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Chen S, Zheng B, Tang W, Ding S, Sui Y, Yu X, Zhong Z, Kong Q, Liu W, Guo Y. The longitudinal changes in multiparametric MRI during neoadjuvant chemotherapy can predict treatment response early in patients with HER2-positive breast cancer. Eur J Radiol 2024; 178:111656. [PMID: 39098252 DOI: 10.1016/j.ejrad.2024.111656] [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: 12/05/2023] [Revised: 07/17/2024] [Accepted: 07/29/2024] [Indexed: 08/06/2024]
Abstract
PURPOSE To investigate whether longitudinal changes in multiparametric MRI can predict early response to neoadjuvant chemotherapy (NAC) for HER2-positive breast cancer (BC) and to further establish quantitative models based on these features. METHODS A total of 164 HER2-positive BC patients from three centers were included. MRI was performed at baseline and after two cycles of NAC (early post-NAC). Clinicopathological characteristics were enrolled. MRI features were evaluated at baseline and early post-NAC, as well as longitudinal changes in multiparametric MRI, including changes in the largest diameter (LD) of the tumor (ΔLD), apparent diffusion coefficient (ADC) values (ΔADC), and time-signal intensity curve (TIC) (ΔTIC). The patients were divided into a training set (n = 95), an internal validation set (n = 31), and an independent external validation set (n = 38). Univariate and multivariate logistic regression analyses were used to identify the independent indicators of pCR, which were then used to establish the clinicopathologic model and combined model. The AUC was used to evaluate the predictive power of the different models and calibration curves were used to evaluate the consistency of the prediction of pCR in different models. Additionally, decision curve analysis (DCA) was employed to determine the clinical usefulness of the different models. RESULTS Two models were enrolled in this study, including the clinicopathologic model and the combined model. The LD at early post-NAC (OR=0.913, 95 % CI=0.953-0.994 p = 0.026), ΔADC (OR=1.005, 95 % CI=1.005-1.008, p = 0.007), and ΔTIC (OR=3.974, 95 % CI=1.276-12.358, p = 0.017) were identified as the best predictors of NAC response. The combined model constructed by the combination of LD at early post-NAC, ΔADC, and ΔTIC showed good predictive performance in the training set (AUC=0.87), internal validation set (AUC=0.78), and external validation set (AUC=0.79), which performed better than the clinicopathologic model in all sets. CONCLUSIONS The changes in multiparametric MRI can predict early treatment response for HER2-positive BC and may be helpful for individualized treatment planning.
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Affiliation(s)
- Siyi Chen
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Bingjie Zheng
- Department of Radiology, Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 150001, China.
| | - Wenjie Tang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Shishen Ding
- Department of Radiology, Liuzhou People's Hospital, Guangxi Medical University, Liuzhou 545006, China.
| | - Yi Sui
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Xiaomeng Yu
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Zhidan Zhong
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Qingcong Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Road, Tianhe District, Guangzhou 510630, China.
| | - Weifeng Liu
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
| | - Yuan Guo
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, No.1 Panfu Road, Guangzhou 510180, China.
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Qu M, Feng W, Liu X, Li Z, Li Y, Lu X, Lei J. Investigation of synthetic MRI with quantitative parameters for discriminating axillary lymph nodes status in invasive breast cancer. Eur J Radiol 2024; 175:111452. [PMID: 38604092 DOI: 10.1016/j.ejrad.2024.111452] [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: 11/29/2023] [Revised: 03/25/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
OBJECTIVE To investigate the potential value of quantitative parameters derived from synthetic magnetic resonance imaging (syMRI) for discriminating axillary lymph nodes metastasis (ALNM) in breast cancer patients. MATERIALS AND METHODS A total of 56 females with histopathologically proven invasive breast cancer who underwent both conventional breast MRI and additional syMRI examinations were enrolled in this study, including 30 patients with ALNM and 26 with non-ALNM. SyMRI has enabled quantification of T1 relaxation time (T1), T2 relaxation time (T2) and proton density (PD). The syMRI quantitative parameters of breast primary tumors before (T1tumor, T2tumor, PDtumor) and after (T1+tumor, T2+tumor, PD+tumor) contrast agent injection were obtained. Similarly, measurements were taken for axillary lymph nodes before (T1LN, T2LN, PDLN) and after (T1+LN, T2+LN, PD+LN) the injection, then theΔT1 (T1-T1+), ΔT2 (T2-T2+), ΔPD (PD-PD+), T1/T2 and T1+/T2+ were calculated. All parameters were compared between ANLM and non-ALNM group. Intraclass correlation coefficient for assessing interobserver agreement. The independent Student's t test or Mann-Whitney U test to determine the relationship between the mean quantitative values and the ALNM. Multivariate logistic regression analyses followed by