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Harada TL, Uematsu T, Nakashima K, Sugino T, Nishimura S, Takahashi K, Hayashi T, Tadokoro Y. Non-contrast-enhanced breast MRI for evaluation of tumor volume change after neoadjuvant chemotherapy. Eur J Radiol 2024; 177:111555. [PMID: 38880053 DOI: 10.1016/j.ejrad.2024.111555] [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: 01/23/2024] [Revised: 05/06/2024] [Accepted: 06/05/2024] [Indexed: 06/18/2024]
Abstract
PURPOSE Three-dimensional contrast-enhanced magnetic resonance imaging (3D-Ce-MRI) is a most powerful tool for evaluation of neoadjuvant chemotherapy (NAC). However, the use of contrast agent is invasive, expensive, and time consuming, Thus, contrast agent-free imaging is preferable. We aimed to investigate the tumor volume change after NAC using maximum intensity projection diffusion-weighted image (MIP-DWI) and 3D-Ce-MRI. METHOD We finally enrolled 55 breast cancer patients who underwent NAC in 2018. All MRI analyses were performed using SYNAPSE VINCENT® medical imaging system (Fujifilm Medical, Tokyo, Japan). We evaluated the tumor volumes before, during, and after NAC. Tumor volume before NAC on 3D-Ce-MRI was termed Pre-CE and those during and after NAC were termed Post-CE. The observer raised the lower end of the window width until the tumor was clearly visible and then manually deleted the non-tumor tissues. A month thereafter, the same observer who was blinded to the 3D-Ce-MRI results randomly evaluated the tumor volumes (Pre-DWI and Post-DWI) using MIP-DWI with the same method. Tumor volume change between ΔCE (Pre-CE - Post-CE/Pre-CE) and ΔDWI (Pre-DWI - Post-DWI/Pre-DWI) and the processing time for both methods (Time-DWI and Time-CE) were compared. RESULTS We enrolled 55 patients. Spearman's rho between ΔDWI and ΔCE for pure mass lesions, and non-mass enhancement (NME) was 0.89 (p < 0.01), 0.63(p < 0.01) respectively. Time-DWI was significantly shorter than Time-CE (41.3 ± 21.2 and 199.5 ± 98.3 respectively, p < 0.01). CONCLUSIONS Non-contrast-enhanced Breast MRI enables appropriate and faster evaluation of tumor volume change after NAC than 3D-Ce-MRI especially for mass lesions.
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Affiliation(s)
- Taiyo L Harada
- Division of Breast Imaging and Breast Interventional Radiology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Takayoshi Uematsu
- Division of Breast Imaging and Breast Interventional Radiology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan.
| | - Kazuaki Nakashima
- Division of Breast Imaging and Breast Interventional Radiology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Takashi Sugino
- Division of Pathology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Seiichirou Nishimura
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Kaoru Takahashi
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Tomomi Hayashi
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Yukiko Tadokoro
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
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Ohashi A, Kataoka M, Iima M, Honda M, Ota R, Urushibata Y, Dominik Nickel M, Toi M, Zackrisson S, Nakamoto Y. A multiparametric approach to predict triple-negative breast cancer including parameters derived from ultrafast dynamic contrast-enhanced MRI. Eur Radiol 2023; 33:8132-8141. [PMID: 37286791 DOI: 10.1007/s00330-023-09730-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 03/12/2023] [Accepted: 03/21/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVE Triple-negative breast cancer (TNBC) is a highly proliferative breast cancer subtype. We aimed to identify TNBC among invasive cancers presenting as masses using maximum slope (MS) and time to enhancement (TTE) measured on ultrafast (UF) DCE-MRI, ADC measured on DWI, and rim enhancement on UF DCE-MRI and early-phase DCE-MRI. METHODS This retrospective single-center study, between December 2015 and May 2020, included patients with breast cancer presenting as masses. Early-phase DCE-MRI was performed immediately after UF DCE-MRI. Interrater agreements were evaluated using the intraclass correlation coefficient (ICC) and Cohen's kappa. Univariate and multivariate logistic regression analyses of the MRI parameters, lesion size, and patient age were performed to predict TNBC and create a prediction model. The programmed death-ligand 1 (PD-L1) expression statuses of the patients with TNBCs were also evaluated. RESULTS In total, 187 women (mean age, 58 years ± 12.9 [standard deviation]) with 191 lesions (33 TNBCs) were evaluated. The ICC for MS, TTE, ADC, and lesion size were 0.95, 0.97, 0.83, and 0.99, respectively. The kappa values of rim enhancements on UF and early-phase DCE-MRI were 0.88 and 0.84, respectively. MS on UF DCE-MRI and rim enhancement on early-phase DCE-MRI remained significant parameters after multivariate analyses. The prediction model created using these significant parameters yielded an area under the curve of 0.74 (95% CI, 0.65, 0.84). The PD-L1-expressing TNBCs tended to have higher rim enhancement rates than the non-PD-L1-expressing TNBCs. CONCLUSION A multiparametric model using UF and early-phase DCE-MRI parameters may be a potential imaging biomarker to identify TNBCs. CLINICAL RELEVANCE STATEMENT Prediction of TNBC or non-TNBC at an early point of diagnosis is crucial for appropriate management. This study offers the potential of UF and early-phase DCE-MRI to offer a solution to this clinical issue. KEY POINTS • It is crucial to predict TNBC at an early clinical period. • Parameters on UF DCE-MRI and early-phase conventional DCE-MRI help in predicting TNBC. • Prediction of TNBC by MRI may be useful in determining appropriate clinical management.
