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Bai B, Cui L, Chu F, Wang Z, Zhao K, Wang S, Wang S, Yan X, Wang M, Kamel IR, Yang G, Qu J. Multiple diffusion models for predicting pathologic response of esophageal squamous cell carcinoma to neoadjuvant chemotherapy. Abdom Radiol (NY) 2024:10.1007/s00261-024-04474-7. [PMID: 38954001 DOI: 10.1007/s00261-024-04474-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/22/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND To assess the feasibility and diagnostic performance of the fractional order calculus (FROC), continuous-time random-walk (CTRW), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), mono-exponential (MEM) and stretched exponential models (SEM) for predicting response to neoadjuvant chemotherapy (NACT) in patients with esophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS This study prospectively included consecutive ESCC patients with baseline and follow up MR imaging and pathologically confirmed cT1-4aN + M0 or T3-4aN0M0 and underwent radical resection after neoadjuvant chemotherapy (NACT) between July 2019 and January 2023. Patients were divided into pCR (TRG 0) and non-pCR (TRG1 + 2 + 3) groups according to tumor regression grading (TRG). The Pre-, Post- and Delta-treatment models were built. 18 predictive models were generated according to different feature categories, based on six models by five-fold cross-validation. Areas under the curve (AUCs) of the models were compared by using DeLong method. RESULTS Overall, 90 patients (71 men, 19 women; mean age, 64 years ± 6 [SD]) received NACT and underwent baseline and Post-NACT esophageal MRI, with 29 patients in the pCR group and 61 patients in the non-pCR group. Among 18 predictive models, The Pre-, Post-, and Delta-CTRW model showed good predictive efficacy (AUC = 0.722, 0.833 and 0.790). Additionally, the Post-FROC model (AUC = 0.907) also exhibited good diagnostic performance. CONCLUSIONS Our study indicates that the CTRW model, along with the Post-FROC model, holds significant promise for the future of NACT efficacy prediction in ESCC patients.
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Affiliation(s)
- Bingmei Bai
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Long Cui
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Funing Chu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Zhaoqi Wang
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Keke Zhao
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Shuting Wang
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Shaoyu Wang
- MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China
| | - Mengzhu Wang
- MR Research Collaboration, Siemens Healthineers Ltd, Beijing, 100000, China
| | - Ihab R Kamel
- Department of Radiology, Anschutz Medical Campus, University of Colorado Denver, 12401 East 17Th Avenue, Aurora, CO, 80045, USA
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Jinrong Qu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China.
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Zhang L, Ning N, Liang H, Zhao S, Gao X, Liu A, Song Q, Duan X, Yang J, Xie L. The contrast-free diffusion MRI multiple index for the early prediction of pathological response to neoadjuvant chemotherapy in breast cancer. NMR IN BIOMEDICINE 2024:e5176. [PMID: 38884131 DOI: 10.1002/nbm.5176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 04/21/2024] [Accepted: 04/21/2024] [Indexed: 06/18/2024]
Abstract
Early tumor response prediction can help avoid overtreatment with unnecessary chemotherapy sessions. It is important to determine whether multiple apparent diffusion coefficient indices (S index, ADC-diff) are effective in the early prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer (BC). Patients with stage II and III BCs who underwent T1WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI using a 3 T system were included. They were divided into two groups: major histological responders (MHRs, Miller-Payne G4/5) and nonmajor histological responders (nMHRs, Miller-Payne G1-3). Three b values were used for DWI to derive the S index; ADC-diff values were obtained using b = 0 and 1000 s/mm2. The different interquartile ranges of percentile S-index and ADC-diff values after treatment were calculated and compared. The assessment was performed at baseline and after two and four NAC cycles. A total of 59 patients were evaluated. There are some correlations of interquartile ranges of S-index parameters and ADC-diff values with histopathological prognostic factors (such as estrogen receptor and human epidermal growth factor receptor 2 expression, all p < 0.05), but no significant differences were found in some other interquartile ranges of S-index parameters or ADC-diff values between progesterone receptor positive and negative or for Ki-67 tumors (all P > 0.05). No differences were found in the dynamic contrast-enhanced MRI characteristics between the two groups. HER-2 expression and kurtosis of the S-index distribution were screened out as independent risk factors for predicting MHR group (p < 0.05, area under the curve (AUC) = 0.811) before NAC. After early NAC (two cycles), only the 10th percentile S index was statistically significant between the two groups (p < 0.05, AUC = 0.714). No significant differences were found in ADC-diff value at any time point of NAC between the two groups (P > 0.1). These findings demonstrate that the S-index value may be used as an early predictor of pathological response to NAC in BC; the value of ADC-diff as an imaging biomarker of NAC needs to be further confirmed by ongoing multicenter prospective trials.
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Affiliation(s)
- Lina Zhang
- PET-CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ning Ning
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Hongbing Liang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Siqi Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xue Gao
- Department of Pathology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ailian Liu
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Qingwei Song
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xiaoyi Duan
- PET-CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jie Yang
- School of Public Health, Dalian Medical University, Dalian, China
| | - Lizhi Xie
- GE Healthcare, MR Research China, Beijing, China
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Zhang N, Song Q, Liang H, Wang Z, Wu Q, Zhang H, Zhang L, Liu A, Wang H, Wang J, Lin L. Early prediction of pathological response to neoadjuvant chemotherapy of breast tumors: a comparative study using amide proton transfer-weighted, diffusion weighted and dynamic contrast enhanced MRI. Front Med (Lausanne) 2024; 11:1295478. [PMID: 38298813 PMCID: PMC10827983 DOI: 10.3389/fmed.2024.1295478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/05/2024] [Indexed: 02/02/2024] Open
Abstract
Objective To examine amide proton transfer-weighted (APTw) combined with diffusion weighed (DWI) and dynamic contrast enhanced (DCE) MRI for early prediction of pathological response to neoadjuvant chemotherapy in invasive breast cancer. Materials In this prospective study, 50 female breast cancer patients (49.58 ± 10.62 years old) administered neoadjuvant chemotherapy (NAC) were enrolled with MRI carried out both before NAC (T0) and at the end of the second cycle of NAC (T1). The patients were divided into 2 groups based on tumor response according to the Miller-Payne Grading (MPG) system. Group 1 included patients with a greater degree of decrease in major histologic responder (MHR, Miller-Payne G4-5), while group 2 included non-MHR cases (Miller-Payne G1-3). Traditional imaging protocols (T1 weighted, T2 weighted, diffusion weighted, and DCE-MRI) and APTw imaging were scanned for each subject before and after treatment. APTw value (APTw0 and APTw1), Dmax (maximum diameter, Dmax0 and Dmax1), V (3D tumor volume, V0 and V1), and ADC (apparent diffusion coefficient, ADC0 and ADC1) before and after treatment, as well as changes between the two times points (ΔAPT, ΔDmax, ΔV, ΔADC) for breast tumors were compared between the two groups. Results APT0 and APT1 values significantly differed between the two groups (p = 0.034 and 0.01). ΔAPTw values were significantly lower in non-MHR tumors compared with MHR tumors (p = 0.015). ΔDmax values were significantly higher in MHR tumors compared with non-MHR tumors (p = 0.005). ADC0 and ADC1 values were significantly higher in MHR tumors than in non-MHR tumors (p = 0.038 and 0.035). AUC (Dmax+DWI + APTw) = AUC (Dmax+APTw) > AUC (APTw) > AUC (Dmax+DWI) > AUC (Dmax). Conclusion APTw imaging along with change of tumor size showed a significant potential in early prediction of MHR for NAC treatment in breast cancer, which might allow timely regimen refinement before definitive surgical treatment.
