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Śledzińska-Bebyn P, Furtak J, Bebyn M, Serafin Z. Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling. Magn Reson Imaging 2024; 112:63-81. [PMID: 38914147 DOI: 10.1016/j.mri.2024.06.004] [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: 04/04/2024] [Revised: 05/20/2024] [Accepted: 06/20/2024] [Indexed: 06/26/2024]
Abstract
This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.
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Affiliation(s)
- Paulina Śledzińska-Bebyn
- Department of Radiology, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland.
| | - Jacek Furtak
- Department of Clinical Medicine, Faculty of Medicine, University of Science and Technology, Bydgoszcz, Poland; Department of Neurosurgery, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Marek Bebyn
- Department of Internal Diseases, 10th Military Clinical Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland
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Hutchings K, Al Zaki A, Bhadkamkar N, Willis J. Symptomatic pseudoprogression in metastatic colorectal cancer. BMJ Case Rep 2024; 17:e258816. [PMID: 38871645 DOI: 10.1136/bcr-2023-258816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024] Open
Abstract
A man in his 70s with metastatic colorectal cancer presented with worsening clinical symptoms and imaging studies concerning for disease progression. He had received two cycles of pembrolizumab, but due to his symptomatic presentation and significant decline in performance status, there was concern for worsening disease. Transitioning to hospice was briefly considered, given his clinical decline and the notable increase in tumour size. Despite the presence of clinical symptoms and radiographic findings, pseudoprogression-defined as an increase in the size(s) of and/or visual appearance of new lesion(s), followed by a response-was also considered as part of the diagnostic possibilities. Consequently, the decision was made to proceed with a third cycle of pembrolizumab. During his subsequent outpatient follow-up, the patient showed significant symptomatic improvement and reported a decrease in his palpable right flank mass. With further immunotherapy, the patient continued to demonstrate symptomatic and radiological improvement.
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Affiliation(s)
- Kasen Hutchings
- Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ajlan Al Zaki
- General Oncology, The University of Texas MD Anderson Cancer Center Division of Cancer Medicine, Houston, Texas, USA
| | - Nishin Bhadkamkar
- General Oncology, The University of Texas MD Anderson Cancer Center Division of Cancer Medicine, Houston, Texas, USA
- Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center Division of Cancer Medicine, Houston, Texas, USA
| | - Jason Willis
- Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center Division of Cancer Medicine, Houston, Texas, USA
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Ogbonnaya CN, Alsaedi BSO, Alhussaini AJ, Hislop R, Pratt N, Steele JD, Kernohan N, Nabi G. Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging. Int J Mol Sci 2024; 25:5379. [PMID: 38791417 PMCID: PMC11121591 DOI: 10.3390/ijms25105379] [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: 04/13/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer.
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Affiliation(s)
- Chidozie N. Ogbonnaya
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | | | - Abeer J. Alhussaini
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | - Robert Hislop
- Cytogenetic, Human Genetics Unit, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK; (R.H.); (N.P.)
| | - Norman Pratt
- Cytogenetic, Human Genetics Unit, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK; (R.H.); (N.P.)
| | - J. Douglas Steele
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | - Neil Kernohan
- Department of Pathology, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK;
| | - Ghulam Nabi
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
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Lopez-Rueda A, Puig J, Thió-Henestrosa S, Moreno-Negrete JL, Zwanzger C, Pujol T, Aldecoa I, Pineda E, Valduvieco I, González JJ, Oleaga L. Texture Analysis of the Apparent Diffusion Coefficient Focused on Contrast-Enhancing Lesions in Predicting Survival for Bevacizumab-Treated Patients with Recurrent Glioblastoma. Cancers (Basel) 2023; 15:cancers15113026. [PMID: 37296988 DOI: 10.3390/cancers15113026] [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: 01/23/2023] [Revised: 04/19/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
PURPOSE Glioblastoma often recurs after treatment. Bevacizumab increases progression-free survival in some patients with recurrent glioblastoma. Identifying pretreatment predictors of survival can help clinical decision making. Magnetic resonance texture analysis (MRTA) quantifies macroscopic tissue heterogeneity indirectly linked to microscopic tissue properties. We investigated the usefulness of MRTA in predicting survival in patients with recurrent glioblastoma treated with bevacizumab. METHODS We evaluated retrospective longitudinal data from 33 patients (20 men; mean age 56 ± 13 years) who received bevacizumab on the first recurrence of glioblastoma. Volumes of contrast-enhancing lesions segmented on postcontrast T1-weighted sequences were co-registered on apparent diffusion coefficient maps to extract 107 radiomic features. To assess the performance of textural parameters in predicting progression-free survival and overall survival, we used receiver operating characteristic curves, univariate and multivariate regression analysis, and Kaplan-Meier plots. RESULTS Longer progression-free survival (>6 months) and overall survival (>1 year) were associated with lower values of major axis length (MAL), a lower maximum 2D diameter row (m2Ddr), and higher skewness values. Longer progression-free survival was also associated with higher kurtosis, and longer overall survival with higher elongation values. The model combining MAL, m2Ddr, and skewness best predicted progression-free survival at 6 months (AUC 0.886, 100% sensitivity, 77.8% specificity, 50% PPV, 100% NPV), and the model combining m2Ddr, elongation, and skewness best predicted overall survival (AUC 0.895, 83.3% sensitivity, 85.2% specificity, 55.6% PPV, 95.8% NPV). CONCLUSIONS Our preliminary analyses suggest that in patients with recurrent glioblastoma pretreatment, MRTA helps to predict survival after bevacizumab treatment.
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Affiliation(s)
- Antonio Lopez-Rueda
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Josep Puig
- Department of Radiology (IDI) and IDIBGI Hospital Universitari de Girona Doctor Josep Trueta, 17190 Girona, Spain
| | - Santiago Thió-Henestrosa
- Department of Computer Science Applied Mathmatics and Statistics, University of Girona, 17003 Girona, Spain
| | | | | | - Teresa Pujol
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Iban Aldecoa
- Department of Anatomical Pathology, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Estela Pineda
- Translational Genomics and Targeted Therapeutics in Solid Tumors Group, Medical Oncology Department, Hospital Clínic de Barcelona, IDIBAPS, University of Barcelona, 08036 Barcelona, Spain
| | - Izaskun Valduvieco
- Radiotherapy Oncology Service, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - José Juan González
- Department of Neurosurgery, Laboratory of Experimental Oncological Neurosurgery, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Laura Oleaga
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
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Wang G, Zhou J. The value of whole-volume apparent diffusion coefficient histogram analysis in preoperatively distinguishing intracranial solitary fibrous tumor and transitional meningioma. Front Oncol 2023; 13:1155162. [PMID: 37260978 PMCID: PMC10228830 DOI: 10.3389/fonc.2023.1155162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/04/2023] [Indexed: 06/02/2023] Open
Abstract
Purpose To investigate the value of whole-volume apparent diffusion coefficient (ADC) histogram analysis in preoperatively distinguishing intracranial solitary fibrous tumors (SFT) from transitional meningiomas (TM), thereby assisting the establishment of the treatment protocol. Methods Preoperative diffusion-weighted imaging datasets of 24 patients with SFT and 28 patients with TM were used to extract whole-volume ADC histogram parameters, including variance, skewness, kurtosis, and mean, as well as 1st (AP1), 10th (AP10), 50th (AP50), 90th (AP90), and 99th (AP99) percentiles of ADC using MaZda software. The independent t-test or Mann-Whitney U test was used to compare the differences between ADC histogram parameters of SFT and TM. Receiver operating characteristic (ROC) curves were generated to evaluate the performance of significant ADC histogram parameters. Spearman's correlation coefficients were calculated to evaluate correlations between these parameters and the Ki-67 expression levels. Results SFT exhibited significantly higher variance, and lower AP1 and AP10 (all P < 0.05) than TM. The best diagnostic performance was obtained by variance, with an area under the ROC curve of 0.848 (0.722-0.933). However, there was no significant difference in skewness, kurtosis, mean, or other percentiles of ADC between the two groups (all P > 0.05). Significant correlations were also observed between the Ki-67 proliferation index and variance (r = 0.519), AP1 (r = -0.425), and AP10 (r = -0.372) (all P < 0.05). Conclusion Whole-volume ADC histogram analysis is a feasible tool for non-invasive preoperative discrimination between intracranial SFT and TM, with variance being the most promising prospective parameter.
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Affiliation(s)
- Gang Wang
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
| | - Junlin Zhou
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
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Tanişman Ö, Kiziltepe FT, Yildirim Ç, Coşar ZS. Prediction of prognostic factors in breast cancer: A noninvasive method utilizing histogram parameters derived from Adc maps. Heliyon 2023; 9:e16282. [PMID: 37251865 PMCID: PMC10208937 DOI: 10.1016/j.heliyon.2023.e16282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 05/31/2023] Open
Abstract
Objective The aim of this study is to investigate the relationship between histogram parameters and prognostic factors of breast cancer and to reveal the diagnostic performance of histogram parameters in predicting prognostic factors status. Materials and methods Ninety-two patients with a confirmed histopathological diagnosis of breast cancer were included in the study. Magnetic resonance imaging (MRI) was performed using a 1.5T scanner and two different b values were used for diffusion-weighted imaging (DWI) (b values: 0 s/mm2, b: 800 s/mm2). For 3D histogram analysis, regions of interest (ROI) were drawn each slice of the lesion on apparent diffusion coefficient (ADC) maps. The following data were derived from the histogram analysis data: percentiles, skewness, kurtosis, and entropy. The relationship between prognostic factors and histogram analysis data was investigated using the Kolmogorov-Smirnov test, Shapiro-Wilk test, skewness-kurtosis test, independent t-test, Mann-Whitney U test, and Kruskal-Wallis test. Receiver operator characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the histogram parameters. Results ADCmax, kurtosis, and entropy parameters were statistically significantly correlated with tumor diameter (p = 0.002, p = 0.008, and p = 0.001, respectively). There was a significant difference in ADC90% and ADCmax values, depending on estrogen receptor (ER) and progesterone receptor (PR) status. These values were lower in ER- and PR-positive than ER- and PR-negative patients (p = 0.02 and p = 0.001 vs. p = 0.018, p = 0.008). All ADC percentage values were lower in patients with a positive Ki-67 proliferation index as compared with those with a negative Ki-67 proliferation index (all p = 0.001). The entropy value was high in high-grade lesions and lesions with axillary involvement (p = 0.039 and p = 0.048, respectively). The highest area under the curve (AUC) for ER and PR status was calculated for the ADC90% value with ROC curve analysis. The highest AUC for Ki-67 proliferation index was found for the ADC50%. Conclusion Histogram analysis parameters derived from of ADC maps of whole lesions can reflect histopathological features of the tumors. Based on our study, it was concluded that histogram analysis parameters were related to the prognostic factors of the tumor.
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Affiliation(s)
- Özge Tanişman
- Deparment of Radiology, Oltu State Hospital, Erzurum, Turkey
| | - Fatma Tuba Kiziltepe
- Deparment of Radiology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Çiğdem Yildirim
- Department of Pathology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Zehra Sumru Coşar
- Deparment of Radiology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
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Qi D, Li J, Quarles CC, Fonkem E, Wu E. Assessment and prediction of glioblastoma therapy response: challenges and opportunities. Brain 2023; 146:1281-1298. [PMID: 36445396 PMCID: PMC10319779 DOI: 10.1093/brain/awac450] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/03/2022] [Accepted: 11/10/2022] [Indexed: 11/30/2022] Open
Abstract
Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.