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Affiliation(s)
- Akane Ohashi
- Department of Translational Medicine, Diagnostic Radiology, Lund University, Malmö, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, Malmö, Sweden
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-Cho Shogoin Sakyo-Ku, Kyoto-Shi, Kyoto, Japan
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-Cho Shogoin Sakyo-Ku, Kyoto-Shi, Kyoto, Japan.
| | - Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-Cho Shogoin Sakyo-Ku, Kyoto-Shi, Kyoto, Japan
- Institute of Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, Kyoto, Japan
| | - Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-Cho Shogoin Sakyo-Ku, Kyoto-Shi, Kyoto, Japan
- Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan
| | - Rie Ota
- Department of Radiology, Tenri Hospital, Nara, Japan
| | | | | | - Masakazu Toi
- Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Sophia Zackrisson
- Department of Translational Medicine, Diagnostic Radiology, Lund University, Malmö, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, Malmö, Sweden
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-Cho Shogoin Sakyo-Ku, Kyoto-Shi, Kyoto, Japan
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Xu WJ, Zheng BJ, Lu J, Liu SY, Li HL. Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture analysis of dynamic contrast-enhanced MRI. BMC Med Imaging 2023; 23:70. [PMID: 37264313 DOI: 10.1186/s12880-023-01022-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/23/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Triple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC. METHODS This retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (Ktrans, Kep and Ve) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived. RESULTS The binary logistic regression analysis revealed that Kep_Range (p = 0.032) and Ve_SumVariance (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of Kep_Compactness2 (p = 0.049), Kep_SphericalDisproportion (p = 0.049), and Ve_GlcmEntropy (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group. CONCLUSION Histogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.
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Affiliation(s)
- Wen-Juan Xu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Bing-Jie Zheng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Jun Lu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Si-Yun Liu
- GE healthcare (China), Beijing, 100176, China
| | - Hai-Liang Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
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Szep M, Pintican R, Boca B, Perja A, Duma M, Feier D, Epure F, Fetica B, Eniu D, Roman A, Dudea SM, Chiorean A. Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status. Diagnostics (Basel) 2023; 13:diagnostics13081414. [PMID: 37189515 DOI: 10.3390/diagnostics13081414] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/04/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
There are different breast cancer molecular subtypes with differences in incidence, treatment response and outcome. They are roughly divided into estrogen and progesterone receptor (ER and PR) negative and positive cancers. In this retrospective study, we included 185 patients augmented with 25 SMOTE patients and divided them into two groups: the training group consisted of 150 patients and the validation cohort consisted of 60 patients. Tumors were manually delineated and whole-volume tumor segmentation was used to extract first-order radiomic features. The ADC-based radiomics model reached an AUC of 0.81 in the training cohort and was confirmed in the validation set, which yielded an AUC of 0.93, in differentiating ER/PR positive from ER/PR negative status. We also tested a combined model using radiomics data together with ki67% proliferation index and histological grade, and obtained a higher AUC of 0.93, which was also confirmed in the validation group. In conclusion, whole-volume ADC texture analysis is able to predict hormonal status in breast cancer masses.
