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Huang J, Liu D, Chen J, Wang X, Tang L, Zhang J, Tan Y, Lan X, Yin T, Nickel D, Wu J, Zhang J. Differential diagnosis of thyroid nodules by DCE-MRI based on compressed sensing volumetric interpolated breath-hold examination: A feasibility study. Magn Reson Imaging 2024; 111:138-147. [PMID: 38729225 DOI: 10.1016/j.mri.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/29/2024] [Accepted: 05/06/2024] [Indexed: 05/12/2024]
Abstract
OBJECTIVES To explore the potential and performance of quantitative and semi-quantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on compressed sensing volumetric interpolated breath-hold (CS-VIBE) examination in the differential diagnosis of thyroid nodules. MATERIALS AND METHODS A total of 208 patients with 259 thyroid nodules scheduled for surgery operation were prospectively recruited. All participants underwent routine and DCE-MRI. DCE-MRI quantitative parameters [Ktrans, Kep, Ve], semi-quantitative parameters [wash-in, wash-out, time to peak (TTP), arrival time (AT), peak enhancement intensity (PEI), and initial area under curve in 60 s (iAUC)] and time-intensity curve (TIC) types were analyzed. Differential diagnostic performances were assessed using area under the receiver operating characteristic curve (AUC) and compared with the Delong test. RESULTS Ktrans, Kep, Ve, wash-in, wash-out, PEI and iAUC were statistically significantly different between malignant and benign nodules (P < 0.001). Among these parameters, ROC analysis revealed that Ktrans showed the highest diagnostic performance in the differentiation of benign and malignant nodules, followed by wash-in. ROC analysis also revealed that Ktrans achieved the best diagnostic performance for distinguishing papillary thyroid carcinoma (PTC) from non-PTC, follicular adenoma (FA) from non-FA, nodular goiter (NG) from non-NG, with AUC values of 0.854, 0.895 and 0.609, respectively. Type III curve is frequently observed in benign thyroid nodules, accounting for 77.4% (82/106). While malignant nodules are more common in type II, accounting for 57.5% (88/153). CONCLUSION Thyroid examination using CS-VIBE based DCE-MRI is a feasible, non-invasive method to identify benign and malignant thyroid nodules and pathological types.
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Affiliation(s)
- Junhao Huang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Daihong Liu
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Jiao Chen
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Xiaoxia Wang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Lin Tang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Jing Zhang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Yong Tan
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Xiaosong Lan
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Ting Yin
- MR Collaborations, Siemens Healthineers Ltd., Chengdu, China
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Jian Wu
- Head and Neck Cancer Center, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China.
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Martín-Noguerol T, Santos-Armentia E, Fernandez-Palomino J, López-Úbeda P, Paulano-Godino F, Luna A. Role of advanced MRI sequences for thyroid lesions assessment. A narrative review. Eur J Radiol 2024; 176:111499. [PMID: 38735157 DOI: 10.1016/j.ejrad.2024.111499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 04/12/2024] [Accepted: 05/05/2024] [Indexed: 05/14/2024]
Abstract
Despite not being the first imaging modality for thyroid gland assessment, Magnetic Resonance Imaging (MRI), thanks to its optimal tissue contrast and spatial resolution, has provided some advancements in detecting and characterizing thyroid abnormalities. Recent research has been focused on improving MRI sequences and employing advanced techniques for a more comprehensive understanding of thyroid pathology. Although not yet standard practice, advanced MRI sequences have shown high accuracy in preliminary studies, correlating well with histopathological results. They particularly show promise in determining malignancy risk in thyroid lesions, which may reduce the need for invasive procedures like biopsies. In this line, functional MRI sequences like Diffusion Weighted Imaging (DWI), Dynamic Contrast-Enhanced MRI (DCE-MRI), and Arterial Spin Labeling (ASL) have demonstrated their potential usefulness in evaluating both diffuse thyroid conditions and focal lesions. Multicompartmental DWI models, such as Intravoxel Incoherent Motion (IVIM) and Diffusion Kurtosis Imaging (DKI), and novel methods like Amide Proton Transfer (APT) imaging or artificial intelligence (AI)-based analyses are being explored for their potential valuable insights into thyroid diseases. This manuscript reviews the critical physical principles and technical requirements for optimal functional MRI sequences of the thyroid and assesses the clinical utility of each technique. It also considers future prospects in the context of advanced MR thyroid imaging and analyzes the current role of advanced MRI sequences in routine practice.