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Affiliation(s)
- Dan Qi
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
| | - Jing Li
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - C Chad Quarles
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Ekokobe Fonkem
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
| | - Erxi Wu
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
- Department of Pharmaceutical Sciences, Irma Lerma Rangel School of Pharmacy, Texas A&M University, College Station, TX 77843, USA
- Department of Oncology and LIVESTRONG Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
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Can Imaging Using Radiomics and Fat Fraction Analysis Detect Early Tissue Changes on Historical CT Scans in the Regions of the Pancreas Gland That Subsequently Develop Adenocarcinoma? Diagnostics (Basel) 2023; 13:diagnostics13050941. [PMID: 36900085 PMCID: PMC10001321 DOI: 10.3390/diagnostics13050941] [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: 01/31/2023] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
Despite a growing number of effective therapeutic options for patients with pancreatic adenocarcinoma, the prognosis remains dismal mostly due to the late-stage presentation and spread of the cancer to other organs. Because a genomic analysis of pancreas tissue revealed that it may take years, if not decades, for pancreatic cancer to develop, we performed radiomics and fat fraction analysis on contrast-enhanced CT (CECT) scans of patients with historical scans showing no evidence of cancer but who subsequently went on to develop pancreas cancer years later, in an attempt to identify specific imaging features of the normal pancreas that may portend the subsequent development of the cancer. In this IRB-exempt, retrospective, single institution study, CECT chest, abdomen, and pelvis (CAP) scans of 22 patients who had evaluable historical imaging data were analyzed. The images from the "healthy" pancreas were obtained between 3.8 and 13.9 years before the diagnosis of pancreas cancer was established. Afterwards, the images were used to divide and draw seven regions of interest (ROIs) around the pancreas (uncinate, head, neck-genu, body (proximal, middle, and distal) and tail). Radiomic analysis on these pancreatic ROIs consisted of first order quantitative texture analysis features such as kurtosis, skewness, and fat quantification. Of all the variables tested, fat fraction in the pancreas tail (p = 0.029) and asymmetry of the histogram frequency curve (skewness) of pancreas tissue (p = 0.038) were identified as the most important imaging signatures for subsequent cancer development. Changes in the texture of the pancreas as measured on the CECT of patients who developed pancreas cancer years later could be identified, confirming the utility of radiomics-based imaging as a potential predictor of oncologic outcomes. Such findings may be potentially useful in the future to screen patients for pancreatic cancer, thereby helping detect pancreas cancer at an early stage and improving survival.
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Roques M, Catalaa I, Raveneau M, Attal J, Siegfried A, Darcourt J, Cognard C, de Champfleur NM, Bonneville F. Assessment of the hypervascularized fraction of glioblastomas using a volume analysis of dynamic susceptibility contrast-enhanced MRI may help to identify pseudoprogression. PLoS One 2022; 17:e0270216. [PMID: 36227862 PMCID: PMC9560146 DOI: 10.1371/journal.pone.0270216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 06/07/2022] [Indexed: 11/22/2022] Open
Abstract
PURPOSE Although perfusion magnetic resonance imaging (MRI) is widely used to identify pseudoprogression, this advanced technique lacks clinical reliability. Our aim was to develop a parameter assessing the hypervascularized fraction of glioblastomas based on volume analysis of dynamic susceptibility contrast-enhanced MRI and evaluate its performance in the diagnosis of pseudoprogression. METHODS Patients with primary glioblastoma showing lesion progression on the first follow-up MRI after chemoradiotherapy were enrolled retrospectively. On both initial and first follow-up MRIs, the leakage-corrected cerebral blood volume (CBV) maps were post-processed using the conventional hot-spot method and a volume method, after manual segmentation of the contrast-enhanced delineated lesion. The maximum CBV (rCBVmax) was calculated with both methods. Secondly, the threshold of 2 was applied to the CBV values contained in the entire segmented volume, defining our new parameter: %rCBV>2. The probability of pseudoprogression based on rCBVmax and %rCBV>2 was calculated in logistic regression models and diagnostic performance assessed by receiving operator characteristic curves. RESULTS Out of 25 patients, 11 (44%) were classified with pseudoprogression and 14 (56%) with true progression based on the Response Assessement in Neuro-Oncology criteria. rCBVmax was lower for pseudoprogression (3.4 vs. 7.6; p = 0.033) on early follow-up MRI. %rCBV>2, was lower for pseudoprogression on both initial (57.5% vs. 71.3%; p = 0.033) and early follow-up MRIs (22.1% vs. 51.8%; p = 0.0006). On early follow-up MRI, %rCBV>2 had the largest area under the curve for the diagnosis of pseudoprogression: 0.909 [0.725-0.986]. CONCLUSION The fraction of hypervascularization of glioblastomas as assessed by %rCBV>2 was lower in tumours that subsequently developed pseudoprogression both on the initial and early follow-up MRIs. This fractional parameter may help identify pseudoprogression with greater accuracy than rCBVmax.
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Affiliation(s)
- Margaux Roques
- Department of Neuroradiology, Toulouse Hospital, Toulouse, France
- * E-mail:
| | - Isabelle Catalaa
- Department of Neuroradiology, Toulouse Hospital, Toulouse, France
| | - Magali Raveneau
- Department of Neuroradiology, Toulouse Hospital, Toulouse, France
| | - Justine Attal
- Department of Radiotherapy, IUCT Toulouse (Toulouse University Cancer Institute), Toulouse, France
| | | | - Jean Darcourt
- Department of Neuroradiology, Toulouse Hospital, Toulouse, France
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Genomic and Epigenomic Features of Glioblastoma Multiforme and its Biomarkers. JOURNAL OF ONCOLOGY 2022; 2022:4022960. [PMID: 36185622 PMCID: PMC9519330 DOI: 10.1155/2022/4022960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/14/2022] [Accepted: 09/10/2022] [Indexed: 11/22/2022]
Abstract
Glioblastoma multiforme is a serious and life-threatening tumor of central nervous system, characterized by aggressive behavior, poor prognosis, and low survival rate. Despite of the availability of aggressive antitumor therapeutic regimen for glioblastoma (radiotherapy followed by chemotherapeutic dose), recovery rate, and patients' survival ratio is attributed to the lack of selectivity of therapeutic drugs and less advancement in cancer therapeutics over last decade. Moreover, tools employed in conventional diagnosis of glioblastoma are more invasive and painful, making the process excruciating for the patients. These challenges urge for the need of novel biomarkers for diagnosis, prognosis, and prediction purpose with less invasiveness and more patient compliance. This article will explore the genetic biomarkers isocitrate dehydrogenase mutation, MGMT mutations, and EGFR that can be deployed as an analytical tool in diagnosis of disease and prognosis of a therapeutic course. The review also highlights the importance of employing novel microRNAs as prognostic biomarkers. Recent clinical advancements to treat GBM and to prevent relapse of the disease are also discussed in this article in the hope of finding a robust and effective method to treat GBM.
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Qin D, Yang G, Jing H, Tan Y, Zhao B, Zhang H. Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma. Cancers (Basel) 2022; 14:cancers14153771. [PMID: 35954435 PMCID: PMC9367286 DOI: 10.3390/cancers14153771] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Glioma is the most common primary malignant tumor of the adult central nervous system. Despite aggressive multimodal treatment, its prognosis remains poor. During follow-up, it remains challenging to distinguish treatment-related changes from tumor progression in treated patients with gliomas due to both share clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions). The early effective identification of tumor progression and treatment-related changes is of great significance for the prognosis and treatment of gliomas. We believe that advanced neuroimaging techniques can provide additional information for distinguishing both at an early stage. In this article, we focus on the research of magnetic resonance imaging technology and artificial intelligence in tumor progression and treatment-related changes. Finally, it provides new ideas and insights for clinical diagnosis. Abstract As the most common neuro-epithelial tumors of the central nervous system in adults, gliomas are highly malignant and easy to recurrence, with a dismal prognosis. Imaging studies are indispensable for tracking tumor progression (TP) or treatment-related changes (TRCs). During follow-up, distinguishing TRCs from TP in treated patients with gliomas remains challenging as both share similar clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions) and fulfill criteria for progression. Thus, the early identification of TP and TRCs is of great significance for determining the prognosis and treatment. Histopathological biopsy is currently the gold standard for TP and TRC diagnosis. However, the invasive nature of this technique limits its clinical application. Advanced imaging methods (e.g., diffusion magnetic resonance imaging (MRI), perfusion MRI, magnetic resonance spectroscopy (MRS), positron emission tomography (PET), amide proton transfer (APT) and artificial intelligence (AI)) provide a non-invasive and feasible technical means for identifying of TP and TRCs at an early stage, which have recently become research hotspots. This paper reviews the current research on using the abovementioned advanced imaging methods to identify TP and TRCs of gliomas. First, the review focuses on the pathological changes of the two entities to establish a theoretical basis for imaging identification. Then, it elaborates on the application of different imaging techniques and AI in identifying the two entities. Finally, the current challenges and future prospects of these techniques and methods are discussed.
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Affiliation(s)
- Danlei Qin
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School, Hospital of Stomatology, Taiyuan 030001, China
| | - Guoqiang Yang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
| | - Hui Jing
- Department of MRI, The Six Hospital, Shanxi Medical University, Taiyuan 030008, China;
| | - Yan Tan
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
| | - Bin Zhao
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School, Hospital of Stomatology, Taiyuan 030001, China
- Correspondence: (B.Z.); (H.Z.)
| | - Hui Zhang
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
- Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province, Taiyuan 030001, China
- Correspondence: (B.Z.); (H.Z.)
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12
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Taylor C, Ekert JO, Sefcikova V, Fersht N, Samandouras G. Discriminators of pseudoprogression and true progression in high-grade gliomas: A systematic review and meta-analysis. Sci Rep 2022; 12:13258. [PMID: 35918373 PMCID: PMC9345984 DOI: 10.1038/s41598-022-16726-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 07/14/2022] [Indexed: 11/09/2022] Open
Abstract
High-grade gliomas remain the most common primary brain tumour with limited treatments options and early recurrence rates following adjuvant treatments. However, differentiating true tumour progression (TTP) from treatment-related effects or pseudoprogression (PsP), may critically influence subsequent management options. Structural MRI is routinely employed to evaluate treatment responses, but misdiagnosis of TTP or PsP may lead to continuation of ineffective or premature cessation of effective treatments, respectively. A systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses method. Embase, MEDLINE, Web of Science and Google Scholar were searched for methods applied to differentiate PsP and TTP, and studies were selected using pre-specified eligibility criteria. The sensitivity and specificity of included studies were summarised. Three of the identified methods were compared in a separate subgroup meta-analysis. Thirty studies assessing seven distinct neuroimaging methods in 1372 patients were included in the systematic review. The highest performing methods in the subgroup analysis were DWI (AUC = 0.93 [0.91-0.95]) and DSC-MRI (AUC = 0.93 [0.90-0.95]), compared to DCE-MRI (AUC = 0.90 [0.87-0.93]). 18F-fluoroethyltyrosine PET (18F-FET PET) and amide proton transfer-weighted MRI (APTw-MRI) also showed high diagnostic accuracy, but results were based on few low-powered studies. Both DWI and DSC-MRI performed with high sensitivity and specificity for differentiating PsP from TTP. Considering the technical parameters and feasibility of each identified method, the authors suggested that, at present, DSC-MRI technique holds the most clinical potential.
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Affiliation(s)
- Chris Taylor
- UCL Queen Square Institute of Neurology, University College London, Gower St., Bloomsbury, Queen Square, London, WC1E 6BT, UK.
| | - Justyna O Ekert
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, UK
| | - Viktoria Sefcikova
- UCL Queen Square Institute of Neurology, University College London, Gower St., Bloomsbury, Queen Square, London, WC1E 6BT, UK
| | - Naomi Fersht
- Department of Oncology, University College London Hospitals NHS Foundation Trust, London, UK
| | - George Samandouras
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, UK
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
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13
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COŞKUN N, YÜKSEL AÖ, CANYİĞİT M, ÖZDEMİR E. Radiomics analysis of pre-treatment F-18 FDG PET/CT for predicting response to transarterial radioembolization in liver tumors. JOURNAL OF HEALTH SCIENCES AND MEDICINE 2022. [DOI: 10.32322/jhsm.1118649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Aim: To investigate the relationship between the textural features extracted from pre-treatment fluorine-18 fluorodeoxyglucose positron emission with computed tomography (F-18 FDG PET/CT) and the response to treatment in patients undergoing transarterial radioembolization (TARE) due to primary or metastatic liver tumors.
Material and Method: A total of 25 liver lesions from the pre-treatment F-18 PET/CT images of 14 patients were segmented manually. Standard uptake value (SUV) metrics and radiomics features were extracted for each lesion. Metabolic treatment response was determined according to PERCIST criteria in 18F-FDG PET/CT imaging performed 2 months after the treatment. Feature selection was done with recursive feature elimination (RFE). The association between selected features and treatment response was evaluated with logistic regression analysis.
Results: Eventually, 13 lesions responded to TARE, while 12 lesions remain stable or progressed. All standard uptake values and 27 out of 30 textural heterogeneity indicators were significantly higher in lesions that responded to treatment. SUVmax, kurtosis and dissimilarity features were selected by the RFE algorithm for the prediction of response to TARE. Logistic regression analysis revealed that all three parameters were significantly associated with treatment outcome.
Conclusion: Textural features extracted from pre-treatment F-18 FDG PET/CT in patients undergoing TARE due to liver tumors are promising biomarkers that can be potentially used to predict metabolic treatment response.
