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Faustino R, Lopes C, Jantarada A, Mendonça A, Raposo R, Ferrão C, Freitas J, Mateus C, Pinto A, Almeida E, Gomes N, Marques L, Palavra F. Neuroimaging characterization of multiple sclerosis lesions in pediatric patients: an exploratory radiomics approach. Front Neurosci 2024; 18:1294574. [PMID: 38370435 PMCID: PMC10869542 DOI: 10.3389/fnins.2024.1294574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction Multiple sclerosis (MS), a chronic inflammatory immune-mediated disease of the central nervous system (CNS), is a common condition in young adults, but it can also affect children. The aim of this study was to construct radiomic models of lesions based on magnetic resonance imaging (MRI, T2-weighted-Fluid-Attenuated Inversion Recovery), to understand the correlation between extracted radiomic features, brain and lesion volumetry, demographic, clinical and laboratorial data. Methods The neuroimaging data extracted from eleven scans of pediatric MS patients were analyzed. A total of 60 radiomic features based on MR T2-FLAIR images were extracted and used to calculate gray level co-occurrence matrix (GLCM). The principal component analysis and ROC analysis were performed to select the radiomic features, respectively. The realized classification task by the logistic regression models was performed according to these radiomic features. Results Ten most relevant features were selected from data extracted. The logistic regression applied to T2-FLAIR radiomic features revealed significant predictor for multiple sclerosis (MS) lesion detection. Only the variable "contrast" was statistically significant, indicating that only this variable played a significant role in the model. This approach enhances the classification of lesions from normal tissue. Discussion and conclusion Our exploratory results suggest that the radiomic models based on MR imaging (T2-FLAIR) may have a potential contribution to characterization of brain tissues and classification of lesions in pediatric MS.
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Affiliation(s)
- Ricardo Faustino
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
- Faculty of Science, Institute of Biophysics and Biomedical Engineering, University of Lisbon, Lisbon, Portugal
- Biomedical Research Group, Faculty of Engineering, Faculty of Veterinary Medicine NICiTeS, Lusófona University, Lisbon, Portugal
| | - Cristina Lopes
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Afonso Jantarada
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Ana Mendonça
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Rafael Raposo
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Cristina Ferrão
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Joana Freitas
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Constança Mateus
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Ana Pinto
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Ellen Almeida
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Nuno Gomes
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Liliana Marques
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Filipe Palavra
- Centre for Child Development – Neuropediatrics Unit, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Laboratory of Pharmacology and Experimental Therapeutics, Faculty of Medicine, Coimbra Institute for Clinical and Biomedical Research, University of Coimbra, Coimbra, Portugal
- Clinical Academic Center of Coimbra, Coimbra, Portugal
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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.
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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
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Saleh M, Virarkar M, Mahmoud HS, Wong VK, Gonzalez Baerga CI, Parikh M, Elsherif SB, Bhosale PR. Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer. World J Radiol 2023; 15:304-314. [PMID: 38058604 PMCID: PMC10696186 DOI: 10.4329/wjr.v15.i11.304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/20/2023] [Accepted: 10/23/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Radiomics can assess prognostic factors in several types of tumors, but considering its prognostic ability in pancreatic cancer has been lacking. AIM To evaluate the performance of two different radiomics software in assessing survival outcomes in pancreatic cancer patients. METHODS We retrospectively reviewed pretreatment contrast-enhanced dual-energy computed tomography images from 48 patients with biopsy-confirmed pancreatic ductal adenocarcinoma who later underwent neoadjuvant chemoradiation and surgery. Tumors were segmented using TexRad software for 2-dimensional (2D) analysis and MIM software for 3D analysis, followed by radiomic feature extraction. Cox proportional hazard modeling correlated texture features with overall survival (OS) and progression-free survival (PFS). Cox regression was used to detect differences in OS related to pretreatment tumor size and residual tumor following treatment. The Wilcoxon test was used to show the relationship between tumor volume and the percent of residual tumor. Kaplan-Meier analysis was used to compare survival in patients with different tumor densities in Hounsfield units for both 2D and 3D analysis. RESULTS 3D analysis showed that higher mean tumor density [hazard ratio (HR) = 0.971, P = 0.041)] and higher median tumor density (HR = 0.970, P = 0.037) correlated with better OS. 2D analysis showed that higher mean tumor density (HR = 0.963, P = 0.014) and higher mean positive pixels (HR = 0.962, P = 0.014) correlated with better OS; higher skewness (HR = 3.067, P = 0.008) and higher kurtosis (HR = 1.176, P = 0.029) correlated with worse OS. Higher entropy correlated with better PFS (HR = 0.056, P = 0.036). Models determined that patients with increased tumor size greater than 1.35 cm were likely to have a higher percentage of residual tumors of over 10%. CONCLUSION Several radiomics features can be used as prognostic tools for pancreatic cancer. However, results vary between 2D and 3D analyses. Mean tumor density was the only variable that could reliably predict OS, irrespective of the analysis used.
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Affiliation(s)
- Mohammed Saleh
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Mayur Virarkar
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Hagar S Mahmoud
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Vincenzo K Wong
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Carlos Ignacio Gonzalez Baerga
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Miti Parikh
- Keck School of Medicine, University of South California, Los Angeles, CA 90033, United States
| | - Sherif B Elsherif
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Priya R Bhosale
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
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Deniffel D, McAlpine K, Harder FN, Jain R, Lawson KA, Healy GM, Hui S, Zhang X, Salinas-Miranda E, van der Kwast T, Finelli A, Haider MA. Predicting the recurrence risk of renal cell carcinoma after nephrectomy: potential role of CT-radiomics for adjuvant treatment decisions. Eur Radiol 2023; 33:5840-5850. [PMID: 37074425 DOI: 10.1007/s00330-023-09551-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: 09/25/2022] [Revised: 01/09/2023] [Accepted: 02/12/2023] [Indexed: 04/20/2023]
Abstract
OBJECTIVES Previous trial results suggest that only a small number of patients with non-metastatic renal cell carcinoma (RCC) benefit from adjuvant therapy. We assessed whether the addition of CT-based radiomics to established clinico-pathological biomarkers improves recurrence risk prediction for adjuvant treatment decisions. METHODS This retrospective study included 453 patients with non-metastatic RCC undergoing nephrectomy. Cox models were trained to predict disease-free survival (DFS) using post-operative biomarkers (age, stage, tumor size and grade) with and without radiomics selected on pre-operative CT. Models were assessed using C-statistic, calibration, and decision curve analyses (repeated tenfold cross-validation). RESULTS At multivariable analysis, one of four selected radiomic features (wavelet-HHL_glcm_ClusterShade) was prognostic for DFS with an adjusted hazard ratio (HR) of 0.44 (p = 0.02), along with American Joint Committee on Cancer (AJCC) stage group (III versus I, HR 2.90; p = 0.002), grade 4 (versus grade 1, HR 8.90; p = 0.001), age (per 10 years HR 1.29; p = 0.03), and tumor size (per cm HR 1.13; p = 0.003). The discriminatory ability of the combined clinical-radiomic model (C = 0.80) was superior to that of the clinical model (C = 0.78; p < 0.001). Decision curve analysis revealed a net benefit of the combined model when used for adjuvant treatment decisions. At an exemplary threshold probability of ≥ 25% for disease recurrence within 5 years, using the combined versus the clinical model was equivalent to treating 9 additional patients (per 1000 assessed) who would recur without treatment (i.e., true-positive predictions) with no increase in false-positive predictions. CONCLUSION Adding CT-based radiomic features to established prognostic biomarkers improved post-operative recurrence risk assessment in our internal validation study and may help guide decisions regarding adjuvant therapy. KEY POINTS In patients with non-metastatic renal cell carcinoma undergoing nephrectomy, CT-based radiomics combined with established clinical and pathological biomarkers improved recurrence risk assessment. Compared to a clinical base model, the combined risk model enabled superior clinical utility if used to guide decisions on adjuvant treatment.
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Affiliation(s)
- Dominik Deniffel
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
| | - Kristen McAlpine
- Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Felix N Harder
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
| | - Rahi Jain
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Keith A Lawson
- Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Gerard M Healy
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
| | - Shirley Hui
- Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Xiaoyu Zhang
- Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Emmanuel Salinas-Miranda
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON, M5G 1X5, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
| | - Theodorus van der Kwast
- Department of Pathology, Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
| | - Antonio Finelli
- Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Masoom A Haider
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Avenue, Toronto, ON, M5G 1X5, Canada.
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada.
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Lu C, Xing ZX, Xia XG, Long ZD, Chen B, Zhou P, Wang R. Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma. World J Gastrointest Oncol 2023; 15:1241-1252. [PMID: 37546550 PMCID: PMC10401473 DOI: 10.4251/wjgo.v15.i7.1241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/14/2023] [Accepted: 06/12/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma (PHC). Previous reports have shown that over 10% of patients with PHC experience postoperative pulmonary infections. Thus, it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC.
AIM To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.
METHODS We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery. Radiomics data were selected for statistical analysis, and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables. We then developed a pulmonary infection prediction model using three different models: An artificial neural network model; a random forest model; and a generalized linear regression model. Finally, we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses.
RESULTS Among the 505 patients, 86 developed a postoperative pulmonary infection, resulting in an incidence rate of 17.03%. Based on the gray-level co-occurrence matrix, we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models. Among these, energy, contrast, the sum of squares (SOS), the inverse difference (IND), mean sum (MES), sum variance (SUV), sum entropy (SUE), and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models. The random forest model algorithm, in combination with IND, SOS, MES, SUE, SUV, and entropy, demonstrated the highest prediction efficiency in both the training and internal verification sets, with areas under the curve of 0.823 and 0.801 and a 95% confidence interval of 0.766-0.880 and 0.744-0.858, respectively. The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95% confidence intervals of 0.677-0.791 and 0.766-0.864, respectively.
CONCLUSION Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND, SOS, MES, SUE, SUV, energy, and entropy. The prediction model in this study based on diffusion-weighted images, especially the random forest model algorithm, can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy, providing valuable guidance for postoperative management.
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Affiliation(s)
- Chao Lu
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Zhi-Xiang Xing
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Xi-Gang Xia
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Zhi-Da Long
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Bo Chen
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Peng Zhou
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Rui Wang
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
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Legaria-Peña JU, Sánchez-Morales F, Cortés-Poza Y. Evaluation of entropy and fractal dimension as biomarkers for tumor growth and treatment response using cellular automata. J Theor Biol 2023; 564:111462. [PMID: 36921839 DOI: 10.1016/j.jtbi.2023.111462] [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: 11/30/2022] [Revised: 02/16/2023] [Accepted: 03/03/2023] [Indexed: 03/14/2023]
Abstract
Cell-based models provide a helpful approach for simulating complex systems that exhibit adaptive, resilient qualities, such as cancer. Their focus on individual cell interactions makes them a particularly appropriate strategy to study cancer therapies' effects, which are often designed to disrupt single-cell dynamics. In this work, we propose them as viable methods for studying the time evolution of cancer imaging biomarkers (IBM). We propose a cellular automata model for tumor growth and three different therapies: chemotherapy, radiotherapy, and immunotherapy, following well-established modeling procedures documented in the literature. The model generates a sequence of tumor images, from which a time series of two biomarkers: entropy and fractal dimension, is obtained. Our model shows that the fractal dimension increased faster at the onset of cancer cell dissemination. At the same time, entropy was more responsive to changes induced in the tumor by the different therapy modalities. These observations suggest that the prognostic value of the proposed biomarkers could vary considerably with time. Thus, it is essential to assess their use at different stages of cancer and for different imaging modalities. Another observation derived from the results was that both biomarkers varied slowly when the applied therapy attacked cancer cells scattered along the automatons' area, leaving multiple independent clusters of cells at the end of the treatment. Thus, patterns of change of simulated biomarkers time series could reflect on essential qualities of the spatial action of a given cancer intervention.
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Affiliation(s)
- Juan Uriel Legaria-Peña
- IIMAS, Unidad Académica de Yucatán, Universidad Nacional Autónoma de México (UNAM), Yuc., Mexico
| | - Félix Sánchez-Morales
- IIMAS, Unidad Académica de Yucatán, Universidad Nacional Autónoma de México (UNAM), Yuc., Mexico
| | - Yuriria Cortés-Poza
- IIMAS, Unidad Académica de Yucatán, Universidad Nacional Autónoma de México (UNAM), Yuc., Mexico.
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Xue G, Liu H, Cai X, Zhang Z, Zhang S, Liu L, Hu B, Wang G. Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors. Front Oncol 2023; 13:1167745. [PMID: 37091167 PMCID: PMC10113560 DOI: 10.3389/fonc.2023.1167745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 03/21/2023] [Indexed: 04/08/2023] Open
Abstract
ObjectiveTo evaluate the impact of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) on abdominal CT radiomic features acquired in portal venous phase in liver tumor patients.MethodsSixty patients with liver tumors who underwent contrast-enhanced abdominal CT were retrospectively enrolled. Six groups including filtered back projection (FBP), ASIR-V (30%, 70%) and DLIR at low (DLIR-L), medium (DLIR-M and high (DLIR-H), were reconstructed using portal venous phase data. CT-based radiomic features (first-order, texture and wavelet features) were extracted from 2D and 3D liver tumors, peritumor and liver parenchyma. All features were analyzed for comparison. P < 0.05 indicated statistically different. The consistency of 3D lesion feature extraction was assessed by calculating intraclass correlation coefficient (ICC).ResultsDifferent reconstruction algorithms influenced most radiomic features. The percentages of first-order, texture and wavelet features without statistical difference among 2D and 3D lesions, peritumor and liver parenchyma for all six groups were 27.78% (5/18), 5.33% (4/75) and 5.56% (1/18), respectively (all p > 0.05), and they decreased while the level of reconstruction strengthened for both ASIR-V and DLIR. Compared with FBP, the features of ASIR-V30% and 70% without statistical difference decreased from 71.31% to 23.95%, and DLIR-L, DLIR-M, and DLIR-H decreased from 31.65% to 27.11% and 23.73%. Among texture features, unaffected features of peritumor were larger than those of lesions and liver parenchyma, and unaffected 3D lesions features were larger than those of 2D lesions. The consistency of 3D lesion first-order features was excellent, with intra- and inter-observer ICCs ranging from 0.891 to 0.999 and 0.880 to 0.998.ConclusionsBoth ASIR-V and DLIR algorithms with different strengths influenced the radiomic features of abdominal CT images in portal venous phase, and the influences aggravated as reconstruction strength increased.