receiver operating characteristics (ROC) analysis for discriminating ALN status. A P value < 0.05 was considered statistically significant. RESULTS The short-diameter of lymph nodes (DLN) in ALNM group was significantly longer than that in the non-ALNM group (10.22 ± 3.58 mm vs. 5.28 ± 1.39 mm, P < 0.001). The optimal cutoff value was determined to be 5.78 mm, with an AUC of 0.894 (95 % CI: 0.838-0.939), a sensitivity of 86.7 %, and a specificity of 90.2 %. In syMRI quantitative parameters of breast tumors, T2tumor, ΔT2tumor and ΔPDtumor values showed statistically significant differences between the two groups (P < 0.05). T2tumor value had the best performance in discriminating ALN status (AUC = 0.712), and the optimal cutoff was 90.12 ms, the sensitivity and specificity were 65.0 % and 83.6 % respectively. In terms of syMRI quantitative parameters of lymph nodes, T1LN, T2LN, T1LN/T2LN, T2+LN and ΔT1LN values were significantly different between the two groups (P < 0.05), and their AUCs were 0.785, 0.840, 0.886, 0.702 and 0.754, respectively. Multivariate analyses indicated that the T1LN value was the only independent predictor of ALNM (OR=1.426, 95 % CI: 1.130-1.798, P = 0.039). The diagnostic sensitivity and specificity of T1LN was 86.7 % and 69.4 % respectively at the best cutoff point of 1371.00 ms. The combination of T1LN, T2LN, T1LN/T2LN, ΔT1LN and DLN had better performance for differentiating ALNM and non-ALNM, with AUCs of 0.905, 0.957, 0.964 and 0.897, respectively. CONCLUSION The quantitative parameters derived from syMRI have certain value for discriminating ALN status in invasive breast cancer, with T2tumor showing the highest diagnostic efficiency among breast lesions parameters. Moreover, T1LN acted as an independent predictor of ALNM.
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Affiliation(s)
- Mengmeng Qu
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Wen Feng
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Xinran Liu
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Zhifan Li
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Yixiang Li
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Xingru Lu
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China; Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou 730000, China
| | - Junqiang Lei
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China; Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou 730000, China.
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Espinoza AF, Patel RH, Patel KR, Badachhape AA, Whitlock R, Srivastava RK, Govindu SR, Duong A, Kona A, Kureti P, Armbruster B, Kats D, Srinivasan RR, Dobrolecki LE, Yu X, Najaf Panah MJ, Zorman B, Sarabia SF, Urbicain M, Major A, Bissig KD, Keller C, Lewis MT, Heczey A, Sumazin P, López-Terrada DH, Woodfield SE, Vasudevan SA. A novel treatment strategy utilizing panobinostat for high-risk and treatment-refractory hepatoblastoma. J Hepatol 2024; 80:610-621. [PMID: 38242326 DOI: 10.1016/j.jhep.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 12/28/2023] [Accepted: 01/03/2024] [Indexed: 01/21/2024]
Abstract
BACKGROUND & AIMS Patients with metastatic, treatment-refractory, and relapsed hepatoblastoma (HB) have survival rates of less than 50% due to limited treatment options. To develop new therapeutic strategies for these patients, our laboratory has developed a preclinical testing pipeline. Given that histone deacetylase (HDAC) inhibition has been proposed for HB, we hypothesized that we could find an effective combination treatment strategy utilizing HDAC inhibition. METHODS RNA sequencing, microarray, NanoString, and immunohistochemistry data of patient HB samples were analyzed for HDAC class expression. Patient-derived spheroids (PDSp) were used to screen combination chemotherapy with an HDAC inhibitor, panobinostat. Patient-derived xenograft (PDX) mouse models were developed and treated with the combination therapy that showed the highest efficacy in the PDSp drug screen. RESULTS HDAC RNA and protein expression were elevated in HB tumors compared to normal livers. Panobinostat (IC50 of 0.013-0.059 μM) showed strong in vitro effects and was associated with lower cell viability than other HDAC inhibitors. PDSp demonstrated the highest level of cell death with combination treatment of vincristine/irinotecan/panobinostat (VIP). All four models responded to VIP therapy with a decrease in tumor size compared to placebo. After 6 weeks of treatment, two models demonstrated necrotic cell death, with lower Ki67 expression, decreased serum alpha fetoprotein and reduced tumor burden compared to paired VI- and placebo-treated groups. CONCLUSIONS Utilizing a preclinical HB pipeline, we demonstrate that panobinostat in combination with VI chemotherapy can induce an effective tumor response in models developed from patients with high-risk, relapsed, and treatment-refractory HB. IMPACT AND IMPLICATIONS Patients with treatment-refractory hepatoblastoma have limited treatment options with survival rates of less than 50%. Our manuscript demonstrates that combination therapy with vincristine, irinotecan, and panobinostat reduces the size of high-risk, relapsed, and treatment-refractory tumors. With this work we provide preclinical evidence to support utilizing this combination therapy as an arm in future clinical trials.