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Affiliation(s)
- Nazım COŞKUN
- SAĞLIK BİLİMLERİ ÜNİVERSİTESİ, ANKARA ŞEHİR SAĞLIK UYGULAMA VE ARAŞTIRMA MERKEZİ, DAHİLİ TIP BİLİMLERİ BÖLÜMÜ
| | - Alptuğ Özer YÜKSEL
- SAĞLIK BİLİMLERİ ÜNİVERSİTESİ, ANKARA ŞEHİR SAĞLIK UYGULAMA VE ARAŞTIRMA MERKEZİ, DAHİLİ TIP BİLİMLERİ BÖLÜMÜ, NÜKLEER TIP ANABİLİM DALI
| | - Murat CANYİĞİT
- YILDIRIM BEYAZIT ÜNİVERSİTESİ, TIP FAKÜLTESİ, DAHİLİ TIP BİLİMLERİ BÖLÜMÜ, RADYOLOJİ ANABİLİM DALI
| | - Elif ÖZDEMİR
- YILDIRIM BEYAZIT ÜNİVERSİTESİ, TIP FAKÜLTESİ, DAHİLİ TIP BİLİMLERİ BÖLÜMÜ, NÜKLEER TIP ANABİLİM DALI
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14
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Yang H, Liu X, Jiang J, Zhou J. Apparent diffusion coefficient histogram analysis to preoperative evaluate intracranial solitary fibrous tumor: Relationship to Ki-67 proliferation index. Clin Neurol Neurosurg 2022; 220:107364. [PMID: 35872434 DOI: 10.1016/j.clineuro.2022.107364] [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: 05/17/2022] [Revised: 06/22/2022] [Accepted: 07/11/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE To explore the value of apparent diffusion coefficient (ADC) histogram analysis in preoperative evaluating intracranial solitary fibrous tumor (SFT) and further investigate the relationship between ADC histogram parameters and the Ki-67 proliferation index. METHODS From January 2014 to March 2022, 37 patients with intracranial SFT (grade 2, n = 20; grade 3, n = 17) who underwent preoperative diffusion-weighted imaging were enrolled in this study. For each tumor, nine histogram parameters were automatically extracted and selected using MaZda software based on the axial ADC maps of the whole tumor, including mean, variance, skewness, kurtosis, as well as the 1st, 10th, 50th, 90th, and 99th percentile ADC (Perc.01, Perc.10, Perc.50, Perc.90, Perc.99). Differences in ADC histogram parameters between grade 2 and 3 intracranial SFT were compared. Receiver operating characteristic (ROC) curves were drawn to determine the diagnostic performance, and Pearson's correlation coefficient was used to investigate the relationship between these parameters and the Ki-67 proliferation index. RESULTS The mean, Perc.01, Perc.10, Perc.50, Perc.90, and Perc.99 were significantly lower in grade 3 than in grade 2 intracranial SFT (all P < 0.05). ROC analysis showed that these parameters can effectively distinguish between the two groups, with Perc.01 generating the best differentiation performance. Significant negative correlations were also observed between these parameters and the Ki-67 proliferation index (r = -0.436 ~ -0.522, all P < 0.05). However, there was no significant difference in variance, skewness, or kurtosis between the two groups (all P > 0.05). CONCLUSIONS ADC histogram analysis enables effective preoperative distinction of grade 2 and grade 3 intracranial SFT.
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Affiliation(s)
- Haiting Yang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Jian Jiang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China.
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15
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Chawla S, Bukhari S, Afridi OM, Wang S, Yadav SK, Akbari H, Verma G, Nath K, Haris M, Bagley S, Davatzikos C, Loevner LA, Mohan S. Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma. NMR IN BIOMEDICINE 2022; 35:e4719. [PMID: 35233862 PMCID: PMC9203929 DOI: 10.1002/nbm.4719] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 02/22/2022] [Accepted: 02/25/2022] [Indexed: 05/15/2023]
Abstract
Pseudoprogression (PsP) refers to treatment-related clinico-radiologic changes mimicking true progression (TP) that occurs in patients with glioblastoma (GBM), predominantly within the first 6 months after the completion of surgery and concurrent chemoradiation therapy (CCRT) with temozolomide. Accurate differentiation of TP from PsP is essential for making informed decisions on appropriate therapeutic intervention as well as for prognostication of these patients. Conventional neuroimaging findings are often equivocal in distinguishing between TP and PsP and present a considerable diagnostic dilemma to oncologists and radiologists. These challenges have emphasized the need for developing alternative imaging techniques that may aid in the accurate diagnosis of TP and PsP. In this review, we encapsulate the current state of knowledge in the clinical applications of commonly used metabolic and physiologic magnetic resonance (MR) imaging techniques such as diffusion and perfusion imaging and proton spectroscopy in distinguishing TP from PsP. We also showcase the potential of promising imaging techniques, such as amide proton transfer and amino acid-based positron emission tomography, in providing useful information about the treatment response. Additionally, we highlight the role of "radiomics", which is an emerging field of radiology that has the potential to change the way in which advanced MR techniques are utilized in assessing treatment response in GBM patients. Finally, we present our institutional experiences and discuss future perspectives on the role of multiparametric MR imaging in identifying PsP in GBM patients treated with "standard-of-care" CCRT as well as novel/targeted therapies.
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Affiliation(s)
- Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sultan Bukhari
- Rowan School of Osteopathic Medicine at Rowan University, Voorhees, New Jersey, USA
| | - Omar M. Afridi
- Rowan School of Osteopathic Medicine at Rowan University, Voorhees, New Jersey, USA
| | - Sumei Wang
- Department of Cardiology, Lenox Hill Hospital, Northwell Health, New York, New York, USA
| | - Santosh K. Yadav
- Laboratory of Functional and Molecular Imaging, Sidra Medicine, Doha, Qatar
| | - Hamed Akbari
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Gaurav Verma
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Kavindra Nath
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mohammad Haris
- Laboratory of Functional and Molecular Imaging, Sidra Medicine, Doha, Qatar
| | - Stephen Bagley
- Department of Hematology-Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laurie A. Loevner
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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16
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Lo CM, Hung PH. Computer-aided diagnosis of ischemic stroke using multi-dimensional image features in carotid color Doppler. Comput Biol Med 2022; 147:105779. [DOI: 10.1016/j.compbiomed.2022.105779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 05/24/2022] [Accepted: 06/19/2022] [Indexed: 11/17/2022]
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17
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Yildirim M, Baykara M. Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma. Acta Neurol Belg 2022; 122:363-368. [PMID: 33555560 DOI: 10.1007/s13760-021-01607-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 01/18/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE Conventional magnetic resonance imaging (MRI) technics are insufficient in the differentiation of tumor progression from post-treatment changes in patients with treated glioblastoma. Previous studies have suggested that histogram analysis is a useful tool in the assessment of treatment response in different cancer types. The aim of the study was to to evaluate the effectiveness of MRI histogram analysis in the differentiation of tumor progression from pseudoprogression in patients with treated glioblastoma. METHODS Forty-six patients with glioblastoma who newly developed enhancing lesions following chemoradiation treatment were included in this retrospective study. Histogram analysis was performed from new enhancing lesions on T1-weighted contrast-enhanced MRI. Histogram analysis findings of patients with progression (23) and pseudoprogression (23) were compared. RESULTS Mean, minimum, median, maximum, standard deviation, variance, entropy, skewness, uniformity values were found to be significantly higher in progressive disease (p < 0.05). A receiver-operating characteristic (ROC) curve analysis was performed for mean value, and area under the curve (AUC) was found as 0.975. When the threshold value was selected as 528.86, two groups could be differentiated with 95.7% sensitivity and 87.0% specificity. CONCLUSION MRI histogram analysis can be used for the differentiation of progressive disease from pseudoprogression.
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18
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Stumpo V, Guida L, Bellomo J, Van Niftrik CHB, Sebök M, Berhouma M, Bink A, Weller M, Kulcsar Z, Regli L, Fierstra J. Hemodynamic Imaging in Cerebral Diffuse Glioma-Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions. Cancers (Basel) 2022; 14:1342. [PMID: 35267650 PMCID: PMC8909110 DOI: 10.3390/cancers14051342] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 02/05/2023] Open
Abstract
Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.
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Affiliation(s)
- Vittorio Stumpo
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Lelio Guida
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Jacopo Bellomo
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Christiaan Hendrik Bas Van Niftrik
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Martina Sebök
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Moncef Berhouma
- Department of Neurosurgical Oncology and Vascular Neurosurgery, Pierre Wertheimer Neurological and Neurosurgical Hospital, Hospices Civils de Lyon, 69500 Lyon, France;
| | - Andrea Bink
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
- Department of Neuroradiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Michael Weller
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
- Department of Neurology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Zsolt Kulcsar
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
- Department of Neuroradiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Luca Regli
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Jorn Fierstra
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
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19
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Henriksen OM, del Mar Álvarez-Torres M, Figueiredo P, Hangel G, Keil VC, Nechifor RE, Riemer F, Schmainda KM, Warnert EAH, Wiegers EC, Booth TC. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques. Front Oncol 2022; 12:810263. [PMID: 35359414 PMCID: PMC8961422 DOI: 10.3389/fonc.2022.810263] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/05/2022] [Indexed: 01/16/2023] Open
Abstract
Objective Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments. Methods Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics). Results High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies. Conclusion Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.
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Affiliation(s)
- Otto M. Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | | | - Patricia Figueiredo
- Department of Bioengineering and Institute for Systems and Robotics-Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Gilbert Hangel
- Department of Neurosurgery, Medical University, Vienna, Austria
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University, Vienna, Austria
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Ruben E. Nechifor
- International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Department of Clinical Psychology and Psychotherapy, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | | | - Evita C. Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Thomas C. Booth
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School of Biomedical Engineering and Imaging Sciences, St. Thomas’ Hospital, King’s College London, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
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20
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Fu R, Szidonya L, Barajas RF, Ambady P, Varallyay C, Neuwelt EA. Diagnostic performance of DSC perfusion MRI to distinguish tumor progression and treatment-related changes: a systematic review and meta-analysis. Neurooncol Adv 2022; 4:vdac027. [PMID: 35386567 PMCID: PMC8982196 DOI: 10.1093/noajnl/vdac027] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background In patients with high-grade glioma (HGG), true disease progression and treatment-related changes often appear similar on magnetic resonance imaging (MRI), making it challenging to evaluate therapeutic response. Dynamic susceptibility contrast (DSC) MRI has been extensively studied to differentiate between disease progression and treatment-related changes. This systematic review evaluated and synthesized the evidence for using DSC MRI to distinguish true progression from treatment-related changes. Methods We searched Ovid MEDLINE and the Ovid MEDLINE in-process file (January 2005-October 2019) and the reference lists. Studies on test performance of DSC MRI using relative cerebral blood volume in HGG patients were included. One investigator abstracted data, and a second investigator confirmed them; two investigators independently assessed study quality. Meta-analyses were conducted to quantitatively synthesize area under the receiver operating curve (AUROC), sensitivity, and specificity. Results We screened 1177 citations and included 28 studies with 638 patients with true tumor progression, and 430 patients with treatment-related changes. Nineteen studies reported AUROC and the combined AUROC is 0.85 (95% CI, 0.81-0.90). All studies contributed data for sensitivity and specificity, and the pooled sensitivity and specificity are 0.84 (95% CI, 0.80-0.88), and 0.78 (95% CI, 0.72-0.83). Extensive subgroup analyses based on study, treatment, and imaging characteristics generally showed similar results. Conclusions There is moderate strength of evidence that relative cerebral blood volume obtained from DSC imaging demonstrated "excellent" ability to discriminate true tumor progression from treatment-related changes, with robust sensitivity and specificity.