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Affiliation(s)
- Gongbo Xue
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- Graduate School, Dalian Medical University, Dalian, China
| | - Hongyan Liu
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Xiaoyi Cai
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- Graduate School, Dalian Medical University, Dalian, China
| | - Zhen Zhang
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Shuai Zhang
- CT Imaging Research Center, GE Healthcare China, Shanghai, China
| | - Ling Liu
- CT Imaging Research Center, GE Healthcare China, Shanghai, China
| | - Bin Hu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Guohua Wang, ; Bin Hu,
| | - Guohua Wang
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- *Correspondence: Guohua Wang, ; Bin Hu,
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Ogbonnaya CN, Alsaedi BSO, Alhussaini AJ, Hislop R, Pratt N, Nabi G. Radiogenomics Reveals Correlation between Quantitative Texture Radiomic Features of Biparametric MRI and Hypoxia-Related Gene Expression in Men with Localised Prostate Cancer. J Clin Med 2023; 12:jcm12072605. [PMID: 37048688 PMCID: PMC10095552 DOI: 10.3390/jcm12072605] [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: 03/03/2023] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 04/14/2023] Open
Abstract
OBJECTIVES To perform multiscale correlation analysis between quantitative texture feature phenotypes of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes. MATERIALS AND METHODS Images from pre-biopsy 3T bpMRI scans in clinically localised PCa patients of various risk categories (n = 15) were used to extract textural features. The genomic landscape of hypoxia-related gene expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the important hypoxia-related genes with 28 radiomic texture features. Then, cBioportal was accessed, and a gene-specific query was executed to extract the Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Based on each selected gene profile, correlation analysis using Pearson's coefficients and survival analysis using Kaplan-Meier estimators were performed. RESULTS The quantitative bpMR imaging textural features, including the histogram and grey level co-occurrence matrix (GLCM), correlated with three hypoxia-related genes (ANGPTL4, VEGFA, and P4HA1) based on RNA sequencing using the TempO-Seq method. Further radiogenomic analysis, including data accessed from the cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcomes, with a median survival ratio of 81.11:133.00 months in those with and without alterations in genes, respectively. CONCLUSION This study found that there is a correlation between the radiomic texture features extracted from bpMRI in localised prostate cancer and the hypoxia-related genes that are differentially expressed. The analysis of expression data based on cBioportal revealed that these hypoxia-related genes, which were the focus of the study, are linked to an unfavourable survival outcomes in prostate cancer patients.
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Affiliation(s)
- Chidozie N Ogbonnaya
- Division of Imaging Science and Technology, University of Dundee, Dundee DD1 4HN, UK
- College of Basic Medical Sciences, Abia State University, Uturu 441103, Nigeria
| | - Basim S O Alsaedi
- Statistics Department, University of Tabuk, Tabuk 47512, Saudi Arabia
| | - Abeer J Alhussaini
- Division of Imaging Science and Technology, University of Dundee, Dundee DD1 4HN, UK
- Department of Medical Imaging, Al-Amiri Hospital, Ministry of Health, Sulaibikhat 1300, Kuwait
| | - Robert Hislop
- Cytogenetic, Human Genetics Unit, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
| | - Norman Pratt
- Cytogenetic, Human Genetics Unit, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
| | - Ghulam Nabi
- Division of Imaging Science and Technology, University of Dundee, Dundee DD1 4HN, UK
- School of Medicine, Ninewells Hospital, Dundee DD1 9SY, UK
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9
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Wang F, Zhao Y, Xu J, Shao S, Yu D. Development and external validation of a radiomics combined with clinical nomogram for preoperative prediction prognosis of resectable pancreatic ductal adenocarcinoma patients. Front Oncol 2022; 12:1037672. [PMID: 36518321 PMCID: PMC9742428 DOI: 10.3389/fonc.2022.1037672] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/02/2022] [Indexed: 11/25/2023] Open
Abstract
PURPOSE To develop and externally validate a prognosis nomogram based on contrast-enhanced computed tomography (CECT) combined clinical for preoperative prognosis prediction of patients with pancreatic ductal adenocarcinoma (PDAC). METHODS 184 patients from Center A with histopathologically confirmed PDAC who underwent CECT were included and allocated to training cohort (n=111) and internal validation cohort (n=28). The radiomic score (Rad - score) for predicting overall survival (OS) was constructed by using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariable Cox regression analysis was used to construct clinic-pathologic features. Finally, a radiomics nomogram incorporating the Rad - score and clinical features was established. External validation was performed using Center B dataset (n = 45). The validation of nomogram was evaluated by calibration curve, Harrell's concordance index (C-index) and decision curve analysis (DCA). The Kaplan-Meier (K-M) method was used for OS analysis. RESULTS Univariate and multivariate analysis indicated that Rad - score, preoperative CA 19-9 and postoperative American Joint Committee on Cancer (AJCC) TNM stage were significant prognostic factors. The nomogram based on Rad - score and preoperative CA19-9 was found to exhibit excellent prediction ability: in the training cohort, C-index was superior to that of the preoperative CA19-9 (0.713 vs 0.616, P< 0.001) and AJCC TNM stage (0.713 vs 0.614, P< 0.001); the C-index was also had good performance in the validation cohort compared with CA19-9 (internal validation cohort: 0.694 vs 0.555, P< 0.001; external validation cohort: 0.684 vs 0.607, P< 0.001) and AJCC TNM stage (internal validation cohort: 0.694 vs 0.563, P< 0.001; external validation cohort: 0.684 vs 0.596, P< 0.001). The calibration plot and DCA showed excellent predictive accuracy in the validation cohort. CONCLUSION We established a well-designed nomogram to accurately predict OS of PDAC preoperatively. The nomogram showed a satisfactory prediction effect and was worthy of further evaluation in the future.
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Affiliation(s)
- Fangqing Wang
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Yuxuan Zhao
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Jianwei Xu
- Department of Pancreatic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Sai Shao
- Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Dexin Yu
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, China
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Gerwing M, Hoffmann E, Kronenberg K, Hansen U, Masthoff M, Helfen A, Geyer C, Wachsmuth L, Höltke C, Maus B, Hoerr V, Krähling T, Hiddeßen L, Heindel W, Karst U, Kimm MA, Schinner R, Eisenblätter M, Faber C, Wildgruber M. Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer. Front Oncol 2022; 12:1000036. [PMID: 36408159 PMCID: PMC9667047 DOI: 10.3389/fonc.2022.1000036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2022] Open
Abstract
Objective The objective of this study was to non-invasively differentiate the degree of malignancy in two murine breast cancer models based on identification of distinct tissue characteristics in a metastatic and non-metastatic tumor model using a multiparametric Magnetic Resonance Imaging (MRI) approach. Methods The highly metastatic 4T1 breast cancer model was compared to the non-metastatic 67NR model. Imaging was conducted on a 9.4 T small animal MRI. The protocol was used to characterize tumors regarding their structural composition, including heterogeneity, intratumoral edema and hemorrhage, as well as endothelial permeability using apparent diffusion coefficient (ADC), T1/T2 mapping and dynamic contrast-enhanced (DCE) imaging. Mice were assessed on either day three, six or nine, with an i.v. injection of the albumin-binding contrast agent gadofosveset. Ex vivo validation of the results was performed with laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), histology, immunhistochemistry and electron microscopy. Results Significant differences in tumor composition were observed over time and between 4T1 and 67NR tumors. 4T1 tumors showed distorted blood vessels with a thin endothelial layer, resulting in a slower increase in signal intensity after injection of the contrast agent. Higher permeability was further reflected in higher Ktrans values, with consecutive retention of gadolinium in the tumor interstitium visible in MRI. 67NR tumors exhibited blood vessels with a thicker and more intact endothelial layer, resulting in higher peak enhancement, as well as higher maximum slope and area under the curve, but also a visible wash-out of the contrast agent and thus lower Ktrans values. A decreasing accumulation of gadolinium during tumor progression was also visible in both models in LA-ICP-MS. Tissue composition of 4T1 tumors was more heterogeneous, with intratumoral hemorrhage and necrosis and corresponding higher T1 and T2 relaxation times, while 67NR tumors mainly consisted of densely packed tumor cells. Histogram analysis of ADC showed higher values of mean ADC, histogram kurtosis, range and the 90th percentile (p90), as markers for the heterogenous structural composition of 4T1 tumors. Principal component analysis (PCA) discriminated well between the two tumor models. Conclusions Multiparametric MRI as presented in this study enables for the estimation of malignant potential in the two studied tumor models via the assessment of certain tumor features over time.
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Affiliation(s)
- Mirjam Gerwing
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
- *Correspondence: Mirjam Gerwing,
| | - Emily Hoffmann
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Katharina Kronenberg
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Uwe Hansen
- Institute for Musculoskeletal Medicine, University of Münster, Münster, Germany
| | - Max Masthoff
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Anne Helfen
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Christiane Geyer
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Lydia Wachsmuth
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Carsten Höltke
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Bastian Maus
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Verena Hoerr
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
- Heart Center Bonn, Department of Internal Medicine II, University of Bonn, Bonn, Germany
| | - Tobias Krähling
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Lena Hiddeßen
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Walter Heindel
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Uwe Karst
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Melanie A. Kimm
- Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Regina Schinner
- Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Michel Eisenblätter
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
- Department of Diagnostic and Interventional Radiology, University of Freiburg, Freiburg, Germany
| | - Cornelius Faber
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Moritz Wildgruber
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
- Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
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11
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Fiz F, Bottoni G, Bini F, Cerroni F, Marinozzi F, Conte M, Treglia G, Morana G, Sorrentino S, Garaventa A, Siri G, Piccardo A. Prognostic value of texture analysis of the primary tumour in high-risk neuroblastoma: An 18 F-DOPA PET study. Pediatr Blood Cancer 2022; 69:e29910. [PMID: 35920594 DOI: 10.1002/pbc.29910] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/22/2022] [Accepted: 07/14/2022] [Indexed: 01/01/2023]
Abstract
PURPOSE To evaluate the prognostic value of texture analysis of the primary tumour with 18 fluorine-dihydroxyphenylalanine positron emission tomography/X-ray computed tomography (18 F-DOPA PET/CT) in patients affected by high-risk neuroblastoma (HR-NBL). METHODS We retrospectively analysed 18 patients with HR-NBL, which had been prospectively enrolled in the course of a previous trial investigating the diagnostic role of 18 F-DOPA PET/CT at the time of the first onset. Texture analysis of the primary tumour was carried out on the PET images using LifeX. Conventional indices, histogram parameters, grey level co-occurrence (GLCM), run-length (GLRLM), neighbouring difference (NGLDM) and zone-length (GLZLM) matrices parameter were extracted; their values were compared with the overall metastatic load, expressed by means of whole-body metabolic burden (WBMB) score and the progression-free/overall survival (PFS and OS). RESULTS There was a direct correlation between WBMB and radiomics parameter describing uptake intensity (SUVmean : p = .004) and voxel heterogeneity (entropy: p = .026; GLCM_Contrast: p = .001). Conversely, texture indices of homogeneity showed an inverse correlation with WBMB (energy: p = .026; GLCM_Homogeneity: p = .006). On the multivariate model, WBMB (p < .01) and the first standardised uptake value (SUV) quartile (p < .001) predicted PFS; OS was predicted by WBMB and the N-myc proto-oncogene protein (MYCN) amplification (p < .05) for both. CONCLUSIONS Textural parameters describing heterogeneity and metabolic intensity of the primary HR-NBL are closely associated with its overall metastatic burden. In turn, the whole-body tumour load appears to be one of the most relevant predictors of progression-free and overall survival.
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Affiliation(s)
- Francesco Fiz
- Department of Nuclear Medicine, E.O. 'Ospedali Galliera', Genoa, Italy
| | - Gianluca Bottoni
- Department of Nuclear Medicine, E.O. 'Ospedali Galliera', Genoa, Italy
| | - Fabiano Bini
- Department of Mechanical and Aerospace Engineering, 'Sapienza' University of Rome, Rome, Italy
| | - Francesca Cerroni
- Department of Mechanical and Aerospace Engineering, 'Sapienza' University of Rome, Rome, Italy
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, 'Sapienza' University of Rome, Rome, Italy
| | - Massimo Conte
- Oncology Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Giorgio Treglia
- Clinic of Nuclear Medicine, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland.,Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Giovanni Morana
- Pediatric Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy.,Department of Neurosciences, University of Turin, Turin, Italy
| | | | | | - Giacomo Siri
- Scientific Directorate, E.O. 'Ospedali Galliera', Genoa, Italy
| | - Arnoldo Piccardo
- Department of Nuclear Medicine, E.O. 'Ospedali Galliera', Genoa, Italy
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12
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Soliman MA, Kelahan LC, Magnetta M, Savas H, Agrawal R, Avery RJ, Aouad P, Liu B, Xue Y, Chae YK, Salem R, Benson AB, Yaghmai V, Velichko YS. A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials. JCO Clin Cancer Inform 2022; 6:e2200023. [PMID: 36332157 PMCID: PMC9668564 DOI: 10.1200/cci.22.00023] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 07/01/2022] [Accepted: 09/21/2022] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Variability in computed tomography images intrinsic to individual scanners limits the application of radiomics in clinical and research settings. The development of reproducible and generalizable radiomics-based models to assess lesions requires harmonization of data. The purpose of this study was to develop, test, and analyze the efficacy of a radiomics data harmonization model. MATERIALS AND METHODS Radiomic features from biopsy-proven untreated hepatic metastasis (N = 380) acquired from 167 unique patients with pancreatic, colon, and breast cancers were analyzed. Radiomic features from volume-match 551 samples of normal liver tissue and 188 hepatic cysts were included as references. A novel linear mixed effect model was used to identify effects associated with lesion size, tissue type, and scanner model. Six separate machine learning models were then used to test the effectiveness of radiomic feature harmonization using multivariate analysis. RESULTS Proposed model identifies and removes scanner-associated effects while preserving cancer-specific functional dependence of radiomic features on the tumor size. Data harmonization improves the performance of classification models by reducing the scanner-associated variability. For example, the multiclass logistic regression model, LogitBoost, demonstrated the improvement in sensitivity in the range from 15% to 40% for each type of liver metastasis, whereas the overall model accuracy and the kappa coefficient increased by 5% and 8% accordingly. CONCLUSION The model removed scanner-associated effects while preserving cancer-specific functional dependence of radiomic features.
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Affiliation(s)
- Moataz A.S. Soliman
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Linda C. Kelahan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Michael Magnetta
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Hatice Savas
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Rishi Agrawal
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Ryan J. Avery
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Pascale Aouad
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Benjamin Liu
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Yue Xue
- Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Young K. Chae
- Department of Medicine, Division of Hematology/Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL
| | - Riad Salem
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL
| | - Al B. Benson
- Department of Medicine, Division of Hematology/Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL
| | - Vahid Yaghmai
- Department of Radiological Sciences, University of California, Irvine UCI Health, University of California Irvine, Orange, CA
| | - Yuri S. Velichko
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL
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13
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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.
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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
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14
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Varriano G, Guerriero P, Santone A, Mercaldo F, Brunese L. Explainability of radiomics through formal methods. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106824. [PMID: 35483269 DOI: 10.1016/j.cmpb.2022.106824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 04/12/2022] [Accepted: 04/18/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Artificial Intelligence has proven to be effective in radiomics. The main problem in using Artificial Intelligence is that researchers and practitioners are not able to know how the predictions are generated. This is currently an open issue because results' explainability is advantageous in understanding the reasoning behind the model, both for patients than for implementing a feedback mechanism for medical specialists using decision support systems. METHODS Addressing transparency issues related to the Artificial Intelligence field, the innovative technique of Formal methods use a mathematical logic reasoning to produce an automatic, quick and reliable diagnosis. In this paper we analyze results given by the adoption of Formal methods for the diagnosis of the Coronavirus disease: specifically, we want to analyse and understand, in a more medical way, the meaning of some radiomic features to connect them with clinical or radiological evidences. RESULTS In particular, the usage of Formal methods allows the authors to do statistical analysis on the feature value distributions, to do pattern recognition on disease models, to generalize the model of a disease and to reach high performances of results and interpretation of them. A further step for explainability can be accounted by the localization and selection of the most important slices in a multi-slice approach. CONCLUSIONS In conclusion, we confirmed the clinical significance of some First order features as Skewness and Kurtosis. On the other hand, we suggest to decline the use of the Minimum feature because of its intrinsic connection with the Computational Tomography exam of the lung.
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Affiliation(s)
- Giulia Varriano
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy.
| | - Pasquale Guerriero
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy.
| | - Antonella Santone
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy.
| | - Francesco Mercaldo
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy.
| | - Luca Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy.
