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Duron L, Heraud A, Charbonneau F, Zmuda M, Savatovsky J, Fournier L, Lecler A. A Magnetic Resonance Imaging Radiomics Signature to Distinguish Benign From Malignant Orbital Lesions. Invest Radiol 2021; 56:173-180. [PMID: 32932375 DOI: 10.1097/rli.0000000000000722] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVES Distinguishing benign from malignant orbital lesions remains challenging both clinically and with imaging, leading to risky biopsies. The objective was to differentiate benign from malignant orbital lesions using radiomics on 3 T magnetic resonance imaging (MRI) examinations. MATERIALS AND METHODS This institutional review board-approved prospective single-center study enrolled consecutive patients presenting with an orbital lesion undergoing a 3 T MRI prior to surgery from December 2015 to July 2019. Radiomics features were extracted from 6 MRI sequences (T1-weighted images [WIs], DIXON-T2-WI, diffusion-WI, postcontrast DIXON-T1-WI) using the Pyradiomics software. Features were selected based on their intraobserver and interobserver reproducibility, nonredundancy, and with a sequential step forward feature selection method. Selected features were used to train and optimize a Random Forest algorithm on the training set (75%) with 5-fold cross-validation. Performance metrics were computed on a held-out test set (25%) with bootstrap 95% confidence intervals (95% CIs). Five residents, 4 general radiologists, and 3 expert neuroradiologists were evaluated on their ability to visually distinguish benign from malignant lesions on the test set. Performance comparisons between reader groups and the model were performed using McNemar test. The impact of clinical and categorizable imaging data on algorithm performance was also assessed. RESULTS A total of 200 patients (116 [58%] women and 84 [42%] men; mean age, 53.0 ± 17.9 years) with 126 of 200 (63%) benign and 74 of 200 (37%) malignant orbital lesions were included in the study. A total of 606 radiomics features were extracted. The best performing model on the training set was composed of 8 features including apparent diffusion coefficient mean value, maximum diameter on T1-WIs, and texture features. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity on the test set were respectively 0.869 (95% CI, 0.834-0.898), 0.840 (95% CI, 0.806-0.874), 0.684 (95% CI, 0.615-0.751), and 0.935 (95% CI, 0.905-0.961). The radiomics model outperformed all reader groups, including expert neuroradiologists (P < 0.01). Adding clinical and categorizable imaging data did not significantly impact the algorithm performance (P = 0.49). CONCLUSIONS An MRI radiomics signature is helpful in differentiating benign from malignant orbital lesions and may outperform expert radiologists.
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Affiliation(s)
| | | | | | - Mathieu Zmuda
- Department of Orbitopalpebral Surgery, Fondation Adolphe de Rothschild Hospital
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102
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Hearn N, Blazak J, Vivian P, Vignarajah D, Cahill K, Atwell D, Lagopoulos J, Min M. Prostate cancer GTV delineation with biparametric MRI and 68Ga-PSMA-PET: comparison of expert contours and semi-automated methods. Br J Radiol 2021; 94:20201174. [PMID: 33507812 DOI: 10.1259/bjr.20201174] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE The optimal method for delineation of dominant intraprostatic lesions (DIL) for targeted radiotherapy dose escalation is unclear. This study evaluated interobserver and intermodality variability of delineations on biparametric MRI (bpMRI), consisting of T2 weighted (T2W) and diffusion-weighted (DWI) sequences, and 68Ga-PSMA-PET/CT; and compared manually delineated GTV contours with semi-automated segmentations based on quantitative thresholding of intraprostatic apparent diffusion coefficient (ADC) and standardised uptake values (SUV). METHODS 16 patients who had bpMRI and PSMA-PET scanning performed prior to any treatment were eligible for inclusion. Four observers (two radiation oncologists, two radiologists) manually delineated the DIL on: (1) bpMRI (GTVMRI), (2) PSMA-PET (GTVPSMA) and (3) co-registered bpMRI/PSMA-PET (GTVFused) in separate sittings. Interobserver, intermodality and semi-automated comparisons were evaluated against consensus Simultaneous Truth and Performance Level Estimation (STAPLE) volumes, created from the relevant manual delineations of all observers with equal weighting. Comparisons included the Dice Similarity Coefficient (DSC), mean distance to agreement (MDA) and other metrics. RESULTS Interobserver agreement was significantly higher (p < 0.05) for GTVPSMA (DSC: 0.822, MDA: 1.12 mm) and GTVFused (DSC: 0.787, MDA: 1.34 mm) than for GTVMRI (DSC: 0.705, MDA 2.44 mm). Intermodality agreement between GTVMRI and GTVPSMA was low (DSC: 0.440, MDA: 4.64 mm). Agreement between semi-automated volumes and consensus GTV was low for MRI (DSC: 0.370, MDA: 8.16 mm) and significantly higher for PSMA-PET (0.571, MDA: 4.45 mm, p < 0.05). CONCLUSION 68Ga-PSMA-PET appears to improve interobserver consistency of DIL localisation vs bpMRI and may be more viable for simple quantitative delineation approaches; however, more sophisticated approaches to semi-automatic delineation factoring for patient- and disease-related heterogeneity are likely required. ADVANCES IN KNOWLEDGE This is the first study to evaluate the interobserver variability of prostate GTV delineations with co-registered bpMRI and 68Ga-PSMA-PET.
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Affiliation(s)
- Nathan Hearn
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, Australia.,ICON Cancer Centre, Maroochydore, Australia.,University of the Sunshine Coast, Sippy Downs, Australia
| | - John Blazak
- Department of Medical Imaging, Sunshine Coast University Hospital, Birtinya, Australia
| | - Philip Vivian
- Department of Medical Imaging, Sunshine Coast University Hospital, Birtinya, Australia
| | - Dinesh Vignarajah
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, Australia.,ICON Cancer Centre, Maroochydore, Australia
| | - Katelyn Cahill
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, Australia
| | - Daisy Atwell
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, Australia.,ICON Cancer Centre, Maroochydore, Australia.,University of the Sunshine Coast, Sippy Downs, Australia
| | - Jim Lagopoulos
- University of the Sunshine Coast, Sippy Downs, Australia.,Sunshine Coast Mind and Neuroscience - Thompson Institute, University of the Sunshine Coast, Birtinya, Australia
| | - Myo Min
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, Australia.,ICON Cancer Centre, Maroochydore, Australia.,University of the Sunshine Coast, Sippy Downs, Australia
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103
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Bozdağ M, Er A, Ekmekçi S. Association of apparent diffusion coefficient with Ki-67 proliferation index, progesterone-receptor status and various histopathological parameters, and its utility in predicting the high grade in meningiomas. Acta Radiol 2021; 62:401-413. [PMID: 32397733 DOI: 10.1177/0284185120922142] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Accurate preoperative determination of the histological grade and cellular proliferative potential of meningioma by non-invasive imaging is of paramount importance. PURPOSE To evaluate the utility of apparent diffusion coefficient (ADC) in determining the histological grade of meningioma, and to investigate the correlation of ADC with Ki-67 proliferation index (PI), progesterone receptor (PR) status, and a number of other histopathological parameters. MATERIAL AND METHODS Histopathologically confirmed 94 meningioma patients (72 low-grade, 22 high-grade) who had undergone preoperative diffusion-weighted imaging were retrospectively evaluated. ADC values were obtained by manually drawing the regions of interest (ROIs) within the solid components of the tumor. The relationship between ADC and Ki-67 values, PR status, and multiple histopathological parameters were investigated, and the ADC values of high-grade and low-grade meningiomas were compared. Independent sample t-test, Mann-Whitney U test, receiver operating characteristic, Pearson correlation, and multiple logistic regression analysis were used for statistical assessment. RESULTS All ADC and rADC values were significantly lower in high-grade meningiomas than in low-grade meningiomas (all P < 0.05). ADC values showed significantly negative correlations with Ki-67 and mitotic index (P < 0.001 for each). Numerous ADC parameters were significantly lower in meningiomas demonstrating hypercellularity and necrosis features (P < 0.05). ADC values did not show a significant correlation with PR score (all P > 0.05). CONCLUSION ADC can be utilized as a reliable imaging biomarker for predicting the proliferative potential and histological grade in meningiomas.
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Affiliation(s)
- Mustafa Bozdağ
- Department of Radiology, Tepecik Training and Research Hospital, Konak, Izmir, Turkey
| | - Ali Er
- Department of Radiology, Tepecik Training and Research Hospital, Konak, Izmir, Turkey
| | - Sümeyye Ekmekçi
- Department of Pathology, Tepecik Training and Research Hospital, Konak, Izmir, Turkey
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Meyer HJ, Wienke A, Surov A. Diffusion weighted imaging to predict nodal status in breast cancer: A systematic review and meta-analysis. Breast J 2021; 27:495-498. [PMID: 33615603 DOI: 10.1111/tbj.14200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale, Germany
| | - Alexey Surov
- Department of Radiology and Nuclear Medicine, University of Magdeburg, Magdeburg, Germany
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105
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Buizza G, Paganelli C, Ballati F, Sacco S, Preda L, Iannalfi A, Alexander DC, Baroni G, Palombo M. Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI. Med Phys 2021; 48:1250-1261. [PMID: 33369744 DOI: 10.1002/mp.14689] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 11/08/2020] [Accepted: 12/17/2020] [Indexed: 12/29/2022] Open
Abstract
PURPOSE Proton therapy could benefit from noninvasively gaining tumor microstructure information, at both planning and monitoring stages. The anatomical location of brain tumors, such as meningiomas, often hinders the recovery of such information from histopathology, and conventional noninvasive imaging biomarkers, like the apparent diffusion coefficient (ADC) from diffusion-weighted MRI (DW-MRI), are nonspecific. The aim of this study was to retrieve discriminative microstructural markers from conventional ADC for meningiomas treated with proton therapy. These markers were employed for tumor grading and tumor response assessment. METHODS DW-MRIs from patients affected by meningioma and enrolled in proton therapy were collected before (n = 35) and 3 months after (n = 25) treatment. For the latter group, the risk of an adverse outcome was inferred by their clinical history. Using Monte Carlo methods, DW-MRI signals were simulated from packings of synthetic cells built with well-defined geometrical and diffusion properties. Patients' ADC was modeled as a weighted sum of selected simulated signals. The weights that best described a patient's ADC were determined through an optimization procedure and used to estimate a set of markers of tumor microstructure: diffusion coefficient (D), volume fraction (vf), and radius (R). Apparent cellularity (ρapp ) was estimated from vf and R for an easier clinical interpretability. Differences between meningothelial and atypical subtypes, and low- and high-grade meningiomas were assessed with nonparametric statistical tests, whereas sensitivity and specificity with ROC analyses. Similar analyses were performed for patients showing low or high risk of an adverse outcome to preliminary evaluate response to treatment. RESULTS Significant (P < 0.05) differences in median ADC, D, vf, R, and ρapp values were found when comparing meningiomas' subtypes and grades. ROC analyses showed that estimated microstructural parameters reached higher specificity than ADC for subtyping (0.93 for D and vf vs 0.80 for ADC) and grading (0.75 for R vs 0.67 for ADC). High- and low-risk patients showed significant differences in ADC and microstructural parameters. The skewness of ρapp was the parameter with highest AUC (0.90) and sensitivity (0.75). CONCLUSIONS Matching measured with simulated ADC yielded a set of potential imaging markers for meningiomas grading and response monitoring in proton therapy, showing higher specificity than conventional ADC. These markers can provide discriminative information about spatial patterns of tumor microstructure implying important advantages for patient-specific proton therapy workflows.
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Affiliation(s)
- Giulia Buizza
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, 20133, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, 20133, Italy
| | - Francesco Ballati
- Diagnostic Radiology Residency School, University of Pavia, Pavia, 27100, Italy
| | - Simone Sacco
- Diagnostic Radiology Residency School, University of Pavia, Pavia, 27100, Italy
| | - Lorenzo Preda
- Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
| | - Alberto Iannalfi
- Clinical Department, National Center of Oncological Hadrontherapy (CNAO), Pavia, 27100, Italy
| | - Daniel C Alexander
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London (UCL), London, WC1V6LJ, UK
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, 20133, Italy.,Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Pavia, 27100, Italy
| | - Marco Palombo
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London (UCL), London, WC1V6LJ, UK
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106
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Surov A, Pech M, Omari J, Fischbach F, Damm R, Fischbach K, Powerski M, Relja B, Wienke A. Diffusion-Weighted Imaging Reflects Tumor Grading and Microvascular Invasion in Hepatocellular Carcinoma. Liver Cancer 2021; 10:10-24. [PMID: 33708636 PMCID: PMC7923880 DOI: 10.1159/000511384] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 09/06/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND To date, there are inconsistent data about relationships between diffusion-weighted imaging (DWI) and tumor grading/microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Our purpose was to systematize the reported results regarding the role of DWI in prediction of tumor grading/MVI in HCC. METHOD MEDLINE library, Scopus, and Embase data bases were screened up to December 2019. Overall, 29 studies with 2,715 tumors were included into the analysis. There were 20 studies regarding DWI and tumor grading, 8 studies about DWI and MVI, and 1 study investigated DWI, tumor grading, and MVI in HCC. RESULTS In 21 studies (1,799 tumors), mean apparent diffusion coefficient (ADC) values (ADCmean) were used for distinguishing HCCs. ADCmean of G1-3 lesions overlapped significantly. In 4 studies (461 lesions), minimum ADC (ADCmin) was used. ADCmin values in G1/2 lesions were over 0.80 × 10-3 mm2/s and in G3 tumors below 0.80 × 10-3 mm2/s. In 4 studies (241 tumors), true diffusion (D) was reported. A significant overlapping of D values between G1, G2, and G3 groups was found. ADCmean and MVI were analyzed in 9 studies (1,059 HCCs). ADCmean values of MIV+/MVI- lesions overlapped significantly. ADCmin was used in 4 studies (672 lesions). ADCmin values of MVI+ tumors were in the area under 1.00 × 10-3 mm2/s. In 3 studies (227 tumors), D was used. Also, D values of MVI+ lesions were predominantly in the area under 1.00 × 10-3 mm2/s. CONCLUSION ADCmin reflects tumor grading, and ADCmin and D predict MVI in HCC. Therefore, these DWI parameters should be estimated for every HCC lesion for pretreatment tumor stratification. ADCmean cannot predict tumor grading/MVI in HCC.