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Affiliation(s)
- Andres F Espinoza
- Pediatric Surgical Oncology Laboratory, Divisions of Pediatric Surgery and Surgical Research, Michael E. DeBakey Department of Surgery, Texas Children's Surgical Oncology Program, Texas Children's Liver Tumor Program, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Roma H Patel
- Pediatric Surgical Oncology Laboratory, Divisions of Pediatric Surgery and Surgical Research, Michael E. DeBakey Department of Surgery, Texas Children's Surgical Oncology Program, Texas Children's Liver Tumor Program, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kalyani R Patel
- Department of Pathology and Immunology, Baylor College of Medicine, Texas Children's Department of Pathology, Houston, TX 77030, USA
| | - Andrew A Badachhape
- Department of Radiology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Richard Whitlock
- Pediatric Surgical Oncology Laboratory, Divisions of Pediatric Surgery and Surgical Research, Michael E. DeBakey Department of Surgery, Texas Children's Surgical Oncology Program, Texas Children's Liver Tumor Program, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Rohit K Srivastava
- Pediatric Surgical Oncology Laboratory, Divisions of Pediatric Surgery and Surgical Research, Michael E. DeBakey Department of Surgery, Texas Children's Surgical Oncology Program, Texas Children's Liver Tumor Program, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Saiabhiroop R Govindu
- Pediatric Surgical Oncology Laboratory, Divisions of Pediatric Surgery and Surgical Research, Michael E. DeBakey Department of Surgery, Texas Children's Surgical Oncology Program, Texas Children's Liver Tumor Program, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ashley Duong
- Pediatric Surgical Oncology Laboratory, Divisions of Pediatric Surgery and Surgical Research, Michael E. DeBakey Department of Surgery, Texas Children's Surgical Oncology Program, Texas Children's Liver Tumor Program, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Abhishek Kona
- Pediatric Surgical Oncology Laboratory, Divisions of Pediatric Surgery and Surgical Research, Michael E. DeBakey Department of Surgery, Texas Children's Surgical Oncology Program, Texas Children's Liver Tumor Program, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Pavan Kureti
- Pediatric Surgical Oncology Laboratory, Divisions of Pediatric Surgery and Surgical Research, Michael E. DeBakey Department of Surgery, Texas Children's Surgical Oncology Program, Texas Children's Liver Tumor Program, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bryan Armbruster
- Pediatric Surgical Oncology Laboratory, Divisions of Pediatric Surgery and Surgical Research, Michael E. DeBakey Department of Surgery, Texas Children's Surgical Oncology Program, Texas Children's Liver Tumor Program, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Dina Kats
- Pediatric Cancer Biology, Children's Cancer Therapy Development Institute, Beaverton, OR, United States
| | | | - Lacey E Dobrolecki
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Xinjian Yu
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital and Cancer Center, Houston, TX, USA
| | - Mohammad J Najaf Panah
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital and Cancer Center, Houston, TX, USA
| | - Barry Zorman
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital and Cancer Center, Houston, TX, USA
| | - Stephen F Sarabia
- Department of Pathology and Immunology, Baylor College of Medicine, Texas Children's Department of Pathology, Houston, TX 77030, USA
| | - Martin Urbicain
- Department of Pathology and Immunology, Baylor College of Medicine, Texas Children's Department of Pathology, Houston, TX 77030, USA
| | - Angela Major
- Department of Pathology and Immunology, Baylor College of Medicine, Texas Children's Department of Pathology, Houston, TX 77030, USA
| | - Karl-Dimiter Bissig
- Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, NC, USA
| | - Charles Keller
- Pediatric Cancer Biology, Children's Cancer Therapy Development Institute, Beaverton, OR, United States
| | - Michael T Lewis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Andras Heczey
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital and Cancer Center, Houston, TX, USA
| | - Pavel Sumazin
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital and Cancer Center, Houston, TX, USA
| | - Dolores H López-Terrada
- Department of Pathology and Immunology, Baylor College of Medicine, Texas Children's Department of Pathology, Houston, TX 77030, USA
| | - Sarah E Woodfield
- Pediatric Surgical Oncology Laboratory, Divisions of Pediatric Surgery and Surgical Research, Michael E. DeBakey Department of Surgery, Texas Children's Surgical Oncology Program, Texas Children's Liver Tumor Program, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sanjeev A Vasudevan
- Pediatric Surgical Oncology Laboratory, Divisions of Pediatric Surgery and Surgical Research, Michael E. DeBakey Department of Surgery, Texas Children's Surgical Oncology Program, Texas Children's Liver Tumor Program, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA.