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15
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Yang CC, Chen CY, Kuo YT, Ko CC, Wu WJ, Liang CH, Yun CH, Huang WM. Radiomics for the Prediction of Response to Antifibrotic Treatment in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study. Diagnostics (Basel) 2022; 12:diagnostics12041002. [PMID: 35454050 PMCID: PMC9028756 DOI: 10.3390/diagnostics12041002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 02/06/2023] Open
Abstract
Antifibrotic therapy has changed the treatment paradigm for idiopathic pulmonary fibrosis (IPF); however, a subset of patients still experienced rapid disease progression despite treatment. This study aimed to determine whether CT-based radiomic features can predict therapeutic response to antifibrotic agents. In this retrospective study, 35 patients with IPF on antifibrotic treatment enrolled from two centers were divided into training (n = 26) and external validation (n = 9) sets. Clinical and pulmonary function data were collected. The patients were categorized into stable disease (SD) and progressive disease (PD) groups based on functional or radiologic criteria. From pretreatment non-enhanced high-resolution CT (HRCT) images, twenty-six radiomic features were extracted through whole-lung texture analysis, and six parenchymal patterns were quantified using dedicated imaging platforms. The predictive factors for PD were determined via univariate and multivariate logistic regression analyses. In the training set (SD/PD: 12/14), univariate analysis identified eight radiomic features and ground-glass opacity percentage (GGO%) as potential predicators of PD. However, multivariate analysis found that the single independent predictor was the sum entropy (accuracy, 80.77%; AUC, 0.75). The combined sum entropy-GGO% model improved the predictive performance in the training set (accuracy, 88.46%; AUC, 0.77). The overall accuracy of the combined model in the validation set (SD/PD: 7/2) was 66.67%. Our preliminary results demonstrated that radiomic features based on pretreatment HRCT could predict the response of patients with IPF to antifibrotic treatment.
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Affiliation(s)
- Cheng-Chun Yang
- Department of Medical Imaging, Chi Mei Hospital, Tainan 710, Taiwan; (C.-C.Y.); (C.-Y.C.); (Y.-T.K.); (C.-C.K.)
| | - Chin-Yu Chen
- Department of Medical Imaging, Chi Mei Hospital, Tainan 710, Taiwan; (C.-C.Y.); (C.-Y.C.); (Y.-T.K.); (C.-C.K.)
| | - Yu-Ting Kuo
- Department of Medical Imaging, Chi Mei Hospital, Tainan 710, Taiwan; (C.-C.Y.); (C.-Y.C.); (Y.-T.K.); (C.-C.K.)
- Department of Radiology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi Mei Hospital, Tainan 710, Taiwan; (C.-C.Y.); (C.-Y.C.); (Y.-T.K.); (C.-C.K.)
- Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan 717, Taiwan
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 804, Taiwan
| | - Wen-Jui Wu
- Division of Pulmonary and Critical Care Medicine, Mackay Memorial Hospital, Taipei 104, Taiwan;
| | - Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan;
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Chun-Ho Yun
- Department of Radiology, Mackay Memorial Hospital, Taipei 104, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, New Taipei City 252, Taiwan
- Correspondence: (C.-H.Y.); (W.-M.H.)
| | - Wei-Ming Huang
- Department of Radiology, Mackay Memorial Hospital, Taipei 104, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, New Taipei City 252, Taiwan
- Correspondence: (C.-H.Y.); (W.-M.H.)
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16
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Dercle L, Zhao B, Gönen M, Moskowitz CS, Firas A, Beylergil V, Connors DE, Yang H, Lu L, Fojo T, Carvajal R, Karovic S, Maitland ML, Goldmacher GV, Oxnard GR, Postow MA, Schwartz LH. Early Readout on Overall Survival of Patients With Melanoma Treated With Immunotherapy Using a Novel Imaging Analysis. JAMA Oncol 2022; 8:385-392. [PMID: 35050320 PMCID: PMC8778619 DOI: 10.1001/jamaoncol.2021.6818] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
IMPORTANCE Existing criteria to estimate the benefit of a therapy in patients with cancer rely almost exclusively on tumor size, an approach that was not designed to estimate survival benefit and is challenged by the unique properties of immunotherapy. More accurate prediction of survival by treatment could enhance treatment decisions. OBJECTIVE To validate, using radiomics and machine learning, the performance of a signature of quantitative computed tomography (CT) imaging features for estimating overall survival (OS) in patients with advanced melanoma treated with immunotherapy. DESIGN, SETTING, AND PARTICIPANTS This prognostic study used radiomics and machine learning to retrospectively analyze CT images obtained at baseline and first follow-up and their associated clinical metadata. Data were prospectively collected in the KEYNOTE-002 (Study of Pembrolizumab [MK-3475] Versus Chemotherapy in Participants With Advanced Melanoma; 2017 analysis) and KEYNOTE-006 (Study to Evaluate the Safety and Efficacy of Two Different Dosing Schedules of Pembrolizumab [MK-3475] Compared to Ipilimumab in Participants With Advanced Melanoma; 2016 analysis) multicenter clinical trials. Participants included 575 patients with a diagnosis of advanced melanoma who were randomly assigned to training and validation sets. Data for the present study were collected from November 20, 2012, to June 3, 2019, and analyzed from July 1, 2019, to September 15, 2021. INTERVENTIONS KEYNOTE-002 featured trial groups testing intravenous pembrolizumab, 2 mg/kg or 10 mg/kg every 2 or every 3 weeks based on randomization, or investigator-choice chemotherapy; KEYNOTE-006 featured trial groups testing intravenous ipilimumab, 3 mg/kg every 3 weeks and intravenous pembrolizumab, 10 mg/kg every 2 or 3 weeks based on randomization. MAIN OUTCOMES AND MEASURES The performance of the signature CT imaging features for estimating OS at the month 6 posttreatment landmark in patients who received pembrolizumab was measured using an area under the time-dependent receiver operating characteristics curve (AUC). RESULTS A random forest model combined 25 imaging features extracted from tumors segmented on CT images to identify the combination (signature) that best estimated OS with pembrolizumab in 575 patients. The signature combined 4 imaging features, 2 related to tumor size and 2 reflecting changes in tumor imaging phenotype. In the validation set (287 patients treated with pembrolizumab), the signature reached an AUC for estimation of OS status of 0.92 (95% CI, 0.89-0.95). The standard method, Response Evaluation Criteria in Solid Tumors 1.1, achieved an AUC of 0.80 (95% CI, 0.75-0.84) and classified tumor outcomes as partial or complete response (93 of 287 [32.4%]), stable disease (90 of 287 [31.3%]), or progressive disease (104 of 287 [36.2%]). CONCLUSIONS AND RELEVANCE The findings of this prognostic study suggest that the radiomic signature discerned from conventional CT images at baseline and on first follow-up may be used in clinical settings to provide an accurate early readout of future OS probability in patients with melanoma treated with single-agent programmed cell death 1 blockade.
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Affiliation(s)
- Laurent Dercle
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Binsheng Zhao
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Mithat Gönen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Chaya S. Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ahmed Firas
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Volkan Beylergil
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Dana E. Connors
- Foundation for the National Institutes of Health, North Bethesda, Maryland
| | - Hao Yang
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Tito Fojo
- Columbia University Herbert Irving Comprehensive Cancer Center, New York, New York
| | - Richard Carvajal
- Columbia University Herbert Irving Comprehensive Cancer Center, New York, New York
| | - Sanja Karovic
- Inova Center for Personalized Health and Schar Cancer Institute, Fairfax, Virginia
| | - Michael L. Maitland
- Inova Center for Personalized Health and Schar Cancer Institute, Fairfax, Virginia,University of Virginia Cancer Center, Charlottesville
| | | | - Geoffrey R. Oxnard
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Michael A. Postow
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York,Department of Medicine, Weill Cornell Medical College, New York, New York
| | - Lawrence H. Schwartz
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
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Kelahan LC, Kim D, Soliman M, Avery RJ, Savas H, Agrawal R, Magnetta M, Liu BP, Velichko YS. Role of hepatic metastatic lesion size on inter-reader reproducibility of CT-based radiomics features. Eur Radiol 2022; 32:4025-4033. [PMID: 35080646 DOI: 10.1007/s00330-021-08526-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 12/05/2021] [Accepted: 12/10/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To evaluate the effect of hepatic metastatic lesion size on inter-reader reproducibility of CT-based 2D radiomics imaging features. METHODS Computerized tomography (CT) scans of 59 liver metastases from 34 patients with colorectal cancer were evaluated. Image segmentation was performed manually by three readers blinded to each other's results. For each radiomics feature, we created two datasets by sorting measurements according to size, i.e., (i) from the smallest to the largest lesion and (ii) from the largest to the smallest lesion. The Lin concordance correlation coefficient (CCC) was employed to analyze the reproducibility of radiomics features. In particular, the CCC was computed as a function of a number of elements in the dataset, by gradually adding lesions from each sorted dataset. To evaluate the effect of lesion size, we analyzed the difference between these two functions thus assessing the contribution of small and large lesions into the reproducibility of radiomics features. RESULTS Inter-reader reproducibility of CT-based 2D radiomics features assessed using Lin's CCC demonstrates tumor-size dependence. For example, the Lin CCC for GLCM contrast equals 0.88 (95% C.I. 0.84 to 0.92, p < 0.003) and could change by an additional + / - 0.06 depending on the presence of large or small lesions. CONCLUSIONS Groups of "large" and "small" lesions show different inter-reader reproducibility. The inter-reader reproducibility from the mixed group consisting of "large" and "small" lesions depends on the lesion-size distribution and can vary widely. This finding could partially explain variability in reproducibility of radiomics features in the literature. KEY POINTS • Groups of "large" and "small" lesions show different inter-reader reproducibility. • The inter-reader reproducibility from the mixed group consisting of "large" and "small" lesions depends on the lesion-size distribution.
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Affiliation(s)
- Linda C Kelahan
- Department of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA
| | - Donald Kim
- Department of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA
| | - Moataz Soliman
- Department of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA
| | - Ryan J Avery
- Department of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA
| | - Hatice Savas
- Department of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA
| | - Rishi Agrawal
- Department of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA
| | - Michael Magnetta
- Department of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA
| | - Benjamin P Liu
- Department of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA
| | - Yuri S Velichko
- Department of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA.
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Dercle L, Zhao B, Gönen M, Moskowitz CS, Connors DE, Yang H, Lu L, Reidy-Lagunes D, Fojo T, Karovic S, Maitland ML, Oxnard GR, Schwartz LH. An imaging signature to predict outcome in metastatic colorectal cancer using routine computed tomography scans. Eur J Cancer 2022; 161:138-147. [PMID: 34916122 PMCID: PMC10018811 DOI: 10.1016/j.ejca.2021.10.029] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/10/2021] [Accepted: 10/24/2021] [Indexed: 01/25/2023]
Abstract
BACKGROUND & AIMS Quantitative analysis of computed tomography (CT) scans of patients with metastatic colorectal cancer (mCRC) can identify imaging signatures that predict overall survival (OS). METHODS We retrospectively analysed CT images from 1584 mCRC patients on two phase III trials evaluating FOLFOX ± panitumumab (n = 331, 350) and FOLFIRI ± aflibercept (n = 437, 466). In the training set (n = 720), an algorithm was trained to predict OS landmarked from month 2; the output was a signature value on a scale from 0 to 1 (most to least favourable predicted OS). In the validation set (n = 864), hazard ratios (HRs) evaluated the association of the signature with OS using RECIST1.1 as a benchmark of comparison. RESULTS In the training set, the selected signature combined three features - change in tumour volume, change in tumour spatial heterogeneity, and tumour volume - to predict OS. In the validation set, RECIST1.1 classified patients in three categories: response (n = 166, 19.2%), stable disease (n = 636, 73.6%), and progression (n = 62, 7.2%). The HR was 3.93 (2.79-5.54). Using the same distribution for the signature, the HR was 21.04 (14.88-30.58), showing an incremental prognostic separation. Stable disease by RECIST1.1 was reclassified by the signature along a continuum where patients belonging to the most and least favourable signature quartiles had a median OS of 40.73 (28.49 to NA) months (n = 94) and 7.03 (5.66-7.89) months (n = 166), respectively. CONCLUSIONS A signature combining three imaging features provides early prognostic information that can improve treatment decisions for individual patients and clinical trial analyses.
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Affiliation(s)
- Laurent Dercle
- Department of Radiology, Columbia University Medical Center and New York Presbyterian Hospital, 710 West 168th St., New York, NY 10032, USA.
| | - Binsheng Zhao
- Department of Radiology, Columbia University Medical Center and New York Presbyterian Hospital, 710 West 168th St., New York, NY 10032, USA
| | - Mithat Gönen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Chaya S Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Dana E Connors
- Foundation for the National Institutes of Health (FNIH), 11400 Rockville Pike, Suite 600, North Bethesda, MD 20852, USA
| | - Hao Yang
- Department of Radiology, Columbia University Medical Center and New York Presbyterian Hospital, 710 West 168th St., New York, NY 10032, USA
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center and New York Presbyterian Hospital, 710 West 168th St., New York, NY 10032, USA
| | - Diane Reidy-Lagunes
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Tito Fojo
- Columbia University Herbert Irving Comprehensive Cancer Center, 161 Fort Washington Ave., New York, NY 10032, USA
| | - Sanja Karovic
- Inova Center for Personalized Health and Schar Cancer Institute, 8100 Innovation Park Dr, Fairfax, VA 22031, USA
| | - Michael L Maitland
- Inova Center for Personalized Health and Schar Cancer Institute, 8100 Innovation Park Dr, Fairfax, VA 22031, USA; University of Virginia Cancer Center, 1240 Lee St., Charlottesville, VA 22903, USA
| | - Geoffrey R Oxnard
- Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave., Boston, MA 02215, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Medical Center and New York Presbyterian Hospital, 710 West 168th St., New York, NY 10032, USA
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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.
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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.
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20
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Intra-scan inter-tissue variability can help harmonize radiomics features in CT. Eur Radiol 2021; 32:783-792. [PMID: 34363133 DOI: 10.1007/s00330-021-08154-8] [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: 12/28/2020] [Revised: 06/03/2021] [Accepted: 06/14/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE We studied the repeatability and the relative intra-scan variability across acquisition protocols in CT using phantom and unenhanced abdominal series. METHODS We used 17 CT scans from the Credence Cartridge Radiomics Phantom database and 20 unenhanced multi-site non-pathologic abdominal patient series for which we measured spleen and liver tissues. We performed multiple measurements in extracting 9 radiomics features. We defined a "tandem" as the measurement of a given tissue (or material) by a given radiomics. For each tandem, we assessed the proportion of the variability attributable to repetitions, acquisition protocols, material, or patient. We analyzed the distribution of the intra-scan correlation between pairs of tandems and checked the impact of correlation coefficient greater than 0.90 in comparing paired and unpaired differences. RESULTS The repeatability of radiomics features depends on the measured material; 56% of tandems were highly repeatable. Histogram-derived radiomics were generally less repeatable. Nearly 60% of relative radiomics measurements had a correlation coefficient higher than 0.90 allowing paired measurements to improve reliability in detecting the difference between two materials. The analysis of liver and spleen tissues showed that measurement variability was negligible with respect to other variabilities. As for phantom data, we found that gray level zone length matrix (GLZLM)-derived radiomics and gray level co-occurrence matrix (GLCM)-derived radiomics were the most correlating features. For these features, relative intra-scan measurements improved the detection of different materials or tissues. CONCLUSIONS We identified radiomics features for which the intra-scan measurements between tissues are linearly correlated. This property represents an opportunity to improve tissue characterization and inter-site harmonization. KEY POINTS • The repeatability of radiomics features on CT depends on the measured material or tissue. • Some tandems of radiomics features/tissues are linearly affected by the variability of acquisition protocols on CT. • Relative intra-scan measurements are an opportunity for improving quantitative imaging on CT.
