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Liu D, Huang J, Zhang Y, Shen H, Wang X, Huang Z, Chen X, Qiao Z, Hu C. Multimodal MRI-based radiomics models for the preoperative prediction of lymphovascular space invasion of endometrial carcinoma. BMC Med Imaging 2024; 24:252. [PMID: 39304802 DOI: 10.1186/s12880-024-01430-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2024] [Accepted: 09/17/2024] [Indexed: 09/22/2024] Open
Abstract
PURPOSE To evaluate the predictive capabilities of MRI-based radiomics for detecting lymphovascular space invasion (LVSI) in patients diagnosed with endometrial carcinoma (EC). MATERIALS AND METHODS A retrospective analysis was conducted on 160 female patients diagnosed with EC. The radiomics model including T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) images was established. Additionally, a conventional MRI model, which incorporated MRI-reported FIGO stage, deep myometrial infiltration (DMI), adnexal involvement, and vaginal/parametrial involvement, was established. Finally, a combined model was created by integrating the radiomics signature and conventional MRI characteristics. The predictive performance was validated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. A stratified analysis was conducted to compare the differences between the three models by Delong test. RESULTS In predicting LVSI, the radiomics model outperformed the clinical model in the training cohort (AUC: 0.899 vs. 0.8862) but not in the test cohort (AUC: 0.812 vs. 0.8758). The combined model demonstrated superior performance in both the training and test cohorts (training cohort: AUC = 0.934, 95% CI: 0.8807-0.9873; testing cohort: AUC = 0.905, 95% CI: 0.7679-1). CONCLUSIONS The combined model exhibited utility in preoperatively predicting LVSI in patients with EC, offering potential benefits for clinical decision-making.
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Affiliation(s)
- Dong Liu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jinyu Huang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yufeng Zhang
- Department of Radiology, Luodian Hospital, Baoshan district, Shanghai, China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medical, Suzhou, China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhou Huang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xue Chen
- Department of Radiology, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
| | - Zhenguo Qiao
- Department of Gastroenterology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
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Zhu X, He Y, Wang M, Shu Y, Lai X, Gan C, Liu L. Intratumoral and Peritumoral Multiparametric MRI-Based Radiomics Signature for Preoperative Prediction of Ki-67 Proliferation Status in Glioblastoma: A Two-Center Study. Acad Radiol 2024; 31:1560-1571. [PMID: 37865602 DOI: 10.1016/j.acra.2023.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/28/2023] [Accepted: 09/08/2023] [Indexed: 10/23/2023]
Abstract
RATIONALE AND OBJECTIVES To assess the predictive ability of intratumoral and peritumoral multiparametric magnetic resonance imaging (MRI)-based radiomics signature (RS) for preoperative prediction of Ki-67 proliferation status in glioblastoma. MATERIALS AND METHODS: A total of 205 patients with glioblastoma at two institutions were retrospectively analyzed. Data from institution 1 (n = 158) were used to develop the predictive model, and as an internal test dataset, data from institution 2 (n = 47) constitute the external test dataset. Feature selection was performed using spearman correlation coefficient, univariate ranking method, and the least absolute shrinkage and selection operator algorithm. RSs were established using a logistic regression algorithm. The predictive performance of the RSs was assessed using calibration curve, decision curve analysis (DCA), and area under the curve (AUC). RESULTS In the RSs based on single-parametric (contrast-enhanced T1-weighted image, T2-weighted image, or apparent diffusion coefficient maps), the AUCs of intratumoral, peritumoral, and combined area (intratumoral and peritumoral) were 0.60-0.67, with no significant difference among them. The RSs that using multiparametric features (integrating the previously mentioned three sequences) showed improved AUC compared to the single-parametric RSs; AUC reached 0.75-0.89. Among them, the multiparametric RS based on radiomics features of the combined area (Multi-Com) exhibited the highest performance, with an internal test dataset AUC of 0.89 (95% confidence interval (CI) 0.75-1.00) and an external test dataset AUC of 0.88 (95% CI 0.78-0.97). The calibration curve and DCA display RS (Multi-Com) have good calibration ability and clinical applicability. CONCLUSION The multiparametric MRI-based RS combining intratumoral and peritumoral features can serve as a noninvasive and effective tool for preoperative assessment of Ki-67 proliferation status in glioblastoma.
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Affiliation(s)
- Xuechao Zhu
- Departments of Radiology, Jiangxi Tumor Hospital, Nanchang, Jiangxi, China (X.Z., Y.S., C.G., L.L.)
| | - Yulin He
- Departments of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China (Y.H., M.W., X.L.)
| | - Mengting Wang
- Departments of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China (Y.H., M.W., X.L.)
| | - Yuqin Shu
- Departments of Radiology, Jiangxi Tumor Hospital, Nanchang, Jiangxi, China (X.Z., Y.S., C.G., L.L.)
| | - Xunfu Lai
- Departments of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China (Y.H., M.W., X.L.)
| | - Cuihua Gan
- Departments of Radiology, Jiangxi Tumor Hospital, Nanchang, Jiangxi, China (X.Z., Y.S., C.G., L.L.)
| | - Lan Liu
- Departments of Radiology, Jiangxi Tumor Hospital, Nanchang, Jiangxi, China (X.Z., Y.S., C.G., L.L.).
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Eisazadeh R, Shahbazi-Akbari M, Mirshahvalad SA, Pirich C, Beheshti M. Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [ 18F]F-FDG Tracers Part II. [ 18F]F-FLT, [ 18F]F-FET, [ 11C]C-MET and Other Less-Commonly Used Radiotracers. Semin Nucl Med 2024; 54:293-301. [PMID: 38331629 DOI: 10.1053/j.semnuclmed.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 02/10/2024]
Abstract
Following the previous part of the narrative review on artificial intelligence (AI) applications in positron emission tomography (PET) using tracers rather than 18F-fluorodeoxyglucose ([18F]F-FDG), in this part we review the impact of PET-derived radiomics data on the diagnostic performance of other PET radiotracers, 18F-O-(2-fluoroethyl)-L-tyrosine ([18F]F-FET), 18F-Fluorothymidine ([18F]F-FLT) and 11C-Methionine ([11C]C-MET). [18F]F-FET-PET, using an artificial amino acid taken up into upregulated tumoral cells, showed potential in lesion detection and tumor characterization, especially with its ability to reflect glioma heterogeneity. [18F]F-FET-PET-derived textural features appeared to have the potential to reveal considerable information for accurate delineation for guiding biopsy and treatment, differentiate between low-grade and high-grade glioma and related wild-type genotypes, and distinguish pseudoprogression from true progression. In addition, models built using clinical parameters and [18F]F-FET-PET-derived radiomics features showed acceptable results for survival stratification of glioblastoma patients. [18F]F-FLT-PET-based characteristics also showed potential in evaluating glioma patients, correlating with Ki-67 and patient prognosis. AI-based PET-volumetry using this radiotracer as a proliferation marker also revealed promising preliminary results in terms of guide-targeting bone marrow-preserving adaptive radiation therapy. Similar to [18F]F-FET, the other amino acid tracer which reflects cellular proliferation, [11C]C-MET, has also shown acceptable performance in predicting tumor grade, distinguishing brain tumor recurrence from radiation necrosis, and treatment monitoring by PET-derived radiomics models. In addition, PET-derived radiomics features of various radiotracers such as [18F]F-DOPA, [18F]F-FACBC, [18F]F-NaF, [68Ga]Ga-CXCR-4 and [18F]F-FMISO may also provide useful information for tumor characterization and predict of disease outcome. In conclusion, AI using tracers beyond [18F]F-FDG could improve the diagnostic performance of PET-imaging for specific indications and help clinicians in their daily routine by providing features that are often not detectable by the naked eye.
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Affiliation(s)
- Roya Eisazadeh
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Malihe Shahbazi-Akbari
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research center for Nuclear Medicine, Department of Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Ali Mirshahvalad
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research center for Nuclear Medicine, Department of Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran; Joint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, Ontario, Canada
| | - Christian Pirich
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Mohsen Beheshti
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
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Cellina M, Irmici G, Pepa GD, Ce M, Chiarpenello V, Alì M, Papa S, Carrafiello G. Radiomics and Artificial Intelligence in Renal Lesion Assessment. Crit Rev Oncog 2024; 29:65-75. [PMID: 38505882 DOI: 10.1615/critrevoncog.2023051084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
| | - Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Ce
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Vittoria Chiarpenello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Marco Alì
- Radiology Unit, CDI, Centro Diagnostico Italiano, 20147 Milan, Italy
| | - Sergio Papa
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
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Semenescu LE, Tataranu LG, Dricu A, Ciubotaru GV, Radoi MP, Rodriguez SMB, Kamel A. A Neurosurgical Perspective on Brain Metastases from Renal Cell Carcinoma: Multi-Institutional, Retrospective Analysis. Biomedicines 2023; 11:2485. [PMID: 37760926 PMCID: PMC10526360 DOI: 10.3390/biomedicines11092485] [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: 07/31/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND While acknowledging the generally poor prognostic features of brain metastases from renal cell carcinoma (BM RCC), it is important to be aware of the fact that neurosurgery still plays a vital role in managing this disease, even though we have entered an era of targeted therapies. Notwithstanding their initial high effectiveness, these agents often fail, as tumors develop resistance or relapse. METHODS The authors of this study aimed to evaluate patients presenting with BM RCC and their outcomes after being treated in the Neurosurgical Department of Clinical Emergency Hospital "Bagdasar-Arseni", and the Neurosurgical Department of the National Institute of Neurology and Neurovascular Diseases, Bucharest, Romania. The study is based on a thorough appraisal of the patient's demographic and clinicopathological data and is focused on the strategic role of neurosurgery in BM RCC. RESULTS A total of 24 patients were identified with BM RCC, of whom 91.6% had clear-cell RCC (ccRCC) and 37.5% had a prior nephrectomy. Only 29.1% of patients harbored extracranial metastases, while 83.3% had a single BM RCC. A total of 29.1% of patients were given systemic therapy. Neurosurgical resection of the BM was performed in 23 out of 24 patients. Survival rates were prolonged in patients who underwent nephrectomy, in patients who received systemic therapy, and in patients with a single BM RCC. Furthermore, higher levels of hemoglobin were associated in our study with a higher number of BMs. CONCLUSION Neurosurgery is still a cornerstone in the treatment of symptomatic BM RCC. Among the numerous advantages of neurosurgical intervention, the most important is represented by the quick reversal of neurological manifestations, which in most cases can be life-saving.
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Affiliation(s)
- Liliana Eleonora Semenescu
- Department of Biochemistry, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, Str. Petru Rares nr. 2–4, 710204 Craiova, Romania; (L.E.S.); (A.D.)
| | - Ligia Gabriela Tataranu
- Neurosurgical Department, Clinical Emergency Hospital “Bagdasar-Arseni”, Soseaua Berceni 12, 041915 Bucharest, Romania; (G.V.C.); (S.M.B.R.); (A.K.)
