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Yan R, Murakami W, Mortazavi S, Yu T, Chu FI, Lee-Felker S, Sung K. Quantitative assessment of background parenchymal enhancement is associated with lifetime breast cancer risk in screening MRI. Eur Radiol 2024; 34:6358-6368. [PMID: 38683385 PMCID: PMC11399191 DOI: 10.1007/s00330-024-10758-9] [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: 10/02/2023] [Revised: 03/07/2024] [Accepted: 03/16/2024] [Indexed: 05/01/2024]
Abstract
OBJECTIVES To compare the quantitative background parenchymal enhancement (BPE) in women with different lifetime risks and BRCA mutation status of breast cancer using screening MRI. MATERIALS AND METHODS This study included screening MRI of 535 women divided into three groups based on lifetime risk: nonhigh-risk women, high-risk women without BRCA mutation, and BRCA1/2 mutation carriers. Six quantitative BPE measurements, including percent enhancement (PE) and signal enhancement ratio (SER), were calculated on DCE-MRI after segmentation of the whole breast and fibroglandular tissue (FGT). The associations between lifetime risk factors and BPE were analyzed via linear regression analysis. We adjusted for risk factors influencing BPE using propensity score matching (PSM) and compared the BPE between different groups. A two-sided Mann-Whitney U-test was used to compare the BPE with a threshold of 0.1 for multiple testing issue-adjusted p values. RESULTS Age, BMI, menopausal status, and FGT level were significantly correlated with quantitative BPE based on the univariate and multivariable linear regression analyses. After adjusting for age, BMI, menopausal status, hormonal treatment history, and FGT level using PSM, significant differences were observed between high-risk non-BRCA and BRCA groups in PEFGT (11.5 vs. 8.0%, adjusted p = 0.018) and SERFGT (7.2 vs. 9.3%, adjusted p = 0.066). CONCLUSION Quantitative BPE varies in women with different lifetime breast cancer risks and BRCA mutation status. These differences may be due to the influence of multiple lifetime risk factors. Quantitative BPE differences remained between groups with and without BRCA mutations after adjusting for known risk factors associated with BPE. CLINICAL RELEVANCE STATEMENT BRCA germline mutations may be associated with quantitative background parenchymal enhancement, excluding the effects of known confounding factors. This finding can provide potential insights into the cancer pathophysiological mechanisms behind lifetime risk models. KEY POINTS Expanding understanding of breast cancer pathophysiology allows for improved risk stratification and optimized screening protocols. Quantitative BPE is significantly associated with lifetime risk factors and differs between BRCA mutation carriers and noncarriers. This research offers a possible understanding of the physiological mechanisms underlying quantitative BPE and BRCA germline mutations.
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Affiliation(s)
- Ran Yan
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering, University of California, Los Angeles, CA, USA.
| | - Wakana Murakami
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Radiology, Showa University Graduate School of Medicine, Tokyo, Japan
| | - Shabnam Mortazavi
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Tiffany Yu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Fang-I Chu
- Department of Radiation Oncology, University of California, Los Angeles, CA, USA
| | - Stephanie Lee-Felker
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kyunghyun Sung
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering, University of California, Los Angeles, CA, USA
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Mohamed RM, Panthi B, Adrada BE, Boge M, Candelaria RP, Chen H, Guirguis MS, Hunt KK, Huo L, Hwang KP, Korkut A, Litton JK, Moseley TW, Pashapoor S, Patel MM, Reed B, Scoggins ME, Son JB, Thompson A, Tripathy D, Valero V, Wei P, White J, Whitman GJ, Xu Z, Yang W, Yam C, Ma J, Rauch GM. Multiparametric MRI-based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer. Sci Rep 2024; 14:16073. [PMID: 38992094 PMCID: PMC11239818 DOI: 10.1038/s41598-024-66220-9] [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: 07/26/2023] [Accepted: 06/28/2024] [Indexed: 07/13/2024] Open
Abstract
Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.