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21
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Shen H, Chen L, Liu K, Zhao K, Li J, Yu L, Ye H, Zhu W. A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes. Quant Imaging Med Surg 2021; 11:2918-2932. [PMID: 34249623 DOI: 10.21037/qims-20-1182] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 03/03/2021] [Indexed: 01/06/2023]
Abstract
Background This study classifies lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using subregion-based radiomics features extracted from positron emission tomography/computed tomography (PET/CT) images. Methods In this study, the standard 18F-fluorodeoxyglucose (FDG) PET/CT images of 150 patients with lung ADC and 100 patients with SCC were retrospectively collected from the PET Center of the First Affiliated Hospital, College of Medicine, Zhejiang University. First, the 3D feature vector of each tumor voxel (whose basis is PET value, CT value, and CT local dominant orientation) was extracted. Using K-means individual clustering and population clustering, each tumor was divided into 4 subregions that reflect intratumoral regional heterogeneity. Next, based on each subregion, 385 radiomics features were extracted. Clinical features including age, gender, and smoking history were included. Thus, there were a total of 1,543 features extracted from PET/CT images and clinical reports. Statistical tests were then used to eliminate irrelevant and redundant features, and the recursive feature elimination (RFE) algorithm was used to select the best feature subset to classify SCC and ADC. Finally, 7 types of classifiers were tested to achieve the optimized model for the classification: support vector machine (SVM) with linear kernel, SVM with radial basis function kernel (SVM-RBF), random forest, logistic regression, Gaussian process classifier, linear discriminant analysis, and the AdaBoost classifier. Furthermore, 5-fold cross-validation was applied to obtain the sensitivity, specificity, accuracy, and area under the curve (AUC) for performance evaluation. Results Our model exhibited the best performance with the subregion radiomics features and SVM-RBF classifier, with a 5-fold cross-validation sensitivity, specificity, accuracy, and AUC of 0.8538, 0.8758, 0.8623, and 0.9155, respectively. The interquartile range feature from subregion 2 of CT and the gender feature from the clinical reports are the 2 optimized features that achieved the highest comprehensive score. Conclusions Our proposed model showed that SCC and ADC could be classified successfully using PET/CT images, which could be a promising tool to assist radiologists or medical physicists during diagnosis. The subregion-based method illustrated that non-small cell lung cancer (NSCLC) depicts intratumoral regional heterogeneity on both CT and PET images. By defining these heterogeneities through a subregion-based method, the diagnostic performance was improved. The 3D feature vector (whose basis is PET value, CT value, and CT local dominant orientation) showed superiority in reflecting NSCLC intratumoral regional heterogeneity.
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Affiliation(s)
- Hui Shen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Ling Chen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Kanfeng Liu
- PET Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Kui Zhao
- PET Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jingsong Li
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Lijuan Yu
- The Affiliated Cancer Hospital of Hainan Medical University, Haikou, China
| | - Hongwei Ye
- MinFound Medical System Co., Ltd, Shaoxing, China
| | - Wentao Zhu
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
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22
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Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future. ACTA ACUST UNITED AC 2021; 28:2351-2372. [PMID: 34202321 PMCID: PMC8293249 DOI: 10.3390/curroncol28040217] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 06/14/2021] [Accepted: 06/21/2021] [Indexed: 12/13/2022]
Abstract
Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer’s molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.
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Ganeshan B, Miles K, Afaq A, Punwani S, Rodriguez M, Wan S, Walls D, Hoy L, Khan S, Endozo R, Shortman R, Hoath J, Bhargava A, Hanson M, Francis D, Arulampalam T, Dindyal S, Chen SH, Ng T, Groves A. Texture Analysis of Fractional Water Content Images Acquired during PET/MRI: Initial Evidence for an Association with Total Lesion Glycolysis, Survival and Gene Mutation Profile in Primary Colorectal Cancer. Cancers (Basel) 2021; 13:2715. [PMID: 34072712 PMCID: PMC8199380 DOI: 10.3390/cancers13112715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 01/07/2023] Open
Abstract
To assess the capability of fractional water content (FWC) texture analysis (TA) to generate biologically relevant information from routine PET/MRI acquisitions for colorectal cancer (CRC) patients. Thirty consecutive primary CRC patients (mean age 63.9, range 42-83 years) prospectively underwent FDG-PET/MRI. FWC tumor parametric images generated from Dixon MR sequences underwent TA using commercially available research software (TexRAD). Data analysis comprised (1) identification of functional imaging correlates for texture features (TF) with low inter-observer variability (intraclass correlation coefficient: ICC > 0.75), (2) evaluation of prognostic performance for FWC-TF, and (3) correlation of prognostic imaging signatures with gene mutation (GM) profile. Of 32 FWC-TF with ICC > 0.75, 18 correlated with total lesion glycolysis (TLG, highest: rs = -0.547, p = 0.002). Using optimized cut-off values, five MR FWC-TF identified a good prognostic group with zero mortality (lowest: p = 0.017). For the most statistically significant prognostic marker, favorable prognosis was significantly associated with a higher number of GM per patient (medians: 7 vs. 1.5, p = 0.009). FWC-TA derived from routine PET/MRI Dixon acquisitions shows good inter-operator agreement, generates biological relevant information related to TLG, GM count, and provides prognostic information that can unlock new clinical applications for CRC patients.
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Affiliation(s)
- Balaji Ganeshan
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Kenneth Miles
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Asim Afaq
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
- Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Shonit Punwani
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Manuel Rodriguez
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Simon Wan
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Darren Walls
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Luke Hoy
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Saif Khan
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Raymond Endozo
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Robert Shortman
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - John Hoath
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Aman Bhargava
- Institute of Health Barts and London Medical School, Queen Mary University of London (QMUL), London E1 2AD, UK;
| | - Matthew Hanson
- Division of Cancer and Clinical Support, Barking, Havering and Redbridge University Hospitals NHS Trust, Queens and King George Hospitals, Essex IG3 8YB, UK;
| | - Daren Francis
- Department of Colorectal Surgery, Royal Free London NHS Foundation Trust, Barnet and Chase Farm Hospitals, London NW3 2QG, UK;
| | - Tan Arulampalam
- Department of Surgery, East Suffolk and North Essex NHS Foundation Trust, Colchester General Hospital, Colchester CO4 5JL, UK;
| | - Sanjay Dindyal
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Shih-Hsin Chen
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
- Department of Nuclear Medicine, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan
| | - Tony Ng
- School of Cancer & Pharmaceutical Sciences, King’s College London (KCL), London WC2R 2LS, UK;
| | - Ashley Groves
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
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Dercle L, Lu L, Schwartz LH, Qian M, Tejpar S, Eggleton P, Zhao B, Piessevaux H. Radiomics Response Signature for Identification of Metastatic Colorectal Cancer Sensitive to Therapies Targeting EGFR Pathway. J Natl Cancer Inst 2021; 112:902-912. [PMID: 32016387 DOI: 10.1093/jnci/djaa017] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 11/05/2019] [Accepted: 01/24/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The authors sought to forecast survival and enhance treatment decisions for patients with liver metastatic colorectal cancer by using on-treatment radiomics signature to predict tumor sensitiveness to irinotecan, 5-fluorouracil, and leucovorin (FOLFIRI) alone (F) or in combination with cetuximab (FC). METHODS We retrospectively analyzed 667 metastatic colorectal cancer patients treated with F or FC. Computed tomography quality was classified as high (HQ) or standard (SD). Four datasets were created using the nomenclature (treatment) - (quality). Patients were randomly assigned (2:1) to training or validation sets: FCHQ: 78:38, FCSD: 124:62, FHQ: 78:51, FSD: 158:78. Four tumor-imaging biomarkers measured quantitative radiomics changes between standard of care computed tomography scans at baseline and 8 weeks. Using machine learning, the performance of the signature to classify tumors as treatment sensitive or treatment insensitive was trained and validated using receiver operating characteristic (ROC) curves. Hazard ratio and Cox regression models evaluated association with overall survival (OS). RESULTS The signature (area under the ROC curve [95% confidence interval (CI)]) used temporal decrease in tumor spatial heterogeneity plus boundary infiltration to successfully predict sensitivity to antiepidermal growth factor receptor therapy (FCHQ: 0.80 [95% CI = 0.69 to 0.94], FCSD: 0.72 [95% CI = 0.59 to 0.83]) but failed with chemotherapy (FHQ: 0.59 [95% CI = 0.44 to 0.72], FSD: 0.55 [95% CI = 0.43 to 0.66]). In cetuximab-containing sets, radiomics signature outperformed existing biomarkers (KRAS-mutational status, and tumor shrinkage by RECIST 1.1) for detection of treatment sensitivity and was strongly associated with OS (two-sided P < .005). CONCLUSIONS Radiomics response signature can serve as an intermediate surrogate marker of OS. The signature outperformed known biomarkers in providing an early prediction of treatment sensitivity and could be used to guide cetuximab treatment continuation decisions.
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Affiliation(s)
- Laurent Dercle
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY, USA.,Gustave Roussy, Université Paris-Saclay, Villejuif, France
| | - Lin Lu
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY, USA
| | - Lawrence H Schwartz
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY, USA
| | - Min Qian
- Department of Biostatistics, Columbia University Medical Center, New York, NY, USA
| | - Sabine Tejpar
- Molecular Digestive Oncology, University Hospitals Leuven and KU Leuven, Leuven, Belgium
| | | | - Binsheng Zhao
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY, USA
| | - Hubert Piessevaux
- Department of Hepato-Gastroenterology, Cliniques Universitaires Saint-Luc, UCLouvain Brussels, Brussels, Belgium
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Velichko YS, Mozafarykhamseh A, Trabzonlu TA, Zhang Z, Rademaker AW, Yaghmai V. Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis. Acad Radiol 2021; 28:e93-e100. [PMID: 32303447 PMCID: PMC10029938 DOI: 10.1016/j.acra.2020.03.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 02/28/2020] [Accepted: 03/04/2020] [Indexed: 01/05/2023]
Abstract
PURPOSE To evaluate the effect of the anatomic size on 3D radiomic imaging features of the breast cancer hepatic metastases. MATERIALS AND METHODS CT scans of 81 liver metastases from 54 patients with breast cancer were evaluated. Ten most common 3D radiomic features from the histogram and gray level co-occurrence matrix (GLCM) categories were calculated for the hepatic metastases (HM) and compared to normal liver (NL). The effect of size was evaluated by using linear mixed-effects regression models. The effect of size on different radiomic features was analyzed for both liver lesions and background liver. RESULTS Three-dimensional radiomic features from GLCM demonstrate an important size dependence. The texture-feature size dependence was found to be different among feature categories and between the HM and NL, thus demonstrating a discriminatory power for the tissue type. Significant difference in the slope was found for GLCM homogeneity (NL slope = 0.004, slope difference 95% confidence interval [CI] 0.06-0.1, p <0.001), contrast (NL slope = 45, slope difference 95% CI 205-305, p <0.001), correlation (NL slope = 0.04, slope difference 95% CI 0.11-0.21, p <0.001), and dissimilarity (NL slope = 0.7, slope difference 95% CI 3.6-5.4, p <0.001). The GLCM energy (NL slope = 0.002, slope difference 95% CI -0.0005 to -0.0003, p <0.007), and entropy (NL slope = 1.49, slope difference 95% CI 0.07-0.52, p <0.009) exhibited size-dependence for both NL and HM, although demonstrating a difference in the slope between themselves. CONCLUSION Radiomic features of breast cancer hepatic metastasis exhibited significant correlation with tumor size. This finding demonstrates the complex behavior of imaging features and the need to include feature-specific properties into radiomic models.
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Affiliation(s)
- Yuri S Velichko
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois; Quantitative Imaging Core Lab, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
| | | | - Tugce Agirlar Trabzonlu
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Zhuoli Zhang
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois; Quantitative Imaging Core Lab, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Alfred W Rademaker
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Vahid Yaghmai
- Quantitative Imaging Core Lab, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
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Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis. Can J Gastroenterol Hepatol 2021; 2021:6677821. [PMID: 33791254 PMCID: PMC7997774 DOI: 10.1155/2021/6677821] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 01/28/2021] [Accepted: 03/03/2021] [Indexed: 12/30/2022] Open
Abstract
Purpose. To compare the diagnostic value of texture analysis- (TA-) derived parameters from out-of-phase T1W, in-phase T1W, and T2W images in the classification of the early stage of liver fibrosis. Methods. Patients clinically diagnosed with hepatitis B infection, who underwent liver biopsy and noncontrast MRI scans, were enrolled. TA parameters were extracted from out-of-phase T1-weighted (T1W), in-phase T1W, and T2-weighted (T2W) images and calculated using Artificial Intelligent Kit (AK). Features were extracted including first-order, shape, gray-level cooccurrence matrix, gray-level run-length matrix, neighboring gray one tone difference matrix, and gray-level differential matrix. After statistical analyses, final diagnostic models were constructed. Receiver operating curves (ROCs) and areas under the ROC (AUCs) were used to assess the diagnostic value of each final model and 100-time repeated cross-validation was applied to assess the stability of the logistic regression models. Results. A total of 57 patients were enrolled in this study, with 27 in the fibrosis stage < 2 and 30 in stages ≥ 2. Overall, 851 features were extracted per ROI. Eight features with high correlation were selected by the maximum relevance method in each sequence, and all had a good diagnostic performance. ROC analysis of the final models showed that all sequences had a preferable performance with AUCs of 0.87, 0.90, and 0.96 in T2W and in-phase and out-of-phase T1W, respectively. Cross-validation results reported the following values of mean accuracy, specificity, and sensitivity: 0.98 each for out-of-phase T1W; 0.90, 0.89, and 0.90 for in-phase T1W; and 0.86, 0.88, 0.84 for T2W in the training set, and 0.76, 0.81, and 0.72 for out-of-phase T1W; 0.74, 0.72, and 0.75 for in-phase T1W; and 0.63, 0.64, and 0.63 for T2W for the test group, respectively. Conclusion. Noncontrast MRI scans with texture analysis are viable for classifying the early stages of liver fibrosis, exhibiting excellent diagnostic performance.
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Ammari S, Pitre-Champagnat S, Dercle L, Chouzenoux E, Moalla S, Reuze S, Talbot H, Mokoyoko T, Hadchiti J, Diffetocq S, Volk A, El Haik M, Lakiss S, Balleyguier C, Lassau N, Bidault F. Influence of Magnetic Field Strength on Magnetic Resonance Imaging Radiomics Features in Brain Imaging, an In Vitro and In Vivo Study. Front Oncol 2021; 10:541663. [PMID: 33552944 PMCID: PMC7855708 DOI: 10.3389/fonc.2020.541663] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The development and clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for the identification of parameters altering radiomics reproducibility. The aim of this study was to assess the impact of magnetic field strength on magnetic resonance imaging (MRI) radiomics features in neuroradiology clinical practice. METHODS T1 3D SPGR sequence was acquired on two phantoms and 10 healthy volunteers with two clinical MR devices from the same manufacturer using two different magnetic fields (1.5 and 3T). Phantoms varied in terms of gadolinium concentrations and textural heterogeneity. 27 regions of interest were segmented (phantom: 21, volunteers: 6) using the LIFEX software. 34 features were analyzed. RESULTS In the phantom dataset, 10 (67%) out of 15 radiomics features were significantly different when measured at 1.5T or 3T (student's t-test, p < 0.05). Gray levels resampling, and pixel size also influence part of texture features. These findings were validated in healthy volunteers. CONCLUSIONS According to daily used protocols for clinical examinations, radiomic features extracted on 1.5T should not be used interchangeably with 3T when evaluating texture features. Such confounding factor should be adjusted when adapting the results of a study to a different platform, or when designing a multicentric trial.