- Department of Neurosurgery, Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 020022 Bucharest, Romania
| | - Anica Dricu
- Department of Biochemistry, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, Str. Petru Rares nr. 2–4, 710204 Craiova, Romania; (L.E.S.); (A.D.)
| | - Gheorghe Vasile Ciubotaru
- Neurosurgical Department, Clinical Emergency Hospital “Bagdasar-Arseni”, Soseaua Berceni 12, 041915 Bucharest, Romania; (G.V.C.); (S.M.B.R.); (A.K.)
| | - Mugurel Petrinel Radoi
- Neurosurgical Department, National Institute of Neurology and Neurovascular Diseases, Soseaua Berceni 10, 041914 Bucharest, Romania;
| | - Silvia Mara Baez Rodriguez
- Neurosurgical Department, Clinical Emergency Hospital “Bagdasar-Arseni”, Soseaua Berceni 12, 041915 Bucharest, Romania; (G.V.C.); (S.M.B.R.); (A.K.)
| | - Amira Kamel
- Neurosurgical Department, Clinical Emergency Hospital “Bagdasar-Arseni”, Soseaua Berceni 12, 041915 Bucharest, Romania; (G.V.C.); (S.M.B.R.); (A.K.)
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Li Y, Xu W, Fei Y, Wu M, Yuan J, Qiu L, Zhang Y, Chen G, Cheng Y, Cao Y, Zhou S. A MRI-based radiomics model for predicting the response to anlotinb combined with temozolomide in recurrent malignant glioma patients. Discov Oncol 2023; 14:154. [PMID: 37612579 PMCID: PMC10447352 DOI: 10.1007/s12672-023-00751-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/08/2023] [Indexed: 08/25/2023] Open
Abstract
OBJECTIVE Anlotinib is a multitarget anti-angiogenic drug that combined with temozolomide (TMZ) can effectively prolongs the overall survival (OS) of recurrent malignant glioma(rMG),but some patients do not respond to anlotinib combined with TMZ. These patients were associated with a worse prognosis and lack effective identification methods. Therefore, it is necessary to differentiate patients who may have good response to anlotinb in combination with TMZ from those who are not, in order to provide personalized targeted therapies. METHODS Fifty three rMG patients (42 in training cohort and 11 in testing cohort) receiving anlotinib combined with TMZ were enrolled. A total of 3668 radiomics features were extracted from the recurrent MRI images. Radiomics features are reduced and filtered by hypothesis testing and Least Absolute Shrinkage And Selection (LASSO) regression. Eight machine learning models construct the radiomics model, and then screen out the optimal model. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness with validation. RESULTS Fifty three patients with rMG were enrolled in our study. Thirty four patients displayed effective treatment response, showed a higher survival benefits than non-response group, the median progression-free survival(PFS) was 8.53 months versus 5.33 months (p = 0.06) and the median OS was 19.9 months and 7.33 months (p = 0.029), respectively. Three radiomics features were incorporated into the model construction as final variables after LASSO regression analysis. In testing cohort, Logistic Regression (LR) model has the best performance with an Area Under the Curve (AUC) of 0.93 compared with other models, which can effectively predict the response of rMG patients to anlotinib in combination with TMZ. The calibration curve confirmed the agreement between the observed actual and prediction probability. Within the reasonable threshold probability range (0.38-0.88), the radiomics model shows good clinical utility. CONCLUSIONS The above-described radiomics model performed well, which can serve as a clinical tool for individualized prediction of the response to anlotinb combined with TMZ in rMG patients.
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Affiliation(s)
- Yurong Li
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weilin Xu
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China
| | - Yinjiao Fei
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China
| | - Mengxing Wu
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China
| | - Jinling Yuan
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China
| | - Lei Qiu
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China
| | - Yumeng Zhang
- Department of Radiation Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, 201204, China
| | - Guanhua Chen
- Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yu Cheng
- Department of Oncology, The Second Hospital of Nanjing, Nanjing, China
| | - Yuandong Cao
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China.
| | - Shu Zhou
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing, China.
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Kolossváry E, Farkas K, Karahan O, Golledge J, Schernthaner GH, Karplus T, Bernardo JJ, Marschang S, Abola MT, Heinzmann M, Edmonds M, Catalano M. The importance of socio-economic determinants of health in the care of patients with peripheral artery disease: A narrative review from VAS. Vasc Med 2023; 28:241-253. [PMID: 37154387 PMCID: PMC10265288 DOI: 10.1177/1358863x231169316] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Socio-economic determinants of health (SDoH) include various nonmedical factors in the socio-economic sphere with a potentially significant impact on health outcomes. Their effects manifest through several mediators/moderators (behavioral characteristics, physical environment, psychosocial circumstances, access to care, and biological factors). Various critical covariates (age, gender/sex, race/ethnicity, culture/acculturation, and disability status) also interact. Analyzing the effects of these factors is challenging due to their enormous complexity. Although the significance of SDoH for cardiovascular diseases is well documented, research regarding their impact on peripheral artery disease (PAD) occurrence and care is less well documented. This narrative review explores to what extent SDoH are multifaceted in PAD and how they are associated with its occurrence and care. Additionally, methodological issues that may hamper this effort are addressed. Finally, the most important question, whether this association may contribute to reasonable interventions aimed at SDoH, is analyzed. This endeavor requires attention to the social context, a whole systems approach, multilevel-thinking, and a broader alliance that reaches out to more stakeholders outside the medical sphere. More research is needed to justify the power in this concept to improve PAD-related outcomes like lower extremity amputations. At the present time, some evidence, reasonable consideration, and intuitive reasoning support the implementation of various interventions in SDoH in this field.
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Affiliation(s)
- Endre Kolossváry
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- Department of Angiology, St Imre University Teaching Hospital, Budapest, Hungary
| | - Katalin Farkas
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- Department of Angiology, St Imre University Teaching Hospital, Budapest, Hungary
| | - Oguz Karahan
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- Department of Cardiovascular Surgery, Medical School of Alaaddin Keykubat University, Alanya/Antalya, Diyarbakir, Turkey
| | - Jonathan Golledge
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- James Cook University & Townsville University Hospital, Townsville, QLD, Australia
| | - Gerit-Holger Schernthaner
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- Department of Internal Medicine II, Medical University of Vienna, Vienna, Austria
| | - Thomas Karplus
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- Department of Vascular Medicine, Concord Repatriation General Hospital, Sydney, NSW, Australia
| | - Jonathan James Bernardo
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- Department of Vascular Medicine, St Luke’s Medical Center, Quezon, NCR, Philippines
| | - Sascha Marschang
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- Department Managing Committee, VAS-European Independent Foundation in Angiology/Vascular Medicine, Bruxelles, Belgium
| | - Maria Teresa Abola
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- University of the Philippines College of Medicine–Philippine, Philippine Heart Center, Quezon, Philippines
| | - Monica Heinzmann
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- Angiology Unit, Allende Sanatorium, Nueva, Cordóba, Argentina
| | - Michael Edmonds
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- King’s College Hospital, Diabetic Foot Clinic, London, UK
| | - Mariella Catalano
- VAS-European Independent Foundation in Angiology/Vascular Medicine
- VAS-International Consortium – International PAD Strategic Network
- Inter-University Research Center on Vascular Disease, Department Biomedical and Clinical Science, University of Milan, Milan, Italy
- Department of Biomedical and Clinical Sciences L Sacco Hospital, Inter-University Research Center on Vascular Disease, University of Milan, Milan, Italy
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Shehata M, Abouelkheir RT, Gayhart M, Van Bogaert E, Abou El-Ghar M, Dwyer AC, Ouseph R, Yousaf J, Ghazal M, Contractor S, El-Baz A. Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review. Cancers (Basel) 2023; 15:2835. [PMID: 37345172 DOI: 10.3390/cancers15102835] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.
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Affiliation(s)
- Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Rasha T Abouelkheir
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | | | - Eric Van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Mohamed Abou El-Ghar
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Amy C Dwyer
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Rosemary Ouseph
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
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Lanza C, Carriero S, Biondetti P, Angileri SA, Carrafiello G, Ierardi AM. Advances in imaging guidance during percutaneous ablation of renal tumors. Semin Ultrasound CT MR 2023; 44:162-169. [DOI: 10.1053/j.sult.2023.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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10
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Ferro M, Musi G, Marchioni M, Maggi M, Veccia A, Del Giudice F, Barone B, Crocetto F, Lasorsa F, Antonelli A, Schips L, Autorino R, Busetto GM, Terracciano D, Lucarelli G, Tataru OS. Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects. Int J Mol Sci 2023; 24:4615. [PMID: 36902045 PMCID: PMC10003020 DOI: 10.3390/ijms24054615] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology (IEO) IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology (IEO) IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, 66100 Chieti, Italy
- Urology Unit, SS. Annunziata Hospital, 66100 Chieti, Italy
- Department of Urology, ASL Abruzzo 2, 66100 Chieti, Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, University of Rome, 00161 Rome, Italy
| | - Alessandro Veccia
- Department of Urology, Azienda Ospedaliera Universitaria Integrata of Verona, University of Verona, 37126 Verona, Italy
| | - Francesco Del Giudice
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, University of Rome, 00161 Rome, Italy
| | - Biagio Barone
- Department of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy
| | - Felice Crocetto
- Department of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy
| | - Francesco Lasorsa
- Urology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari Aldo Moro, 70124 Bari, Italy
| | - Alessandro Antonelli
- Department of Urology, Azienda Ospedaliera Universitaria Integrata of Verona, University of Verona, 37126 Verona, Italy
| | - Luigi Schips
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, 66100 Chieti, Italy
- Urology Unit, SS. Annunziata Hospital, 66100 Chieti, Italy
- Department of Urology, ASL Abruzzo 2, 66100 Chieti, Italy
| | | | - Gian Maria Busetto
- Department of Urology and Renal Transplantation, University of Foggia, 71122 Foggia, Italy
| | - Daniela Terracciano
- Department of Translational Medical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari Aldo Moro, 70124 Bari, Italy
| | - Octavian Sabin Tataru
- Department of Simulation Applied in Medicine, The Institution Organizing University Doctoral Studies (I.O.S.U.D.), George Emil Palade University of Medicine, Pharmacy, Sciences, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania
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11
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Patkulkar P, Subbalakshmi AR, Jolly MK, Sinharay S. Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis. ACS OMEGA 2023; 8:6126-6138. [PMID: 36844580 PMCID: PMC9948167 DOI: 10.1021/acsomega.2c06659] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 01/05/2023] [Indexed: 05/14/2023]
Abstract
Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics, and spatial transcriptomics allow recording of patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multiscale dynamics of its evolution. Here, we review the latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed burgeoning growth in the recent past in terms of mapping heterogeneity within tumor cell types as well as the stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor and a more systematic investigation of the implications of heterogeneity for patient outcomes.
