neuroendocrine Tumors’, and looked at the established methods for demonstrating heterogeneity, each with its advantages and drawbacks. Also, the available theranostic radiotracers targeting PSMA and somatostatin receptors combined with targeted systemic agents, have been described. Lu-177 labeled PSMA and DOTATATE are the ‘standard of care’ radionuclide therapeutic tracers for management of progressive treatment-resistant prostate cancer and NET. These approved therapies have shown reasonable benefit in treatment outcome, with improvement in quality of life parameters. Various biomarkers and predictors of response to radionuclide therapies targeting TH which are currently available and those which can be explored have been elaborated in details. Imaging-based features using artificial intelligence (AI) need to be developed to further predict the presence of TH. Also, novel theranostic tools binding to newer targets on surface of cancer cell should be explored to overcome the treatment resistance to current treatment regimens.
Collapse
|
39
|
de Causans A, Carré A, Roux A, Tauziède-Espariat A, Ammari S, Dezamis E, Dhermain F, Reuzé S, Deutsch E, Oppenheim C, Varlet P, Pallud J, Edjlali M, Robert C. Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis. Front Oncol 2021; 11:638262. [PMID: 34327133 PMCID: PMC8315001 DOI: 10.3389/fonc.2021.638262] [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: 12/05/2020] [Accepted: 06/17/2021] [Indexed: 01/06/2023] Open
Abstract
Objectives To differentiate Glioblastomas (GBM) and Brain Metastases (BM) using a radiomic features-based Machine Learning (ML) classifier trained from post-contrast three-dimensional T1-weighted (post-contrast 3DT1) MR imaging, and compare its performance in medical diagnosis versus human experts, on a testing cohort. Methods We enrolled 143 patients (71 GBM and 72 BM) in a retrospective bicentric study from January 2010 to May 2019 to train the classifier. Post-contrast 3DT1 MR images were performed on a 3-Tesla MR unit and 100 radiomic features were extracted. Selection and optimization of the Machine Learning (ML) classifier was performed using a nested cross-validation. Sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were calculated as performance metrics. The model final performance was cross-validated, then evaluated on a test set of 37 patients, and compared to human blind reading using a McNemar’s test. Results The ML classifier had a mean [95% confidence interval] sensitivity of 85% [77; 94], a specificity of 87% [78; 97], a balanced accuracy of 86% [80; 92], and an AUC of 92% [87; 97] with cross-validation. Sensitivity, specificity, balanced accuracy and AUC were equal to 75, 86, 80 and 85% on the test set. Sphericity 3D radiomic index highlighted the highest coefficient in the logistic regression model. There were no statistical significant differences observed between the performance of the classifier and the experts’ blinded examination. Conclusions The proposed diagnostic support system based on radiomic features extracted from post-contrast 3DT1 MR images helps in differentiating solitary BM from GBM with high diagnosis performance and generalizability.