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Affiliation(s)
- Alexey Surov
- Department of Radiology and Nuclear Medicine University of Magdeburg, Magdeburg, Germany,*Alexey Surov, Department of Radiology and Nuclear Medicine, Ott-Von-Guericke University Magdeburg, Leipziger St., 44, DE–39112 Magdeburg (Germany),
| | - Maciej Pech
- Department of Radiology and Nuclear Medicine University of Magdeburg, Magdeburg, Germany
| | - Jazan Omari
- Department of Radiology and Nuclear Medicine University of Magdeburg, Magdeburg, Germany
| | - Frank Fischbach
- Department of Radiology and Nuclear Medicine University of Magdeburg, Magdeburg, Germany
| | - Robert Damm
- Department of Radiology and Nuclear Medicine University of Magdeburg, Magdeburg, Germany
| | - Katharina Fischbach
- Department of Radiology and Nuclear Medicine University of Magdeburg, Magdeburg, Germany
| | - Maciej Powerski
- Department of Radiology and Nuclear Medicine University of Magdeburg, Magdeburg, Germany
| | - Borna Relja
- Department of Radiology and Nuclear Medicine University of Magdeburg, Magdeburg, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
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Relationships between apparent diffusion coefficient (ADC) histogram analysis parameters and PD-L 1-expression in head and neck squamous cell carcinomas: a preliminary study. Radiol Oncol 2021; 55:150-157. [PMID: 33764703 PMCID: PMC8042826 DOI: 10.2478/raon-2021-0005] [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: 10/22/2020] [Accepted: 12/13/2020] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Immunotherapy has become a cornerstone of the modern cancer treatment. It might be crucial to predict its expression non-invasively by imaging. The present study used diffusion-weighted imaging (DWI) quantified by whole lesion apparent diffusion coefficient (ADC) values to elucidate possible associations with programmed cell death ligand 1(PD-L1) expression in head and neck squamous cell cancer (HNSCC). PATIENTS AND METHODS Overall, 29 patients with primary HNSCC of different localizations were involved in the study. DWI was obtained by using a sequence with b - values of 0 and 800 s/mm2 on a 3 T MRI. ADC values were evaluated with a whole lesion measurement and a histogram approach. PD-L1 expression was estimated on bioptic samples before any form of treatment using 3 scores, tumor positive score (TPS), immune cell score (ICS), and combined positive score (CPS). RESULTS An inverse correlation between skewness derived from ADC values and ICS was identified (r = -0.38, p = 0.04). ADCmax tended to correlate with ICS (r = -0.35, p = 0.06). Other ADC parameters did not show any association with the calculated scores. CONCLUSIONS There is a weak association between skewness derived from ADC values and PD-L1 expression in HNSCC, which might not be strong enough to predict PD-L1 expression in clinical routine. Presumably, ADC values are more influenced by complex histopathology compartments, comprising cellular and extracellular aspects of tumors than only of a single subset of tumor associated cells.
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108
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Verburg N, Koopman T, Yaqub MM, Hoekstra OS, Lammertsma AA, Barkhof F, Pouwels PJW, Reijneveld JC, Heimans JJ, Rozemuller AJM, Bruynzeel AME, Lagerwaard F, Vandertop WP, Boellaard R, Wesseling P, de Witt Hamer PC. Improved detection of diffuse glioma infiltration with imaging combinations: a diagnostic accuracy study. Neuro Oncol 2021; 22:412-422. [PMID: 31550353 PMCID: PMC7058442 DOI: 10.1093/neuonc/noz180] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 09/13/2019] [Indexed: 11/22/2022] Open
Abstract
Background Surgical resection and irradiation of diffuse glioma are guided by standard MRI: T2/fluid attenuated inversion recovery (FLAIR)–weighted MRI for non-enhancing and T1-weighted gadolinium-enhanced (T1G) MRI for enhancing gliomas. Amino acid PET has been suggested as the new standard. Imaging combinations may improve standard MRI and amino acid PET. The aim of the study was to determine the accuracy of imaging combinations to detect glioma infiltration. Methods We included 20 consecutive adults with newly diagnosed non-enhancing glioma (7 diffuse astrocytomas, isocitrate dehydrogenase [IDH] mutant; 1 oligodendroglioma, IDH mutant and 1p/19q codeleted; 1 glioblastoma IDH wildtype) or enhancing glioma (glioblastoma, 9 IDH wildtype and 2 IDH mutant). Standardized preoperative imaging (T1-, T2-, FLAIR-weighted, and T1G MRI, perfusion and diffusion MRI, MR spectroscopy and O-(2-[18F]-fluoroethyl)-L-tyrosine ([18F]FET) PET) was co-localized with multiregion stereotactic biopsies preceding resection. Tumor presence in the biopsies was assessed by 2 neuropathologists. Diagnostic accuracy was determined using receiver operating characteristic analysis. Results A total of 174 biopsies were obtained (63 from 9 non-enhancing and 111 from 11 enhancing gliomas), of which 129 contained tumor (50 from non-enhancing and 79 from enhancing gliomas). In enhancing gliomas, the combination of apparent diffusion coefficient (ADC) with [18F]FET PET (area under the curve [AUC], 95% CI: 0.89, 0.79‒0.99) detected tumor better than T1G MRI (0.56, 0.39‒0.72; P < 0.001) and [18F]FET PET (0.76, 0.66‒0.86; P = 0.001). In non-enhancing gliomas, no imaging combination detected tumor significantly better than standard MRI. FLAIR-weighted MRI had an AUC of 0.81 (0.65–0.98) compared with 0.69 (0.56–0.81; P = 0.019) for [18F]FET PET. Conclusion Combining ADC and [18F]FET PET detects glioma infiltration better than standard MRI and [18F]FET PET in enhancing gliomas, potentially enabling better guidance of local therapy.
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Affiliation(s)
- Niels Verburg
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center (UMC), Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Thomas Koopman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Maqsood M Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Otto S Hoekstra
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands.,University College London Institute of Neurology and Healthcare Engineering, London, UK
| | - Petra J W Pouwels
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Jaap C Reijneveld
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center (UMC), Amsterdam, Netherlands.,Department of Neurology, Amsterdam UMC, VUmc, Amsterdam, Netherlands
| | - Jan J Heimans
- Department of Neurology, Amsterdam UMC, VUmc, Amsterdam, Netherlands
| | | | | | - Frank Lagerwaard
- Department of Radiotherapy, Amsterdam UMC, VUmc, Amsterdam, Netherlands
| | - William P Vandertop
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center (UMC), Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Pieter Wesseling
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center (UMC), Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, Amsterdam UMC, Amsterdam, Netherlands.,Department of Pathology, Amsterdam UMC, VUmc, Amsterdam, Netherlands.,Princess Máxima Center for Pediatric Oncology and Department of Pathology, UMC Utrecht, Utrecht, Netherlands
| | - Philip C de Witt Hamer
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center (UMC), Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
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Surov A, Meyer HJ, Pech M, Powerski M, Omari J, Wienke A. Apparent diffusion coefficient cannot discriminate metastatic and non-metastatic lymph nodes in rectal cancer: a meta-analysis. Int J Colorectal Dis 2021; 36:2189-2197. [PMID: 34184127 PMCID: PMC8426255 DOI: 10.1007/s00384-021-03986-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/16/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND Our aim was to provide data regarding use of diffusion-weighted imaging (DWI) for distinguishing metastatic and non-metastatic lymph nodes (LN) in rectal cancer. METHODS MEDLINE library, EMBASE, and SCOPUS database were screened for associations between DWI and metastatic and non-metastatic LN in rectal cancer up to February 2021. Overall, 9 studies were included into the analysis. Number, mean value, and standard deviation of DWI parameters including apparent diffusion coefficient (ADC) values of metastatic and non-metastatic LN were extracted from the literature. The methodological quality of the studies was investigated according to the QUADAS-2 assessment. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian, and Laird random-effects models with inverse-variance weights were used to account the heterogeneity between the studies. Mean DWI values including 95% confidence intervals were calculated for metastatic and non-metastatic LN. RESULTS ADC values were reported for 1376 LN, 623 (45.3%) metastatic LN, and 754 (54.7%) non-metastatic LN. The calculated mean ADC value (× 10-3 mm2/s) of metastatic LN was 1.05, 95%CI (0.94, 1.15). The calculated mean ADC value of the non-metastatic LN was 1.17, 95%CI (1.01, 1.33). The calculated sensitivity and specificity were 0.81, 95%CI (0.74, 0.89) and 0.67, 95%CI (0.54, 0.79). CONCLUSION No reliable ADC threshold can be recommended for distinguishing of metastatic and non-metastatic LN in rectal cancer.
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Affiliation(s)
- Alexey Surov
- grid.5807.a0000 0001 1018 4307Department of Radiology and Nuclear Medicine, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Hans-Jonas Meyer
- grid.9647.c0000 0004 7669 9786Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Maciej Pech
- grid.5807.a0000 0001 1018 4307Department of Radiology and Nuclear Medicine, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Maciej Powerski
- grid.5807.a0000 0001 1018 4307Department of Radiology and Nuclear Medicine, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Jasan Omari
- grid.5807.a0000 0001 1018 4307Department of Radiology and Nuclear Medicine, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Andreas Wienke
- grid.9018.00000 0001 0679 2801Institute of Medical Epidemiology, Martin-Luther-University Halle-Wittenberg, Biostatistics, and Informatics, Halle (Saale), Germany
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Long-term apparent diffusion coefficient value changes in patients undergoing radiosurgical treatment of meningiomas. Acta Neurochir (Wien) 2021; 163:89-95. [PMID: 32909068 DOI: 10.1007/s00701-020-04567-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/02/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE A noninvasive method to predict the progress or treatment response of meningiomas is desirable to improve the tumor management. Studies showed that apparent diffusion coefficient (ADC) pretreatment values can predict treatment response in brain tumors. The aim of this study was to analyze changes of intratumoral ADC values in patients with meningiomas undergoing conservative or radiosurgery. METHOD MR images of 51 patients with diagnose of meningiomas were retrospectively reviewed. Twenty-five patients undergoing conservative or radiosurgery treatment, respectively, were included in the study. The follow-up data ranged between 1 and 10 years. Based on ROI analysis, the mean ADC values, ADC10%min, and ADC90%max were evaluated at different time points during follow-up. RESULTS Baseline ADC values in between both groups were similar. The ADCmean values, ADC10%min, and ADC90%max within the different groups did not show any significant changes during the follow-up times in the untreated (ADCmean over 10 years period: 0.87 ± 0.05 × 10-3 mm2/s) and radiosurgically treated (ADCmean over 4 years period: 1.02 ± 0.12 × 10-3 mm2/s) group. However, statistically significant difference was observed when comparing the ADCmean and ADC90%max values of untreated with radiosurgically treated (p < 0.0001) meningiomas. Also, ADC10%min revealed statistically significant difference between the untreated and the radiosurgery group (p < 0.05). CONCLUSIONS ADC values in conservatively managed meningiomas remain stable during the follow-up. However, meningiomas undergoing radiosurgery reveal significant change of the mean ADC values over time, suggesting that ADC may reflect a change in the biological behavior of the tumor. These observations might suggest the value of ADC changes as an indicator of treatment response.
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Carmona-Bozo JC, Manavaki R, Woitek R, Torheim T, Baxter GC, Caracò C, Provenzano E, Graves MJ, Fryer TD, Patterson AJ, Gilbert FJ. Hypoxia and perfusion in breast cancer: simultaneous assessment using PET/MR imaging. Eur Radiol 2021; 31:333-344. [PMID: 32725330 PMCID: PMC7755870 DOI: 10.1007/s00330-020-07067-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 05/12/2020] [Accepted: 07/03/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Hypoxia is associated with poor prognosis and treatment resistance in breast cancer. However, the temporally variant nature of hypoxia can complicate interpretation of imaging findings. We explored the relationship between hypoxia and vascular function in breast tumours through combined 18F-fluoromisonidazole (18 F-FMISO) PET/MRI, with simultaneous assessment circumventing the effect of temporal variation in hypoxia and perfusion. METHODS Women with histologically confirmed, primary breast cancer underwent a simultaneous 18F-FMISO-PET/MR examination. Tumour hypoxia was assessed using influx rate constant Ki and hypoxic fractions (%HF), while parameters of vascular function (Ktrans, kep, ve, vp) and cellularity (ADC) were derived from dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI, respectively. Additional correlates included histological subtype, grade and size. Relationships between imaging variables were assessed using Pearson correlation (r). RESULTS Twenty-nine women with 32 lesions were assessed. Hypoxic fractions > 1% were observed in 6/32 (19%) cancers, while 18/32 (56%) tumours showed a %HF of zero. The presence of hypoxia in lesions was independent of histological subtype or grade. Mean tumour Ktrans correlated negatively with Ki (r = - 0.38, p = 0.04) and %HF (r = - 0.33, p = 0.04), though parametric maps exhibited intratumoural heterogeneity with hypoxic regions colocalising with both hypo- and hyperperfused areas. No correlation was observed between ADC and DCE-MRI or PET parameters. %HF correlated positively with lesion size (r = 0.63, p = 0.001). CONCLUSION Hypoxia measured by 18F-FMISO-PET correlated negatively with Ktrans from DCE-MRI, supporting the hypothesis of perfusion-driven hypoxia in breast cancer. Intratumoural hypoxia-perfusion relationships were heterogeneous, suggesting that combined assessment may be needed for disease characterisation, which could be achieved using simultaneous multimodality imaging. KEY POINTS • At the tumour level, hypoxia measured by 18F-FMISO-PET was negatively correlated with perfusion measured by DCE-MRI, which supports the hypothesis of perfusion-driven hypoxia in breast cancer. • No associations were observed between 18F-FMISO-PET parameters and tumour histology or grade, but tumour hypoxic fractions increased with lesion size. • Intratumoural hypoxia-perfusion relationships were heterogeneous, suggesting that the combined hypoxia-perfusion status of tumours may need to be considered for disease characterisation, which can be achieved via simultaneous multimodality imaging as reported here.
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Affiliation(s)
- Julia C Carmona-Bozo
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Roido Manavaki
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Ramona Woitek
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Turid Torheim
- Cancer Research UK - Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Gabrielle C Baxter
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Corradina Caracò
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Elena Provenzano
- Cancer Research UK - Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
- Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Box 97, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Martin J Graves
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- MRIS Unit, Cambridge University Hospitals NHS Foundation Trust, Box 162, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Tim D Fryer
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Box 65, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Andrew J Patterson
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- MRIS Unit, Cambridge University Hospitals NHS Foundation Trust, Box 162, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Fiona J Gilbert
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
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Meyer HJ, Höhn AK, Woidacki K, Andric M, Powerski M, Pech M, Surov A. Associations between IVIM histogram parameters and histopathology in rectal cancer. Magn Reson Imaging 2020; 77:21-27. [PMID: 33316358 DOI: 10.1016/j.mri.2020.12.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 11/18/2020] [Accepted: 12/08/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE Histogram analysis can better reflect tumor heterogeneity than conventional imaging analysis. The present study analyzed possible correlations between histogram analysis parameters derived from Intravoxel-incoherent imaging (IVIM) and histopathological features in rectal cancer (RC). METHODS Seventeen patients with histopathologically proven rectal adenocarcinomas were retrospectively acquired. In all cases, pelvic MRI was performed. Diffusion weighted imaging was obtained using a multi-slice single-shot echo-planar imaging sequence with b values of 0, 50, 200, 500 and 1000 s/mm2. Simplified IVIM analysis was performed using the IntelliSpace portal, version 10 and the following images were generated: f (perfusion fraction) map, D (true diffusion coefficient) map, and ADC map utilizing all b-values. Histogram based analysis of signal intensities was performed for every IVIM map using an in-house matlab tool. Histopathology was investigated using Ki 67 specimens with calculation of Ki 67-index and cellularity. CD31 stained specimens were used for calculation of microvessel density (MVD). RESULTS There were statistically significant correlations between Ki 67 index and mode derived from ADC as well as entropy from f, r=-0.50, p=.04 and r=-0.55, p=.02, respectively. MVD correlated well with parameters derived from f. CONCLUSION IVIM histogram analysis parameters can reflect histopathology in RC. ADC and D values are associated with proliferation potential. Perfusion fraction f is associated with MVD.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
| | | | - Katja Woidacki
- Section Experimental Radiology, Department of Radiology and Nuclear Medicine, Otto-von-Guericke-University of Magdeburg, Magdeburg, Germany
| | - Mihailo Andric
- Department of Surgery, Otto-von-Guericke-University of Magdeburg, Magdeburg, Germany
| | - Maciej Powerski
- Department of Radiology and Nuclear Medicine, Otto-von-Guericke-University of Magdeburg, Magdeburg, Germany
| | - Maciej Pech
- Department of Radiology and Nuclear Medicine, Otto-von-Guericke-University of Magdeburg, Magdeburg, Germany
| | - Alexey Surov
- Department of Radiology and Nuclear Medicine, Otto-von-Guericke-University of Magdeburg, Magdeburg, Germany
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Serour DK, Adel KM, Osman AMA. Post-treatment benign changes versus recurrence in non-lymphoid head and neck malignancies: can diffusion-weighted magnetic resonance imaging end up the diagnostic challenge? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-020-00177-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Abstract
Background
The aim of this prospective cohort study is to substantiate the added value of diffusion-weighted magnetic resonance imaging (DW-MRI) over conventional MRI assessment in the differentiation between locoregional recurrence/residual tumour and post-treatment benign changes in patients with non-lymphoid head and neck malignancies.