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12
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Liu J, Lin Z, Wang K, Fang D, Zhang Y, Wang X, Zhang X, Wang H, Wang X. A preliminary radiomics model for predicting perirenal fat invasion on renal cell carcinoma with contrast-enhanced CT images. Abdom Radiol (NY) 2023; 48:649-658. [PMID: 36414745 DOI: 10.1007/s00261-022-03699-8] [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: 04/07/2022] [Revised: 08/09/2022] [Accepted: 09/30/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVE The aim is to develop a radiomics model based on contrast-enhanced CT scans for preoperative prediction of perirenal fat invasion (PFI) in patients with renal cell carcinoma (RCC). METHODS The CT data of 131 patients with pathology-confirmed PFI status (64 positives) were retrospectively collected and randomly assigned to the training and test datasets. The kidneys and the masses were annotated by semi-automatic segmentation. Eight types of regions of interest (ROI) were chosen for the training of the radiomics models. The areas under the curves (AUCs) from the receiver operating characteristic (ROC) curve analysis were used to analyze the diagnostic performance. Eight types of models with different ROIs have been developed. The models with the highest AUC in the test dataset were used for construction of the corresponding final model, and comparison with radiologists' diagnosis. RESULTS The AUCs of the models for each ROI was 0.783-0.926, and there was no statistically significant difference between them (P > 0.05). Model 4 was using the ROI of the outer half-ring which extended along the edge of the mass at the outer edge of the kidney into the perirenal fat space with a thickness of 3 mm. It yielded the highest AUC (0.926) and its diagnostic accuracy was higher than the radiologists' diagnosis. CONCLUSION We have developed and validated a radiomics model for prediction of PFI on RCC with contrast-enhanced CT scans. The model proved to be more accurate than the radiologists' diagnosis.
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Affiliation(s)
- Jia Liu
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Zhiyong Lin
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China
| | - Dong Fang
- Department of Urology, Peking University First Hospital, Beijing, 100034, China
| | - Yaofeng Zhang
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, 100011, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, 100011, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - He Wang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
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Ferro M, Crocetto F, Barone B, del Giudice F, Maggi M, Lucarelli G, Busetto GM, Autorino R, Marchioni M, Cantiello F, Crocerossa F, Luzzago S, Piccinelli M, Mistretta FA, Tozzi M, Schips L, Falagario UG, Veccia A, Vartolomei MD, Musi G, de Cobelli O, Montanari E, Tătaru OS. Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review. Ther Adv Urol 2023; 15:17562872231164803. [PMID: 37113657 PMCID: PMC10126666 DOI: 10.1177/17562872231164803] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/04/2023] [Indexed: 04/29/2023] Open
Abstract
Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.
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Affiliation(s)
| | - Felice Crocetto
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Biagio Barone
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Francesco del Giudice
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation
Unit, Department of Emergency and Organ Transplantation, University of Bari,
Bari, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | | | - Michele Marchioni
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti,
Italy
| | - Francesco Cantiello
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Fabio Crocerossa
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Stefano Luzzago
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Mattia Piccinelli
- Cancer Prognostics and Health Outcomes Unit,
Division of Urology, University of Montréal Health Center, Montréal, QC,
Canada
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Tozzi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Luigi Schips
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
| | | | - Alessandro Veccia
- Urology Unit, Azienda Ospedaliera
Universitaria Integrata Verona, University of Verona, Verona, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology,
George Emil Palade University of Medicine, Pharmacy, Science and Technology
of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of
Vienna, Vienna, Austria
| | - Gennaro Musi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca’
Granda – Ospedale Maggiore Policlinico, Department of Clinical Sciences and
Community Health, University of Milan, Milan, Italy
| | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral
Studies (IOSUD), George Emil Palade University of Medicine, Pharmacy,
Science and Technology of Târgu Mures, Târgu Mures, Romania
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CT-Based Radiomics Can Predict the Efficacy of Anlotinib in Advanced Non-Small-Cell Lung Cancer. JOURNAL OF ONCOLOGY 2022; 2022:4182540. [PMID: 36600966 PMCID: PMC9807313 DOI: 10.1155/2022/4182540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 11/25/2022] [Accepted: 11/29/2022] [Indexed: 12/27/2022]
Abstract
Anlotinib is a small-molecule RTK inhibitor that has achieved certain results in further-line treatment, but many patients do not respond to this drug and lack effective methods for identification. Although radiomics has been widely used in lung cancer, very few studies have been conducted in the field of antiangiogenic drugs. This study aims to develop a new model to predict the efficacy of patients receiving anlotinib by combining pretreatment computed tomography (CT) radiomic characters with clinical characters, in order to assist precision medicine of pulmonary cancer. 254 patients from seven institutions were involved in the study. Lesions were selected according to the RECIST 1.1 criteria, and the corresponding radiomic features were obtained. We constructed prediction models based on clinical, NCE-CT, and CE-CT radiomic features, respectively, and evaluated the prediction performance of the models for training sets, internal validation sets, and external validation sets. In the RAD score only model, the area under curve(AUC) of the NCE-CT cohort was 0.740 (95% CI: 0.622, 0.857) for the training set, 0.711 (95% CI: 0.480, 0.942) for the internal validation set, and 0.633(95% CI: 0.479, 0.787) for the external validation set, while that of the CE-CT cohort was 0.815 (95% CI: 0.705, 0.926) for the training set, 0.771 (95% CI: 0.539, 1.000) for the internal validation set, and 0.701 (95% CI: 0.489, 0.913) for the external validation set. In the RAD score-combined model, the AUC of the NCE-CT cohort was 0.796 (95% CI: 0.691, 0.901) for the training set, 0.579 (95% CI: 0.309, 0.848) for the internal validation set, and 0.590 (95% CI: 0.427, 0.753) for the external validation set, while that of the CE-CT cohort was 0.902 (95% CI: 0.828, 0.977) for the training set, 0.865 (95% CI: 0.696, 1.000) for the internal validation set, and 0.837 (95% CI: 0.682, 0.992) for the external validation set. In conclusion, radiomics has accurate predictions for the efficacy of anlotinib. CE-CT-based radiomic models have the best predictive potential in predicting the efficacy of anlotinib, and model predictions become better when they are combined with clinical characteristics.
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15
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Robustness of radiomics to variations in segmentation methods in multimodal brain MRI. Sci Rep 2022; 12:16712. [PMID: 36202934 PMCID: PMC9537186 DOI: 10.1038/s41598-022-20703-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 09/16/2022] [Indexed: 11/09/2022] Open
Abstract
Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77–0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3–0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods.
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Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma. Cancers (Basel) 2022; 14:cancers14082008. [PMID: 35454914 PMCID: PMC9028737 DOI: 10.3390/cancers14082008] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/05/2022] [Accepted: 04/12/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Mantle cell lymphoma (MCL) is an aggressive lymphoid tumour with a poor prognosis. There exist no routine biomarkers for the early prediction of relapse. Our study compared the potential of radiomics-based machine learning and 3D deep learning models as non-invasive biomarkers to risk-stratify MCL patients, thus promoting precision imaging in clinical oncology. Abstract Mantle cell lymphoma (MCL) is a rare lymphoid malignancy with a poor prognosis characterised by frequent relapse and short durations of treatment response. Most patients present with aggressive disease, but there exist indolent subtypes without the need for immediate intervention. The very heterogeneous behaviour of MCL is genetically characterised by the translocation t(11;14)(q13;q32), leading to Cyclin D1 overexpression with distinct clinical and biological characteristics and outcomes. There is still an unfulfilled need for precise MCL prognostication in real-time. Machine learning and deep learning neural networks are rapidly advancing technologies with promising results in numerous fields of application. This study develops and compares the performance of deep learning (DL) algorithms and radiomics-based machine learning (ML) models to predict MCL relapse on baseline CT scans. Five classification algorithms were used, including three deep learning models (3D SEResNet50, 3D DenseNet, and an optimised 3D CNN) and two machine learning models based on K-nearest Neighbor (KNN) and Random Forest (RF). The best performing method, our optimised 3D CNN, predicted MCL relapse with a 70% accuracy, better than the 3D SEResNet50 (62%) and the 3D DenseNet (59%). The second-best performing method was the KNN-based machine learning model (64%) after principal component analysis for improved accuracy. Our optimised CNN developed by ourselves correctly predicted MCL relapse in 70% of the patients on baseline CT imaging. Once prospectively tested in clinical trials with a larger sample size, our proposed 3D deep learning model could facilitate clinical management by precision imaging in MCL.
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Zhang H, Yin F, Chen M, Yang L, Qi A, Cui W, Yang S, Wen G. Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I-III Renal Cell Carcinoma. Front Oncol 2022; 11:742547. [PMID: 35155180 PMCID: PMC8830916 DOI: 10.3389/fonc.2021.742547] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/29/2021] [Indexed: 11/17/2022] Open
Abstract
Background Many patients experience recurrence of renal cell carcinoma (RCC) after radical and partial nephrectomy. Radiomics nomogram is a newly used noninvasive tool that could predict tumor phenotypes. Objective To investigate Radiomics Features (RFs) associated with progression-free survival (PFS) of RCC, assessing its incremental value over clinical factors, and to develop a visual nomogram in order to provide reference for individualized treatment. Methods The RFs and clinicopathological data of 175 patients (125 in the training set and 50 in the validation set) with clear cell RCC (ccRCC) were retrospectively analyzed. In the training set, RFs were extracted from multiphase enhanced CT tumor volume and selected using the stability LASSO feature selection algorithm. A radiomics nomogram final model was developed that incorporated the RFs weighted sum and selected clinical predictors based on the multivariate Cox proportional hazard regression. The performances of a clinical variables-only model, RFs-only model, and the final model were compared by receiver operator characteristic (ROC) analysis and DeLong test. Nomogram performance was determined and validated with respect to its discrimination, calibration, reclassification, and clinical usefulness. Results The radiomics nomogram included age, clinical stage, KPS score, and RFs weighted sum, which consisted of 6 selected RFs. The final model showed good discrimination, with a C-index of 0.836 and 0.706 in training and validation, and good calibration. In the training set, the C-index of the final model was significantly larger than the clinical-only model (DeLong test, p = 0.008). From the clinical variables-only model to the final model, the reclassification of net reclassification improvement was 18.03%, and the integrated discrimination improvement was 19.08%. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram. Conclusion The CT-based RF is an improvement factor for clinical variables-only model. The radiomics nomogram provides individualized risk assessment of postoperative PFS for patients with RCC.
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Affiliation(s)
- Haijie Zhang
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China.,PET/CT Center, Department of Nuclear Medicine, First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Fu Yin
- College of Information Engineering, Shenzhen University, Shenzhen, China
| | - Menglin Chen
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Liyang Yang
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Anqi Qi
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Weiwei Cui
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Shanshan Yang
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ge Wen
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Lisson CS, Lisson CG, Achilles S, Mezger MF, Wolf D, Schmidt SA, Thaiss WM, Bloehdorn J, Beer AJ, Stilgenbauer S, Beer M, Götz M. Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL). Cancers (Basel) 2022; 14:393. [PMID: 35053554 PMCID: PMC8773890 DOI: 10.3390/cancers14020393] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/29/2021] [Accepted: 01/06/2022] [Indexed: 02/06/2023] Open
Abstract
The study's primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years. Radiomic analysis of all targets (n = 745) was performed and features selected using the Mann Whitney U test; the discriminative power of identifying "high-risk MCL" was evaluated by receiver operating characteristics (ROC). The four radiomic features, "Uniformity", "Entropy", "Skewness" and "Difference Entropy" showed predictive significance for relapse (p < 0.05)-in contrast to the routine size measurements, which showed no relevant difference. The best prognostication for relapse achieved the feature "Uniformity" (AUC-ROC-curve 0.87; optimal cut-off ≤0.0159 to predict relapse with 87% sensitivity, 65% specificity, 69% accuracy). Several radiomic features, including the parameter "Short Axis," were associated with sustained remission. CT-derived 3D radiomics improves the predictive estimation of MCL patients; in combination with the ability to identify potential radiomic features that are characteristic for sustained remission, it may assist physicians in the clinical management of MCL.