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Affiliation(s)
- Madalina Szep
- Department of Radiology, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Roxana Pintican
- Department of Radiology, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Bianca Boca
- Department of Medical Imaging, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Andra Perja
- Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, 400347 Cluj-Napoca, Romania
| | | | - Diana Feier
- Department of Radiology, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
- Medimages Breast Center, 400462 Cluj-Napoca, Romania
| | - Flavia Epure
- Medical Imaging Department, Medisprof Cancer Center, 400641 Cluj Napoca, Romania
| | - Bogdan Fetica
- Department of Pathology, "Ion Chiricuţă" Oncology Institute, 400015 Cluj-Napoca, Romania
| | - Dan Eniu
- Department of Surgical Oncology, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Andrei Roman
- Department of Radiology, "Ion Chiricuță" Oncology Institute, 400015 Cluj-Napoca, Romania
| | - Sorin Marian Dudea
- Department of Radiology, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
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Fang J, Zhang Y, Li R, Liang L, Yu J, Hu Z, Zhou L, Liu R. The utility of diffusion-weighted imaging for differentiation of phyllodes tumor from fibroadenoma and breast cancer. Front Oncol 2023; 13:938189. [PMID: 36937381 PMCID: PMC10018141 DOI: 10.3389/fonc.2023.938189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Objective To evaluate the utility of apparent diffusion coefficient (ADC) values for differentiating breast tumors. Methods The medical records of 17 patients with phyllodes tumor [PT; circular regions of interest (ROI-cs) n = 171], 74 patients with fibroadenomas (FAs; ROI-cs, n = 94), and 57 patients with breast cancers (BCs; ROI-cs, n = 104) confirmed by surgical pathology were retrospectively reviewed. Results There were significant differences between PTs, FAs, and BCs in ADCmean, ADCmax, and ADCmin values. The cutoff ADCmean for differentiating PTs from FAs was 1.435 × 10-3 mm2/s, PTs from BCs was 1.100 × 10-3 mm2/s, and FAs from BCs was 0.925 × 10-3 mm2/s. There were significant differences between benign PTs, borderline PTs, and malignant PTs in ADCmean, ADCmax, and ADCmin values. The cutoff ADCmean for differentiating benign PTs from borderline PTs was 1.215 × 10-3 mm2/s, and borderline PTs from malignant PTs was 1.665 × 10-3 mm2/s. Conclusion DWI provides quantitative information that can help distinguish breast tumors.
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Affiliation(s)
- Jinzhi Fang
- Department of Radiology, Affiliated Longhua People’s Hospital, Southern Medical University (Longhua People’s Hospital), Shenzhen, China
| | - Yuzhong Zhang
- Department of Radiology, Affiliated Longhua People’s Hospital, Southern Medical University (Longhua People’s Hospital), Shenzhen, China
| | - Ruifeng Li
- Department of Radiology, Affiliated Longhua People’s Hospital, Southern Medical University (Longhua People’s Hospital), Shenzhen, China
| | - Lanlan Liang
- Clinical Medical College of Dali University, Dali, China
- Department of Radiology, Affiliated Hospital of Yunnan University, Kunming, China
| | - Juan Yu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen, China
| | - Ziqi Hu
- Department of Radiology, Affiliated Longhua People’s Hospital, Southern Medical University (Longhua People’s Hospital), Shenzhen, China
| | - Lingling Zhou
- Department of Radiology, Affiliated Longhua People’s Hospital, Southern Medical University (Longhua People’s Hospital), Shenzhen, China
| | - Renwei Liu
- Department of Radiology, Affiliated Longhua People’s Hospital, Southern Medical University (Longhua People’s Hospital), Shenzhen, China
- *Correspondence: Renwei Liu,
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Zheng M, Huang Y, Peng J, Xia Y, Cui Y, Han X, Wang S, Xie H. Optimal Selection of Imaging Examination for Lymph Node Detection of Breast Cancer With Different Molecular Subtypes. Front Oncol 2022; 12:762906. [PMID: 35912264 PMCID: PMC9326026 DOI: 10.3389/fonc.2022.762906] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 05/30/2022] [Indexed: 11/30/2022] Open
Abstract