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Affiliation(s)
- Rania M Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bikash Panthi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Beatriz E Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
- Koc University Hospital, Istanbul, Turkey
| | - Rosalind P Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Huiqin Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mary S Guirguis
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Kelly K Hunt
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lei Huo
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tanya W Moseley
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sanaz Pashapoor
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Miral M Patel
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Brandy Reed
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marion E Scoggins
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Debu Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jason White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gary J Whitman
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Zhan Xu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wei Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Clinton Yam
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gaiane M Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA.
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Arefan D, Zuley ML, Berg WA, Yang L, Sumkin JH, Wu S. Assessment of Background Parenchymal Enhancement at Dynamic Contrast-enhanced MRI in Predicting Breast Cancer Recurrence Risk. Radiology 2024; 310:e230269. [PMID: 38259203 PMCID: PMC10831474 DOI: 10.1148/radiol.230269] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 11/17/2023] [Accepted: 12/07/2023] [Indexed: 01/24/2024]
Abstract
Background Background parenchymal enhancement (BPE) at dynamic contrast-enhanced (DCE) MRI of cancer-free breasts increases the risk of developing breast cancer; implications of quantitative BPE in ipsilateral breasts with breast cancer are largely unexplored. Purpose To determine whether quantitative BPE measurements in one or both breasts could be used to predict recurrence risk in women with breast cancer, using the Oncotype DX recurrence score as the reference standard. Materials and Methods This HIPAA-compliant retrospective single-institution study included women diagnosed with breast cancer between January 2007 and January 2012 (development set) and between January 2012 and January 2017 (internal test set). Quantitative BPE was automatically computed using an in-house-developed computer algorithm in both breasts. Univariable logistic regression was used to examine the association of BPE with Oncotype DX recurrence score binarized into high-risk (recurrence score >25) and low- or intermediate-risk (recurrence score ≤25) categories. Models including BPE measures were assessed for their ability to distinguish patients with high risk versus those with low or intermediate risk and the actual recurrence outcome. Results The development set included 127 women (mean age, 58 years ± 10.2 [SD]; 33 with high risk and 94 with low or intermediate risk) with an actual local or distant recurrence rate of 15.7% (20 of 127) at a minimum 10 years of follow-up. The test set included 60 women (mean age, 57.8 years ± 11.6; 16 with high risk and 44 with low or intermediate risk). BPE measurements quantified in both breasts were associated with increased odds of a high-risk Oncotype DX recurrence score (odds ratio range, 1.27-1.66 [95% CI: 1.02, 2.56]; P < .001 to P = .04). Measures of BPE combined with tumor radiomics helped distinguish patients with a high-risk Oncotype DX recurrence score from those with a low- or intermediate-risk score, with an area under the receiver operating characteristic curve of 0.94 in the development set and 0.79 in the test set. For the combined models, the negative predictive values were 0.97 and 0.93 in predicting actual distant recurrence and local recurrence, respectively. Conclusion Ipsilateral and contralateral DCE MRI measures of BPE quantified in patients with breast cancer can help distinguish patients with high recurrence risk from those with low or intermediate recurrence risk, similar to Oncotype DX recurrence score. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Zhou and Rahbar in this issue.