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Affiliation(s)
- Catharina Silvia Lisson
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Christoph Gerhard Lisson
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Sherin Achilles
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Marc Fabian Mezger
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany
| | - Daniel Wolf
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany
| | - Stefan Andreas Schmidt
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Wolfgang M Thaiss
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Johannes Bloehdorn
- Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Ambros J Beer
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Translational Imaging "From Molecule to Man" (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- i2SouI-Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Stephan Stilgenbauer
- Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Comprehensive Cancer Center Ulm (CCCU), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Translational Imaging "From Molecule to Man" (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- i2SouI-Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Michael Götz
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- German Cancer Research Center (DKFZ), Division Medical Image Computing, 69120 Heidelberg, Germany
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Yao X, Mao L, Yi K, Han Y, Li W, Xiao Y, Ji J, Wang Q, Ren K. Radiomic Signature as a Diagnostic Factor for Classification of Histologic Subtypes of Lung Cancer. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
<sec> <title>Objectives:</title> To discuss the application of radiomics using Computerized Tomography (CT) analysis, for improving its diagnostic efficacy in lung, specifically in distinguishing Squamous Cell Carcinoma (SCC), lung Adenocarcinoma (ADC),
and Small Cell Lung Cancer (SCLC). </sec> <sec> <title>Methods:</title> The pathology of 189 identified cases of lung cancer was analyzed, retrospectively (60 patients with SCC, 69 patients with lung ADC and 60 patients with SCLC). A neural network was used
to determine whether the pulmonary or mediastinal window was selected to extract effective radiomic features. The key features of radiomic signature were retrieved by a Least Absolute Shrinkage and Selection Operator (LASSO) multiple logistic regression model. Next, receiver operating characteristic
curve and Area Under the Curve (AUC) analysis were used to evaluate the performance of the radiomic signature in both, training(129 patients) and validation cohorts (60 patients). </sec> <sec> <title>Results:</title> About 295 features were extracted from
a manually outlined tumor region. Features extracted from mediastinal window CT scans had a better prognostic ability than pulmonary window scans. The average accuracy for mediastinal window scans was 0.933. Our analysis revealed that the radiomic features extracted from mediastinal window
scans had the potential to build a prediction model for distinguishing between SCC, lung ADC, and SCLC. The performance of the radiomic signature to diagnose SCC and SCLC in validation cohorts proved effective, with AUC values of 0.869 and 0.859, respectively. </sec> <sec> <title>Conclusions:</title>
A unique radiomic signature was constructed as a diagnostic factor for different histologic subtypes of lung cancer. Patients with lung cancer may benefit from this proposed radiomic signature. </sec>
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Affiliation(s)
- Xiang Yao
- Department of Radiology, Xiang’an Hospital of XiaMen University, XiaMen 361000, Fujian, China
| | - Ling Mao
- The School of Economics, XiaMen University, XiaMen, Fujian, 361000, China
| | - Ke Yi
- Department of Respiratory and Critical Care Medicine, Sichuan Science City Hospital, Mianyang, Sichuan, 621000, China
| | - Yuxiao Han
- Yang Zhou University, Yangzhou, Jiangsu, 225000, China
| | - Wentao Li
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210000, China
| | - Yingqi Xiao
- West China School of Nursing/West China Hospital, Sichuan University, Chengdu, Sichuan, 610000, China
| | - Jun Ji
- Department of Pathology, Sunning People’s Hospital, Xuzhou, Jiangsu, 221000, China
| | - Qingqing Wang
- Department of Nephrology, Xuzhou Children’s Hospital, Xuzhou, Jiangsu, 221000, China
| | - Ke Ren
- Department of Radiology, Xiang’an Hospital of XiaMen University, XiaMen 361000, Fujian, China
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20
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Texture Analysis of Computed Tomography Images in the Lung of Patients With Breast Cancer. J Comput Assist Tomogr 2021; 45:837-842. [PMID: 34347709 DOI: 10.1097/rct.0000000000001198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of this study was to investigate whether the texture features of lung computed tomography images were altered by primary breast cancer without pulmonary metastasis. METHODS Texture analysis was performed on the regions of interest of lung computed tomography images from 36 patients with breast cancer and 36 healthy controls. Texture parameters between subjects with different clinical stages and hormone receptor (HR) statuses in patients with breast cancer were analyzed. RESULTS Three texture parameters (mean, SD, and variance) were significantly different between patients with breast cancer and healthy controls and between preoperative and postoperative stages in patients with breast cancer. All 3 parameters showed an increasing trend under the tumor-bearing state. These parameters were significantly higher in the stage III + IV group than in the stage I + II group. The variance parameter was significantly higher in the HR-negative group than in the HR-positive group. CONCLUSIONS Texture analysis may serve as a novel additional tool for discovering conventionally invisible changes in the lung tissue of patients with breast cancer.
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21
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Fournier L, Costaridou L, Bidaut L, Michoux N, Lecouvet FE, de Geus-Oei LF, Boellaard R, Oprea-Lager DE, Obuchowski NA, Caroli A, Kunz WG, Oei EH, O'Connor JPB, Mayerhoefer ME, Franca M, Alberich-Bayarri A, Deroose CM, Loewe C, Manniesing R, Caramella C, Lopci E, Lassau N, Persson A, Achten R, Rosendahl K, Clement O, Kotter E, Golay X, Smits M, Dewey M, Sullivan DC, van der Lugt A, deSouza NM, European Society Of Radiology. Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers. Eur Radiol 2021; 31:6001-6012. [PMID: 33492473 PMCID: PMC8270834 DOI: 10.1007/s00330-020-07598-8] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/16/2020] [Accepted: 12/03/2020] [Indexed: 02/07/2023]
Abstract
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.
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Affiliation(s)
- Laure Fournier
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
| | - Lena Costaridou
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- School of Medicine, University of Patras, University Campus, Rio, 26 500, Patras, Greece
| | - Luc Bidaut
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- College of Science, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Nicolas Michoux
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Frederic E Lecouvet
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Lioe-Fee de Geus-Oei
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands
| | - Ronald Boellaard
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
| | - Daniela E Oprea-Lager
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
| | - Nancy A Obuchowski
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - Anna Caroli
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - Wolfgang G Kunz
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Edwin H Oei
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - James P B O'Connor
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Marius E Mayerhoefer
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Manuela Franca
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Centro Hospitalar Universitário do Porto, Instituto de Ciências Biomédicas de Abel Salazar, University of Porto, Porto, Portugal
| | - Angel Alberich-Bayarri
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers in Medicine (QUIBIM), Valencia, Spain
| | - Christophe M Deroose
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Christian Loewe
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cardiovascular and Interventional Radiology, Dept. for Bioimaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Rashindra Manniesing
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Caroline Caramella
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Radiology Department, Hôpital Marie Lannelongue, Institut d'Oncologie Thoracique, Université Paris-Saclay, Le Plessis-Robinson, France
| | - Egesta Lopci
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, Humanitas Clinical and Research Hospital - IRCCS, Rozzano, MI, Italy
| | - Nathalie Lassau
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Imaging Department, Gustave Roussy Cancer Campus Grand, Paris, UMR 1281, INSERM, CNRS, CEA, Universite Paris-Saclay, Saint-Aubin, France
| | - Anders Persson
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, and Department of Health, Medicine and Caring Sciences, Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Rik Achten
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Medical Imaging, Ghent University Hospital, Gent, Belgium
| | - Karen Rosendahl
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Hospital of North Norway, Tromsø, Norway
| | - Olivier Clement
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
| | - Elmar Kotter
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Xavier Golay
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Queen Square Institute of Neurology, University College London, London, UK
| | - Marion Smits
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Marc Dewey
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel C Sullivan
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Dept. of Radiology, Duke University, 311 Research Dr, Durham, NC, 27710, USA
| | - Aad van der Lugt
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Nandita M deSouza
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria.
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium.
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA.
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK.
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22
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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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23
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Radiomics for Renal Cell Carcinoma: Predicting Outcomes from Immunotherapy and Targeted Therapies-A Narrative Review. Eur Urol Focus 2021; 7:717-721. [PMID: 33994170 DOI: 10.1016/j.euf.2021.04.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/19/2021] [Accepted: 04/29/2021] [Indexed: 01/03/2023]
Abstract
T-cell immunotherapy and molecular targeted therapies have become standard-of-care treatments for renal cell carcinoma (RCC). There is a need to develop robust biomarkers that predict patient outcomes to targeted therapies to personalise treatment. In recent years, quantitative analysis of imaging features, termed radiomics, has been used to extract tumour features. This narrative mini review summarises the evidence for radiomics prediction of immunotherapy and molecular targeted therapy outcomes in RCC. Radiomics may predict survival, treatment response, and disease progression in RCC treated with tyrosine kinase inhibitors (eg, sunitinib) and immune checkpoint inhibitors (eg, nivolumab). Further validation is necessary in large-scale studies. PATIENT SUMMARY: We summarise evidence on the ability of features extracted from CT (computed tomography) scans to predict patient outcomes from new treatments for kidney cancer. Although these features can predict treatment outcomes for patients, including survival, treatment response, and cancer progression, further research is necessary before this technology can be applied clinically.
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24
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Jena R, Narain TA, Singh UP, Srivastava A. Role of positron emission tomography/computed tomography in the evaluation of renal cell carcinoma. Indian J Urol 2021; 37:125-132. [PMID: 34103794 PMCID: PMC8173953 DOI: 10.4103/iju.iju_268_20] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 08/12/2020] [Accepted: 10/04/2020] [Indexed: 01/03/2023] Open
Abstract
Introduction: Positron emission tomography (PET) is not a standard recommendation in most of the major guidelines for the evaluation of renal cell carcinoma (RCC). Earlier studies evaluating PET scan in patients with RCC have provided discordant results. However, with the advent of newer hybrid PET/computed tomography (CT) scanning systems, this modality has shown increased efficacy in the evaluation of primary renal masses along with the detection of extrarenal metastases, restaging recurrent RCC, and also in monitoring response to targeted therapy. We performed a systematic review of the existing literature on the role of PET scan in the evaluation of RCC. Methodology: We systematically searched the databases of PubMed/Medline, Embase, and Google Scholar to identify studies on the use of PET scan in RCC. Using Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines, 94 full-text articles were selected, of which 54 relevant articles were then reviewed, after a consensus by the authors. Results: Several studies have shown similar sensitivity and specificity of fluoro-2-deoxy-2-d-glucose-PET (FDG-PET) scan as compared to conventional CT scan for the initial diagnosis of RCC, and an improved sensitivity and specificity for the detection of metastases and recurrences following curative therapy. The PET scan may also play a role in predicting the initial tumor biology and pathology and predicting the prognosis as well as the response to therapy. Conclusion: The current guidelines do not recommend PET scan in the staging armamentarium of RCCs. However, FDG-PET scan is as efficacious, if not better than conventional imaging alone, in the evaluation of the primary and metastatic RCC, as well as in evaluating the response to therapy, due to its ability to pick up areas of increased metabolic activity early on. Newer tracers such as Ga68 prostate specific membrane antigen-labeled ligands may help in opening up newer avenues of theragnostics.