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Yoen H, Kim SY, Lee DW, Lee HB, Cho N. Prediction of Tumor Progression During Neoadjuvant Chemotherapy and Survival Outcome in Patients With Triple-Negative Breast Cancer. Korean J Radiol 2023; 24:626-639. [PMID: 37404105 DOI: 10.3348/kjr.2022.0974] [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: 12/07/2022] [Revised: 03/31/2023] [Accepted: 05/01/2023] [Indexed: 07/06/2023] Open
Abstract
OBJECTIVE To investigate the association of clinical, pathologic, and magnetic resonance imaging (MRI) variables with progressive disease (PD) during neoadjuvant chemotherapy (NAC) and distant metastasis-free survival (DMFS) in patients with triple-negative breast cancer (TNBC). MATERIALS AND METHODS This single-center retrospective study included 252 women with TNBC who underwent NAC between 2010 and 2019. Clinical, pathologic, and treatment data were collected. Two radiologists analyzed the pre-NAC MRI. After random allocation to the development and validation sets in a 2:1 ratio, we developed models to predict PD and DMFS using logistic regression and Cox proportional hazard regression, respectively, and validated them. RESULTS Among the 252 patients (age, 48.3 ± 10.7 years; 168 in the development set; 84 in the validation set), PD was occurred in 17 patients and 9 patients in the development and validation sets, respectively. In the clinical-pathologic-MRI model, the metaplastic histology (odds ratio [OR], 8.0; P = 0.032), Ki-67 index (OR, 1.02; P = 0.044), and subcutaneous edema (OR, 30.6; P = 0.004) were independently associated with PD in the development set. The clinical-pathologic-MRI model showed a higher area under the receiver-operating characteristic curve (AUC) than the clinical-pathologic model (AUC: 0.69 vs. 0.54; P = 0.017) for predicting PD in the validation set. Distant metastases occurred in 49 patients and 18 patients in the development and validation sets, respectively. Residual disease in both the breast and lymph nodes (hazard ratio [HR], 6.0; P = 0.005) and the presence of lymphovascular invasion (HR, 3.3; P < 0.001) were independently associated with DMFS. The model consisting of these pathologic variables showed a Harrell's C-index of 0.86 in the validation set. CONCLUSION The clinical-pathologic-MRI model, which considered subcutaneous edema observed using MRI, performed better than the clinical-pathologic model for predicting PD. However, MRI did not independently contribute to the prediction of DMFS.
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Affiliation(s)
- Heera Yoen
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Soo-Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
| | - Dae-Won Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Han-Byoel Lee
- Department of Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Nariya Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
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Klein K, Gabriel Schafigh D, Schömig-Markiefka B, Campbell GM, Weiss K, Malter W, Maintz D, Hellmich M, Barbara Krug K. Intermodal correlation of quantitative CT-data and MRI-biomarkers derived from synchronous spectral CT-maps and breast MRI-examinations with molecular biomarkers in invasive ductal breast carcinomas. Eur J Radiol 2023; 165:110919. [PMID: 37302338 DOI: 10.1016/j.ejrad.2023.110919] [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: 04/14/2023] [Revised: 05/19/2023] [Accepted: 05/31/2023] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To asses the correlation of data derived from dual-layer (DL)-CT material-maps and breast MRI data with molecular biomarkers in invasive breast carcinomas. METHODS All patients at the University Breast Cancer Center who underwent a clinically indicated DLCT-scan and a breast MRI for staging of invasive ductal breast cancer from 2016 to 2020 were prospectively included. Iodine concentration-maps, and Zeffective-maps were reconstructed from the CT-datasets. T1w- and T2w-signal intensities, ADC and the clustered shapes of the dynamic-curves (washout, plateau, persistent) were derived from the MRI-datasets. ROI-based evaluations of the cancers and the reference "musculature" were performed semi-automatically in identical anatomical positions using dedicated evaluation software. Statistical analysis was essentially descriptive using Spearmańs rank correlation and (multivariable) partial correlation. RESULTS The signal intensities measured in the 3rd phase of the contrast dynamics correlated at an intermediate level of significance with the iodine content and the Zeffective-values derived from the breast target lesions (Spearmańs rank correlation-coefficient r = 0.237/0.236, p = 0.002/0.003). The bivariate and the multivariate analyses displayed correlations of an intermediate significance level of the iodine content and the Zeff-values measured in the breast target lesions with immunhistochemical subtyping (r = 0.211-0.243, p = 0.002-0.009, respectively). The Zeff-values showed the strongest correlations when normalized to the values measured in the musculature and in the aorta (r = -0.237 to -0.305, r=<0.001-0.003). The MRI-assessments showed correlations of intermediate to high significance and low to intermediate significance between the ratios of the T2w-signal intensities and the trends of the dynamic curves measured in the breast target lesions and in musculature and immunohistochemical cancer subtyping, respectively (T2w: r = 0.232-0.249, p = 0.003/0.002; dynamics: r = -0.322/-0.245, p=<0.001/0.002). The ratios of the clustered trends of the dynamic curves measured in the breast target lesions and in musculature correlated with tumor grading on intermediate significance level (r = -0.213 and -0.194, p = 0.007/0.016) and with Ki-67 on a low significance level (bivariate analysis: r = -0.160, p = 0.040). There was only a weak correlation between the ADC-values measured in the breast target lesions and HER2-expression (bivariate ansalysis: r = 0.191, p = 0.030). CONCLUSIONS Our preliminary results indicate that evaluation of perfusion based DLCT-data and MRI-biomarkers show correlations with the immunhistochemical subtyping of invasive ductal breast carcinomas. Further clinical research is warranted in order to validate the value of the results and define clinical situations in which the use of the described DLCT-biomaker and MRI biomarkers may be helpful in clinical patient care.
