1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Zhan T, Dai J, Li Y. Noninvasive identification of HER2-zero, -low, or -overexpressing breast cancers: Multiparametric MRI-based quantitative characterization in predicting HER2-low status of breast cancer. Eur J Radiol 2024; 177:111573. [PMID: 38905803 DOI: 10.1016/j.ejrad.2024.111573] [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: 01/20/2024] [Revised: 03/28/2024] [Accepted: 06/12/2024] [Indexed: 06/23/2024]
Abstract
PURPOSE To evaluate the effectiveness of both synthetic magnetic resonance imaging (SyMRI) and conventional diffusion-weighted imaging (DWI) for identifying the human epidermal growth factor receptor 2 (HER2) status in breast cancer (BC) patients. METHOD In this retrospective study, 114 women with DWI and SyMRI were pathologically classified into three groups: HER2-overexpressing (n = 40), HER2-low-expressing (n = 53), and HER2-zero-expressing (n = 21). T1 and T2 relaxation times and proton density (PD) were assessed before and after enhancement, and the resulting quantitative parameters produced by SyMRI were recorded as T1, T2, and PD and T1e, T2e, and PDe. Logistic regression was used to identify the best indicators for classifying patients based on HER2 expression. The discriminative performance of the models was evaluated using receiver operating characteristic (ROC) curves. RESULTS Our preliminary study revealed significant differences in progesterone receptor (PR) status, Ki-67 index, and axillary lymph node (ALN) count among the HER2-zero, -low, and -overexpressing groups (p < 0.001 to p = 0.03). SyMRI quantitative indices showed significant differences among BCs in the three HER2 subgroups, except for ΔT2 (p < 0.05). our results indicate that PDe achieved an area under the curve(AUC)of 0.849 (95 % CI: 0.760-0.915) for distinguishing HER2-low and -overexpressing BCs. Further investigation revealed that both the PDe and ADC were indicators for predicting differences among patients with HER2-zero and HER2-low-expressing BC, with AUCs of 0.765(95 % CI: 0.652-0.855) and 0.684(95 % CI: 0.565-0.787), respectively. The addition of the PDe to the ADC improved the AUC to 0.825(95 % CI: 0.719-0.903). CONCLUSIONS SyMRI could noninvasively and robustly predict the HER2 expression status of patients with BC.
Collapse
Affiliation(s)
- Ting Zhan
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, China
| | | | - Yan Li
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, China.
| |
Collapse
|
3
|
Kim BK, Kim B, You SH, Jang MS, Im GH, Kim KH. Early therapy evaluation of intra-arterial trastuzumab injection in a human breast cancer xenograft model using multiparametric MR imaging. PLoS One 2024; 19:e0300171. [PMID: 38701062 PMCID: PMC11068173 DOI: 10.1371/journal.pone.0300171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 02/22/2024] [Indexed: 05/05/2024] Open
Abstract
PURPOSE To investigate the treatment efficacy of intra-arterial (IA) trastuzumab treatment using multiparametric magnetic resonance imaging (MRI) in a human breast cancer xenograft model. MATERIALS AND METHODS Human breast cancer cells (BT474) were stereotaxically injected into the brains of nude mice to obtain a xenograft model. The mice were divided into four groups and subjected to different treatments (IA treatment [IA-T], intravenous treatment [IV-T], IA saline injection [IA-S], and the sham control group). MRI was performed before and at 7 and 14 d after treatment to assess the efficacy of the treatment. The tumor volume, apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) MRI parameters (Ktrans, Kep, Ve, and Vp) were measured. RESULTS Tumor volumes in the IA-T group at 14 d after treatment were significantly lower than those in the IV-T group (13.1 mm3 [interquartile range 8.48-16.05] vs. 25.69 mm3 [IQR 20.39-30.29], p = 0.005), control group (IA-S, 33.83 mm3 [IQR 32.00-36.30], p<0.01), and sham control (39.71 mm3 [IQR 26.60-48.26], p <0.001). The ADC value in the IA-T group was higher than that in the control groups (IA-T, 7.62 [IQR 7.23-8.20] vs. IA-S, 6.77 [IQR 6.48-6.87], p = 0.044 and vs. sham control, 6.89 [IQR 4.93-7.48], p = 0.004). Ktrans was significantly decreased following the treatment compared to that in the control groups (p = 0.002 and p<0.001 for vs. IA-S and sham control, respectively). Tumor growth was decreased in the IV-T group compared to that in the sham control group (25.69 mm3 [IQR 20.39-30.29] vs. 39.71 mm3 [IQR 26.60-48.26], p = 0.27); there was no significant change in the MRI parameters. CONCLUSION IA treatment with trastuzumab potentially affects the early response to treatment, including decreased tumor growth and decrease of Ktrans, in a preclinical brain tumor model.
Collapse
Affiliation(s)
- Bo Kyu Kim
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
| | - Byungjun Kim
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
| | - Sung-Hye You
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
| | - Moon-Sun Jang
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine and Center for Molecular and Cellular Imaging, Samsung Biomedical Research Institute, Seoul, Korea
| | - Geun Ho Im
- Center for Neuroscience Imaging Research, SungKyunkwan University, Suwon, Korea
| | - Keon-Ha Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| |
Collapse
|
4
|
Wu C, Hormuth DA, Easley T, Pineda F, Karczmar GS, Yankeelov TE. Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom. J Med Imaging (Bellingham) 2024; 11:024002. [PMID: 38463607 PMCID: PMC10921778 DOI: 10.1117/1.jmi.11.2.024002] [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: 06/06/2023] [Revised: 01/26/2024] [Accepted: 02/19/2024] [Indexed: 03/12/2024] Open
Abstract
Purpose Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Approach A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to 300 μ m / 60 s ) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to 300 μ m / 1 to 10 s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity. Results The observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization. Conclusion An in silico validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.
Collapse
Affiliation(s)
- Chengyue Wu
- University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, Texas, United States
- MD Anderson Cancer Center, Department of Imaging Physics, Houston, Texas, United States
- MD Anderson Cancer Center, Department of Breast Imaging, Houston, Texas, United States
- MD Anderson Cancer Center, Department of Biostatistics, Houston, Texas, United States
| | - David A. Hormuth
- University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, Texas, United States
- University of Texas at Austin, Livestrong Cancer Institutes, Austin, Texas, United States
| | - Ty Easley
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Federico Pineda
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Gregory S. Karczmar
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Thomas E. Yankeelov
- University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, Texas, United States
- MD Anderson Cancer Center, Department of Imaging Physics, Houston, Texas, United States
- University of Texas at Austin, Livestrong Cancer Institutes, Austin, Texas, United States
- University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States
- University of Texas at Austin, Department of Diagnostic Medicine, Austin, Texas, United States
- University of Texas at Austin, Department of Oncology, Austin, Texas, United States
| |
Collapse
|
5
|
Elsayed B, Alksas A, Shehata M, Mahmoud A, Zaky M, Alghandour R, Abdelwahab K, Abdelkhalek M, Ghazal M, Contractor S, El-Din Moustafa H, El-Baz A. Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review. Cancers (Basel) 2023; 15:5288. [PMID: 37958461 PMCID: PMC10648987 DOI: 10.3390/cancers15215288] [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: 09/02/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.
Collapse
Affiliation(s)
- Basma Elsayed
- Biomedical Engineering Program, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Alksas
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Ali Mahmoud
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mona Zaky
- Diagnostic Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt;
| | - Reham Alghandour
- Medical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt;
| | - Khaled Abdelwahab
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohamed Abdelkhalek
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA;
| | | | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| |
Collapse
|
6
|
Zhao R, Du S, Gao S, Shi J, Zhang L. Time Course Changes of Synthetic Relaxation Time During Neoadjuvant Chemotherapy in Breast Cancer: The Optimal Parameter for Treatment Response Evaluation. J Magn Reson Imaging 2023; 58:1290-1302. [PMID: 36621982 DOI: 10.1002/jmri.28597] [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: 10/13/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Synthetic MRI (syMRI) has enabled quantification of multiple relaxation parameters (T1/T2 relaxation time [T1/T2], proton density [PD]), and their longitudinal change during neoadjuvant chemotherapy (NAC) promises to be valuable parameters for treatment response evaluation in breast cancer. PURPOSE To investigate the time course changes of syMRI parameters during NAC and evaluate their value as predictors for pathological complete response (pCR) in breast cancer. STUDY TYPE Retrospective, longitudinal. POPULATION A total of 129 women (median age, 50 years; range, 28-69 years) with locally advanced breast cancer who underwent NAC; all performed multiple conventional breast MRI examinations with added syMRI during NAC. FIELD STRENGTH/SEQUENCE A 3.0 T, T1-weighted dynamic contrast enhanced and syMRI acquired by a multiple-dynamic, multiple-echo sequence. ASSESSMENT Breast MRI was set at four time-points: baseline, after one cycle, after three or four cycles of NAC and preoperation. SyMRI parameters and tumor diameters were measured and their changes from baseline were calculated. All parameters were compared between pCR and non-pCR. Interaction between syMRI parameters and clinicopathological features was analyzed. STATISTICAL TESTS Mann-Whitney U tests, random effects model of repeated measurement, receiver operating characteristic (ROC) analysis, interaction analysis. RESULTS Median synthetic T1/T2/PD and tumor diameter generally decreased throughout NAC. Absolute T1 at early-NAC, T1, and PD at mid-NAC were significantly lower in the pCR group. After early-NAC, the T1 change was significantly higher in the pCR (median ± IQR, 18.17 ± 11.33) than the non-pCR group (median ± IQR, 10.90 ± 10.03), with the highest area under the ROC curves (AUC) of 0.769 (95% CI, 0.684-0.838). Interaction analysis showed that histological grade III patients had higher odds ratio (OR) (OR = 1.206) compared to grade II patients (OR = 1.067). DATA CONCLUSION Synthetic T1 changes after one cycle of NAC maybe useful for early evaluating NAC response in breast cancer during whole treatment cycles. However, its discriminative ability is significantly affected by histological grade. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Ruimeng Zhao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Siyao Du
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Si Gao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Jing Shi
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Lina Zhang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| |
Collapse
|
7
|
Bressler I, Ben Bashat D, Buchsweiler Y, Aizenstein O, Limon D, Bokestein F, Blumenthal TD, Nevo U, Artzi M. Model-free dynamic contrast-enhanced MRI analysis: differentiation between active tumor and necrotic tissue in patients with glioblastoma. MAGMA (NEW YORK, N.Y.) 2023; 36:33-42. [PMID: 36287282 DOI: 10.1007/s10334-022-01045-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/09/2022] [Accepted: 10/13/2022] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Treatment response assessment in patients with high-grade gliomas (HGG) is heavily dependent on changes in lesion size on MRI. However, in conventional MRI, treatment-related changes can appear as enhancing tissue, with similar presentation to that of active tumor tissue. We propose a model-free data-driven method for differentiation between these tissues, based on dynamic contrast-enhanced (DCE) MRI. MATERIALS AND METHODS The study included a total of 66 scans of patients with glioblastoma. Of these, 48 were acquired from 1 MRI vendor and 18 scans were acquired from a different MRI vendor and used as test data. Of the 48, 24 scans had biopsy results. Analysis included semi-automatic arterial input function (AIF) extraction, direct DCE pharmacokinetic-like feature extraction, and unsupervised clustering of the two tissue types. Validation was performed via (a) comparison to biopsy result (b) correlation to literature-based DCE curves for each tissue type, and (c) comparison to clinical outcome. RESULTS Consistency between the model prediction and biopsy results was found in 20/24 cases. An average correlation of 82% for active tumor and 90% for treatment-related changes was found between the predicted component and population-based templates. An agreement between the predicted results and radiologist's assessment, based on RANO criteria, was found in 11/12 cases. CONCLUSION The proposed method could serve as a non-invasive method for differentiation between lesion tissue and treatment-related changes.