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Affiliation(s)
- Rahul Jena
- Department of Urology and Renal Transplant, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
| | - Tushar Aditya Narain
- Department of Urology, All India Institute of Medical Sciences, Rishikesh, Uttarkhand, India
| | - Uday Pratap Singh
- Department of Urology and Renal Transplant, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
| | - Aneesh Srivastava
- Department of Urology and Renal Transplant, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
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Long L, Sun J, Jiang L, Hu Y, Li L, Tan Y, Cao M, Lan X, Zhang J. MRI-based traditional radiomics and computer-vision nomogram for predicting lymphovascular space invasion in endometrial carcinoma. Diagn Interv Imaging 2021; 102:455-462. [PMID: 33741266 DOI: 10.1016/j.diii.2021.02.008] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 02/24/2021] [Accepted: 02/25/2021] [Indexed: 01/06/2023]
Abstract
PURPOSE To determine the capabilities of MRI-based traditional radiomics and computer-vision (CV) nomogram for predicting lymphovascular space invasion (LVSI) in patients with endometrial carcinoma (EC). MATERIALS AND METHODS A total of 184 women (mean age, 52.9±9.0 [SD] years; range, 28-82 years) with EC were retrospectively included. Traditional radiomics features and CV features were extracted from preoperative T2-weighted and dynamic contrast-enhanced MR images. Two models (Model 1, the radiomics model; Model 2, adding CV radiomics signature into the Model 1) were built. The performance of the models was evaluated by the area under the curve (AUC) of the receiver operator characteristic (ROC) in the training and test cohorts. A nomogram based on clinicopathological metrics and radiomics signatures was developed. The predictive performance of the nomogram was assessed by AUC of the ROC in the training and test cohorts. RESULTS For predicting LVSI, the AUC values of Model 1 in the training and test cohorts were 0.79 (95% confidence interval [CI]: 0.702-0.889; accuracy: 65.9%; sensitivity: 88.8%; specificity: 57.8%) and 0.75 (95% CI: 0.585-0.914; accuracy: 69.5%; sensitivity: 85.7%; specificity: 62.5%), respectively. The AUC values of Model 2 in the training and test cohorts were 0.93 (95% CI: 0.875-0.991; accuracy: 94.9%; sensitivity: 91.6%; specificity: 96.0%) and 0.81 (95% CI: 0.666-0.962; accuracy: 71.7%; sensitivity: 92.8%; specificity: 62.5%), respectively. The discriminative ability of Model 2 was significantly improved compared to Model 1 (Net Reclassification Improvement [NRI]=0.21; P=0.04). Based on histologic grade, FIGO stage, Rad-score and CV-score, AUC values of the nomogram to predict LVSI in the training and test cohorts were 0.98 (95% CI: 0.955-1; accuracy: 91.6%; sensitivity: 91.6%; specificity: 96.0%) and 0.92 (95% CI: 0.823-1; accuracy: 91.3%; sensitivity: 78.5%; specificity: 96.8%), respectively. CONCLUSIONS MRI-based traditional radiomics and computer-vision nomogram are useful for preoperative risk stratification in patients with EC and may facilitate better clinical decision-making.
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Affiliation(s)
- Ling Long
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, 400030 Chongqing, PR China
| | - Jianqing Sun
- Shukun (Beijing) Technology Co., Ltd., Jinhui Bd, Qiyang Rd, 100102 Beijing, China
| | - Liling Jiang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, 400030 Chongqing, PR China
| | - Yixin Hu
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, 400030 Chongqing, PR China
| | - Lan Li
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, 400030 Chongqing, PR China
| | - Yong Tan
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, 400030 Chongqing, PR China
| | - Meimei Cao
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, 400030 Chongqing, PR China
| | - Xiaosong Lan
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, 400030 Chongqing, PR China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, 400030 Chongqing, PR China.
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Mühlbauer J, Egen L, Kowalewski KF, Grilli M, Walach MT, Westhoff N, Nuhn P, Laqua FC, Baessler B, Kriegmair MC. Radiomics in Renal Cell Carcinoma-A Systematic Review and Meta-Analysis. Cancers (Basel) 2021; 13:cancers13061348. [PMID: 33802699 PMCID: PMC8002585 DOI: 10.3390/cancers13061348] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/07/2021] [Accepted: 03/10/2021] [Indexed: 12/23/2022] Open
Abstract
Simple Summary Radiomics may answer questions where the conventional interpretation of medical imaging has limitations. The aim of our systematic review and meta-analysis was to assess the (current) status of evidence in the application of radiomics in the field of renal masses. We focused on its role in diagnosis, sub-entity discrimination and treatment response assessment in renal cell carcinoma (RCC) and benign renal masses. Our quantitative synthesis showed promising results in discrimination of tumor dignity, nevertheless, the value added to human assessment remains unclear and should be the focus of future research. Furthermore, the benefit regarding treatment response assessment remains unclear as well, since the existing studies are investigating already abandoned systemic therapies (ST), which no longer represent the current “reference” standard. Open science could enable to establish technical and clinical validity of radiomic signatures prior to the incorporation of radiomics into everyday clinical practice. Abstract Radiomics may increase the diagnostic accuracy of medical imaging for localized and metastatic RCC (mRCC). A systematic review and meta-analysis was performed. Doing so, we comprehensively searched literature databases until May 2020. Studies investigating the diagnostic value of radiomics in differentiation of localized renal tumors and assessment of treatment response to ST in mRCC were included and assessed with respect to their quality using the radiomics quality score (RQS). A total of 113 out of 1098 identified studies met the criteria and were included in qualitative synthesis. Median RQS of all studies was 13.9% (5.0 points, IQR 0.25–7.0 points), and RQS increased over time. Thirty studies were included into the quantitative synthesis: For distinguishing angiomyolipoma, oncocytoma or unspecified benign tumors from RCC, the random effects model showed a log odds ratio (OR) of 2.89 (95%-CI 2.40–3.39, p < 0.001), 3.08 (95%-CI 2.09–4.06, p < 0.001) and 3.57 (95%-CI 2.69–4.45, p < 0.001), respectively. For the general discrimination of benign tumors from RCC log OR was 3.17 (95%-CI 2.73–3.62, p < 0.001). Inhomogeneity of the available studies assessing treatment response in mRCC prevented any meaningful meta-analysis. The application of radiomics seems promising for discrimination of renal tumor dignity. Shared data and open science may assist in improving reproducibility of future studies.
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Affiliation(s)
- Julia Mühlbauer
- Department of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (J.M.); (L.E.); (K.-F.K.); (M.T.W.); (N.W.); (P.N.)
| | - Luisa Egen
- Department of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (J.M.); (L.E.); (K.-F.K.); (M.T.W.); (N.W.); (P.N.)
| | - Karl-Friedrich Kowalewski
- Department of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (J.M.); (L.E.); (K.-F.K.); (M.T.W.); (N.W.); (P.N.)
| | - Maurizio Grilli
- Library of the Medical Faculty Mannheim of the University of Heidelberg, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany;
| | - Margarete T. Walach
- Department of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (J.M.); (L.E.); (K.-F.K.); (M.T.W.); (N.W.); (P.N.)
| | - Niklas Westhoff
- Department of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (J.M.); (L.E.); (K.-F.K.); (M.T.W.); (N.W.); (P.N.)
| | - Philipp Nuhn
- Department of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (J.M.); (L.E.); (K.-F.K.); (M.T.W.); (N.W.); (P.N.)
| | - Fabian C. Laqua
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (F.C.L.); (B.B.)
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (F.C.L.); (B.B.)
| | - Maximilian C. Kriegmair
- Department of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (J.M.); (L.E.); (K.-F.K.); (M.T.W.); (N.W.); (P.N.)
- Correspondence:
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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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Zhao Y, Liu G, Sun Q, Zhai G, Wu G, Li ZC. Validation of CT radiomics for prediction of distant metastasis after surgical resection in patients with clear cell renal cell carcinoma: exploring the underlying signaling pathways. Eur Radiol 2021; 31:5032-5040. [PMID: 33439312 DOI: 10.1007/s00330-020-07590-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 11/06/2020] [Accepted: 12/02/2020] [Indexed: 01/05/2023]
Abstract
OBJECTIVES To develop a radiomics model using preoperative multiphasic CT for predicting distant metastasis after surgical resection in patients with localized clear cell renal cell carcinoma (ccRCC) and to identify key biological pathways underlying the predictive radiomics features using RNA sequencing data. METHODS In this multi-institutional retrospective study, a CT radiomics metastasis score (RMS) was developed from a radiomics analysis cohort (n = 184) for distant metastasis prediction. Using a gene expression analysis cohort (n = 326), radiomics-associated gene modules were identified. Based on a radiogenomics discovery cohort (n = 42), key biological pathways were enriched from the gene modules. Furthermore, a multigene signature associated with RMS was constructed and validated on an independent radiogenomics validation cohort (n = 37). RESULTS The 9-feature-based RMS predicted distant metastasis with an AUC of 0.861 in validation set and was independent with clinical factors (p < 0.001). A gene module comprising 114 genes was identified to be associated with all nine radiomics features (p < 0.05). Four enriched pathways were identified, including ECM-receptor interaction, focal adhesion, protein digestion and absorption, and PI3K-Akt pathways. Most of them play important roles in tumor progression and metastasis. A 19-gene signature was constructed from the radiomics-associated gene module and predicted metastasis with an AUC of 0.843 in the radiogenomics validation cohort. CONCLUSIONS CT radiomics features can predict distant metastasis after surgical resection of localized ccRCC while the predictive radiomics phenotypes may be driven by key biological pathways related to cancer progression and metastasis. KEY POINTS • Radiomics features from primary tumor in preoperative CT predicted distant metastasis after surgical resection in patients with localized ccRCC. • CT radiomics features predictive of distant metastasis were associated with key signaling pathways related to tumor progression and metastasis. • Gene signature associated with radiomics metastasis score predicted distant metastasis in localized ccRCC.
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Affiliation(s)
- Yuanshen Zhao
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, No. 1068, Xueyuan Avenue, Shenzhen, 518055, Guangdong Province, China
| | - Guiqin Liu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No.1630, Dongfang Road, Pudong, Shanghai, 200127, China
| | - Qiuchang Sun
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, No. 1068, Xueyuan Avenue, Shenzhen, 518055, Guangdong Province, China
| | - Guangtao Zhai
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guangyu Wu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No.1630, Dongfang Road, Pudong, Shanghai, 200127, China.
| | - Zhi-Cheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, No. 1068, Xueyuan Avenue, Shenzhen, 518055, Guangdong Province, China.
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Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA. Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging 2020; 4:24. [PMID: 34191197 PMCID: PMC8218106 DOI: 10.1186/s41824-020-00094-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/26/2020] [Indexed: 12/20/2022] Open
Abstract
Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.
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Affiliation(s)
- Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
- Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Francesco Bartoli
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Andrea Marciano
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberta Zanca
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Riemer H J A Slart
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands
- Faculty of Science and Technology, Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands
| | - Paola A Erba
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands.
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Alvarez-Jimenez C, Sandino AA, Prasanna P, Gupta A, Viswanath SE, Romero E. Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results. Cancers (Basel) 2020; 12:cancers12123663. [PMID: 33297357 PMCID: PMC7762258 DOI: 10.3390/cancers12123663] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/12/2020] [Accepted: 11/13/2020] [Indexed: 12/18/2022] Open
Abstract
(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas.