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Affiliation(s)
- Nan Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qingwei Song
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Hongbing Liang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Zhuo Wang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Qi Wu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Haonan Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Lina Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Ailian Liu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Huali Wang
- Department of Pathology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Jiazheng Wang
- MSC Clinical and Technical Solutions, Philips Healthcare, Beijing, China
| | - Liangjie Lin
- MSC Clinical and Technical Solutions, Philips Healthcare, Beijing, China
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Kataoka M, Iima M, Miyake KK, Honda M. Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment. Invest Radiol 2024; 59:26-37. [PMID: 37994113 DOI: 10.1097/rli.0000000000001044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
ABSTRACT A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.
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Affiliation(s)
- Masako Kataoka
- From the Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine Kyoto University, Kyoto, Japan (M.K., M.I., M.H.); Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan (M.I.); Department of Advanced Imaging in Medical Magnetic Resonance, Graduate School of Medicine Kyoto University, Kyoto, Japan (K.K.M); and Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan (M.H.)
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Cheung SM, Wu WS, Senn N, Sharma R, McGoldrick T, Gagliardi T, Husain E, Masannat Y, He J. Towards detection of early response in neoadjuvant chemotherapy of breast cancer using Bayesian intravoxel incoherent motion. Front Oncol 2023; 13:1277556. [PMID: 38125950 PMCID: PMC10731248 DOI: 10.3389/fonc.2023.1277556] [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: 08/14/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
Introduction The early identification of good responders to neoadjuvant chemotherapy (NACT) holds a significant potential in the optimal treatment of breast cancer. A recent Bayesian approach has been postulated to improve the accuracy of the intravoxel incoherent motion (IVIM) model for clinical translation. This study examined the prediction and early sensitivity of Bayesian IVIM to NACT response. Materials and methods Seventeen female patients with breast cancer were scanned at baseline and 16 patients were scanned after Cycle 1. Tissue diffusion and perfusion from Bayesian IVIM were calculated at baseline with percentage change at Cycle 1 computed with reference to baseline. Cellular proliferative activity marker Ki-67 was obtained semi-quantitatively with percentage change at excision computed with reference to core biopsy. Results The perfusion fraction showed a significant difference (p = 0.042) in percentage change between responder groups at Cycle 1, with a decrease in good responders [-7.98% (-19.47-1.73), n = 7] and an increase in poor responders [10.04% (5.09-28.93), n = 9]. There was a significant correlation between percentage change in perfusion fraction and percentage change in Ki-67 (p = 0.042). Tissue diffusion and pseudodiffusion showed no significant difference in percentage change between groups at Cycle 1, nor was there a significant correlation against percentage change in Ki-67. Perfusion fraction, tissue diffusion, and pseudodiffusion showed no significant difference between groups at baseline, nor was there a significant correlation against Ki-67 from core biopsy. Conclusion The alteration in tumour perfusion fraction from the Bayesian IVIM model, in association with cellular proliferation, showed early sensitivity to good responders in NACT. Clinical trial registration https://clinicaltrials.gov/ct2/show/NCT03501394, identifier NCT03501394.
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Affiliation(s)
- Sai Man Cheung
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
| | - Wing-Shan Wu
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
| | - Nicholas Senn
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
| | - Ravi Sharma
- Department of Oncology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Trevor McGoldrick
- Department of Oncology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Tanja Gagliardi
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
- Department of Radiology, Royal Marsden Hospital, London, United Kingdom
| | - Ehab Husain
- Department of Pathology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Yazan Masannat
- Breast Unit, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Jiabao He
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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Si L, Liu X, Li X, Yang K, Wang L. Diffusion kurtosis imaging and intravoxel incoherent motion imaging parameters in breast lesions: Effect of radiologists' experience and region-of-interest selection. Eur J Radiol 2023; 158:110633. [PMID: 36470051 DOI: 10.1016/j.ejrad.2022.110633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 11/14/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE To investigate the influence of ROI placement methods and radiologists' experience on diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) parameters' diagnostic performance in differentiating benign and malignant lesions based on the mass and non-mass enhancement (NME). METHODS We evaluated 138 lesions in 131 patients retrospectively. The IVIM and DKI parameter values were measured by three radiologists with different experiences independently using two different ROI placement methods. IVIM parameters include diffusion coefficient (ADCstand), true diffusion coefficient (ADCslow), pseudo-diffusion coefficient (ADCfast) and perfusion fraction (f). DKI parameters include mean diffusivity (MD) and mean kurtosis (MK). Each radiologist measured the lesions twice with a 3-month interval. We utilized intra-class correlation (ICC) to determine the inter- and intra-reader agreement for mass and NME, respectively. ROC analysis compared the diagnostic performance of parameters between different radiologists, ROI methods, and between mass and NME. RESULTS In mass lesions, inter- and intra-observer agreement were perfect for all parameters (ICC: 0.800-989). In NME, the inter-observer agreement was substantial to perfect for all parameters(ICC: 0.703-877), the intra-observer agreement of the senior and intermediate radiologists was substantial to perfect(ICC: 0.748-931) and the intra-observer agreement of the junior radiologist was moderate to substantial(ICC: 0.569-784). The diagnostic performance of ADCslow (Z = 2.209, P = 0.023), MD (mean diffusivity) (Z = 2.887, P = 0.004), and MK (mean kurtosis) (Z = 2.080, P = 0.038) in the small ROI measured by the senior radiologist was better than that of the junior radiologist for NME. The diagnostic performance of ADCslow in the large ROI measured by the senior radiologist (Z = 2.281, P = 0.023) and intermediate radiologist (Z = 2.867, P = 0.0041) was better than the junior radiologist for mass lesions. The diagnostic performance of ADCslow, ADCstand, MD, and MK did not show a significant difference between the two ROI placement methods (P > 0.05). CONCLUSION The observers' experience can influence the ROI selection and the diagnostic performance of ADCslow, ADCstand, MD, and MK measured using different methods show equal diagnostic performance.
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Affiliation(s)
- Lifang Si
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, People's Republic of China
| | - Xiaojuan Liu
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, People's Republic of China.
| | - Xinyue Li
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, People's Republic of China
| | - Kaiyan Yang
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, People's Republic of China
| | - Li Wang
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, People's Republic of China
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Liao L, Liu T, Wei B. Prediction of short-term treatment outcome of nasopharyngeal carcinoma based on voxel incoherent motion imaging and arterial spin labeling quantitative parameters. Eur J Radiol Open 2022; 10:100466. [PMID: 36590328 PMCID: PMC9794885 DOI: 10.1016/j.ejro.2022.100466] [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: 06/13/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
Purpose To evaluate the early response of chemoradiotherapy (CRT) in nasopharyngeal carcinoma (NPC) based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and three-dimensional pseudo-continuous arterial spin labeling (3D pCASL). Materials and methods Forty patients diagnosed with NPC were recruited and divided into complete remission (CR) and partial remission (PR) group after CRT. All patients underwent IVIM and ASL and the related parameters was obtained. These parameters include pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), average blood flow ( BFavg), minimum blood flow (BFmin), and maximum blood flow (BFmax). Student's t test was used to compare the difference in ASL and IVIM derived parameters between CR and PR. The Areas under curve (AUC) of the receiver operating characteristic (ROC) was used to analyze the diagnostic performance of each parameter of ASL and IVIM to the treatment outcome. Results the D value of IVIM in CR group was lower than that of the PR group ( P = 0.014),. Among the parameters of ASL, the BFavg and BFmax of the CR group were higher than those of the PR group(p = 0.004,0.013), but the BFmin had no statistical significance in the two groups(P = 0.54). AUC of D, BFavg, and BFmax is about 0.731, 0.753, and 0.724, respectively, all of their combined AUC diagnosis was 0.812. Conclusion The early response of NPC after CRT can predict by IVIM's diffusion parameters and ASL-related blood flow parameters.