Thirty adult patients, each with a suspicious lesion on post-treatment imaging scans at the primary site of a previously treated non-lymphoid head and neck malignancy, were evaluated by MRI and diffusion-weighted imaging (DWI). The apparent diffusion coefficient (ADC) values of the lesions were calculated.
Results
Diffusion-weighted MRI yielded an accuracy of 90%, a sensitivity of 88.9%, a specificity of 91.7%, a positive predictive value of 94.1% and a negative predictive value of 84.6%. The mean ADC value of the lesions was lower in the “locoregional recurrence/residual tumour” group (1.08 × 10−3 mm2/s) compared to the “post-treatment benign changes” group (1.95 × 10−3 mm2/s); P < 0.001. An ADC cutoff value of 1.43 × 10−3 mm2/s achieved the same accuracy as the visual assessment by DW-MRI.
Conclusion
Incorporating the DWI sequence into the post-treatment imaging assessment protocol brings a substantial added value to conventional MRI assessment in patients with non-lymphoid head and neck malignancies. This valuable merit of DW-MRI can help avoid or, at least, largely minimize unnecessary or unfeasible tissue sampling. An ADC cutoff value of 1.43 × 10−3 mm2/s can also be utilized to aid in the assessment process.
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Schell M, Pflüger I, Brugnara G, Isensee F, Neuberger U, Foltyn M, Kessler T, Sahm F, Wick A, Nowosielski M, Heiland S, Weller M, Platten M, Maier-Hein KH, Von Deimling A, Van Den Bent MJ, Gorlia T, Wick W, Bendszus M, Kickingereder P. Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial. Neuro Oncol 2020; 22:1667-1676. [PMID: 32393964 PMCID: PMC7690360 DOI: 10.1093/neuonc/noaa120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND This study validated a previously described diffusion MRI phenotype as a potential predictive imaging biomarker in patients with recurrent glioblastoma receiving bevacizumab (BEV). METHODS A total of 396/596 patients (66%) from the prospective randomized phase II/III EORTC-26101 trial (with n = 242 in the BEV and n = 154 in the non-BEV arm) met the inclusion criteria with availability of anatomical and diffusion MRI sequences at baseline prior treatment. Apparent diffusion coefficient (ADC) histograms from the contrast-enhancing tumor volume were fitted to a double Gaussian distribution and the mean of the lower curve (ADClow) was used for further analysis. The predictive ability of ADClow was assessed with biomarker threshold models and multivariable Cox regression for overall survival (OS) and progression-free survival (PFS). RESULTS ADClow was associated with PFS (hazard ratio [HR] = 0.625, P = 0.007) and OS (HR = 0.656, P = 0.031). However, no (predictive) interaction between ADClow and the treatment arm was present (P = 0.865 for PFS, P = 0.722 for OS). Independent (prognostic) significance of ADClow was retained after adjusting for epidemiological, clinical, and molecular characteristics (P ≤ 0.02 for OS, P ≤ 0.01 PFS). The biomarker threshold model revealed an optimal ADClow cutoff of 1241*10-6 mm2/s for OS. Thereby, median OS for BEV-patients with ADClow ≥ 1241 was 10.39 months versus 8.09 months for those with ADClow < 1241 (P = 0.004). Similarly, median OS for non-BEV patients with ADClow ≥ 1241 was 9.80 months versus 7.79 months for those with ADClow < 1241 (P = 0.054). CONCLUSIONS ADClow is an independent prognostic parameter for stratifying OS and PFS in patients with recurrent glioblastoma. Consequently, the previously suggested role of ADClow as predictive imaging biomarker could not be confirmed within this phase II/III trial.
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Affiliation(s)
- Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Irada Pflüger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Foltyn
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Kessler
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | - Antje Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Nowosielski
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
- Department of Neurology, Medical University, Innsbruck, Austria
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Michael Platten
- Department of Neurology, Mannheim Medical Center, University of Heidelberg, Mannheim, Germany
| | - Klaus H Maier-Hein
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Department of Neurology, Mannheim Medical Center, University of Heidelberg, Mannheim, Germany
| | - Andreas Von Deimling
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | | | - Thierry Gorlia
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Kickingereder
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
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Meyer HJ, Schneider I, Emmer A, Kornhuber M, Surov A. Associations between apparent diffusion coefficient values and histopathological tissue alterations in myopathies. Brain Behav 2020; 10:e01809. [PMID: 32860496 PMCID: PMC7667360 DOI: 10.1002/brb3.1809] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 08/02/2020] [Accepted: 08/04/2020] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES Diffusion-weighted imaging (DWI) can reflect histopathologic changes in muscle disorders. The present study sought to elucidate possible associations between histopathology derived from muscle biopsies and DWI in myositis and other myopathies. METHODS Nineteen patients (10 women, 52.6%) with a mean age 51.43 ± 19 years were included in this retrospective study. Apparent diffusion coefficients (ADC) were evaluated with a histogram approach of the biopsied muscle. The histopathology analysis included the scoring systems proposed by Tateyama et al., Fanin et al., Allenbach et al. and immunhistochemical stainings for MHC, CD68, CD8, and CD4. RESULTS There was a tendency that skewness was lowered with increasing Tateyama score, but it did not reach statistical significance (p = .14). No statistical differences for the other scores were identified. There was a tendency that kurtosis was higher in MHC negative stained patient compared to positive patients, but statistically significance was not reached (p = .07). ADC histogram parameters did not correlate with CD68 and CD8 positive stained cells. There was a trend for skewness to correlate with the amount of CD4-positive cells (r = .57, p = .07). CONCLUSION The present study could not identify statistical significant associations between DWI and histopathology in muscle diseases based upon a small patient sample. Presumably, the investigated histopathology scores are more specific for certain disease aspects, whereas ADC values reflect the whole cellularity of the investigated muscle, which might cause the negative results.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Ilka Schneider
- Department of Neurology, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Alexander Emmer
- Department of Neurology, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Malte Kornhuber
- Department of Neurology, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Magdeburg, Magdeburg, Germany
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Franklin A, Yaxley WJ, Raveenthiran S, Coughlin G, Gianduzzo T, Kua B, McEwan L, Wong D, Delahunt B, Egevad L, Samaratunga H, Brown N, Parkinson R, Roberts MJ, Yaxley JW. Histological comparison between predictive value of preoperative 3-T multiparametric MRI and 68 Ga-PSMA PET/CT scan for pathological outcomes at radical prostatectomy and pelvic lymph node dissection for prostate cancer. BJU Int 2020; 127:71-79. [PMID: 32524748 DOI: 10.1111/bju.15134] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
OBJECTIVE To evaluate the ability of preoperative multiparametric magnetic resonance imaging (mpMRI) and a gallium-68 prostate-specific membrane antigen positron emission tomography/computed tomography (68 Ga-PSMA PET/CT) scan to predict pathological outcomes and also identify a group of men with a <5% risk of histological pelvic lymph node metastasis (LNM) at pelvic lymph node dissection (PLND) performed during a robot-assisted laparoscopic radical prostatectomy (RALP) for prostate cancer. We then aimed to compare these results to known risk calculators for LNM, including the Cancer of the Prostate Risk Assessment (CAPRA) score, Memorial Sloan Kettering Cancer Centre (MSKCC) and Briganti nomograms. PATIENTS AND METHODS Between July 2014 and September 2019 only men who had both a preoperative mpMRI and staging 68 Ga-PSMA PET/CT at our institution followed by a RALP with PLND referred to a single specialist uropathology laboratory were considered for inclusion. The data were collected retrospectively prior to February 2019 and in a prospective manner thereafter. A model was built to allocate probabilities of the men with a negative 68 Ga-PSMA PET/CT scan having a <5% risk of histologically LNM at RALP based on the preoperative radiological staging. RESULTS A total of 233 consecutive men met the inclusion criteria of which 58 men (24.9%) had a LNM identified on PLND histology. The median (range) International Society of Urological Pathology (ISUP) Grade was 5 (1-5) and the median (range) prostate-specific antigen level was 7.4 (1.5-72) ng/mL. The median (range) number of resected lymph nodes was 16 (1-53) and the median (range) number of positive nodes identified on histology was 2 (1-22). Seminal vesicle invasion on mpMRI was more common in node-positive men than in the absence of LNM (31% vs 12%). The maximum standardised uptake value of the primary tumour on 68 Ga-PSMA PET/CT was higher in men with LNM (median 9.2 vs 7.2, P = 0.02). Suspected LNM were identified in 42/233 (18.0%) men with 68 Ga-PSMA PET/CT compared with 22/233 (9.4%) men with mpMRI (P = 0.023). The positive and negative predictive value for 68 Ga-PSMA PET/CT was 66.7% and 84.3% respectively, compared to 59.1% and 78.7% for mpMRI. A predictive model showed only two men (4.2%) with a negative preoperative 68 Ga-PSMA PET/CT would be positive for a histological LNM if they are ISUP Grade < 5 and Prostate Imaging-Reporting and Data System (PI-RADS) <5; or ISUP Grade 5 with PI-RADS < 4. An inspection of three additional variables: CAPRA score, MSKCC and Briganti nomograms did not improve the predictive probability for this group. However, of the 61 men with ISUP Grade 4-5 malignancy and also a PI-RADS 5 mpMRI, 20 (32.8%) men had a microscopic LNM despite a negative preoperative 68 Ga-PSMA PET/CT. CONCLUSION Preoperative 68 Ga-PSMA/PET CT was more sensitive in identifying histological pelvic LNM than 3-T mpMRI. Men with a negative 68 Ga-PSMA PET/CT have a lower risk of LNM than predicted with CAPRA scores or MSKCC and Briganti nomograms. We identified that the combination of a negative preoperative 68 Ga-PSMA PET/CT, ISUP biopsy Grade <5 and PI-RADS <5 prostate mpMRI, or an ISUP Grade 5 with PI-RADS <4 on mpMRI was associated with a <5% risk of a LNM. The addition of CAPRA scores, MSKCC and Briganti nomograms did not improve the predictive probability within this model. Conversely, men with ISUP Grade 4-5 malignancy associated with a PI-RADS 5 prostate mpMRI had a >30% risk of microscopic LNM despite a negative preoperative 68 Ga-PSMA PET/CT and this high-risk group would appear suitable for an extended PLND at the time of a radical prostatectomy.
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Affiliation(s)
- Anthony Franklin
- The Wesley Hospital, Brisbane, Queensland, Australia.,Wesley Medical Research, Brisbane, Queensland, Australia.,The University of Queensland, Brisbane, Queensland, Australia
| | - William J Yaxley
- The University of Queensland, Brisbane, Queensland, Australia.,Royal Brisbane and Women's Hospital, Herston, Queensland, Australia
| | | | | | - Troy Gianduzzo
- The Wesley Hospital, Brisbane, Queensland, Australia.,The University of Queensland, Brisbane, Queensland, Australia
| | - Boon Kua
- The Wesley Hospital, Brisbane, Queensland, Australia
| | - Lousie McEwan
- Wesley Medical Imaging, Brisbane, Queensland, Australia
| | - David Wong
- Wesley Medical Imaging, Brisbane, Queensland, Australia
| | - Brett Delahunt
- Aquesta Pathology, Milton, Queensland, Australia.,Department of Pathology and Molecular Medicine, Wellington School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand
| | - Lars Egevad
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | | | - Nicholas Brown
- The University of Queensland, Brisbane, Queensland, Australia.,Wesley Medical Imaging, Brisbane, Queensland, Australia
| | - Rob Parkinson
- Wesley Medical Imaging, Brisbane, Queensland, Australia
| | - Matthew J Roberts
- The University of Queensland, Brisbane, Queensland, Australia.,Royal Brisbane and Women's Hospital, Herston, Queensland, Australia
| | - John W Yaxley
- The Wesley Hospital, Brisbane, Queensland, Australia.,The University of Queensland, Brisbane, Queensland, Australia.,Royal Brisbane and Women's Hospital, Herston, Queensland, Australia
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Kedves A, Tóth Z, Emri M, Fábián K, Sipos D, Freihat O, Tollár J, Cselik Z, Lakosi F, Bajzik G, Repa I, Kovács Á. Predictive Value of Diffusion, Glucose Metabolism Parameters of PET/MR in Patients With Head and Neck Squamous Cell Carcinoma Treated With Chemoradiotherapy. Front Oncol 2020; 10:1484. [PMID: 32983984 PMCID: PMC7492555 DOI: 10.3389/fonc.2020.01484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 07/13/2020] [Indexed: 12/15/2022] Open
Abstract
Purpose: This study aims to evaluate the predictive value of the pretreatment, metabolic, and diffusion parameters of a primary tumor assessed with PET/MR on patient clinical outcomes. Methods: Retrospective evaluation was performed using PET/MR image data sets acquired using the single tracer injection dual imaging of 68 histologically proven head and neck cancer patients 4 weeks before receiving definitive chemoradiotherapy (CRT). PET/MR was performed before the CRT and 12 weeks after the CRT for response evaluation. Image data (PET and MRI diffusion-weighted imaging [DWI]) was used to specify the maximum standard uptake value, the peak lean body mass corrected, SUVmax, the metabolic tumor volume, the total lesion glycolysis (SUVmax, SULpeak, MTV, and TLG), and the mean apparent diffusion coefficient (ADCmean) of the primary tumor. Based on the results of the therapeutic response evaluation, two patient subgroups were created: one with a viable tumor and another without. Metabolic and diffusion data, from the pretreatment PET/MR and the therapeutic response, were correlated using Spearman's correlation coefficient and Wilcoxon's test. Results: After completing the CRT, a viable residual tumor was detected in 36/68 (53%) cases, and 32/68 (47%) patients showed complete remission. However, no significant correlation was found between the pretreatment parameter, ADCmean (p = 0.88), and the therapeutic success. The PET parameters, SUVmax and SULpeak, MTV, and TLG (p = 0.032, p = 0.01, p < 0.0001, p = 0.0004) were statistically significantly different between the two patient subgroups. Conclusion: This study found that MRI-based (ADCmean) data from FDG PET/MR pretreatment could not be used to predict therapeutic response although the PET parameters SUVmax, SULpeak, MTV, and TLG proved to be more useful; thus, their inclusion in risk stratification may also be of additional value.