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Affiliation(s)
| | | | | | | | | | - Antonio Luna
- MRI unit, Radiology department. HT medica, Carmelo Torres 2, 23007 Jaén, Spain.
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Zheng T, Wang L, Wang H, Tang L, Xie X, Fu Q, Wu PY, Song B. Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules. BMC Cancer 2024; 24:256. [PMID: 38395783 PMCID: PMC10885392 DOI: 10.1186/s12885-024-11995-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND The low specificity of Thyroid Imaging Reporting and Data System (TI-RADS) for preoperative benign-malignant diagnosis leads to a large number of unnecessary biopsies. This study developed and validated a predictive model based on MRI morphological features to improve the specificity. METHODS A retrospective analysis was conducted on 825 thyroid nodules pathologically confirmed postoperatively. Univariate and multivariate logistic regression were used to obtain β coefficients, construct predictive models and nomogram incorporating MRI morphological features in the training cohort, and validated in the validation cohort. The discrimination, calibration, and decision curve analysis of the nomogram were performed. The diagnosis efficacy, area under the curve (AUC) and net reclassification index (NRI) were calculated and compared with TI-RADS. RESULTS 572 thyroid nodules were included (training cohort: n = 397, validation cohort: n = 175). Age, low signal intensity on T2WI, restricted diffusion, reversed halo sign in delay phase, cystic degeneration and wash-out pattern were independent predictors of malignancy. The nomogram demonstrated good discrimination and calibration both in the training cohort (AUC = 0.972) and the validation cohort (AUC = 0.968). The accuracy, sensitivity, specificity, PPV, NPV and AUC of MRI-based prediction were 94.4%, 96.0%, 93.4%, 89.9%, 96.5% and 0.947, respectively. The MRI-based prediction model exhibited enhanced accuracy (NRI>0) in comparison to TI-RADSs. CONCLUSIONS The prediction model for diagnosis of benign and malignant thyroid nodules demonstrated a more notable diagnostic efficacy than TI-RADS. Compared with the TI-RADSs, predictive model had better specificity along with a high sensitivity and can reduce overdiagnosis and unnecessary biopsies.
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Affiliation(s)
- Tingting Zheng
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Lanyun Wang
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Hao Wang
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Lang Tang
- Department of Ultrasound, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Xiaoli Xie
- Department of Pathology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Qingyin Fu
- Department of Ultrasound, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Pu-Yeh Wu
- GE Healthcare, MR Research China, Beijing, China
| | - Bin Song
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China.
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LoCastro E, Paudyal R, Konar AS, LaViolette PS, Akin O, Hatzoglou V, Goh AC, Bochner BH, Rosenberg J, Wong RJ, Lee NY, Schwartz LH, Shukla-Dave A. A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology. Tomography 2023; 9:2052-2066. [PMID: 37987347 PMCID: PMC10661267 DOI: 10.3390/tomography9060161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/12/2023] [Accepted: 10/18/2023] [Indexed: 11/22/2023] Open
Abstract
There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.
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Affiliation(s)
- Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Peter S. LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA;
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Alvin C. Goh
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Bernard H. Bochner
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Jonathan Rosenberg
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Richard J. Wong
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Nancy Y. Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Lawrence H. Schwartz
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
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Bagher-Ebadian H, Brown SL, Ghassemi MM, Nagaraja TN, Movsas B, Ewing JR, Chetty IJ. Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI. Sci Rep 2023; 13:10693. [PMID: 37394559 DOI: 10.1038/s41598-023-37723-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/27/2023] [Indexed: 07/04/2023] Open
Abstract
Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs' feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Physics, Oakland University, Rochester, MI, 48309, USA.
| | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Mohammad M Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tavarekere N Nagaraja
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - James R Ewing
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Wayne State University, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
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Bagher-Ebadian H, Brown SL, Ghassemi MM, Nagaraja TN, Valadie OG, Acharya PC, Cabral G, Divine G, Knight RA, Lee IY, Xu JH, Movsas B, Chetty IJ, Ewing JR. Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models. Sci Rep 2023; 13:9672. [PMID: 37316579 PMCID: PMC10267191 DOI: 10.1038/s41598-023-36483-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 06/05/2023] [Indexed: 06/16/2023] Open
Abstract
We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, vp, Ktrans, and ve, respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Physics, Oakland University, Rochester, MI, 48309, USA.
| | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Mohammad M Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tavarekere N Nagaraja
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Olivia Grahm Valadie
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Prabhu C Acharya
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
| | - Glauber Cabral
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
| | - George Divine
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Epidemiology and Biostatistics, Michigan State University, E. Lansing, MI, 48824, USA
| | - Robert A Knight
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
| | - Ian Y Lee
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Jun H Xu
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - James R Ewing
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Wayne State University, Detroit, MI, 48202, USA
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7
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Liu L, Jia C, Li G, Shi Q, Du L, Wu R. Nomogram incorporating preoperative clinical and ultrasound indicators to predict aggressiveness of solitary papillary thyroid carcinoma. Front Oncol 2023; 13:1009958. [PMID: 36798828 PMCID: PMC9927212 DOI: 10.3389/fonc.2023.1009958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 01/16/2023] [Indexed: 02/04/2023] Open
Abstract
Objective To construct a nomogram based on preoperative clinical and ultrasound indicators to predict aggressiveness of solitary papillary thyroid carcinoma (PTC). Methods Preoperative clinical and ultrasound data from 709 patients diagnosed with solitary PTC between January 2017 and December 2020 were analyzed retrospectively. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with PTC aggressiveness, and these factors were used to construct a predictive nomogram. The nomogram's performance was evaluated in the primary and validation cohorts. Results The 709 patients were separated into a primary cohort (n = 424) and a validation cohort (n = 285). Univariate analysis in the primary cohort showed 13 variables to be associated with aggressive PTC. In multivariate logistic regression analysis, the independent predictors of aggressive behavior were age (OR, 2.08; 95% CI, 1.30-3.35), tumor size (OR, 4.0; 95% CI, 2.17-7.37), capsule abutment (OR, 2.53; 95% CI, 1.50-4.26), and suspected cervical lymph nodes metastasis (OR, 2.50; 95% CI, 1.20-5.21). The nomogram incorporating these four predictors showed good discrimination and calibration in both the primary cohort (area under the curve, 0.77; 95% CI, 0.72-0.81; Hosmer-Lemeshow test, P = 0.967 and the validation cohort (area under the curve, 0.72; 95% CI, 0.66-0.78; Hosmer-Lemeshow test, P = 0.251). Conclusion The proposed nomogram shows good ability to predict PTC aggressiveness and could be useful during treatment decision making. Advances in knowledge Our nomogram-based on four indicators-provides comprehensive assessment of aggressive behavior of PTC and could be a useful tool in the clinic.
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Affiliation(s)
- Long Liu
- Department of Ultrasound, Shanghai General Hospital of Nanjing Medical University, Shanghai, China,Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chao Jia
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gang Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiusheng Shi
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lianfang Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rong Wu
- Department of Ultrasound, Shanghai General Hospital of Nanjing Medical University, Shanghai, China,Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,*Correspondence: Rong Wu,
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8
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Stetson PD, Cantor MN, Gonen M. When Predictive Models Collide. JCO Clin Cancer Inform 2021; 4:547-550. [PMID: 32543898 DOI: 10.1200/cci.20.00024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Peter D Stetson
- Department of Medicine, Digital Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Michael N Cantor
- Department of Medicine, Digital Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Mithat Gonen
- Department of Medicine, Digital Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
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Gomes AL, Gribben J, Siow B, Passaro D, Bonnet D. Dynamic contrast-enhanced magnetic resonance imaging quantification of leukemia-induced changes in bone marrow vascular function. Haematologica 2021; 106:2281-2286. [PMID: 33764000 PMCID: PMC8327741 DOI: 10.3324/haematol.2020.277269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Indexed: 11/09/2022] Open
Abstract
Not available.
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Affiliation(s)
- Ana L Gomes
- Hematopoietic Stem Cell Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT
| | - John Gribben
- Department of Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ
| | - Bernard Siow
- In Vivo Imaging, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT.
| | - Diana Passaro
- Hematopoietic Stem Cell Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, United Kingdom; Present address: Leukemia and Niche Dynamics Laboratory, Université de Paris, Institut Cochin, INSERM, CNRS, F-75014 PARIS.
| | - Dominique Bonnet
- Hematopoietic Stem Cell Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT.