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Key Words
- 3DpCASL, three-dimensional quasi-continuous arterial spin labeling
- ADC, apparent diffusion coefficient
- AUC, area under the curve
- Arterial spin labeling
- BFavg, average of blood flow
- BFmax, maximum blood flow
- BFmin, minimum blood flow
- CR, complete remission
- CRT, chemoradiotherapy
- Chemoradiotherapy
- D*, pseudo-diffusion coefficient
- D, pure diffusion coefficient
- DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging
- IVIM-DWI, intravoxel incoherent motion diffusion-weighted imaging
- Intravoxel incoherent motion diffusion-weighted imaging
- NPC, nasopharyngeal carcinoma
- Nasopharyngeal carcinoma
- PR, partial remission
- f, perfusion fraction
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Affiliation(s)
- Liping Liao
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China,Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China,Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China,Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Teng Liu
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China,Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China,Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China,Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Bo Wei
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China,Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China,Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China,Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China,Corresponding author at: Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
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Sahib MA, Arvin A, Ahmadinejad N, Bustan RA, Dakhil HA. Assessment of intravoxel incoherent motion MR imaging for differential diagnosis of breast lesions and evaluation of response: a systematic review. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00770-8] [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
The current study aimed to assess the performance for quantitative differentiation and evaluation of response in categorized observations from intravoxel incoherent motion analyses of patients based on breast tumors. To assess the presence of heterogeneity, the Cochran's Q tests for heterogeneity with a significance level of P < 0.1 and I2 statistic with values > 75% were used. A random-effects meta-analysis model was used to estimate pooled sensitivity and specificity. The standardized mean difference (SMD) and 95% confidence intervals of the true diffusivity (D), pseudo-diffusivity (D*), perfusion fraction (f) and apparent diffusion coefficient (ADC) were calculated, and publication bias was evaluated using the Begg's and Egger's tests and also funnel plot. Data were analyzed by STATA v 16 (StataCorp, College Station).
Results
The pooled D value demonstrated good measurement performance showed a sensitivity 86%, specificity 86%, and AUC 0.91 (SMD − 1.50, P < 0.001) in the differential diagnosis of breast lesions, which was comparable to that of the ADC that showed a sensitivity of 76%, specificity 79%, and AUC 0.85 (SMD 1.34, P = 0.01), then by the f it showed a sensitivity 80%, specificity 76%, and AUC 0.85 (SMD 0.89, P = 0.001), and D* showed a sensitivity 84%, specificity 59%, and AUC 0.71 (SMD − 0.30, P = 0.20).
Conclusion
The estimated sensitivity and specificity in the current meta-analysis were acceptable. So, this approach can be used as a suitable method in the differentiation and evaluation response of breast tumors.
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Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review. Cancers (Basel) 2022; 14:cancers14205076. [PMID: 36291865 PMCID: PMC9599928 DOI: 10.3390/cancers14205076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers. Abstract Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.
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Value of shear wave elasticity in predicting the efficacy of neoadjuvant chemotherapy in different molecular types. Clin Imaging 2022; 89:97-103. [DOI: 10.1016/j.clinimag.2022.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/06/2022] [Accepted: 06/16/2022] [Indexed: 11/29/2022]
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Mendez AM, Fang LK, Meriwether CH, Batasin SJ, Loubrie S, Rodríguez-Soto AE, Rakow-Penner RA. Diffusion Breast MRI: Current Standard and Emerging Techniques. Front Oncol 2022; 12:844790. [PMID: 35880168 PMCID: PMC9307963 DOI: 10.3389/fonc.2022.844790] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.
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Affiliation(s)
- Ashley M. Mendez
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Lauren K. Fang
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Claire H. Meriwether
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Summer J. Batasin
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Stéphane Loubrie
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Ana E. Rodríguez-Soto
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Rebecca A. Rakow-Penner
- Department of Radiology, University of California San Diego, La Jolla, CA, United States,Department of Bioengineering, University of California San Diego, La Jolla, CA, United States,*Correspondence: Rebecca A. Rakow-Penner,
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12
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Kong X, Zhang Q, Wu X, Zou T, Duan J, Song S, Nie J, Tao C, Tang M, Wang M, Zou J, Xie Y, Li Z, Li Z. Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer. Front Oncol 2022; 12:816297. [PMID: 35669440 PMCID: PMC9163342 DOI: 10.3389/fonc.2022.816297] [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: 11/16/2021] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
Neoadjuvant chemotherapy (NAC) is increasingly widely used in breast cancer treatment, and accurate evaluation of its response provides essential information for treatment and prognosis. Thus, the imaging tools used to quantify the disease response are critical in evaluating and managing patients treated with NAC. We discussed the recent progress, advantages, and disadvantages of common imaging methods in assessing the efficacy of NAC for breast cancer.
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Affiliation(s)
- Xianshu Kong
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Qian Zhang
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Xuemei Wu
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Tianning Zou
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jiajun Duan
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Shujie Song
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jianyun Nie
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Chu Tao
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Mi Tang
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Maohua Wang
- First Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jieya Zou
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Yu Xie
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
- *Correspondence: Zhen Li, ; Zhenhui Li,
| | - Zhen Li
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
- *Correspondence: Zhen Li, ; Zhenhui Li,
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13
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Volumetric Analysis of Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Predicting the Response to Chemotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer. J Comput Assist Tomogr 2022; 46:406-412. [PMID: 35405718 DOI: 10.1097/rct.0000000000001282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE We aimed to prospectively investigate intravoxel incoherent motion parameters to predict the response to chemotherapy in locally advanced non-small cell lung cancer (NSCLC) patients. METHODS From July 2016 to March 2018, 30 advanced NSCLC patients were enrolled and underwent chest intravoxel incoherent motion-diffusion-weighted imaging at Siemens 3T magnetic resonance imaging before and at the end of the first cycle of chemotherapy. Regions of interest were drawn including the whole tumor volume to derive the apparent diffusion coefficient value, D, D*, and f, respectively. Time-dependent receiver operating characteristic curves were generated to evaluate the cutoff values of continuous variables. A Cox proportional hazards model was used to assess the independent predictors of progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curves and log-rank test were generated. RESULTS Among the 30 patients, 28 cases (93.3%) died and 2 cases (6.7%) survived till the closeout date. Univariate Cox regression analyses revealed that the significant predictors of PFS and OS were the tumor size reduction rate, the change rates of D and apparent diffusion coefficient values, and the D value before therapy (PFS: P = 0.015, hazard ratio [HR] = 2.841; P < 0.001, HR = 5.840; P = 0.044, HR = 2.457; and P = 0.027, HR = 2.715; OS: P = 0.008, HR = 2.987; P < 0.001, HR = 4.357; P = 0.006, HR = 3.313; and P = 0.013, HR = 2.941, respectively). Multivariate Cox regression analysis suggested that △D% was identified as independent predictors of both PFS and OS (P = 0.003, HR = 9.200 and P = 0.016, HR = 4.617). In addition, the cutoff value of △D% was 21.06% calculated by receiver operating characteristic curve analysis. In the Kaplan-Meier analysis, the PFS and OS were significantly greater in the group of patients with △D% larger than 21.06% (log-rank test, χ2 = 16.453, P < 0.001; χ2 = 13.952, P < 0.001). CONCLUSIONS Intravoxel incoherent motion-diffusion-weighted imaging was preferred for predicting the prognosis of advanced NSCLC patients treated with chemotherapy. A D increase more than 21.06% at 1 month was associated with a lower rate of disease progression and death.