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Affiliation(s)
- András Kedves
- Doctoral School of Health Sciences, University of Pécs, Pécs, Hungary.,Department of Medical Imaging, Faculty of Health Sciences, University of Pécs, Pécs, Hungary.,Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, "Moritz Kaposi" Teaching Hospital, Kaposvár, Hungary
| | - Zoltán Tóth
- Doctoral School of Health Sciences, University of Pécs, Pécs, Hungary.,MEDICOPUS Healthcare Provider and Public Nonprofit Ltd., Somogy County Moritz Kaposi Teaching Hospital, Kaposvár, Hungary
| | - Miklós Emri
- Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, "Moritz Kaposi" Teaching Hospital, Kaposvár, Hungary.,Department of Medical Imaging, Faculty of Health Sciences, University of Debrecen, Debrecen, Hungary
| | - Krisztián Fábián
- Department of Medical Imaging, Faculty of Health Sciences, University of Pécs, Pécs, Hungary
| | - Dávid Sipos
- Doctoral School of Health Sciences, University of Pécs, Pécs, Hungary.,Department of Medical Imaging, Faculty of Health Sciences, University of Pécs, Pécs, Hungary.,Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, "Moritz Kaposi" Teaching Hospital, Kaposvár, Hungary
| | - Omar Freihat
- Doctoral School of Health Sciences, University of Pécs, Pécs, Hungary
| | - József Tollár
- Department of Medical Imaging, Faculty of Health Sciences, University of Pécs, Pécs, Hungary.,Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, "Moritz Kaposi" Teaching Hospital, Kaposvár, Hungary
| | - Zsolt Cselik
- Oncoradiology, Csolnoky Ferenc County Hospital, Veszprém, Hungary
| | - Ferenc Lakosi
- Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, "Moritz Kaposi" Teaching Hospital, Kaposvár, Hungary
| | - Gábor Bajzik
- Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, "Moritz Kaposi" Teaching Hospital, Kaposvár, Hungary
| | - Imre Repa
- Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, "Moritz Kaposi" Teaching Hospital, Kaposvár, Hungary
| | - Árpád Kovács
- Doctoral School of Health Sciences, University of Pécs, Pécs, Hungary.,Department of Medical Imaging, Faculty of Health Sciences, University of Pécs, Pécs, Hungary.,Department of Oncoradiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
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Hearn N, Bugg W, Chan A, Vignarajah D, Cahill K, Atwell D, Lagopoulos J, Min M. Manual and semi-automated delineation of locally advanced rectal cancer subvolumes with diffusion-weighted MRI. Br J Radiol 2020; 93:20200543. [PMID: 32877210 DOI: 10.1259/bjr.20200543] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES To evaluate interobserver agreement for T2 weighted (T2W) and diffusion-weighted MRI (DW-MRI) contours of locally advanced rectal cancer (LARC); and to evaluate manual and semi-automated delineations of restricted diffusion tumour subvolumes. METHODS 20 cases of LARC were reviewed by 2 radiation oncologists and 2 radiologists. Contours of gross tumour volume (GTV) on T2W, DW-MRI and co-registered T2W/DW-MRI were independently delineated and compared using Dice Similarity Coefficient (DSC), mean distance to agreement (MDA) and other metrics of interobserver agreement. Restricted diffusion subvolumes within GTVs were manually delineated and compared to semi-automatically generated contours corresponding to intratumoral apparent diffusion coefficient (ADC) centile values. RESULTS Observers were able to delineate subvolumes of restricted diffusion with moderate agreement (DSC 0.666, MDA 1.92 mm). Semi-automated segmentation based on the 40th centile intratumoral ADC value demonstrated moderate average agreement with consensus delineations (DSC 0.581, MDA 2.44 mm), with errors noted in image registration and luminal variation between acquisitions. A small validation set of four cases with optimised planning MRI demonstrated improvement (DSC 0.669, MDA 1.91 mm). CONCLUSION Contours based on co-registered T2W and DW-MRI could be used for delineation of biologically relevant tumour subvolumes. Semi-automated delineation based on patient-specific intratumoral ADC thresholds may standardise subvolume delineation if registration between acquisitions is sufficiently accurate. ADVANCES IN KNOWLEDGE This is the first study to evaluate the feasibility of semi-automated diffusion-based subvolume delineation in LARC. This approach could be applied to dose escalation or 'dose painting' protocols to improve delineation reproducibility.
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Affiliation(s)
- Nathan Hearn
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, QLD, Australia.,ICON Cancer Centre, Maroochydore, QLD, Australia.,University of the Sunshine Coast, Sippy Downs, QLD, Australia
| | - William Bugg
- Department of Medical Imaging, Sunshine Coast University Hospital, Birtinya, QLD, Australia
| | - Anthony Chan
- Department of Medical Imaging, Sunshine Coast University Hospital, Birtinya, QLD, Australia
| | - Dinesh Vignarajah
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, QLD, Australia.,ICON Cancer Centre, Maroochydore, QLD, Australia
| | - Katelyn Cahill
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, QLD, Australia
| | - Daisy Atwell
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, QLD, Australia.,ICON Cancer Centre, Maroochydore, QLD, Australia.,University of the Sunshine Coast, Sippy Downs, QLD, Australia
| | - Jim Lagopoulos
- University of the Sunshine Coast, Sippy Downs, QLD, Australia.,Sunshine Coast Mind and Neuroscience - Thompson Institute, University of the Sunshine Coast, Birtinya, QLD, Australia
| | - Myo Min
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, QLD, Australia.,ICON Cancer Centre, Maroochydore, QLD, Australia.,University of the Sunshine Coast, Sippy Downs, QLD, Australia
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Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging Methods in Nonenhancing Gliomas. World Neurosurg 2020; 141:123-130. [DOI: 10.1016/j.wneu.2020.05.278] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 05/29/2020] [Accepted: 05/30/2020] [Indexed: 12/21/2022]
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Chu JP, Song YK, Tian YS, Qiu HS, Huang XH, Wang YL, Huang YQ, Zhao J. Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability. Eur Radiol 2020; 31:729-739. [PMID: 32857204 DOI: 10.1007/s00330-020-07204-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 07/05/2020] [Accepted: 08/18/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVES Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data. METHODS Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed. RESULTS The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (p < 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the IDH-1 mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81). CONCLUSIONS Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma. KEY POINTS • The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods. • DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma. • The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.
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Affiliation(s)
- Jian-Ping Chu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Yu-Kun Song
- Department of Radiology, The First Affiliated Hospital of Xiamen University, Xiamen, 361003, China
| | - Yi-Su Tian
- Department of Radiology, SICHUAN Cancer Hospital and Research Institute, Chengdu, 610041, China
| | - Hai-Shan Qiu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Xia-Hua Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Yu-Liang Wang
- Department of Radiology, Shenzhen City Nanshan District People's Hospital, Shenzhen, 518000, China
| | - Ying-Qian Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China.
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ADC values of benign and high grade meningiomas and associations with tumor cellularity and proliferation - A systematic review and meta-analysis. J Neurol Sci 2020; 415:116975. [PMID: 32535250 DOI: 10.1016/j.jns.2020.116975] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 05/27/2020] [Accepted: 06/01/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION The aim of the present systematic review and meta-analysis was to compare the reported ADC values in different meningiomas and to analyze associations between ADC and cell count and proliferation activity in this tumor entity. METHOD MEDLINE library and SCOPUS database were screened for papers investigating ADC values of meningiomas up November 2019. The first primary endpoint of the systematic review was the reported ADC mean value of the meningioma groups. The second primary endpoint was the correlation coefficient between ADC values and proliferation index Ki 67 and cellularity. RESULTS For the discrimination analysis between benign and high grade meningioma 17 studies were suitable. There were 766 grade I tumors and 289 high grade meningiomas. The calculated mean ADC value of the benign grade I tumors was 0.93 × 10-3mm2/s [95%-Confidence interval 0.84;1.03] and the mean value of the high-grade tumors was 0.77 × 10-3mm2/s [95%-Confidence interval 0.73-0.80]. The pooled correlation coefficient between ADC and the proliferation index Ki 67 was r = -0.36 [95% CI -0.43; -0.28]. The pooled correlation coefficient between ADC and cellularity was r = -0.43 [95% CI -0.61; - 0.26]. CONCLUSION No validated ADC threshold can be recommended for distinguishing benign from high grade meningiomas. Only a moderate inverse correlation was identified between ADC values and tumor microstructure in meningiomas and, therefore, ADC might not accurately enough to predict proliferation potential and cellularity in this entity.
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Radiomics in diffusion data: a test-retest, inter- and intra-reader DWI phantom study. Clin Radiol 2020; 75:798.e13-798.e22. [PMID: 32723501 DOI: 10.1016/j.crad.2020.06.024] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 06/17/2020] [Indexed: 12/14/2022]
Abstract
AIM The aim of this study was to evaluate the robustness of radiomics features of a MRI (magnetic resonance imaging) phantom in quantitative diffusion-weighted imaging (DWI) and depending on the image resolution. MATERIALS AND METHODS Scanning of an in-house developed DWI phantom was performed at a 1.5 T MRI scanner (Magnetom AERA, Siemens, Erlangen, Germany) using an echo planar imaging (EPI) DWI sequence (b=0,500,1,000 s/mm2) with low (3×3 mm2) and high (2×2 mm2) image resolutions. Scans were repeated after phantom repositioning to evaluate retest reliability. Radiomics features were extracted after semi-automatic segmentation and standardised pre-processing. Intra-/interobserver reproducibility and test-retest robustness were assessed using intraclass correlation coefficients (ICC). Differences were tested with non-parametric Wilcoxon's signed-rank and Friedman's test (p < 0.05) with Dunn's post-hoc analysis. RESULTS Test-retest ICC was overall high with >0.90 for 39/46 radiomics features in all sequences/resolutions. Decreased test-retest ICCs were pronounced for conventional Min-value (overall ICC=0.817), and grey-level zone length matrix (GLZLM) features Short-Zone Emphasis (SZE) and Short-Zone Low Grey-level Emphasis (SZLGE) (for both overall ICC=0.927). Test-retest reproducibility was significantly different between b=500, 1,000 and apparent diffusion coefficient (ADC) (mean 0.975±0.050, 0.974±0.051 and 0.966±0.063), which remained significant after post-hoc analysis between b=1,000 and ADC (p = 0.022). ICCs were not significantly different between resolutions of 2×2 and 3×3 mm2 regarding b=500 (mean: 0.977±0.052 and 0.974±0.049, p = 0.612), b=1,000 (mean: 0.973±0.059 and 0.974±0.054, p = 0.516), and ADC (mean: 0.972±0.049 and 0.955±0.101, p = 0.851). Inter- and intra-observer reliability was consistently high for all sequences (overall mean 0.992±0.021 and 0.990±0.028). CONCLUSION Under ex-vivo conditions, DWI provided robust radiomics features with those from ADC being slightly less robust than from raw DWI (b=500, 1,000 s/mm2). No significant difference was detected for different resolutions. Although, ex-vivo reliability of DWI radiomics features was high, no implications can be made regarding in-vivo analyses.
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Conficoni A, Feraco P, Mazzatenta D, Zoli M, Asioli S, Zenesini C, Fabbri VP, Cellerini M, Bacci A. Biomarkers of pituitary macroadenomas aggressive behaviour: a conventional MRI and DWI 3T study. Br J Radiol 2020; 93:20200321. [PMID: 32628097 DOI: 10.1259/bjr.20200321] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVE Pituitary macroadenomas (PAs) are usually defined as benign intracranial tumors. However, they may present local aggressive course. High Ki67 labelling index (LI) values have been related to an aggressive tumor behavior. A recent clinicopathological classification of PA based on local invasiveness and proliferation indexes, divided them in groups with different prognosis. We evaluated the utility of conventional MRI (cMRI) and diffusion-weighted imaging (DWI), in predicting the Ki67- LI according the clinicopathological classification. METHODS 17 patients (12 M and 5 F) who underwent surgical removal of a PA were studied. cMRI features, quantification of T1W and T2W signal intensity, degree of contrast uptake (enhancement ratio, ER) and apparent diffusion coefficient (ADC) values were evaluated by using a 3 T scan. Statistics included Mann-Whitney test, Spearman's test, and receiver operating characteristic analysis. A value of p ≤ 0.05 was considered significant for all the tests. RESULTS Negative correlations were observed between Ki-67 LI, ADCm (ρ = - 0.67, p value = 0.005) and ER values (ρ = -0.62; p = 0.008). ER values were significantly lower in the proliferative PA group (p = 0.028; p = 0.017). ADCm showed sensitivity and specificity of 90 and 85% respectively into predict Ki67-LI value. A value of ADCm ≤0, 711 x 10-6 mm2 emerged as a cut-off of a value of Ki67-LI ≥ 3%. CONCLUSION Adding quantitative measures of ADC values to cMRI could be used routinely as a non-invasive marker of specific predictive biomarker of the proliferative activity of PA. ADVANCES IN KNOWLEDGE Routinely use of DWI on diagnostic work-up of pituitary adenomas may help in establish the likely biological aggressive lesions.
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Affiliation(s)
- Alberto Conficoni
- Department of Radiology, Neuroradiology Unit, Azienda Ospedaliero-Universitaria di Ferrara, Via Aldo Moro, 44124 Ferrara, Italy.,Department of Neuroradiology, Ospedale Bellaria, IRCCS Institute of Neurological Sciences of Bologna, Via Altura, 3; 40100 Bolgona, Italy
| | - Paola Feraco
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Via S. Giacomo 14, 40138 Bologna, Italy.,Department of Neuroradiology, Ospedale S. Chiara, Azienda Provinciale per i Servizi Sanitari, Largo medaglie d'oro 9, 38122 , Trento, Italy
| | - Diego Mazzatenta
- Department of Biomedical and Neuromotor Sciences (DIBINEM) of Neurological Sciences of Bologna, Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic and Pituitary Diseases, Bologna, Italy
| | - Matteo Zoli
- Department of Biomedical and Neuromotor Sciences (DIBINEM) of Neurological Sciences of Bologna, Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic and Pituitary Diseases, Bologna, Italy
| | - Sofia Asioli
- Section of Anatomic Pathology 'M. Malpighi', Bellaria Hospital, Bologna, Italy, Via Altura9; 40100 Bolgona, Italy
| | - Corrado Zenesini
- Epidemiology and Statistics Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Viscardo Paolo Fabbri
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Via S. Giacomo 14, 40138 Bologna, Italy.,Section of Anatomic Pathology 'M. Malpighi', Bellaria Hospital, Bologna, Italy, Via Altura9; 40100 Bolgona, Italy
| | - Martino Cellerini
- Department of Neuroradiology, Ospedale Bellaria, IRCCS Institute of Neurological Sciences of Bologna, Via Altura, 3; 40100 Bolgona, Italy
| | - Antonella Bacci
- Department of Neuroradiology, Ospedale Bellaria, IRCCS Institute of Neurological Sciences of Bologna, Via Altura, 3; 40100 Bolgona, Italy
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Ono T, Kishimoto K, Tajima S, Maeda I, Takagi M, Suzuki N, Mimura H. Apparent diffusion coefficient (ADC) values of serous, endometrioid, and clear cell carcinoma of the ovary: pathological correlation. Acta Radiol 2020; 61:992-1000. [PMID: 31698924 DOI: 10.1177/0284185119883392] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Primary epithelial ovarian cancer is divided into several subtypes. The relationships between apparent diffusion coefficient (ADC) values and their subtypes have not yet been established. PURPOSE To investigate whether ADC values of epithelial ovarian cancer vary according to histologic tumor cellularity and evaluate the difference of clear cell carcinoma (CCC), high-grade serous carcinoma (HGSC), and endometrioid carcinoma (EC). MATERIAL AND METHODS This retrospective study included 51 cases of epithelial ovarian cancer (17 CCC, 20 HGSC, and 14 EC) identified by magnetic resonance imaging with pathological confirmation. All patients underwent diffusion-weighted imaging and the ADC values of lesions were measured. We counted the tumor cells in three high-power fields and calculated the average for each case. The Spearman's correlation coefficient test was used to analyze correlation between ADC values and tumor cellularity. The ADC values of HGSC, EC, and CCC were compared using the Steel-Dwass test. RESULTS The ADC values of all cases were significantly inversely correlated with tumor cellularity (rs = -0.761; P < 0.001). The mean ± SD ADC values (×10-3 mm2/s) of CCC, HGSC, and EC were 1.24 ± 0.17 (range 0.98--1.65), 0.84 ± 0.10 (range 0.67--1.06), and 0.84 ± 0.10 (range 0.67--1.07). The mean ± SD tumor cellularity of CCC, HGSC, and EC was 162.88 ± 63.28 (range 90.33--305.67), 440.60 ± 119.86 (range 204.67--655.67), and 461.02 ± 81.86 (range 333.33--602.33). CONCLUSION There is a significant inverse correlation between ADC values and tumor cellularity in epithelial ovarian cancer. The mean ADC value of CCC was higher than those of HGSC and EC, seemingly due to the low cellularity of CCC.