Collapse
Affiliation(s)
- Alix de Causans
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | - Alexandre Carré
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Alexandre Roux
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Arnault Tauziède-Espariat
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neuropathologie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Samy Ammari
- Département de Radiologie, Gustave Roussy, Université Paris Saclay, Villejuif, France.,BioMaps UMR1281, Université Paris-Saclay, CNRS, INSERM, CEA, Orsay, France
| | - Edouard Dezamis
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Frederic Dhermain
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Sylvain Reuzé
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Eric Deutsch
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Catherine Oppenheim
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | | | - Johan Pallud
- Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France.,Service de Neurochirurgie, GHU Paris - Psychiatrie et Neurosciences - Hôpital Sainte-Anne, Paris, France
| | - Myriam Edjlali
- Neuroradiology Department, Hôpital Sainte-Anne, GHU-Paris Psychiatrie et Neurosciences, Paris, France.,Université de Paris, Paris, France.,Inserm, UMR1266, IMA-Brain, Institut de Psychiatrie et Neurosciences, Paris, France
| | - Charlotte Robert
- Radiothérapie Moléculaire et Innovation Thérapeutique, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris Saclay, Villejuif, France
| |
Collapse
|
40
|
Carles M, Fechter T, Grosu AL, Sörensen A, Thomann B, Stoian RG, Wiedenmann N, Rühle A, Zamboglou C, Ruf J, Martí-Bonmatí L, Baltas D, Mix M, Nicolay NH. 18F-FMISO-PET Hypoxia Monitoring for Head-and-Neck Cancer Patients: Radiomics Analyses Predict the Outcome of Chemo-Radiotherapy. Cancers (Basel) 2021; 13:3449. [PMID: 34298663 PMCID: PMC8303992 DOI: 10.3390/cancers13143449] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 12/24/2022] Open
Abstract
Tumor hypoxia is associated with radiation resistance and can be longitudinally monitored by 18F-fluoromisonidazole (18F-FMISO)-PET/CT. Our study aimed at evaluating radiomics dynamics of 18F-FMISO-hypoxia imaging during chemo-radiotherapy (CRT) as predictors for treatment outcome in head-and-neck squamous cell carcinoma (HNSCC) patients. We prospectively recruited 35 HNSCC patients undergoing definitive CRT and longitudinal 18F-FMISO-PET/CT scans at weeks 0, 2 and 5 (W0/W2/W5). Patients were classified based on peritherapeutic variations of the hypoxic sub-volume (HSV) size (increasing/stable/decreasing) and location (geographically-static/geographically-dynamic) by a new objective classification parameter (CP) accounting for spatial overlap. Additionally, 130 radiomic features (RF) were extracted from HSV at W0, and their variations during CRT were quantified by relative deviations (∆RF). Prediction of treatment outcome was considered statistically relevant after being corrected for multiple testing and confirmed for the two 18F-FMISO-PET/CT time-points and for a validation cohort. HSV decreased in 64% of patients at W2 and in 80% at W5. CP distinguished earlier disease progression (geographically-dynamic) from later disease progression (geographically-static) in both time-points and cohorts. The texture feature low grey-level zone emphasis predicted local recurrence with AUCW2 = 0.82 and AUCW5 = 0.81 in initial cohort (N = 25) and AUCW2 = 0.79 and AUCW5 = 0.80 in validation cohort. Radiomics analysis of 18F-FMISO-derived hypoxia dynamics was able to predict outcome of HNSCC patients after CRT.
Collapse
Affiliation(s)
- Montserrat Carles
- Department of Radiation Oncology, Division of Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (T.F.); (B.T.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain;
| | - Tobias Fechter
- Department of Radiation Oncology, Division of Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (T.F.); (B.T.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
| | - Anca L. Grosu
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Arnd Sörensen
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Benedikt Thomann
- Department of Radiation Oncology, Division of Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (T.F.); (B.T.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
| | - Raluca G. Stoian
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Nicole Wiedenmann
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Alexander Rühle
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Constantinos Zamboglou
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Juri Ruf
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Luis Martí-Bonmatí
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain;
| | - Dimos Baltas
- Department of Radiation Oncology, Division of Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (T.F.); (B.T.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
| | - Michael Mix
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Nuclear Medicine, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Nils H. Nicolay
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (A.L.G.); (A.S.); (R.G.S.); (N.W.); (A.R.); (C.Z.); (J.R.); (M.M.); (N.H.N.)