Objective Axillary lymph node management is an important part of breast cancer surgery and the accuracy of preoperative imaging evaluation can provide adequate information to guide operation. Different molecular subtypes of breast cancer have distinct imaging characteristics. This article was aimed to evaluate the predictive ability of imaging methods in accessing the status of axillary lymph node in different molecular subtypes. Methods A total of 2,340 patients diagnosed with primary invasive breast cancer after breast surgery from 2013 to 2018 in Jiangsu Breast Disease Center, the First Affiliated Hospital with Nanjing Medical University were included in the study. We collected lymph node assessment results from mammography, ultrasounds, and MRIs, performed receiver operating characteristic (ROC) analysis, and calculated the sensitivity and specificity of each test. The C-statistic among different imaging models were compared in different molecular subtypes to access the predictive abilities of these imaging models in evaluating the lymph node metastasis. Results In Her-2 + patients, the C-statistic of ultrasound was better than that of MRI (0.6883 vs. 0.5935, p=0.0003). The combination of ultrasound and MRI did not raise the predictability compared to ultrasound alone (p=0.492). In ER/PR+HER2- patients, the C-statistic of ultrasound was similar with that of MRI (0.7489 vs. 0.7650, p=0.5619). Ultrasound+MRI raised the prediction accuracy compared to ultrasound alone (p=0.0001). In ER/PR-HER2- patients, the C-statistics of ultrasound was similar with MRI (0.7432 vs. 0.7194, p=0.5579). Combining ultrasound and MRI showed no improvement in the prediction accuracy compared to ultrasound alone (p=0.0532). Conclusion From a clinical perspective, for Her-2+ patients, ultrasound was the most recommended examination to assess the status of axillary lymph node metastasis. For ER/PR+HER2- patients, we suggested that the lymph node should be evaluated by ultrasound plus MRI. For ER/PR-Her2- patients, ultrasound or MRI were both optional examinations in lymph node assessment. Furthermore, more new technologies should be explored, especially for Her2+ patients, to further raise the prediction accuracy of lymph node assessment.
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Affiliation(s)
| | | | | | | | | | | | - Shui Wang
- *Correspondence: Shui Wang, ; Hui Xie,
| | - Hui Xie
- *Correspondence: Shui Wang, ; Hui Xie,
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Jiménez de los Santos ME, Reyes-Pérez JA, Domínguez Osorio V, Villaseñor-Navarro Y, Moreno-Astudillo L, Vela-Sarmiento I, Sollozo-Dupont I. Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer. World J Gastroenterol 2022; 28:2609-2624. [PMID: 35949349 PMCID: PMC9254137 DOI: 10.3748/wjg.v28.i23.2609] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/25/2021] [Accepted: 04/25/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Whole-tumor apparent diffusion coefficient (ADC) histogram analysis is relevant to predicting the neoadjuvant chemoradiation therapy (nCRT) response in patients with locally advanced rectal cancer (LARC).
AIM To evaluate the performance of ADC histogram-derived parameters for predicting the outcomes of patients with LARC.
METHODS This is a single-center, retrospective study, which included 48 patients with LARC. All patients underwent a pre-treatment magnetic resonance imaging (MRI) scan for primary tumor staging and a second restaging MRI for response evaluation. The sample was distributed as follows: 18 responder patients (R) and 30 non-responders (non-R). Eight parameters derived from the whole-lesion histogram analysis (ADCmean, skewness, kurtosis, and ADC10th, 25th, 50th, 75th, 90th percentiles), as well as the ADCmean from the hot spot region of interest (ROI), were calculated for each patient before and after treatment. Then all data were compared between R and non-R using the Mann-Whitney U test. Two measures of diagnostic accuracy were applied: the receiver operating characteristic curve and the diagnostic odds ratio (DOR). We also reported intra- and interobserver variability by calculating the intraclass correlation coefficient (ICC).
RESULTS Post-nCRT kurtosis, as well as post-nCRT skewness, were significantly lower in R than in non-R (both P < 0.001, respectively). We also found that, after treatment, R had a larger loss of both kurtosis and skewness than non-R (∆%kurtosis and ∆skewness, P < 0.001). Other parameters that demonstrated changes between groups were post-nCRT ADC10th, ∆%ADC10th, ∆%ADCmean, and ROI ∆%ADCmean. However, the best diagnostic performance was achieved by ∆%kurtosis at a threshold of 11.85% (Area under the receiver operating characteristic curve [AUC] = 0.991, DOR = 376), followed by post-nCRT kurtosis = 0.78 × 10-3 mm2/s (AUC = 0.985, DOR = 375.3), ∆skewness = 0.16 (AUC = 0.885, DOR = 192.2) and post-nCRT skewness = 1.59 × 10-3 mm2/s (AUC = 0.815, DOR = 168.6). Finally, intraclass correlation coefficient analysis showed excellent intraobserver and interobserver agreement, ensuring the implementation of histogram analysis into routine clinical practice.