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LoCastro E, Paudyal R, Mazaheri Y, Hatzoglou V, Oh JH, Lu Y, Konar AS, Vom Eigen K, Ho A, Ewing JR, Lee N, Deasy JO, Shukla-Dave A. Computational Modeling of Interstitial Fluid Pressure and Velocity in Head and Neck Cancer Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging: Feasibility Analysis. ACTA ACUST UNITED AC 2021; 6:129-138. [PMID: 32548289 PMCID: PMC7289251 DOI: 10.18383/j.tom.2020.00005] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
We developed and tested the feasibility of computational fluid modeling (CFM) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for quantitative estimation of interstitial fluid pressure (IFP) and velocity (IFV) in patients with head and neck (HN) cancer with locoregional lymph node metastases. Twenty-two patients with HN cancer, with 38 lymph nodes, underwent pretreatment standard MRI, including DCE-MRI, on a 3-Tesla scanner. CFM simulation was performed with the finite element method in COMSOL Multiphysics software. The model consisted of a partial differential equation (PDE) module to generate 3D parametric IFP and IFV maps, using the Darcy equation and Ktrans values (min−1, estimated from the extended Tofts model) to reflect fluid influx into tissue from the capillary microvasculature. The Spearman correlation (ρ) was calculated between total tumor volumes and CFM estimates of mean tumor IFP and IFV. CFM-estimated tumor IFP and IFV mean ± standard deviation for the neck nodal metastases were 1.73 ± 0.39 (kPa) and 1.82 ± 0.9 × (10−7 m/s), respectively. High IFP estimates corresponds to very low IFV throughout the tumor core, but IFV rises rapidly near the tumor boundary where the drop in IFP is precipitous. A significant correlation was found between pretreatment total tumor volume and CFM estimates of mean tumor IFP (ρ = 0.50, P = 0.004). Future studies can validate these initial findings in larger patients with HN cancer cohorts using CFM of the tumor in concert with DCE characterization, which holds promise in radiation oncology and drug-therapy clinical trials.
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Affiliation(s)
| | | | - Yousef Mazaheri
- Departments of Medical Physics and.,Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Vaios Hatzoglou
- Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Yonggang Lu
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI
| | | | | | - Alan Ho
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - James R Ewing
- Departments of Neurology and.,Neurosurgery, Henry Ford Hospital, Detroit, MI; and
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Amita Shukla-Dave
- Departments of Medical Physics and.,Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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11
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Kinh Do R, Reyngold M, Paudyal R, Oh JH, Konar AS, LoCastro E, Goodman KA, Shukla-Dave A. Diffusion-Weighted and Dynamic Contrast-Enhanced MRI Derived Imaging Metrics for Stereotactic Body Radiotherapy of Pancreatic Ductal Adenocarcinoma: Preliminary Findings. ACTA ACUST UNITED AC 2021; 6:261-271. [PMID: 32548304 PMCID: PMC7289241 DOI: 10.18383/j.tom.2020.00015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
We aimed to assess longitudinal changes in quantitative imaging metric values obtained from diffusion-weighted (DW-) and dynamic contrast-enhanced magnetic resonance imaging (DCE)-MRI at pre-treatment (TX[0]), immediately after the first fraction of stereotactic body radiotherapy (D1-TX[1]), and 6 weeks post-TX (Post-TX[2]) in patients with pancreatic ductal adenocarcinoma. Ten enrolled patients (n = 10) underwent DW- and DCE-MRI examinations on a 3.0 T scanner. The apparent diffusion coefficient, ADC (mm2/s), was derived from DW imaging data using a monoexponential model. The tissue relaxation rate, R 1t, time-course data were fitted with a shutter-speed model, which provides estimates of the volume transfer constant, K trans (min-1), extravascular extracellular volume fraction, ve , and mean lifetime of intracellular water protons, τ i (seconds). Wilcoxon rank-sum test compared the mean values, standard deviation, skewness, kurtosis, and relative percentage (r, %) changes (Δ) in ADC, K trans, ve , and τ i values between the magnetic resonance examinations. rADCΔ2-0 values were significantly greater than rADCΔ1-0 values (P = .009). rK trans Δ2-0 values were significantly lower than rK trans Δ1-0 values (P = .048). rve Δ2-1 and rveΔ2-0 values were significantly different (P = .016). rτ i Δ2-1 values were significantly lower than rτ i Δ2-0 values (P = .008). For group comparison, the pre-TX mean and kurtosis of ADC (P = .18 and P = .14), skewness and kurtosis of K trans values (P = .14 for both) showed a leaning toward significant difference between patients who experienced local control (n = 2) and failed early (n = 4). DW- and DCE-MRI-derived quantitative metrics could be useful biomarkers to evaluate longitudinal changes to stereotactic body radiotherapy in patients with pancreatic ductal adenocarcinoma.