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Affiliation(s)
- Dooman Arefan
- From the Department of Radiology, University of Pittsburgh School of
Medicine, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213 (D.A., M.L.Z., W.A.B.,
L.Y., J.H.S., S.W.); Department of Radiology, Magee-Womens Hospital, University
of Pittsburgh Medical Center, Pittsburgh, PA, 15213 (M.L.Z., W.A.B., J.H.S.);
Chongqing Key Laboratory of Translational Research for Cancer Metastasis and
Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China
(L.Y.); and Department of Biomedical Informatics (S.W.), Department of
Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of
Pittsburgh, Pittsburgh, Pa
| | - Margarita L. Zuley
- From the Department of Radiology, University of Pittsburgh School of
Medicine, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213 (D.A., M.L.Z., W.A.B.,
L.Y., J.H.S., S.W.); Department of Radiology, Magee-Womens Hospital, University
of Pittsburgh Medical Center, Pittsburgh, PA, 15213 (M.L.Z., W.A.B., J.H.S.);
Chongqing Key Laboratory of Translational Research for Cancer Metastasis and
Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China
(L.Y.); and Department of Biomedical Informatics (S.W.), Department of
Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of
Pittsburgh, Pittsburgh, Pa
| | - Wendie A. Berg
- From the Department of Radiology, University of Pittsburgh School of
Medicine, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213 (D.A., M.L.Z., W.A.B.,
L.Y., J.H.S., S.W.); Department of Radiology, Magee-Womens Hospital, University
of Pittsburgh Medical Center, Pittsburgh, PA, 15213 (M.L.Z., W.A.B., J.H.S.);
Chongqing Key Laboratory of Translational Research for Cancer Metastasis and
Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China
(L.Y.); and Department of Biomedical Informatics (S.W.), Department of
Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of
Pittsburgh, Pittsburgh, Pa
| | - Lu Yang
- From the Department of Radiology, University of Pittsburgh School of
Medicine, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213 (D.A., M.L.Z., W.A.B.,
L.Y., J.H.S., S.W.); Department of Radiology, Magee-Womens Hospital, University
of Pittsburgh Medical Center, Pittsburgh, PA, 15213 (M.L.Z., W.A.B., J.H.S.);
Chongqing Key Laboratory of Translational Research for Cancer Metastasis and
Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China
(L.Y.); and Department of Biomedical Informatics (S.W.), Department of
Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of
Pittsburgh, Pittsburgh, Pa
| | - Jules H. Sumkin
- From the Department of Radiology, University of Pittsburgh School of
Medicine, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213 (D.A., M.L.Z., W.A.B.,
L.Y., J.H.S., S.W.); Department of Radiology, Magee-Womens Hospital, University
of Pittsburgh Medical Center, Pittsburgh, PA, 15213 (M.L.Z., W.A.B., J.H.S.);
Chongqing Key Laboratory of Translational Research for Cancer Metastasis and
Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China
(L.Y.); and Department of Biomedical Informatics (S.W.), Department of
Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of
Pittsburgh, Pittsburgh, Pa
| | - Shandong Wu
- From the Department of Radiology, University of Pittsburgh School of
Medicine, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213 (D.A., M.L.Z., W.A.B.,
L.Y., J.H.S., S.W.); Department of Radiology, Magee-Womens Hospital, University
of Pittsburgh Medical Center, Pittsburgh, PA, 15213 (M.L.Z., W.A.B., J.H.S.);
Chongqing Key Laboratory of Translational Research for Cancer Metastasis and
Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China
(L.Y.); and Department of Biomedical Informatics (S.W.), Department of
Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of
Pittsburgh, Pittsburgh, Pa
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Liu Z, Yao B, Wen J, Wang M, Ren Y, Chen Y, Hu Z, Li Y, Liang D, Liu X, Zheng H, Luo D, Zhang N. Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions. Eur Radiol 2024; 34:182-192. [PMID: 37566270 DOI: 10.1007/s00330-023-10102-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/03/2023] [Accepted: 07/08/2023] [Indexed: 08/12/2023]
Abstract
OBJECTIVES To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications. MATERIALS AND METHODS From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes. RESULTS The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p = 0.01 and 0.875vs. 0.804, p = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p = 0.006 and 0.890 vs. 0.596, p = 0.020), but not in predicting histologic grades (p = 0.820 and 0.970). CONCLUSION In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity. CLINICAL RELEVANCE STATEMENT Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions. KEY POINTS • Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions. • The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions. • This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions.
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Affiliation(s)
- Zhou Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 113 Baohe Avenue, 518116, Shenzhen, China
| | - Bingyu Yao
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen, China
- College of Computer and Information Engineering, Xiamen University of Technology, 600 Ligong Road, Xiamen, China
| | - Jie Wen
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 113 Baohe Avenue, 518116, Shenzhen, China
| | - Meng Wang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 113 Baohe Avenue, 518116, Shenzhen, China
| | - Ya Ren
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 113 Baohe Avenue, 518116, Shenzhen, China
| | - Yuming Chen
- College of Computer and Information Engineering, Xiamen University of Technology, 600 Ligong Road, Xiamen, China
| | - Zhanli Hu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen, China
| | - Ye Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen, China
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen, China
| | - Dehong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 113 Baohe Avenue, 518116, Shenzhen, China.
| | - Na Zhang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen, China.