CONCLUSION Whole-tumor ADC histogram parameters, particularly kurtosis and skewness, are relevant biomarkers for predicting the nCRT response in LARC. Both parameters appear to be more reliable than ADCmean from one-slice ROI.
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Affiliation(s)
| | | | | | | | | | - Itzel Vela-Sarmiento
- Department of Gastrointestinal Surgery, National Cancer Institute, Mexico 14080, Mexico
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Sha Y, Chen J. MRI-based radiomics for the diagnosis of triple-negative breast cancer: a meta-analysis. Clin Radiol 2022; 77:655-663. [DOI: 10.1016/j.crad.2022.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 04/21/2022] [Indexed: 11/03/2022]
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Chen H, Min Y, Xiang K, Chen J, Yin G. DCE-MRI Performance in Triple Negative Breast Cancers: Comparison with Non-Triple Negative Breast Cancers. Curr Med Imaging 2022; 18:970-976. [PMID: 35232365 DOI: 10.2174/1573405618666220225090944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/20/2021] [Accepted: 01/31/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Triple negative breast cancers is considered to have the worst prognosis in breast cancer. Dynamic contrast enhanced magnetic resonance imaging has been widely used in the diagnosis of breast cancer because that is more sensitive to breast cancer. However, there are few reports about the MRI characteristics of triple negative breast cancers. OBJECTIVE The aim of the study was to evaluate the imaging finding in triple negative breast cancers compared with non-TNBC and attempt to predict it. METHOD In total, 223 patients with a preoperative diagnosis of breast cancer were enrolled in the study. Dynamic contrast enhanced magnetic resonance imaging was performed before being diagnosed with breast cancer, and histopathological assessment was confirmed after biopsy or operation. The patients were divided into 2 groups based on immunohistochemical, namely the triple negative breast cancers or non-triple negative breast cancers. RESULTS The 2 groups demonstrated significant differences regarding the tumor size, margin, outline, burr sign, enhancement, inverted nipple(P<0.05). A multivariate logistic regression analysis was performed to further validate the association of these features, however, only margin [odds ratio (OR), 0.038; 95% confidence interval (CI), 0.014-0.100; <0.001], outline [odds ratio (OR), 0.039; 95% confidence interval (CI), 0.008-0.200; <0.001], burr sign [odds ratio (OR), 2.786; 95% confidence interval (CI), 1.225-6.333; 0.014] and enhancement [odds ratio (OR), 0.131; 95% confidence interval (CI), 0.037-0.457; P=0.001] were associated with TNBC. CONCLUSION The results indicated that the specific dynamic contrast enhanced magnetic resonance imaging features can be possible predictors of pathological results, with a consequent prognostic value.
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Affiliation(s)
- Hang Chen
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing 404100, P.R. China
| | - Yu Min
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing 404100, P.R. China
| | - Ke Xiang
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing 404100, P.R. China
| | - Jialin Chen
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing 404100, P.R. China
| | - Guobing Yin
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing 404100, P.R. China
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Matsuda M, Tsuda T, Ebihara R, Toshimori W, Okada K, Takeda S, Okumura A, Shiraishi Y, Suekuni H, Kamei Y, Kurata M, Kitazawa R, Mochizuki T, Kido T. Triple-negative breast cancer on contrast-enhanced MRI and synthetic MRI: A comparison with non-triple-negative breast carcinoma. Eur J Radiol 2021; 142:109838. [PMID: 34217136 DOI: 10.1016/j.ejrad.2021.109838] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 06/22/2021] [Accepted: 06/24/2021] [Indexed: 12/20/2022]
Abstract
PURPOSE This study aimed to compare the characteristics of triple-negative breast cancer (TNBC) with non-TNBC on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and synthetic MRI. METHOD This retrospective study included 79 patients with histopathologically proven breast cancer (TNBC: 16, non-TNBC: 63) who underwent synthetic MRI. Using synthetic MR images, we obtained T1 and T2 relaxation times in breast lesions before (Pre-T1, Pre-T2, Pre-PD) and after (Gd-T1, Gd-T2, Gd-PD) contrast agent injection. Subsequently, we calculated the ΔT1 (Pre-T1 - Gd-T1), ΔT2 (Pre-T2 - Gd-T2), Pre-T1/T2, and Gd-T1/T2. We compared the aforementioned quantitative values, as well as several morphologic features between TNBCs and non-TNBCs that were identified on DCE-MRI. RESULTS The multivariate analyses revealed that the Pre-T2 (P = 0.037) and the presence of rim enhancement (P-RIM) (P = 0.034) were significant and independent predictors of TNBC. The area under the receiver operating characteristics curve for all breast cancers was greater when a combination of Pre-T2 and P-RIM (Pre-T2+P-RIM; Method 3, AUC (area under the curve) = 0.858) was used to distinguish between TNBCs and non-TNBCs versus the use of either Pre-T2 alone (Method 1, AUC = 0.786) or P-RIM alone (Method 2, AUC = 0.747). CONCLUSIONS Pre-T2 obtained using synthetic MRI and P-RIM identified on DCE-MRI allowed the differentiation between TNBCs and non-TNBCs. However, these results are preliminary and need to be verified by further studies.