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Affiliation(s)
- Takafumi Ono
- Department of Radiology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Keiko Kishimoto
- Department of Radiology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Shinya Tajima
- Department of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Ichiro Maeda
- Department of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Masayuki Takagi
- Department of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Nao Suzuki
- Department of Obstetrics and Gynecology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Hidefumi Mimura
- Department of Radiology, St. Marianna University School of Medicine, Kawasaki, Japan
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Long L, Zhang H, He X, Zhou J, Guo D, Liu X. Value of intravoxel incoherent motion magnetic resonance imaging for differentiating metastatic from nonmetastatic mesorectal lymph nodes with different short-axis diameters in rectal cancer. J Cancer Res Ther 2020; 15:1508-1515. [PMID: 31939430 DOI: 10.4103/jcrt.jcrt_76_19] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background Conventional magnetic resonance imaging (MRI) does not accurately evaluate lymph node (LN) status, which is essential for the treatment and prognosis assessment in patients with rectal cancer. Objective The aim of this study is to evaluate the diagnostic value of intravoxel incoherent motion (IVIM) MRI in differentiating metastatic and nonmetastatic mesorectal LNs with different short-axis diameters in rectal cancer patients. Materials and Methods Forty patients (154 LNs) were divided into three groups based on short-axis diameter: 3 mm ≤ × ≤5 mm, 5 mm < × ≤7 mm, and × >7 mm. MRI characteristics and IVIM parameters were compared between the metastatic and nonmetastatic LNs to determine the diagnostic value for discriminating them. Results In the 3 mm ≤ × ≤ 5 mm group, mean D values were significantly lower in metastatic than in the nonmetastatic LNs (P < 0.001). In the 5 mm < × ≤7 mm group, mean f values were significantly lower in metastatic than nonmetastatic LNs (P < 0.05). In the × >7 mm group, only the short-axis diameter of metastatic LNs was significantly greater than that of nonmetastatic LNs (P < 0.05). The area under the curve, sensitivity, specificity, and cutoff values were used for differentiating the metastatic from the nonmetastatic LNs. Conclusion IVIM parameters can differentiate metastatic from nonmetastatic LNs with smaller short-axis diameters (× ≤7 mm) in rectal cancer, and the short-axis diameter is a significant factor in identifying metastatic and nonmetastatic LNs in larger short-axis diameter groups (× >7 mm).
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Affiliation(s)
- Ling Long
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
| | - Haiping Zhang
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
| | - Xiaojing He
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
| | - Jun Zhou
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
| | - Dajing Guo
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
| | - Xinjie Liu
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, China
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Surov A, Wienke A, Meyer HJ. Pretreatment apparent diffusion coefficient does not predict therapy response to neoadjuvant chemotherapy in breast cancer. Breast 2020; 53:59-67. [PMID: 32652460 PMCID: PMC7375564 DOI: 10.1016/j.breast.2020.06.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 05/30/2020] [Accepted: 06/01/2020] [Indexed: 12/12/2022] Open
Abstract
Background Some reports indicated that apparent diffusion coefficient can predict pathologic response to treatment in breast cancer (BC). The purpose of the present meta-analysis was to provide evident data regarding use of ADC values for prediction of treatment response in BC. Methods MEDLINE library, EMBASE and SCOPUS databases were screened for associations between ADC and treatment response for neoadjuvant chemotherapy in breast cancer (BC) up to March 2020. Overall, 22 studies met the inclusion criteria. For the present analysis, the following data were extracted from the collected studies: authors, year of publication, study design, number of patients/lesions, mean and standard deviation of the pretreatment ADC values. The methodological quality of the included studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for responders and non responders. Results The acquired 22 studies comprised 1827 patients with different BC. Of the 1827 patients, 650 (35.6%) were reported as responders and 1177 (64.4%) as non-responders to the neoadjuvant chemotherapy. The pooled calculated pretreatment mean ADC value of BC in responders was 0.98 (95% CI = [0.94; 1.03]). In non-responders, it was 1.05 (95% CI = [1.00; 1.10]). The ADC values of the groups overlapped significantly. Conclusion Pretreatment ADC alone cannot predict response to neoadjuvant chemotherapy in BC.
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Affiliation(s)
- Alexey Surov
- Department of Radiology and Nuclear Medicine, Otto-von-Guericke University of Magdeburg, Germany.
| | - Andreas Wienke
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Germany.
| | - Hans Jonas Meyer
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Germany.
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Jones EF, Hathi DK, Freimanis R, Mukhtar RA, Chien AJ, Esserman LJ, van’t Veer LJ, Joe BN, Hylton NM. Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy. Cancers (Basel) 2020; 12:E1511. [PMID: 32527022 PMCID: PMC7352259 DOI: 10.3390/cancers12061511] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 12/24/2022] Open
Abstract
In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.
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Affiliation(s)
- Ella F. Jones
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Deep K. Hathi
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Rita Freimanis
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Rita A. Mukhtar
- Department of Surgery, University of California, San Francisco, CA 94115, USA;
| | - A. Jo Chien
- School of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94115, USA; (A.J.C.); (L.J.v.V.)
| | - Laura J. Esserman
- Department of Surgery, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94115, USA;
| | - Laura J. van’t Veer
- School of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94115, USA; (A.J.C.); (L.J.v.V.)
| | - Bonnie N. Joe
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Nola M. Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
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Jiang J, Fu Y, Hu X, Cui L, Hong Q, Gu X, Yin J, Cai R, Xu G. The value of diffusion-weighted imaging based on monoexponential and biexponential models for the diagnosis of benign and malignant lung nodules and masses. Br J Radiol 2020; 93:20190400. [PMID: 32163295 PMCID: PMC10993207 DOI: 10.1259/bjr.20190400] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 02/29/2020] [Accepted: 03/09/2020] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES The objective is to compare the efficacy of diffusion-weighted imaging (DWI) parameters of mean and minimum apparent diffusion coefficient (ADCmean and ADCmin) and intravoxel incoherent motion (IVIM) in the differentiation of benign and malignant lung nodules and masses. METHODS Lung lesions measured larger than 1.5 cm on CT were included between August 2015 and September 2018. DWI (10 b-values, 0-1000 s/mm2) scans were performed, and the data were post-processed to derive the ADCmean, ADCmin and IVIM parameters of true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f). An independent sample t-test or Mann-Whitney U test was used to compare benign and malignant parameters. Receiver operating characteristic curves were generated and a Z test was used. RESULTS 121 patients were finally enrolled, each with one lesion. Examined 121 lesions were malignant in 88 (72.7%) and benign in 33 (27.3%). The ADCmean of malignant pulmonary nodules was significantly lower than that of benign pulmonary nodules (t = 3.156, p = 0.006), whereas the other parameters revealed no significant differences (p = 0.162-0.690). Receiver operating characteristic curve analysis revealed that an ADCmean threshold value of 1.43 × 10-3 mm2/s yielded 88.57% sensitivity and 64.29% specificity. While for lung masses, the ADCmean, ADCmin, D and D* values in malignant pulmonary masses were significantly lower (P﹤0.001-0.011). Among them, the D value exhibited the best diagnostic performance when the threshold of D was 1.23 × 10-3mm2/s, which yielded a sensitivity of 90.57% and a specificity of 89.47% (Z = 2.230, 3.958, 2.877 and p = 0.026, ﹤0.001 and 0.004, respectively). CONCLUSION ADC is the most robust parameter to differentiate benign and malignant lung nodules, whereas D is the most robust parameter to differentiate benign and malignant lung masses. ADVANCES IN KNOWLEDGE This is the first study to compare all the quantitative parameters of DWI and IVIM mentioned in the literatures for assessing lung lesions; Second, we divided the lesions into lung nodules and lung masses with the size of 3 cm as the boundary.
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Affiliation(s)
- Jianqin Jiang
- Department of Radiology, Yancheng City No.1 People's
Hospital, Yancheng,
China
| | - Yigang Fu
- Department of Radiology, Yancheng City No.1 People's
Hospital, Yancheng,
China
| | - Xiaoyun Hu
- Department of Radiology, Wuxi People's Hospital,
Wuxi, China
| | - Lei Cui
- Department of Radiology, Second Affiliated Hospital of Nantong
University, Nantong,
China
| | - Qin Hong
- Department of Radiology, Yancheng City No.1 People's
Hospital, Yancheng,
China
| | - Xiaowen Gu
- Department of Radiology, Suzhou Municipal
Hospital, Suzhou,
China
| | - Jianbing Yin
- Department of Radiology, Second Affiliated Hospital of Nantong
University, Nantong,
China
| | - Rongfang Cai
- Department of Radiology, Second Affiliated Hospital of Nantong
University, Nantong,
China
| | - Gaofeng Xu
- Department of Radiology, Yancheng City No.1 People's
Hospital, Yancheng,
China
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Meyer HJ, Wienke A, Surov A. Discrimination between clinical significant and insignificant prostate cancer with apparent diffusion coefficient - a systematic review and meta analysis. BMC Cancer 2020; 20:482. [PMID: 32460795 PMCID: PMC7254689 DOI: 10.1186/s12885-020-06942-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 05/10/2020] [Indexed: 11/26/2022] Open
Abstract
Background Prostate MRI has become a corner stone in diagnosis of prostate cancer (PC). Diffusion weighted imaging and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure. The present analysis sought to compare ADC values of clinically insignificant with clinical significant PC based upon a large patient sample. Methods MEDLINE library and SCOPUS databases were screened for the associations between ADC and Gleason score (GS) in PC up to May 2019. The primary endpoint of the systematic review was the ADC value of PC groups according to Gleason score. In total 26 studies were suitable for the analysis and included into the present study. The included studies comprised a total of 1633 lesions. Results Clinically significant PCs (GS ≥ 7) were diagnosed in 1078 cases (66.0%) and insignificant PCs (GS 5 and 6) in 555 cases (34.0%). The pooled mean ADC value derived from monoexponenantially fitted ADCmean of the clinically significant PC was 0.86 × 10− 3 mm2/s [95% CI 0.83–0.90] and the pooled mean value of insignificant PC was 1.1 × 10− 3 mm2/s [95% CI 1.03–1.18]. Clinical significant PC showed lower ADC values compared to non-significant PC. The pooled ADC values of clinically insignificant PCs were no lower than 0.75 × 10− 3 mm2/s. Conclusions We evaluated the published literature comparing clinical insignificant with clinically prostate cancer in regard of the Apparent diffusion coefficient values derived from magnetic resonance imaging. We identified that the clinically insignificant prostate cancer have lower ADC values than clinically significant, which may aid in tumor noninvasive tumor characterization in clinical routine.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
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Does multiparametric imaging with 18F-FDG-PET/MRI capture spatial variation in immunohistochemical cancer biomarkers in head and neck squamous cell carcinoma? Br J Cancer 2020; 123:46-53. [PMID: 32382113 PMCID: PMC7341803 DOI: 10.1038/s41416-020-0876-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 04/07/2020] [Accepted: 04/15/2020] [Indexed: 11/18/2022] Open
Abstract
Background The purpose of this study is to test if functional multiparametric imaging with 18F-FDG-PET/MRI correlates spatially with immunohistochemical biomarker status within a lesion of head and neck squamous cell carcinoma (HNSCC), and also whether a biopsy with the highest FDG uptake was more likely to have the highest PD-L1 expression or the highest percentage of vital tumour cells (VTC) compared with a random biopsy. Methods Thirty-one patients with HNSCC were scanned on an integrated PET/MRI scanner with FDG prior to surgery in this prospective study. Imaging was quantified with SUV, ADC and Ktrans. A 3D-morphometric MRI scan of the specimen was used to co-register the patient and the specimen scans. All specimens were sectioned in consecutive slices, and slices from six different locations were selected randomly from each tumour. Core biopsies were performed to construct TMA blocks for IHC staining with the ten predefined biomarkers. The spatial correlation was assessed with a partial correlation analysis. Results Twenty-eight patients with a total of 33 lesions were eligible for further analysis. There were significant correlations between the three imaging biomarkers and some of the IHC biomarkers. Moreover, a biopsy taken from the most FDG-avid part of the tumour did not have a statistically significantly higher probability of higher PD-L1 expression or VTC, compared with a random biopsy. Conclusion We found statistically significant correlations between functional imaging parameters and key molecular cancer markers.