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Affiliation(s)
- Charlems Alvarez-Jimenez
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Alvaro A. Sandino
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA;
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA;
| | - Satish E. Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Eduardo Romero
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
- Correspondence:
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Antunes JT, Ofshteyn A, Bera K, Wang EY, Brady JT, Willis JE, Friedman KA, Marderstein EL, Kalady MF, Stein SL, Purysko AS, Paspulati R, Gollamudi J, Madabhushi A, Viswanath SE. Radiomic Features of Primary Rectal Cancers on Baseline T 2 -Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study. J Magn Reson Imaging 2020; 52:1531-1541. [PMID: 32216127 PMCID: PMC7529659 DOI: 10.1002/jmri.27140] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 03/03/2020] [Accepted: 03/04/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Twenty-five percent of rectal adenocarcinoma patients achieve pathologic complete response (pCR) to neoadjuvant chemoradiation and could avoid proctectomy. However, pretreatment clinical or imaging markers are lacking in predicting response to chemoradiation. Radiomic texture features from MRI have recently been associated with therapeutic response in other cancers. PURPOSE To construct a radiomics texture model based on pretreatment MRI for identifying patients who will achieve pCR to neoadjuvant chemoradiation in rectal cancer, including validation across multiple scanners and sites. STUDY TYPE Retrospective. SUBJECTS In all, 104 rectal cancer patients staged with MRI prior to long-course chemoradiation followed by proctectomy; curated from three institutions. FIELD STRENGTH/SEQUENCE 1.5T-3.0T, axial higher resolution T2 -weighted turbo spin echo sequence. ASSESSMENT Pathologic response was graded on postsurgical specimens. In total, 764 radiomic features were extracted from single-slice sections of rectal tumors on processed pretreatment T2 -weighted MRI. STATISTICAL TESTS Three feature selection schemes were compared for identifying radiomic texture descriptors associated with pCR via a discovery cohort (one site, N = 60, cross-validation). The top-selected radiomic texture features were used to train and validate a random forest classifier model for pretreatment identification of pCR (two external sites, N = 44). Model performance was evaluated via area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS Laws kernel responses and gradient organization features were most associated with pCR (P ≤ 0.01); as well as being commonly identified across all feature selection schemes. The radiomics model yielded a discovery AUC of 0.699 ± 0.076 and a hold-out validation AUC of 0.712 with 70.5% accuracy (70.0% sensitivity, 70.6% specificity) in identifying pCR. Radiomic texture features were resilient to variations in magnetic field strength as well as being consistent between two different expert annotations. Univariate analysis revealed no significant associations of baseline clinicopathologic or MRI findings with pCR (P = 0.07-0.96). DATA CONCLUSION Radiomic texture features from pretreatment MRIs may enable early identification of potential pCR to neoadjuvant chemoradiation, as well as generalize across sites. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Jacob T. Antunes
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH 44106
| | - Asya Ofshteyn
- University Hospitals Cleveland Medical Center, Department of Surgery, Cleveland, OH, 44106
| | - Kaustav Bera
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH 44106
| | - Erik Y. Wang
- University Hospitals Cleveland Medical Center, Department of Surgery, Cleveland, OH, 44106
| | - Justin T. Brady
- University Hospitals Cleveland Medical Center, Department of Surgery, Cleveland, OH, 44106
| | - Joseph E. Willis
- University Hospitals Cleveland Medical Center, Department of Pathology, Cleveland, OH, 44106
| | - Kenneth A. Friedman
- University Hospitals Cleveland Medical Center, Department of Pathology, Cleveland, OH, 44106
| | - Eric L. Marderstein
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, 44106
| | - Matthew F. Kalady
- Cleveland Clinic, Department of Colorectal Surgery, Cleveland, OH, 44106
| | - Sharon L. Stein
- University Hospitals Cleveland Medical Center, Department of Surgery, Cleveland, OH, 44106
| | - Andrei S. Purysko
- Cleveland Clinic, Section of Abdominal Imaging and Nuclear Radiology Department, Cleveland, OH, 44195
| | - Rajmohan Paspulati
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, 44106
| | - Jayakrishna Gollamudi
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, 44106
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH 44106
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, 44106
| | - Satish E. Viswanath
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH 44106
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Shiyam Sundar LK, Muzik O, Buvat I, Bidaut L, Beyer T. Potentials and caveats of AI in hybrid imaging. Methods 2020; 188:4-19. [PMID: 33068741 DOI: 10.1016/j.ymeth.2020.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/05/2020] [Accepted: 10/07/2020] [Indexed: 12/18/2022] Open
Abstract
State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research.
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Affiliation(s)
- Lalith Kumar Shiyam Sundar
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | | | - Irène Buvat
- Laboratoire d'Imagerie Translationnelle en Oncologie, Inserm, Institut Curie, Orsay, France
| | - Luc Bidaut
- College of Science, University of Lincoln, Lincoln, UK
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
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Enhanced Computed Tomography-Based Radiomics Signature Combined With Clinical Features in Evaluating Nuclear Grading of Renal Clear Cell Carcinoma. J Comput Assist Tomogr 2020; 44:730-736. [PMID: 32558771 DOI: 10.1097/rct.0000000000001041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE The aim of the study was to explore the value of enhanced computed tomography (CT)-based radiomics signature combined with clinical features in evaluating nuclear grading of clear cell renal cell carcinoma (ccRCC). METHODS One hundred one patients with ccRCC were classified into low- and high-grade group, and the data were divided into training set and verification set. Radiomics signatures were constructed in the training set in enhanced 3 stages and the combination of them. The predictive nomogram was constructed. The classification efficiency and the clinical practicability of the integrated radiomics model were evaluated. RESULTS The classification efficiency of enhanced 3-stage integrated histology model was higher than that of each single-phase model. The predictive nomogram incorporated the best radiomics signature, and the independent clinical risk factors showed good performance. A decision curve analysis curve shows that the net benefit of the combined model. CONCLUSIONS It is feasible to evaluate the nuclear grading of ccRCC based on enhanced CT radiomics signature combined with clinical features.
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Aizaz M, Moonen RPM, van der Pol JAJ, Prieto C, Botnar RM, Kooi ME. PET/MRI of atherosclerosis. Cardiovasc Diagn Ther 2020; 10:1120-1139. [PMID: 32968664 DOI: 10.21037/cdt.2020.02.09] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Myocardial infarction and stroke are the most prevalent global causes of death. Each year 15 million people worldwide die due to myocardial infarction or stroke. Rupture of a vulnerable atherosclerotic plaque is the main underlying cause of stroke and myocardial infarction. Key features of a vulnerable plaque are inflammation, a large lipid-rich necrotic core (LRNC) with a thin or ruptured overlying fibrous cap, and intraplaque hemorrhage (IPH). Noninvasive imaging of these features could have a role in risk stratification of myocardial infarction and stroke and can potentially be utilized for treatment guidance and monitoring. The recent development of hybrid PET/MRI combining the superior soft tissue contrast of MRI with the opportunity to visualize specific plaque features using various radioactive tracers, paves the way for comprehensive plaque imaging. In this review, the use of hybrid PET/MRI for atherosclerotic plaque imaging in carotid and coronary arteries is discussed. The pros and cons of different hybrid PET/MRI systems are reviewed. The challenges in the development of PET/MRI and potential solutions are described. An overview of PET and MRI acquisition techniques for imaging of atherosclerosis including motion correction is provided, followed by a summary of vessel wall imaging PET/MRI studies in patients with carotid and coronary artery disease. Finally, the future of imaging of atherosclerosis with PET/MRI is discussed.
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Affiliation(s)
- Mueez Aizaz
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Rik P M Moonen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Jochem A J van der Pol
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingenieria, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingenieria, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - M Eline Kooi
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics. Sci Rep 2020; 10:12340. [PMID: 32704007 PMCID: PMC7378556 DOI: 10.1038/s41598-020-69298-z] [Citation(s) in RCA: 126] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Accepted: 07/06/2020] [Indexed: 02/06/2023] Open
Abstract
Radiomics relies on the extraction of a wide variety of quantitative image-based features to provide decision support. Magnetic resonance imaging (MRI) contributes to the personalization of patient care but suffers from being highly dependent on acquisition and reconstruction parameters. Today, there are no guidelines regarding the optimal pre-processing of MR images in the context of radiomics, which is crucial for the generalization of published image-based signatures. This study aims to assess the impact of three different intensity normalization methods (Nyul, WhiteStripe, Z-Score) typically used in MRI together with two methods for intensity discretization (fixed bin size and fixed bin number). The impact of these methods was evaluated on first- and second-order radiomics features extracted from brain MRI, establishing a unified methodology for future radiomics studies. Two independent MRI datasets were used. The first one (DATASET1) included 20 institutional patients with WHO grade II and III gliomas who underwent post-contrast 3D axial T1-weighted (T1w-gd) and axial T2-weighted fluid attenuation inversion recovery (T2w-flair) sequences on two different MR devices (1.5 T and 3.0 T) with a 1-month delay. Jensen–Shannon divergence was used to compare pairs of intensity histograms before and after normalization. The stability of first-order and second-order features across the two acquisitions was analysed using the concordance correlation coefficient and the intra-class correlation coefficient. The second dataset (DATASET2) was extracted from the public TCIA database and included 108 patients with WHO grade II and III gliomas and 135 patients with WHO grade IV glioblastomas. The impact of normalization and discretization methods was evaluated based on a tumour grade classification task (balanced accuracy measurement) using five well-established machine learning algorithms. Intensity normalization highly improved the robustness of first-order features and the performances of subsequent classification models. For the T1w-gd sequence, the mean balanced accuracy for tumour grade classification was increased from 0.67 (95% CI 0.61–0.73) to 0.82 (95% CI 0.79–0.84, P = .006), 0.79 (95% CI 0.76–0.82, P = .021) and 0.82 (95% CI 0.80–0.85, P = .005), respectively, using the Nyul, WhiteStripe and Z-Score normalization methods compared to no normalization. The relative discretization makes unnecessary the use of intensity normalization for the second-order radiomics features. Even if the bin number for the discretization had a small impact on classification performances, a good compromise was obtained using the 32 bins considering both T1w-gd and T2w-flair sequences. No significant improvements in classification performances were observed using feature selection. A standardized pre-processing pipeline is proposed for the use of radiomics in MRI of brain tumours. For models based on first- and second-order features, we recommend normalizing images with the Z-Score method and adopting an absolute discretization approach. For second-order feature-based signatures, relative discretization can be used without prior normalization. In both cases, 32 bins for discretization are recommended. This study may pave the way for the multicentric development and validation of MR-based radiomics biomarkers.
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Abstract
Radiomics has been shown to have considerable potential and value in quantifying the tumor phenotype and predicting the treatment response. In most scenarios, the commercial and open-source software programs are available for quantitative analysis in medical images to streamline radiomics research. However, at this stage, most of these programs are local applications and require users to have experience in programming and software engineering, which clinicians usually do not have. Therefore, in this article, a web-based tool was proposed to flexibly support radiomics research workflow tasks. Radiomics in RayPlus requires zero installation, is easy to maintain, and accessible anywhere via any PC or MAC with an Internet connection. The system provides functions including multimodality image import and viewing, ROI definition, feature extraction, and data sharing. As a web application, it appears an effective way to multi-institution and multi-department collaborative radiomics research and moreover, its transparency, flexibility, and portability can greatly accelerate the pace of clinical data analysis.