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Geng X, Zhang D, Suo S, Chen J, Cheng F, Zhang K, Zhang Q, Li L, Lu Y, Hua J, Zhuang Z. Using the apparent diffusion coefficient histogram analysis to predict response to neoadjuvant chemotherapy in patients with breast cancer: comparison among three region of interest selection methods. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:323. [PMID: 35433990 PMCID: PMC9011214 DOI: 10.21037/atm-22-1078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/18/2022] [Indexed: 11/06/2022]
Abstract
Background The apparent diffusion coefficient (ADC) value using histogram analysis is helpful to predict responses to neoadjuvant chemotherapy (NAC) in breast cancer. However, the measurement method has not reached a consensus. This study was to assess the diagnostic performance of the ADC histogram analysis at predicting patient response prior to NAC in breast cancer patients using different region of interest (ROI) selection methods. Methods A total of 75 patients who underwent diffusion weighted imaging (DWI) prior to NAC were retrospectively enrolled from February 2017 to December 2019. Images were measured using small 2-dimensional (2D) ROI, large 2D ROI, and volume ROI methods. The measurement time and ROI size were recorded. Histopathologic responses were acquired using the Miller-Payne grading system after surgery. The inter- and intra-observer repeatability was analyzed and the ADC histogram values from the three ROI methods were compared. The efficacy of each method at predicting patient response prior to NAC was assessed using the area under the receiver operating characteristic curve (AUC) for the whole study population and subgroups according to molecular subtype. Results Among the 75 enrolled patients, 26 (34.67%) were responsive to NAC therapy. The ADC histogram values were significantly different among the three ROI methods (P≤0.038). Inter- and intra-observer repeatability of the large 2D ROI method and the volume ROI method was generally greater than that observed with the 2D ROI method. The 10% ADC value of the large 2D ROI method showed the greatest AUC (0.701) in the whole study population and in the luminal subgroup (AUC 0.804). The volume ROI method required significantly more time than the other two ROI methods (P<0.001). Conclusions The small 2D ROI method is not appropriate for predicting response prior to NAC in breast cancer patients due to the poor repeatability. When choosing the ROI method and the histogram parameters for predicting response prior to NAC in breast cancer patients using ADC-derived histogram analysis, 10% of the large 2D ROI method is recommended, especially in luminal A subtype patients.
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Affiliation(s)
- Xiaochuan Geng
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dandan Zhang
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shiteng Suo
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Chen
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fang Cheng
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kebei Zhang
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qing Zhang
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lan Li
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Lu
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jia Hua
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiguo Zhuang
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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15
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van der Hoogt KJJ, Schipper RJ, Winter-Warnars GA, Ter Beek LC, Loo CE, Mann RM, Beets-Tan RGH. Factors affecting the value of diffusion-weighted imaging for identifying breast cancer patients with pathological complete response on neoadjuvant systemic therapy: a systematic review. Insights Imaging 2021; 12:187. [PMID: 34921645 PMCID: PMC8684570 DOI: 10.1186/s13244-021-01123-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 11/06/2021] [Indexed: 12/18/2022] Open
Abstract
This review aims to identify factors causing heterogeneity in breast DWI-MRI and their impact on its value for identifying breast cancer patients with pathological complete response (pCR) on neoadjuvant systemic therapy (NST). A search was performed on PubMed until April 2020 for studies analyzing DWI for identifying breast cancer patients with pCR on NST. Technical and clinical study aspects were extracted and assessed for variability. Twenty studies representing 1455 patients/lesions were included. The studies differed with respect to study population, treatment type, DWI acquisition technique, post-processing (e.g., mono-exponential/intravoxel incoherent motion/stretched exponential modeling), and timing of follow-up studies. For the acquisition and generation of ADC-maps, various b-value combinations were used. Approaches for drawing regions of interest on longitudinal MRIs were highly variable. Biological variability due to various molecular subtypes was usually not taken into account. Moreover, definitions of pCR varied. The individual areas under the curve for the studies range from 0.50 to 0.92. However, overlapping ranges of mean/median ADC-values at pre- and/or during and/or post-NST were found for the pCR and non-pCR groups between studies. The technical, clinical, and epidemiological heterogeneity may be causal for the observed variability in the ability of DWI to predict pCR accurately. This makes implementation of DWI for pCR prediction and evaluation based on one absolute ADC threshold for all breast cancer types undesirable. Multidisciplinary consensus and appropriate clinical study design, taking biological and therapeutic variation into account, is required for obtaining standardized, reliable, and reproducible DWI measurements for pCR/non-pCR identification.
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Affiliation(s)
- Kay J J van der Hoogt
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. .,GROW School of Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands.
| | - Robert J Schipper
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Gonneke A Winter-Warnars
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Leon C Ter Beek
- Department of Medical Physics, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Claudette E Loo
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Ritse M Mann
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School of Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands.,Danish Colorectal Cancer Unit South, Institute of Regional Health Research, Vejle University Hospital, University of Southern Denmark, Odense, Denmark
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16
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Feng W, Gao Y, Lu XR, Xu YS, Guo ZZ, Lei JQ. Correlation between molecular prognostic factors and magnetic resonance imaging intravoxel incoherent motion histogram parameters in breast cancer. Magn Reson Imaging 2021; 85:262-270. [PMID: 34740800 DOI: 10.1016/j.mri.2021.10.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 07/26/2021] [Accepted: 10/17/2021] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To explore the efficacy of the quantitative parameter histogram analysis of intravoxel incoherent motion (IVIM) for different molecular prognostic factors of breast cancer. MATERIALS AND METHODS A total of 72 patients with breast cancer who were confirmed by surgical pathology and underwent preoperative magnetic resonance imaging (MRI) were analyzed retrospectively. A region of interest (ROI) was drawn in each slice of the IVIM images. Whole-tumor histogram parameters were obtained with Firevoxel's software by accumulating all ROIs. Next, Kolmogorov-Smirnov test, Student's t-test, Mann-Whitney U test, receiver operating characteristic curve analysis and spearman rank correlation analysis were used to assess the relationship between histogram parameters and molecular prognostic factors of breast cancer. RESULTS Among estrogen receptor (ER)-negative ROCs, the apparent diffusion coefficient (ADC) 10th percentile had the highest ROC of 0.792, with a cut-off value of 0.788 × 10-3 mm2/s, and sensitivity and specificity of 0.714 and 0.867, respectively. The negative correlation between lymph node metastasis status and ADC standard deviation was significant (ρ = -0.44, the correlation coefficients was represented by ρ). Positive correlations were observed between hormonal expression of ER and progesterone receptor (PR) with heterogeneity metrics of ADC or perfusion fraction (f), such as ADC inhomogeneity (ρ = 0.37, ρ = 0.29) and f skewness (ρ = 0.32, ρ = 0.28). Negative correlations were observed with numerical metrics, such as the ADC median (ρ = -0.31, ρ = -0.34) and f 45th percentile (ρ = -0.35, ρ = -0.28). The positive correlations between human epidermal receptor factor-2 (HER2) and pseudo-diffusivity (Dp) numerical metrics, Ki-67 expression, and heterogeneity metrics of Dp were high. CONCLUSIONS The ADC 10th percentile had the largest area under the curve in the ER-negative ROC analysis, and the ADC standard deviation was the most valuable in the correlation analysis of lymph node metastasis. Whole-lesion quantitative histogram parameters of IVIM could, therefore, provide a scientific basis for radiomics to further guide clinical practice in the prognosis of breast cancer.
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Affiliation(s)
- Wen Feng
- The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu, China; Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Ya Gao
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Xing-Ru Lu
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Yong-Sheng Xu
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Zhuan-Zhuan Guo
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, Shanxi, China
| | - Jun-Qiang Lei
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China.