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Theruvath AJ, Siedek F, Muehe AM, Garcia-Diaz J, Kirchner J, Martin O, Link MP, Spunt S, Pribnow A, Rosenberg J, Herrmann K, Gatidis S, Schäfer JF, Moseley M, Umutlu L, Daldrup-Link HE. Therapy Response Assessment of Pediatric Tumors with Whole-Body Diffusion-weighted MRI and FDG PET/MRI. Radiology 2020; 296:143-151. [PMID: 32368961 DOI: 10.1148/radiol.2020192508] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background Whole-body diffusion-weighted (DW) MRI can help detect cancer with high sensitivity. However, the assessment of therapy response often requires information about tumor metabolism, which is measured with fluorine 18 fluorodeoxyglucose (FDG) PET. Purpose To compare tumor therapy response with whole-body DW MRI and FDG PET/MRI in children and young adults. Materials and Methods In this prospective, nonrandomized multicenter study, 56 children and young adults (31 male and 25 female participants; mean age, 15 years ± 4 [standard deviation]; age range, 6-22 years) with lymphoma or sarcoma underwent 112 simultaneous whole-body DW MRI and FDG PET/MRI between June 2015 and December 2018 before and after induction chemotherapy (ClinicalTrials.gov identifier: NCT01542879). The authors measured minimum tumor apparent diffusion coefficients (ADCs) and maximum standardized uptake value (SUV) of up to six target lesions and assessed therapy response after induction chemotherapy according to the Lugano classification or PET Response Criteria in Solid Tumors. The authors evaluated agreements between whole-body DW MRI- and FDG PET/MRI-based response classifications with Krippendorff α statistics. Differences in minimum ADC and maximum SUV between responders and nonresponders and comparison of timing for discordant and concordant response assessments after induction chemotherapy were evaluated with the Wilcoxon test. Results Good agreement existed between treatment response assessments after induction chemotherapy with whole-body DW MRI and FDG PET/MRI (α = 0.88). Clinical response prediction according to maximum SUV (area under the receiver operating characteristic curve = 100%; 95% confidence interval [CI]: 99%, 100%) and minimum ADC (area under the receiver operating characteristic curve = 98%; 95% CI: 94%, 100%) were similar (P = .37). Sensitivity and specificity were 96% (54 of 56 participants; 95% CI: 86%, 99%) and 100% (56 of 56 participants; 95% CI: 54%, 100%), respectively, for DW MRI and 100% (56 of 56 participants; 95% CI: 93%, 100%) and 100% (56 of 56 participants; 95% CI: 54%, 100%) for FDG PET/MRI. In eight of 56 patients who underwent imaging after induction chemotherapy in the early posttreatment phase, chemotherapy-induced changes in tumor metabolism preceded changes in proton diffusion (P = .002). Conclusion Whole-body diffusion-weighted MRI showed significant agreement with fluorine 18 fluorodeoxyglucose PET/MRI for treatment response assessment in children and young adults. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- Ashok J Theruvath
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Florian Siedek
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Anne M Muehe
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Jordi Garcia-Diaz
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Julian Kirchner
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Ole Martin
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Michael P Link
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Sheri Spunt
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Allison Pribnow
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Jarrett Rosenberg
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Ken Herrmann
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Sergios Gatidis
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Jürgen F Schäfer
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Michael Moseley
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Lale Umutlu
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
| | - Heike E Daldrup-Link
- From the Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Rd, Stanford, CA 94304 (A.J.T., F.S., A.M.M., J.G.D., J.R., M.M., H.E.D.L.); Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (A.J.T.); Institute of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany (F.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty, University Düsseldorf, Düsseldorf, Germany (J.K., O.M.); Department of Pediatrics, Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, Calif (M.P.L., S.S., A.P., H.E.D.L.); Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (K.H.); Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.G., J.F.S.); Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany (L.U.)
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Wang Y, Bai G, Guo L, Chen W. Associations Between Apparent Diffusion Coefficient Value With Pathological Type, Histologic Grade, and Presence of Lymph Node Metastases of Esophageal Carcinoma. Technol Cancer Res Treat 2020; 18:1533033819892254. [PMID: 31782340 PMCID: PMC6886268 DOI: 10.1177/1533033819892254] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Objective: To investigate the application value of apparent diffusion coefficient value in the pathological type, histologic grade, and presence of lymph node metastases of esophageal carcinoma. Materials and Methods: Eighty-six patients with pathologically confirmed esophageal carcinoma were divided into different groups according to pathological type, histological grade, and lymph node status. All patients underwent conventional magnetic resonance imaging and diffusion-weighted imaging scan, and apparent diffusion coefficient values of tumors were measured. Independent sample t test and 1-way variance were used to compare the difference of apparent diffusion coefficient value in different pathological types, histologic grades, and lymph node status. Correlation between the apparent diffusion coefficient value and the histologic grade was evaluated using Spearman rank correlation test. Receiver operating characteristic curve of apparent diffusion coefficient value was generated to evaluate the differential diagnostic efficiency of poorly and well/moderately differentiated esophageal carcinoma. Results: No significant difference was observed in apparent diffusion coefficient value between esophageal squamous cell carcinoma and adenocarcinoma and in patients between those with and without lymph node metastases (P > .05). The differences of apparent diffusion coefficient value were statistically significant between different histologic grades of esophageal carcinoma (P < .05). The apparent diffusion coefficient value was positively correlated with histologic grade (rs = 0.802). The apparent diffusion coefficient value ≤1.25 × 10−3 mm2/s as the cutoff value for diagnosis of poorly differentiated esophageal carcinoma with the sensitivity of 84.3%, and the specificity was 94.3%. Conclusions: The performance of apparent diffusion coefficient value was contributing to predict the histologic grade of esophageal carcinoma, which might increase lesions characterization before choosing the best therapeutic alternative. However, they do not correlate with pathological type and the presence of lymph node metastases of esophageal carcinoma.
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Affiliation(s)
- Yating Wang
- Department of Medical Imaging, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
| | - Genji Bai
- Department of Medical Imaging, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
| | - Lili Guo
- Department of Medical Imaging, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
| | - Wei Chen
- Department of Medical Imaging, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
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133
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Magnetic Resonance Imaging Derived Biomarkers of IDH Mutation Status and Overall Survival in Grade III Astrocytomas. Diagnostics (Basel) 2020; 10:diagnostics10040247. [PMID: 32340318 PMCID: PMC7236014 DOI: 10.3390/diagnostics10040247] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 04/16/2020] [Accepted: 04/21/2020] [Indexed: 12/29/2022] Open
Abstract
The evaluation of the isocitrate dehydrogenase (IDH) mutation status in the glioma decision-making process has diagnostic, prognostic and therapeutic implications. The aim of this study was to evaluate whether conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) can noninvasively predict the most common IDH mutational status (R132H) in GIII-astrocytomas and the overall survival (OS). Hence, twenty-two patients (9-F, 13-M) with a histological diagnosis of GIII-astrocytoma and evaluation of IDH-mutation status (12-wild type, 10-mutant) were retrospectively evaluated. Imaging studies were reviewed for the morphological feature and mean ADC values (ADCm). Statistics included a Fisher’s exact test, Student’s t-test, Spearman’s Test and receiver operating characteristic analysis. A p ≤ 0.05 value was considered statistically significant for all the tests. A younger age and a frontal location were more likely related to mutational status. IDH-wild type (Wt) exhibited a slight enhancement (p = 0.039). The ADCm values in IDH-mutant (Mut) patients were higher than those of IDH-Wt patients (p < 0.0004). The value of ADC ≥ 0.99 × 10−3 mm2/s emerged as a “cut-off” to differentiate the mutation state. In the overall group, a positive relationship between the ADCm values and OS was detected (p = 0.003; r = 0.62). Adding quantitative measures of ADC values to conventional MR imaging could be used routinely as a noninvasive marker of specific molecular patterns.
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134
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Dreher C, Linde P, Boda-Heggemann J, Baessler B. Radiomics for liver tumours. Strahlenther Onkol 2020; 196:888-899. [PMID: 32296901 PMCID: PMC7498486 DOI: 10.1007/s00066-020-01615-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 03/20/2020] [Indexed: 12/15/2022]
Abstract
Current research, especially in oncology, increasingly focuses on the integration of quantitative, multiparametric and functional imaging data. In this fast-growing field of research, radiomics may allow for a more sophisticated analysis of imaging data, far beyond the qualitative evaluation of visible tissue changes. Through use of quantitative imaging data, more tailored and tumour-specific diagnostic work-up and individualized treatment concepts may be applied for oncologic patients in the future. This is of special importance in cross-sectional disciplines such as radiology and radiation oncology, with already high and still further increasing use of imaging data in daily clinical practice. Liver targets are generally treated with stereotactic body radiotherapy (SBRT), allowing for local dose escalation while preserving surrounding normal tissue. With the introduction of online target surveillance with implanted markers, 3D-ultrasound on conventional linacs and hybrid magnetic resonance imaging (MRI)-linear accelerators, individualized adaptive radiotherapy is heading towards realization. The use of big data such as radiomics and the integration of artificial intelligence techniques have the potential to further improve image-based treatment planning and structured follow-up, with outcome/toxicity prediction and immediate detection of (oligo)progression. The scope of current research in this innovative field is to identify and critically discuss possible application forms of radiomics, which is why this review tries to summarize current knowledge about interdisciplinary integration of radiomics in oncologic patients, with a focus on investigations of radiotherapy in patients with liver cancer or oligometastases including multiparametric, quantitative data into (radio)-oncologic workflow from disease diagnosis, treatment planning, delivery and patient follow-up.
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Affiliation(s)
- Constantin Dreher
- Department of Radiation Oncology, University Hospital Mannheim, Medical Faculty of Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany
| | - Philipp Linde
- Department of Radiation Oncology, Medical Faculty and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Judit Boda-Heggemann
- Department of Radiation Oncology, University Hospital Mannheim, Medical Faculty of Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany.
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
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Duchêne G, Abarca-Quinones J, Feza-Bingi N, Leclercq I, Duprez T, Peeters F. Double Diffusion Encoding for Probing Radiation-Induced Microstructural Changes in a Tumor Model: A Proof-of-Concept Study With Comparison to the Apparent Diffusion Coefficient and Histology. J Magn Reson Imaging 2020; 52:941-951. [PMID: 32147929 DOI: 10.1002/jmri.27119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 02/17/2020] [Accepted: 02/18/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Microstructure analyses are gaining interest in cancer MRI as an alternative to the conventional apparent diffusion coefficient (ADC), of which the determinants remain unclear. PURPOSE To assess the sensitivity of parameters calculated from a double diffusion encoding (DDE) sequence to changes in a tumor's microstructure early after radiotherapy and to compare them with ADC and histology. STUDY TYPE Cohort study on experimental tumors. ANIMAL MODEL Sixteen WAG/Rij rats grafted with one rhabdomyosarcoma fragment in each thigh. Thirty-one were imaged at days 1 and 4, of which 17 tumors received a 20 Gy radiation dose after the first imagery. FIELD STRENGTH/SEQUENCE 3T. Diffusion-weighted imaging, DDE with flow compensated, and noncompensated measurements. ASSESSMENTS 1) To compare, after irradiation, DDE-derived parameters (intracellular fraction, cell size, and cell density) to their histological counterparts (fraction of stained area, minimal Feret diameter, and nuclei count, respectively). 2) To compare percentage changes in DDE-derived parameters and ADC. 3) To evaluate the evolution of DDE-derived parameters describing perfusion. STATISTICAL TESTS Wilcoxon rank sum test. RESULTS 1) Intracellular fraction, cell size, and cell density were respectively lower (-24%, P < 0.001), higher (+7.5%, P < 0.001) and lower (-38%, P < 0.001) in treated tumors as compared to controls. Fraction of stained area, minimal Feret diameter, and nuclei count were respectively lower (-20%, P < 0.001), higher (+28%, P < 0.001), and lower (-34%, P < 0.001) in treated tumors. 2) The magnitude of ADC's percentage change due to irradiation (16.4%) was superior to the one of cell size (8.4%, P < 0.01) but inferior to those of intracellular fraction (35.5%, P < 0.001) and cell density (42%, P < 0.001). 3) After treatment, the magnitude of the vascular fraction's decrease was higher than the increase of flow velocity (33.3%, vs. 13.3%, P < 0.001). DATA CONCLUSION The DDE sequence allows quantitatively monitoring the effects of radiotherapy on a tumor's microstructure, whereas ADC only reveals global changes. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 4. J. Magn. Reson. Imaging 2020;52:941-951.
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Affiliation(s)
- Gaëtan Duchêne
- Department of medical imaging, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
| | - Jorge Abarca-Quinones
- Department of medical imaging, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium.,MRI unit, Department of medical imaging, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Natacha Feza-Bingi
- Department of medical imaging, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium.,Laboratory of Hepato-gastroenterology, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Brussels, Belgium
| | - Isabelle Leclercq
- Department of medical imaging, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium.,MRI unit, Department of medical imaging, Cliniques Universitaires Saint-Luc, Brussels, Belgium.,Laboratory of Hepato-gastroenterology, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Brussels, Belgium
| | - Thierry Duprez
- Department of medical imaging, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium.,MRI unit, Department of medical imaging, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Frank Peeters
- MRI unit, Department of medical imaging, Cliniques Universitaires Saint-Luc, Brussels, Belgium
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136
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Asenjo García B, Navarro Guirado F, Nagib Raya F, Vidal Denis M, Bravo Rodríguez F, Galán Montenegro P. ADC quantification to classify patients candidate to receive bevacizumab treatment for recurrent glioblastoma. Acta Radiol 2020; 61:404-413. [PMID: 31357873 DOI: 10.1177/0284185119864842] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background Recurrent high-grade gliomas progressing after surgery and temozolomide plus radiation therapy have traditionally been treated using antiangiogenic drugs as the first-line therapy. Since the phase 3 EORTC 26101 trial showed no significant benefit of administering antiangiogenic drugs, the need to identify a biomarker to classify subgroups of potential responders has increased. Purpose To investigate the feasibility of using apparent diffusion coefficient as a predictor of the response of recurrent high-grade gliomas to bevacizumab or classifier for patients showing better response. Material and Methods This retrospective study analyzed magnetic resonance images obtained from 39 patients at the time of high-grade glioma progression who were treated using bevacizumab. The apparent diffusion coefficient maps were quantified and modelled as a mixture of Gaussian functions. The correlation of their descriptors and the time to second progression was studied. Log-rank tests were performed to determine the power of these descriptors as the classifiers for patients exhibiting better survival. Results None of the descriptors showed correlation with time to second progression (r < 0.35) but several of them stratified subgroups showing a better time to second progression passing log-rank tests ( P < 0.02). Conclusion Apparent diffusion coefficient cannot be used to predict the time to second progression of recurrent high-grade gliomas treated with bevacizumab, but it can stratify groups with better time to second progression distributions.
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Affiliation(s)
| | | | | | - María Vidal Denis
- Department of Radiology, Regional University Hospital of Málaga, Spain
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137
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Gihr GA, Horvath-Rizea D, Hekeler E, Ganslandt O, Henkes H, Hoffmann KT, Scherlach C, Schob S. Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: in vivo Characterization of Tumor Architecture and Corresponding Neuropathology. Front Oncol 2020; 10:206. [PMID: 32158691 PMCID: PMC7051987 DOI: 10.3389/fonc.2020.00206] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 02/06/2020] [Indexed: 02/01/2023] Open
Abstract
Background: Low-grade gliomas (LGG) in adults are usually slow growing and frequently asymptomatic brain tumors, originating from glial cells of the central nervous system (CNS). Although regarded formally as “benign” neoplasms, they harbor the potential of malignant transformation associated with high morbidity and mortality. Their complex and unpredictable tumor biology requires a reliable and conclusive presurgical magnetic resonance imaging (MRI). A promising and emerging MRI approach in this context is histogram based apparent diffusion coefficient (ADC) profiling, which recently proofed to be capable of providing prognostic relevant information in different tumor entities. Therefore, our study investigated whether histogram profiling of ADC distinguishes grade I from grade II glioma, reflects the proliferation index Ki-67, as well as the IDH (isocitrate dehydrogenase) mutation and MGMT (methylguanine-DNA methyl-transferase) promotor methylation status. Material and Methods: Pre-treatment ADC volumes of 26 LGG patients were used for histogram-profiling. WHO-grade, Ki-67 expression, IDH mutation, and MGMT promotor methylation status were evaluated. Comparative and correlative statistics investigating the association between histogram-profiling and neuropathology were performed. Results: Almost the entire ADC profile (p25, p75, p90, mean, median) was significantly lower in grade II vs. grade I gliomas. Entropy, as second order histogram parameter of ADC volumes, was significantly higher in grade II gliomas compared with grade I gliomas. Mean, maximum value (ADCmax) and the percentiles p10, p75, and p90 of ADC histogram were significantly correlated with Ki-67 expression. Furthermore, minimum ADC value (ADCmin) was significantly associated with MGMT promotor methylation status as well as ADC entropy with IDH-1 mutation status. Conclusions: ADC histogram-profiling is a valuable radiomic approach, which helps differentiating tumor grade, estimating growth kinetics and probably prognostic relevant genetic as well as epigenetic alterations in LGG.