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Affiliation(s)
- Rongwei Fu
- Oregon Health & Science University-Portland State University, School of Public Health, Portland, Oregon, USA.,Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Laszlo Szidonya
- Department of Radiology, Oregon Health & Science University, Portland, Oregon, USA.,Neuro-Oncology Program, Oregon Health & Science University, Portland, Oregon, USA.,Heart and Vascular Center, Diagnostic Radiology, Semmelweis University, Budapest, Hungary
| | - Ramon F Barajas
- Department of Radiology, Oregon Health & Science University, Portland, Oregon, USA.,Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Knight Cancer Institute Translational Oncology Program, Oregon Health & Science University, Portland, Oregon, USA
| | - Prakash Ambady
- Neuro-Oncology Program, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Edward A Neuwelt
- Neuro-Oncology Program, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurosurgery, Oregon Health and Sciences University, Portland, Oregon, USA.,Office of Research and Development, Department of Veterans Affairs Medical Center, Portland, Oregon, USA
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21
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Singh M, Jindal D, Agarwal V, Pathak D, Sharma M, Pancham P, Mani S, Rachana. New phase therapeutic pursuits for targeted drug delivery in glioblastoma multiforme. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2022; 3:866-888. [PMID: 36654821 PMCID: PMC9834280 DOI: 10.37349/etat.2022.00118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 08/19/2022] [Indexed: 12/31/2022] Open
Abstract
Glioblastoma multiforme (GBM) is known as the most aggressive and prevalent brain tumor with a high mortality rate. It is reported in people who are as young as 10 years old to as old as over 70 years old, exhibiting inter and intra tumor heterogeneity. There are several genomic and proteomic investigations that have been performed to find the unexplored potential targets of the drug against GBM. Therefore, certain effective targets have been taken to further validate the studies embarking on the robustness in the field of medicinal chemistry followed by testing in clinical trials. Also, The Cancer Genome Atlas (TCGA) project has identified certain overexpressed targets involved in the pathogenesis of GBM in three major pathways, i.e., tumor protein 53 (p53), retinoblastoma (RB), and receptor tyrosine kinase (RTK)/rat sarcoma virus (Ras)/phosphoinositide 3-kinase (PI3K) pathways. This review focuses on the compilation of recent developments in the fight against GBM thus, directing future research into the elucidation of pathogenesis and potential cure for GBM. Also, it highlights the potential biomarkers that have undergone extensive research and have promising prognostic and predictive values. Additionally, this manuscript analyses the advent of gene therapy and immunotherapy, unlocking the way to consider treatment approaches other than, or in addition to, conventional chemo-radiation therapies. This review study encompasses all the relevant research studies associated with the pathophysiology, occurrence, diagnostic tools, and therapeutic intervention for GBM. It highlights the evolution of various therapeutic perspectives against GBM from the most conventional form of radiotherapy to the recent advancement of gene/cell/immune therapy. Further, the review focuses on various targeted therapies for GBM including chemotherapy sensitization, radiotherapy, nanoparticles based, immunotherapy, cell therapy, and gene therapy which would offer a comprehensive account for exploring several facets related to GBM prognostics.
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Affiliation(s)
- Manisha Singh
- Department of Biotechnology, Jaypee Institute of Information Technology (JIIT), Noida 201301, India,Correspondence: Manisha Singh, Department of Biotechnology, Jaypee Institute of Information Technology (JIIT), Noida 201301, India.
| | - Divya Jindal
- Department of Biotechnology, Jaypee Institute of Information Technology (JIIT), Noida 201301, India
| | - Vinayak Agarwal
- Department of Biotechnology, Jaypee Institute of Information Technology (JIIT), Noida 201301, India
| | - Deepanshi Pathak
- Department of Biotechnology, Jaypee Institute of Information Technology (JIIT), Noida 201301, India
| | - Mansi Sharma
- Department of Biotechnology, Jaypee Institute of Information Technology (JIIT), Noida 201301, India
| | - Pranav Pancham
- Department of Biotechnology, Jaypee Institute of Information Technology (JIIT), Noida 201301, India
| | - Shalini Mani
- Department of Biotechnology, Jaypee Institute of Information Technology (JIIT), Noida 201301, India
| | - Rachana
- Department of Biotechnology, Jaypee Institute of Information Technology (JIIT), Noida 201301, India
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22
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Tomaszewski MR, Latifi K, Boyer E, Palm RF, El Naqa I, Moros EG, Hoffe SE, Rosenberg SA, Frakes JM, Gillies RJ. Delta radiomics analysis of Magnetic Resonance guided radiotherapy imaging data can enable treatment response prediction in pancreatic cancer. Radiat Oncol 2021; 16:237. [PMID: 34911546 PMCID: PMC8672552 DOI: 10.1186/s13014-021-01957-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 11/22/2021] [Indexed: 12/22/2022] Open
Abstract
Background Magnetic Resonance Image guided Stereotactic body radiotherapy (MRgRT) is an emerging technology that is increasingly used in treatment of visceral cancers, such as pancreatic adenocarcinoma (PDAC). Given the variable response rates and short progression times of PDAC, there is an unmet clinical need for a method to assess early RT response that may allow better prescription personalization. We hypothesize that quantitative image feature analysis (radiomics) of the longitudinal MR scans acquired before and during MRgRT may be used to extract information related to early treatment response. Methods Histogram and texture radiomic features (n = 73) were extracted from the Gross Tumor Volume (GTV) in 0.35T MRgRT scans of 26 locally advanced and borderline resectable PDAC patients treated with 50 Gy RT in 5 fractions. Feature ratios between first (F1) and last (F5) fraction scan were correlated with progression free survival (PFS). Feature stability was assessed through region of interest (ROI) perturbation. Results Linear normalization of image intensity to median kidney value showed improved reproducibility of feature quantification. Histogram skewness change during treatment showed significant association with PFS (p = 0.005, HR = 2.75), offering a potential predictive biomarker of RT response. Stability analyses revealed a wide distribution of feature sensitivities to ROI delineation and was able to identify features that were robust to variability in contouring. Conclusions This study presents a proof-of-concept for the use of quantitative image analysis in MRgRT for treatment response prediction and providing an analysis pipeline that can be utilized in future MRgRT radiomic studies. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-021-01957-5.
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Affiliation(s)
- M R Tomaszewski
- Cancer Physiology Department, H. Lee Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Dr, Tampa, FL, 33612, USA.,Translation Imaging Department, Merck & Co, West Point, PA, USA
| | - K Latifi
- Medical Physics Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - E Boyer
- Radiation Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - R F Palm
- Radiation Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - I El Naqa
- Machine Learning Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - E G Moros
- Medical Physics Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - S E Hoffe
- Radiation Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - S A Rosenberg
- Radiation Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J M Frakes
- Radiation Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - R J Gillies
- Cancer Physiology Department, H. Lee Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Dr, Tampa, FL, 33612, USA.
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23
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Mohammed S, Bharath K, Kurtek S, Rao A, Baladandayuthapani V. RADIOHEAD: Radiogenomic analysis incorporating tumor heterogeneity in imaging through densities. Ann Appl Stat 2021. [DOI: 10.1214/21-aoas1458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Shariq Mohammed
- Department of Biostatistics, Department of Computational Medicine and Bioinformatics, University of Michigan
| | | | | | - Arvind Rao
- Department of Biostatistics, Department of Computational Medicine and Bioinformatics, University of Michigan
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24
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Haghighi Borujeini M, Farsizaban M, Yazdi SR, Tolulope Agbele A, Ataei G, Saber K, Hosseini SM, Abedi-Firouzjah R. Grading of meningioma tumors based on analyzing tumor volumetric histograms obtained from conventional MRI and apparent diffusion coefficient images. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [DOI: 10.1186/s43055-021-00545-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Abstract
Background
Our purpose was to evaluate the application of volumetric histogram parameters obtained from conventional MRI and apparent diffusion coefficient (ADC) images for grading the meningioma tumors.
Results
Tumor volumetric histograms of preoperative MRI images from 45 patients with the diagnosis of meningioma at different grades were analyzed to find the histogram parameters. Kruskal-Wallis statistical test was used for comparison between the parameters obtained from different grades. Multi-parametric regression analysis was used to find the model and parameters with high predictive value for the classification of meningioma. Mode; standard deviation on post-contrast T1WI, T2-FLAIR, and ADC images; kurtosis on post-contrast T1WI and T2-FLAIR images; mean and several percentile values on ADC; and post-contrast T1WI images showed significant differences among different tumor grades (P < 0.05). The multi-parametric linear regression showed that the ADC histogram parameters model had a higher predictive value, with cutoff values of 0.212 (sensitivity = 79.6%, specificity = 84.3%) and 0.180 (sensitivity = 70.9%, specificity = 80.8%) for differentiating the grade I from II, and grade II from III, respectively.
Conclusions
The multi-parametric model of volumetric histogram parameters in some of the conventional MRI series (i.e., post-contrast T1WI and T2-FLAIR images) along with the ADC images are appropriate for predicting the meningioma tumors’ grade.
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25
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Li S, Liang P, Wang Y, Feng C, Shen Y, Hu X, Hu D, Meng X, Li Z. Combining volumetric apparent diffusion coefficient histogram analysis with vesical imaging reporting and data system to predict the muscle invasion of bladder cancer. Abdom Radiol (NY) 2021; 46:4301-4310. [PMID: 33909091 DOI: 10.1007/s00261-021-03091-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/06/2021] [Accepted: 04/10/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVE The objective of this study was to explore whether volumetric apparent diffusion coefficient (ADC) histogram analysis can provide additional value to Vesical Imaging Reporting and Data System (VI-RADS) in differentiating muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC). MATERIALS AND METHODS 80 patients were retrospectively reviewed with pathologically proven NMIBC (n = 53) or MIBC (n = 27). All patients underwent MRI including diffusion-weighted imaging (DWI) (b = 0, 800 s/mm2), and the VI-RADS score was evaluated based on DWI. Volumetric ADC histogram parameters were calculated from the volumetric of interest (VOI) on DWI, including the min ADC, mean ADC, median ADC, max ADC, 10th, 25th, 75th, 90th percentiles ADC, skewness, kurtosis, and entropy. The Mann-Whitney U-test was used to compare histogram parameters between NMIBC and MIBC. Receiver operating characteristic analysis was used to evaluate the diagnostic value of each significant parameter. RESULTS Among all parameters, the VI-RADS yield the highest Area Under the Curve (AUC, 0.88; sensitivity, 88.89%; specificity, 83.61%). MIBC had significantly lower min ADC, mean ADC, median ADC, 10th, 25th, 75th, and 90th percentiles ADC than NMIBC (p = 0.002, p < 0.001, p < 0.001, p = 0.003, p = 0.004, p < 0.001, p < 0.001). Skewness and kurtosis of MIBC were significantly higher than those of NMIBC (p < 0.001, p < 0.001). The combination of VI-RADS and skewness showed significantly higher AUC (AUC 0.923; 95% CI 0.847-0.969) than only with VI-RADS (AUC 0.880; 95% CI 0.793-0.940). CONCLUSION Volumetric ADC histogram analysis and VI-RADS are both useful methods in differentiating MIBC from NMIBC, and the volumetric ADC histogram analysis can provide additional value to VI-RADS.
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Affiliation(s)
- Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Yanchun Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Cui Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Yaqi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Xuemei Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China.
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
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26
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Clancy K, Dadashzadeh ER, Handzel R, Rieser C, Moses JB, Rosenblum L, Wu S. Machine learning for the prediction of pathologic pneumatosis intestinalis. Surgery 2021; 170:797-805. [PMID: 33926706 PMCID: PMC8405549 DOI: 10.1016/j.surg.2021.03.049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/19/2021] [Accepted: 03/22/2021] [Indexed: 11/18/2022]
Abstract
BACKGROUND The radiographic finding of pneumatosis intestinalis can indicate a spectrum of underlying processes ranging from a benign finding to a life-threatening condition. Although radiographic pneumatosis intestinalis is relatively common, there is no validated clinical tool to guide surgical management. METHODS Using a retrospective cohort of 300 pneumatosis intestinalis cases from a single institution, we developed 3 machine learning models for 2 clinical tasks: (1) the distinction of benign from pathologic pneumatosis intestinalis cases and (2) the determination of patients who would benefit from an operation. The 3 models are (1) an imaging model based on radiomic features extracted from computed tomography scans, (2) a clinical model based on clinical variables, and (3) a combination model using both the imaging and clinical variables. RESULTS The combination model achieves an area under the curve of 0.91 (confidence interval: 0.87-0.94) for task I and an area under the curve of 0.84 (confidence interval: 0.79-0.88) for task II. The combination model significantly (P < .05) outperforms the imaging model and the clinical model for both tasks. The imaging model achieves an area under the curve of 0.72 (confidence interval: 0.57-0.87) for task I and 0.68 (confidence interval: 0.61-0.74) for task II. The clinical model achieves an area under the curve of 0.87 (confidence interval: 0.83-0.91) for task I and 0.76 (confidence interval: 0.70-0.81) for task II. CONCLUSION This study suggests that combined radiographic and clinical features can identify pathologic pneumatosis intestinalis and aid in patient selection for surgery. This tool may better inform the surgical decision-making process for patients with pneumatosis intestinalis.
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Affiliation(s)
- Kadie Clancy
- Department of Computer Science, University of Pittsburgh, PA
| | | | - Robert Handzel
- Department of Surgery, University of Pittsburgh Medical Center, PA
| | - Caroline Rieser
- Department of Surgery, University of Pittsburgh Medical Center, PA
| | - J B Moses
- Department of Surgery, University of Pittsburgh Medical Center, PA
| | - Lauren Rosenblum
- Department of Surgery, University of Pittsburgh Medical Center, PA
| | - Shandong Wu
- Departments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, PA.