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Affiliation(s)
| | | | - Ramesh Paudyal
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; and
| | - Jung Hun Oh
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; and
| | | | - Eve LoCastro
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; and
| | - Karyn A Goodman
- Tisch Cancer Institute at Mount Sinai Hospital, New York, NY
| | - Amita Shukla-Dave
- Departments of Radiology.,Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; and
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12
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Li KL, Lewis D, Coope DJ, Roncaroli F, Agushi E, Pathmanaban ON, King AT, Zhao S, Jackson A, Cootes T, Zhu X. The LEGATOS technique: A new tissue-validated dynamic contrast-enhanced MRI method for whole-brain, high-spatial resolution parametric mapping. Magn Reson Med 2021; 86:2122-2136. [PMID: 33991126 DOI: 10.1002/mrm.28842] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 04/23/2021] [Accepted: 04/24/2021] [Indexed: 01/06/2023]
Abstract
PURPOSE A DCE-MRI technique that can provide both high spatiotemporal resolution and whole-brain coverage for quantitative microvascular analysis is highly desirable but currently challenging to achieve. In this study, we sought to develop and validate a novel dual-temporal resolution (DTR) DCE-MRI-based methodology for deriving accurate, whole-brain high-spatial resolution microvascular parameters. METHODS Dual injection DTR DCE-MRI was performed and composite high-temporal and high-spatial resolution tissue gadolinium-based-contrast agent (GBCA) concentration curves were constructed. The high-temporal but low-spatial resolution first-pass GBCA concentration curves were then reconstructed pixel-by-pixel to higher spatial resolution using a process we call LEGATOS. The accuracy of kinetic parameters (Ktrans , vp , and ve ) derived using LEGATOS was evaluated through simulations and in vivo studies in 17 patients with vestibular schwannoma (VS) and 13 patients with glioblastoma (GBM). Tissue from 15 tumors (VS) was examined with markers for microvessels (CD31) and cell density (hematoxylin and eosin [H&E]). RESULTS LEGATOS derived parameter maps offered superior spatial resolution and improved parameter accuracy compared to the use of high-temporal resolution data alone, provided superior discrimination of plasma volume and vascular leakage effects compared to other high-spatial resolution approaches, and correlated with tissue markers of vascularity (P ≤ 0.003) and cell density (P ≤ 0.006). CONCLUSION The LEGATOS method can be used to generate accurate, high-spatial resolution microvascular parameter estimates from DCE-MRI.
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Affiliation(s)
- Ka-Loh Li
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Daniel Lewis
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom.,Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.,Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, United Kingdom
| | - David J Coope
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.,Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, United Kingdom.,Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Federico Roncaroli
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, United Kingdom.,Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Erjon Agushi
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Omar N Pathmanaban
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.,Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, United Kingdom.,Division of Cell Matrix Biology & Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Andrew T King
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.,Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, United Kingdom.,Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Sha Zhao
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Alan Jackson
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Timothy Cootes
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Xiaoping Zhu
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom
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13
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Julie L, Ikram D, Mailyn PL, Augustin L, Afef B, Joevin S, Bentoumi I, Cuenod CA, Daniel B. A free time point model for dynamic contrast enhanced exploration. Magn Reson Imaging 2021; 80:39-49. [PMID: 33905829 DOI: 10.1016/j.mri.2021.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 04/08/2021] [Accepted: 04/21/2021] [Indexed: 02/07/2023]
Abstract
Dynamic-Contrast-Enhanced (DCE) Imaging has been widely studied to characterize microcirculatory disorders associated with various diseases. Although numerous studies have demonstrated its diagnostic interest, the physiological interpretation using pharmacokinetic models often remains debatable. Indeed, to be interpretable, a model must provide, at first instance, an accurate description of the DCE data. However, the evaluation and optimization of this accuracy remain rather limited in DCE. Here we established a non-linear Free-Time-Point-Hermite (FTPH) data-description model designed to fit DCE data accurately. Its performance was evaluated on data generated using two contrasting pharmacokinetic microcirculatory hypotheses (MH). The accuracy of data description of the models was evaluated by calculating the mean squared error (QE) from initial and assessed tissue impulse responses. Then, FTPH assessments were provided to blinded observers to evaluate if these assessments allowed observers to identify MH in their data. Regardless of the initial pharmacokinetic model used for data generation, QE was lower than 3% for the noise-free datasets and increased up to 10% for a signal-to-noise-ratio (SNR) of 20. Under SNR = 20, the sensitivity and specificity of the MH identification were over 80%. The performance of the FTPH model was higher than that of the B-Spline model used as a reference. The accuracy of the FTPH model regardless of the initial MH provided an opportunity to have a reference to check the accuracy of other pharmacokinetic models.