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Li Z, Li J, Lu X, Qu M, Tian J, Lei J. The diagnostic performance of diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in evaluating the pathological response of breast cancer to neoadjuvant chemotherapy: A meta-analysis. Eur J Radiol 2021; 143:109931. [PMID: 34492627 DOI: 10.1016/j.ejrad.2021.109931] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/10/2021] [Accepted: 08/18/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE To evaluate and compare the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the pathological response of breast cancer to neoadjuvant chemotherapy (NAC). METHODS We searched PubMed, EMBASE, Cochrane Library, and Web of Science systematically to identify relevant studies from inception to December 2020. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess the methodological quality of the included studies. We extracted sufficient data to construct 2 × 2 tables and then used STATA 12.0 to perform data pooling, heterogeneity testing, meta-regression analysis and subgroup analysis. RESULTS A total of 41 articles were enrolled in this study, including 27 studies (2107 patients) on DCE-MRI and 23 studies (1321 patients) on DWI. The pooled sensitivity and specificity of DCE-MRI were 0.75 and 0.79, and the pooled sensitivity and specificity of DWI were 0.77 and 0.75. There was no significant difference in sensitivity (P = 0.598) and specificity (P = 0.218) between DCE-MRI and DWI. And meta-regression analysis showed that both magnetic field strength and the time of examination had significant effects on heterogeneity. CONCLUSIONS DWI might be a potential substitute for DCE-MRI in predicting the pathological response of breast cancer to NAC as there was no significant difference in the diagnostic performance between the two. However, considering that not all included studies directly compared the diagnostic performance of DWI and DCE-MRI in the same patients and the heterogeneity of the included studies, caution should be exercised in applying our results.
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Affiliation(s)
- Zhifan Li
- The first Clinical Medical College of Lanzhou University, Lanzhou 730000, China; First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Jinkui Li
- The first Clinical Medical College of Lanzhou University, Lanzhou 730000, China; First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Xingru Lu
- First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Mengmeng Qu
- The first Clinical Medical College of Lanzhou University, Lanzhou 730000, China; First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China; Key Laboratory of Evidence-based Medicine and Knowledge Translation of Gansu Province, Lanzhou University, Lanzhou 730000, China.
| | - Junqiang Lei
- First Hospital of Lanzhou University, Lanzhou 730000, China.
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18
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Ye YJ, Huang XJ, Luo BC, Wang XY, Cai XR. Application of Multiparametric Magnetic Resonance Imaging to Monitor the Early Antitumor Effect of CuS@GOD Nanoparticles in a 4 T1 Breast Cancer Xenograft Model. J Magn Reson Imaging 2021; 55:301-310. [PMID: 34427359 DOI: 10.1002/jmri.27867] [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: 04/06/2021] [Revised: 07/20/2021] [Accepted: 07/20/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND We have developed hybrid nanoparticles (NPs) by co-loading copper sulfide (CuS) NPs and glucose oxidase (GOD) (CuS@GOD NPs) to explore their antitumor properties. PURPOSE To investigate the feasibility of using multiparametric magnetic resonance imaging (MRI) including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and R2 * mapping to quantitatively assess the early antitumor effect of CuS@GOD NPs. STUDY TYPE Prospective. ANIMAL MODEL The orthotopic BALB/c mice 4 T1 breast cancer model. The 4 T1 xenografts in group 1 mice received normal saline, group 2 received CuS@GOD NPs, group 3 received CuS NPs plus laser, and group 4 received CuS@GOD NPs plus laser (n = 28 for each group). FIELD STRENGTH/SEQUENCE A 3.0 T/IVIM-DWI MRI single-shot echo-planar imaging, R2 * mapping spoiled gradient recalled echo (SPGR) sequence, T2-weighted images (T2WI) and T1-weighted images (T1WI) fast spin echo (FSE) sequence. ASSESSMENT The IVIM-DWI and R2 * mapping were performed before and after treatment at 0 hour, 0.5 hour, 1 hour, 2 hours, 4 hours, and 24 hours in four groups and the MRI parameters were obtained. Correlation analysis between the MRI parameters and histological analyses was conducted. STATISTICAL TESTS One-way ANOVA, Pearson's correlation analysis, two independent samples t test, intraclass correlation coefficient. P < 0.05 was considered to be statistically significant. RESULTS In group 4, the tumoral D value was significantly higher than that of group 2 at 24 hours (0.541 ± 0.065 vs. 0.492 ± 0.051). The f value of group 4 was significantly lower than that of groups 1 and 2 at 2 hours (10.83 ± 2.16 vs. 14.28 ± 1.86, 16.67 ± 3.53, respectively). The R2 * value was significantly increased at 0 hour in group 4 compared to that of groups 1 and 2 (64.552 ± 4.663 vs. 42.441 ± 1.516, 43.165 ± 1.709, respectively). D, f, and R2 * were correlated with the histological staining results (r = 0.695-0.970). DATA CONCLUSION The IVIM-DWI-derived D and f and R2 * mapping-derived R2 * could monitor early response to CuS@GOD NPs treatment in vivo. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yao-Jiang Ye
- Medical Imaging Center, the First Affiliated Hospital of Jinan University, No. 613 West Huangpu Avenue, Tianhe District, Guangzhou, China
| | - Xiu-Jie Huang
- State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, China
| | - Bi-Chong Luo
- State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, China
| | - Xiao-Ying Wang
- State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, China
| | - Xiang-Ran Cai
- Medical Imaging Center, the First Affiliated Hospital of Jinan University, No. 613 West Huangpu Avenue, Tianhe District, Guangzhou, China
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19
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Jerome NP, Vidić I, Egnell L, Sjøbakk TE, Østlie A, Fjøsne HE, Goa PE, Bathen TF. Understanding diffusion-weighted MRI analysis: Repeatability and performance of diffusion models in a benign breast lesion cohort. NMR IN BIOMEDICINE 2021; 34:e4508. [PMID: 33738878 DOI: 10.1002/nbm.4508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 02/26/2021] [Accepted: 02/27/2021] [Indexed: 06/12/2023]
Abstract
Diffusion-weighted MRI (DWI) is an important tool for oncology research, with great clinical potential for the classification and monitoring of breast lesions. The utility of parameters derived from DWI, however, is influenced by specific analysis choices. The purpose of this study was to critically evaluate repeatability and curve-fitting performance of common DWI signal representations, for a prospective cohort of patients with benign breast lesions. Twenty informed, consented patients with confirmed benign breast lesions underwent repeated DWI (3 T) using: sagittal single-shot spin-echo echo planar imaging, bipolar encoding, TR/TE: 11,600/86 ms, FOV: 180 x 180 mm, matrix: 90 x 90, slices: 60 x 2.5 mm, iPAT: GRAPPA 2, fat suppression, and 13 b-values: 0-700 s/mm2 . A phase-reversed scan (b = 0 s/mm2 ) was acquired for distortion correction. Voxel-wise repeat-measures coefficients of variation (CoVs) were derived for monoexponential (apparent diffusion coefficient [ADC]), biexponential (intravoxel incoherent motion: f, D, D*) and stretched exponential (α, DDC) across the parameter histograms for lesion regions of interest (ROIs). Goodness-of-fit for each representation was assessed by Bayesian information criterion. The volume of interest (VOI) definition was repeatable (CoV 13.9%). Within lesions, and across both visits and the cohort, there was no dominant best-fit model, with all representations giving the best fit for a fraction of the voxels. Diffusivity measures from the signal representations (ADC, D, DDC) all showed good repeatability (CoV < 10%), whereas parameters associated with pseudodiffusion (f, D*) performed poorly (CoV > 50%). The stretching exponent α was repeatable (CoV < 12%). This pattern of repeatability was consistent over the central part of the parameter percentiles. Assumptions often made in diffusion studies about analysis choices will influence the detectability of changes, potentially obscuring useful information. No single signal representation prevails within or across lesions, or across repeated visits; parameter robustness is therefore a critical consideration. Our results suggest that stretched exponential representation is more repeatable than biexponential, with pseudodiffusion parameters unlikely to provide clinically useful biomarkers.