Collapse
Affiliation(s)
- Idan Bressler
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Dafna Ben Bashat
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Yuval Buchsweiler
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Orna Aizenstein
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Division of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Dror Limon
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Division of Oncology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Felix Bokestein
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - T Deborah Blumenthal
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Uri Nevo
- The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Moran Artzi
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. .,Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel. .,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| |
Collapse
|
8
|
Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI. Sci Rep 2023; 13:1171. [PMID: 36670144 PMCID: PMC9859781 DOI: 10.1038/s41598-023-27518-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 01/03/2023] [Indexed: 01/22/2023] Open
Abstract
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients' pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the validation, respectively. The model achieved an AUC of 0.86 ± 0.03 when evaluated in the independent testing group of 32 patients. In an additional prospective blinded testing group of 48 patients, the model achieved an AUC of 0.83 ± 0.02. These results demonstrated that DL based on multiparametric MRI can potentially differentiate TNBC patients with pCR or non-pCR in the breast early during NAST.
Collapse
|
9
|
Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer. PLoS One 2023; 18:e0280320. [PMID: 36649274 PMCID: PMC9844845 DOI: 10.1371/journal.pone.0280320] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 12/27/2022] [Indexed: 01/18/2023] Open
Abstract
PURPOSE To predict pathological complete response (pCR) after neoadjuvant chemotherapy using extreme gradient boosting (XGBoost) with MRI and non-imaging data at multiple treatment timepoints. MATERIAL AND METHODS This retrospective study included breast cancer patients (n = 117) who underwent neoadjuvant chemotherapy. Data types used included tumor ADC values, diffusion-weighted and dynamic-contrast-enhanced MRI at three treatment timepoints, and patient demographics and tumor data. GLCM textural analysis was performed on MRI data. An extreme gradient boosting machine learning algorithm was used to predict pCR. Prediction performance was evaluated using the area under the curve (AUC) of the receiver operating curve along with precision and recall. RESULTS Prediction using texture features of DWI and DCE images at multiple treatment time points (AUC = 0.871; 95% CI: (0.768, 0.974; p<0.001) and (AUC = 0.903 95% CI: 0.854, 0.952; p<0.001) respectively), outperformed that using mean tumor ADC (AUC = 0.850 (95% CI: 0.764, 0.936; p<0.001)). The AUC using all MRI data was 0.933 (95% CI: 0.836, 1.03; p<0.001). The AUC using non-MRI data was 0.919 (95% CI: 0.848, 0.99; p<0.001). The highest AUC of 0.951 (95% CI: 0.909, 0.993; p<0.001) was achieved with all MRI and all non-MRI data at all time points as inputs. CONCLUSION Using XGBoost on extracted GLCM features and non-imaging data accurately predicts pCR. This early prediction of response can minimize exposure to toxic chemotherapy, allowing regimen modification mid-treatment and ultimately achieving better outcomes.
Collapse
|
10
|
Liang X, Chen X, Yang Z, Liao Y, Wang M, Li Y, Fan W, Dai Z, Zhang Y. Early prediction of pathological complete response to neoadjuvant chemotherapy combining DCE-MRI and apparent diffusion coefficient values in breast Cancer. BMC Cancer 2022; 22:1250. [PMID: 36460972 PMCID: PMC9716688 DOI: 10.1186/s12885-022-10315-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 11/14/2022] [Indexed: 12/04/2022] Open
Abstract
INTRODUCTION Improving the early prediction of neoadjuvant chemotherapy (NAC) efficacy in breast cancer can lead to an improved prediction of the final prognosis of patients, which would be useful for promoting individualized treatment. This study aimed to explore the value of the combination of dynamic contrast-enhanced (DCE)-MRI parameters and apparent diffusion coefficient (ADC) values in the early prediction of pathological complete response (pCR) to NAC for breast cancer. METHODS A total of 119 (range, 28-69 years) patients with biopsy-proven breast cancer who received two cycles of NAC before breast surgery were retrospectively enrolled from our hospital database. Patients were divided into pCR and non pCR groups according to their pathological responses; a total of 24 patients achieved pCR, while 95 did not. The quantitative (Ktrans; Kep; Ve; IAUC) and semiquantitative parameters (W-in; W-out; TTP) of DCE-MRI that were significantly different between groups were combined with ADC values to explore their value in the early prediction of pCR to NAC for breast cancer. The independent T test was performed to compare the differences in DCE-MRI parameters and ADC values between the two groups. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC), sensitivity and specificity were calculated to evaluate the performance of the prediction. RESULTS The Ktrans, Kep, IAUC, ADC, W-in and TTP values were significantly different between the pCR and non pCR groups after NAC. The AUC (0.845) and specificity (95.79%) of the combined Ktrans, Kep, IAUC and ADC values were both higher than those of the individual parameters. The combination of W-in, TTP and ADC values had the highest AUC value (0.886) in predicting pCR, with a sensitivity and specificity of 87.5% and 82.11%, respectively. CONCLUSIONS The results suggested that the combination of ADC values and quantitative and semiquantitative DCE-MRI parameters, especially the combination of W-in, TTP, and ADC values, may improve the early prediction of pCR in breast cancer.
Collapse
Affiliation(s)
- Xinhong Liang
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Xiaofeng Chen
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Zhiqi Yang
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | | | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthineers, Guangzhou, 510620 China
| | - Yulin Li
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Weixiong Fan
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Zhuozhi Dai
- grid.452734.3Department of Radiology, Shantou Central Hospital, Guangdong, 515041 China
| | - Yunuo Zhang
- grid.459766.fDepartment of Oncology, Meizhou People’s Hospital, Meizhou, 514031 China
| |
Collapse
|
11
|
Jones MA, Islam W, Faiz R, Chen X, Zheng B. Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction. Front Oncol 2022; 12:980793. [PMID: 36119479 PMCID: PMC9471147 DOI: 10.3389/fonc.2022.980793] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/04/2022] [Indexed: 12/27/2022] Open
Abstract
Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.
Collapse
Affiliation(s)
- Meredith A. Jones
- School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States
| | - Warid Islam
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Rozwat Faiz
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| |
Collapse
|
12
|
Assessment of Early Response to Lung Cancer Chemotherapy by Semiquantitative Analysis of Dynamic Contrast-Enhanced MRI. DISEASE MARKERS 2022; 2022:2669281. [PMID: 35915736 PMCID: PMC9338849 DOI: 10.1155/2022/2669281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/30/2022] [Accepted: 07/04/2022] [Indexed: 11/28/2022]
Abstract
Objective To evaluate the early chemotherapy response in patients with lung cancer using semiquantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). Methods Twenty-two patients with lung cancer treated with chemotherapy were subjected to DCE-MRI at two time points: before starting treatment and after one week of therapy. The image data were collected by DCE-MRI, and the semiquantitative parameters including positive enhancement integral (PEI), signal enhancement ratio (SER), maximum slope of increase (MSI), and time to peak (TTP) were calculated. After chemotherapy, the parameters and relevant variations between the responders and nonresponders were compared with Mann–Whitney U tests. Student's t-test for paired samples was used to evaluate the temporal changes between pre- and posttreatment images. Results The patients were categorized as 13 responders and 9 nonresponders based on the tumor response evaluation. After chemotherapy, the PEI, SER, and MSI were significantly increased in responders compared with the pretreatment values (P < 0.05), while no obvious decrease in TTP was observed (P > 0.05). However, 9 nonresponders showed no significant changes in PEI, SER, MSI, and TTP values, as compared with those of pretreatment (P > 0.05). Moreover, the increase of PEI was more dramatically in responders than in nonresponders (P < 0.05), but no significantly differences were observed in SER, MSI, and TTP (P > 0.05). Conclusion Semiquantitative analysis of DCE-MRI could provide a reliable noninvasive method for assessing early chemotherapy response in lung cancer patients.
Collapse
|
13
|
Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy. Cancers (Basel) 2022; 14:cancers14143515. [PMID: 35884576 PMCID: PMC9316501 DOI: 10.3390/cancers14143515] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/06/2022] [Accepted: 07/16/2022] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Neoadjuvant chemotherapy (NAC) followed with surgery is the standard strategy in the treatment of locally advanced breast cancer, but the individual efficacy varies. Early and accurate prediction of complete responders determines the NAC regimens and prognosis. Breast MRI has been recommended to monitor NAC response before, during, and after treatment. Radiomics has been heralded as a breakthrough in medicine and regarded to have changed the landscape of biomedical research in oncology. Delta-radiomics characterizing the change in feature values by applying radiomics to multiple time points, is a promising strategy for predicting response after NAC. In our study, the delta-radiomics model built with the change of radiomic features before and after one cycle NAC could effectively predict pathological complete response (pCR) in breast cancer. The model provides strong support for clinical decision-making at the earliest stage and helps patients benefit the most from NAC. Abstract Objective: To investigate the value of delta-radiomics after the first cycle of neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced (DCE) MRI for early prediction of pathological complete response (pCR) in patients with breast cancer. Methods: From September 2018 to May 2021, a total of 140 consecutive patients (training, n = 98: validation, n = 42), newly diagnosed with breast cancer who received NAC before surgery, were prospectively enrolled. All patients underwent DCE-MRI at pre-NAC (pre-) and after the first cycle (1st-) of NAC. Radiomic features were extracted from the postcontrast early, peak, and delay phases. Delta-radiomics features were computed in each contrast phases. Least absolute shrinkage and selection operator (LASSO) and a logistic regression model were used to select features and build models. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. Results: The delta-radiomics model based on the early phases of DCE-MRI showed a highest AUC (0.917/0.842 for training/validation cohort) compared with that using the peak and delay phases images. The delta-radiomics model outperformed the pre-radiomics model (AUC = 0.759/0.617, p = 0.011/0.047 for training/validation cohort) in early phase. Based on the optimal model, longitudinal fusion radiomic models achieved an AUC of 0.871/0.869 in training/validation cohort. Clinical-radiomics model generated good calibration and discrimination capacity with AUC 0.934 (95%CI: 0.882, 0.986)/0.864 (95%CI: 0.746, 0.982) for training and validation cohort. Delta-radiomics based on early contrast phases of DCE-MRI combined clinicopathology information could predict pCR after one cycle of NAC in patients with breast cancer.
Collapse
|
14
|
Herrero Vicent C, Tudela X, Moreno Ruiz P, Pedralva V, Jiménez Pastor A, Ahicart D, Rubio Novella S, Meneu I, Montes Albuixech Á, Santamaria MÁ, Fonfria M, Fuster-Matanzo A, Olmos Antón S, Martínez de Dueñas E. Machine Learning Models and Multiparametric Magnetic Resonance Imaging for the Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2022; 14:cancers14143508. [PMID: 35884572 PMCID: PMC9317428 DOI: 10.3390/cancers14143508] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/07/2022] [Accepted: 07/14/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Achieving pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) is crucial, as pCR is a surrogate marker for survival. However, only 10–30% of patients achieve it. It is therefore essential to develop tools that enable to accurately predict response. Recently, different studies have demonstrated the feasibility of applying machine learning approaches to non-invasively predict pCR before NAC administration from magnetic resonance imaging (MRI) data. Some of those models are based on radiomics, an emerging field that allows the automated extraction of clinically relevant information from radiologic images. However, the research is still at an early stage and further data are needed. Here, we determine whether the combination of imaging data (radiomic features and perfusion/diffusion imaging biomarkers) extracted from multiparametric MRIs and clinical variables can improve pCR prediction to NAC compared to models only including imaging or clinical data, potentially avoiding unnecessary treatment and delays to surgery. Abstract Background: Most breast cancer (BC) patients fail to achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). The aim of this study was to evaluate whether imaging features (perfusion/diffusion imaging biomarkers + radiomic features) extracted from pre-treatment multiparametric (mp)MRIs were able to predict, alone or in combination with clinical data, pCR to NAC. Methods: Patients with stage II-III BC receiving NAC and undergoing breast mpMRI were retrospectively evaluated. Imaging features were extracted from mpMRIs performed before NAC. Three different machine learning models based on imaging features, clinical data or imaging features + clinical data were trained to predict pCR. Confusion matrices and performance metrics were obtained to assess model performance. Statistical analyses were conducted to evaluate differences between responders and non-responders. Results: Fifty-eight patients (median [range] age, 52 [45–58] years) were included, of whom 12 showed pCR. The combined model improved pCR prediction compared to clinical and imaging models, yielding 91.5% of accuracy with no false positive cases and only 17% false negative results. Changes in different parameters between responders and non-responders suggested a possible increase in vascularity and reduced tumour heterogeneity in patients with pCR, with the percentile 25th of time-to-peak (TTP), a classical perfusion parameter, being able to discriminate both groups in a 75% of the cases. Conclusions: A combination of mpMRI-derived imaging features and clinical variables was able to successfully predict pCR to NAC. Specific patient profiles according to tumour vascularity and heterogeneity might explain pCR differences, where TTP could emerge as a putative surrogate marker for pCR.