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Affiliation(s)
- Levebvre Julie
- Université de Paris, PARCC, INSERM, Paris F-75015, France
| | - Djebali Ikram
- Université de Paris, PARCC, INSERM, Paris F-75015, France
| | | | | | | | - Sourdon Joevin
- Université de Paris, PARCC, INSERM, Paris F-75015, France.
| | - Isma Bentoumi
- Université de Paris, PARCC, INSERM, Paris F-75015, France
| | - Charles-André Cuenod
- Université de Paris, PARCC, INSERM, Paris F-75015, France; Service Radiologie, AP-HP, Hôpital Européen Georges Pompidou, F-75015, France.
| | - Balvay Daniel
- Université de Paris, PARCC, INSERM, Paris F-75015, France; Université de Paris, Plateforme d'Imageries du Vivant, F-75015, France.
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14
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Paudyal R, Chen L, Oh JH, Zakeri K, Hatzoglou V, Tsai CJ, Lee N, Shukla-Dave A. Nongaussian Intravoxel Incoherent Motion Diffusion Weighted and Fast Exchange Regime Dynamic Contrast-Enhanced-MRI of Nasopharyngeal Carcinoma: Preliminary Study for Predicting Locoregional Failure. Cancers (Basel) 2021; 13:1128. [PMID: 33800762 PMCID: PMC7961986 DOI: 10.3390/cancers13051128] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/02/2021] [Accepted: 03/02/2021] [Indexed: 12/28/2022] Open
Abstract
The aim of the present study was to identify whether the quantitative metrics from pre-treatment (TX) non-Gaussian intravoxel incoherent motion (NGIVIM) diffusion weighted (DW-) and fast exchange regime (FXR) dynamic contrast enhanced (DCE)-MRI can predict patients with locoregional failure (LRF) in nasopharyngeal carcinoma (NPC). Twenty-nine NPC patients underwent pre-TX DW- and DCE-MRI on a 3T MR scanner. DW imaging data from primary tumors were fitted to monoexponential (ADC) and NGIVIM (D, D*, f, and K) models. The metrics Ktrans, ve, and τi were estimated using the FXR model. Cumulative incidence (CI) analysis and Fine-Gray (FG) modeling were performed considering death as a competing risk. Mean ve values were significantly different between patients with and without LRF (p = 0.03). Mean f values showed a trend towards the difference between the groups (p = 0.08). Histograms exhibited inter primary tumor heterogeneity. The CI curves showed significant differences for the dichotomized cutoff value of ADC ≤ 0.68 × 10-3 (mm2/s), D ≤ 0.74 × 10-3 (mm2/s), and f ≤ 0.18 (p < 0.05). τi ≤ 0.89 (s) cutoff value showed borderline significance (p = 0.098). FG's modeling showed a significant difference for the K cutoff value of ≤0.86 (p = 0.034). Results suggest that the role of pre-TX NGIVIM DW- and FXR DCE-MRI-derived metrics for predicting LRF in NPC than alone.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.P.); (J.H.O.)
| | - Linda Chen
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (L.C.); (K.Z.); (C.J.T.); (N.L.)
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.P.); (J.H.O.)
| | - Kaveh Zakeri
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (L.C.); (K.Z.); (C.J.T.); (N.L.)
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - C. Jillian Tsai
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (L.C.); (K.Z.); (C.J.T.); (N.L.)
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (L.C.); (K.Z.); (C.J.T.); (N.L.)