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Affiliation(s)
- Konstantin Klein
- Dept. of Diagnostic and Interventional Radiology, University Hospital of Cologne, Cologne, Germany
| | - Darius Gabriel Schafigh
- Dept. of Diagnostic and Interventional Radiology, University Hospital of Cologne, Cologne, Germany; Dept. of ENT Surgery, University Hospital of Cologne, Cologne, Germany
| | | | | | | | - Wolfram Malter
- Breast Cancer Center, Department of Gynecology and Obstetrics, University of Cologne, Cologne, Germany
| | - David Maintz
- Dept. of Diagnostic and Interventional Radiology, University Hospital of Cologne, Cologne, Germany
| | - Martin Hellmich
- Institute of Medical Statistics and Bioinformatics, Medical Faculty, University of Cologne, Germany
| | - Kathrin Barbara Krug
- Dept. of Diagnostic and Interventional Radiology, University Hospital of Cologne, Cologne, Germany.
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Tanişman Ö, Kiziltepe FT, Yildirim Ç, Coşar ZS. Prediction of prognostic factors in breast cancer: A noninvasive method utilizing histogram parameters derived from Adc maps. Heliyon 2023; 9:e16282. [PMID: 37251865 PMCID: PMC10208937 DOI: 10.1016/j.heliyon.2023.e16282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 05/31/2023] Open
Abstract
Objective The aim of this study is to investigate the relationship between histogram parameters and prognostic factors of breast cancer and to reveal the diagnostic performance of histogram parameters in predicting prognostic factors status. Materials and methods Ninety-two patients with a confirmed histopathological diagnosis of breast cancer were included in the study. Magnetic resonance imaging (MRI) was performed using a 1.5T scanner and two different b values were used for diffusion-weighted imaging (DWI) (b values: 0 s/mm2, b: 800 s/mm2). For 3D histogram analysis, regions of interest (ROI) were drawn each slice of the lesion on apparent diffusion coefficient (ADC) maps. The following data were derived from the histogram analysis data: percentiles, skewness, kurtosis, and entropy. The relationship between prognostic factors and histogram analysis data was investigated using the Kolmogorov-Smirnov test, Shapiro-Wilk test, skewness-kurtosis test, independent t-test, Mann-Whitney U test, and Kruskal-Wallis test. Receiver operator characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the histogram parameters. Results ADCmax, kurtosis, and entropy parameters were statistically significantly correlated with tumor diameter (p = 0.002, p = 0.008, and p = 0.001, respectively). There was a significant difference in ADC90% and ADCmax values, depending on estrogen receptor (ER) and progesterone receptor (PR) status. These values were lower in ER- and PR-positive than ER- and PR-negative patients (p = 0.02 and p = 0.001 vs. p = 0.018, p = 0.008). All ADC percentage values were lower in patients with a positive Ki-67 proliferation index as compared with those with a negative Ki-67 proliferation index (all p = 0.001). The entropy value was high in high-grade lesions and lesions with axillary involvement (p = 0.039 and p = 0.048, respectively). The highest area under the curve (AUC) for ER and PR status was calculated for the ADC90% value with ROC curve analysis. The highest AUC for Ki-67 proliferation index was found for the ADC50%. Conclusion Histogram analysis parameters derived from of ADC maps of whole lesions can reflect histopathological features of the tumors. Based on our study, it was concluded that histogram analysis parameters were related to the prognostic factors of the tumor.
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Affiliation(s)
- Özge Tanişman
- Deparment of Radiology, Oltu State Hospital, Erzurum, Turkey
| | - Fatma Tuba Kiziltepe
- Deparment of Radiology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Çiğdem Yildirim
- Department of Pathology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Zehra Sumru Coşar
- Deparment of Radiology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
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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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8
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Zhang L, Hao J, Guo J, Zhao X, Yin X. Predicting of Ki-67 Expression Level Using Diffusion-Weighted and Synthetic Magnetic Resonance Imaging in Invasive Ductal Breast Cancer. Breast J 2023; 2023:6746326. [PMID: 37063453 PMCID: PMC10098409 DOI: 10.1155/2023/6746326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/26/2023] [Accepted: 03/27/2023] [Indexed: 04/18/2023]
Abstract
Objectives To investigate the association between quantitative parameters generated using synthetic magnetic resonance imaging (SyMRI) and diffusion-weighted imaging (DWI) and Ki-67 expression level in patients with invasive ductal breast cancer (IDC). Method We retrospectively reviewed the records of patients with IDC who underwent SyMRI and DWI before treatment. Precontrast and postcontrast relaxation times (T1, longitudinal; T2, transverse), proton density (PD) parameters, and apparent diffusion coefficient (ADC) values were measured in breast lesions. Univariate and multivariate regression analyses were performed to screen for statistically significant variables to differentiate the high (≥30%) and low (<30%) Ki-67 expression groups. Their performance was evaluated by receiver operating characteristic (ROC) curve analysis. Results We analyzed 97 patients. Multivariate regression analysis revealed that the high Ki-67 expression group (n = 57) had significantly higher parameters generated using SyMRI (pre-T1, p=0.001) and lower ADC values (p=0.036) compared with the low Ki-67 expression group (n = 40). Pre-T1 showed the best diagnostic performance for predicting the Ki-67 expression level in patients with invasive ductal breast cancer (areas under the ROC curve (AUC), 0.711; 95% confidence interval (CI), 0.609-0.813). Conclusions Pre-T1 could be used to predict the pretreatment Ki-67 expression level in invasive ductal breast cancer.