Collapse
Affiliation(s)
- Carmen Herrero Vicent
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
- Correspondence:
| | - Xavier Tudela
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Paula Moreno Ruiz
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Víctor Pedralva
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Ana Jiménez Pastor
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Daniel Ahicart
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Silvia Rubio Novella
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Isabel Meneu
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Ángela Montes Albuixech
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Miguel Ángel Santamaria
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - María Fonfria
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Almudena Fuster-Matanzo
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Santiago Olmos Antón
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Eduardo Martínez de Dueñas
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| |
Collapse
|
15
|
Dynamic characterization of breast cancer response to neoadjuvant therapy using biophysical metrics of spatial proliferation. Sci Rep 2022; 12:11718. [PMID: 35810187 PMCID: PMC9271064 DOI: 10.1038/s41598-022-15801-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/29/2022] [Indexed: 11/08/2022] Open
Abstract
Current tools to assess breast cancer response to neoadjuvant chemotherapy cannot reliably predict disease eradication, which if possible, could allow early cessation of therapy. In this work, we assessed the ability of an image data-driven mathematical modeling approach for dynamic characterization of breast cancer response to neoadjuvant therapy. We retrospectively analyzed patients enrolled in the I-SPY 2 TRIAL at the Atrium Health Wake Forest Baptist Comprehensive Cancer Center. Patients enrolled on the study received four MR imaging examinations during neoadjuvant therapy with acquisitions at baseline (T0), 3-weeks/early-treatment (T1), 12-weeks/mid-treatment (T2), and completion of therapy prior to surgery (T3). We use a biophysical mathematical model of tumor growth to generate spatial estimates of tumor proliferation to characterize the dynamics of treatment response. Using histogram summary metrics to quantify estimated tumor proliferation maps, we found strong correlation of mathematical model-estimated tumor proliferation with residual cancer burden, with Pearson correlation coefficients ranging from 0.88 and 0.97 between T0 and T2, representing a significant improvement from conventional assessment methods of change in mean apparent diffusion coefficient and functional tumor volume. This data shows the significant promise of imaging-based biophysical mathematical modeling methods for dynamic characterization of patient-specific response to neoadjuvant therapy with correlation to residual disease outcomes.
Collapse
|
16
|
MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2703350. [PMID: 35845886 PMCID: PMC9282990 DOI: 10.1155/2022/2703350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/04/2022] [Accepted: 05/25/2022] [Indexed: 11/21/2022]
Abstract
Precision medicine for cancer affords a new way for the most accurate and effective treatment to each individual cancer. Given the high time-evolving intertumor and intratumor heterogeneity features of personal medicine, there are still several obstacles hindering its diagnosis and treatment in clinical practice regardless of extensive exploration on it over the past years. This paper is to investigate radiogenomics methods in the literature for precision medicine for cancer focusing on the heterogeneity analysis of tumors. Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging and molecular expression are conducted. These investigations are intended to (i) fully excavate the multidimensional spatial, temporal, and semantic related information regarding high-dimensional breast magnetic resonance imaging data, with integration of the highly specific structured data of genomics and combination of the diagnosis and cognitive process of doctors, and (ii) establish a radiogenomics data representation model based on multidimensional consistency analysis with multilevel spatial-temporal correlations.
Collapse
|
17
|
Wang PN, Velikina JV, Bancroft LCH, Samsonov AA, Kelcz F, Strigel RM, Holmes JH. The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI. Tomography 2022; 8:1552-1569. [PMID: 35736876 PMCID: PMC9227412 DOI: 10.3390/tomography8030128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/07/2022] [Accepted: 06/09/2022] [Indexed: 11/25/2022] Open
Abstract
Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with Ktrans values ranging from 0.01 to 0.8 min−1 and fixed Ve = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both Ktrans and Ve. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV.
Collapse
Affiliation(s)
- Ping Ni Wang
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI 53705, USA; (P.N.W.); (R.M.S.)
| | - Julia V. Velikina
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA; (J.V.V.); (L.C.H.B.); (A.A.S.); (F.K.)
| | - Leah C. Henze Bancroft
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA; (J.V.V.); (L.C.H.B.); (A.A.S.); (F.K.)
| | - Alexey A. Samsonov
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA; (J.V.V.); (L.C.H.B.); (A.A.S.); (F.K.)
| | - Frederick Kelcz
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA; (J.V.V.); (L.C.H.B.); (A.A.S.); (F.K.)
| | - Roberta M. Strigel
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI 53705, USA; (P.N.W.); (R.M.S.)
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA; (J.V.V.); (L.C.H.B.); (A.A.S.); (F.K.)
- Carbone Cancer Center, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
| | - James H. Holmes
- Department of Radiology, University of Iowa, 169 Newton Road, Iowa City, IA 52333, USA
- Holden Comprehensive Cancer Center, University of Iowa, 169 Newton Road, Iowa City, IA 52333, USA
- Correspondence:
| |
Collapse
|
18
|
Thakran S, Cohen E, Jahani N, Weinstein SP, Pantalone L, Hylton N, Newitt D, DeMichele A, Davatzikos C, Kontos D. Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial. Transl Oncol 2022; 20:101411. [PMID: 35395604 PMCID: PMC8990167 DOI: 10.1016/j.tranon.2022.101411] [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: 01/24/2022] [Revised: 03/09/2022] [Accepted: 03/27/2022] [Indexed: 11/13/2022] Open
Abstract
PURPOSE Image registration plays a vital role in spatially aligning multiple MRI scans for better longitudinal assessment of tumor morphological features. The objective was to evaluate the effect of registration accuracy of six established deformable registration methods(ANTs, DRAMMS, ART, NiftyReg, SSD-FFD, and NMI-FFD) on the predictive value of extracted radiomic features when modeling recurrence-free-survival(RFS) for women after neoadjuvant chemotherapy(NAC) for locally advanced breast cancer. METHODS 130 women had DCE-MRI scans available from the first two visits in the ISPY1/ACRIN-6657 cohort. We calculated the transformation field from each of the different deformable registration methods, and used it to compute voxel-wise parametric-response-maps(PRM) for established four kinetic features.104-radiomic features were computed from each PRM map to characterize intra-tumor heterogeneity. We evaluated performance for RFS using Cox-regression, C-statistic, and Kaplan-Meier(KM) plots. RESULTS A baseline model(F1:Age, Race, and Hormone-receptor-status) had a 0.54 C-statistic, and model F2(baseline + functional-tumor-volume at early treatment visit(FTV2)) had 0.63. The F2+ANTs had the highest C-statistic(0.72) with the smallest landmark differences(5.40±4.40mm) as compared to other models. The KM curve for model F2 gave p=0.004 for separation between women above and below the median hazard compared to the model F1(p=0.31). A models augmented with radiomic features, also achieved significant KM curve separation(p<0.001) except the F2+ART model. CONCLUSION Incorporating image registration in quantifying changes in tumor heterogeneity during NAC can improve prediction of RFS. Radiomic features of PRM maps derived from warping the DCE-MRI kinetic maps using ANTs registration method further improved the early prediction of RFS as compared to other methods.
Collapse
Affiliation(s)
- Snekha Thakran
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Eric Cohen
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Nariman Jahani
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Susan P Weinstein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Lauren Pantalone
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Nola Hylton
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94115 United States
| | - David Newitt
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94115 United States
| | - Angela DeMichele
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 United States.
| |
Collapse
|
19
|
Contrast-free MRI quantitative parameters for early prediction of pathological response to neoadjuvant chemotherapy in breast cancer. Eur Radiol 2022; 32:5759-5772. [PMID: 35267091 DOI: 10.1007/s00330-022-08667-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 02/13/2022] [Accepted: 02/15/2022] [Indexed: 12/16/2022]
Abstract
OBJECTIVES To assess early changes in synthetic relaxometry after neoadjuvant chemotherapy (NAC) for breast cancer and establish a model with contrast-free quantitative parameters for early prediction of pathological response. METHODS From March 2019 to January 2021, breast MRI were performed for a primary cohort of women with breast cancer before (n = 102) and after the first (n = 93) and second (n = 90) cycle of NAC. Tumor size, synthetic relaxometry (T1/T2 relaxation time [T1/T2], proton density), and ADC were obtained, and the changes after treatment were calculated. Prediction models were established by multivariate logistic regression; evaluated with discrimination, calibration, and clinical application; and compared with Delong tests, net reclassification (NRI), and integrated discrimination index (IDI). External validation was performed from February to June 2021 with an independent cohort of 35 patients. RESULTS In the primary cohort, all parameters changed after early treatment. Synthetic relaxometry decreased to a greater degree in major histologic responders (MHR, Miller-Payne G4-5) compared with non-MHR (Miller-Payne G1-3). A model combining ADC after treatment, changes in T1 and tumor size, and cancer subtype achieved the highest AUC after the first (primary/validation cohort, 0.83/0.82) and second cycles (primary/validation cohort, 0.85/0.84). No difference of AUC (p ≥ 0.27), NRI (p ≥ 0.31), and IDI (p ≥ 0.32) was found between models with different cycles and size-measured sequences. Model calibration and decision curves demonstrated a good fitness and clinical benefit, respectively. CONCLUSIONS Early reduction in synthetic relaxometry indicated pathological response to NAC. Contrast-free T1 and ADC combined with size and cancer subtype predicted effectively pathological response after one NAC cycle. KEY POINTS • Synthetic MRI relaxometry changed after early neoadjuvant chemotherapy, which demonstrated pathological response for mass-like breast cancers. • Contrast-free quantitative parameters including T1 relaxation time and apparent diffusion coefficient, combined with tumor size and cancer subtype, stratified major histologic responders. • A contrast-free model predicted an early pathological response after the first treatment cycle of neoadjuvant chemotherapy.