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Affiliation(s)
- Megumi Matsuda
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan.
| | - Takaharu Tsuda
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan.
| | - Rui Ebihara
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Wataru Toshimori
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Kanako Okada
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Shiori Takeda
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Aya Okumura
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Yasuhiro Shiraishi
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan.
| | - Hiroshi Suekuni
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Yoshiaki Kamei
- Breast Center, Ehime University Hospital, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Mie Kurata
- Department of Pathology, Ehime University Proteo-Science Center, Shitsukawa, Toon, Ehime 791-0295, Japan; Department of Analytical Pathology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan.
| | - Riko Kitazawa
- Division of Diagnostic Pathology, Ehime University Hospital, Shitsukawa, Toon, Ehime 791-0295, Japan.
| | - Teruhito Mochizuki
- Department of Radiology, I.M. Sechenov First Moscow State Medical University, 8-2 Trubetskaya str., Moscow 119991, Russian Federation
| | - Teruhito Kido
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
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11
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Tang WJ, Jin Z, Zhang YL, Liang YS, Cheng ZX, Chen LX, Liang YY, Wei XH, Kong QC, Guo Y, Jiang XQ. Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker for Assessing the Level of Tumor-Infiltrating Lymphocytes: Value in Molecular Subtypes of Breast Cancer. Front Oncol 2021; 10:611571. [PMID: 33489920 PMCID: PMC7820903 DOI: 10.3389/fonc.2020.611571] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 11/19/2020] [Indexed: 12/18/2022] Open
Abstract
Purpose To assess whether apparent diffusion coefficient (ADC) metrics can be used to assess tumor-infiltrating lymphocyte (TIL) levels in breast cancer, particularly in the molecular subtypes of breast cancer. Methods In total, 114 patients with breast cancer met the inclusion criteria (mean age: 52 years; range: 29–85 years) and underwent multi-parametric breast magnetic resonance imaging (MRI). The patients were imaged by diffusion-weighted (DW)-MRI (1.5 T) using a single-shot spin-echo echo-planar imaging sequence. Two readers independently drew a region of interest (ROI) on the ADC maps of the whole tumor. The mean ADC and histogram parameters (10th, 25th, 50th, 75th, and 90th percentiles of ADC, skewness, entropy, and kurtosis) were used as features to analyze associations with the TIL levels in breast cancer. Additionally, the correlation between the ADC values and Ki-67 expression were analyzed. Continuous variables were compared with Student’s t-test or Mann-Whitney U test if the variables were not normally distributed. Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test. Associations between TIL levels and imaging features were evaluated by the Mann-Whitney U and Kruskal-Wallis tests. Results A statistically significant difference existed in the 10th and 25th percentile ADC values between the low and high TIL groups in breast cancer (P=0.012 and 0.027). For the luminal subtype of breast cancer, the 10th percentile ADC value was significantly lower in the low TIL group (P=0.041); for the non-luminal subtype of breast cancer, the kurtosis was significantly lower in the low TIL group (P=0.023). The Ki-67 index showed statistical significance for evaluating the TIL levels in breast cancer (P=0.007). Additionally, the skewness was significantly higher for samples with high Ki-67 levels in breast cancer (P=0.029). Conclusions Our findings suggest that whole-lesion ADC histogram parameters can be used as surrogate biomarkers to evaluate TIL levels in molecular subtypes of breast cancer.
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Affiliation(s)
- Wen-Jie Tang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Zhe Jin
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yan-Ling Zhang
- Department of Ultrasound, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yun-Shi Liang
- Department of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Zi-Xuan Cheng
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Lei-Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Ying-Ying Liang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xin-Hua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Qing-Cong Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yuan Guo
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xin-Qing Jiang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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