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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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Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature. Cancers (Basel) 2020; 12:cancers12061387. [PMID: 32481542 PMCID: PMC7352711 DOI: 10.3390/cancers12061387] [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: 04/17/2020] [Revised: 05/22/2020] [Accepted: 05/26/2020] [Indexed: 12/21/2022] Open
Abstract
Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients’ responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine.
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Noortman WA, Vriens D, Grootjans W, Tao Q, de Geus-Oei LF, Van Velden FH. Nuclear medicine radiomics in precision medicine: why we can't do without artificial intelligence. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2020; 64:278-290. [PMID: 32397702 DOI: 10.23736/s1824-4785.20.03263-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In recent years, radiomics, defined as the extraction of large amounts of quantitative features from medical images, has gained emerging interest. Radiomics consists of the extraction of handcrafted features combined with sophisticated statistical methods or machine learning algorithms for modelling, or deep learning algorithms that both learn features from raw data and perform modelling. These features have the potential to serve as non-invasive biomarkers for tumor characterization, prognostic stratification and response prediction, thereby contributing to precision medicine. However, especially in nuclear medicine, variable results are obtained when using radiomics for these purposes. Individual studies show promising results, but due to small numbers of patients per study and little standardization, it is difficult to compare and validate results on other datasets. This review describes the radiomic pipeline, its applications and the increasing role of artificial intelligence within the field. Furthermore, the challenges that need to be overcome to achieve clinical translation are discussed, so that, eventually, radiomics, combined with clinical data and other biomarkers, can contribute to precision medicine, by providing the right treatment to the right patient, with the right dose, at the right time.
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Affiliation(s)
- Wyanne A Noortman
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands - .,Biomedical Photonic Imaging Group, University of Twente, Enschede, the Netherlands -
| | - Dennis Vriens
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands
| | - Willem Grootjans
- Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands
| | - Qian Tao
- Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands
| | - Lioe-Fee de Geus-Oei
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands.,Biomedical Photonic Imaging Group, University of Twente, Enschede, the Netherlands
| | - Floris H Van Velden
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands
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Lu X, Li M, Zhang H, Hua S, Meng F, Yang H, Li X, Cao D. A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma. Phys Med Biol 2020; 65:055012. [PMID: 31978901 DOI: 10.1088/1361-6560/ab6f98] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
To predict the epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using quantitative radiomic biomarkers and semantic features. We analyzed the computed tomography (CT) images and medical record data of 104 patients with lung adenocarcinoma who underwent surgical excision and EGFR mutation detection from 2016 to 2018 at our center. CT radiomic and semantic features that reflect the tumors' heterogeneity and phenotype were extracted from preoperative non-enhanced CT scans. The least absolute shrinkage and selection operator method was applied to select the most distinguishable features. Three logistic regression models were built to predict the EGFR mutation status by combining the CT semantic with clinicopathological characteristics, using the radiomic features alone, and by combining the radiomic and clinicopathological features. Receiver operating characteristic (ROC) curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for predicting EGFR mutation. Furthermore, radiomic nomograms were constructed to demonstrate the performance of the model. In total, 1025 radiomic features were extracted and reduced to 13 features as the most important predictors to build the radiomic signature. The combined radiomic and clinicopathological features model was developed based on the radiomic signature, sex, smoking, vascular infiltration, and pathohistological type. The AUC was 0.90 ± 0.02 for the training, 0.88 ± 0.11 for the verification, and 0.894 for the test dataset. This model was superior to the other prediction models that used the combined CT semantic and clinicopathological features (AUC for the test dataset: 0.768) and radiomic features alone (AUC for the test dataset: 0.837). The prediction model built by radiomic biomarkers and clinicopathological features, including the radiomic signature, sex, smoking, vascular infiltration, and pathological type, outperformed the other two models and could effectively predict the EGFR mutation status in patients with peripheral lung adenocarcinoma. The radiomic nomogram of this model is expected to become an effective biomarker for patients with lung adenocarcinoma requiring adjuvant targeted treatment.
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Affiliation(s)
- Xiaoqian Lu
- Department of Radiology, the First Hospital of Jilin University, 130021 Changchun, People's Republic of China. These authors contributed equally to this work
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Mytsyk Y, Pasichnyk S, Dutka I, Dats I, Vorobets D, Skrzypczyk M, Uteuliyev Y, Botikova A, Gazdikova K, Kubatka P, Urdzik P, Kruzliak P. Systemic treatment of the metastatic renal cell carcinoma: usefulness of the apparent diffusion coefficient of diffusion-weighted MRI in prediction of early therapeutic response. Clin Exp Med 2020; 20:277-287. [PMID: 32026157 DOI: 10.1007/s10238-020-00612-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 01/27/2020] [Indexed: 12/21/2022]
Abstract
Accurate prediction of early treatment response to systemic therapy (ST) with tyrosine kinase inhibitors (TKI) in patients with metastatic renal cell carcinoma (mRCC) could help avoid ineffective and expensive treatment with serious side effects. Neither RECIST v.1.1 nor Choi criteria successfully discriminate between patients with mRCC who received ST having a short or long time to progression (TTP). There is no biomarker, which is able to predict early therapeutic response to TKIs application in patients with mRCC. The goal of our study was to investigate the potential of apparent diffusion coefficient (ADC) of diffusion-weighted imaging (DWI) of MRI in prediction of early therapeutic response to ST with pazopanib in patients with mRCC. The retrospective study enrolled 32 adult patients with conventional mRCC who received pazopanib (mean duration-7.5 ± 3.45). The mean duration of follow-up was 11.85 ± 4.34 months. In all patients as baseline examination and 1 month after treatment, 1.5T MRI including DWI sequence was performed followed by ADC measurement of the main renal lesion. For assessment of the therapeutic response, RECIST 1.1 is used. Partial response (PR), stable disease (SD) and progressive disease (PD) were observed in 12 (37.50%), 10 (31.25%) and 10 (31.25%) cases with mean TTP of 10.33 ± 2.06 months (95% confidence interval, CI = 9.05-11.61), 7.40 ± 2.50 months (95% CI = 5.61-9.19) and 4.20 ± 1.99 months (95% CI = 2.78-5.62) accordingly (p < 0.05). There was no difference in change of main lesions' longest size 1 month after ST in patients with PR, SD and PD. Comparison of mean ADC values before and 1 month after systemic treatment showed significant decrease by 19.11 ± 10.64% (95% CI = 12.35-25.87) and by 7.66 ± 6.72% (95% CI = 2.86-12.47) in subgroups with PR and SD, respectively (p < 0.05). There was shorter TTP in patients with mRCC if ADC of the main renal lesion 1 month after the ST increased from the baseline less than 1.73% compared to patients with ADC levels above this threshold: 5.29 ± 3.45 versus 9.50 ± 2.04 months accordingly (p < 0.001). Overall, our findings highlighted the use of ADC as a predictive biomarker for early therapeutic response assessment. Use of ADC will be effective and useful for reliable prediction of responders and non-responders to systemic treatment with pazopanib.
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Affiliation(s)
- Yulian Mytsyk
- Department of Urology, Lviv National Medical University n.a. Danylo Halytsky, Pekarska Str. 69, Lviv, Ukraine
| | - Serhiy Pasichnyk
- Department of Urology, Lviv National Medical University n.a. Danylo Halytsky, Pekarska Str. 69, Lviv, Ukraine
| | - Ihor Dutka
- Medical center "Euroclinic", Lviv, Ukraine
| | - Ihor Dats
- Department of Radiology, Lviv National Medical University n.a. Danylo Halytsky, Lviv, Ukraine
| | - Dmytro Vorobets
- Department of Urology, Lviv National Medical University n.a. Danylo Halytsky, Pekarska Str. 69, Lviv, Ukraine
| | - Michał Skrzypczyk
- Department of Urology, Centre of Postgraduate Medical Education, Warsaw, Poland
| | - Yerzhan Uteuliyev
- Department of Postgraduate Education and Research, Kazakhstan Medical University, Almaty, Kazakhstan
| | - Andrea Botikova
- Faculty of Health and Social Work, Trnava University, Trnava, Slovakia
| | - Katarina Gazdikova
- Department of General Medicine, Faculty of Medicine, Slovak Medical University, Limbova 12, 8303, Bratislava, Slovakia
| | - Peter Kubatka
- Department of Medical Biology, Jessenius Faculty of Medicine in Martin, Comenius University, Martin, Slovakia
| | - Peter Urdzik
- Department of Gynaecology and Obstetrics, Faculty of Medicine, Pavol Jozef Safarik University and Louis Pasteur University Hospital, Tr. SNP 1, 04001, Kosice, Slovakia
| | - Peter Kruzliak
- Department of Internal Medicine, Brothers of Mercy Hospital, Polni 553/3, 63900, Brno, Czech Republic.
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Lacroix M, Frouin F, Dirand AS, Nioche C, Orlhac F, Bernaudin JF, Brillet PY, Buvat I. Correction for Magnetic Field Inhomogeneities and Normalization of Voxel Values Are Needed to Better Reveal the Potential of MR Radiomic Features in Lung Cancer. Front Oncol 2020; 10:43. [PMID: 32083003 PMCID: PMC7006432 DOI: 10.3389/fonc.2020.00043] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 01/10/2020] [Indexed: 12/23/2022] Open
Abstract
Purpose: To design and validate a preprocessing procedure dedicated to T2-weighted MR images of lung cancers so as to improve the ability of radiomic features to distinguish between adenocarcinoma and other histological types. Materials and Methods: A discovery set of 52 patients with advanced lung cancer who underwent T2-weighted MR imaging at 3 Tesla in a single center study from August 2017 to May 2019 was used. Findings were then validated using a validation set of 19 additional patients included from May to October 2019. Tumor type was obtained from the pathology report after trans-thoracic needle biopsy, metastatic lymph node or metastasis samples, or surgical excisions. MR images were preprocessed using N4ITK bias field correction and by normalizing voxel intensities with fat as a reference region. Segmentation and extraction of radiomic features were performed with LIFEx software on the raw images, on the N4ITK-corrected images and on the fully preprocessed images. Two analyses were conducted where radiomic features were extracted: (1) from the whole tumor volume (3D analysis); (2) from all slices encompassing the tumor (2D analysis). Receiver operating characteristic (ROC) analysis was used to identify features that could distinguish between adenocarcinoma and other histological types. Sham experiments were also designed to control the number of false positive findings. Results: There were 31 (12) adenocarcinomas and 21 (7) other histological types in the discovery (validation) set. In 2D, preprocessing increased the number of discriminant radiomic features from 8 without preprocessing to 22 with preprocessing. 2D analysis yielded more features able to identify adenocarcinoma than 3D analysis (12 discriminant radiomic features after preprocessing in 3D). Preprocessing did not increase false positive findings as no discriminant features were identified in any of the sham experiments. The greatest sensitivity of the 2D analysis applied to preprocessed data was confirmed in the validation set. Conclusion: Correction for magnetic field inhomogeneities and normalization of voxel values are essential to reveal the full potential of radiomic features to identify the tumor histological type from MR T2-weighted images, with classification performance similar to those reported in PET/CT and in multiphase CT in lung cancers.