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Affiliation(s)
- Liying Zhang
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Jisen Hao
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Jia Guo
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Xin Zhao
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Xing Yin
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
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9
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Multiparametric MRI Features of Breast Cancer Molecular Subtypes. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121716. [PMID: 36556918 PMCID: PMC9785392 DOI: 10.3390/medicina58121716] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/01/2022] [Accepted: 11/15/2022] [Indexed: 11/25/2022]
Abstract
Background and Objectives: Breast cancer (BC) molecular subtypes have unique incidence, survival and response to therapy. There are five BC subtypes described by immunohistochemistry: luminal A, luminal B HER2 positive and HER2 negative, triple negative (TNBC) and HER2-enriched. Multiparametric breast MRI (magnetic resonance imaging) provides morphological and functional characteristics of breast tumours and is nowadays recommended in the preoperative setting. Aim: To evaluate the multiparametric MRI features (T2-WI, ADC values and DCE) of breast tumours along with breast density and background parenchymal enhancement (BPE) features among different BC molecular subtypes. Materials and Methods: This was a retrospective study which included 344 patients. All underwent multiparametric breast MRI (T2WI, ADC and DCE sequences) and features were extracted according to the latest BIRADS lexicon. The inter-reader agreement was assessed using the intraclass coefficient (ICC) between the ROI of ADC obtained from the two breast imagers (experienced and moderately experienced). Results: The study population was divided as follows: 89 (26%) with luminal A, 39 (11.5%) luminal B HER2 positive, 168 (48.5%) luminal B HER2 negative, 41 (12%) triple negative (TNBC) and 7 (2%) with HER2 enriched. Luminal A tumours were associated with special histology type, smallest tumour size and persistent kinetic curve (all p-values < 0.05). Luminal B HER2 negative tumours were associated with lowest ADC value (0.77 × 10−3 mm2/s2), which predicts the BC molecular subtype with an accuracy of 0.583. TNBC were associated with asymmetric and moderate/marked BPE, round/oval masses with circumscribed margins and rim enhancement (all p-values < 0.05). HER2 enriched BC were associated with the largest tumour size (mean 37.28 mm, p-value = 0.02). Conclusions: BC molecular subtypes can be associated with T2WI, ADC and DCE MRI features. ADC can help predict the luminal B HER2 negative cases.
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10
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El-Rawy AS, Abdallah HY, Suliman MA, Habba MR, Gad AA. Magnetic resonance imaging correlation with molecular and epigenetic markers in assessment of breast cancer. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00901-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Breast cancer is a heterogeneous disease with a wide range of clinical behavior, histologic subtypes, therapeutic options, and outcomes. The different biology and histology of breast cancer display different tumor morphology at breast magnetic resonance imaging (MRI). However, few studies have examined the relationship between the MRI morphological, kinetic features and molecular and epigenetic markers in breast cancer assessment. The study aimed to evaluate the correlation between MRI morphological and kinetic features, molecular and an epigenetic marker (linc-ITGB1) in breast cancer cases. A total of 115 women (80 cases and 35 controls) with BIRAD 4 category breast lesions were included. The association between the MRI morphological & kinetic features, apparent diffusion coefficient (ADC) values, and molecular and the epigenetic marker (linc-ITGB1) was evaluated using Mann–Whitney and Chi-square tests.
Results
The shape (p = 0.009), size of the lesion (p = 0.003), and pattern of enhancement (p ≤ 0.001) were significantly correlated with the molecular markers. Luminal subtypes are more likely to be presented with irregular shaped and non-circumscribed margin masses (97% for luminal A and 94.1% for luminal B). Triple-negative cancers are frequently presented with regular masses, circumscribed margins, and peripheral rim enhancement (50% of TN tumors). HER2-positive cancers are more likely to be multifocal/multicentric and are more associated with non-mass pattern of enhancement as compared to HER2-negative cancers. Perilesional edema was also significantly correlated with HER2-positive lesions (p = 0.009). Although the epigenetic marker linc-ITGB1 was overexpressed by 4.85-folds in breast cancer cases compared to benign controls, we could not find any significant correlation between its expression level and the MRI features or molecular subtypes (p = 0.948).
Conclusions
MRI features can be a reliable predictor of breast cancer molecular subtypes. The epigenetic marker linc-ITGB1 has a potential role in breast cancer pathogenesis but with no significant correlation with either the MRI features or molecular subtypes of the lesions.