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Su GH, Xiao Y, You C, Zheng RC, Zhao S, Sun SY, Zhou JY, Lin LY, Wang H, Shao ZM, Gu YJ, Jiang YZ. Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets. SCIENCE ADVANCES 2023; 9:eadf0837. [PMID: 37801493 PMCID: PMC10558123 DOI: 10.1126/sciadv.adf0837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 09/06/2023] [Indexed: 10/08/2023]
Abstract
Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast-enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.
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Affiliation(s)
- Guan-Hua Su
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ren-Cheng Zheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 201203, China
| | - Shen Zhao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Shi-Yun Sun
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Jia-Yin Zhou
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Lu-Yi Lin
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 201203, China
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ya-Jia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
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Karam R, Elmokadem AH, El-Rakhawy MM, Soliman N, Elnahas W, Abdel-Khalek AM. Clinical utility of abbreviated breast MRI based on diffusion tensor imaging in patients underwent breast conservative therapy. LA RADIOLOGIA MEDICA 2023; 128:289-298. [PMID: 36763315 DOI: 10.1007/s11547-023-01600-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 01/24/2023] [Indexed: 02/11/2023]
Abstract
PURPOSE To evaluate the added value of the diffusion tensor imaging (DTI) parameters to abbreviated breast MRI protocol in differentiating recurrent breast cancer from post-operative changes in cases of breast conservative surgery (BCS). METHODS This prospective study was approved by our institutional review board. Written informed consent was obtained in all patients. 47 female patients (mean age, 49 years; range, 32-66 years) that previously underwent breast conservative surgery with a palpable mass were included in this study (62 breast lesions). Two abbreviated MRI protocols were compared using 1.5 Tesla MRI, AB-MRI 1 (axial T1, T2, pre-contrast T1, 1st post-contrast and subtracted images) and AB-MRI 2 (same sequences plus adding DTI). In both protocols, the wash-in rate was calculated. Histopathology was used as the standard of reference. Appropriate statistical tests were used to assess sensitivity, specificity, and diagnostic accuracy for each protocol. RESULTS The mean total acquisition time was of 6 min for AB-MRI 1 and 10 min for AB-MRI 2 protocols while the mean interpretation time was of 57.5 and 75 s, respectively. Among analyzed DTI parameters, MD (mean diffusivity) showed the highest sensitivity (96.43%) and specificity (91.18%) (P value = < 0.001). FA (fractional anisotropy), AD (axial diffusivity) and RD (radial diffusivity) showed sensitivity = (78.57%, 82.14% and 85.71%), specificity = (88.24, 85.29% and 79.41%), respectively, P value (< 0.001). CONCLUSION DTI may be included in abbreviated MRI protocols without a significant increase in acquisition time and with the advantage of increasing specificity and clinical utility in the characterization of post-conservative breast lesions.
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Affiliation(s)
- Rasha Karam
- Department of Radiology, Mansoura University, Elgomhoria St. 35516, Mansoura, Egypt
| | - Ali H Elmokadem
- Department of Radiology, Mansoura University, Elgomhoria St. 35516, Mansoura, Egypt.