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Affiliation(s)
| | | | - Elena Hekeler
- Department for Pathology, Katharinenhospital Stuttgart, Stuttgart, Germany
| | - Oliver Ganslandt
- Katharinenhospital Stuttgart, Clinic for Neurosurgery, Stuttgart, Germany
| | - Hans Henkes
- Katharinenhospital Stuttgart, Clinic for Neuroradiology, Stuttgart, Germany
| | - Karl-Titus Hoffmann
- Department for Neuroradiology, University Hospital Leipzig, Leipzig, Germany
| | - Cordula Scherlach
- Department for Neuroradiology, University Hospital Leipzig, Leipzig, Germany
| | - Stefan Schob
- Department for Neuroradiology, University Hospital Leipzig, Leipzig, Germany
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138
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Rotili A, Trimboli RM, Penco S, Pesapane F, Tantrige P, Cassano E, Sardanelli F. Double reading of diffusion-weighted magnetic resonance imaging for breast cancer detection. Breast Cancer Res Treat 2020; 180:111-120. [PMID: 31938940 DOI: 10.1007/s10549-019-05519-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 12/31/2019] [Indexed: 12/29/2022]
Abstract
PURPOSE To estimate the performance of diffusion-weighted imaging (DWI) for breast cancer detection. METHODS Consecutive breast magnetic resonance imaging examinations performed from January to September 2016 were retrospectively evaluated. Examinations performed before/after neoadjuvant therapy, lacking DWI sequences or reference standard were excluded; breasts after mastectomy were also excluded. Two experienced breast radiologists (R1, R2) independently evaluated only DWI. Final pathology or > 1-year follow-up served as reference standard. Mc Nemar, χ2, and κ statistics were applied. RESULTS Of 1,131 examinations, 672 (59.4%) lacked DWI sequence, 41 (3.6%) had no reference standard, 30 (2.7%) were performed before/after neoadjuvant therapy, and 10 (0.9%) had undergone bilateral mastectomy. Thus, 378 women aged 49 ± 11 years (mean ± standard deviation) were included, 51 (13%) with unilateral mastectomy, totaling 705 breasts. Per-breast cancer prevalence was 96/705 (13.6%). Per-breast sensitivity was 83/96 (87%, 95% confidence interval 78-93%) for both R1 and R2, 89/96 (93%, 86-97%) for double reading (DR) (p = 0.031); per-lesion DR sensitivity for cancers ≤ 10 mm was 22/31 (71%, 52-86%). Per-breast specificity was 562/609 (93%, 90-94%) for R1, 538/609 (88%, 86-91%) for R2, and 526/609 (86%¸ 83-89%) for DR (p < 0.001). Inter-observer agreement was substantial (κ = 0.736). Acquisition time varied from 3:00 to 6:22 min:s. Per-patient median interpretation time was 46 s (R1) and 51 s (R2). CONCLUSIONS DR DWI showed a 93% sensitivity and 88% specificity, with 71% sensitivity for cancers ≤ 10 mm, pointing out a potential for DWI as stand-alone screening method.
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Affiliation(s)
- Anna Rotili
- IEO, European Institute of Oncology IRCCS, Milan, Via Giuseppe Ripamonti, 435, 20141, Milan, Italy.
| | - Rubina Manuela Trimboli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy
| | - Silvia Penco
- IEO, European Institute of Oncology IRCCS, Milan, Via Giuseppe Ripamonti, 435, 20141, Milan, Italy
| | - Filippo Pesapane
- IEO, European Institute of Oncology IRCCS, Milan, Via Giuseppe Ripamonti, 435, 20141, Milan, Italy.,Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Priyan Tantrige
- Unit of Radiology, King's College Hospital, Denmark Hill, Brixton, London, SE5 9RS, UK
| | - Enrico Cassano
- IEO, European Institute of Oncology IRCCS, Milan, Via Giuseppe Ripamonti, 435, 20141, Milan, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy.,Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy
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139
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Surov A, Meyer HJ, Wienke A. Apparent Diffusion Coefficient for Distinguishing Between Malignant and Benign Lesions in the Head and Neck Region: A Systematic Review and Meta-Analysis. Front Oncol 2020; 9:1362. [PMID: 31970081 PMCID: PMC6960101 DOI: 10.3389/fonc.2019.01362] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 11/18/2019] [Indexed: 12/18/2022] Open
Abstract
Background: The purpose of the present meta-analysis was to provide evident data about use of apparent diffusion coefficient (ADC) values for distinguishing malignant and benign lesions in the head and neck region. Material and Methods: MEDLINE and Scopus databases were screened for associations between ADC and malignancy/benignancy of head and neck lesions up to December 2018. Overall, 22 studies met the inclusion criteria. The following data were extracted: authors, year of publication, study design, number of patients/lesions, lesion type, mean value, and standard deviation of ADC. The primary endpoint of the systematic review was the analysis of the association between lesion nature and ADC values. The methodological quality of the involved studies was checked according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malignant lesions. Results: The acquired 22 studies comprised 1,227 lesions. Different malignant lesions were diagnosed in 818 cases (66.7%) and benign lesions in 409 cases (33.3%). The mean ADC value of the malignant lesions was 1.04 × 10−3 mm2/s, and the mean value of the benign lesions was 1.46 × 10−3 mm2/s. Lymphomas and sarcomas showed the lowest calculated mean ADC values, 0.7 and 0.79 × 10−3 mm2/s, respectively. Adenoid cystic carcinomas had the highest ADC values (1.5 × 10−3 mm2/s). None of the analyzed malignant tumors had mean ADC values above 1.75 × 10−3 mm2/s. Conclusion: ADC values play a limited role in distinguishing between malignant and benign lesions in the head and neck region. It may be only suggested that lesions with mean ADC values above 1.75 × 10−3 mm2/s are probably benign. Further large studies are needed for the analysis of the role of diffusion-weighted imaging (DWI)/ADC in the discrimination of benign and malignant lesions in the head and neck region.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
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140
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Diffusion-Weighted MR Imaging of Primary and Secondary Lung Cancer: Predictive Value for Response to Transpulmonary Chemoembolization and Transarterial Chemoperfusion. J Vasc Interv Radiol 2019; 31:301-310. [PMID: 31899107 DOI: 10.1016/j.jvir.2019.08.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 08/20/2019] [Accepted: 08/26/2019] [Indexed: 12/19/2022] Open
Abstract
PURPOSE To examine predictive value of apparent diffusion coefficient (ADC) in diffusion-weighted imaging (DWI) for response of patients with primary and secondary lung neoplasms undergoing transpulmonary chemoembolization (TPCE) and transarterial chemoperfusion (TACP) treatment. MATERIALS AND METHODS Thirty-one patients (mean age ± SD 64 ± 12.4 y) with 42 lung target lesions (13 primary and 29 secondary) underwent DWI and subsequent ADC analysis on a 1.5T MR imaging scanner before and 30.3 days ± 6.4 after first session of TPCE or TACP. After 3.1 treatment sessions ± 1.4 performed in 2- to 4-week intervals, morphologic response was analyzed by comparing tumor diameter and volume before and after treatment on unenhanced T1-weighted MR images. On a per-lesion basis, response was classified according to Response Evaluation Criteria In Solid Tumors. RESULTS Threshold ADC increase of 20.7% indicated volume response with 88% sensitivity and 78% specificity (area under the curve [AUC] = 0.84). Differences between ADC changes in volume response groups were significant (P = .002). AUC for volume response predicted by ADC before treatment was 0.77. Median ADC before treatment and mean ADC change were 1.09 × 10-3 mm2/second and 0.36 × 10-3 mm2/second ± 0.23, 1.45 × 10-3 mm2/second and 0.14 × 10-3 mm2/second ± 0.16, and 1.30 × 10-3 mm2/second and 0.06 × 10-3 mm2/second ± 0.19 in partial response, stable disease, and progressive disease groups. In primary lung cancer lesions, strong negative correlation of ADC change with change in diameter (ρ = -.87, P < .001) and volume (ρ = -.66, P = .016) was found. In metastases, respective correlation coefficients were ρ = -.18 (P = .356) and ρ = -.35 (P = .061). CONCLUSIONS ADC quantification shows considerable diagnostic value for predicting response and monitoring TPCE and TACP treatment of patients with primary and secondary lung neoplasms.
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141
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Ianuş A, Santiago I, Galzerano A, Montesinos P, Loução N, Sanchez-Gonzalez J, Alexander DC, Matos C, Shemesh N. Higher-order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer. Magn Reson Med 2019; 84:348-364. [PMID: 31850546 DOI: 10.1002/mrm.28102] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 11/05/2019] [Accepted: 11/07/2019] [Indexed: 01/02/2023]
Abstract
PURPOSE Mesorectal lymph node staging plays an important role in treatment decision making. Here, we explore the benefit of higher-order diffusion MRI models accounting for non-Gaussian diffusion effects to classify mesorectal lymph nodes both 1) ex vivo at ultrahigh field correlated with histology and 2) in vivo in a clinical scanner upon patient staging. METHODS The preclinical investigation included 54 mesorectal lymph nodes, which were scanned at 16.4 T with an extensive diffusion MRI acquisition. Eight diffusion models were compared in terms of goodness of fit, lymph node classification ability, and histology correlation. In the clinical part of this study, 10 rectal cancer patients were scanned with diffusion MRI at 1.5 T, and 72 lymph nodes were analyzed with Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), Kurtosis, and IVIM-Kurtosis. RESULTS Compartment models including restricted and anisotropic diffusion improved the preclinical data fit, as well as the lymph node classification, compared to standard ADC. The comparison with histology revealed only moderate correlations, and the highest values were observed between diffusion anisotropy metrics and cell area fraction. In the clinical study, the diffusivity from IVIM-Kurtosis was the only metric showing significant differences between benign (0.80 ± 0.30 μm2 /ms) and malignant (1.02 ± 0.41 μm2 /ms, P = .03) nodes. IVIM-Kurtosis also yielded the largest area under the receiver operating characteristic curve (0.73) and significantly improved the node differentiation when added to the standard visual analysis by experts based on T2 -weighted imaging. CONCLUSION Higher-order diffusion MRI models perform better than standard ADC and may be of added value for mesorectal lymph node classification in rectal cancer patients.
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Affiliation(s)
- Andrada Ianuş
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.,Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Ines Santiago
- Champalimaud Clinical Centre, Champalimaud Centre for the Unknown, Lisbon, Portugal.,Nova Medical School, Lisbon, Portugal
| | - Antonio Galzerano
- Champalimaud Clinical Centre, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | | | | | | | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Celso Matos
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.,Champalimaud Clinical Centre, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
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142
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Egnell L, Vidić I, Jerome NP, Bofin AM, Bathen TF, Goa PE. Stromal Collagen Content in Breast Tumors Correlates With In Vivo Diffusion-Weighted Imaging: A Comparison of Multi b-Value DWI With Histologic Specimen From Benign and Malignant Breast Lesions. J Magn Reson Imaging 2019; 51:1868-1878. [PMID: 31837076 DOI: 10.1002/jmri.27018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 11/22/2019] [Accepted: 11/22/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Increased deposition and reorientation of stromal collagen fibers are associated with breast cancer progression and invasiveness. Diffusion-weighted imaging (DWI) may be sensitive to the collagen fiber organization in the stroma and could provide important biomarkers for breast cancer characterization. PURPOSE To understand how collagen fibers influence water diffusion in vivo and evaluate the relationship between collagen content and the apparent diffusion coefficient (ADC) and the signal fractions of the biexponential model using a high b-value scheme. STUDY TYPE Prospective. SUBJECTS/SPECIMENS Forty-five patients with benign (n = 8), malignant (n = 36), and ductal carcinoma in situ (n = 1) breast tumors. Lesions and normal fibroglandular tissue (n = 9) were analyzed using sections of formalin-fixed, paraffin-embedded tissue stained with hematoxylin, erythrosine, and saffron. FIELD STRENGTH/SEQUENCE MRI (3T) protocols: Protocol I: Twice-refocused spin-echo echo-planar imaging with: echo time (TE) 85 msec; repetition time (TR) 9300/11600 msec; matrix 90 × 90 × 60; voxel size 2 × 2 × 2.5 mm3 ; b-values: 0 and 700 s/mm2 . Protocol II: Stejskal-Tanner spin-echo echo-planar imaging with: TE: 88 msec; TR: 10600/11800 msec, matrix 90 × 90 × 60; voxel size 2 × 2 × 2.5 mm3 ; b-values [0, 200, 600, 1200, 1800, 2400, 3000] s/mm2 . ASSESSMENT Area fractions of cellular and collagen content in histologic sections were quantified using whole-slide image analysis and compared with the corresponding DWI parameters. STATISTICAL TESTS Correlations were assessed using Pearson's r. Univariate analysis of group median values was done using the Mann-Whitney U-test. RESULTS Collagen content correlated with the fast signal fraction (r = 0.67, P < 0.001) and ADC (r = 0.58, P < 0.001) and was lower (P < 0.05) in malignant lesions than benign and normal tissues. Cellular content correlated inversely with the fast signal fraction (r = -0.67, P < 0.001) and ADC (r = -0.61, P < 0.001) and was different (P < 0.05) between malignant, benign, and normal tissues. DATA CONCLUSION Our findings suggest stromal collagen content increases diffusivity observed by MRI and is associated with higher ADC and fast signal fraction of the biexponential model. LEVEL OF EVIDENCE 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1868-1878.
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Affiliation(s)
- Liv Egnell
- Department of Physics, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Clinic of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | - Igor Vidić
- Department of Physics, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Neil P Jerome
- Clinic of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway.,Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Anna M Bofin
- Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Tone F Bathen
- Clinic of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway.,Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Pål Erik Goa
- Department of Physics, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Clinic of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
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143
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Mohammad Ahmad MI, Sabr M, Roshy E. Assessment of apparent diffusion coefficient value as prognostic factor for renal cell carcinoma aggressiveness. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2019. [DOI: 10.1186/s43055-019-0038-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Assurance of prognostic elements is important for the management of renal cell carcinoma (RCC). Our goal was to check the relation between apparent diffusion coefficient (ADC) values and parameters predicting prognosis of RCC. Fifty pathologically confirmed RCC underwent diffusion-weighted (DW) MRI. ADC values were calculated using b factor (800 s/mm2). The correlation between ADC values and tumor size, cystic/necrotic feature, growth pattern, unenhanced T1, histological grade, clinical stage, and distant metastasis were analyzed.