Collapse
|
20
|
Liu L, Mei N, Yin B, Peng W. Correlation of DCE-MRI Perfusion Parameters and Molecular Biology of Breast Infiltrating Ductal Carcinoma. Front Oncol 2021; 11:561735. [PMID: 34722229 PMCID: PMC8548684 DOI: 10.3389/fonc.2021.561735] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 09/21/2021] [Indexed: 12/14/2022] Open
Abstract
Objective We aimed to investigate the correlation of the perfusion parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with the molecular biological expression of breast infiltrating ductal carcinoma (IDC) in order to guide appropriate therapeutic advice and clinical outcome prediction. Materials and Methods In a prospective analysis of 67 patients with breast IDC, preoperative DCE-MRI and routine MRI images were obtained. The double-chamber model (extended Tofts model) was employed to calculate the perfusion parameters. Postoperative pathological immunohistochemistry was examined, including human epidermal growth factor receptor 2 (HER-2), estrogen receptor (ER), progesterone receptor (PR), cell nuclear-associated antigen (Ki-67), cytokeratin 5/6 (CK5/6), and epidermal growth factor receptor (EGFR). Statistical analysis was applied to explore the relationship between the perfusion parameters and the molecular biomarkers of breast cancer. Results A total of 67 lesions were included in our study. The mean maximum diameter of lesions was 4.48 ± 1.73 cm. Perfusion parameters had no correlation with tumor diameters (p > 0.05). The volume transfer constant (K trans) and the rate constant (k ep) had positive correlations with Ki-67 (p < 0.05). The plasma volume ratio (v p) had a statistical difference between CK5/6 positivity and CK5/6 negativity. The maximum rising slope (MAX Slope) was higher in HER-2-enriched tumors than that in luminal A or B tumors (p < 0.05). k ep was higher in HER-2-enriched tumors than that in luminal A tumors (p < 0.05). The extravascular extracellular space volume fraction (v e) was higher in triple-negative tumors than that in HER-2-enriched and in luminal A and B tumors (p < 0.05). The time to peak enhancement (TTP) was lower in HER-2-enriched tumors than that in luminal A and B tumors (p < 0.05). Maximum concentration (MAX Conc) was higher in triple-negative tumors than that in luminal B tumors (p < 0.05). Conclusion DCE-MRI perfusion parameters can behave as a noninvasive tool to assess the molecular biological expression and the molecular subtypes of breast IDC. They may aid in predicting breast IDC invasiveness, metastasis, and prognosis.
Collapse
Affiliation(s)
- Li Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Nan Mei
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| |
Collapse
|
21
|
Li Z, Li J, Lu X, Qu M, Tian J, Lei J. The diagnostic performance of diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in evaluating the pathological response of breast cancer to neoadjuvant chemotherapy: A meta-analysis. Eur J Radiol 2021; 143:109931. [PMID: 34492627 DOI: 10.1016/j.ejrad.2021.109931] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/10/2021] [Accepted: 08/18/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE To evaluate and compare the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the pathological response of breast cancer to neoadjuvant chemotherapy (NAC). METHODS We searched PubMed, EMBASE, Cochrane Library, and Web of Science systematically to identify relevant studies from inception to December 2020. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess the methodological quality of the included studies. We extracted sufficient data to construct 2 × 2 tables and then used STATA 12.0 to perform data pooling, heterogeneity testing, meta-regression analysis and subgroup analysis. RESULTS A total of 41 articles were enrolled in this study, including 27 studies (2107 patients) on DCE-MRI and 23 studies (1321 patients) on DWI. The pooled sensitivity and specificity of DCE-MRI were 0.75 and 0.79, and the pooled sensitivity and specificity of DWI were 0.77 and 0.75. There was no significant difference in sensitivity (P = 0.598) and specificity (P = 0.218) between DCE-MRI and DWI. And meta-regression analysis showed that both magnetic field strength and the time of examination had significant effects on heterogeneity. CONCLUSIONS DWI might be a potential substitute for DCE-MRI in predicting the pathological response of breast cancer to NAC as there was no significant difference in the diagnostic performance between the two. However, considering that not all included studies directly compared the diagnostic performance of DWI and DCE-MRI in the same patients and the heterogeneity of the included studies, caution should be exercised in applying our results.
Collapse
Affiliation(s)
- Zhifan Li
- The first Clinical Medical College of Lanzhou University, Lanzhou 730000, China; First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Jinkui Li
- The first Clinical Medical College of Lanzhou University, Lanzhou 730000, China; First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Xingru Lu
- First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Mengmeng Qu
- The first Clinical Medical College of Lanzhou University, Lanzhou 730000, China; First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China; Key Laboratory of Evidence-based Medicine and Knowledge Translation of Gansu Province, Lanzhou University, Lanzhou 730000, China.
| | - Junqiang Lei
- First Hospital of Lanzhou University, Lanzhou 730000, China.
| |
Collapse
|
22
|
Hormuth DA, Phillips CM, Wu C, Lima EABF, Lorenzo G, Jha PK, Jarrett AM, Oden JT, Yankeelov TE. Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data. Cancers (Basel) 2021; 13:3008. [PMID: 34208448 PMCID: PMC8234316 DOI: 10.3390/cancers13123008] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/07/2021] [Accepted: 06/13/2021] [Indexed: 01/03/2023] Open
Abstract
Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2-3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.
Collapse
Affiliation(s)
- David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Ernesto A. B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78758, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Prashant K. Jha
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Angela M. Jarrett
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
| | - J. Tinsley Oden
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Mathematics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| |
Collapse
|
23
|
Adrada BE, Candelaria R, Moulder S, Thompson A, Wei P, Whitman GJ, Valero V, Litton JK, Santiago L, Scoggins ME, Moseley TW, White JB, Ravenberg EE, Yang WT, Rauch GM. Early ultrasound evaluation identifies excellent responders to neoadjuvant systemic therapy among patients with triple-negative breast cancer. Cancer 2021; 127:2880-2887. [PMID: 33878210 DOI: 10.1002/cncr.33604] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 03/06/2021] [Accepted: 03/18/2021] [Indexed: 01/12/2023]
Abstract
BACKGROUND Heterogeneity exists in the response of triple-negative breast cancer (TNBC) to standard anthracycline (AC)/taxane-based neoadjuvant systemic therapy (NAST), with 40% to 50% of patients having a pathologic complete response (pCR) to therapy. Early assessment of the imaging response during NAST may identify a subset of TNBCs that are likely to have a pCR upon completion of treatment. The authors aimed to evaluate the performance of early ultrasound (US) after 2 cycles of neoadjuvant NAST in identifying excellent responders to NAST among patients with TNBC. METHODS Two hundred fifteen patients with TNBC were enrolled in the ongoing ARTEMIS (A Robust TNBC Evaluation Framework to Improve Survival) clinical trial. The patients were divided into a discovery cohort (n = 107) and a validation cohort (n = 108). A receiver operating characteristic analysis with 95% confidence intervals (CIs) and a multivariate logistic regression analysis were performed to model the probability of a pCR on the basis of the tumor volume reduction (TVR) percentage by US from the baseline to after 2 cycles of AC. RESULTS Overall, 39.3% of the patients (42 of 107) achieved a pCR. A positive predictive value (PPV) analysis identified a cutoff point of 80% TVR after 2 cycles; the pCR rate was 77% (17 of 22) in patients with a TVR ≥ 80%, and the area under the curve (AUC) was 0.84 (95% CI, 0.77-0.92; P < .0001). In the validation cohort, the pCR rate was 44%. The PPV for pCR with a TVR ≥ 80% after 2 cycles was 76% (95% CI, 55%-91%), and the AUC was 0.79 (95% CI, 0.70-0.87; P < .0001). CONCLUSIONS The TVR percentage by US evaluation after 2 cycles of NAST may be a cost-effective early imaging biomarker for a pCR to AC/taxane-based NAST.
Collapse
Affiliation(s)
- Beatriz E Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Rosalind Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Stacy Moulder
- Department of Breast Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alastair Thompson
- Department of Breast Surgery, University of Baylor College of Medicine, Houston, Texas.,Lester and Sue Smith Breast Cancer, University of Baylor College of Medicine, Houston, Texas
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Gary J Whitman
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lumarie Santiago
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Marion E Scoggins
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tanya W Moseley
- Department of Breast Imaging and Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jason B White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Elizabeth E Ravenberg
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Wei T Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Gaiane M Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| |
Collapse
|
24
|
Xing D, Mao N, Dong J, Ma H, Chen Q, Lv Y. Quantitative analysis of contrast enhanced spectral mammography grey value for early prediction of pathological response of breast cancer to neoadjuvant chemotherapy. Sci Rep 2021; 11:5892. [PMID: 33723322 PMCID: PMC7960703 DOI: 10.1038/s41598-021-85353-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 03/01/2021] [Indexed: 12/15/2022] Open
Abstract
A quantitative analysis of contrast-enhanced spectral mammography (CESM) enhancement was conducted for the early prediction of the pathological response after neoadjuvant chemotherapy (NAC). Retrospective analysis of the data of 111 patients was conducted, and all of them underwent NAC in our hospital and surgical resection after the end of all cycles from January 2018 to May 2019. They were divided into pathological complete response (PCR) and non-PCR groups. We determined whether a statistical difference in the percentage of CESM grey value reduction (ΔCGV) was present in the PCR and non-PCR groups and whether a statistical difference was observed in the diagnostic efficiency of craniocaudal (CC) and mediolateral oblique (MLO) view subtraction images. Independent sample t-test was used to compare different groups, the receiver operating characteristic (ROC) curve was used to compare the diagnostic efficacy of CC and MLO for pathological response after NAC, and the Delong test was used to compare the area under the ROC curve (AUC). Statistical significance was considered at P < 0.05. A statistical difference was observed in the ΔCGV in the PCR and non-PCR groups. No statistical difference was observed in the AUCs of CC and MLO view subtraction images. The ΔCGV can be used as a quantitative index to predict PCR early, and no statistical difference was observed in the diagnostic efficacy of CC and MLO view subtraction images.
Collapse
Affiliation(s)
- Dong Xing
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Road, Yantai, 264000, Shandong, People's Republic of China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Road, Yantai, 264000, Shandong, People's Republic of China
| | - Jianjun Dong
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Road, Yantai, 264000, Shandong, People's Republic of China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Road, Yantai, 264000, Shandong, People's Republic of China
| | - Qianqian Chen
- GE Healthcare, Institute of Precision Medicine, No. 1 Huatuo Road, Shanghai, 201203, People's Republic of China
| | - Yongbin Lv
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Road, Yantai, 264000, Shandong, People's Republic of China.
| |
Collapse
|
25
|
Ha R, Chin C, Karcich J, Liu MZ, Chang P, Mutasa S, Pascual Van Sant E, Wynn RT, Connolly E, Jambawalikar S. Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset. J Digit Imaging 2020; 32:693-701. [PMID: 30361936 DOI: 10.1007/s10278-018-0144-1] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
We hypothesize that convolutional neural networks (CNN) can be used to predict neoadjuvant chemotherapy (NAC) response using a breast MRI tumor dataset prior to initiation of chemotherapy. An institutional review board-approved retrospective review of our database from January 2009 to June 2016 identified 141 locally advanced breast cancer patients who (1) underwent breast MRI prior to the initiation of NAC, (2) successfully completed adriamycin/taxane-based NAC, and (3) underwent surgical resection with available final surgical pathology data. Patients were classified into three groups based on their NAC response confirmed on final surgical pathology: complete (group 1), partial (group 2), and no response/progression (group 3). A total of 3107 volumetric slices of 141 tumors were evaluated. Breast tumor was identified on first T1 postcontrast dynamic images and underwent 3D segmentation. CNN consisted of ten convolutional layers, four max-pooling layers, and dropout of 50% after a fully connected layer. Dropout, augmentation, and L2 regularization were implemented to prevent overfitting of data. Non-linear functions were modeled by a rectified linear unit (ReLU). Batch normalization was used between the convolutional and ReLU layers to limit drift of layer activations during training. A three-class neoadjuvant prediction model was evaluated (group 1, group 2, or group 3). The CNN achieved an overall accuracy of 88% in three-class prediction of neoadjuvant treatment response. Three-class prediction discriminating one group from the other two was analyzed. Group 1 had a specificity of 95.1% ± 3.1%, sensitivity of 73.9% ± 4.5%, and accuracy of 87.7% ± 0.6%. Group 2 (partial response) had a specificity of 91.6% ± 1.3%, sensitivity of 82.4% ± 2.7%, and accuracy of 87.7% ± 0.6%. Group 3 (no response/progression) had a specificity of 93.4% ± 2.9%, sensitivity of 76.8% ± 5.7%, and accuracy of 87.8% ± 0.6%. It is feasible for current deep CNN architectures to be trained to predict NAC treatment response using a breast MRI dataset obtained prior to initiation of chemotherapy. Larger dataset will likely improve our prediction model.
