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Stanzione A, Ponsiglione A, Alessandrino F, Brembilla G, Imbriaco M. Beyond diagnosis: is there a role for radiomics in prostate cancer management? Eur Radiol Exp 2023; 7:13. [PMID: 36907973 PMCID: PMC10008761 DOI: 10.1186/s41747-023-00321-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/05/2023] [Indexed: 03/13/2023] Open
Abstract
The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | | | - Giorgio Brembilla
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
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Devan SP, Jiang X, Kang H, Luo G, Xie J, Zu Z, Stokes AM, Gore JC, McKnight CD, Kirschner AN, Xu J. Towards differentiation of brain tumor from radiation necrosis using multi-parametric MRI: Preliminary results at 4.7 T using rodent models. Magn Reson Imaging 2022; 94:144-150. [PMID: 36209946 PMCID: PMC10167709 DOI: 10.1016/j.mri.2022.10.002] [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: 07/19/2022] [Revised: 09/15/2022] [Accepted: 10/01/2022] [Indexed: 02/13/2023]
Abstract
BACKGROUND It remains a clinical challenge to differentiate brain tumors from radiation-induced necrosis in the brain. Despite significant improvements, no single MRI method has been validated adequately in the clinical setting. METHODS Multi-parametric MRI (mpMRI) was performed to differentiate 9L gliosarcoma from radiation necrosis in animal models. Five types of MRI methods probed complementary information on different scales i.e., T2 (relaxation), CEST based APT (probing mobile proteins/peptides) and rNOE (mobile macromolecules), qMT (macromolecules), diffusion based ADC (cell density) and SSIFT iAUC (cell size), and perfusion based DSC (blood volume and flow). RESULTS For single MRI parameters, iAUC and ADC provide the best discrimination of radiation necrosis and brain tumor. For mpMRI, a combination of iAUC, ADC, and APT shows the best classification performance based on a two-step analysis with the Lasso and Ridge regressions. CONCLUSION A general mpMRI approach is introduced to choosing candidate multiple MRI methods, identifying the most effective parameters from all the mpMRI parameters, and finding the appropriate combination of chosen parameters to maximize the classification performance to differentiate tumors from radiation necrosis.
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Affiliation(s)
- Sean P Devan
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, United States
| | - Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Guozhen Luo
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jingping Xie
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Zhongliang Zu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Ashley M Stokes
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
| | - John C Gore
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States
| | - Colin D McKnight
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Austin N Kirschner
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States.
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Liberini V, Laudicella R, Balma M, Nicolotti DG, Buschiazzo A, Grimaldi S, Lorenzon L, Bianchi A, Peano S, Bartolotta TV, Farsad M, Baldari S, Burger IA, Huellner MW, Papaleo A, Deandreis D. Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics. Eur Radiol Exp 2022; 6:27. [PMID: 35701671 PMCID: PMC9198151 DOI: 10.1186/s41747-022-00282-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 04/20/2022] [Indexed: 11/21/2022] Open
Abstract
In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.
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Affiliation(s)
- Virginia Liberini
- Medical Physiopathology - A.O.U. Città della Salute e della Scienza di Torino, Division of Nuclear Medicine, Department of Medical Science, University of Torino, 10126, Torino, Italy. .,Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy.