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Affiliation(s)
- Samy Ammari
- Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- BioMaps (UMR1281), Université Paris-Saclay, CNRS, INSERM, CEA, Orsay and Gustave Roussy, Villejuif, France
| | - Stephanie Pitre-Champagnat
- BioMaps (UMR1281), Université Paris-Saclay, CNRS, INSERM, CEA, Orsay and Gustave Roussy, Villejuif, France
| | - Laurent Dercle
- Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- Immunology of Tumours and Immunotherapy INSERM U1015, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France
- Radiology Department, Columbia University Medical Center, New York Presbyterian Hospital, New York, NY, United States
| | - Emilie Chouzenoux
- Center for Visual Computing, CentraleSupelec, Inria, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Salma Moalla
- Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
| | - Sylvain Reuze
- Department of Radiotherapy - Medical Physics, Gustave Roussy, Université ParisSaclay, Villejuif, France
| | - Hugues Talbot
- Center for Visual Computing, CentraleSupelec, Inria, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Tite Mokoyoko
- Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
| | - Joya Hadchiti
- Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
| | - Sebastien Diffetocq
- Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
| | - Andreas Volk
- BioMaps (UMR1281), Université Paris-Saclay, CNRS, INSERM, CEA, Orsay and Gustave Roussy, Villejuif, France
| | - Mickeal El Haik
- Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
| | - Sara Lakiss
- Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
| | - Corinne Balleyguier
- Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- BioMaps (UMR1281), Université Paris-Saclay, CNRS, INSERM, CEA, Orsay and Gustave Roussy, Villejuif, France
| | - Nathalie Lassau
- Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- BioMaps (UMR1281), Université Paris-Saclay, CNRS, INSERM, CEA, Orsay and Gustave Roussy, Villejuif, France
| | - Francois Bidault
- Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- BioMaps (UMR1281), Université Paris-Saclay, CNRS, INSERM, CEA, Orsay and Gustave Roussy, Villejuif, France
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Alongi P, Stefano A, Comelli A, Laudicella R, Scalisi S, Arnone G, Barone S, Spada M, Purpura P, Bartolotta TV, Midiri M, Lagalla R, Russo G. Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients. Eur Radiol 2021; 31:4595-4605. [PMID: 33443602 DOI: 10.1007/s00330-020-07617-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/10/2020] [Accepted: 12/07/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVE The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging. MATERIAL AND METHODS Ninety-four high-risk PCa patients who underwent restaging Cho-PET/CT were analyzed. Follow-up data were recorded for a minimum of 13 months after the PET/CT scan. PET images were imported in LIFEx toolbox to extract 51 features from each lesion. A statistical system based on correlation matrix and point-biserial-correlation coefficient has been implemented for features reduction and selection, while Discriminant analysis (DA) was used as a method for features classification in a whole sample and sub-groups for primary tumor or local relapse (T), nodal disease (N), and metastatic disease (M). RESULTS In the whole group, 2 feature (HISTO_Entropy_log10; HISTO_Energy_Uniformity) results were able to discriminate the occurrence of disease progression at follow-up, obtaining the best performance in DA classification (sensitivity 47.1%, specificity 76.5%, positive predictive value (PPV) 46.7%, and accuracy 67.6%). In the sub-group analysis, the best performance in DA classification for T was obtained by selecting 3 features (SUVmin; SHAPE_Sphericity; GLCM_Correlation) with a sensitivity of 91.6%, specificity 84.1%, PPV 79.1%, and accuracy 87%; for N by selecting 2 features (HISTO = _Energy Uniformity; GLZLM_SZLGE) with a sensitivity of 68.1%, specificity 91.4%, PPV 83%, and accuracy 82.6%; and for M by selecting 2 features (HISTO_Entropy_log10 - HISTO_Entropy_log2) with a sensitivity 64.4%, specificity 74.6%, PPV 40.6%, and accuracy 72.5%. CONCLUSION This machine learning model demonstrated to be feasible and useful to select Cho-PET features for T, N, and M with valuable association with high-risk PCa patients' outcomes. KEY POINTS • Artificial intelligence applications are feasible and useful to select Cho-PET features. • Our model demonstrated the presence of specific features for T, N, and M with valuable association with high-risk PCa patients' outcomes. • Further prospective studies are necessary to confirm our results and to develop the application of artificial intelligence in PET imaging of PCa.
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Affiliation(s)
- Pierpaolo Alongi
- Nuclear Medicine Unit, Fondazione Istituto G. Giglio, Contrada Pietrapollastra Pisciotto, 90015, Cefalù, PA, Italy.
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (CNR), Cefalù, PA, Italy
| | | | - Riccardo Laudicella
- Department of Biomedical and Dental Sciences and of Morpho-functional Imaging, Nuclear Medicine Unit, University of Messina, Messina, Italy
| | - Salvatore Scalisi
- Nuclear Medicine Unit, Fondazione Istituto G. Giglio, Contrada Pietrapollastra Pisciotto, 90015, Cefalù, PA, Italy
| | - Giuseppe Arnone
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Stefano Barone
- Dipartimento di Scienze Agronomiche, Alimentari e Forestali (SAAF), University of Palermo, Palermo, Italy
| | | | - Pierpaolo Purpura
- Department of Radiology, Fondazione Istituto Giuseppe Giglio Ct.da Pietrapollastra, Via Pisciotto, 90015, Cefalù (Palermo), Italy
| | - Tommaso Vincenzo Bartolotta
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
- Department of Radiology, Fondazione Istituto Giuseppe Giglio Ct.da Pietrapollastra, Via Pisciotto, 90015, Cefalù (Palermo), Italy
| | - Massimo Midiri
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Roberto Lagalla
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (CNR), Cefalù, PA, Italy
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Ziegelmayer S, Kaissis G, Harder F, Jungmann F, Müller T, Makowski M, Braren R. Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP). J Clin Med 2020; 9:jcm9124013. [PMID: 33322559 PMCID: PMC7764649 DOI: 10.3390/jcm9124013] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 11/25/2020] [Accepted: 12/08/2020] [Indexed: 12/12/2022] Open
Abstract
The differentiation of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) poses a relevant diagnostic challenge and can lead to misdiagnosis and consequently poor patient outcome. Recent studies have shown that radiomics-based models can achieve high sensitivity and specificity in predicting both entities. However, radiomic features can only capture low level representations of the input image. In contrast, convolutional neural networks (CNNs) can learn and extract more complex representations which have been used for image classification to great success. In our retrospective observational study, we performed a deep learning-based feature extraction using CT-scans of both entities and compared the predictive value against traditional radiomic features. In total, 86 patients, 44 with AIP and 42 with PDACs, were analyzed. Whole pancreas segmentation was automatically performed on CT-scans during the portal venous phase. The segmentation masks were manually checked and corrected if necessary. In total, 1411 radiomic features were extracted using PyRadiomics and 256 features (deep features) were extracted using an intermediate layer of a convolutional neural network (CNN). After feature selection and normalization, an extremely randomized trees algorithm was trained and tested using a two-fold shuffle-split cross-validation with a test sample of 20% (n = 18) to discriminate between AIP or PDAC. Feature maps were plotted and visual difference was noted. The machine learning (ML) model achieved a sensitivity, specificity, and ROC-AUC of 0.89 ± 0.11, 0.83 ± 0.06, and 0.90 ± 0.02 for the deep features and 0.72 ± 0.11, 0.78 ± 0.06, and 0.80 ± 0.01 for the radiomic features. Visualization of feature maps indicated different activation patterns for AIP and PDAC. We successfully trained a machine learning model using deep feature extraction from CT-images to differentiate between AIP and PDAC. In comparison to traditional radiomic features, deep features achieved a higher sensitivity, specificity, and ROC-AUC. Visualization of deep features could further improve the diagnostic accuracy of non-invasive differentiation of AIP and PDAC.
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Affiliation(s)
- Sebastian Ziegelmayer
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany; (S.Z.); (G.K.); (F.H.); (F.J.); (T.M.); (M.M.)
| | - Georgios Kaissis
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany; (S.Z.); (G.K.); (F.H.); (F.J.); (T.M.); (M.M.)
- Department of Computing, Faculty of Engineering, Technology and Medicine, Imperial College of Science, London SW7 2BU, UK
| | - Felix Harder
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany; (S.Z.); (G.K.); (F.H.); (F.J.); (T.M.); (M.M.)
| | - Friederike Jungmann
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany; (S.Z.); (G.K.); (F.H.); (F.J.); (T.M.); (M.M.)
| | - Tamara Müller
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany; (S.Z.); (G.K.); (F.H.); (F.J.); (T.M.); (M.M.)
| | - Marcus Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany; (S.Z.); (G.K.); (F.H.); (F.J.); (T.M.); (M.M.)
| | - Rickmer Braren
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany; (S.Z.); (G.K.); (F.H.); (F.J.); (T.M.); (M.M.)
- German Cancer Consortium, Partner Site Technical University of Munich, D-69120 Heidelberg, Germany
- Correspondence: ; Tel.: +49-89-4140-5627
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Vuong D, Tanadini-Lang S, Wu Z, Marks R, Unkelbach J, Hillinger S, Eboulet EI, Thierstein S, Peters S, Pless M, Guckenberger M, Bogowicz M. Radiomics Feature Activation Maps as a New Tool for Signature Interpretability. Front Oncol 2020; 10:578895. [PMID: 33364192 PMCID: PMC7753181 DOI: 10.3389/fonc.2020.578895] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 10/22/2020] [Indexed: 12/25/2022] Open
Abstract
Introduction In the field of personalized medicine, radiomics has shown its potential to support treatment decisions. However, the limited feature interpretability hampers its introduction into the clinics. Here, we propose a new methodology to create radiomics feature activation maps, which allows to identify the spatial-anatomical locations responsible for signature activation based on local radiomics. The feasibility of this technique will be studied for histological subtype differentiation (adenocarcinoma versus squamous cell carcinoma) in non-small cell lung cancer (NSCLC) using computed tomography (CT) radiomics. Materials and Methods Pre-treatment CT scans were collected from a multi-centric Swiss trial (training, n=73, IIIA/N2 NSCLC, SAKK 16/00) and an independent cohort (validation, n=32, IIIA/N2/IIIB NSCLC). Based on the gross tumor volume (GTV), four peritumoral region of interests (ROI) were defined: lung_exterior (expansion into the lung), iso_exterior (expansion into lung and soft tissue), gradient (GTV border region), GTV+Rim (GTV and iso_exterior). For each ROI, 154 radiomic features were extracted using an in-house developed software implementation (Z-Rad, Python v2.7.14). Features robust against delineation variability served as an input for a multivariate logistic regression analysis. Model performance was quantified using the area under the receiver operating characteristic curve (AUC) and verified using five-fold cross validation and internal validation. Local radiomic features were extracted from the GTV+Rim ROI using non-overlapping 3x3x3 voxel patches previously marked as GTV or rim. A binary activation map was created for each patient using the median global feature value from the training. The ratios of activated/non-activated patches of GTV and rim regions were compared between histological subtypes (Wilcoxon test). Results Iso_exterior, gradient, GTV+Rim showed good performances for histological subtype prediction (AUCtraining=0.68-0.72 and AUCvalidation=0.73-0.74) whereas GTV and lung_exterior models failed validation. GTV+Rim model feature activation maps showed that local texture feature distribution differed significantly between histological subtypes in the rim (p=0.0481) but not in the GTV (p=0.461). Conclusion In this exploratory study, radiomics-based prediction of NSCLC histological subtypes was predominantly based on the peritumoral region indicating that radiomics activation maps can be useful for tracing back the spatial location of regions responsible for signature activation.
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Affiliation(s)
- Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Ze Wu
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Robert Marks
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Jan Unkelbach
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Sven Hillinger
- Department of Thoracic Surgery, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Eric Innocents Eboulet
- Department of Clinical Trial Management, Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Sandra Thierstein
- Department of Clinical Trial Management, Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Solange Peters
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Miklos Pless
- Department of Medical Oncology, Kantonsspital Winterthur, Winterthur, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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Radiomics of Liver Metastases: A Systematic Review. Cancers (Basel) 2020; 12:cancers12102881. [PMID: 33036490 PMCID: PMC7600822 DOI: 10.3390/cancers12102881] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/03/2020] [Accepted: 10/05/2020] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Patients with liver metastases can be scheduled for different therapies (e.g., chemotherapy, surgery, radiotherapy, and ablation). The choice of the most appropriate treatment should rely on adequate understanding of tumor biology and prediction of survival, but reliable biomarkers are lacking. Radiomics is an innovative approach to medical imaging: it identifies invisible-to-the-human-eye radiological patterns that can predict tumor aggressiveness and patients outcome. We reviewed the available literature to elucidate the role of radiomics in patients with liver metastases. Thirty-two papers were analyzed, mostly (56%) concerning metastases from colorectal cancer. Even if available studies are still preliminary, radiomics provided effective prediction of response to chemotherapy and of survival, allowing more accurate and earlier prediction than standard predictors. Entropy and homogeneity were the radiomic features with the strongest clinical impact. In the next few years, radiomics is expected to give a consistent contribution to the precision medicine approach to patients with liver metastases. Abstract Multidisciplinary management of patients with liver metastases (LM) requires a precision medicine approach, based on adequate profiling of tumor biology and robust biomarkers. Radiomics, defined as the high-throughput identification, analysis, and translational applications of radiological textural features, could fulfill this need. The present review aims to elucidate the contribution of radiomic analyses to the management of patients with LM. We performed a systematic review of the literature through the most relevant databases and web sources. English language original articles published before June 2020 and concerning radiomics of LM extracted from CT, MRI, or PET-CT were considered. Thirty-two papers were identified. Baseline higher entropy and lower homogeneity of LM were associated with better survival and higher chemotherapy response rates. A decrease in entropy and an increase in homogeneity after chemotherapy correlated with radiological tumor response. Entropy and homogeneity were also highly predictive of tumor regression grade. In comparison with RECIST criteria, radiomic features provided an earlier prediction of response to chemotherapy. Lastly, texture analyses could differentiate LM from other liver tumors. The commonest limitations of studies were small sample size, retrospective design, lack of validation datasets, and unavailability of univocal cut-off values of radiomic features. In conclusion, radiomics can potentially contribute to the precision medicine approach to patients with LM, but interdisciplinarity, standardization, and adequate software tools are needed to translate the anticipated potentialities into clinical practice.
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Efficacy of ZOOMit coronal diffusion-weighted imaging and MR texture analysis for differentiating between benign and malignant distal bile duct strictures. Abdom Radiol (NY) 2020; 45:2418-2429. [PMID: 32562051 DOI: 10.1007/s00261-020-02625-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
PURPOSE To investigate the diagnostic efficacy of ZOOMit coronal diffusion-weighted imaging (Z-DWI) and MR texture analysis (MRTA) for differentiating benign from malignant distal bile duct strictures. METHODS We retrospectively enrolled a total of 71 patients with distal bile duct stricture who underwent magnetic resonance cholangiopancreatography (MRCP). For quantitative analysis, the average apparent diffusion coefficient (ADC) value at suspected stricture sites was assessed on both Z-DWI and conventional DWI (C-DWI). For qualitative analysis, two reviewers independently reviewed two image sets containing different diffusion-weighted images, and receiver operating characteristic (ROC) curve analysis was performed. Several MRTA parameters were extracted from the area of the stricture on the ADC map of the ZOOMit coronal diffusion-weighted images using commercially available software. RESULTS Among 71 patients, 26 patients were diagnosed with malignant stricture. On quantitative analysis, the average ADC value of the malignant and benign strictures, using Z-DWI, was 1.124 × 10-3 mm2/s and 1.522 × 10-3 mm2/s, respectively (P < 0.001). The average ADC value of the malignant and benign strictures, using C-DWI, was 1.107 × 10-3 mm2/s and 1.519 × 10-3 mm2/s, respectively (P < 0.001). On qualitative analysis, for each reviewer, the area under the ROC curve (AUC) values for differentiating benign from malignant stricture was 0.928 and 0.939, respectively, for the ZOOMit diffusion set and 0.851 and 0.824, respectively, for the conventional diffusion set. Multiple MRTA parameters showed a significantly different distribution for the benign and malignant strictures, including mean, entropy, mean of positive pixels, and kurtosis at spatial filtration values of 0, 5, and 6 mm. CONCLUSION The addition of Z-DWI to conventional MRCP is helpful in differentiating benign from malignant bile duct strictures, and some MRTA parameters also can be helpful in differentiating benign from malignant distal bile duct strictures.