| | | | - Nermin Soliman
- Department of Radiology, Mansoura University, Elgomhoria St. 35516, Mansoura, Egypt
| | - Waleed Elnahas
- Department of Surgical Oncology, Oncology Center, Mansoura University, Mansoura, Egypt
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Ragusi MAA, Bismeijer T, van der Velden BHM, Loo CE, Canisius S, Wesseling J, Wessels LFA, Elias SG, Gilhuijs KGA. Contralateral parenchymal enhancement on MRI is associated with tumor proteasome pathway gene expression and overall survival of early ER+/HER2-breast cancer patients. Breast 2021; 60:230-237. [PMID: 34763270 PMCID: PMC8591464 DOI: 10.1016/j.breast.2021.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/26/2021] [Accepted: 11/02/2021] [Indexed: 11/29/2022] Open
Abstract
Purpose To assess whether contralateral parenchymal enhancement (CPE) on MRI is associated with gene expression pathways in ER+/HER2-breast cancer, and if so, whether such pathways are related to survival. Methods Preoperative breast MRIs were analyzed of early ER+/HER2-breast cancer patients eligible for breast-conserving surgery included in a prospective observational cohort study (MARGINS). The contralateral parenchyma was segmented and CPE was calculated as the average of the top-10% delayed enhancement. Total tumor RNA sequencing was performed and gene set enrichment analysis was used to reveal gene expression pathways associated with CPE (N = 226) and related to overall survival (OS) and invasive disease-free survival (IDFS) in multivariable survival analysis. The latter was also done for the METABRIC cohort (N = 1355). Results CPE was most strongly correlated with proteasome pathways (normalized enrichment statistic = 2.04, false discovery rate = .11). Patients with high CPE showed lower tumor proteasome gene expression. Proteasome gene expression had a hazard ratio (HR) of 1.40 (95% CI = 0.89, 2.16; P = .143) for OS in the MARGINS cohort and 1.53 (95% CI = 1.08, 2.14; P = .017) for IDFS, in METABRIC proteasome gene expression had an HR of 1.09 (95% CI = 1.01, 1.18; P = .020) for OS and 1.10 (95% CI = 1.02, 1.18; P = .012) for IDFS. Conclusion CPE was negatively correlated with tumor proteasome gene expression in early ER+/HER2-breast cancer patients. Low tumor proteasome gene expression was associated with improved survival in the METABRIC data. Contralateral parenchymal enhancement on MRI was associated with tumor proteasome gene expression in ER+/HER2-breast cancer. A high contralateral parenchymal enhancement was associated with a low proteasome gene expression in the breast cancer. Low proteasome tumor gene expression was associated with improved survival in an independent patient cohort.
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Affiliation(s)
- Max A A Ragusi
- Department of Radiology / Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands; Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands.
| | - Tycho Bismeijer
- Division of Molecular Carcinogenesis - Oncode Institute, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Bas H M van der Velden
- Department of Radiology / Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Claudette E Loo
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Sander Canisius
- Division of Molecular Carcinogenesis - Oncode Institute, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands; Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Jelle Wesseling
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands; Department of Pathology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands
| | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis - Oncode Institute, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands; Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Mekelweg 5, 2628 CD Delft, the Netherlands
| | - Sjoerd G Elias
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CG Utrecht, the Netherlands
| | - Kenneth G A Gilhuijs
- Department of Radiology / Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
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8
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Hu X, Jiang L, You C, Gu Y. Fibroglandular Tissue and Background Parenchymal Enhancement on Breast MR Imaging Correlates With Breast Cancer. Front Oncol 2021; 11:616716. [PMID: 34660251 PMCID: PMC8515131 DOI: 10.3389/fonc.2021.616716] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 09/16/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives To evaluate the association of breast cancer with both the background parenchymal enhancement intensity and volume (BPEI and BPEV, respectively) and the amount of fibroglandular tissue (FGT) using an automatic quantitative assessment method in breast magnetic resonance imaging (MRI). Materials and Methods Among 17,274 women who underwent breast MRI, 132 normal women (control group), 132 women with benign breast lesions (benign group), and 132 women with breast cancer (cancer group) were randomly selected and matched by age and menopausal status. The area under the receiver operating characteristic curve (AUC) was compared in Cancer vs Control and Cancer vs Benign groups to assess the discriminative ability of BPEI, BPEV and FGT. Results Compared with the control groups, the cancer group showed a significant difference in BPEV with a maximum AUC of 0.715 and 0.684 for patients in premenopausal and postmenopausal subgroup, respectively. And the cancer group showed a significant difference in BPEV with a maximum AUC of 0.622 and 0.633 for patients in premenopausal and postmenopausal subgroup, respectively, when compared with the benign group. FGT showed no significant difference when breast cancer group was compared with normal control and benign lesion group, respectively. Compared with the control groups, BPEI showed a slight difference in the cancer group. Compared with the benign group, no significant difference was seen in cancer group. Conclusion Increased BPEV is correlated with a high risk of breast cancer While FGT is not.