| | - Riccardo Laudicella
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8006, Zurich, Switzerland.,Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and of Morpho-Functional Imaging, University of Messina, 98125, Messina, Italy.,Nuclear Medicine Unit, Fondazione Istituto G. Giglio, Ct.da Pietrapollastra Pisciotto, Cefalù, Palermo, Italy
| | - Michele Balma
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | | | - Ambra Buschiazzo
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | - Serena Grimaldi
- Medical Physiopathology - A.O.U. Città della Salute e della Scienza di Torino, Division of Nuclear Medicine, Department of Medical Science, University of Torino, 10126, Torino, Italy
| | - Leda Lorenzon
- Medical Physics Department, Central Bolzano Hospital, 39100, Bolzano, Italy
| | - Andrea Bianchi
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | - Simona Peano
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | | | - Mohsen Farsad
- Nuclear Medicine, Central Hospital Bolzano, 39100, Bolzano, Italy
| | - Sergio Baldari
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and of Morpho-Functional Imaging, University of Messina, 98125, Messina, Italy
| | - Irene A Burger
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8006, Zurich, Switzerland.,Department of Nuclear Medicine, Kantonsspital Baden, 5004, Baden, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8006, Zurich, Switzerland
| | - Alberto Papaleo
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | - Désirée Deandreis
- Medical Physiopathology - A.O.U. Città della Salute e della Scienza di Torino, Division of Nuclear Medicine, Department of Medical Science, University of Torino, 10126, Torino, Italy
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Ouyang Z, Zhao S, Cheng Z, Duan Y, Chen Z, Zhang N, Liang D, Hu Z. Dynamic PET Imaging Using Dual Texture Features. Front Comput Neurosci 2022; 15:819840. [PMID: 35069162 PMCID: PMC8782430 DOI: 10.3389/fncom.2021.819840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 12/10/2021] [Indexed: 11/16/2022] Open
Abstract
Purpose: This study aims to explore the impact of adding texture features in dynamic positron emission tomography (PET) reconstruction of imaging results. Methods: We have improved a reconstruction method that combines radiological dual texture features. In this method, multiple short time frames are added to obtain composite frames, and the image reconstructed by composite frames is used as the prior image. We extract texture features from prior images by using the gray level-gradient cooccurrence matrix (GGCM) and gray-level run length matrix (GLRLM). The prior information contains the intensity of the prior image, the inverse difference moment of the GGCM and the long-run low gray-level emphasis of the GLRLM. Results: The computer simulation results show that, compared with the traditional maximum likelihood, the proposed method obtains a higher signal-to-noise ratio (SNR) in the image obtained by dynamic PET reconstruction. Compared with similar methods, the proposed algorithm has a better normalized mean squared error (NMSE) and contrast recovery coefficient (CRC) at the tumor in the reconstructed image. Simulation studies on clinical patient images show that this method is also more accurate for reconstructing high-uptake lesions. Conclusion: By adding texture features to dynamic PET reconstruction, the reconstructed images are more accurate at the tumor.
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Affiliation(s)
- Zhanglei Ouyang
- School of Physics, Zhengzhou University, Zhengzhou, China
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shujun Zhao
- School of Physics, Zhengzhou University, Zhengzhou, China
| | - Zhaoping Cheng
- Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yanhua Duan
- Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Zixiang Chen
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Na Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Jensen LJ, Kim D, Elgeti T, Steffen IG, Hamm B, Nagel SN. Stability of Radiomic Features across Different Region of Interest Sizes-A CT and MR Phantom Study. ACTA ACUST UNITED AC 2021; 7:238-252. [PMID: 34201012 PMCID: PMC8293351 DOI: 10.3390/tomography7020022] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/19/2021] [Accepted: 06/03/2021] [Indexed: 02/01/2023]
Abstract
We aimed to evaluate radiomic features' stability across different region of interest (ROI) sizes in CT and MR images. We chose a phantom with a homogenous internal structure so no differences for a feature extracted from ROIs of different sizes would be expected. For this, we scanned a plastic cup filled with sodium chloride solution ten times in CT and per MR sequence (T1-weighted-gradient-echo and T2-weighted-turbo-inversion-recovery-magnitude). We placed sphere-shaped ROIs of different diameters (4, 8, and 16 mm, and 4, 8, and 16 pixels) into the phantom's center. Features were extracted using PyRadiomics. We assessed feature stability across ROI sizes with overall concordance correlation coefficients (OCCCs). Differences were tested for significance with the Mann-Whitney U-test. Of 93 features, 87 T1w-derived, 87 TIRM-derived, and 70 CT-derived features were significantly different between ROI sizes. Among MR-derived features, OCCCs showed excellent (>0.90) agreement for mean, median, and root mean squared for ROI sizes between 4 and 16 mm and pixels. We further observed excellent agreement for 10th and 90th percentile in T1w and 10th percentile in T2w TIRM images. There was no excellent agreement among the OCCCs of CT-derived features. In summary, many features indicated significant differences and only few showed excellent agreement across varying ROI sizes, although we examined a homogenous phantom. Since we considered a small phantom in an experimental setting, further studies to investigate this size effect would be necessary for a generalization. Nevertheless, we believe knowledge about this effect is crucial in interpreting radiomics studies, as features that supposedly discriminate disease entities may only indicate a systematic difference in ROI size.
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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