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27
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Wei RL, Wei XT. Advanced Diagnosis of Glioma by Using Emerging Magnetic Resonance Sequences. Front Oncol 2021; 11:694498. [PMID: 34422648 PMCID: PMC8374052 DOI: 10.3389/fonc.2021.694498] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 07/19/2021] [Indexed: 12/15/2022] Open
Abstract
Glioma, the most common primary brain tumor in adults, can be difficult to discern radiologically from other brain lesions, which affects surgical planning and follow-up treatment. Recent advances in MRI demonstrate that preoperative diagnosis of glioma has stepped into molecular and algorithm-assisted levels. Specifically, the histology-based glioma classification is composed of multiple different molecular subtypes with distinct behavior, prognosis, and response to therapy, and now each aspect can be assessed by corresponding emerging MR sequences like amide proton transfer-weighted MRI, inflow-based vascular-space-occupancy MRI, and radiomics algorithm. As a result of this novel progress, the clinical practice of glioma has been updated. Accurate diagnosis of glioma at the molecular level can be achieved ahead of the operation to formulate a thorough plan including surgery radical level, shortened length of stay, flexible follow-up plan, timely therapy response feedback, and eventually benefit patients individually.
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Affiliation(s)
- Ruo-Lun Wei
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin-Ting Wei
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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28
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Xiao M, Ma X, Ma F, Li Y, Zhang G, Qiang J. Whole-tumor histogram analysis of apparent diffusion coefficient for differentiating adenosquamous carcinoma and adenocarcinoma from squamous cell carcinoma in patients with cervical cancer. Acta Radiol 2021; 63:1415-1424. [PMID: 34382429 DOI: 10.1177/02841851211035915] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Differentiating adenosquamous carcinoma (ASC) and adenocarcinoma (AC) from squamous cell carcinoma (SCC) precisely is crucial for treatment strategy and prognosis prediction in patients with cervical cancer (CC). PURPOSE To differentiate ASC and AC from SCC in patients with CC using the apparent diffusion coefficient (ADC) histogram analysis. MATERIAL AND METHODS A total of 118 patients with histologically diagnosed ASC, AC, and SCC were included. The ADC histogram parameters were extracted from ADC maps. Receiver operating characteristic analysis was performed to evaluate the diagnostic performance of each ADC histogram parameter in differentiating the subtypes of CC. The predictors for histologic subtypes were further selected using univariate and multivariate logistic regression analyses. RESULTS The ADCmean, ADCmax, ADCP10, ADCP25, ADCP75, ADCP90, ADCmedian, and ADCmode of the ASC were significantly lower than those of the AC; and ADCkurtosis and ADCskewness of the ASC were lower than those of the SCC. The ADCmean, ADCmax, ADCP10, ADCP25, ADCP75, ADCP90, ADCmedian, and ADCmode of AC were significantly higher than those of the SCC. The ADCP10 and ADCP10 + diameter yielded the AUCs of 0.753 and 0.778 in differentiating ASC from AC. The ADCmedian and ADCmedian + diameter yielded the AUCs of 0.807 and 0.838 in differentiating AC from SCC. The ADCskewness yielded the AUC of 0.713 in differentiating ASC from SCC. CONCLUSION The ADCP10 and ADCP10 + diameter, ADCmedian, and ADCmedian + diameter performed well in differentiating ASC from AC and AC from SCC, respectively. However, ADCskewness exhibited a limited ability in differentiating ASC from SCC.
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Affiliation(s)
- Meiling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, PR China
| | - Xiaoliang Ma
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, PR China
| | - Fenghua Ma
- Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, PR China
| | - Yongai Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, PR China
| | - Guofu Zhang
- Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, PR China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, PR China
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29
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Fung DLX, Liu Q, Zammit J, Leung CKS, Hu P. Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19. J Transl Med 2021; 19:318. [PMID: 34311742 PMCID: PMC8312213 DOI: 10.1186/s12967-021-02992-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 07/17/2021] [Indexed: 12/28/2022] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testing approach. Automatic analysis of the lung CT images is needed to increase the diagnostic efficiency and release the human participant. Deep learning is successful in automatically solving computer vision problems. Thus, it can be introduced to the automatic and rapid COVID-19 CT diagnosis. Many advanced deep learning-based computer vison techniques were developed to increase the model performance but have not been introduced to medical image analysis. Methods In this study, we propose a self-supervised two-stage deep learning model to segment COVID-19 lesions (ground-glass opacity and consolidation) from chest CT images to support rapid COVID-19 diagnosis. The proposed deep learning model integrates several advanced computer vision techniques such as generative adversarial image inpainting, focal loss, and lookahead optimizer. Two real-life datasets were used to evaluate the model’s performance compared to the previous related works. To explore the clinical and biological mechanism of the predicted lesion segments, we extract some engineered features from the predicted lung lesions. We evaluate their mediation effects on the relationship of age with COVID-19 severity, as well as the relationship of underlying diseases with COVID-19 severity using statistic mediation analysis. Results The best overall F1 score is observed in the proposed self-supervised two-stage segmentation model (0.63) compared to the two related baseline models (0.55, 0.49). We also identified several CT image phenotypes that mediate the potential causal relationship between underlying diseases with COVID-19 severity as well as the potential causal relationship between age with COVID-19 severity. Conclusions This work contributes a promising COVID-19 lung CT image segmentation model and provides predicted lesion segments with potential clinical interpretability. The model could automatically segment the COVID-19 lesions from the raw CT images with higher accuracy than related works. The features of these lesions are associated with COVID-19 severity through mediating the known causal of the COVID-19 severity (age and underlying diseases). Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-02992-2.
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Affiliation(s)
- Daryl L X Fung
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Qian Liu
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada.,Department of Biochemistry and Medical Genetics, University of Manitoba, 745 Bannatyne Avenue, Winnipeg, MB, R3E 0J9, Canada
| | - Judah Zammit
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Carson Kai-Sang Leung
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Pingzhao Hu
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada. .,Department of Biochemistry and Medical Genetics, University of Manitoba, 745 Bannatyne Avenue, Winnipeg, MB, R3E 0J9, Canada. .,CancerCare Manitoba Research Institute, CancerCare Manitoba, Winnipeg, MB, R3E 0W3, Canada.
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30
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Fan Z, Sun Z, Fang S, Li Y, Liu X, Liang Y, Liu Y, Zhou C, Zhu Q, Zhang H, Li T, Li S, Jiang T, Wang Y, Wang L. Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas. Front Oncol 2021; 11:616740. [PMID: 34295805 PMCID: PMC8290517 DOI: 10.3389/fonc.2021.616740] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 06/17/2021] [Indexed: 11/21/2022] Open
Abstract
Purpose The present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas. Methods This retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant IDH, 16 patients with astrocytomas with wild-type IDH, and 65 patients with oligodendrogliomas with mutant IDH and 1p/19q codeletion). Radiomic features were extracted from magnetic resonance images, including T1-weighted, T2-weighted, and contrast T1-weighted images. Elastic net and support vector machines with radial basis function kernel were applied in nested 10-fold cross-validation loops to predict the 1p/19q status. Receiver operating characteristic analysis and precision-recall analysis were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of 1p/19q co-deletion prediction in the group with different 1p/19q status. Results Six valuable radiomic features, along with age, were selected with the nested 10-fold cross-validation loops. Five features showed significant difference in patients with different 1p/19q status. The area under curve and accuracy of the predictive model were 0.8079 (95% confidence interval, 0.733–0.8755) and 0.758 (0.6879–0.8217), respectively, and the F1-score of the precision-recall curve achieved 0.6667 (0.5201–0.7705). The posterior probabilities in the 1p/19q co-deletion group were significantly different from the non-deletion group. Conclusion Combined radiomics analysis and machine learning showed potential clinical utility in the preoperative prediction of 1p/19q status, which can aid in making customized neurosurgery plans and glioma management strategies before postoperative pathology.
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Affiliation(s)
- Ziwen Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhiyan Sun
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Shengyu Fang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yiming Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Liu
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yucha Liang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yukun Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunyao Zhou
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qiang Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tianshi Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shaowu Li
- Department of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lei Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Jafari SH, Rabiei N, Taghizadieh M, Mirazimi SMA, Kowsari H, Farzin MA, Razaghi Bahabadi Z, Rezaei S, Mohammadi AH, Alirezaei Z, Dashti F, Nejati M. Joint application of biochemical markers and imaging techniques in the accurate and early detection of glioblastoma. Pathol Res Pract 2021; 224:153528. [PMID: 34171601 DOI: 10.1016/j.prp.2021.153528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/09/2021] [Accepted: 06/14/2021] [Indexed: 11/28/2022]
Abstract
Glioblastoma is a primary brain tumor with the most metastatic effect in adults. Despite the wide range of multidimensional treatments, tumor heterogeneity is one of the main causes of tumor spread and gives great complexity to diagnostic and therapeutic methods. Therefore, featuring noble noninvasive prognostic methods that are focused on glioblastoma heterogeneity is perceived as an urgent need. Imaging neuro-oncological biomarkers including MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status, tumor grade along with other tumor characteristics and demographic features (e.g., age) are commonly referred to during diagnostic, therapeutic and prognostic processes. Therefore, the use of new noninvasive prognostic methods focused on glioblastoma heterogeneity is considered an urgent need. Some neuronal biomarkers, including the promoter methylation status of the promoter MGMT, the characteristics and grade of the tumor, along with the patient's demographics (such as age and sex) are involved in diagnosis, treatment, and prognosis. Among the wide array of imaging techniques, magnetic resonance imaging combined with the more physiologically detailed technique of H-magnetic resonance spectroscopy can be useful in diagnosing neurological cancer patients. In addition, intracranial tumor qualitative analysis and sometimes tumor biopsies help in accurate diagnosis. This review summarizes the evidence for biochemical biomarkers being a reliable biomarker in the early detection and disease management in GBM. Moreover, we highlight the correlation between Imaging techniques and biochemical biomarkers and ask whether they can be combined.
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Affiliation(s)
- Seyed Hamed Jafari
- Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nikta Rabiei
- School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Taghizadieh
- Department of Pathology, School of Medicine, Center for Women's Health Research Zahra, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Sayad Mohammad Ali Mirazimi
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
| | - Hamed Kowsari
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
| | - Mohammad Amin Farzin
- Department of Laboratory Medicine, School of Allied Medical Sciences, Kashan University of Medical Sciences, Kashan, Iran
| | - Zahra Razaghi Bahabadi
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
| | - Samaneh Rezaei
- Department of Medical Biotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amir Hossein Mohammadi
- Research Center for Biochemistry and Nutrition in Metabolic Diseases, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran
| | - Zahra Alirezaei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Paramedical School, Bushehr University of Medical Sciences, Bushehr, Iran.
| | - Fatemeh Dashti
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran.
| | - Majid Nejati
- Anatomical Sciences Research Center, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran.
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Orsatti G, Zucchetta P, Varotto A, Crimì F, Weber M, Cecchin D, Bisogno G, Spimpolo A, Giraudo C, Stramare R. Volumetric histograms-based analysis of apparent diffusion coefficients and standard uptake values for the assessment of pediatric sarcoma at staging: preliminary results of a PET/MRI study. Radiol Med 2021; 126:878-885. [PMID: 33683542 DOI: 10.1007/s11547-021-01340-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 02/21/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE To assess the relationship between apparent diffusion coefficients (ADC) and standard uptake values (SUV) of pediatric sarcomas at staging by using volumetric histograms analyses. METHODS Children with histologically proven sarcoma, referring to our tertiary center for a whole-body 18F-FDG PET/MRI for staging and including diffusion weighted imaging in the MRI protocol were investigated. Firstly, turbo inversion recovery magnitude (TIRM) and PET images were resliced and resampled according to the ADC maps. Regions of interests were drawn along tumor margins on TIRM images and then copied on PET and ADC datasets. Pixel-based SUVs and ADCs were collected from the entire volume of each lesion. Mean, median, skewness, and kurtosis of SUVs and ADCs values were computed, and the Pearson correlation coefficient was then applied (for the entire population and for histological subgroups with more than five patients). RESULTS Thirteen patients met the inclusion criteria (six females; mean age 8.31 ± 6.03 years). Histology revealed nine rhabdomyosarcomas, three Ewing sarcomas, and one chondroblastic osteosarcoma. A significant negative correlation between ADCs' and SUVs' mean (rmean = - 0.501, P < 0.001), median (rmedian = - 0.519, P < 0,001), and skewness (rskewness = - 0.550, P < 0.001) emerged for the entire population and for rhabdomyosarcomas (rmean = - 0.541, P = 0.001, rmedian = - 0.597, P < 0.001, rskewness = - 0.568, P < 0.001), whereas a significant positive correlation was found for kurtosis (rkurtosis = 0.346, P < 0.001, and rkurtosis = 0.348, P < 0.001 for the entire population and for rhabdomyosarcomas, respectively). CONCLUSION Our preliminary results demonstrate that, using volumetric histograms, simultaneously collected SUVs and ADCs are dependent biomarkers in pediatric FDG-avid sarcomas. Further studies, on a larger population, are necessary to confirm this evidence and assess its clinical implications.