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Affiliation(s)
- Maxime Lacroix
- Service d'Imagerie Médicale, AP-HP, Hôpital Avicenne, Bobigny, France.,Laboratoire IMIV, UMR 1023 Inserm-CEA-Université Paris Sud, ERL 9218 CNRS, Université Paris Saclay, Orsay, France
| | - Frédérique Frouin
- Laboratoire IMIV, UMR 1023 Inserm-CEA-Université Paris Sud, ERL 9218 CNRS, Université Paris Saclay, Orsay, France
| | - Anne-Sophie Dirand
- Laboratoire IMIV, UMR 1023 Inserm-CEA-Université Paris Sud, ERL 9218 CNRS, Université Paris Saclay, Orsay, France
| | - Christophe Nioche
- Laboratoire IMIV, UMR 1023 Inserm-CEA-Université Paris Sud, ERL 9218 CNRS, Université Paris Saclay, Orsay, France
| | - Fanny Orlhac
- Laboratoire IMIV, UMR 1023 Inserm-CEA-Université Paris Sud, ERL 9218 CNRS, Université Paris Saclay, Orsay, France
| | | | | | - Irène Buvat
- Laboratoire IMIV, UMR 1023 Inserm-CEA-Université Paris Sud, ERL 9218 CNRS, Université Paris Saclay, Orsay, France
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Yuan Q, Zhang H, Deng T, Tang S, Yuan X, Tang W, Xie Y, Ge H, Wang X, Zhou Q, Xiao X. Role of Artificial Intelligence in Kidney Disease. Int J Med Sci 2020; 17:970-984. [PMID: 32308551 PMCID: PMC7163364 DOI: 10.7150/ijms.42078] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 03/17/2020] [Indexed: 12/17/2022] Open
Abstract
Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.
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Affiliation(s)
- Qiongjing Yuan
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Haixia Zhang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China.,Department of Nephrology, Second Affiliated Hospital of Soochow University, 1055 Sanxiang Road, Suzhou, Jiangsu 215000, China
| | - Tianci Deng
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Shumei Tang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiangning Yuan
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Wenbin Tang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Yanyun Xie
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Huipeng Ge
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiufen Wang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Qiaoling Zhou
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiangcheng Xiao
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
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Textural features of hypoxia PET predict survival in head and neck cancer during chemoradiotherapy. Eur J Nucl Med Mol Imaging 2019; 47:1056-1064. [PMID: 31773233 DOI: 10.1007/s00259-019-04609-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 11/07/2019] [Indexed: 01/24/2023]
Abstract
PURPOSE The aim of this study was to investigate whether textural features of tumour hypoxia, assessed with serial [18F]fluoromisonidazole (FMISO)-PET, were able to predict clinical outcome in patients with head and neck squamous cell carcinoma (HNSCC, T1-4, N+, M0) during chemoradiotherapy (CRT). METHODS In a preliminary evaluation of a prospective trial, tumour hypoxia was evaluated in 29 patients via serial FMISO-PET before and during CRT. All patients received an initial [18F]fluorodeoxyglucose (FDG)-PET before CRT, and tumour regions were defined on this FDG-PET. The first-order metrics tumour-to-background ratio (TBRmean, TBRmax, TBRpeak), coefficient of variation, total lesion uptake and integral non-uniformity were calculated for all scans. Further, 3 second-order (textural) features from two grey-level matrices were calculated, as well as differential non-uniformity (udiff). Prognostic value was examined by median split for group separation (GS) in Kaplan-Meier estimates and correlated with overall survival (OS), quantified via log-rank tests (p ≤ 0.05) and group-relative hazard ratios (HR). RESULTS Within a median follow-up of 29.6 months (95% CI: 16.8-48.0 months), no first-order metrics predicted OS with a significant GS (all p > 0.05) on any FMISO-PET scan. Only udiff before and in week 2 during CRT (p = 0.03, HR = 10.8 and p = 0.05, HR = 5.2) and non-uniformity from grey-level run length matrix in week 2 separated prognostic groups (p = 0.05, HR = 5.3); lower values were correlated with better OS. Further, the decrease in udiff from before CRT to week 2 was correlated with better OS (p = 0.04, HR = 9.4). FDG-PET before CRT did not predict outcome in any measure. CONCLUSIONS Textural features on FMISO-PET scans before CRT, in week 2 and, to a limited degree, the change of features during CRT, were able to identify head and neck squamous cell carcinoma patients with better OS, suggesting that a higher homogeneity of the degree of hypoxia in tumours could correlate with a better outcome after CRT.
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Bologna M, Corino VDA, Montin E, Messina A, Calareso G, Greco FG, Sdao S, Mainardi LT. Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images. J Digit Imaging 2019; 31:879-894. [PMID: 29725965 PMCID: PMC6261192 DOI: 10.1007/s10278-018-0092-9] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
The objectives of the study are to develop a new way to assess stability and discrimination capacity of radiomic features without the need of test-retest or multiple delineations and to use information obtained to perform a preliminary feature selection. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of two groups of patients: 18 with soft tissue sarcomas (STS) and 18 with oropharyngeal cancers (OPC). Sixty-nine radiomic features were computed, using three different histogram discretizations (16, 32, and 64 bins). Geometrical transformations (translations) of increasing entity were applied to the regions of interest (ROIs), and the intra-class correlation coefficient (ICC) was used to compare the features computed on the original and modified ROIs. The distribution of ICC values for minimal and maximal entity translations (ICC10 and ICC100, respectively) was used to adjust thresholds of ICC (ICCmin and ICCmax) used to discriminate between good, unstable (ICC10 < ICCmin), and non-discriminative features (ICC100 > ICCmax). Fifty-four and 59 radiomic features passed the stability-based selection for all the three histogram discretizations for the OPC and STS datasets, respectively. The excluded features were similar across the different histogram discretizations (Jaccard’s index 0.77 ± 0.13 and 0.9 ± 0.1 for OPC and STS, respectively) but different between datasets (Jaccard’s index 0.19 ± 0.02). The results suggest that the observed radiomic features are mainly stable and discriminative, but the stability depends on the region of the body under observation. The method provides a way to assess stability without the need of test-retest or multiple delineations.
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Affiliation(s)
- Marco Bologna
- Departement of Electronics, Information and Bioengineering, Milan, Italy.
| | | | - Eros Montin
- Departement of Electronics, Information and Bioengineering, Milan, Italy
| | | | | | | | - Silvana Sdao
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Luca T Mainardi
- Departement of Electronics, Information and Bioengineering, Milan, Italy
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Um H, Tixier F, Bermudez D, Deasy JO, Young RJ, Veeraraghavan H. Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets. ACTA ACUST UNITED AC 2019; 64:165011. [DOI: 10.1088/1361-6560/ab2f44] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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47
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AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. Eur J Nucl Med Mol Imaging 2019; 46:2673-2699. [PMID: 31292700 DOI: 10.1007/s00259-019-04414-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 06/21/2019] [Indexed: 12/13/2022]
Abstract
INTRODUCTION The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes. OBJECTIVE The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.
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48
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Yin P, Mao N, Wang S, Sun C, Hong N. Clinical-radiomics nomograms for pre-operative differentiation of sacral chordoma and sacral giant cell tumor based on 3D computed tomography and multiparametric magnetic resonance imaging. Br J Radiol 2019; 92:20190155. [PMID: 31276426 DOI: 10.1259/bjr.20190155] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVE To develop and validate clinical-radiomics nomograms based on three-dimensional CT and multiparametric MRI (mpMRI) for pre-operative differentiation of sacral chordoma (SC) and sacral giant cell tumor (SGCT). METHODS A total of 83 SC and 54 SGCT patients diagnosed through surgical pathology were retrospectively analyzed. We built six models based on CT, CT enhancement (CTE), T1 weighted, T2 weighted, diffusion-weighted imaging (DWI), and contrast-enhanced T1 weighted features, two radiomics nomograms and two clinical-radiomics nomograms combined radiomics mixed features with clinical data. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) analysis were used to assess the performance of the models. RESULTS SC and SGCT presented significant differences in terms of age, sex, and tumor location (tage = 9.00, χ2sex = 10.86, χ2location = 26.20; p < 0.01). For individual scan, the radiomics model based on diffusion-weighted imaging features yielded the highest AUC of 0.889 and ACC of 0.885, followed by CT (AUC = 0.857; ACC = 0.846) and CT enhancement (AUC = 0.833; ACC = 0.769). For the combined features, the radiomics model based on mixed CT features exhibited a better AUC of 0.942 and ACC of 0.880, whereas mixed MRI features achieved a lower performance than the individual scan. The clinical-radiomics nomogram based on combined CT features achieved the highest AUC of 0.948 and ACC of 0.920. CONCLUSIONS The radiomics model based on CT and multiparametricMRI present a certain predictive value in distinguishing SC and SGCT, which can be used for auxiliary diagnosis before operation. The clinical-radiomics nomograms performed better than radiomics nomograms. ADVANCES IN KNOWLEDGE Clinical-radiomics nomograms based on CT and mpMRI features can be used for preoperative differentiation of SC and SGCT.
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Affiliation(s)
- Ping Yin
- 1Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, PR China
| | - Ning Mao
- 2Department of Radiology, Qingdao University Medical College Affiliated Yantai Yuhuangding Hospital, Yantai, Shandong 264000, PR China
| | - Sicong Wang
- 3GE Healthcare Life Sciences, Beijing 100176, China
| | - Chao Sun
- 1Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, PR China
| | - Nan Hong
- 1Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, PR China
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Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives. Korean J Radiol 2019; 20:1124-1137. [PMID: 31270976 PMCID: PMC6609433 DOI: 10.3348/kjr.2018.0070] [Citation(s) in RCA: 204] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 04/07/2019] [Indexed: 02/06/2023] Open
Abstract
Radiomics, which involves the use of high-dimensional quantitative imaging features for predictive purposes, is a powerful tool for developing and testing medical hypotheses. Radiologic and statistical challenges in radiomics include those related to the reproducibility of imaging data, control of overfitting due to high dimensionality, and the generalizability of modeling. The aims of this review article are to clarify the distinctions between radiomics features and other omics and imaging data, to describe the challenges and potential strategies in reproducibility and feature selection, and to reveal the epidemiological background of modeling, thereby facilitating and promoting more reproducible and generalizable radiomics research.
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Affiliation(s)
- Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Seo Young Park
- Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Hwa Jung Kim
- Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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Thomas R, Qin L, Alessandrino F, Sahu SP, Guerra PJ, Krajewski KM, Shinagare A. A review of the principles of texture analysis and its role in imaging of genitourinary neoplasms. Abdom Radiol (NY) 2019; 44:2501-2510. [PMID: 30448920 DOI: 10.1007/s00261-018-1832-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Advances in the management of genitourinary neoplasms have resulted in a trend towards providing patients with personalized care. Texture analysis of medical images, is one of the tools that is being explored to provide information such as detection and characterization of tumors, determining their aggressiveness including grade and metastatic potential and for prediction of survival rates and risk of recurrence. In this article we review the basic principles of texture analysis and then detail its current role in imaging of individual neoplasms of the genitourinary system.
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