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Affiliation(s)
- Xiaoxin Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
| | - Luan Jiang
- Center for Advanced Medical Imaging Technology, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
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9
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Differentiating Breast Tumors from Background Parenchymal Enhancement at Contrast-Enhanced Mammography: The Role of Radiomics-A Pilot Reader Study. Diagnostics (Basel) 2021; 11:diagnostics11071248. [PMID: 34359332 PMCID: PMC8305277 DOI: 10.3390/diagnostics11071248] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 07/09/2021] [Accepted: 07/10/2021] [Indexed: 12/27/2022] Open
Abstract
Background: The purpose of this study was to assess the effectiveness of the radiomic analysis of contrast-enhanced spectral mammography (CESM) in discriminating between breast cancers and background parenchymal enhancement (BPE). Methods: This retrospective study included 38 patients that underwent CESM examinations for clinical purposes between January 2019–December 2020. A total of 57 malignant breast lesions and 23 CESM examinations with 31 regions of BPE were assessed through radiomic analysis using MaZda software. The parameters that demonstrated to be independent predictors for breast malignancy were exported into the B11 program and a k-nearest neighbor classifier (k-NN) was trained on the initial groups of patients and was tested using a validation group. Histopathology results obtained after surgery were considered the gold standard. Results: Radiomic analysis found WavEnLL_s_2 parameter as an independent predictor for breast malignancies with a sensitivity of 68.42% and a specificity of 83.87%. The prediction model that included CH1D6SumAverg, CN4D6Correlat, Kurtosis, Perc01, Perc10, Skewness, and WavEnLL_s_2 parameters had a sensitivity of 73.68% and a specificity of 80.65%. Higher values were obtained of WavEnLL_s_2 and the prediction model for tumors than for BPEs. The comparison between the ROC curves provided by the WaveEnLL_s_2 and the entire prediction model did not show statistically significant results (p = 0.0943). The k-NN classifier based on the parameter WavEnLL_s_2 had a sensitivity and specificity on training and validating groups of 71.93% and 45.16% vs. 60% and 44.44%, respectively. Conclusion: Radiomic analysis has the potential to differentiate CESM between malignant lesions and BPE. Further quantitative insight into parenchymal enhancement patterns should be performed to facilitate the role of BPE in personalized clinical decision-making and risk assessment.
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10
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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.
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11
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Tezcan S, Ozturk FU, Uslu N, Akcay EY. The Role of Combined Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MRI for Differentiating Malignant From Benign Breast Lesions Presenting Washout Curve. Can Assoc Radiol J 2020; 72:460-469. [PMID: 32157892 DOI: 10.1177/0846537120907098] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
PURPOSE The aim of this study is to evaluate the diagnostic performance of combined breast magnetic resonance imaging (MRI) protocol including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in patients with enhancing lesions that demonstrated washout curve and to determine whether applying apparent diffusion coefficient (ADC) cutoff value could improve the diagnostic value of breast MRI. METHODS The retrospective study included 116 patients with 116 suspicious breast lesions, which showed washout curve on DCE-MRI, who underwent subsequent biopsy. Morphologic characteristics on DCE-MRI and ADC values on DWI were evaluated. Apparent diffusion coefficient values and morphologic features of benign and malignant lesions were compared. Diagnostic values of DCE-MRI and combined MRI, including DCE-MRI and DWI (applying an ADC cutoff value) for distinguishing malignancy from benign lesions, were calculated. RESULTS Of the 116 breast lesions, 79 were malignant and 37 were benign. The ADC value of malignant tumors (median ADC, 0.72 × 10-3 mm2/s) was significantly lower than that of benign lesions (median ADC, 1.03 × 10-3 mm2/s; P < .000). The sensitivity and specificity of an ADC cutoff value of 0.89 × 10-3 mm2/s were 92% and 95%, respectively. Dynamic contrast-enhanced MRI alone presented 100% sensitivity and 59.4% specificity. Adding an ADC cutoff value of 0.89 × 10-3 mm2/s provided 100% sensitivity and 81% specificity, which would have prevented biopsy for 21.6% of benign lesions without missing any malignancies. CONCLUSION Applying an ADC cutoff value to DCE-MRI provides an improvement in the diagnostic value of breast MRI for differentiating among lesions presenting washout curve.