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Affiliation(s)
- Giovanna Orsatti
- Department of Medicine - DIMED, Institute of Radiology, Padova University, Via Giustiniani 2, 35100, Padua, Italy
| | - Pietro Zucchetta
- Nuclear Medicine Unit, Department of Medicine - DIMED, University of Padova, Padua, Italy
| | | | - Filippo Crimì
- Department of Medicine - DIMED, Institute of Radiology, Padova University, Via Giustiniani 2, 35100, Padua, Italy
| | - Michael Weber
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine - DIMED, University of Padova, Padua, Italy
| | - Gianni Bisogno
- Hematology and Oncology Division, Department of Women's and Children's Health, University of Padova, Padua, Italy
| | - Alessandro Spimpolo
- Nuclear Medicine Unit, Department of Medicine - DIMED, University of Padova, Padua, Italy
| | - Chiara Giraudo
- Department of Medicine - DIMED, Institute of Radiology, Padova University, Via Giustiniani 2, 35100, Padua, Italy.
| | - Roberto Stramare
- Department of Medicine - DIMED, Institute of Radiology, Padova University, Via Giustiniani 2, 35100, Padua, Italy
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Strauss SB, Meng A, Ebani EJ, Chiang GC. Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis. Neuroimaging Clin N Am 2021; 31:103-120. [PMID: 33220823 DOI: 10.1016/j.nic.2020.09.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.
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Affiliation(s)
- Sara B Strauss
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Alicia Meng
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Edward J Ebani
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Gloria C Chiang
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA.
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Le Fèvre C, Constans JM, Chambrelant I, Antoni D, Bund C, Leroy-Freschini B, Schott R, Cebula H, Noël G. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Part 2 - Radiological features and metric markers. Crit Rev Oncol Hematol 2021; 159:103230. [PMID: 33515701 DOI: 10.1016/j.critrevonc.2021.103230] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 01/10/2021] [Accepted: 01/16/2021] [Indexed: 12/28/2022] Open
Abstract
After chemoradiotherapy for glioblastoma, pseudoprogression can occur and must be distinguished from true progression to correctly manage glioblastoma treatment and follow-up. Conventional treatment response assessment is evaluated via conventional MRI (contrast-enhanced T1-weighted and T2/FLAIR), which is unreliable. The emergence of advanced MRI techniques, MR spectroscopy, and PET tracers has improved pseudoprogression diagnostic accuracy. This review presents a literature review of the different imaging techniques and potential imaging biomarkers to differentiate pseudoprogression from true progression.
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Affiliation(s)
- Clara Le Fèvre
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Jean-Marc Constans
- Department of Radiology, Amiens-Picardie University Hospital, 1 rond-point du Professeur Christian Cabrol, 80054, Amiens Cedex 1, France.
| | - Isabelle Chambrelant
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Delphine Antoni
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Caroline Bund
- Department of Nuclear Medicine, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Benjamin Leroy-Freschini
- Department of Nuclear Medicine, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Roland Schott
- Departement of Medical Oncology, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Hélène Cebula
- Departement of Neurosurgery, Hautepierre University Hospital, 1, avenue Molière, 67200, Strasbourg, France.
| | - Georges Noël
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
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Nakanishi R, Oki E, Hasuda H, Sano E, Miyashita Y, Sakai A, Koga N, Kuriyama N, Nonaka K, Fujimoto Y, Jogo T, Hokonohara K, Hu Q, Hisamatsu Y, Ando K, Kimura Y, Yoshizumi T, Mori M. Radiomics Texture Analysis for the Identification of Colorectal Liver Metastases Sensitive to First-Line Oxaliplatin-Based Chemotherapy. Ann Surg Oncol 2021; 28:2975-2985. [PMID: 33454878 DOI: 10.1245/s10434-020-09581-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/01/2020] [Indexed: 01/06/2023]
Abstract
OBJECTIVE The aim of this study was to develop a radiomics-based prediction model for the response of colorectal liver metastases to oxaliplatin-based chemotherapy. METHODS Forty-two consecutive patients treated with oxaliplatin-based first-line chemotherapy for colorectal liver metastasis at our institution from August 2013 to October 2019 were enrolled in this retrospective study. Overall, 126 liver metastases were chronologically divided into the training (n = 94) and validation (n = 32) cohorts. Regions of interest were manually segmented, and the best response to chemotherapy was decided based on Response Evaluation Criteria in Solid Tumors (RECIST). Patients who achieved clinical complete and partial response according to RECIST were defined as good responders. Radiomics features were extracted from the pretreatment enhanced computed tomography scans, and a radiomics score was calculated using the least absolute shrinkage and selection operator regression model in a trial cohort. RESULTS The radiomics score significantly discriminated good responders in both the trial (area under the curve [AUC] 0.8512, 95% confidence interval [CI] 0.7719-0.9305; p < 0.0001) and validation (AUC 0.7792, 95% CI 0.6176-0.9407; p < 0.0001) cohorts. Multivariate analysis revealed that high radiomics scores greater than - 0.06 (odds ratio [OR] 23.803, 95% CI 8.432-80.432; p < 0.0001), clinical non-T4 (OR 6.054, 95% CI 2.164-18.394; p = 0.0005), and metachronous disease (OR 11.787, 95% CI 2.333-70.833; p = 0.0025) were independently associated with good response. CONCLUSIONS Radiomics signatures may be a potential biomarker for the early prediction of chemosensitivity in colorectal liver metastases. This approach may support the treatment strategy for colorectal liver metastasis.
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Affiliation(s)
- Ryota Nakanishi
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
| | - Eiji Oki
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hirofumi Hasuda
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Eiki Sano
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yu Miyashita
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Akihiro Sakai
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Naomichi Koga
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Naotaka Kuriyama
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kentaro Nonaka
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshiaki Fujimoto
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tomoko Jogo
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kentaro Hokonohara
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Qingjiang Hu
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yuichi Hisamatsu
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Koji Ando
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yasue Kimura
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tomoharu Yoshizumi
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masaki Mori
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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Liu P, Luo B, Chen L, Wang QX, Yuan G, Jiang GH, Zhang J. Baseline Volumetric T2 Relaxation Time Histogram Analysis: Can It Be Used to Predict the Response to Intravenous Methylprednisolone Therapy in Patients With Thyroid-Associated Ophthalmopathy? Front Endocrinol (Lausanne) 2021; 12:614536. [PMID: 33716970 PMCID: PMC7947366 DOI: 10.3389/fendo.2021.614536] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 01/13/2021] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Prediction of therapy response to intravenous methylprednisolone pulses (ivMP) is crucial for thyroid-associated ophthalmopathy (TAO). Image histograms may offer sensitive imaging biomarkers for therapy effect prediction. This study aimed to investigate whether pretherapeutic, multiparametric T2 relaxation time(T2RT) histogram features of extraocular muscles (EOMs) can be used to predict therapy response. MATERIALS AND METHODS Forty-five active and moderate-severe TAO patients, who were treated with standard ivMP and underwent orbital MRI before therapy, were retrospectively included in this study. The patients were divided into responsive (n = 24, 48 eyes) and unresponsive group(n = 21, 42 eyes) according to clinical evaluation. Baseline clinical features of patients and histogram-derived T2RT parameters of the EOMs were analyzed and compared. Logistic regression model was conducted to determine independent predictors, and a histogram features nomogram was formulated for personalized prediction. RESULTS Responsive group displayed lower values for 5th, 10th percentiles (P < 0.050, respectively), and higher values for 75th, 90th, and 95th percentiles, skewness, entropy, and inhomogeneity (P < 0.050, respectively) than unresponsive group. Multivariate logistic regression analysis showed that 95th percentile of >88.1 [odds ratio (OR) = 12.078; 95% confidence interval (CI) = 3.98-36.655, p < 0.001], skewness of >0.31 (OR = 3.935; 95% CI = 2.28-6.788, p < 0.001) and entropy of >3.41 (OR = 4.375; 95% CI = 2.604-7.351, p < 0.001) were independent predictors for favorable response. The nomogram integration of three independent predictors demonstrated optimal predictive efficiency, with a C-index of 0.792. CONCLUSIONS Pre-treatment volumetric T2RT histogram features of EOMs could function to predict the response to ivMP in patients with TAO. The nomogram based on histogram features facilitates the selection of patients who will derive maximal benefit from ivMP.
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Affiliation(s)
- Ping Liu
- Department of Radiology, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Ban Luo
- Department of Ophthalmology, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
| | - Lang Chen
- Department of Radiology, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
| | - Qiu-Xia Wang
- Department of Radiology, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
| | - Gang Yuan
- Department of Endocrinology and Metabolism, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
| | - Gui-hua Jiang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Jing Zhang
- Department of Radiology, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
- *Correspondence: Jing Zhang,
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Noninvasive Assessment of Liver Parenchyma Using Gray-Scale Ultrasound-Based Histogram Analysis in Patients With Chronic Hepatitis B Infection. Ultrasound Q 2020; 36:69-73. [PMID: 30855417 DOI: 10.1097/ruq.0000000000000438] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The aims of this study were to examine the alterations of liver echo-intensity histogram parameters in chronic hepatitis B (CHB) patients and to assess the potential role of histogram parameters in the evaluation of hepatic fibrosis. A total of 52 patients with CHB who underwent liver biopsies were included in the study. The control group consisted of 30 healthy individuals. Histogram parameters were obtained from histogram analysis of gray-scale ultrasound images of both groups. The histogram parameters of the groups were compared. The association of histogram parameters with the grading and staging of histological activity index (HAI) in patients with CHB were evaluated. The patient group had statistically significant lower skewness, kurtosis, and higher variance, mean, 50th, and 90th percentile values compared with control group. When patients with CHB were divided into subgroups according to HAI stage, there was the increasing trend in skewness values and decreasing trend in kurtosis values across subgroups. The first percentile values showed negative correlation with HAI staging in patients with CHB. Ultrasound is a fast, inexpensive, and reproducible imaging method; histogram analysis of gray-scale ultrasound images may provide useful information for evaluation of hepatic fibrosis in CHB patients.
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Le Fèvre C, Lhermitte B, Ahle G, Chambrelant I, Cebula H, Antoni D, Keller A, Schott R, Thiery A, Constans JM, Noël G. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review: Part 1 - Molecular, morphological and clinical features. Crit Rev Oncol Hematol 2020; 157:103188. [PMID: 33307200 DOI: 10.1016/j.critrevonc.2020.103188] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 11/12/2020] [Accepted: 11/23/2020] [Indexed: 01/04/2023] Open
Abstract
With new therapeutic protocols, more patients treated for glioblastoma have experienced a suspicious radiologic image of progression (pseudoprogression) during follow-up. Pseudoprogression should be differentiated from true progression because the disease management is completely different. In the case of pseudoprogression, the follow-up continues, and the patient is considered stable. In the case of true progression, a treatment adjustment is necessary. Presently, a pseudoprogression diagnosis certainly needs to be pathologically confirmed. Some important efforts in the radiological, histopathological, and genomic fields have been made to differentiate pseudoprogression from true progression, and the assessment of response criteria exists but remains limited. The aim of this paper is to highlight clinical and pathological markers to differentiate pseudoprogression from true progression through a literature review.
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Affiliation(s)
- Clara Le Fèvre
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Benoît Lhermitte
- Département of Pathology, Hautepierre University Hospital, 1, Avenue Molière, 67200, Strasbourg, France
| | - Guido Ahle
- Departement of Neurology, Hôpitaux Civils de Colmar, 39 Avenue de la Liberté, 68024, Colmar, France
| | - Isabelle Chambrelant
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Hélène Cebula
- Departement of Neurosurgery, Hautepierre University Hospital, 1, Avenue Molière, 67200, Strasbourg, France
| | - Delphine Antoni
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Audrey Keller
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Roland Schott
- Departement of Medical Oncology, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Alicia Thiery
- Department of Public Health, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Jean-Marc Constans
- Department of Radiology, Amiens-Pïcardie University Hospital, 1 rond point du Professeur Christian Cabrol, 80054 Amiens Cedex 1, France
| | - Georges Noël
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France.