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Affiliation(s)
- Neil Peter Jerome
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Clinic of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim, Norway
| | - Igor Vidić
- Department of Physics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Liv Egnell
- Clinic of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim, Norway
- Department of Physics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Torill E Sjøbakk
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Clinic of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim, Norway
| | - Agnes Østlie
- Department of Radiology, St. Olavs Hospital, Trondheim, Norway
| | - Hans E Fjøsne
- Department of Radiology, St. Olavs Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Pål Erik Goa
- Department of Physics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Clinic of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim, Norway
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20
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Li K, Machireddy A, Tudorica A, Moloney B, Oh KY, Jafarian N, Partridge SC, Li X, Huang W. Discrimination of Malignant and Benign Breast Lesions Using Quantitative Multiparametric MRI: A Preliminary Study. ACTA ACUST UNITED AC 2021; 6:148-159. [PMID: 32548291 PMCID: PMC7289240 DOI: 10.18383/j.tom.2019.00028] [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: 12/13/2022]
Abstract
We aimed to compare diagnostic performance in discriminating malignant and benign breast lesions between two intravoxel incoherent motion (IVIM) analysis methods for diffusion-weighted magnetic resonance imaging (DW-MRI) data and between DW- and dynamic contrast-enhanced (DCE)-MRI, and to determine if combining DW- and DCE-MRI further improves diagnostic accuracy. DW-MRI with 12 b-values and DCE-MRI were performed on 26 patients with 28 suspicious breast lesions before biopsies. The traditional biexponential fitting and a 3-b-value method were used for independent IVIM analysis of the DW-MRI data. Simulations were performed to evaluate errors in IVIM parameter estimations by the two methods across a range of signal-to-noise ratio (SNR). Pharmacokinetic modeling of DCE-MRI data was performed. Conventional radiological MRI reading yielded 86% sensitivity and 21% specificity in breast cancer diagnosis. At the same sensitivity, specificity of individual DCE- and DW-MRI markers improved to 36%–57% and that of combined DCE- or combined DW-MRI markers to 57%–71%, with DCE-MRI markers showing better diagnostic performance. The combination of DCE- and DW-MRI markers further improved specificity to 86%–93% and the improvements in diagnostic accuracy were statistically significant (P < .05) when compared with standard clinical MRI reading and most individual markers. At low breast DW-MRI SNR values (<50), like those typically seen in clinical studies, the 3-b-value approach for IVIM analysis generates markers with smaller errors and with comparable or better diagnostic performances compared with biexponential fitting. This suggests that the 3-b-value method could be an optimal IVIM-MRI method to be combined with DCE-MRI for improved diagnostic accuracy.
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Affiliation(s)
- Kurt Li
- International School of Beaverton, Aloha, OR
| | - Archana Machireddy
- Center for Spoken Language Understanding, Oregon Health & Science University, Portland, OR
| | - Alina Tudorica
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, OR
| | - Brendan Moloney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR; and
| | - Karen Y Oh
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, OR
| | - Neda Jafarian
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, OR
| | | | - Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR; and
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR; and
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21
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Suo S, Yin Y, Geng X, Zhang D, Hua J, Cheng F, Chen J, Zhuang Z, Cao M, Xu J. Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models. J Transl Med 2021; 19:236. [PMID: 34078388 PMCID: PMC8173748 DOI: 10.1186/s12967-021-02886-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 05/14/2021] [Indexed: 12/24/2022] Open
Abstract
Background To investigate the performance of diffusion-weighted (DW) MRI with mono-, bi- and stretched-exponential models in predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) for breast cancer, and further outline a predictive model of pCR combining DW MRI parameters, contrast-enhanced (CE) MRI findings, and/or clinical-pathologic variables. Methods In this retrospective study, 144 women who underwent NACT and subsequently received surgery for invasive breast cancer were included. Breast MRI including multi-b-value DW imaging was performed before (pre-treatment), after two cycles (mid-treatment), and after all four cycles (post-treatment) of NACT. Quantitative DW imaging parameters were computed according to the mono-exponential (apparent diffusion coefficient [ADC]), bi-exponential (pseudodiffusion coefficient and perfusion fraction), and stretched-exponential (distributed diffusion coefficient and intravoxel heterogeneity index) models. Tumor size and relative enhancement ratio of the tumor were measured on contrast-enhanced MRI at each time point. Pre-treatment parameters and changes in parameters at mid- and post-treatment relative to baseline were compared between pCR and non-pCR groups. Receiver operating characteristic analysis and multivariate regression analysis were performed. Results Of the 144 patients, 54 (37.5%) achieved pCR after NACT. Overall, among all DW and CE MRI measures, flow-insensitive ADC change (ΔADC200,1000) at mid-treatment showed the highest diagnostic performance for predicting pCR, with an area under the receiver operating characteristic curve (AUC) of 0.831 (95% confidence interval [CI]: 0.747, 0.915; P < 0.001). The model combining pre-treatment estrogen receptor and human epidermal growth factor receptor 2 statuses and mid-treatment ΔADC200,1000 improved the AUC to 0.905 (95% CI: 0.843, 0.966; P < 0.001). Conclusion Mono-exponential flow-insensitive ADC change at mid-treatment was a predictor of pCR after NACT in breast cancer.
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Affiliation(s)
- Shiteng Suo
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd, Shanghai, 200127, China.,Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Yin
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd, Shanghai, 200127, China
| | - Xiaochuan Geng
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd, Shanghai, 200127, China
| | - Dandan Zhang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd, Shanghai, 200127, China
| | - Jia Hua
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd, Shanghai, 200127, China.
| | - Fang Cheng
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd, Shanghai, 200127, China
| | - Jie Chen
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd, Shanghai, 200127, China.
| | - Zhiguo Zhuang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd, Shanghai, 200127, China
| | - Mengqiu Cao
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd, Shanghai, 200127, China
| | - Jianrong Xu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd, Shanghai, 200127, China
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22
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Multi-parametric MRI lesion heterogeneity biomarkers for breast cancer diagnosis. Phys Med 2020; 80:101-110. [PMID: 33137621 DOI: 10.1016/j.ejmp.2020.10.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 10/07/2020] [Accepted: 10/10/2020] [Indexed: 02/07/2023] Open
Abstract
PURPOSE To identify intra-lesion imaging heterogeneity biomarkers in multi-parametric Magnetic Resonance Imaging (mpMRI) for breast lesion diagnosis. METHODS Dynamic Contrast Enhanced (DCE) and Diffusion Weighted Imaging (DWI) of 73 female patients, with 85 histologically verified breast lesions were acquired. Non-rigid multi-resolution registration was utilized to spatially align sequences. Four (4) DCE (2nd post-contrast frame, Initial-Enhancement, Post-Initial-Enhancement and Signal-Enhancement-Ratio) and one (1) DWI (Apparent-Diffusion-Coefficient) representations were analyzed, considering a representative lesion slice. 11 1st-order-statistics and 16 texture features (Gray-Level-Co-occurrence-Matrix (GLCM) and Gray-Level-Run-Length-Matrix (GLRLM) based) were derived from lesion segments, provided by Fuzzy C-Means segmentation, across the 5 representations, resulting in 135 features. Least-Absolute-Shrinkage and Selection-Operator (LASSO) regression was utilized to select optimal feature subsets, subsequently fed into 3 classification schemes: Logistic-Regression (LR), Random-Forest (RF), Support-Vector-Machine-Sequential-Minimal-Optimization (SVM-SMO), assessed with Receiver-Operating-Characteristic (ROC) analysis. RESULTS LASSO regression resulted in 7, 6 and 7 features subsets from DCE, DWI and mpMRI, respectively. Best classification performance was obtained by the RF multi-parametric scheme (Area-Under-ROC-Curve, (AUC) ± Standard-Error (SE), AUC ± SE = 0.984 ± 0.025), as compared to DCE (AUC ± SE = 0.961 ± 0.030) and DWI (AUC ± SE = 0.938 ± 0.032) and statistically significantly higher as compared to DWI. The selected mpMRI feature subset highlights the significance of entropy (1st-order-statistics and 2nd-order-statistics (GLCM)) and percentile features extracted from 2nd post-contrast frame, PIE, SER maps and ADC map. CONCLUSION Capturing breast intra-lesion heterogeneity, across mpMRI lesion segments with 1st-order-statistics and texture features (GLCM and GLRLM based), offers a valuable diagnostic tool for breast cancer.