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| |
Collapse
|
41
|
Shiri I, Maleki H, Hajianfar G, Abdollahi H, Ashrafinia S, Hatt M, Zaidi H, Oveisi M, Rahmim A. Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms. Mol Imaging Biol 2021; 22:1132-1148. [PMID: 32185618 DOI: 10.1007/s11307-020-01487-8] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the same time, NSCLC management through KRAS and EGFR mutation profiling faces challenges. In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC patients based on radiomic features from low-dose computed tomography (CT), contrast-enhanced diagnostic quality CT (CTD), and positron emission tomography (PET) imaging modalities and use of machine learning algorithms. METHODS Our study involved NSCLC patients including 150 PET, low-dose CT, and CTD images. Radiomic features from original and preprocessed (including 64 bin discretizing, Laplacian-of-Gaussian (LOG), and Wavelet) images were extracted. Conventional clinically used standard uptake value (SUV) parameters and metabolic tumor volume (MTV) were also obtained from PET images. Highly correlated features were pre-eliminated, and false discovery rate (FDR) correction was performed with the resulting q-values reported for univariate analysis. Six feature selection methods and 12 classifiers were then used for multivariate prediction of gene mutation status (provided by polymerase chain reaction (PCR)) in patients. We performed 10-fold cross-validation for model tuning to improve robustness, and our developed models were assessed on an independent validation set with 68 patients (common in all three imaging modalities). The average area under the receiver operator characteristic curve (AUC) was utilized for performance evaluation. RESULTS The best predictive power for conventional PET parameters was achieved by SUVpeak (AUC 0.69, p value = 0.0002) and MTV (AUC 0.55, p value = 0.0011) for EGFR and KRAS, respectively. Univariate analysis of extracted radiomics features improved AUC performance to 0.75 (q-value 0.003, Short-Run Emphasis feature of GLRLM from LOG preprocessed image of PET with sigma value 1.5) and 0.71 (q-value 0.00005, Large Dependence Low Gray-Level Emphasis feature of GLDM in LOG preprocessed image of CTD with sigma value 5) for EGFR and KRAS, respectively. Furthermore, multivariate machine learning-based AUC performances were significantly improved to 0.82 for EGFR (LOG preprocessed image of PET with sigma 3 with variance threshold (VT) feature selector and stochastic gradient descent (SGD) classifier (q-value = 4.86E-05) and 0.83 for KRAS (LOG preprocessed image of CT with sigma 3.5 with select model (SM) feature selector and SGD classifier (q-value = 2.81E-09). CONCLUSION Our work demonstrated that non-invasive and reliable radiomics analysis can be successfully used to predict EGFR and KRAS mutation status in NSCLC patients. We demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients and showed improved predictive power relative to conventional image-derived metrics.
Collapse
Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Hasan Maleki
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.,Department of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Hamid Abdollahi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.,Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Saeed Ashrafinia
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA.,Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.,Geneva University Neurocenter, Geneva University, Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mehrdad Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.,Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Arman Rahmim
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA. .,Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada. .,Department of Integrative Oncology, BC Cancer Research Centre, 675 West 10th Ave, Vancouver, BC, V5Z 1L3, Canada.
| |
Collapse
|
42
|
Carles M, Fechter T, Martí-Bonmatí L, Baltas D, Mix M. Experimental phantom evaluation to identify robust positron emission tomography (PET) radiomic features. EJNMMI Phys 2021; 8:46. [PMID: 34117929 PMCID: PMC8197692 DOI: 10.1186/s40658-021-00390-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 05/12/2021] [Indexed: 12/14/2022] Open
Abstract