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11
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Kazama T, Takahara T, Kwee TC, Nakamura N, Kumaki N, Niikura N, Niwa T, Hashimoto J. Quantitative Values from Synthetic MRI Correlate with Breast Cancer Subtypes. Life (Basel) 2022; 12:life12091307. [PMID: 36143344 PMCID: PMC9501941 DOI: 10.3390/life12091307] [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: 07/31/2022] [Revised: 08/21/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
The purpose of this study is to correlate quantitative T1, T2, and proton density (PD) values with breast cancer subtypes. Twenty-eight breast cancer patients underwent MRI of the breast including synthetic MRI. T1, T2, and PD values were correlated with Ki-67 and were compared between ER-positive and ER-negative cancers, and between Luminal A and Luminal B cancers. The effectiveness of T1, T2, and PD in differentiating the ER-negative from the ER-positive group and Luminal A from Luminal B cancers was evaluated using receiver operating characteristic analysis. Mean T2 relaxation of ER-negative cancers was significantly higher than that of ER-positive cancers (p < 0.05). The T1, T2, and PD values exhibited a strong positive correlation with Ki-67 (Pearson’s r = 0.75, 0.69, and 0.60 respectively; p < 0.001). Among ER-positive cancers, T1, T2, and PD values of Luminal A cancers were significantly lower than those of Luminal B cancers (p < 0.05). The area under the curve (AUC) of T2 for discriminating ER-negative from ER-positive cancers was 0.87 (95% CI: 0.69−0.97). The AUC of T1 for discriminating Luminal A from Luminal B cancers was 0.83 (95% CI: 0.61−0.95). In conclusion, quantitative values derived from synthetic MRI show potential for subtyping of invasive breast cancers.
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Affiliation(s)
- Toshiki Kazama
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan
- Correspondence: ; Tel.: +81-463-93-1121
| | - Taro Takahara
- Department of Biomedical Engineering, Tokai University School of Engineering, Hiratsuka 259-1207, Japan
| | - Thomas C. Kwee
- Department of Radiology, Nuclear Medicine, and Molecular Imaging, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
| | - Noriko Nakamura
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan
| | - Nobue Kumaki
- Department of Pathology, Tokai University School of Medicine, Isehara 259-1193, Japan
| | - Naoki Niikura
- Department of Breast Oncology, Tokai University School of Medicine, Isehara 259-1193, Japan
| | - Tetsu Niwa
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan
| | - Jun Hashimoto
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan
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12
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The potential of predictive and prognostic breast MRI (P2-bMRI). Eur Radiol Exp 2022; 6:42. [PMID: 35989400 PMCID: PMC9393116 DOI: 10.1186/s41747-022-00291-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 06/08/2022] [Indexed: 11/10/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.
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13
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Chen H, Li W, Wan C, Zhang J. Correlation of dynamic contrast-enhanced MRI and diffusion-weighted MR imaging with prognostic factors and subtypes of breast cancers. Front Oncol 2022; 12:942943. [PMID: 35992872 PMCID: PMC9389013 DOI: 10.3389/fonc.2022.942943] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
Objective To determine the preoperative magnetic resonance imaging (MRI) findings of breast cancer on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DWI) in different molecular subtypes. Materials and methods A retrospective study was conducted on 116 breast cancer subjects who underwent preoperative MRI and surgery or biopsy. Three radiologists retrospectively assessed the morphological and kinetic characteristics on DCE-MRI and tumor detectability on DWI, by using apparent diffusion coefficient (ADC) values of lesions. The clinicopathologic and MRI features of four subtypes were compared. The correlation between clinical and MRI findings with molecular subtypes was evaluated using the chi-square and ANOVA tests, while the Mann–Whitney test was used to analyze the relationship between ADC and prognostic factors. Results One hundred and sixteen women diagnosed with breast cancer confirmed by surgery or biopsy had the following subtypes of breast cancer: luminal A (27, 23.3%), luminal B (56, 48.2%), HER2 positive (14, 12.1%), and triple-negative breast cancer (TNBC) (19, 16.4%), respectively. Among the subtypes, significant differences were found in axillary node metastasis, histological grade, tumor shape, rim enhancement, margin, lesion type, intratumoral T2 signal intensity, Ki-67 index, and paratumoral enhancement (p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, and p = 0.02, respectively). On DWI, the mean ADC value of TNBC (0.910 × 10−3 mm2/s) was the lowest compared to luminal A (1.477×10−3 mm2/s), luminal B (0.955 × 10−3 mm2/s), and HER2 positive (0.996 × 10−3 mm2/s) (p < 0.001). Analysis of the correlation between different prognostic factors and ADC value showed that only axillary lymph node status and ADC value had a statistically significant difference (p = 0.009). Conclusion The morphologic features of MRI can be used as imaging biomarkers to identify the molecular subtypes of breast cancer. In addition, quantitative assessments of ADC values on DWI may also provide biological clues about molecular subtypes.
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Affiliation(s)
- Hui Chen
- Department of Oncology, Tianmen First People’s Hospital, Tianmen, China
| | - Wei Li
- Department of Oncology, Tianmen First People’s Hospital, Tianmen, China
| | - Chao Wan
- Department of Oncology, Tianmen First People’s Hospital, Tianmen, China
| | - Jue Zhang
- Department of CT/MRI, Tianmen First People's Hospital, Tianmen, China
- *Correspondence: Jue Zhang,
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14
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Kazama T, Takahara T, Hashimoto J. Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review. Life (Basel) 2022; 12:life12040490. [PMID: 35454981 PMCID: PMC9028183 DOI: 10.3390/life12040490] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 02/20/2022] [Accepted: 03/08/2022] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.