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23
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Tsarouchi MI, Vlachopoulos GF, Karahaliou AN, Costaridou LI. Diagnostic value of apparent diffusion coefficient lesion texture biomarkers in breast MRI. HEALTH AND TECHNOLOGY 2020. [DOI: 10.1007/s12553-020-00452-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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24
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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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25
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Wang H, Mao X. Evaluation of the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer. DRUG DESIGN DEVELOPMENT AND THERAPY 2020; 14:2423-2433. [PMID: 32606609 PMCID: PMC7308147 DOI: 10.2147/dddt.s253961] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 05/07/2020] [Indexed: 12/12/2022]
Abstract
Neoadjuvant chemotherapy is increasingly used in breast cancer, especially for downstaging the primary tumor in the breast and the metastatic axillary lymph node. Accurate evaluations of the response to neoadjuvant chemotherapy provide important information on the impact of systemic therapies on breast cancer biology, prognosis, and guidance for further therapy. Moreover, pathologic complete response is a validated and valuable surrogate prognostic factor of survival after therapy. Evaluations of neoadjuvant chemotherapy response are very important in clinical work and basic research. In this review, we will elaborate on evaluations of the efficacy of neoadjuvant chemotherapy in breast cancer and provide a clinical evaluation procedure for neoadjuvant chemotherapy.
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Affiliation(s)
- Huan Wang
- Department of Breast Surgery, The First Affiliated Hospital of China Medical University, Shenyang City, Liaoning Province, People's Republic of China
| | - Xiaoyun Mao
- Department of Breast Surgery, The First Affiliated Hospital of China Medical University, Shenyang City, Liaoning Province, People's Republic of China
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26
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Meng N, Wang XJ, Sun J, Huang L, Wang Z, Wang KY, Wang J, Han DM, Wang MY. Comparative Study of Amide Proton Transfer-Weighted Imaging and Intravoxel Incoherent Motion Imaging in Breast Cancer Diagnosis and Evaluation. J Magn Reson Imaging 2020; 52:1175-1186. [PMID: 32369256 DOI: 10.1002/jmri.27190] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 04/20/2020] [Accepted: 04/21/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Amide proton transfer-weighted imaging (APTWI) and intravoxel incoherent motion imaging (IVIM) are valuable MRI techniques applied to cancer. PURPOSE To compare APTWI and IVIM in the diagnosis of benign and malignant breast lesions and to evaluate the correlations between different parameters (MTRasym [3.5 ppm], D, D*, and f) and prognostic factors for breast cancer. STUDY TYPE Retrospective. POPULATION In all, 123 breast lesions were studied before treatment, including 58 benign lesions and 65 malignant lesions. FIELD STRENGTH/SEQUENCE Conventional MRI (T1 WI, T2 WI, and diffusion-weighted imaging [DWI]), APTWI, and IVIM MRI at 3T. ASSESSMENT The magnetization transfer ratio asymmetry at 3.5 ppm (MTRasym [3.5 ppm]), diffusion coefficient (D), pseudo diffusion coefficient (D*), and perfusion fraction (f) values were compared between the benign and malignant groups and between groups with different expression levels of prognostic factors. STATISTICAL TESTS Individual sample t-test, χ2 test, Spearman correlation, logistic regression, and the Delong test. RESULTS The D and MTRasym (3.5 ppm) values of the malignant group were lower than those of the benign group; however, D* and f values were higher than those of the benign group (all P < 0.05). The areas under the curve (AUCs) of D, MTRasym (3.5 ppm), D*, and f were 0.809, 0.778, 0.670, and 0.766, respectively; however, only the difference between AUC (D) and AUC (D*) was significant (Z = 2.374, P < 0.05). The D value showed a low correlation with the pathological grade and Ki-67 expression (| r | = 0.294, 0.367); the f value showed a low correlation with estrogen receptor (ER) expression (| r | = 0.382); and the MTRasym (3.5 ppm) value showed a low correlation with pathological grade (| r | = 0.371). DATA CONCLUSION This analysis revealed that both IVIM and APTWI could be used for the differential diagnosis of benign and malignant breast lesions, and APTWI-derived MTRasym (3.5 ppm), IVIM-derived D, D*, and f values showed correlations with some prognostic factors for breast cancer. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1175-1186.
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Affiliation(s)
- Nan Meng
- Department of Radiology, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China.,Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Xue-Jia Wang
- Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Jing Sun
- Department of Pediatrics, Zhengzhou Central Hospital, Zhengzhou University, Zhengzhou, China
| | - Ling Huang
- Department of Obstetrics and Gynecology, The Women & Infants Hospital of Zhengzhou & Zhengzhou Maternity Hospital Affiliated to Henan University, Zhengzhou, China
| | - Zhe Wang
- Department of Anesthesiology, The Third Affiliated Hospital, Xinxiang Medical University, Xinxiang, China
| | - Kai-Yu Wang
- GE Healthcare, MR Research China, Beijing, China
| | - Jing Wang
- Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Dong-Ming Han
- Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Mei-Yun Wang
- Department of Radiology, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China.,Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
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27
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Reig B, Heacock L, Lewin A, Cho N, Moy L. Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer. J Magn Reson Imaging 2020; 52. [DOI: 10.1002/jmri.27145] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 12/25/2022] Open
Affiliation(s)
- Beatriu Reig
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Laura Heacock
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Alana Lewin
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Nariya Cho
- Department of Radiology Seoul National University Hospital Seoul Republic of Korea
- Department of Radiology Seoul National University College of Medicine Seoul Republic of Korea
| | - Linda Moy
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
- Bernard and Irene Schwartz Center for Biomedical Imaging Department of Radiology, New York University Grossman School of Medicine New York New York USA
- Center for Advanced Imaging Innovation and Research (CAI2 R) New York University Grossman School of Medicine New York New York USA
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28
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Iima M, Honda M, Sigmund EE, Ohno Kishimoto A, Kataoka M, Togashi K. Diffusion MRI of the breast: Current status and future directions. J Magn Reson Imaging 2019; 52:70-90. [PMID: 31520518 DOI: 10.1002/jmri.26908] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 08/12/2019] [Indexed: 12/30/2022] Open
Abstract
Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.
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Affiliation(s)
- Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Clinical Innovative Medicine, Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan
| | - Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Eric E Sigmund
- Department of Radiology, NYU Langone Health, New York, New York, USA.,Center for Advanced Imaging and Innovation (CAI2R), New York, New York, USA
| | - Ayami Ohno Kishimoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kaori Togashi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
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29
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Carlin D, Weller A, Kramer G, Liu Y, Waterton JC, Chiti A, Sollini M, Joop de Langen A, O'Brien MER, Urbanowicz M, Jacobs BK, deSouza N. Evaluation of diffusion-weighted MRI and (18F) fluorothymidine-PET biomarkers for early response assessment in patients with operable non-small cell lung cancer treated with neoadjuvant chemotherapy. BJR Open 2019; 1:20190029. [PMID: 33178953 PMCID: PMC7592464 DOI: 10.1259/bjro.20190029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 07/09/2019] [Indexed: 12/13/2022] Open
Abstract
Objective: To correlate changes in the apparent diffusion coefficient (ADC) from diffusion-weighted (DW)-MRI and standardised uptake value (SUV) from fluorothymidine (18FLT)-PET/CT with histopathological estimates of response in patients with non-small cell lung cancer (NSCLC) treated with neoadjuvant chemotherapy and track longitudinal changes in these biomarkers in a multicentre, multivendor setting. Methods: 14 patients with operable NSCLC recruited to a prospective, multicentre imaging trial (EORTC-1217) were treated with platinum-based neoadjuvant chemotherapy. 13 patients had DW-MRI and FLT-PET/CT at baseline (10 had both), 12 were re-imaged at Day 14 (eight dual-modality) and nine after completing chemotherapy, immediately before surgery (six dual-modality). Surgical specimens (haematoxylin-eosin and Ki67 stained) estimated the percentage of residual viable tumour/necrosis and proliferation index. Results: Despite the small numbers,significant findings were possible. ADCmedian increased (p < 0.001) and SUVmean decreased (p < 0.001) significantly between baseline and Day 14; changes between Day 14 and surgery were less marked. All responding tumours (>30% reduction in unidimensional measurement pre-surgery), showed an increase at Day 14 in ADC75th centile and reduction in total lesion proliferation (SUVmean x proliferative volume) greater than established measurement variability. Change in imaging biomarkers did not correlate with histological response (residual viable tumour, necrosis). Conclusion: Changes in ADC and FLT-SUV following neoadjuvant chemotherapy in NSCLC were measurable by Day 14 and preceded changes in unidimensional size but did not correlate with histopathological response. However, the magnitude of the changes and their utility in predicting (non-) response (tumour size/clinical outcome) remains to be established. Advances in knowledge: During treatment, ADC increase precedes size reductions, but does not reflect histopathological necrosis.