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de Castro Dytz O, de Azevedo Berger P, Dytz MG, Barbosa BA, Júnior AJ, Reggatieri NAT, Disegna A, de Paula WD, Casulari LA, Naves LA. Entropy and uniformity as additional parameters to optimize the effectiveness of bone CT in the evaluation of acromegalic patients. Endocrine 2020; 69:368-376. [PMID: 32524503 DOI: 10.1007/s12020-020-02358-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 05/18/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE Acromegaly is considered an important cause of secondary osteoporosis. However, studies on bone mineral density (BMD) have yielded conflicting results and there are few studies that evaluate an accurate imaging method for early diagnosis of osteoporosis in these patients. The objective of this study was to assess whether entropy and uniformity on computed tomography (CT) scans are useful parameters for optimization of assessment of bone fragility in patients with acromegaly. METHODS We included 34 patients and 36 controls matched for age and sex in a cross-sectional study. Patients and controls underwent CT scan of the lumbosacral spine, dual-energy x-ray absorptiometry (DXA) and blood tests. A software was developed to calculate the entropy and uniformity by a region of interest (ROI) of the trabecular bone of the first lumbar vertebra (L1). RESULTS The acromegalic group presented higher mean bone entropy (6.87 ± 0.98 vs. 6.03 ± 1.68, p = 0.013) and lower mean bone uniformity (0.035 ± 0.704 vs. 0.113 ± 0.205, p = 0.035) than control group. Analyzing only acromegalics, mean bone entropy was higher and bone uniformity was lower in patients with hypogonadism than patients without hypogonadism (7.28 ± 0.36 vs. 6.74 ± 1.08, p = 0.038 and 0.008 ± 0.002 vs. 0.043 ± 0.079, p = 0.031) respectively. Patients with acromegaly presented higher BMD and Z-score in the femoral neck than control group (1.156 ± 0.108 vs. 0.925 ± 0.326 g/cm2, p = 0.043 and 0.6 ± 0.6 vs. -0.05 ± 0.8, p = 0.041, respectively). Entropy was negatively correlated with T-score of the lumbar spine (rp = -0.357, p = 0.033) in control group and uniformity was positively correlated with T-score of the lumbar spine, neck, and total hip, respectively (rp = 0.371, p = 0.031; rp = 0.348, p = 0.043 and rp = 0.341, p = 0.049) in acromegalic group. CONCLUSIONS The study identified that entropy and uniformity are a relevant parameters data in bone fragility assessment in acromegalic patients.
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Affiliation(s)
- Olga de Castro Dytz
- Department of Endocrinology, University Hospital of Brasilia, University of Brasilia, Brasilia, Brazil.
| | - Pedro de Azevedo Berger
- Faculty of Medicine, University of Brasilia, Brasilia, Brazil
- Department of Computer Science, University of Brasilia, Brasilia, Brazil
| | - Márcio Garrison Dytz
- Department of Endocrinology, University Hospital of Brasilia, University of Brasilia, Brasilia, Brazil
| | - Bernardo Alves Barbosa
- Department of Endocrinology, University Hospital of Brasilia, University of Brasilia, Brasilia, Brazil
| | - Armindo Jreige Júnior
- Department of Endocrinology, University Hospital of Brasilia, University of Brasilia, Brasilia, Brazil
- Faculty of Medicine, University of Brasilia, Brasilia, Brazil
| | | | - Arthur Disegna
- Department of Endocrinology, University Hospital of Brasilia, University of Brasilia, Brasilia, Brazil
- Faculty of Medicine, University of Brasilia, Brasilia, Brazil
| | - Wagner Diniz de Paula
- Department of Radiology, University Hospital of Brasilia, University of Brasilia, Brasilia, Brazil
| | - Luiz Augusto Casulari
- Department of Endocrinology, University Hospital of Brasilia, University of Brasilia, Brasilia, Brazil
- Faculty of Medicine, University of Brasilia, Brasilia, Brazil
| | - Luciana Ansaneli Naves
- Department of Endocrinology, University Hospital of Brasilia, University of Brasilia, Brasilia, Brazil
- Faculty of Medicine, University of Brasilia, Brasilia, Brazil
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Template Creation for High-Resolution Computed Tomography Scans of the Lung in R Software. Acad Radiol 2020; 27:e204-e215. [PMID: 31843391 PMCID: PMC7292778 DOI: 10.1016/j.acra.2019.10.030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 10/29/2019] [Accepted: 10/29/2019] [Indexed: 12/21/2022]
Abstract
Rationale and Objectives. A standard lung template could improve population-level analyses for computed tomography (CT) scans of the lung. We develop a fully-automated pre-processing pipeline for image analysis of the lungs using updated methodologies and R software that results in the creation of a standard lung template. We apply this pipeline to CT scans from a sarcoidosis population, exploring the influence of registration on radiomic analyses. Materials and Methods. Using 65 high-resolution CT scans from healthy adults, we create a standard lung template by segmenting the left and right lungs, non-linearly registering lung masks to an initial template mask, and using an unbiased, iterative procedure to converge to a standard lung shape (Dice similarity coefficient ≥0.99). We compare three-dimensional radiomic features between control and sarcoidosis patients, before and after registration to a study-specific lung template. Results. The final lung template had a right lung volume of 2967 cm3 and left lung volume of 2623 cm3, with a median HU = −862. Registration significantly affected radiomic features, shifting the HU distribution to the left, decreasing variability, and increasing smoothness (p<0.0001). The registration improved detective ability of radiomics; for contrast, autocorrelation, energy and homogeneity, the group effect was significant post-registration (p<0.05), but was not significant pre-registration. Conclusion. The final lung template and software used for its creation are publicly available via the lungct R package to facilitate its use in practice. This study advances lung imaging by developing tools to improve population-level analyses for various lung diseases.
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Dercle L, Henry T, Carré A, Paragios N, Deutsch E, Robert C. Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives. Methods 2020; 188:44-60. [PMID: 32697964 DOI: 10.1016/j.ymeth.2020.07.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 07/02/2020] [Accepted: 07/06/2020] [Indexed: 12/14/2022] Open
Abstract
Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Imaging is now playing a critical role on deployment of the clinical workflow, both for treatment planning and treatment delivery. Machine-learning analysis of predefined features extracted from medical images, i.e. radiomics, has emerged as a promising clinical tool for a wide range of clinical problems addressing drug development, clinical diagnosis, treatment selection and implementation as well as prognosis. Radiomics denotes a paradigm shift redefining medical images as a quantitative asset for data-driven precision medicine. The adoption of machine-learning in a clinical setting and in particular of radiomics features requires the selection of robust, representative and clinically interpretable biomarkers that are properly evaluated on a representative clinical data set. To be clinically relevant, radiomics must not only improve patients' management with great accuracy but also be reproducible and generalizable. Hence, this review explores the existing literature and exposes its potential technical caveats, such as the lack of quality control, standardization, sufficient sample size, type of data collection, and external validation. Based upon the analysis of 165 original research studies based on PET, CT-scan, and MRI, this review provides an overview of new concepts, and hypotheses generating findings that should be validated. In particular, it describes evolving research trends to enhance several clinical tasks such as prognostication, treatment planning, response assessment, prediction of recurrence/relapse, and prediction of toxicity. Perspectives regarding the implementation of an AI-based radiotherapy workflow are presented.
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Affiliation(s)
- Laurent Dercle
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, USA
| | - Theophraste Henry
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Nuclear Medicine and Endocrine Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Alexandre Carré
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | | | - Eric Deutsch
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Charlotte Robert
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France.
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MRI texture features differentiate clinicopathological characteristics of cervical carcinoma. Eur Radiol 2020; 30:5384-5391. [PMID: 32382845 DOI: 10.1007/s00330-020-06913-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 04/23/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC). METHODS Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC. RESULTS Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively. CONCLUSIONS Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC. KEY POINTS • First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.
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Liu X, Li Y, Li S, Fan X, Sun Z, Yang Z, Wang K, Zhang Z, Jiang T, Liu Y, Wang L, Wang Y. IDH mutation-specific radiomic signature in lower-grade gliomas. Aging (Albany NY) 2020; 11:673-696. [PMID: 30696801 PMCID: PMC6366985 DOI: 10.18632/aging.101769] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 01/06/2019] [Indexed: 12/16/2022]
Abstract
Unravelling the heterogeneity is the central challenge for glioma precession oncology. In this study, we extracted quantitative image features from T2-weighted MR images and revealed that the isocitrate dehydrogenase (IDH) wild type and mutant lower grade gliomas (LGGs) differed in their expression of 146 radiomic descriptors. The logistic regression model algorithm further reduced these to 86 features. The classification model could discriminate the two types in both the training and validation sets with area under the curve values of 1.0000 and 0.9932, respectively. The transcriptome-radiomic analysis revealed that these features were associated with the immune response, biological adhesion, and several malignant behaviors, all of which are consistent with biological processes that are differentially expressed in IDH wild type and IDH mutant LGGs. Finally, a prognostic signature showed an ability to stratify IDH mutant LGGs into high and low risk groups with distinctive outcomes. By extracting a large number of radiomic features, we identified an IDH mutation-specific radiomic signature with prognostic implications. This radiomic signature may provide a way to non-invasively discriminate lower-grade gliomas as with or without the IDH mutation.
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Affiliation(s)
- Xing Liu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yiming Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Shaowu Li
- Neurological Imaging Center, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xing Fan
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhiyan Sun
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Kai Wang
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Jiang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Chinese Glioma Genome Atlas Network (CGGA) and Asian Glioma Genome Atlas Network (AGGA)
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lei Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Monti S, Brancato V, Di Costanzo G, Basso L, Puglia M, Ragozzino A, Salvatore M, Cavaliere C. Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol. Cancers (Basel) 2020; 12:cancers12020390. [PMID: 32046196 PMCID: PMC7072162 DOI: 10.3390/cancers12020390] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 01/27/2020] [Accepted: 02/05/2020] [Indexed: 12/20/2022] Open
Abstract
Prostate cancer (PCa) is a disease affecting an increasing number of men worldwide. Several efforts have been made to identify imaging biomarkers to non-invasively detect and characterize PCa, with substantial improvements thanks to multiparametric Magnetic Resonance Imaging (mpMRI). In recent years, diffusion kurtosis imaging (DKI) was proposed to be directly related to tissue physiological and pathological characteristic, while the radiomic approach was proven to be a key method to study cancer imaging phenotypes. Our aim was to compare a standard radiomic model for PCa detection, built using T2-weighted (T2W) and Apparent Diffusion Coefficient (ADC), with an advanced one, including DKI and quantitative Dynamic Contrast Enhanced (DCE), while also evaluating differences in prediction performance when using 2D or 3D lesion segmentation. The obtained results in terms of diagnostic accuracy were high for all of the performed comparisons, reaching values up to 0.99 for the area under a receiver operating characteristic curve (AUC), and 0.98 for both sensitivity and specificity. In comparison, the radiomic model based on standard features led to prediction performances higher than those of the advanced model, while greater accuracy was achieved by the model extracted from 3D segmentation. These results provide new insights into active topics of discussion, such as choosing the most convenient acquisition protocol and the most appropriate postprocessing pipeline to accurately detect and characterize PCa.
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Affiliation(s)
- Serena Monti
- IRCCS SDN, 80143 Naples, Italy; (S.M.); (L.B.); (M.S.); (C.C.)
| | - Valentina Brancato
- IRCCS SDN, 80143 Naples, Italy; (S.M.); (L.B.); (M.S.); (C.C.)
- Correspondence: ; Tel.: +39-081-2408-299
| | | | - Luca Basso
- IRCCS SDN, 80143 Naples, Italy; (S.M.); (L.B.); (M.S.); (C.C.)
| | - Marta Puglia
- Ospedale S. Maria delle Grazie, 80078 Pozzuoli, Italy; (G.D.C.); (M.P.); (A.R.)
| | - Alfonso Ragozzino
- Ospedale S. Maria delle Grazie, 80078 Pozzuoli, Italy; (G.D.C.); (M.P.); (A.R.)
| | - Marco Salvatore
- IRCCS SDN, 80143 Naples, Italy; (S.M.); (L.B.); (M.S.); (C.C.)
| | - Carlo Cavaliere
- IRCCS SDN, 80143 Naples, Italy; (S.M.); (L.B.); (M.S.); (C.C.)
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Bereby-Kahane M, Dautry R, Matzner-Lober E, Cornelis F, Sebbag-Sfez D, Place V, Mezzadri M, Soyer P, Dohan A. Prediction of tumor grade and lymphovascular space invasion in endometrial adenocarcinoma with MR imaging-based radiomic analysis. Diagn Interv Imaging 2020; 101:401-411. [PMID: 32037289 DOI: 10.1016/j.diii.2020.01.003] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 12/21/2019] [Accepted: 01/02/2020] [Indexed: 01/07/2023]
Abstract
PURPOSE To evaluate the capabilities of two-dimensional magnetic resonance imaging (MRI)-based texture analysis features, tumor volume, tumor short axis and apparent diffusion coefficient (ADC) in predicting histopathological high-grade and lymphovascular space invasion (LVSI) in endometrial adenocarcinoma. MATERIALS AND METHODS Seventy-three women (mean age: 66±11.5 [SD] years; range: 45-88 years) with endometrial adenocarcinoma who underwent MRI of the pelvis at 1.5-T before hysterectomy were retrospectively included. Texture analysis was performed using TexRAD® software on T2-weighted images and ADC maps. Primary outcomes were high-grade and LVSI prediction using histopathological analysis as standard of reference. After data reduction using ascending hierarchical classification analysis, a predictive model was obtained by stepwise multivariate logistic regression and performances were assessed using cross-validated receiver operator curve (ROC). RESULTS A total of 72 texture features per tumor were computed. Texture model yielded 52% sensitivity and 75% specificity for the diagnosis of high-grade tumor (areas under ROC curve [AUC]=0.64) and 71% sensitivity and 59% specificity for the diagnosis of LVSI (AUC=0.59). Volumes and tumor short axis were greater for high-grade tumors (P=0.0002 and P=0.004, respectively) and for patients with LVSI (P=0.004 and P=0.0279, respectively). No differences in ADC values were found between high-grade and low-grade tumors and for LVSI. A tumor short axis≥20mm yielded 95% sensitivity and 75% specificity for the diagnosis of high-grade tumor (AUC=0.86). CONCLUSION MRI-based texture analysis is of limited value to predict high grade and LVSI of endometrial adenocarcinoma. A tumor short axis≥20mm is the best predictor of high grade and LVSI.