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Affiliation(s)
- Toshiki Kazama
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
- Correspondence: ; Tel.: +81-463-93-1121
| | - Taro Takahara
- Department of Biomedical Engineering, Tokai University School of Engineering, Hiratsuka 259-1207, Japan;
| | - Jun Hashimoto
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
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Song SE, Cho KR, Cho Y, Kim K, Jung SP, Seo BK, Woo OH. Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer. Eur Radiol 2021; 32:853-863. [PMID: 34383145 DOI: 10.1007/s00330-021-08127-x] [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: 04/30/2021] [Revised: 05/20/2021] [Accepted: 06/09/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVES To investigate whether machine learning-based prediction models using 3-T multiparametric MRI (mpMRI) can predict Ki-67 and histologic grade in stage I-II luminal cancer. METHODS Between 2013 and 2019, consecutive women with luminal cancers who underwent preoperative MRI with diffusion-weighted imaging (DWI) and surgery were included. For prediction models, morphology, kinetic features using computer-aided diagnosis (CAD), and apparent diffusion coefficient (ADC) at DWI were evaluated by two radiologists. Logistic regression analysis was used to identify mpMRI features for predicting Ki-67 and grade. Diagnostic performance was assessed using eight machine learning algorithms incorporating mpMRI features and compared using the DeLong method. RESULTS Of 300 women, 203 (67.7%) had low Ki-67 and 97 (32.3%) had high Ki-67; 242 (80.7%) had low grade and 58 (19.3%) had high grade. In multivariate analysis, independent predictors for higher Ki-67 were washout component > 13.5% (odds ratio [OR] = 4.16; p < 0.001) and intratumoral high SI on T2-weighted image (OR = 1.89; p = 0.022). Those for higher grade were washout component > 15.5% (OR = 7.22; p < 0.001), rim enhancement (OR = 2.59; p = 0.022), and ADC value < 0.945 × 10-3 mm2/s (OR = 2.47; p = 0.015). Among eight models using these predictors, six models showed the equivalent performance for Ki-67 (area under the receiver operating characteristic curve [AUC]: 0.70) and Naive Bayes classifier showed the highest performance for grade (AUC: 0.79). CONCLUSIONS A prediction model incorporating mpMRI features shows good diagnostic performance for predicting Ki-67 and histologic grade in patients with luminal breast cancers. KEY POINTS • Among multiparametric MRI features, kinetic feature of washout component >13.5% and intratumoral high signal intensity on T2-weighted image were associated with higher Ki-67. • Washout component >15.5%, rim enhancement, and mean apparent diffusion coefficient value < 0.945 × 10-3 mm2/s were associated with higher histologic grade. • Machine learning-based prediction models incorporating multiparametric MRI features showed good diagnostic performance for Ki-67 and histologic grade in luminal breast cancers.
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Affiliation(s)
- Sung Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Kyu Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
| | - Yongwon Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Kwangsoo Kim
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Seung Pil Jung
- Department of Surgery, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Bo Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea
| | - Ok Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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Xu M, Tang Q, Li M, Liu Y, Li F. An analysis of Ki-67 expression in stage 1 invasive ductal breast carcinoma using apparent diffusion coefficient histograms. Quant Imaging Med Surg 2021; 11:1518-1531. [PMID: 33816188 DOI: 10.21037/qims-20-615] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Background To investigate the value of apparent diffusion coefficient (ADC) histograms in differentiating Ki-67 expression in T1 stage invasive ductal breast carcinoma (IDC). Methods The records of 111 patients with pathologically confirmed T1 stage IDC who underwent magnetic resonance imaging prior to surgery were retrospectively reviewed. The expression of Ki-67 in tumor tissue samples from the patients was assessed using immunohistochemical (IHC) staining, with a cut-off value of 25% for high Ki-67 labeling index (LI). ADC images of the maximum lay of tumors were selected, and the region of interest (ROI) of each lay was delineated using the MaZda software and analyzed by histogram. The correlations between the histogram characteristic parameters and the Ki-67 LI were investigated. Additionally, the histogram characteristic parameters of the high Ki-67 group (n=54) and the low Ki-67 group (n=57) were statistically analyzed to determine the characteristic parameters with significant difference. Receiver operator characteristic (ROC) analyses were further performed for the significant parameters. Results The mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles were found to be negatively correlated with the expression of Ki-67 (all P values <0.001), with a correlation coefficient of -0.624, -0.749, -0.717, -0.621, -0.500, and -0.410, respectively. In the high Ki-67 group, the mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles extracted by the histogram were significantly lower (all P values <0.05) than that of the low Ki-67 group, with areas under the ROC curves ranging from 0.717-0.856. However, the variance, skewness, and kurtosis did not differ between the two groups (all P values >0.05). Conclusions Histogram-derived parameters for ADC images can serve as a reliable tool in the prediction of Ki-67 proliferation status in patients with T1 stage IDC. Among the significant ADC histogram values, the 1st and 10th percentiles showed the best predictive values.
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Affiliation(s)
- Maolin Xu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Tang
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Manxiu Li
- Department of Breast Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fang Li
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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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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