Background Radiomics analysis usually involves, especially in multicenter and large hospital studies, different imaging protocols for acquisition, reconstruction, and processing of data. Differences in protocols can lead to differences in the quantification of the biomarker distribution, leading to radiomic feature variability. The aim of our study was to identify those radiomic features robust to the different degrading factors in positron emission tomography (PET) studies. We proposed the use of the standardized measurements of the European Association Research Ltd. (EARL) accreditation to retrospectively identify the radiomic features having low variability to the different systems and reconstruction protocols. In addition, we presented a reproducible procedure to identify PET radiomic features robust to PET/CT imaging metal artifacts. In 27 heterogeneous homemade phantoms for which ground truth was accurately defined by CT segmentation, we evaluated the segmentation accuracy and radiomic feature reliability given by the contrast-oriented algorithm (COA) and the 40% threshold PET segmentation. In the comparison of two data sets, robustness was defined by Wilcoxon rank tests, bias was quantified by Bland–Altman (BA) plot analysis, and strong correlations were identified by Spearman correlation test (r > 0.8 and p satisfied multiple test Bonferroni correction). Results Forty-eight radiomic features were robust to system, 22 to resolution, 102 to metal artifacts, and 42 to different PET segmentation tools. Overall, only 4 radiomic features were simultaneously robust to all degrading factors. Although both segmentation approaches significantly underestimated the volume with respect to the ground truth, with relative deviations of −62 ± 36% for COA and −50 ± 44% for 40%, radiomic features derived from the ground truth were strongly correlated and/or robust to 98 radiomic features derived from COA and to 102 from 40%. Conclusion In multicenter studies, we recommend the analysis of EARL accreditation measurements in order to retrospectively identify the robust PET radiomic features. Furthermore, 4 radiomic features (area under the curve of the cumulative SUV volume histogram, skewness, kurtosis, and gray-level variance derived from GLRLM after application of an equal probability quantization algorithm on the voxels within lesion) were robust to all degrading factors. In addition, the feasibility of 40% and COA segmentations for their use in radiomics analysis has been demonstrated. Supplementary Information The online version contains supplementary material available at 10.1186/s40658-021-00390-7.
Collapse
Affiliation(s)
- Montserrat Carles
- Division of Medical Physics, Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany. .,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany. .,Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), La Fe Health Research Institute, Av. Fernando Abril Martorell, 106, 46026, Valencia, Spain.
| | - Tobias Fechter
- Division of Medical Physics, Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Luis Martí-Bonmatí
- Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), La Fe Health Research Institute, Av. Fernando Abril Martorell, 106, 46026, Valencia, Spain
| | - Dimos Baltas
- Division of Medical Physics, Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Mix
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Department of Medical Imaging and Clinical Oncology, Nuclear Medicine Division, Faculty of Medicine and Health Science, Stellenbosch University, Stellenbosch, South Africa
| |
Collapse
|
43
|
Vercellino L, de Jong D, di Blasi R, Kanoun S, Reshef R, Schwartz LH, Dercle L. Current and Future Role of Medical Imaging in Guiding the Management of Patients With Relapsed and Refractory Non-Hodgkin Lymphoma Treated With CAR T-Cell Therapy. Front Oncol 2021; 11:664688. [PMID: 34123825 PMCID: PMC8195284 DOI: 10.3389/fonc.2021.664688] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 05/05/2021] [Indexed: 12/21/2022] Open
Abstract
Chimeric antigen receptor (CAR) T-cells are a novel immunotherapy available for patients with refractory/relapsed non-Hodgkin lymphoma. In this indication, clinical trials have demonstrated that CAR T-cells achieve high rates of response, complete response, and long-term response (up to 80%, 60%, and 40%, respectively). Nonetheless, the majority of patients ultimately relapsed. This review provides an overview about the current and future role of medical imaging in guiding the management of non-Hodgkin lymphoma patients treated with CAR T-cells. It discusses the value of predictive and prognostic biomarkers to better stratify the risk of relapse, and provide a patient-tailored therapeutic strategy. At baseline, high tumor volume (assessed on CT-scan or on [18F]-FDG PET/CT) is a prognostic factor associated with treatment failure. Response assessment has not been studied extensively yet. Available data suggests that current response assessment developed on CT-scan or on [18F]-FDG PET/CT for cytotoxic systemic therapies remains relevant to estimate lymphoma response to CAR T-cell therapy. Nonetheless, atypical patterns of response and progression have been observed and should be further analyzed. The potential advantages as well as limitations of artificial intelligence and radiomics as tools providing high throughput quantitative imaging features is described.