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Affiliation(s)
- M Bereby-Kahane
- Department of Radiology A, Hôpital Cochin, AP-HP, 75014 Paris, France
| | - R Dautry
- Department of Radiology A, Hôpital Cochin, AP-HP, 75014 Paris, France
| | - E Matzner-Lober
- CREST UMR 9194, ENSAE formation continue, 91120 Palaiseau, France
| | - F Cornelis
- Department of Pathology, Hôpital Lariboisière, AP-HP, 75010 Paris, France
| | - D Sebbag-Sfez
- Department of Radiology, Hôpital Lariboisière, AP-HP, 75010 Paris, France
| | - V Place
- Department of Radiology, Hôpital Lariboisière, AP-HP, 75010 Paris, France
| | - M Mezzadri
- Department of Gynecology, Hôpital Lariboisière, AP-HP, 75010 Paris, France
| | - P Soyer
- Department of Radiology A, Hôpital Cochin, AP-HP, 75014 Paris, France; Université de Paris, Descartes-Paris 5, 75006 Paris, France
| | - A Dohan
- Department of Radiology A, Hôpital Cochin, AP-HP, 75014 Paris, France; Université de Paris, Descartes-Paris 5, 75006 Paris, France; Institut Cochin, 75014 Paris, France.
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Basirjafari S, Poureisa M, Shahhoseini B, Zarei M, Aghayari Sheikh Neshin S, Anvari Aria S, Nouri-Vaskeh M. Apparent diffusion coefficient values and non-homogeneity of diffusion in brain tumors in diffusion-weighted MRI. Acta Radiol 2020; 61:244-252. [PMID: 31264441 DOI: 10.1177/0284185119856887] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Background The values that have been received from apparent diffusion coefficient (ADC) maps of diffusion-weighted magnetic resonance imaging (DW-MRI) might play a vital role in evaluating tumors and their grading scale. Purpose To investigate the predictive role of this heterogeneity in brain tumor pathologies and its correlation with Ki-67. Material and Methods A total of 124 patients with brain tumors underwent brain MRI with gadolinium injection. ADC and standard deviation of each lesion have been obtained from manual localization of the region of interest on the ADC map. A receiver operating characteristic analysis was conducted to determine the minimum cut-off values of the mean ADC and mean standard deviation of ADC maps having the highest sensitivity and specificity to differentiate high-grade and low-grade tumors. Results Mean ADC values in the region of interest were significantly lower for malignant tumors (grade IV and metastasis) than grade I brain tumors, while a higher mean standard deviation was observed. In a more detailed comparison of tumor groups, the mean standard deviation of the ADC for glioblastoma multiform was significantly higher than meningioma grade I ( P < 0.001) and metastasis was significantly higher than grade III and IV astrocytic tumors ( P = 0.004). The analysis of Ki-67 proliferation index and mean ADC values in gliomas showed a significant inverse correlation between the parameters (r = –0.0429, P < 0.001) and direct correlation between Ki-67 and mean standard deviation of the ADC (r = 0.551, P < 0.001). As an index for the ADC to differentiate high-grade and low-grade tumors, the cut-off values of 1.40*10−3 mm2/s for mean ADC and 45*10−3 mm2/s for mean standard deviation have the highest combination of sensitivity, specificity, and area under the curve. Conclusion The mean value and standard deviation of the ADC could be considered for differentiating between low-grade and high-grade brain tumors, as two available non-invasive methods.
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Affiliation(s)
| | - Masoud Poureisa
- Department of Radiology, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Babak Shahhoseini
- Imam Khomeini Hospital, North Khorasan University of Medical Sciences, Shirvan, Iran
| | - Mohammad Zarei
- Department of Pharmacology, Toxicology and Therapeutic Chemistry, Faculty of Pharmacy, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
| | | | - Sheida Anvari Aria
- Department of Growth and Reproduction, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Masoud Nouri-Vaskeh
- Neurosciences Research Center (NSRC), Tabriz University of Medical Sciences, Tabriz, Iran
- Connective Tissue Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
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Ma J, Dercle L, Lichtenstein P, Wang D, Chen A, Zhu J, Piessevaux H, Zhao J, Schwartz LH, Lu L, Zhao B. Automated Identification of Optimal Portal Venous Phase Timing with Convolutional Neural Networks. Acad Radiol 2020; 27:e10-e18. [PMID: 31151901 DOI: 10.1016/j.acra.2019.02.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 02/25/2019] [Accepted: 02/26/2019] [Indexed: 12/15/2022]
Abstract
OBJECTIVES To develop a deep learning-based algorithm to automatically identify optimal portal venous phase timing (PVP-timing) so that image analysis techniques can be accurately performed on post contrast studies. METHODS 681 CT-scans (training: 479 CT-scans; validation: 202 CT-scans) from a multicenter clinical trial in patients with liver metastases from colorectal cancer were retrospectively analyzed for algorithm development and validation. An additional external validation was performed on a cohort of 228 CT-scans from gastroenteropancreatic neuroendocrine cancer patients. Image acquisition was performed according to each centers' standard CT protocol for single portal venous phase, portal venous acquisition. The reference gold standard for the classification of PVP-timing as either optimal or nonoptimal was based on experienced radiologists' consensus opinion. The algorithm performed automated localization (on axial slices) of the portal vein and aorta upon which a novel dual input Convolutional Neural Network calculated a probability of the optimal PVP-timing. RESULTS The algorithm automatically computed a PVP-timing score in 3 seconds and reached area under the curve of 0.837 (95% CI: 0.765, 0.890) in validation set and 0.844 (95% CI: 0.786, 0.889) in external validation set. CONCLUSION A fully automated, deep-learning derived PVP-timing algorithm was developed to classify scans' contrast-enhancement timing and identify scans with optimal PVP-timing. The rapid identification of such scans will aid in the analysis of quantitative (radiomics) features used to characterize tumors and changes in enhancement with treatment in a multitude of settings including quantitative response criteria such as Choi and MASS which rely on reproducible measurement of enhancement.
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Affiliation(s)
- Jingchen Ma
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032
| | - Laurent Dercle
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032; Gustave Roussy, Université Paris-Saclay, Université Paris-Saclay, Département D'imagerie Médicale, Villejuif, France
| | - Philip Lichtenstein
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032
| | - Deling Wang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Aiping Chen
- Department of Radiology, First Affiliated Hospital of NanJing Medical University, Nanjing, China
| | - Jianguo Zhu
- Department of Radiology, the Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | | | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032.
| | - Binsheng Zhao
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032
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Xie T, Wang X, Li M, Tong T, Yu X, Zhou Z. Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection. Eur Radiol 2020; 30:2513-2524. [PMID: 32006171 DOI: 10.1007/s00330-019-06600-2] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 11/15/2019] [Accepted: 11/21/2019] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To identify a CT-based radiomics nomogram for survival prediction in patients with resected pancreatic ductal adenocarcinoma (PDAC). METHODS A total of 220 patients (training cohort n = 147; validation cohort n = 73) with PDAC were enrolled. A total of 300 radiomics features were extracted from CT images. And the least absolute shrinkage and selection operator algorithm were applied to select features and develop a radiomics score (Rad-score). The radiomics nomogram was constructed by multivariate regression analysis. Nomogram discrimination, calibration, and clinical usefulness were evaluated. The association of the Rad-score and recurrence pattern in PDAC was evaluated. RESULTS The Rad-score was significantly associated with PDAC patient's disease-free survival (DFS) and overall survival (OS) (both p < 0.001 in two cohorts). Incorporating the Rad-score into the radiomics nomogram resulted in better performance of the survival prediction than that of the clinical model and TNM staging system. In addition, the radiomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. There was no association between the Rad-score and recurrence pattern. CONCLUSIONS The radiomics nomogram integrating the Rad-score and clinical data provided better prognostic prediction in resected PDAC patients, which may hold great potential for guiding personalized care for these patients. The Rad-score was not a predictor of the recurrence pattern in resected PDAC patients. KEY POINTS • The Rad-score developed by CT radiomics features was significantly associated with PDAC patients' prognosis. • The radiomics nomogram integrating the Rad-score and clinical data has value to permit non-invasive, low-cost, and personalized evaluation of prognosis in PDAC patients. • The radiomics nomogram outperformed clinical model and the TNM staging system in terms of survival estimation.
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Affiliation(s)
- Tiansong Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, No.270, Dongan Rd, Shanghai, 200032, People's Republic of China
| | - Xuanyi Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Menglei Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, No.270, Dongan Rd, Shanghai, 200032, People's Republic of China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, No.270, Dongan Rd, Shanghai, 200032, People's Republic of China
| | - Xiaoli Yu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Zhengrong Zhou
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China. .,Department of Oncology, Shanghai Medical College, Fudan University, No.270, Dongan Rd, Shanghai, 200032, People's Republic of China. .,Department of Radiology, Minhang Branch of Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
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Xi IL, Zhao Y, Wang R, Chang M, Purkayastha S, Chang K, Huang RY, Silva AC, Vallières M, Habibollahi P, Fan Y, Zou B, Gade TP, Zhang PJ, Soulen MC, Zhang Z, Bai HX, Stavropoulos SW. Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging. Clin Cancer Res 2020; 26:1944-1952. [PMID: 31937619 DOI: 10.1158/1078-0432.ccr-19-0374] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/30/2019] [Accepted: 01/10/2020] [Indexed: 11/16/2022]
Abstract
PURPOSE With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging. EXPERIMENTAL DESIGN Preoperative MR images (T2-weighted and T1-postcontrast sequences) of 1,162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables and T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model. RESULTS Among the 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, P = 0.053), sensitivity (0.92 vs. 0.80, P = 0.017), and specificity (0.41 vs. 0.35, P = 0.450). Compared with the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, P = 0.081), sensitivity (0.92 vs. 0.79, P = 0.012), and specificity (0.41 vs. 0.39, P = 0.770). CONCLUSIONS Deep learning can noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multi-institutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.
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Affiliation(s)
- Ianto Lin Xi
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yijun Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Robin Wang
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Subhanik Purkayastha
- Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - Ken Chang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Alvin C Silva
- Department of Radiology, Mayo Clinic Hospital, Scottsdale, Arizona
| | - Martin Vallières
- Medical Physics Unit, McGill University, Montreal, Québec, Canada
| | - Peiman Habibollahi
- Department of Radiology, Division of Interventional Radiology, UT Southwestern Medical School, Dallas, Texas
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Beiji Zou
- School of Informatics and Engineering, Central South University, Changsha, Hunan, China
| | - Terence P Gade
- Department of Radiology, Division of Interventional Radiology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Paul J Zhang
- Department of Pathology and Lab Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael C Soulen
- Department of Radiology, Division of Interventional Radiology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zishu Zhang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Harrison X Bai
- Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, Rhode Island.
| | - S William Stavropoulos
- Department of Radiology, Division of Interventional Radiology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania.
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Mokrane FZ, Lu L, Vavasseur A, Otal P, Peron JM, Luk L, Yang H, Ammari S, Saenger Y, Rousseau H, Zhao B, Schwartz LH, Dercle L. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol 2020; 30:558-570. [PMID: 31444598 DOI: 10.1007/s00330-019-06347-w] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 06/12/2019] [Accepted: 06/27/2019] [Indexed: 02/08/2023]
Abstract
PURPOSE To enhance clinician's decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules using quantitative imaging features extracted from triphasic CT scans. MATERIAL AND METHODS We retrospectively analyzed 178 cirrhotic patients from 27 institutions, with biopsy-proven liver nodules classified as indeterminate using the European Association for the Study of the Liver (EASL) guidelines. Patients were randomly assigned to a discovery cohort (142 patients (pts.)) and a validation cohort (36 pts.). Each liver nodule was segmented on each phase of triphasic CT scans, and 13,920 quantitative imaging features (12 sets of 1160 features each reflecting the phenotype at one single phase or its change between two phases) were extracted. Using machine-learning techniques, the signature was trained and calibrated (discovery cohort), and validated (validation cohort) to classify liver nodules as HCC vs. non-HCC. Effects of segmentation and contrast enhancement quality were also evaluated. RESULTS Patients were predominantly male (88%) and CHILD A (65%). Biopsy was positive for HCC in 77% of patients. LI-RADS scores were not different between HCC and non-HCC patients. The signature included a single radiomics feature quantifying changes between arterial and portal venous phases: DeltaV-A_DWT1_LL_Variance-2D and reached area under the receiver operating characteristic curve (AUC) of 0.70 (95%CI 0.61-0.80) and 0.66 (95%CI 0.64-0.84) in discovery and validation cohorts, respectively. The signature was influenced neither by segmentation nor by contrast enhancement. CONCLUSION A signature using a single feature was validated in a multicenter retrospective cohort to diagnose HCC in cirrhotic patients with indeterminate liver nodules. Artificial intelligence could enhance clinicians' decision by identifying a subgroup of patients with high HCC risk. KEY POINTS • In cirrhotic patients with visually indeterminate liver nodules, expert visual assessment using current guidelines cannot accurately differentiate HCC from differential diagnoses. Current clinical protocols do not entail biopsy due to procedural risks. Radiomics can be used to non-invasively diagnose HCC in cirrhotic patients with indeterminate liver nodules, which could be leveraged to optimize patient management. • Radiomics features contributing the most to a better characterization of visually indeterminate liver nodules include changes in nodule phenotype between arterial and portal venous phases: the "washout" pattern appraised visually using EASL and EASL guidelines. • A clinical decision algorithm using radiomics could be applied to reduce the rate of cirrhotic patients requiring liver biopsy (EASL guidelines) or wait-and-see strategy (AASLD guidelines) and therefore improve their management and outcome.
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Affiliation(s)
- Fatima-Zohra Mokrane
- Radiology Department, Rangueil University Hospital, Toulouse, France.
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA.
| | - Lin Lu
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
| | - Adrien Vavasseur
- Radiology Department, Rangueil University Hospital, Toulouse, France
| | - Philippe Otal
- Radiology Department, Rangueil University Hospital, Toulouse, France
| | - Jean-Marie Peron
- Hepatology Department, Purpan University Hospital, Toulouse, France
| | - Lyndon Luk
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
| | - Hao Yang
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
| | - Samy Ammari
- Service de Radiologie, Gustave-Roussy, Université Paris-Saclay, Villejuif, France
| | - Yvonne Saenger
- Department of Medicine, Division of Hematology/Oncology, Columbia University Medical Center/New York Presbyterian, New York, NY, USA
| | - Herve Rousseau
- Radiology Department, Rangueil University Hospital, Toulouse, France
| | - Binsheng Zhao
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
| | - Lawrence H Schwartz
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
| | - Laurent Dercle
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
- INSERM U1015, Gustave Roussy Institute, Université Paris-Saclay, F-94805, Villejuif, France
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Rozenblum L, Mokrane FZ, Yeh R, Sinigaglia M, Besson FL, Seban RD, Zadro C, Dierickx L, Chougnet CN, Partouche E, Revel-Mouroz P, Zhao B, Otal P, Schwartz LH, Dercle L. Imaging-guided precision medicine in non-resectable gastro-entero-pancreatic neuroendocrine tumors: A step-by-step approach. Eur J Radiol 2020; 122:108743. [DOI: 10.1016/j.ejrad.2019.108743] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 11/11/2019] [Indexed: 12/11/2022]
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Is tumour sphericity an important prognostic factor in patients with lung cancer? Radiother Oncol 2019; 143:73-80. [PMID: 31472998 DOI: 10.1016/j.radonc.2019.08.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 07/05/2019] [Accepted: 08/05/2019] [Indexed: 01/05/2023]
Abstract
BACKGROUND AND PURPOSE Quantitative tumour shape features extracted from radiotherapy planning scans have shown potential as prognostic markers. In this study, we investigated if sphericity of the gross tumour volume (GTV) on planning computed tomography (CT) is an independent predictor of overall survival (OS) in lung cancer patients treated with standard radiotherapy. In the analysis, we considered whether tumour sphericity is correlated with clinical prognostic factors or influenced by the inclusion of lymph nodes in the GTV. MATERIALS AND METHODS Sphericity of single GTV delineation was extracted for 457 lung cancer patients. Relationships between sphericity, and common patient and tumour characteristics were investigated via correlation analysis and multivariate Cox regression to assess prognostic value of GTV sphericity. A subset analysis was performed for 290 nodal stage N0 patients to determine prognostic value of primary tumour sphericity. RESULTS Sphericity is correlated with clinical variables: tumour volume, mean lung dose, N stage, and T stage. Sphericity is strongly associated with OS (p < 0.001, hazard ratio (HR) (95% confidence interval (CI)) = 0.13 (0.04-0.41)) in univariate analysis. However, this association did not remain significant in multivariate analysis (p = 0.826, HR (95% CI) = 0.83 (0.16-4.31), and inclusion of sphericity to a clinical model did not improve model performance. In addition, no significant relationship between sphericity and OS was detected in univariate (p = 0.072) or multivariate (p = 0.920) analysis of N0 subset. CONCLUSION Sphericity correlates clearly with clinical prognostic factors, which are often unaccounted for in radiomic studies. Sphericity is also influenced by the presence of nodal involvement within the GTV contour.