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Tsakiris C, Siempis T, Alexiou GA, Zikou A, Sioka C, Voulgaris S, Argyropoulou MI. Differentiation Between True Tumor Progression of Glioblastoma and Pseudoprogression Using Diffusion-Weighted Imaging and Perfusion-Weighted Imaging: Systematic Review and Meta-analysis. World Neurosurg 2020; 144:e100-e109. [DOI: 10.1016/j.wneu.2020.07.218] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 01/08/2023]
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Farrell C, Shi W, Bodman A, Olson JJ. Congress of neurological surgeons systematic review and evidence-based guidelines update on the role of emerging developments in the management of newly diagnosed glioblastoma. J Neurooncol 2020; 150:269-359. [PMID: 33215345 DOI: 10.1007/s11060-020-03607-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 08/23/2020] [Indexed: 12/12/2022]
Abstract
TARGET POPULATION These recommendations apply to adult patients with newly diagnosed or suspected glioblastoma. IMAGING Question What imaging modalities are in development that may be able to provide improvements in diagnosis, and therapeutic guidance for individuals with newly diagnosed glioblastoma? RECOMMENDATION Level III: It is suggested that techniques utilizing magnetic resonance imaging for diffusion weighted imaging, and to measure cerebral blood and magnetic spectroscopic resonance imaging of N-acetyl aspartate, choline and the choline to N-acetyl aspartate index to assist in diagnosis and treatment planning in patients with newly diagnosed or suspected glioblastoma. SURGERY Question What new surgical techniques can be used to provide improved tumor definition and resectability to yield better tumor control and prognosis for individuals with newly diagnosed glioblastoma? RECOMMENDATIONS Level II: The use of 5-aminolevulinic acid is recommended to improve extent of tumor resection in patients with newly diagnosed glioblastoma. Level II: The use of 5-aminolevulinic acid is recommended to improve median survival and 2 year survival in newly diagnosed glioblastoma patients with clinical characteristics suggesting poor prognosis. Level III: It is suggested that, when available, patients be enrolled in properly designed clinical trials assessing the value of diffusion tensor imaging in improving the safety of patients with newly diagnosed glioblastoma undergoing surgery. NEUROPATHOLOGY Question What new pathology techniques and measurement of biomarkers in tumor tissue can be used to provide improved diagnostic ability, and determination of therapeutic responsiveness and prognosis for patients with newly diagnosed glioblastomas? RECOMMENDATIONS Level II: Assessment of tumor MGMT promoter methylation status is recommended as a significant predictor of a longer progression free survival and overall survival in patients with newly diagnosed with glioblastoma. Level II: Measurement of tumor expression of neuron-glia-2, neurofilament protein, glutamine synthetase and phosphorylated STAT3 is recommended as a predictor of overall survival in patients with newly diagnosed with glioblastoma. Level III: Assessment of tumor IDH1 mutation status is suggested as a predictor of longer progression free survival and overall survival in patients with newly diagnosed with glioblastoma. Level III: Evaluation of tumor expression of Phosphorylated Mitogen-Activated Protein Kinase protein, EGFR protein, and Insulin-like Growth Factor-Binding Protein-3 is suggested as a predictor of overall survival in patients with newly diagnosed with glioblastoma. RADIATION Question What radiation therapy techniques are in development that may be used to provide improved tumor control and prognosis for individuals with newly diagnosed glioblastomas? RECOMMENDATIONS Level III: It is suggested that patients with newly diagnosed glioblastoma undergo pretreatment radio-labeled amino acid tracer positron emission tomography to assess areas at risk for tumor recurrence to assist in radiation treatment planning. Level III: It is suggested that, when available, patients be with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of radiation dose escalation, altered fractionation, or new radiation delivery techniques. CHEMOTHERAPY Question What emerging chemotherapeutic agents or techniques are available to provide better tumor control and prognosis for patients with newly diagnosed glioblastomas? RECOMMENDATION Level III: As no emerging chemotherapeutic agents or techniques were identified in this review that improved tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of chemotherapy. MOLECULAR AND TARGETED THERAPY Question What new targeted therapy agents are available to provide better tumor control and prognosis for individuals with newly diagnosed glioblastomas? RECOMMENDATION Level III: As no new molecular and targeted therapies have clearly provided better tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of molecular and targeted therapies IMMUNOTHERAPY: Question What emerging immunotherapeutic agents or techniques are available to provide better tumor control and prognosis for patients with newly diagnosed glioblastomas? RECOMMENDATION Level III: As no immunotherapeutic agents have clearly provided better tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of immunologically-based therapies. NOVEL THERAPIES Question What novel therapies or techniques are in development to provide better tumor control and prognosis for individuals with newly diagnosed glioblastomas? RECOMMENDATIONS Level II: The use of tumor-treating fields is recommended for patients with newly diagnosed glioblastoma who have undergone surgical debulking and completed concurrent chemoradiation without progression of disease at the time of tumor-treating field therapy initiation. Level II: It is suggested that, when available, enrollment in properly designed studies of vector containing herpes simplex thymidine kinase gene and prodrug therapies be considered in patients with newly diagnosed glioblastoma.
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Affiliation(s)
- Christopher Farrell
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA
| | - Wenyin Shi
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | | | - Jeffrey J Olson
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA.
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Advanced multimodal imaging in differentiating glioma recurrence from post-radiotherapy changes. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2020; 151:281-297. [PMID: 32448612 DOI: 10.1016/bs.irn.2020.03.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Gliomas are the most common malignant primary brain tumor, and their prognosis is extremely poor. Radiotherapy is an important treatment for glioma patients, but the changes caused by radiotherapy have brought difficulties in clinical image evaluation because differentiating glioma recurrence from post-radiotherapy changes including pseudo-progression (PD) and radiation necrosis (RN) remains a challenge. Therefore, accurate and reliable imaging evaluation is very important for making clinical decisions. In recent years, advanced multimodal imaging techniques have been applied to achieve the goal of better differentiating glioma recurrence from post-radiotherapy changes for minimizing errors associated with interpretation of treatment effects. In this review, we discuss the recent applications of advanced multimodal imaging such as diffusion MRI sequences, amide proton transfer MRI sequences, perfusion MRI sequences, MR spectroscopy and multinuclides PET/CT in the evaluation of post-radiotherapy treatment response in glioma patients and highlight their potential role in differentiating post-radiotherapy changes from glioma recurrence.
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Li J, Chekkoury A, Prakash J, Glasl S, Vetschera P, Koberstein-Schwarz B, Olefir I, Gujrati V, Omar M, Ntziachristos V. Spatial heterogeneity of oxygenation and haemodynamics in breast cancer resolved in vivo by conical multispectral optoacoustic mesoscopy. LIGHT, SCIENCE & APPLICATIONS 2020; 9:57. [PMID: 32337021 PMCID: PMC7154032 DOI: 10.1038/s41377-020-0295-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 02/10/2020] [Accepted: 03/19/2020] [Indexed: 05/11/2023]
Abstract
The characteristics of tumour development and metastasis relate not only to genomic heterogeneity but also to spatial heterogeneity, associated with variations in the intratumoural arrangement of cell populations, vascular morphology and oxygen and nutrient supply. While optical (photonic) microscopy is commonly employed to visualize the tumour microenvironment, it assesses only a few hundred cubic microns of tissue. Therefore, it is not suitable for investigating biological processes at the level of the entire tumour, which can be at least four orders of magnitude larger. In this study, we aimed to extend optical visualization and resolve spatial heterogeneity throughout the entire tumour volume. We developed an optoacoustic (photoacoustic) mesoscope adapted to solid tumour imaging and, in a pilot study, offer the first insights into cancer optical contrast heterogeneity in vivo at an unprecedented resolution of <50 μm throughout the entire tumour mass. Using spectral methods, we resolve unknown patterns of oxygenation, vasculature and perfusion in three types of breast cancer and showcase different levels of structural and functional organization. To our knowledge, these results are the most detailed insights of optical signatures reported throughout entire tumours in vivo, and they position optoacoustic mesoscopy as a unique investigational tool linking microscopic and macroscopic observations.
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Affiliation(s)
- Jiao Li
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, No.92, Weijin Road, Nankai District, 300072 Tianjin, China
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Andrei Chekkoury
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Jaya Prakash
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
- Department of Instrumentation and Applied Physics, Indian Institute of Science Bangalore, CV Raman Rd, Bengaluru, 560012 Karnataka India
| | - Sarah Glasl
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Paul Vetschera
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Benno Koberstein-Schwarz
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Ivan Olefir
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Vipul Gujrati
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Murad Omar
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Vasilis Ntziachristos
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
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Strauss SB, Meng A, Ebani EJ, Chiang GC. Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis. Radiol Clin North Am 2019; 57:1199-1216. [PMID: 31582045 DOI: 10.1016/j.rcl.2019.07.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.
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Affiliation(s)
- Sara B Strauss
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Alicia Meng
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Edward J Ebani
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Gloria C Chiang
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA.
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Li T, Hong Y, Kong D, Li K. Histogram analysis of diffusion kurtosis imaging based on whole-volume images of breast lesions. J Magn Reson Imaging 2019; 51:627-634. [PMID: 31385429 DOI: 10.1002/jmri.26884] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 06/27/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Breast diffusion kurtosis imaging (DKI) is a novel MRI technique to assess breast cancer but the effectivity still remains to be improved. PURPOSE To investigate the performance of whole-volume histogram parameters derived from a DKI model for differentiating benign and malignant breast lesions. STUDY TYPE Retrospective. POPULATION In all, 120 patients with breast lesions (62 malignant, 58 benign). SEQUENCE DKI sequence with seven b-values (0, 500, 1000, 1500, 2000, 2500, and 3000 s/mm2 ) and DWI sequence with two b-values (0 and 1000 s/mm2 ) on 3.0T MRI. ASSESSMENT Histogram parameters of the DKI model (K and D) and the DWI model (ADC), including the minimum, maximum, mean, percentile values (25th, 50th, 75th, and 95th), standard deviation, kurtosis and skewness, were calculated by two radiologists for the whole lesion volume. STATISTICAL TESTS Student's t-test was used to compare malignant and benign lesions. The diagnostic performances were evaluated by receiver operating characteristic (ROC) analysis. RESULTS Kmax , Dmin , and ADCmin had the highest area under the curve (AUC) (0.875, 0.830, and 0.847, respectively), sensitivity (85.5%, 74.2%, and 77.4%, respectively), and accuracy (85.0%, 79.2%, and 81.7%, respectively) in their individual histogram parameter groups, and Kmax was found to outperform Dmin and ADCmin . ADC histogram parameters (from ADCmin to ADCsd ) were significantly lower than D histogram parameters in all groups. DATA CONCLUSION Kmax , Dmin , and ADCmin were found to be better metrics than the corresponding average values for differentiating benign from malignant tumors. Histogram parameters derived from the DKI model provided more information and had better diagnostic performance than ADC parameters derived from the DWI model. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:627-634.