Collapse
Affiliation(s)
- Richard Ha
- Department of Radiology, Columbia University Irving Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA.
| | - Christine Chin
- Division of Radiation Oncology, Columbia University Medical Center, Presbyterian Hospital Building, 622 West 168th Street, Level B, New York, NY, 10032, USA
| | - Jenika Karcich
- Department of Radiology, Columbia University Irving Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Michael Z Liu
- Department of Medical Physics, Columbia University Medical Center, 177 Ft. Washington Ave., Milstein Bldg Room 3-124B, New York, NY, 10032-3784, USA
| | - Peter Chang
- Department of Radiology, UC San Francisco Medical Center, 505 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Simukayi Mutasa
- Department of Radiology, Columbia University Irving Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Eduardo Pascual Van Sant
- Department of Radiology, Columbia University Irving Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Ralph T Wynn
- Department of Radiology, Columbia University Irving Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Eileen Connolly
- Division of Radiation Oncology, Columbia University Medical Center, Presbyterian Hospital Building, 622 West 168th Street, Level B, New York, NY, 10032, USA
| | - Sachin Jambawalikar
- Department of Medical Physics, Columbia University Medical Center, 177 Ft. Washington Ave., Milstein Bldg Room 3-124B, New York, NY, 10032-3784, USA
| |
Collapse
|
26
|
Duric N, Littrup P, Sak M, Li C, Chen D, Roy O, Bey-Knight L, Brem R. A Novel Marker, Based on Ultrasound Tomography, for Monitoring Early Response to Neoadjuvant Chemotherapy. JOURNAL OF BREAST IMAGING 2020; 2:569-576. [PMID: 33385161 DOI: 10.1093/jbi/wbaa084] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVE To evaluate the combination of tumor volume and sound speed as a potential imaging marker for assessing neoadjuvant chemotherapy (NAC) response. METHODS This study was carried out under an IRB-approved protocol (written consent required). Fourteen patients undergoing NAC for invasive breast cancer were examined with ultrasound tomography (UST) throughout their treatment. The volume (V) and the volume-averaged sound speed (VASS) of the tumors and their changes were measured for each patient. Time-dependent response curves of V and VASS were constructed individually for each patient and then as averages for the complete versus partial response groups in order to characterize differences between the two groups. Differences in group means were assessed for statistical significance using t-tests. Differences in shapes of group curves were evaluated with Kolmogorov-Smirnoff tests. RESULTS On average, tumor volume and sound speed in the partial response group showed a gradual decline in the first 60 days of treatment, while the complete response group showed a much steeper decline (P < 0.05). The shapes of the response curves of the two groups, corresponding to the entire treatment period, were also found to be significantly different (P < 0.05). Furthermore, large simultaneous drops in volume and sound speed in the first 3 weeks of treatment were characteristic only of the complete responders (P < 0.05). CONCLUSION This study demonstrates the feasibility of using UST to monitor NAC response, warranting future studies to better define the potential of UST for noninvasive, rapid identification of partial versus complete responders in women undergoing NAC.
Collapse
Affiliation(s)
- Neb Duric
- Delphinus Medical Technologies, Inc., Novi, MI.,Wayne State University, Barbara Ann Karmanos Cancer Institute, Department of Oncology, Detroit, MI
| | - Peter Littrup
- Delphinus Medical Technologies, Inc., Novi, MI.,Wayne State University, Barbara Ann Karmanos Cancer Institute, Department of Oncology, Detroit, MI
| | - Mark Sak
- Delphinus Medical Technologies, Inc., Novi, MI
| | - Cuiping Li
- Delphinus Medical Technologies, Inc., Novi, MI
| | - Di Chen
- Delphinus Medical Technologies, Inc., Novi, MI
| | - Olivier Roy
- Delphinus Medical Technologies, Inc., Novi, MI
| | - Lisa Bey-Knight
- Delphinus Medical Technologies, Inc., Novi, MI.,Wayne State University, Barbara Ann Karmanos Cancer Institute, Department of Oncology, Detroit, MI
| | - Rachel Brem
- George Washington University, Department of Radiology, Washington, DC
| |
Collapse
|
27
|
Characterization of parotid gland tumors: added value of permeability MR imaging to DWI and DCE-MRI. Eur Radiol 2020; 30:6402-6412. [PMID: 32613285 DOI: 10.1007/s00330-020-07004-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 04/25/2020] [Accepted: 06/03/2020] [Indexed: 01/05/2023]
Abstract
OBJECTIVES To determine added value of permeability MRI in parotid tumor characterization to T2-weighted imaging (T2WI), semi-quantitative analysis of time-intensity curve (TIC), and intra-voxel incoherent motion diffusion-weighted imaging (IVIM-DWI). METHODS This retrospective study was approved by the institutional review board, and the informed consent was waived. Sixty-one parotid tumors in 61 patients were examined using T2WI, IVIM-DWI, and permeability MRI. TIC patterns were categorized as persistent, washout, or plateau. Signal intensity ratio of lesion-to-muscle on T2WI, apparent diffusion coefficients (ADCs), D and f values from IVIM-DWI, and Ktrans, kep, Ve, and Vp values from permeability MRI were measured. Multiple comparisons were applied to determine whether any differences among 4 histopathologic types (pleomorphic adenomas, Warthin's tumors, other benign tumors, and malignant tumors) existed. Diagnostic accuracy was compared before and after modification diagnosis referring to permeability MRI. In a validation study, 60 parotid tumors in 60 patients were examined. RESULTS ADC and D values of malignant tumors were significantly lower than those of benign tumors other than Warthin's tumors, but higher than those of Warthin's tumors. kep and Vp values of Warthin's tumors were significantly higher than those of malignant tumors. Multivariate analyses showed that TIC pattern, D, and kep values were suitable parameters. McNemar's test showed a significant increase of sensitivity (11/12, 92%) and specificity (46/49, 94%) with adding kep. The validation study yielded high sensitivity (14/16, 88%) and specificity (41/44, 93%). CONCLUSION Permeability MRI offers added value to IVIM-MRI and semi-quantitative TIC analysis of DCE-MRI in characterization of parotid tumors KEY POINTS: • Permeability MR imaging offers added value in the characterization of parotid gland tumors in combination with semi-quantitative TIC analysis and IVIM analyses with D parameter. • The combination of TIC pattern, D, and kep might facilitate accurate characterization of parotid gland tumor, thereby avoiding unnecessary surgery for benign tumors or delayed treatment for malignant tumors. • A combination of permeability and diffusion MR imaging can be used to guide the selection of an appropriate biopsy site.
Collapse
|
28
|
Chronaiou I, Giskeødegård GF, Goa PE, Teruel J, Hedayati R, Lundgren S, Huuse EM, Pickles MD, Gibbs P, Sitter B, Bathen TF. Feasibility of contrast-enhanced MRI derived textural features to predict overall survival in locally advanced breast cancer. Acta Radiol 2020; 61:875-884. [PMID: 31744303 DOI: 10.1177/0284185119885116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The prognosis for women with locally advanced breast cancer (LABC) is poor and there is a need for better treatment stratification. Gray-level co-occurrence matrix (GLCM) texture analysis of magnetic resonance (MR) images has been shown to predict pathological response and could become useful in stratifying patients to more targeted treatments. PURPOSE To evaluate the ability of GLCM textural features obtained before neoadjuvant chemotherapy to predict overall survival (OS) seven years after diagnosis of patients with LABC. MATERIAL AND METHODS This retrospective study includes data from 55 patients with LABC. GLCM textural features were extracted from segmented tumors in pre-treatment dynamic contrast-enhanced 3-T MR images. Prediction of OS by GLCM textural features was assessed and compared to predictions using traditional clinical variables. RESULTS Linear mixed-effect models showed significant differences in five GLCM features (f1, f2, f5, f10, f11) between survivors and non-survivors. Using discriminant analysis for prediction of survival, GLCM features from 2 min post-contrast images achieved a classification accuracy of 73% (P < 0.001), whereas traditional prognostic factors resulted in a classification accuracy of 67% (P = 0.005). Using a combination of both yielded the highest classification accuracy (78%, P < 0.001). Median values for features f1, f2, f10, and f11 provided significantly different survival curves in Kaplan-Meier analysis. CONCLUSION This study shows a clear association between textural features from post-contrast images obtained before neoadjuvant chemotherapy and OS seven years after diagnosis. Further studies in larger cohorts should be undertaken to investigate how this prognostic information can be used to benefit treatment stratification.
Collapse
Affiliation(s)
- Ioanna Chronaiou
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Guro Fanneløb Giskeødegård
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Pål Erik Goa
- Department of Physics, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Jose Teruel
- Department of Radiation Oncology, NYU Langone Health, New York, NY, USA
| | - Roja Hedayati
- Cancer clinic, St. Olavs University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Steinar Lundgren
- Cancer clinic, St. Olavs University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Else Marie Huuse
- Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
| | - Martin D Pickles
- Radiology Department, Hull University Teaching Hospitals NHS Trust, Hull, UK
| | - Peter Gibbs
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Beathe Sitter
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Tone Frost Bathen
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
| |
Collapse
|
29
|
Syed AK, Whisenant JG, Barnes SL, Sorace AG, Yankeelov TE. Multiparametric Analysis of Longitudinal Quantitative MRI data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer. Cancers (Basel) 2020; 12:cancers12061682. [PMID: 32599906 PMCID: PMC7352623 DOI: 10.3390/cancers12061682] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 12/11/2022] Open
Abstract
This study identifies physiological tumor habitats from quantitative magnetic resonance imaging (MRI) data and evaluates their alterations in response to therapy. Two models of breast cancer (BT-474 and MDA-MB-231) were imaged longitudinally with diffusion-weighted MRI and dynamic contrast-enhanced MRI to quantify tumor cellularity and vascularity, respectively, during treatment with trastuzumab or albumin-bound paclitaxel. Tumors were stained for anti-CD31, anti-Ki-67, and H&E. Imaging and histology data were clustered to identify tumor habitats and percent tumor volume (MRI) or area (histology) of each habitat was quantified. Histological habitats were correlated with MRI habitats. Clustering of both the MRI and histology data yielded three clusters: high-vascularity high-cellularity (HV-HC), low-vascularity high-cellularity (LV-HC), and low-vascularity low-cellularity (LV-LC). At day 4, BT-474 tumors treated with trastuzumab showed a decrease in LV-HC (p = 0.03) and increase in HV-HC (p = 0.03) percent tumor volume compared to control. MDA-MB-231 tumors treated with low-dose albumin-bound paclitaxel showed a longitudinal decrease in LV-HC percent tumor volume at day 3 (p = 0.01). Positive correlations were found between histological and imaging-derived habitats: HV-HC (BT-474: p = 0.03), LV-HC (MDA-MB-231: p = 0.04), LV-LC (BT-474: p = 0.04; MDA-MB-231: p < 0.01). Physiologically distinct tumor habitats associated with therapeutic response were identified with MRI and histology data in preclinical models of breast cancer.
Collapse
Affiliation(s)
- Anum K Syed
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Jennifer G Whisenant
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Stephanie L Barnes
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Anna G Sorace
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
- O'Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| |
Collapse
|
30
|
Combining Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Apparent Diffusion Coefficient Maps for a Radiomics Nomogram to Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients. J Comput Assist Tomogr 2020; 44:275-283. [PMID: 32004189 DOI: 10.1097/rct.0000000000000978] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
OBJECTIVE The objective of this study was to develop a nomogrom for prediction of pathological complete response (PCR) to neoadjuvant chemotherapy in breast cancer patients. METHODS Ninety-one patients were analyzed. A total of 396 radiomics features were extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator was selected for data dimension reduction to build a radiomics signature. Finally, the nomogram was built to predict PCR. RESULTS The radiomics signature of the model that combined DCE-MRI and ADC maps showed a higher performance (area under the receiver operating characteristic curve [AUC], 0.848) than the models with DCE-MRI (AUC, 0.750) or ADC maps (AUC, 0.785) alone in the training set. The proposed model, which included combined radiomics signature, estrogen receptor, and progesterone receptor, yielded a maximum AUC of 0.837 in the testing set. CONCLUSIONS The combined radiomics features from DCE-MRI and ADC data may serve as potential predictor markers for predicting PCR. The nomogram could be used as a quantitative tool to predict PCR.