Collapse
Affiliation(s)
- Laetitia Vercellino
- Nuclear Medicine Department Saint Louis Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Dorine de Jong
- Center for Cell Engineering, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Roberta di Blasi
- Onco-Hematology Department Saint Louis Hospital, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Salim Kanoun
- Cancer Research Center of Toulouse (CRCT), Team 9, INSERM UMR 1037, Toulouse, France
| | - Ran Reshef
- Blood and Marrow Transplantation and Cell Therapy Program, Division of Hematology/Oncology and Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York City, NY, United States
| | - Lawrence H. Schwartz
- Department of Radiology, New York Presbyterian, Columbia University Irving Medical Center, New York City, NY, United States
| | - Laurent Dercle
- Department of Radiology, New York Presbyterian, Columbia University Irving Medical Center, New York City, NY, United States
| |
Collapse
|
44
|
|
45
|
Mesguich C, Hindie E, de Senneville BD, Tlili G, Pinaquy JB, Marit G, Saut O. Improved 18-FDG PET/CT diagnosis of multiple myeloma diffuse disease by radiomics analysis. Nucl Med Commun 2021; 42:1135-1143. [PMID: 34001823 DOI: 10.1097/mnm.0000000000001437] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
OBJECTIVES In multiple myeloma, the diagnosis of diffuse bone marrow infiltration on 18-FDG PET/CT can be challenging. We aimed to develop a PET/CT radiomics-based model that could improve the diagnosis of multiple myeloma diffuse disease on 18-FDG PET/CT. METHODS We prospectively performed PET/CT and whole-body diffusion-weighted MRI in 30 newly diagnosed multiple myeloma. MRI was the reference standard for diffuse disease assessment. Twenty patients were randomly assigned to a training set and 10 to an independent test set. Visual analysis of PET/CT was performed by two nuclear medicine physicians. Spine volumes were automatically segmented, and a total of 174 Imaging Biomarker Standardisation Initiative-compliant radiomics features were extracted from PET and CT. Selection of best features was performed with random forest features importance and correlation analysis. Machine-learning algorithms were trained on the selected features with cross-validation and evaluated on the independent test set. RESULTS Out of the 30 patients, 18 had established diffuse disease on MRI. The sensitivity, specificity and accuracy of visual analysis were 67, 75 and 70%, respectively, with a moderate kappa coefficient of agreement of 0.6. Five radiomics features were selected. On the training set, random forest classifier reached a sensitivity, specificity and accuracy of 93, 86 and 91%, respectively, with an area under the curve of 0.90 (95% confidence interval, 0.89-0.91). On the independent test set, the model achieved an accuracy of 80%. CONCLUSIONS Radiomics analysis of 18-FDG PET/CT images with machine-learning overcame the limitations of visual analysis, providing a highly accurate and more reliable diagnosis of diffuse bone marrow infiltration in multiple myeloma patients.
Collapse
Affiliation(s)
- Charles Mesguich
- Nuclear Medicine Department, CHU Bordeaux.,INSERM U1035, University of Bordeaux, Bordeaux.,University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project team Monc, Talence, France
| | | | | | | | | | | | - Olivier Saut
- University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project team Monc, Talence, France
| |
Collapse
|
46
|
Michalet M, Azria D, Tardieu M, Tibermacine H, Nougaret S. Radiomics in radiation oncology for gynecological malignancies: a review of literature. Br J Radiol 2021; 94:20210032. [PMID: 33882246 DOI: 10.1259/bjr.20210032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Radiomics is the extraction of a significant number of quantitative imaging features with the aim of detecting information in correlation with useful clinical outcomes. Features are extracted, after delineation of an area of interest, from a single or a combined set of imaging modalities (including X-ray, US, CT, PET/CT and MRI). Given the high dimensionality, the analytical process requires the use of artificial intelligence algorithms. Firstly developed for diagnostic performance in radiology, it has now been translated to radiation oncology mainly to predict tumor response and patient outcome but other applications have been developed such as dose painting, prediction of side-effects, and quality assurance. In gynecological cancers, most studies have focused on outcomes of cervical cancers after chemoradiation. This review highlights the role of this new tool for the radiation oncologists with particular focus on female GU oncology.