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Affiliation(s)
- Dominic Carlin
- CRUK Imaging Centre, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, UK
| | | | - Gem Kramer
- Department of Respiratory Diseases, VU University Medical Center, Amsterdam, The Netherlands
| | - Yan Liu
- EORTC Headquarters, Brussels, Belgium
| | - John C Waterton
- Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, Oxford Road Manchester M13 9PL UK
| | | | - Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | | | - Mary E R O'Brien
- The Royal Marsden Hospital, Downs Road, Sutton, Surrey SM2 5PT, UK
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30
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Doudou NR, Kampo S, Liu Y, Ahmmed B, Zeng D, Zheng M, Mohamadou A, Wen QP, Wang S. Monitoring the Early Antiproliferative Effect of the Analgesic-Antitumor Peptide, BmK AGAP on Breast Cancer Using Intravoxel Incoherent Motion With a Reduced Distribution of Four b-Values. Front Physiol 2019; 10:708. [PMID: 31293432 PMCID: PMC6598093 DOI: 10.3389/fphys.2019.00708] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 05/21/2019] [Indexed: 12/31/2022] Open
Abstract
Background: The present study aimed to investigate the possibility of using intravoxel incoherent motion (IVIM) diffusion magnetic resonance imaging (MRI) to quantitatively assess the early therapeutic effect of the analgesic–antitumor peptide BmK AGAP on breast cancer and also evaluate the medical value of a reduced distribution of four b-values. Methods: IVIM diffusion MRI using 10 b-values and 4 b-values (0–1,000 s/mm2) was performed at five different time points on BALB/c mice bearing xenograft breast tumors treated with BmK AGAP. Variability in Dslow, Dfast, PF, and ADC derived from the set of 10 b-values and 4 b-values was assessed to evaluate the antitumor effect of BmK AGAP on breast tumor. Results: The data showed that PF values significantly decreased in rBmK AGAP-treated mice on day 12 (P = 0.044). PF displayed the greatest AUC but with a poor medical value (AUC = 0.65). The data showed no significant difference between IVIM measurements acquired from the two sets of b-values at different time points except in the PF on the day 3. The within-subject coefficients of variation were relatively higher in Dfast and PF. However, except for a case noticed on day 0 in PF measurements, the results indicated no statistically significant difference at various time points in the rBmK AGAP-treated or the untreated group (P < 0.05). Conclusion: IVIM showed poor medical value in the early evaluation of the antiproliferative effect of rBmK AGAP in breast cancer, suggesting sensitivity in PF. A reduced distribution of four b-values may provide remarkable measurements but with a potential loss of accuracy in the perfusion-related parameter PF.
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Affiliation(s)
- Natacha Raissa Doudou
- Department of Radiology, Dalian Medical University, Dalian, China.,Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Sylvanus Kampo
- Department of Anesthesiology, Dalian Medical University, Dalian, China.,Department of Anesthesiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yajie Liu
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Bulbul Ahmmed
- Department of Biochemistry and Molecular Biology, Liaoning Provincial Core Lab of Glycobiology and Glycoengineering, Dalian Medical University, Dalian, China
| | - Dewei Zeng
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Minting Zheng
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Aminou Mohamadou
- Department of Radiology, Dalian Medical University, Dalian, China
| | - Qing-Ping Wen
- Department of Anesthesiology, Dalian Medical University, Dalian, China.,Department of Anesthesiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shaowu Wang
- Department of Radiology, Dalian Medical University, Dalian, China.,Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
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31
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Camps-Herrero J. Diffusion-weighted imaging of the breast: current status as an imaging biomarker and future role. BJR Open 2019; 1:20180049. [PMID: 33178933 PMCID: PMC7592470 DOI: 10.1259/bjro.20180049] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 02/07/2019] [Accepted: 02/12/2019] [Indexed: 12/13/2022] Open
Abstract
Diffusion-weighted imaging (DWI) of the breast is a MRI sequence that shows several advantages when compared to the dynamic contrast-enhanced sequence: it does not need intravenous contrast, it is relatively quick and easy to implement (artifacts notwithstanding). In this review, the current applications of DWI for lesion characterization and prognosis as well as for response evaluation are analyzed from the point of view of the necessary steps to become a useful surrogate of underlying biological processes (tissue architecture and cellularity): from the proof of concept, to the proof of mechanism, the proof of principle and finally the proof of effectiveness. Future applications of DWI in screening, DWI modeling and radiomics are also discussed.
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Affiliation(s)
- Julia Camps-Herrero
- Head of Radiology Department, Breast Unit. Hospital Universitario de la Ribera, Alzira, Spain
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Meyer HJ, Leifels L, Hamerla G, Höhn AK, Surov A. ADC-histogram analysis in head and neck squamous cell carcinoma. Associations with different histopathological features including expression of EGFR, VEGF, HIF-1α, Her 2 and p53. A preliminary study. Magn Reson Imaging 2018; 54:214-217. [PMID: 30189236 DOI: 10.1016/j.mri.2018.07.013] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 07/23/2018] [Accepted: 07/23/2018] [Indexed: 12/27/2022]
Abstract
OBJECTIVE Apparent diffusion coefficient (ADC) values derived from Diffusion-weighted images are able to reflect tumor microstructure, such as cellularity, extracellular matrix or proliferation potential. This present study sought to correlate prognostic relevant histopathologic parameters with ADC values derived from a whole lesion measurement in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS Thirty-four patients with histological proven primary HNSCC were prospectively acquired. Histogram analysis was derived from ADC maps. In all cases, expression of Hif1-alpha, VEGF, EGFR, p53, p16, Her 2 were analyzed. RESULTS In the overall patient sample, ADCmax correlated with p53 expression (p = -0.446, p = 0.009) and ADCmode correlated with Her2-expression (p = -0.354, p = 0.047). In the p16 positive group there were several correlations. P25, P90 and entropy correlated with Hif1-alpha (p = -0.423, p = 0.05, p = -0.494, p = 0.019, p = 0.479, p = 0.024, respectively). Kurtosis correlated with P53 expression (p = -0.466, p = 0.029). For p16 negative carcinomas the following associations could be identified. Mode correlated with VEGF-expression (p = -0.657, p = 0.039). ADCmax, P75, P90, and Std correlated with p53-expression (p = -0.827, p = 0.002, p = -0.736, p = 0.01, p = -0.836, p = 0.001 and p = -0.70, p = 0.016, respectively). There were no statistically significant differences of ADC histogram parameters between p16 positive and p16 negative carcinomas. CONCLUSION ADC histogram values can reflect different histopathological features in HNSCC. Associations between ADC histogram analysis parameters and histopathology depend on p16 status.
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Affiliation(s)
- Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Germany.
| | - Leonard Leifels
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Germany.
| | - Gordian Hamerla
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Germany.
| | | | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Germany.
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