Collapse
|
31
|
Reig B, Heacock L, Lewin A, Cho N, Moy L. Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer. J Magn Reson Imaging 2020; 52. [DOI: 10.1002/jmri.27145] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 12/25/2022] Open
Affiliation(s)
- Beatriu Reig
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Laura Heacock
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Alana Lewin
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
| | - Nariya Cho
- Department of Radiology Seoul National University Hospital Seoul Republic of Korea
- Department of Radiology Seoul National University College of Medicine Seoul Republic of Korea
| | - Linda Moy
- Department of Radiology New York University Grossman School of Medicine New York New York USA
- New York University Laura and Isaac Perlmutter Cancer Center New York New York USA
- Bernard and Irene Schwartz Center for Biomedical Imaging Department of Radiology, New York University Grossman School of Medicine New York New York USA
- Center for Advanced Imaging Innovation and Research (CAI2 R) New York University Grossman School of Medicine New York New York USA
| |
Collapse
|
32
|
Cheng Q, Huang J, Liang J, Ma M, Ye K, Shi C, Luo L. The Diagnostic Performance of DCE-MRI in Evaluating the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer: A Meta-Analysis. Front Oncol 2020; 10:93. [PMID: 32117747 PMCID: PMC7028702 DOI: 10.3389/fonc.2020.00093] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 01/17/2020] [Indexed: 12/23/2022] Open
Abstract
Background: Neoadjuvant chemotherapy (NAC) is commonly utilized in preoperative treatment for local breast cancer, and it gives high clinical response rates and can result in pathologic complete response (pCR) in 6–25% of patients. In recent years, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been increasingly used to assess the pathological response of breast cancer to NAC. In present analysis, we assess the diagnostic performance of DCE-MRI in evaluating the pathological response of breast cancer to NAC. Materials and Methods: A systematic search in PubMed, the Cochrane Library, and Web of Science for original studies was performed. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the methodological quality of the included studies. Patient, study, and imaging characteristics were extracted, and sufficient data to reconstruct 2 × 2 tables were obtained. Data pooling, heterogeneity testing, forest plot construction, meta-regression analysis and sensitivity analysis were performed using Stata version 12.0 (StataCorp LP, College Station, TX). Results: Eighteen studies (969 patients with breast cancer) were included in the present meta-analysis. The pooled sensitivity and specificity of DCE-MRI were 0.80 (95% confidence interval [CI]: 0.70, 0.88) and 0.84 (95% [CI]: 0.79, 0.88), respectively. Meta-regression analysis found no significant factors affecting heterogeneity. Sensitivity analysis showed that studies that set pathological complete response (pCR) (n = 14) as a responder showed a tendency for higher sensitivity compared with those that set pCR and near pCR together (n = 5) as a responder (0.83 vs. 0.72), and studies (n = 14) that used DCE-MRI to early predict the pathological response of breast cancer had a higher sensitivity (0.83 vs. 0.71) and equivalent specificity (0.80 vs. 0.86) compared to studies (n = 5) that assessed the response after NAC completion. Conclusion: Our results indicated that DCE-MRI could be considered an important auxiliary method for evaluating the pathological response of breast cancer to NAC and used as an effective method for dynamically monitoring the efficacy during NAC. DCE-MRI also performed well in predicting the pCR of breast cancer to NAC. However, due to the heterogeneity of the included studies, caution should be exercised in applying our results.
Collapse
Affiliation(s)
- Qingqing Cheng
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jiaxi Huang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jianye Liang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Mengjie Ma
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Kunlin Ye
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Changzheng Shi
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Engineering Research Center of Medical Imaging Artificial Intelligence for Precision Diagnosis and Treatment, Guangzhou, China
| | - Liangping Luo
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Engineering Research Center of Medical Imaging Artificial Intelligence for Precision Diagnosis and Treatment, Guangzhou, China
| |
Collapse
|
33
|
Machireddy A, Thibault G, Tudorica A, Afzal A, Mishal M, Kemmer K, Naik A, Troxell M, Goranson E, Oh K, Roy N, Jafarian N, Holtorf M, Huang W, Song X. Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps. ACTA ACUST UNITED AC 2020; 5:90-98. [PMID: 30854446 PMCID: PMC6403033 DOI: 10.18383/j.tom.2018.00046] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (Ktrans), in the training and testing sets, respectively. The differences in AUC were statistically significant (P < .05) for several parametric maps. Thus, multiresolution analysis that decomposes the texture at various spatial-frequency scales may more accurately capture changes in tumor vascular heterogeneity as measured by DCE-MRI, and therefore provide better early prediction of NACT response.
Collapse
Affiliation(s)
| | | | | | - Aneela Afzal
- Oregon Health and Science University, Portland, OR
| | - May Mishal
- Oregon Health and Science University, Portland, OR
| | | | - Arpana Naik
- Oregon Health and Science University, Portland, OR
| | | | | | - Karen Oh
- Oregon Health and Science University, Portland, OR
| | - Nicole Roy
- Oregon Health and Science University, Portland, OR
| | | | | | - Wei Huang
- Oregon Health and Science University, Portland, OR
| | - Xubo Song
- Oregon Health and Science University, Portland, OR
| |
Collapse
|
34
|
Lingala SG, Guo Y, Bliesener Y, Zhu Y, Lebel RM, Law M, Nayak KS. Tracer kinetic models as temporal constraints during brain tumor DCE-MRI reconstruction. Med Phys 2019; 47:37-51. [PMID: 31663134 PMCID: PMC6980286 DOI: 10.1002/mp.13885] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 10/17/2019] [Accepted: 10/17/2019] [Indexed: 12/11/2022] Open
Abstract
Purpose To apply tracer kinetic models as temporal constraints during reconstruction of under‐sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI). Methods A library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under‐sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in‐vivo 3T datasets. The performances of the proposed under‐sampled reconstruction scheme and an existing compressed sensing‐based temporal finite‐difference (tFD) under‐sampled reconstruction were compared against the fully sampled inverse Fourier Transform‐based reconstruction. Results Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts‐Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO‐based experiments showed good fidelity in recovery of kinetic maps from 20‐fold under‐sampled data. In‐vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under‐sampled reduction factors >= 20. Conclusions Tracer kinetic models can be applied as temporal constraints during brain tumor DCE‐MRI reconstruction. The proposed under‐sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters.
Collapse
Affiliation(s)
- Sajan Goud Lingala
- Roy J Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
| | - Yi Guo
- Snap Inc., San Francisco, CA, USA
| | - Yannick Bliesener
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | | | - R Marc Lebel
- GE Healthcare Applied Sciences Laboratory, Calgary, Canada
| | - Meng Law
- Department of Neuroscience, Monash University, Melbourne, Australia
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| |
Collapse
|
35
|
Kontopodis E, Venianaki M, Manikis GC, Nikiforaki K, Salvetti O, Papadaki E, Papadakis GZ, Karantanas AH, Marias K. Investigating the Role of Model-Based and Model-Free Imaging Biomarkers as Early Predictors of Neoadjuvant Breast Cancer Therapy Outcome. IEEE J Biomed Health Inform 2019; 23:1834-1843. [DOI: 10.1109/jbhi.2019.2895459] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
36
|
Jahani N, Cohen E, Hsieh MK, Weinstein SP, Pantalone L, Hylton N, Newitt D, Davatzikos C, Kontos D. Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration. Sci Rep 2019; 9:12114. [PMID: 31431633 PMCID: PMC6702160 DOI: 10.1038/s41598-019-48465-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 08/05/2019] [Indexed: 12/11/2022] Open
Abstract
We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC = 0.78 (±0.03) vs 0.71 (±0.04), p < 0.05 and RFS (C-statistic = 0.76 ( ± 0.05), vs 0.63 ( ± 0.01)), p < 0.05, while models based on analogous aggregate imaging features did not show appreciable performance changes (p > 0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p < 0.05). Thus, precise measures of voxel-wise changes in tumor heterogeneity extracted from registered DCE-MRI scans can improve early prediction of neoadjuvant treatment outcomes in locally advanced breast cancer.
Collapse
Affiliation(s)
- Nariman Jahani
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Eric Cohen
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Meng-Kang Hsieh
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Susan P Weinstein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauren Pantalone
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nola Hylton
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94115, USA
| | - David Newitt
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94115, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| |
Collapse
|
37
|
Chamseddine IM, Rejniak KA. Hybrid modeling frameworks of tumor development and treatment. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2019; 12:e1461. [PMID: 31313504 PMCID: PMC6898741 DOI: 10.1002/wsbm.1461] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 06/13/2019] [Accepted: 06/13/2019] [Indexed: 12/15/2022]
Abstract
Tumors are complex multicellular heterogeneous systems comprised of components that interact with and modify one another. Tumor development depends on multiple factors: intrinsic, such as genetic mutations, altered signaling pathways, or variable receptor expression; and extrinsic, such as differences in nutrient supply, crosstalk with stromal or immune cells, or variable composition of the surrounding extracellular matrix. Tumors are also characterized by high cellular heterogeneity and dynamically changing tumor microenvironments. The complexity increases when this multiscale, multicomponent system is perturbed by anticancer treatments. Modeling such complex systems and predicting how tumors will respond to therapies require mathematical models that can handle various types of information and combine diverse theoretical methods on multiple temporal and spatial scales, that is, hybrid models. In this update, we discuss the progress that has been achieved during the last 10 years in the area of the hybrid modeling of tumors. The classical definition of hybrid models refers to the coupling of discrete descriptions of cells with continuous descriptions of microenvironmental factors. To reflect on the direction that the modeling field has taken, we propose extending the definition of hybrid models to include of coupling two or more different mathematical frameworks. Thus, in addition to discussing recent advances in discrete/continuous modeling, we also discuss how these two mathematical descriptions can be coupled with theoretical frameworks of optimal control, optimization, fluid dynamics, game theory, and machine learning. All these methods will be illustrated with applications to tumor development and various anticancer treatments. This article is characterized under:Analytical and Computational Methods > Computational Methods Translational, Genomic, and Systems Medicine > Therapeutic Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models
Collapse
Affiliation(s)
- Ibrahim M. Chamseddine
- Department of Integrated Mathematical OncologyH. Lee Moffitt Cancer Center and Research InstituteTampaFlorida
| | - Katarzyna A. Rejniak
- Department of Integrated Mathematical OncologyH. Lee Moffitt Cancer Center and Research InstituteTampaFlorida
- Department of Oncologic Sciences, Morsani College of MedicineUniversity of South FloridaTampaFlorida
| |
Collapse
|
38
|
Arif O, Afzal H, Abbas H, Amjad MF, Wan J, Nawaz R. Accelerated Dynamic MRI Using Kernel-Based Low Rank Constraint. J Med Syst 2019; 43:271. [DOI: 10.1007/s10916-019-1399-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 06/25/2019] [Indexed: 11/24/2022]
|
39
|
MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures. COMPUTERS 2019. [DOI: 10.3390/computers8030052] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Breast tumor segmentation in medical images is a decisive step for diagnosis and treatment follow-up. Automating this challenging task helps radiologists to reduce the high manual workload of breast cancer analysis. In this paper, we propose two deep learning approaches to automate the breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building two fully convolutional neural networks (CNN) based on SegNet and U-Net. The obtained models can handle both detection and segmentation on each single DCE-MRI slice. In this study, we used a dataset of 86 DCE-MRIs, acquired before and after two cycles of chemotherapy, of 43 patients with local advanced breast cancer, a total of 5452 slices were used to train and validate the proposed models. The data were annotated manually by an experienced radiologist. To reduce the training time, a high-performance architecture composed of graphic processing units was used. The model was trained and validated, respectively, on 85% and 15% of the data. A mean intersection over union (IoU) of 68.88 was achieved using SegNet and 76.14% using U-Net architecture.