Collapse
Affiliation(s)
- Morgan Michalet
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Univ Montpellier, Montpellier, France.,INSERM U1194 IRCM, Montpellier, France
| | - David Azria
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Univ Montpellier, Montpellier, France.,INSERM U1194 IRCM, Montpellier, France
| | | | | | | |
Collapse
|
47
|
Hotta M, Minamimoto R, Gohda Y, Miwa K, Otani K, Kiyomatsu T, Yano H. Prognostic value of 18F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery. Ann Nucl Med 2021; 35:843-852. [PMID: 33948903 DOI: 10.1007/s12149-021-01622-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 04/27/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE The aim of this study was to evaluate the ability of texture analysis using pretreatment 18F-FDG PET/CT to predict prognosis in patients with surgically treated rectal cancer. METHODS We analyzed 94 patients with pathologically proven rectal cancer who underwent pretreatment 18F-FDG PET/CT and were subsequently treated with surgery. The volume of interest of the primary tumor was defined using a threshold of 40% of the maximum standardized uptake value (SUVmax), and conventional (SUVmax, metabolic tumor volume [MTV], total lesion glycolysis [TLG]) and textural PET features were extracted. Harmonization of PET features was performed with the ComBat method. The study endpoints were overall survival (OS) and progression-free survival (PFS), and the prognostic value of PET features was evaluated by Cox regression analysis. RESULTS In the follow-up period (median 41.7 [interquartile range, 30.5-60.4] months), 21 (22.3%) and 30 (31.9%) patients had cancer-related death or disease progression, respectively. Univariate analysis revealed a significant association of (1) MTV, TLG, and gray-level co-occurrence matrix (GLCM) entropy with OS; and (2) SUVmax, MTV, TLG, and GLCM entropy with PFS. In multivariate analysis including clinical characteristics, GLCM entropy (≥ 2.13) was the only relevant prognostic PET feature for poor OS (hazard ratio [HR]: 4.16, p = 0.035) and PFS (HR: 2.70, p = 0.046). CONCLUSION GLCM entropy, which indicates metabolic intratumoral heterogeneity, was an independent prognostic factor in patients with surgically treated rectal cancer. Compared with conventional PET features, GLCM entropy has better predictive value and shows potential to facilitate precision medicine.
Collapse
Affiliation(s)
- Masatoshi Hotta
- Division of Nuclear Medicine, Department of Radiology, National Center for Global Health and Medicine, 1-21-1, Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.
| | - Ryogo Minamimoto
- Division of Nuclear Medicine, Department of Radiology, National Center for Global Health and Medicine, 1-21-1, Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan
| | - Yoshimasa Gohda
- Department of Surgery, National Center for Global Health and Medicine, 1-21-1, Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan
| | - Kenta Miwa
- Department of Radiological Sciences, School of Health Science, International University of Health and Welfare, 2600-1 Kitakanemaru, Ohtawara City, Tochigi, 324-8501, Japan
| | - Kensuke Otani
- Department of Surgery, National Center for Global Health and Medicine, 1-21-1, Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan
| | - Tomomichi Kiyomatsu
- Department of Surgery, National Center for Global Health and Medicine, 1-21-1, Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan
| | - Hideaki Yano
- Department of Surgery, National Center for Global Health and Medicine, 1-21-1, Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan
| |
Collapse
|
48
|
Yuan J, Xue C, Lo G, Wong OL, Zhou Y, Yu SK, Cheung KY. Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy. Quant Imaging Med Surg 2021; 11:1870-1887. [PMID: 33936971 DOI: 10.21037/qims-20-865] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Background MRI pulse sequences and imaging parameters substantially influence the variation of MRI radiomics features, thus impose a critical challenge on MRI radiomics reproducibility and reliability. This study aims to prospectively investigate the impact of various imaging parameters on MRI radiomics features in a 3D T2-weighted (T2W) turbo-spin-echo (TSE) pulse sequence for MR-guided-radiotherapy (MRgRT). Methods An anthropomorphic phantom was scanned using a 3D-T2W-TSE MRgRT sequence at 1.5T under a variety of acquisition imaging parameter changes. T1 and T2 relaxation times of the phantom were also measured. 