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Jahani N, Cohen E, Hsieh MK, Weinstein SP, Pantalone L, Hylton N, Newitt D, Davatzikos C, Kontos D. Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration. Sci Rep 2019; 9:12114. [PMID: 31431633 PMCID: PMC6702160 DOI: 10.1038/s41598-019-48465-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 08/05/2019] [Indexed: 12/11/2022] Open
Abstract
We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC = 0.78 (±0.03) vs 0.71 (±0.04), p < 0.05 and RFS (C-statistic = 0.76 ( ± 0.05), vs 0.63 ( ± 0.01)), p < 0.05, while models based on analogous aggregate imaging features did not show appreciable performance changes (p > 0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p < 0.05). Thus, precise measures of voxel-wise changes in tumor heterogeneity extracted from registered DCE-MRI scans can improve early prediction of neoadjuvant treatment outcomes in locally advanced breast cancer.
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Affiliation(s)
- Nariman Jahani
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Eric Cohen
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Meng-Kang Hsieh
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Susan P Weinstein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauren Pantalone
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nola Hylton
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94115, USA
| | - David Newitt
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94115, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Dercle L, Lu L, Lichtenstein P, Yang H, Wang D, Zhu J, Wu F, Piessevaux H, Schwartz LH, Zhao B. Impact of Variability in Portal Venous Phase Acquisition Timing in Tumor Density Measurement and Treatment Response Assessment: Metastatic Colorectal Cancer as a Paradigm. JCO Clin Cancer Inform 2019; 1:1-8. [PMID: 30657405 DOI: 10.1200/cci.17.00108] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
PURPOSE New response patterns to anticancer drugs have led tumor size-based response criteria to shift to also include density measurements. Choi criteria, for instance, categorize antiangiogenic therapy response as a decrease in tumor density > 15% at the portal venous phase (PVP). We studied the effect that PVP timing has on measurement of the density of liver metastases (LM) from colorectal cancer (CRC). METHODS Pretreatment PVP computed tomography images from 291 patients with LM-CRC from the CRYSTAL trial (Cetuximab Combined With Irinotecan in First-Line Therapy for Metastatic Colorectal Cancer; ClinicalTrials.gov identifier: NCT00154102) were included. Four radiologists independently scored the scans' timing according to a three-point scoring system: early, optimal, late PVP. Using this, we developed, by machine learning, a proprietary computer-aided quality-control algorithm to grade PVP timing. The reference standard was a computer-refined consensus. For each patient, we contoured target liver lesions and calculated their mean density. RESULTS Contrast-product administration data were not recorded in the digital imaging and communications in medicine headers for injection volume (94%), type (93%), and route (76%). The PVP timing was early, optimal, and late in 52, 194, and 45 patients, respectively. The mean (95% CI) accuracy of the radiologists for detection of optimal PVP timing was 81.7% (78.3 to 85.2) and was outperformed by the 88.6% (84.8 to 92.4) computer accuracy. The mean ± standard deviation of LM-CRC density was 68 ± 15 Hounsfield units (HU) overall and 59.5 ± 14.9 HU, 71.4 ± 14.1 HU, 62.4 ± 12.5 HU at early, optimal, and late PVP timing, respectively. LM-CRC density was thus decreased at nonoptimal PVP timing by 14.8%: 16.7% at early PVP ( P < .001) and 12.6% at late PVP ( P < .001). CONCLUSION Nonoptimal PVP timing should be identified because it significantly decreased tumor density by 14.8%. Our computer-aided quality-control system outperformed the accuracy, reproducibility, and speed of radiologists' visual scoring. PVP-timing scoring could improve the extraction of tumor quantitative imaging biomarkers and the monitoring of anticancer therapy efficacy at the patient and clinical trial levels.
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Affiliation(s)
- Laurent Dercle
- Laurent Dercle, Lin Lu, Philip Lichtenstein, Hao Yang, Jianguo Zhu, Feiyun Wu, Lawrence H. Schwartz, and Binsheng Zhao, Columbia University Medical Center, and Presbyterian Hospital, New York, NY; Laurent Dercle, Gustave Roussy, Université Paris-Saclay, UMR1015, Villejuif, France; Deling Wang, Sun Yat-sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong; State Key Laboratory of Oncology in South China, Hong Kong, Special Administrative Region, People's Republic of China; and Hubert Piessevaux, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Lin Lu
- Laurent Dercle, Lin Lu, Philip Lichtenstein, Hao Yang, Jianguo Zhu, Feiyun Wu, Lawrence H. Schwartz, and Binsheng Zhao, Columbia University Medical Center, and Presbyterian Hospital, New York, NY; Laurent Dercle, Gustave Roussy, Université Paris-Saclay, UMR1015, Villejuif, France; Deling Wang, Sun Yat-sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong; State Key Laboratory of Oncology in South China, Hong Kong, Special Administrative Region, People's Republic of China; and Hubert Piessevaux, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Philip Lichtenstein
- Laurent Dercle, Lin Lu, Philip Lichtenstein, Hao Yang, Jianguo Zhu, Feiyun Wu, Lawrence H. Schwartz, and Binsheng Zhao, Columbia University Medical Center, and Presbyterian Hospital, New York, NY; Laurent Dercle, Gustave Roussy, Université Paris-Saclay, UMR1015, Villejuif, France; Deling Wang, Sun Yat-sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong; State Key Laboratory of Oncology in South China, Hong Kong, Special Administrative Region, People's Republic of China; and Hubert Piessevaux, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Hao Yang
- Laurent Dercle, Lin Lu, Philip Lichtenstein, Hao Yang, Jianguo Zhu, Feiyun Wu, Lawrence H. Schwartz, and Binsheng Zhao, Columbia University Medical Center, and Presbyterian Hospital, New York, NY; Laurent Dercle, Gustave Roussy, Université Paris-Saclay, UMR1015, Villejuif, France; Deling Wang, Sun Yat-sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong; State Key Laboratory of Oncology in South China, Hong Kong, Special Administrative Region, People's Republic of China; and Hubert Piessevaux, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Deling Wang
- Laurent Dercle, Lin Lu, Philip Lichtenstein, Hao Yang, Jianguo Zhu, Feiyun Wu, Lawrence H. Schwartz, and Binsheng Zhao, Columbia University Medical Center, and Presbyterian Hospital, New York, NY; Laurent Dercle, Gustave Roussy, Université Paris-Saclay, UMR1015, Villejuif, France; Deling Wang, Sun Yat-sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong; State Key Laboratory of Oncology in South China, Hong Kong, Special Administrative Region, People's Republic of China; and Hubert Piessevaux, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Jianguo Zhu
- Laurent Dercle, Lin Lu, Philip Lichtenstein, Hao Yang, Jianguo Zhu, Feiyun Wu, Lawrence H. Schwartz, and Binsheng Zhao, Columbia University Medical Center, and Presbyterian Hospital, New York, NY; Laurent Dercle, Gustave Roussy, Université Paris-Saclay, UMR1015, Villejuif, France; Deling Wang, Sun Yat-sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong; State Key Laboratory of Oncology in South China, Hong Kong, Special Administrative Region, People's Republic of China; and Hubert Piessevaux, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Feiyun Wu
- Laurent Dercle, Lin Lu, Philip Lichtenstein, Hao Yang, Jianguo Zhu, Feiyun Wu, Lawrence H. Schwartz, and Binsheng Zhao, Columbia University Medical Center, and Presbyterian Hospital, New York, NY; Laurent Dercle, Gustave Roussy, Université Paris-Saclay, UMR1015, Villejuif, France; Deling Wang, Sun Yat-sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong; State Key Laboratory of Oncology in South China, Hong Kong, Special Administrative Region, People's Republic of China; and Hubert Piessevaux, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Hubert Piessevaux
- Laurent Dercle, Lin Lu, Philip Lichtenstein, Hao Yang, Jianguo Zhu, Feiyun Wu, Lawrence H. Schwartz, and Binsheng Zhao, Columbia University Medical Center, and Presbyterian Hospital, New York, NY; Laurent Dercle, Gustave Roussy, Université Paris-Saclay, UMR1015, Villejuif, France; Deling Wang, Sun Yat-sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong; State Key Laboratory of Oncology in South China, Hong Kong, Special Administrative Region, People's Republic of China; and Hubert Piessevaux, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Lawrence H Schwartz
- Laurent Dercle, Lin Lu, Philip Lichtenstein, Hao Yang, Jianguo Zhu, Feiyun Wu, Lawrence H. Schwartz, and Binsheng Zhao, Columbia University Medical Center, and Presbyterian Hospital, New York, NY; Laurent Dercle, Gustave Roussy, Université Paris-Saclay, UMR1015, Villejuif, France; Deling Wang, Sun Yat-sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong; State Key Laboratory of Oncology in South China, Hong Kong, Special Administrative Region, People's Republic of China; and Hubert Piessevaux, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Binsheng Zhao
- Laurent Dercle, Lin Lu, Philip Lichtenstein, Hao Yang, Jianguo Zhu, Feiyun Wu, Lawrence H. Schwartz, and Binsheng Zhao, Columbia University Medical Center, and Presbyterian Hospital, New York, NY; Laurent Dercle, Gustave Roussy, Université Paris-Saclay, UMR1015, Villejuif, France; Deling Wang, Sun Yat-sen University Cancer Center; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong; State Key Laboratory of Oncology in South China, Hong Kong, Special Administrative Region, People's Republic of China; and Hubert Piessevaux, Cliniques Universitaires Saint-Luc, Brussels, Belgium
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49
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Rozenblum L, Mokrane FZ, Yeh R, Sinigaglia M, Besson F, Seban RD, Chougnet CN, Revel-Mouroz P, Zhao B, Otal P, Schwartz LH, Dercle L. The role of multimodal imaging in guiding resectability and cytoreduction in pancreatic neuroendocrine tumors: focus on PET and MRI. Abdom Radiol (NY) 2019; 44:2474-2493. [PMID: 30980115 DOI: 10.1007/s00261-019-01994-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Pancreatic neuroendocrine tumors (pNETs) are rare neoplasms that secrete peptides and neuro-amines. pNETs can be sporadic or hereditary, syndromic or non-syndromic with different clinical presentations and prognoses. The role of medical imaging includes locating the tumor, assessing its extent, and evaluating the feasibility of curative surgery or cytoreduction. Pancreatic NETs have very distinctive phenotypes on CT, MRI, and PET. PET have been demonstrated to be very sensitive to detect either well-differentiated pNETs using 68Gallium somatostatin receptor (SSTR) radiotracers, or more aggressive undifferentiated pNETS using 18F-FDG. A comprehensive interpretation of multimodal imaging guides resectability and cytoreduction in pNETs. The imaging phenotype provides information on the differentiation and proliferation of pNETs, as well as the spatial and temporal heterogeneity of tumors with prognostic and therapeutic implications. This review provides a structured approach for standardized reading and reporting of medical imaging studies with a focus on PET and MR techniques. It explains which imaging approach should be used for different subtypes of pNET and what a radiologist should be looking for and reporting when interpreting these studies.
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Affiliation(s)
- Laura Rozenblum
- Sorbonne Université, Service de Médecine Nucléaire, AP-HP, Hôpital La Pitié-Salpêtrière, 75013, Paris, France
| | - Fatima-Zohra Mokrane
- Radiology Department, Toulouse University Hospital, 1 Avenue du Professeur Jean Poulhes, 31059, Toulouse, France
- Department of Radiology, New York Presbyterian Hospital, Columbia University, New York, NY, USA
| | - Randy Yeh
- Memorial Sloan Kettering Cancer Center, Molecular Imaging and Therapy Service, New York, NY, USA
| | - Mathieu Sinigaglia
- Department of Imaging and Nuclear Medicine, Institut Claudius Regaud - Institut Universitaire du Cancer de Toulouse - Oncopole, Toulouse, France
| | - Florent Besson
- Paris Sud University, Kremlin Bicêtre Hospital, Paris, France
| | - Romain-David Seban
- Department of Nuclear Medicine, Institut Curie-René Huguenin, Saint-Cloud, France
| | - Cecile N Chougnet
- Department of Endocrine Oncology, Hôpital Saint Louis, Paris, France
| | - Paul Revel-Mouroz
- Radiology Department, Toulouse University Hospital, 1 Avenue du Professeur Jean Poulhes, 31059, Toulouse, France
| | - Binsheng Zhao
- Department of Radiology, New York Presbyterian Hospital, Columbia University, New York, NY, USA
| | - Philippe Otal
- Radiology Department, Toulouse University Hospital, 1 Avenue du Professeur Jean Poulhes, 31059, Toulouse, France
| | - Lawrence H Schwartz
- Department of Radiology, New York Presbyterian Hospital, Columbia University, New York, NY, USA
| | - Laurent Dercle
- Department of Radiology, New York Presbyterian Hospital, Columbia University, New York, NY, USA.
- UMR 1015, Gustave Roussy Institute, Université Paris-Saclay, Villejuif, 94805, France.
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50
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Nie K, Al-Hallaq H, Li XA, Benedict SH, Sohn JW, Moran JM, Fan Y, Huang M, Knopp MV, Michalski JM, Monroe J, Obcemea C, Tsien CI, Solberg T, Wu J, Xia P, Xiao Y, El Naqa I. NCTN Assessment on Current Applications of Radiomics in Oncology. Int J Radiat Oncol Biol Phys 2019; 104:302-315. [PMID: 30711529 PMCID: PMC6499656 DOI: 10.1016/j.ijrobp.2019.01.087] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 01/17/2019] [Accepted: 01/23/2019] [Indexed: 02/06/2023]
Abstract
Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.
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Affiliation(s)
- Ke Nie
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, New Jersey.
| | - Hania Al-Hallaq
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - Jason W Sohn
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Jean M Moran
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mi Huang
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael V Knopp
- Division of Imaging Science, Department of Radiology, Ohio State University, Columbus, Ohio
| | - Jeff M Michalski
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - James Monroe
- Department of Radiation Oncology, St. Anthony's Cancer Center, St. Louis, Missouri
| | - Ceferino Obcemea
- Radiation Research Program, National Cancer Institute, Bethesda, Maryland
| | - Christina I Tsien
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Timothy Solberg
- Department of Radiation Oncology, University of California-San Francisco, San Francisco, California
| | - Jackie Wu
- Department of Radiation Oncology, Duke University, Durham, North Carolina
| | - Ping Xia
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio
| | - Ying Xiao
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Issam El Naqa
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
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