Results
The optimal ADC threshold for prognosis of RCC appeared to be 1.4 × 10−3 mm2/s. There was a significant inverse correlation between ADC values and growth pattern (R = − 0, P = 0.05), unenhanced T1(R = − 0.41, P = 0.01), cystic/necrotic feature (R = − 0.4, P = 0.01), histological grade (R = − 0.37, P = 0.02), clinical stage (r = − 0.4, P = 0.01), and distant metastasis (R = − 0.33, P = 0.04), and significant linear correlation with tumor size (R = 0.39, P < 0.02).
Conclusion
The performance of ADC value as a newly proposed prognostic parameter follows with the degree of tumor differentiation and that may recognize extremely aggressive RCC. RCC with low ADC values should be inspected extensively for the risk of high pathological grade, high clinical stage, and distant metastasis.
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144
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Meyer HJ, Pönisch W, Schmidt SA, Wienbeck S, Braulke F, Schramm D, Surov A. Clinical and imaging features of myeloid sarcoma: a German multicenter study. BMC Cancer 2019; 19:1150. [PMID: 31775680 PMCID: PMC6882227 DOI: 10.1186/s12885-019-6357-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 11/12/2019] [Indexed: 12/29/2022] Open
Abstract
Background Myeloid sarcoma (MS), also known as chloroma, is an extramedullary manifestation of malignant primitive myeloid cells. Previously, only small studies investigated clinical and imaging features of MS. The purpose of this study was to elucidate clinical and imaging features of MS based upon a multicenter patient sample. Methods Patient records of radiological databases of 4 German university hospitals were retrospectively screened for MS in the time period 01/2001 and 06/2019. Overall, 151 cases/76 females (50.3%) with a mean age of 55.5 ± 15.1 years and 183 histopathological confirmation or clinically suspicious lesions of MS were included into this study. The underlying hematological disease, localizations, and clinical symptoms as well as imaging features on CT and MRI were investigated. Results In 15 patients (9.9% of all 151 cases) the manifestation of MS preceded the systemic hematological disease. In 43 cases (28.4%), first presentation of MS occurred simultaneously with the initial diagnosis of leukemia, and 92 (60.9%) patients presented MS after the initial diagnosis. In 37 patients (24.5%), the diagnosis was made incidentally by imaging. Clinically, cutaneous lesions were detected in 35 of 151 cases (23.2%). Other leading symptoms were pain (n = 28/151, 18.5%), neurological deficit (n = 27/151, 17.9%), swelling (n = 14/151, 9.3%) and dysfunction of the affected organ (n = 10/151, 6.0%). Most commonly, skin was affected (n = 30/151, 16.6%), followed by bone (n = 29/151, 16.0%) and lymphatic tissue (n = 21/151, 11.4%). Other localizations were rare. On CT, most lesions were homogenous. On T2-weighted imaging, most of the lesions were hyperintense. On T1-weighted images, MS was hypointense in n = 22/54 (40.7%) and isointense in n = 30/54 (55.6%). A diffusion restriction was identified in most cases with a mean ADC value of 0.76 ± 0.19 × 10− 3 mm2/s. Conclusions The present study shows clinical and imaging features of MS based upon a large patient sample in a multicenter design. MS occurs in most cases meta-chronous to the hematological disease and most commonly affects the cutis. One fourth of cases were identified incidentally on imaging, which needs awareness of the radiologists for possible diagnosis of MS.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, University Hospital Leipzig, Liebigstraße 20, 04103, Leipzig, Germany.
| | - Wolfram Pönisch
- Department of Hematology and Oncology, University Hospital Leipzig, Leipzig, Germany
| | - Stefan Andreas Schmidt
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081, Ulm, Germany
| | - Susanne Wienbeck
- Department of Diagnostic and Interventional Radiology, University Medicine Göttingen, 37075, Göttingen, Germany
| | - Friederike Braulke
- Department of Hematology and Medical Oncology, University Medicine Göttingen, 37075, Göttingen, Germany
| | - Dominik Schramm
- Department of Diagnostic and Interventional Radiology, University Hospital of Halle (Saale), 06097, Halle (Saale), Germany
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, University Hospital Leipzig, Liebigstraße 20, 04103, Leipzig, Germany
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145
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Surov A, Chang YW, Li L, Martincich L, Partridge SC, Kim JY, Wienke A. Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis. BMC Cancer 2019; 19:1043. [PMID: 31690273 PMCID: PMC6833245 DOI: 10.1186/s12885-019-6298-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 10/27/2019] [Indexed: 12/14/2022] Open
Abstract
Background Radiological imaging plays a central role in the diagnosis of breast cancer (BC). Some studies suggest MRI techniques like diffusion weighted imaging (DWI) may provide further prognostic value by discriminating between tumors with different biologic characteristics including receptor status and molecular subtype. However, there is much contradictory reported data regarding such associations in the literature. The purpose of the present study was to provide evident data regarding relationships between quantitative apparent diffusion coefficient (ADC) values on DWI and pathologic prognostic factors in BC. Methods Data from 5 centers (661 female patients, mean age, 51.4 ± 10.5 years) were acquired. Invasive ductal carcinoma (IDC) was diagnosed in 625 patients (94.6%) and invasive lobular carcinoma in 36 cases (5.4%). Luminal A carcinomas were diagnosed in 177 patients (28.0%), luminal B carcinomas in 279 patients (44.1%), HER 2+ carcinomas in 66 cases (10.4%), and triple negative carcinomas in 111 patients (17.5%). The identified lesions were staged as T1 in 51.3%, T2 in 43.0%, T3 in 4.2%, and as T4 in 1.5% of the cases. N0 was found in 61.3%, N1 in 33.1%, N2 in 2.9%, and N3 in 2.7%. ADC values between different groups were compared using the Mann–Whitney U test and by the Kruskal-Wallis H test. The association between ADC and Ki 67 values was calculated by Spearman’s rank correlation coefficient. Results ADC values of different tumor subtypes overlapped significantly. Luminal B carcinomas had statistically significant lower ADC values compared with luminal A (p = 0.003) and HER 2+ (p = 0.007) lesions. No significant differences of ADC values were observed between luminal A, HER 2+ and triple negative tumors. There were no statistically significant differences of ADC values between different T or N stages of the tumors. Weak statistically significant correlation between ADC and Ki 67 was observed in luminal B carcinoma (r = − 0.130, p = 0.03). In luminal A, HER 2+ and triple negative tumors there were no significant correlations between ADC and Ki 67. Conclusion ADC was not able to discriminate molecular subtypes of BC, and cannot be used as a surrogate marker for disease stage or proliferation activity.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Germany.
| | - Yun-Woo Chang
- Department of Radiology, Soonchunhyang University Hospital, 59 Daesakwan-ro, Yongsan-gu, Seoul, 140-743, Republic of Korea
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Laura Martincich
- Unit of Radiology, Institute for Cancer Research and Treatment (IRCC), Strada Provinciale 142, 10060 Candiolo, Turin, Italy
| | - Savannah C Partridge
- Department of Radiology, University of Washington, Seattle, Washington 825 Eastlake Ave. E, G2-600, Seattle, WA, 98109, USA
| | - Jin You Kim
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute 1-10, Ami-Dong, Seo-gu, Busan, 602-739, South Korea
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Magdeburger Str, 06097, Halle, Germany
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146
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Kang KJ, Jung KH, Choi EJ, Kim H, Do SH, Ko IO, Oh SJ, Lee YJ, Kim JY, Park JA. Monitoring Physiological Changes in Neutron-Exposed Normal Mouse Brain Using FDG-PET and DW-MRI. Radiat Res 2019; 193:54-62. [PMID: 31682543 DOI: 10.1667/rr15405.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
We monitored a physiological response in a neutron-exposed normal mouse brain using two imaging tools, [18F]fluro-deoxy-D-glucose positron emission tomography ([18F]FDG-PET) and diffusion weighted-magnetic resonance imaging (DW-MRI), as an imaging biomarker. We measured the apparent diffusion coefficient (ADC) of DW-MRI and standardized uptake value (SUV) of [18F]FDG-PET, which indicated changes in the cellular environment for neutron irradiation. This approach was sensitive enough to detect cell changes that were not confirmed in hematoxylin and eosin (H&E) results. Glucose transporters (GLUT) 1 and 3, indicators of the GLUT capacity of the brain, were significantly decreased after neutron irradiation, demonstrating that the change in blood-brain-barrier (BBB) permeability affects the GLUT, with changes in both SUV and ADC values. These results demonstrate that combined imaging of the same object can be used as a quantitative indicator for in vivo pathological changes. In particular, the radiation exposure assessment of combined imaging, with specific integrated functions of [18F]FDG-PET and MRI, can be employed repeatedly for noninvasive analysis performed in clinical practice. Additionally, this study demonstrated a novel approach to assess the extent of damage to normal tissues as well as therapeutic effects on tumors.
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Affiliation(s)
- Kyung Jun Kang
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Ki-Hye Jung
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Eun-Ji Choi
- College of Veterinary Medicine, Konkuk University, Seoul, Korea 05029
| | - Hyosung Kim
- College of Veterinary Medicine, Konkuk University, Seoul, Korea 05029
| | - Sun Hee Do
- College of Veterinary Medicine, Konkuk University, Seoul, Korea 05029
| | - In Ok Ko
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Se Jong Oh
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Yong Jin Lee
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Jung Young Kim
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
| | - Ji-Ae Park
- Division of Applied RI, Korea Institute Radiological and Medical Sciences, Seoul, Korea 01812
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147
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Hamerla G, Meyer HJ, Schob S, Ginat DT, Altman A, Lim T, Gihr GA, Horvath-Rizea D, Hoffmann KT, Surov A. Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study. Magn Reson Imaging 2019; 63:244-249. [DOI: 10.1016/j.mri.2019.08.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 07/14/2019] [Accepted: 08/15/2019] [Indexed: 01/05/2023]
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148
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Meyer HJ, Hamerla G, Leifels L, Höhn AK, Surov A. Histogram analysis parameters derived from DCE-MRI in head and neck squamous cell cancer – Associations with microvessel density. Eur J Radiol 2019; 120:108669. [DOI: 10.1016/j.ejrad.2019.108669] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 09/04/2019] [Accepted: 09/10/2019] [Indexed: 01/21/2023]
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149
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Hamerla G, Meyer HJ, Hambsch P, Wolf U, Kuhnt T, Hoffmann KT, Surov A. Radiomics Model Based on Non-Contrast CT Shows No Predictive Power for Complete Pathological Response in Locally Advanced Rectal Cancer. Cancers (Basel) 2019; 11:cancers11111680. [PMID: 31671766 PMCID: PMC6895820 DOI: 10.3390/cancers11111680] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 10/18/2019] [Accepted: 10/25/2019] [Indexed: 01/15/2023] Open
Abstract
(1) Background: About 15% of the patients undergoing neoadjuvant chemoradiation for locally advanced rectal cancer exhibit pathological complete response (pCR). The surgical approach is associated with major risks as well as a potential negative impact on quality of life and has been questioned in the past. Still, there is no evidence of a reliable clinical or radiological surrogate marker for pCR. This study aims to replicate previously reported response predictions on the basis of non-contrast CT scans on an independent patient cohort. (2) Methods: A total of 169 consecutive patients (126 males, 43 females) that underwent neoadjuvant chemoradiation and consecutive total mesorectal excision were included. The solid tumors were segmented on CT scans acquired on the same scanner for treatment planning. To quantify intratumoral 3D spatial heterogeneity, 1819 radiomics parameters were derived per case. Feature selection and algorithmic modeling were performed to classify pCR vs. non-pCR cases. A random forest model was trained on the dataset using 4-fold cross-validation. (3) Results: The model achieved an accuracy of 87%, higher than previously reported. Correction for the imbalanced distribution of pCR and non-PCR cases (13% and 87% respectively) was applied, yielding a balanced accuracy score of 0.5%. An additional experiment to classify a computer-generated random data sample using the same model led to comparable results. (4) Conclusions: There is no evidence of added value of a radiomics model based on on-contrast CT scans for prediction of pCR in rectal cancer. The imbalance of the target variable could be identified as a key issue, leading to a biased model and optimistic predictions.
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Affiliation(s)
- Gordian Hamerla
- Department of Neuroradiology, University of Leipzig, 04103 Leipzig, Germany.
| | - Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, 04103 Leipzig, Germany.
| | - Peter Hambsch
- Department of Radiooncology, University of Leipzig, 04103 Leipzig, Germany.
| | - Ulrich Wolf
- Department of Radiooncology, University of Leipzig, 04103 Leipzig, Germany.
| | - Thomas Kuhnt
- Department of Radiooncology, University of Leipzig, 04103 Leipzig, Germany.
| | - Karl-Titus Hoffmann
- Department of Neuroradiology, University of Leipzig, 04103 Leipzig, Germany.
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, 04103 Leipzig, Germany.
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany.
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150
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Stieb S, Klarhoefer M, Finkenstaedt T, Wurnig MC, Becker AS, Ciritsis A, Rossi C. Correction for fast pseudo-diffusive fluid motion contaminations in diffusion tensor imaging. Magn Reson Imaging 2019; 66:50-56. [PMID: 31655141 DOI: 10.1016/j.mri.2019.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 07/18/2019] [Accepted: 09/15/2019] [Indexed: 11/26/2022]
Abstract
In this prospective study, we quantified the fast pseudo-diffusion contamination by blood perfusion or cerebrospinal fluid (CSF) intravoxel incoherent movements on the measurement of the diffusion tensor metrics in healthy brain tissue. Diffusion-weighted imaging (TR/TE = 4100 ms/90 ms; b-values: 0, 5, 10, 20, 35, 55, 80, 110, 150, 200, 300, 500, 750, 1000, 1300 s/mm2, 20 diffusion-encoding directions) was performed on a cohort of five healthy volunteers at 3 Tesla. The projections of the diffusion tensor along each diffusion-encoding direction were computed using a two b-value approach (2b), by fitting the signal to a monoexponential curve (mono), and by correcting for fast pseudo-diffusion compartments using the biexponential intravoxel incoherent motion model (IVIM) (bi). Fractional anisotropy (FA) and mean diffusivity (MD) of the diffusion tensor were quantified in regions of interest drawn over white matter areas, gray matter areas, and the ventricles. A significant dependence of the MD from the evaluation method was found in all selected regions. A lower MD was computed when accounting for the fast-diffusion compartments. A larger dependence was found in the nucleus caudatus (bi: median 0.86 10-3 mm2/s, Δ2b: -11.2%, Δmono: -14.4%; p = 0.007), in the anterior horn (bi: median 2.04 10-3 mm2/s, Δ2b: -9.4%, Δmono: -11.5%, p = 0.007) and in the posterior horn of the lateral ventricles (bi: median 2.47 10-3 mm2/s, Δ2b: -5.5%, Δmono: -11.7%; p = 0.007). Also for the FA, the signal modeling affected the computation of the anisotropy metrics. The deviation depended on the evaluated region with significant differences mainly in the nucleus caudatus (bi: median 0.15, Δ2b: +39.3%, Δmono: +14.7%; p = 0.022) and putamen (bi: median 0.19, Δ2b: +3.1%, Δmono: +17.3%; p = 0.015). Fast pseudo-diffusive regimes locally affect diffusion tensor imaging (DTI) metrics in the brain. Here, we propose the use of an IVIM-based method for correction of signal contaminations through CSF or perfusion.
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Affiliation(s)
- Sonja Stieb
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland.
| | | | - Tim Finkenstaedt
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Moritz C Wurnig
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Anton S Becker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Alexander Ciritsis
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Cristina Rossi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
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