93 first-order and texture radiomics features in the original and 14 transformed images, yielding 1,395 features in total, were extracted from 10 volumes-of-interest (VOIs). The percentage deviation (d%) of radiomics feature values from the baseline values and intra-class correlation coefficient (ICC) with the baseline were calculated. Robust radiomics features were identified based on the excellent agreement of radiomics feature values with the baseline, i.e., the averaged d% <5% and ICC >0.90 in all VOIs for all imaging parameter variations. Results The radiomics feature values changed considerably but to different degrees with different imaging parameter adjustments, in the ten VOIs. The deviation d% ranged from 0.02% to 321.3%, with a mean of 12.5% averaged for all original features in all ten VOIs. First-order and GLCM features were generally more robust to imaging parameters than other features in the original images. There were also significantly different radiomics feature values (ANOVA, P<0.001) between the original and the transformed images, exhibiting quite different robustness to imaging parameters. 330 out of 1395 features (23.7%) robust to imaging parameters were identified. GLCM and GLSZM features had the most (42.5%, 153/360) and least (3.8%, 9/240) robust features in the original and transformed images, respectively. Conclusions This study helps better understand the quantitative dependence of radiomics feature values on imaging parameters in a 3D-T2W-TSE sequence for MRgRT. Imaging parameter heterogeneity should be considered as a significant source of radiomics variability and uncertainty, which must be well harmonized for reliable clinical use. The identified robust features to imaging parameters are helpful for the pre-selection of radiomics features for reliable radiomics modeling.
Collapse
Affiliation(s)
- Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Cindy Xue
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Gladys Lo
- Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Siu Ki Yu
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Kin Yin Cheung
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| |
Collapse
|
49
|
Xu P, Xue Y, Schoepf UJ, Varga-Szemes A, Griffith J, Yacoub B, Zhou F, Zhou C, Yang Y, Xing W, Zhang L. Radiomics: The Next Frontier of Cardiac Computed Tomography. Circ Cardiovasc Imaging 2021; 14:e011747. [PMID: 33722057 DOI: 10.1161/circimaging.120.011747] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Radiomics uses advanced image analysis to extract massive amounts of quantitative information from digital images, which is not otherwise distinguishable to the human eye. The mined data can be used to explore and establish new and undiscovered correlations between these imaging features and clinical end points. Cardiac computed tomography (CT) is a first-line imaging modality for evaluating coronary artery disease and has a primary role in the assessment of cardiac structures. Conventional interpretation of cardiac CT images relies mostly on subjective and qualitative analysis, as well as basic geometric quantification. To date, some proof-of-concept studies have demonstrated the feasibility and diagnostic performance of cardiac CT radiomics analysis. This review describes the current literature on radiomics in cardiac CT and discusses its advantages, challenges, and future directions. Although much evidences are needed in this field, cardiac CT radiomics has a lot to offer to patients and physicians with potential to define cardiac disease phenotypes on imaging with higher precision.
Collapse
Affiliation(s)
- Pengpeng Xu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Jiangsu, China (P.X., F.Z., C.Z., L.Z.)
| | - Yi Xue
- Department of Diagnostic Radiology, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, Jiangsu Province, China (Y.X., Y.Y., L.Z.)
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., A.V.-S., J.G., B.Y.)
| | - Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., A.V.-S., J.G., B.Y.)
| | - Joseph Griffith
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., A.V.-S., J.G., B.Y.)
| | - Basel Yacoub
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., A.V.-S., J.G., B.Y.)
| | - Fan Zhou
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Jiangsu, China (P.X., F.Z., C.Z., L.Z.)
| | - Changsheng Zhou
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Jiangsu, China (P.X., F.Z., C.Z., L.Z.)
| | - Yuting Yang
- Department of Diagnostic Radiology, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, Jiangsu Province, China (Y.X., Y.Y., L.Z.)