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.P.); (J.H.O.)
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
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15
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Shah AD, Shridhar Konar A, Paudyal R, Oh JH, LoCastro E, Nuñez DA, Swinburne N, Vachha B, Ulaner GA, Young RJ, Holodny AI, Beal K, Shukla-Dave A, Hatzoglou V. Diffusion and Perfusion MRI Predicts Response Preceding and Shortly After Radiosurgery to Brain Metastases: A Pilot Study. J Neuroimaging 2020; 31:317-323. [PMID: 33370467 DOI: 10.1111/jon.12828] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/20/2020] [Accepted: 12/06/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND AND PURPOSE To determine the ability of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict long-term response of brain metastases prior to and within 72 hours of stereotactic radiosurgery (SRS). METHODS In this prospective pilot study, multiple b-value DWI and T1-weighted DCE-MRI were performed in patients with brain metastases before and within 72 hours following SRS. Diffusion-weighted images were analyzed using the monoexponential and intravoxel incoherent motion (IVIM) models. DCE-MRI data were analyzed using the extended Tofts pharmacokinetic model. The parameters obtained with these methods were correlated with brain metastasis outcomes according to modified Response Assessment in Neuro-Oncology Brain Metastases criteria. RESULTS We included 25 lesions from 16 patients; 16 patients underwent pre-SRS MRI and 12 of 16 patients underwent both pre- and early (within 72 hours) post-SRS MRI. The perfusion fraction (f) derived from IVIM early post-SRS was higher in lesions demonstrating progressive disease than in lesions demonstrating stable disease, partial response, or complete response (q = .041). Pre-SRS extracellular extravascular volume fraction, ve , and volume transfer coefficient, Ktrans , derived from DCE-MRI were higher in nonresponders versus responders (q = .041). CONCLUSIONS Quantitative DWI and DCE-MRI are feasible imaging methods in the pre- and early (within 72 hours) post-SRS evaluation of brain metastases. DWI- and DCE-MRI-derived parameters demonstrated physiologic changes (tumor cellularity and vascularity) and offer potentially useful biomarkers that can predict treatment response. This allows for initiation of alternate therapies within an effective time window that may help prevent disease progression.
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Affiliation(s)
- Akash Deelip Shah
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - David Aramburu Nuñez
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Nathaniel Swinburne
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Gary A Ulaner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kathryn Beal
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Amita Shukla-Dave
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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16
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Harrington KA, Shukla-Dave A, Paudyal R, Do RKG. MRI of the Pancreas. J Magn Reson Imaging 2020; 53:347-359. [PMID: 32302044 DOI: 10.1002/jmri.27148] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 02/06/2023] Open
Abstract
MRI has played a critical role in the evaluation of patients with pancreatic pathologies, from screening of patients at high risk for pancreatic cancer to the evaluation of pancreatic cysts and indeterminate pancreatic lesions. The high mortality associated with pancreatic adenocarcinomas has spurred much interest in developing effective screening tools, with MRI using magnetic resonance cholangiopancreatography (MRCP) playing a central role in the hopes of identifying cancers at earlier stages amenable to curative resection. Ongoing efforts to improve the resolution and robustness of imaging of the pancreas using MRI may thus one day reduce the mortality of this deadly disease. However, the increasing use of cross-sectional imaging has also generated a concomitant clinical conundrum: How to manage incidental pancreatic cystic lesions that are found in over a quarter of patients who undergo MRCP. Efforts to improve the specificity of MRCP for patients with pancreatic cysts and with indeterminate pancreatic masses may be achieved with continued technical advances in MRI, including diffusion-weighted and T1 -weighted dynamic contrast-enhanced MRI. However, developments in quantitative MRI of the pancreas remain challenging, due to the small size of the pancreas and its upper abdominal location, adjacent to bowel and below the diaphragm. Further research is needed to improve MRI of the pancreas as a clinical tool, to positively affect the lives of patients with pancreatic abnormalities. This review focuses on various MR techniques such as MRCP, quantitative imaging, and dynamic contrast-enhanced imaging and their clinical applicability in the imaging of the pancreas, with an emphasis on pancreatic malignant and premalignant lesions. Level of Evidence 5 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:347-359.
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Affiliation(s)
- Kate A Harrington
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amita Shukla-Dave
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ramesh Paudyal
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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