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Affiliation(s)
- Ting Li
- The Department of Radiology, First People's Hospital of Changzhou, Jiangsu, P.R. China
| | - Yuan Hong
- School of Mathematical Sciences, Zhejiang University, Hangzhou, P.R. China
| | - Dexing Kong
- School of Mathematical Sciences, Zhejiang University, Hangzhou, P.R. China
| | - Kangan Li
- Department of Radiology, Shanghai General Hospital, Shanghai, P.R. China
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45
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Klimov S, Miligy IM, Gertych A, Jiang Y, Toss MS, Rida P, Ellis IO, Green A, Krishnamurti U, Rakha EA, Aneja R. A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk. Breast Cancer Res 2019; 21:83. [PMID: 31358020 PMCID: PMC6664779 DOI: 10.1186/s13058-019-1165-5] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 06/25/2019] [Indexed: 12/18/2022] Open
Abstract
Background Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mastectomy or adjuvant radiation can reduce recurrence risk, there are significant concerns regarding patient over-/under-treatment. Current clinicopathological markers are insufficient to accurately assess the recurrence risk. To address this issue, we developed a novel machine learning (ML) pipeline to predict risk of ipsilateral recurrence using digitized whole slide images (WSI) and clinicopathologic long-term outcome data from a retrospectively collected cohort of DCIS patients (n = 344) treated with lumpectomy at Nottingham University Hospital, UK. Methods The cohort was split case-wise into training (n = 159, 31 with 10-year recurrence) and validation (n = 185, 26 with 10-year recurrence) sets. The sections from primary tumors were stained with H&E, then digitized and analyzed by the pipeline. In the first step, a classifier trained manually by pathologists was applied to digital slides to annotate the areas of stroma, normal/benign ducts, cancer ducts, dense lymphocyte region, and blood vessels. In the second step, a recurrence risk classifier was trained on eight select architectural and spatial organization tissue features from the annotated areas to predict recurrence risk. Results The recurrence classifier significantly predicted the 10-year recurrence risk in the training [hazard ratio (HR) = 11.6; 95% confidence interval (CI) 5.3–25.3, accuracy (Acc) = 0.87, sensitivity (Sn) = 0.71, and specificity (Sp) = 0.91] and independent validation [HR = 6.39 (95% CI 3.0–13.8), p < 0.0001;Acc = 0.85, Sn = 0.5, Sp = 0.91] cohorts. Despite the limitations of our cohorts, and in some cases inferior sensitivity performance, our tool showed superior accuracy, specificity, positive predictive value, concordance, and hazard ratios relative to tested clinicopathological variables in predicting recurrences (p < 0.0001). Furthermore, it significantly identified patients that might benefit from additional therapy (validation cohort p = 0.0006). Conclusions Our machine learning-based model fills an unmet clinical need for accurately predicting the recurrence risk for lumpectomy-treated DCIS patients. Electronic supplementary material The online version of this article (10.1186/s13058-019-1165-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sergey Klimov
- Department of Biology, Georgia State University, Atlanta, GA, 30303, USA.,Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA
| | - Islam M Miligy
- Department of Cellular Pathology, University of Nottingham, Nottingham, UK
| | - Arkadiusz Gertych
- Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yi Jiang
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA
| | - Michael S Toss
- Department of Cellular Pathology, University of Nottingham, Nottingham, UK
| | - Padmashree Rida
- Department of Biology, Georgia State University, Atlanta, GA, 30303, USA
| | - Ian O Ellis
- Department of Cellular Pathology, University of Nottingham, Nottingham, UK
| | - Andrew Green
- Department of Cellular Pathology, University of Nottingham, Nottingham, UK
| | | | - Emad A Rakha
- Department of Cellular Pathology, University of Nottingham, Nottingham, UK. .,Division of Cancer and Stem Cells School of Medicine, University of Nottingham City Hospital Campus, Nottingham, NG5 1PB, UK.
| | - Ritu Aneja
- Department of Biology, Georgia State University, Atlanta, GA, 30303, USA.
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Xiang P, Zhang X, Liu D, Wang C, Ding L, Wang F, Zhang Z. Distinguishing soft tissue sarcomas of different histologic grades based on quantitative MR assessment of intratumoral heterogeneity. Eur J Radiol 2019; 118:194-199. [PMID: 31439242 DOI: 10.1016/j.ejrad.2019.07.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 06/17/2019] [Accepted: 07/19/2019] [Indexed: 10/26/2022]
Abstract
PURPOSE To explore the role of intratumoral heterogeneity on MRI assessed by histogram analysis in differentiating soft-tissue sarcomas (STS) of different grades. MATERIALS AND METHODS Patients with primary STS undergoing MRI prior to iatrogenic procedures were included retrospectively. The histologic grade was assigned according to Federation Nationale des Centres de Lutte Contre le Cancer grading system. T1WI and T2WI were normalized by dividing mean signal intensity (SI) of contralateral/near unaffected muscles. Contrast-enhanced T1WI was normalized by computing enhancement ratio (ER) map as (SIpost-SIpre)/SIpre×100, where SIpre and SIpost represent SI of each pixel before and after enhancement. A region of interest (ROI) was manually drawn to include entire tumor area on axial slice with largest tumor diameter. Mean, mode, standard deviation, kurtosis and skewness on ROIs were extracted with ImageJ software. ANOVA/Kruskal-Wallis test was used to determine the significance of differences. ROC curve was applied for statistically significant parameters. P value ≤0.05 was considered statistically significant. RESULTS Among involved 67 patients, 8 were assigned to grade 1, 38 to grade 2 and 21 to grade 3. Skewness (P = 0.022) and kurtosis (P = 0.035) on ER maps were significantly different among STS of different grades. The optimal cutoffs of skewness and kurtosis on ER maps were -0.488 (AUC[95% CI] 0.747[0.557-0.937]; sensitivity/specificity, 62.5%/86.4%) and 0.762 (AUC[95% CI] 0.684[0.548-0.821]; sensitivity/specificity, 76.2%/56.5%), respectively. CONCLUSION Intratumoral heterogeneity on MRI quantitatively displayed by histogram parameters can differentiate STS of different grades. Skewness and kurtosis on ER maps show the capacity.
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Affiliation(s)
- Pei Xiang
- Department of Medical Imaging, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan 2nd Road, Guangzhou, Guangdong 510080, PR China
| | - Xiaoling Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan 2nd Road, Guangzhou, Guangdong 510080, PR China
| | - Dawei Liu
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan 2nd Road, Guangzhou, Guangdong 510080, PR China
| | - Chaoyang Wang
- Department of Medical Imaging, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan 2nd Road, Guangzhou, Guangdong 510080, PR China
| | - Lei Ding
- Department of Medical Imaging, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan 2nd Road, Guangzhou, Guangdong 510080, PR China
| | - Fen Wang
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan 2nd Road, Guangzhou, Guangdong 510080, PR China
| | - Zhaohui Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan 2nd Road, Guangzhou, Guangdong 510080, PR China.
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47
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Korn RL, Rahmanuddin S, Borazanci E. Use of Precision Imaging in the Evaluation of Pancreas Cancer. Cancer Treat Res 2019; 178:209-236. [PMID: 31209847 DOI: 10.1007/978-3-030-16391-4_8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Pancreas cancer is an aggressive and fatal disease that will become one of the leading causes of cancer mortality by 2030. An all-out effort is underway to better understand the basic biologic mechanisms of this disease ranging from early development to metastatic disease. In order to change the course of this disease, diagnostic radiology imaging may play a vital role in providing a precise, noninvasive method for early diagnosis and assessment of treatment response. Recent progress in combining medical imaging, advanced image analysis and artificial intelligence, termed radiomics, can offer an innovate approach in detecting the earliest changes of tumor development as well as a rapid method for the detection of response. In this chapter, we introduce the principles of radiomics and demonstrate how it can provide additional information into tumor biology, early detection, and response assessments advancing the goals of precision imaging to deliver the right treatment to the right person at the right time.
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Affiliation(s)
- Ronald L Korn
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA. .,Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, AZ, USA. .,Imaging Endpoints Core Lab, Scottsdale, AZ, USA.
| | | | - Erkut Borazanci
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA.,Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, AZ, USA
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Whole-Tumor Quantitative Apparent Diffusion Coefficient Histogram and Texture Analysis to Differentiation of Minimal Fat Angiomyolipoma from Clear Cell Renal Cell Carcinoma. Acad Radiol 2019; 26:632-639. [PMID: 30087067 DOI: 10.1016/j.acra.2018.06.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 06/12/2018] [Accepted: 06/15/2018] [Indexed: 12/11/2022]
Abstract
AIM To evaluate the diagnostic value of whole-tumor quantitative apparent diffusion coefficient (ADC) histogram and texture analysis for differentiation of minimal fat angiomyolipoma (MFAML) from clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS We retrospectively reviewed 27 patients with MFAML and 113 patients with ccRCC pathologically proven. All patients performed magnetic resonance imaging (MRI) including diffusion-weighted imaging (b = 0, 800s/mm2). Whole-tumor regions of interest were drawn on all slices of diffusion-weighted imaging to obtain histogram and texture parameters, including the mean ADC, median ADC, 10th, 25th, 75th, 90th percentiles ADC, standard deviation, skewness, kurtosis, and entropy. The Student's t test was used to compare the parameters between MFAML and ccRCC. Receiver operating characteristic curves analysis was used to compare the diagnostic value of each significant parameter. RESULTS MFAML had significantly lower mean ADC, median ADC, 10th, 25th, 75th, 90th percentiles ADC than ccRCC (p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, respectively). Skewness of MFAML was significantly higher than that of ccRCC (p = 0.016). However, standard deviation, kurtosis, and entropy were not significantly different (p = 0.409, p = 0.085, p = 0.206, respectively). The 90th percentile ADC generated the highest AUC (AUC, 0.854; Sensitivity, 78.8%; Specificity, 81.5%) for differentiating MFAML from ccRCC. CONCLUSION Whole-tumor ADC histogram and texture analysis could be considered a useful and noninvasive method to help differentiate MFAML of ccRCC with higher accuracy.
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Chang RF, Lee CC, Lo CM. Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition. PLoS One 2019; 14:e0212741. [PMID: 30817804 PMCID: PMC6394937 DOI: 10.1371/journal.pone.0212741] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2018] [Accepted: 02/08/2019] [Indexed: 11/30/2022] Open
Abstract
The lifetime prevalence of shoulder pain is nearly 70% and is mostly attributable to subacromial disorders. A rotator cuff tear is the most severe form of subacromial disorders, and most occur in the supraspinatus. For clinical examination, shoulder ultrasound is recommended to detect supraspinatus tears. In this study, a computer-aided tear classification (CTC) system was developed to identify supraspinatus tears in ultrasound examinations and reduce inter-operator variability. The observed cases included 89 ultrasound images of supraspinatus tendinopathy and 102 of supraspinatus tear from 136 patients. For each case, intensity and texture features were extracted from the entire lesion and combined in a binary logistic regression classifier for lesion classification. The proposed CTC system achieved an accuracy rate of 92% (176/191) and an area under receiver operating characteristic curve (Az) of 0.9694. Based on its diagnostic performance, the CTC system has promise for clinical use.
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Affiliation(s)
- Ruey-Feng Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Chung-Chien Lee
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Orthopedic Surgery, New Taipei City Hospital, New Taipei City, Taiwan
- Department of Orthopedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Chung-Ming Lo
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan
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50
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Han L, Wang S, Miao Y, Shen H, Guo Y, Xie L, Shang Y, Dong J, Li X, Wang W, Song Q. MRI texture analysis based on 3D tumor measurement reflects the IDH1 mutations in gliomas - A preliminary study. Eur J Radiol 2019; 112:169-179. [PMID: 30777207 DOI: 10.1016/j.ejrad.2019.01.025] [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: 09/09/2018] [Revised: 01/10/2019] [Accepted: 01/22/2019] [Indexed: 10/27/2022]
Abstract
OBJECTIVE To evaluate the differentiation efficiency of texture analysis of T1WI, T2WI and contrasted-enhanced T1WI MRI sequences in gliomas with and without IDH1 mutation based on entire tumor region. MATERIALS AND METHODS A total of 42 patients with histopathologically confirmed gliomas, including 21 patients carrying IDH1 mutation (IDH1mutation group) and 21 with wild-type IDH1 (IDH1wt group) were included in this study. The preoperative MRI and clinical data were collected. The regions of interest (ROIs) covering the entire tumor and edema were manually delineated on axial slices using O.K. (Omni Kinetics, GE Healthcare, China) software; and the histogram and GLCM features based on T1WI, T2WI and contrasted-enhanced T1WI sequences were automatically generated. RESULTS Based on contrasted-enhanced T1WI features, the inertia resulted as the best feature for diagnosis, with the AUC of 0.844. Furthermore, the AUC for gliomas prediction with IDH1mutation was 0.800 for cluster prominence. IDH1-mutation was differentiated on T2WI with the highest AUC of 0.848, which corresponded to GLCM Entropy. After modeling, the accuracy of the contrasted-enhanced T1WI, T1WI, and T2WI features model was 0.952, 0.857, and 0.738, respectively. The AUC of Joint VariableT1WI+C for predicting IDH1mutation was 0.984, while the AUC of Joint VariableT1WI for predicting the same mutation was 0.927. The diagnostic efficiency of Joint VariableT2WI was also desirable. CONCLUSION MRI texture analysis could be used as a new noninvasive method for identification of gliomas with IDH1 mutation. The present results show that the Joint Variable derived from conventional MR imaging histogram and GLCM features is suitable for precise detection of IDH1-mutated gliomas.
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Affiliation(s)
- Liang Han
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116000, China.
| | - Siyu Wang
- College of medical imaging, Dalian Medical University, Dalian, 116044, China.
| | - Yanwei Miao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116000, China.
| | - Huicong Shen
- Department of Radiology, Beijing Tian Tan Hospital, Capital Medical University, Beijing, 100050, China.
| | - Yan Guo
- Life science, GE Healthcare, Shenyang, 110000, China
| | - Lizhi Xie
- GE Healthcare, MR Research China, Beijing, China
| | - Yuqing Shang
- Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale University, New Haven, Connecticut, USA
| | - Junyi Dong
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116000, China
| | - Xiaoxin Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116000, China
| | - Weiwei Wang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116000, China
| | - Qingwei Song
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116000, China
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