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University and Changzhou First People's Hospital, Jiangsu, China (W.X.)
| | - Longjiang Zhang
- Department of Diagnostic Radiology, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, Jiangsu Province, China (Y.X., Y.Y., L.Z.)
| |
Collapse
|
50
|
Carles M, Popp I, Starke MM, Mix M, Urbach H, Schimek-Jasch T, Eckert F, Niyazi M, Baltas D, Grosu AL. FET-PET radiomics in recurrent glioblastoma: prognostic value for outcome after re-irradiation? Radiat Oncol 2021; 16:46. [PMID: 33658069 PMCID: PMC7931514 DOI: 10.1186/s13014-020-01744-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 12/29/2020] [Indexed: 12/21/2022] Open
Abstract
Purpose The value of O-(2-[18F]fluoroethyl)-L-tyrosine (FET)-positron emission tomography (PET)-radiomics in the outcome assessment of patients with recurrent glioblastoma (rGBM) has not been evaluated until now.
The aim of this study was to evaluate whether a prognostic model based on FET-PET radiomics features (RF) is feasible and can identify rGBM patients that would most benefit from re-irradiation.
Methods We prospectively recruited rGBM patients who underwent FET-PET before re-irradiation (GLIAA-Pilot trial, DRKS00000633). Tumor volume was delineated using a semi-automatic method with a threshold of 1.8 times the standardized-uptake-value of the background. 135 FET-RF (histogram parameters, shape and texture features) were extracted. The analysis involved the characterization of tumor and non-tumor tissue with FET-RF and the evaluation of the prognostic value of FET-RF for time-to-progression (TTP), overall survival (OS) and recurrence location (RL). Results Thirty-two rGBM patients constituted our cohort. FET-RF discriminated significantly between tumor and non-tumor. The texture feature Small-Zone-Low-Gray-Level-Emphasis (SZLGE) showed the best performance for the prediction of TTP (p = 0.001, satisfying Bonferroni-multiple-test significance level). Additionally, two radiomics signatures could predict TTP (TTP-radiomics-signature, p = 0.001) and OS (OS-radiomics-signature, p = 0.038). SZLGE and the TTP-radiomics-signature additionally predicted RL. Specifically, high values for TTP-radiomics-signature and for SZLGE indicated not only earlier progression, but also a RL within the initial FET-PET active volume. Conclusion Our findings suggest that FET-PET radiomics could contribute to the prognostic assessment and selection of rGBM-patients benefiting from re-irradiation. Trial registration DRKS00000633. Registered on 8th of December in 2010. https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00000633.
Collapse
Affiliation(s)
- Montserrat Carles
- Division of Medical Physics, Department of Radiation Oncology, Medical Center, University of Freiburg, Robert-Koch Str. 3, 79106, Freiburg, Germany. .,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, Heidelberg, Germany. .,Biomedical Imaging Research Group (GIBI230-PREBI), Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), La Fe Health Research Institute, Valencia, Spain.
| | - Ilinca Popp
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, Heidelberg, Germany.,Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michael Maximilian Starke
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michael Mix
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, Heidelberg, Germany.,Department of Nuclear Medicine, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Tanja Schimek-Jasch
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Franziska Eckert
- Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Tübingen, Tübingen, Germany
| | - Maximilian Niyazi
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Cerman Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Munich, Munich, Germany
| | - Dimos Baltas
- Division of Medical Physics, Department of Radiation Oncology, Medical Center, University of Freiburg, Robert-Koch Str. 3, 79106, Freiburg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, Heidelberg, Germany
| | - Anca L Grosu
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, Heidelberg, Germany.,Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| |
Collapse
|