Collapse
|
40
|
Karolak A, Markov DA, McCawley LJ, Rejniak KA. Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues. J R Soc Interface 2019; 15:rsif.2017.0703. [PMID: 29367239 DOI: 10.1098/rsif.2017.0703] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 01/02/2018] [Indexed: 02/06/2023] Open
Abstract
A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.
Collapse
Affiliation(s)
- Aleksandra Karolak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Dmitry A Markov
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Lisa J McCawley
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Katarzyna A Rejniak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA .,Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| |
Collapse
|
41
|
Ianniello C, Madelin G, Moy L, Brown R. A dual-tuned multichannel bilateral RF coil for 1 H/ 23 Na breast MRI at 7 T. Magn Reson Med 2019; 82:1566-1575. [PMID: 31148249 DOI: 10.1002/mrm.27829] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 05/06/2019] [Accepted: 05/07/2019] [Indexed: 12/25/2022]
Abstract
PURPOSE Sodium MRI has shown promise for monitoring neoadjuvant chemotherapy response in breast cancer. The purpose of this work was to build a dual-tuned bilateral proton/sodium breast coil for 7T MRI that provides sufficient SNR to enable sodium breast imaging in less than 10 minutes. METHODS The proton/sodium coil consists of 2 shielded unilateral units: 1 for each breast. Each unit consists of 3 nested layers: (1) a 3-loop solenoid for sodium excitation, (2) a 3-loop solenoid for proton excitation and signal reception, and (3) a 4-channel receive array for sodium signal reception. Benchmark measurements were performed in phantoms with and without the sodium receive array insert. In vivo images were acquired on a healthy volunteer. RESULTS The sodium receive array boosted 1.5 to 3 times the SNR compared with the solenoid. Proton SNR loss due to residual interaction with the sodium array was less than 10%. The coil enabled sodium imaging in vivo with 2.8-mm isotropic nominal resolution (~5-mm real resolution) in 9:36 minutes. CONCLUSION The coil design that we propose addresses challenges associated with sodium's low SNR from a hardware perspective and offers the opportunity to investigate noninvasively breast tumor metabolism as a function of sodium concentration in patients undergoing neoadjuvant chemotherapy.
Collapse
Affiliation(s)
- Carlotta Ianniello
- Center for Advanced Imaging Innovation and Research (CAI2R) and Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,The Sackler Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York
| | - Guillaume Madelin
- Center for Advanced Imaging Innovation and Research (CAI2R) and Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,The Sackler Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York
| | - Linda Moy
- Center for Advanced Imaging Innovation and Research (CAI2R) and Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,The Sackler Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York
| | - Ryan Brown
- Center for Advanced Imaging Innovation and Research (CAI2R) and Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,The Sackler Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York
| |
Collapse
|
42
|
Agha M, Eid AF, Nouh M. 3T MRI of the breast with computer aided diagnosis, can it help to avoid unnecessary invasive procedures??? ALEXANDRIA JOURNAL OF MEDICINE 2019. [DOI: 10.1016/j.ajme.2014.12.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Mahmoud Agha
- Medical Research Institute, Alexandria University, Egypt
- Consultant of Diagnostic Imaging, Almana General Hospital, Saudi Arabia
| | - Ahmed Fathi Eid
- Department of Radiology, National Guard Hospital, Saudi Arabia
| | - Mohamed Nouh
- Faculty of Medicine, Alexandria University, Egypt
| |
Collapse
|
43
|
Dong X, Chunrong Y, Hongjun H, Xuexi Z. Differentiating the lymph node metastasis of breast cancer through dynamic contrast-enhanced magnetic resonance imaging. BJR Open 2019; 1:20180023. [PMID: 33178917 PMCID: PMC7592437 DOI: 10.1259/bjro.20180023] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 02/26/2019] [Accepted: 02/27/2019] [Indexed: 01/30/2023] Open
Abstract
Objective: Lymph node metastasis is an important trait of breast cancer, and tumors with different lymph node statuses require various clinical treatments. This study was designed to evaluate the lymph node metastasis of breast cancer through pharmacokinetic and histogram analysis via dynamic contrast-enhanced (DCE) MRI. Methods and materials: A retrospective analysis was conducted to quantitatively evaluate the lymph node statuses of patients with breast cancer. A total of 75 patients, i.e. 34 patients with lymph node metastasis and 41 patients without lymph node metastasis, were involved in this research. Of the patients with lymph node metastases, 19 had sentinel lymph node metastasis, and 15 had axillary lymph node metastasis. MRI was conducted using a 3.0 T imaging device. Segmentation was carried out on the regions of interest (ROIs) in breast tumors under DCE-MRI, and pharmacokinetic and histogram parameters were calculated from the same ROIs. Mann–Whitney U test was performed, and receiver operating characteristic curves for the parameters of the two groups were constructed to determine their diagnostic values. Results: Pharmacokinetic parameters, including Ktrans, Kep, area under the curve of time–concentration, and time to peak, which were derived from the extended Tofts linear model for DCE-MRI, could highlight the tumor areas in the breast and reveal the increased perfusion. Conversely, the pharmacokinetic parameters showed no significant difference between the patients with and without lymph node metastases. By contrast, the parameters from the histogram analysis yielded promising results. The entropy of the ROIs exhibited the best diagnostic ability between patients with and without lymph node metastases (p < 0.01, area under the curve of receiver operating characteristic = 0.765, specificity = 0.706, sensitivity = 0.780). Conclusion: In comparison with the pharmacokinetic parameters, the histogram analysis of the MR images could reveal the differences between patients with and without lymph node metastases. The entropy from the histogram indicated that the diagnostic ability was highly sensitive and specific. Advances in knowledge: This research gave out a promising result on the differentiating lymph node metastases through histogram analysis on tumors in DCE-MR images. Histogram could reveal the tumors heterogenicity between patients with different lymph node status.
Collapse
Affiliation(s)
- Xu Dong
- WeiHai Central Hospital, Weihai City, ShanDong, China
| | - Yu Chunrong
- WeiHai Central Hospital, Weihai City, ShanDong, China
| | - Hou Hongjun
- WeiHai Central Hospital, Weihai City, ShanDong, China
| | | |
Collapse
|
44
|
Li W, Newitt DC, Wilmes LJ, Jones EF, Arasu V, Gibbs J, La Yun B, Li E, Partridge SC, Kornak J, Esserman LJ, Hylton NM. Additive value of diffusion-weighted MRI in the I-SPY 2 TRIAL. J Magn Reson Imaging 2019; 50:1742-1753. [PMID: 31026118 DOI: 10.1002/jmri.26770] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 04/18/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The change in apparent diffusion coefficient (ADC) measured from diffusion-weighted imaging (DWI) has been shown to be predictive of pathologic complete response (pCR) for patients with locally invasive breast cancer undergoing neoadjuvant chemotherapy. PURPOSE To investigate the additive value of tumor ADC in a multicenter clinical trial setting. STUDY TYPE Retrospective analysis of multicenter prospective data. POPULATION In all, 415 patients who enrolled in the I-SPY 2 TRIAL from 2010 to 2014 were included. FIELD STRENGTH/SEQUENCE 1.5T or 3T MRI system using a fat-suppressed single-shot echo planar imaging sequence with b-values of 0 and 800 s/mm2 for DWI, followed by a T1-weighted sequence for dynamic contrast-enhanced MRI (DCE-MRI) performed at pre-NAC (T0), after 3 weeks of NAC (T1), mid-NAC (T2), and post-NAC (T3). ASSESSMENT Functional tumor volume and tumor ADC were measured at each MRI exam; pCR measured at surgery was assessed as the binary outcome. Breast cancer subtype was defined by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. STATISTICAL TESTS A logistic regression model was used to evaluate associations between MRI predictors with pCR. The cross-validated area under the curve (AUC) was calculated to assess the predictive performance of the model with and without ADC. RESULTS In all, 354 patients (128 HR+/HER2-, 60 HR+/HER2+, 34 HR-/HER2+, 132 HR-/HER2-) were included in the analysis. In the full cohort, adding ADC predictors increased the AUC from 0.76 to 0.78 at mid-NAC and from 0.76 to 0.81 at post-NAC. In HR/HER2 subtypes, the AUC increased from 0.52 to 0.65 at pre-NAC for HR+/HER2-, from 0.67 to 0.73 at mid-NAC and from 0.72 to 0.76 at post-NAC for HR+/HER2+, from 0.71 to 0.81 at post-NAC for triple negatives. DATA CONCLUSION The addition of ADC to standard functional tumor volume MRI showed improvement in the prediction of treatment response in HR+ and triple-negative breast cancer. LEVEL OF EVIDENCE 2 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2019;50:1742-1753.
Collapse
Affiliation(s)
- Wen Li
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - David C Newitt
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Lisa J Wilmes
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Ella F Jones
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Vignesh Arasu
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Jessica Gibbs
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Bo La Yun
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA.,Department of Radiology, Seoul National University Bundang Hospital, Seoul, Korea
| | - Elizabeth Li
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA.,Department of Biomedical Engineering, University of California, Davis, California, USA
| | | | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | -
- Quantum Leap Healthcare Collaborative, San Francisco, California, USA
| | - Laura J Esserman
- Department of Surgery, University of California, San Francisco, California, USA
| | - Nola M Hylton
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| |
Collapse
|
45
|
Yang J, Yin J. Discrimination between breast invasive ductal carcinomas and benign lesions by optimizing quantitative parameters derived from dynamic contrast-enhanced MRI using a semi-automatic method. Int J Clin Oncol 2019; 24:815-824. [PMID: 30810889 DOI: 10.1007/s10147-019-01421-1] [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: 12/26/2018] [Accepted: 02/21/2019] [Indexed: 11/24/2022]
Abstract
BACKGROUND To propose a semi-automatic method for distinguishing invasive ductal carcinomas from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS 142 cases were included. In the conventional method, the region of interest for a breast lesion was drawn manually and the corresponding mean time-signal intensity curve (TIC) was qualitatively categorized. Only one quantitative parameter was obtained: the maximum slope of increase (MSI). By contrast, the proposed method extracted the suspicious breast lesion semi-automatically. Besides MSI, more quantitative parameters reflecting perfusion information were derived from the mean TIC and lesion region, including the signal intensity slope (SIslope), initial percentage of enhancement, percentage of peak enhancement, early signal enhancement ratio, and second enhancement percentage. The mean TIC was categorized quantitatively according to the value of SIslope. Regression models were established. The diagnostic performance differed between the new and conventional methods according to the Wilcoxon rank-sum test and receiver operating characteristic analysis. RESULTS According to the TIC categorization results, the accuracies of the traditional and the new method were 59.16% and 76.05%, respectively (P < 0.05). The accuracy was 63.35% for MSI, which was derived from the manual method. For the semi-automatic method, the accuracies were 81.0% and 78.9% for the lesion region and the corresponding mean TIC regression models, respectively. CONCLUSIONS The results demonstrate that our proposed semi-automatic method is beneficial for discriminating breast IDCs and benign lesions based on DCE-MRI, and this method should be considered as a supplementary tool for subjective diagnosis by clinical radiologists.
Collapse
Affiliation(s)
- Jiawen Yang
- Department of Equipment, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
| |
Collapse
|
46
|
Syed AK, Woodall R, Whisenant JG, Yankeelov TE, Sorace AG. Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer. Neoplasia 2019; 21:17-29. [PMID: 30472501 PMCID: PMC6260456 DOI: 10.1016/j.neo.2018.10.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 10/24/2018] [Accepted: 10/29/2018] [Indexed: 12/21/2022]
Abstract
The purpose of this study is to investigate imaging and histology-based measurements of intratumoral heterogeneity to evaluate early treatment response to targeted therapy in a murine model of HER2+ breast cancer. BT474 tumor-bearing mice (N = 30) were treated with trastuzumab or saline and imaged longitudinally with either dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) or 18F-fluoromisonidazole (FMISO) positron emission tomography (PET). At the imaging study end point (day 4 for MRI or 7 for PET), each tumor was excised for immunohistochemistry analysis. Voxel-based histogram analysis was performed on imaging-derived parametric maps (i.e., Ktrans and ve from DCE-MRI, SUV from 18F-FMISO-PET) of the tumor region of interest to measure heterogeneity. Image processing and histogram analysis of whole tumor slice immunohistochemistry data were performed to validate the in vivo imaging findings. Trastuzumab-treated tumors had increased heterogeneity in quantitative imaging measures of cellularity (ve), with a mean Kolmogorov-Smirnov (K-S) distance of 0.32 (P = .05) between baseline and end point distributions. Trastuzumab-treated tumors had increased vascular heterogeneity (Ktrans) and decreased hypoxic heterogeneity (SUV), with a mean K-S distance of 0.42 (P < .01) and 0.46 (P = .047), respectively, between baseline and study end points. These observations were validated by whole-slice immunohistochemistry analysis with mean interquartile range of CD31 distributions of 1.72 for treated and 0.95 for control groups (P = .02). Quantitative longitudinal changes in tumor cellular and vascular heterogeneity in response to therapy may provide evidence for early prediction of response and guide therapy for patients with HER2+ breast cancer.
Collapse
Affiliation(s)
- Anum K Syed
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712
| | - Ryan Woodall
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712
| | - Jennifer G Whisenant
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712; Department of Oncology, The University of Texas at Austin, Austin, TX 78712; Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712
| | - Anna G Sorace
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712; Department of Oncology, The University of Texas at Austin, Austin, TX 78712; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712.
| |
Collapse
|
47
|
Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, Huang W, Noworolski SM, Young RJ, Shiroishi MS, Kim H, Coolens C, Laue H, Chung C, Rosen M, Boss M, Jackson EF. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2018; 49:e101-e121. [PMID: 30451345 DOI: 10.1002/jmri.26518] [Citation(s) in RCA: 225] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 09/06/2018] [Accepted: 09/06/2018] [Indexed: 12/14/2022] Open
Abstract
Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121.
Collapse
Affiliation(s)
- Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Thomas L Chenevert
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Dariya Malyarenko
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Susan M Noworolski
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Mark S Shiroishi
- Division of Neuroradiology, Department of Radiology, University of Southern California, Los Angeles, California, USA
| | - Harrison Kim
- Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Catherine Coolens
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | | | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Mark Rosen
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michael Boss
- Applied Physics Division, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Edward F Jackson
- Departments of Medical Physics, Radiology, and Human Oncology, University of Wisconsin School of Medicine, Madison, Wisconsin, USA
| |
Collapse
|
48
|
Tietze A, Nielsen A, Klærke Mikkelsen I, Bo Hansen M, Obel A, Østergaard L, Mouridsen K. Bayesian modeling of Dynamic Contrast Enhanced MRI data in cerebral glioma patients improves the diagnostic quality of hemodynamic parameter maps. PLoS One 2018; 13:e0202906. [PMID: 30256797 PMCID: PMC6157834 DOI: 10.1371/journal.pone.0202906] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 08/11/2018] [Indexed: 01/08/2023] Open
Abstract
PURPOSE The purpose of this work is to investigate if the curve-fitting algorithm in Dynamic Contrast Enhanced (DCE) MRI experiments influences the diagnostic quality of calculated parameter maps. MATERIAL AND METHODS We compared the Levenberg-Marquardt (LM) and a Bayesian method (BM) in DCE data of 42 glioma patients, using two compartmental models (extended Toft's and 2-compartment-exchange model). Logistic regression and an ordinal linear mixed model were used to investigate if the image quality differed between the curve-fitting algorithms and to quantify if image quality was affected for different parameters and algorithms. The diagnostic performance to discriminate between high-grade and low-grade gliomas was compared by applying a Wilcoxon signed-rank test (statistical significance p>0.05). Two neuroradiologists assessed different qualitative imaging features. RESULTS Parameter maps based on BM, particularly those describing the blood-brain barrier, were superior those based on LM. The image quality was found to be significantly improved (p<0.001) for BM when assessed through independent clinical scores. In addition, given a set of clinical scores, the generating algorithm could be predicted with high accuracy (area under the receiver operating characteristic curve between 0.91 and 1). Using linear mixed models, image quality was found to be improved when applying the 2-compartment-exchange model compared to the extended Toft's model, regardless of the underlying fitting algorithm. Tumor grades were only differentiated reliably on plasma volume maps when applying BM. The curve-fitting algorithm had, however, no influence on grading when using parameter maps describing the blood-brain barrier. CONCLUSION The Bayesian method has the potential to increase the diagnostic reliability of Dynamic Contrast Enhanced parameter maps in brain tumors. In our data, images based on the 2-compartment-exchange model were superior to those based on the extended Toft's model.
Collapse
Affiliation(s)
- Anna Tietze
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany
- Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark
- * E-mail:
| | - Anne Nielsen
- Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark
| | - Irene Klærke Mikkelsen
- Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark
| | - Mikkel Bo Hansen
- Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark
| | - Annette Obel
- Dept. of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark
- Dept. of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark
| | - Kim Mouridsen
- Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark
| |
Collapse
|
49
|
Sharma U, Agarwal K, Sah RG, Parshad R, Seenu V, Mathur S, Gupta SD, Jagannathan NR. Can Multi-Parametric MR Based Approach Improve the Predictive Value of Pathological and Clinical Therapeutic Response in Breast Cancer Patients? Front Oncol 2018; 8:319. [PMID: 30159254 PMCID: PMC6104482 DOI: 10.3389/fonc.2018.00319] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 07/26/2018] [Indexed: 11/13/2022] Open
Abstract
The potential of total choline (tCho), apparent diffusion coefficient (ADC) and tumor volume, both individually and in combination of all these three parameters (multi-parametric approach), was evaluated in predicting both pathological and clinical responses in 42 patients with locally advanced breast cancer (LABC) enrolled for neoadjuvant chemotherapy (NACT). Patients were sequentially examined by conventional MRI; diffusion weighted imaging and in vivo proton MR spectroscopy at 4 time points (pre-therapy, after I, II, and III NACT) at 1.5 T. Miller Payne grading system was used for pathological assessment of response. Of the 42 patients, 24 were pathological responders (pR) while 18 were pathological non-responders (pNR). Clinical response determination classified 26 patients as responders (cR) while 16 as non-responders (cNR). tCho and ADC showed significant changes after I NACT, however, MR measured tumor volume showed reduction only after II NACT both in pR and cR. After III NACT, the sensitivity to detect responders was highest for MR volume (83.3% for pR and 96.2% for cR) while the specificity was highest for ADC (76.5% for pR and 100% for cR). Combination of all three parameters exhibited lower sensitivity (66.7%) than MR volume for pR prediction, however, a moderate improvement was seen in specificity (58.8%). For the prediction of clinical response, multi-parametric approach showed 84.6% sensitivity with 100% specificity compared to MR volume (sensitivity 96.2%; specificity 80%). Kappa statistics demonstrated substantial agreement of clinical response with MR volume (k = 0.78) and with multi-parametric approach (k = 0.80) while moderate agreement was seen for tCho (k = 0.48) and ADC (k = 0.46). The values of k for tCho, MR volume and ADC were 0.31, 0.38, and 0.18 indicating fair, moderate, and slight agreement, respectively with pathological response. Moderate agreement (k = 0.44) was observed between clinical and pathological responses. Our study demonstrated that both tCho and ADC are strong predictors of assessment of early pathological and clinical responses. Multi-parametric approach yielded 100% specificity in predicting clinical response. Following III NACT, MR volume emerged as highly suitable predictor for both clinical and pathological assessments. PCA demonstrated separate clusters of pR vs. pNR and cR vs. cNR at post-therapy while with some overlap at pre-therapy.
Collapse
Affiliation(s)
- Uma Sharma
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India
| | - Khushbu Agarwal
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India
| | - Rani G Sah
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India
| | - Rajinder Parshad
- Department of Surgical Disciplines, All India Institute of Medical Sciences, New Delhi, India
| | - Vurthaluru Seenu
- Department of Surgical Disciplines, All India Institute of Medical Sciences, New Delhi, India
| | - Sandeep Mathur
- Department of Pathology, All India Institute of Medical Sciences, New Delhi, India
| | - Siddhartha D Gupta
- Department of Pathology, All India Institute of Medical Sciences, New Delhi, India
| | | |
Collapse
|
50
|
Kim Y, Kim SH, Song BJ, Kang BJ, Yim KI, Lee A, Nam Y. Early Prediction of Response to Neoadjuvant Chemotherapy Using Dynamic Contrast-Enhanced MRI and Ultrasound in Breast Cancer. Korean J Radiol 2018; 19:682-691. [PMID: 29962874 PMCID: PMC6005946 DOI: 10.3348/kjr.2018.19.4.682] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 01/15/2018] [Indexed: 01/25/2023] Open
Abstract
Objective To determine the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and DCE ultrasound (DCE-US) for predicting response to neoadjuvant chemotherapy (NAC) in breast cancer patients. Materials and Methods This Institutional Review Board-approved prospective study was performed between 2014 and 2016. Thirty-nine women with breast cancer underwent DCE-US and DCE-MRI before the NAC, follow-up DCE-US after the first cycle of NAC, and follow-up DCE-MRI after the second cycle of NAC. DCE-MRI parameters (transfer constant [Ktrans], reverse constant [kep], and leakage space [Ve]) were assessed with histograms. From DCE-US, peak-enhancement, the area under the curve, wash-in rate, wash-out rate, time to peak, and rise time (RT) were obtained. After surgery, all the imaging parameters and their changes were compared with histopathologic response using the Miller-Payne Grading (MPG) system. Data from minor and good responders were compared using Wilcoxon rank sum test, chi-square test, or Fisher's exact test. Receiver operating characteristic curve analysis was used for assessing diagnostic performance to predict good response. Results Twelve patients (30.8%) showed a good response (MPG 4 or 5) and 27 (69.2%) showed a minor response (MPG 1–3). The mean, 25th, 50th, and 75th percentiles of Ktrans and Kep of post-NAC DCE-MRI differed between the two groups. These parameters showed fair to good diagnostic performance for the prediction of response to NAC (AUC 0.76–0.81, p ≤ 0.007). Among DCE-US parameters, the percentage change in RT showed fair prediction (AUC 0.71, p = 0.023). Conclusion Quantitative analysis of DCE-MRI and DCE-US was helpful for early prediction of response to NAC.
Collapse
Affiliation(s)
- Yunju Kim
- Department of Radiology, National Cancer Center, Goyang 10408, Korea
| | - Sung Hun Kim
- Department of Radiology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul 06591, Korea
| | - Byung Joo Song
- Department of Surgery, College of Medicine, Bucheon St. Mary's Hospital, The Catholic University of Korea, Bucheon 14647, Korea
| | - Bong Joo Kang
- Department of Radiology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul 06591, Korea
| | - Kwang-Il Yim
- Department of Pathology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul 06591, Korea
| | - Ahwon Lee
- Department of Pathology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul 06591, Korea
| | - Yoonho Nam
- Department of Radiology, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul 06591, Korea
| |
Collapse
|