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Affiliation(s)
- Sehnaz Tezcan
- Koru Hospital, Kızılırmak Mah, Cukurambar, Ankara, Turkey
| | - Funda Ulu Ozturk
- Radiology Department, Baskent University Hospital, Bahcelievler, Ankara, Turkey
| | - Nihal Uslu
- Radiology Department, Baskent University Hospital, Bahcelievler, Ankara, Turkey
| | - Eda Yilmaz Akcay
- Pathology Department, Baskent University Hospital, Bahcelievler, Ankara, Turkey
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12
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Molecular Subtypes Recognition of Breast Cancer in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging Phenotypes from Radiomics Data. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:6978650. [PMID: 31827586 PMCID: PMC6885255 DOI: 10.1155/2019/6978650] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 10/10/2019] [Indexed: 12/28/2022]
Abstract
Background and Objective Breast cancer is a major cause of mortality among women if not treated in early stages. Recognizing molecular markers from DCE-MRI directly to distinguish the four molecular subtypes without invasive biopsy is helpful for guiding treatment plans for breast cancer, which provides a fast way to consequential treatment plan decision in early time and best opportunity for patients. Methods This study presents an approach of molecular subtypes recognition from breast cancer image phenotypes by radiomics. An improved region growth algorithm with dynamic threshold without user interaction is proposed for cancer lesion segmentation, which gives the precise border of lesion other than area with background. The lesions are extracted automatically based on radiologists' annotation which guarantees the lesion is segmented correctly. Various features are extracted on lesions data including texture, morphology, dynamic kinetics, and statistics features carried out on a large patient cohort, which are used to validate the relationship between image phenotypes and the molecular subtypes. A new algorithm of multimodel-based recursive feature elimination is applied on the radiomics data generated by the feature extraction process. This method obtains the feature subset with stable performance for different classification models, and the gradient boosting decision tree model gets the best results of both classification performance and imbalance performance on molecular subtypes. Result From the experimental results, 69 optimal features from 143 original features are found by the multimodel-based recursive feature elimination algorithms and the gradient boosting decision tree classifier obtains a good performance with accuracy 0.87, precise 0.88, recall 0.87, and F1-score 0.87. The dataset with 637 patients in this paper has serious imbalance problem on different molecular subtypes, and the the robust features that are generated by multimodel-based recursive feature eliminiation algorithm make the gradient boosting decision tree classifier have good behaviors. The recognition precision for the four molecular subtypes of luminal A, luminal B, HER-2, and basal-like are 0.91, 0.89, 0.83, and 0.87, respectively. Conclusions The improved lesion segmentation method gives more precise lesion edge, which not only saves the time of automatic extraction of lesion region of interest without threshold setting for each case, but also prevents the segmentation error by manual and prejudice from different radiologists. The feature selection algorithm of multimodel-based recursive feature elimination has the ability to find robust and optimal features that distinguish the four molecular subtypes from image phenotypes. The gradient boosting decision tree classifier rather plays a main role in recognition than other models used in this paper.
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13
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Serasanambati M, Broza YY, Haick H. Volatile Compounds Are Involved in Cellular Crosstalk and Upregulation. ACTA ACUST UNITED AC 2019; 3:e1900131. [PMID: 32648725 DOI: 10.1002/adbi.201900131] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 08/16/2019] [Indexed: 12/14/2022]
Abstract
Cell-cell cross talk is of great importance in cancer research due to its major role in proliferation, differentiation, migration, and influence on the apoptotic pathway. Different cell-cell communication mechanisms have come mainly from proteomic and genomic approaches. In this paper, a new route is reported for cross talk between cancer cells that occurs, even when they are far away from each other. Single-cell and culture analysis shows that upregulation of cancer cells emits hundreds of volatile organic compounds (VOCs) into their headspace. Part of the VOCs remains without any change, disregarding the biological environment around it. The other part of the VOCs is exchanged between monocultures of the cells as well as between co-cultures of the cells with no physical contact between them, leading to different changes in growth than when left on their own. The chemical nature and composition of these VOCs have been determined and are discussed herein. Cell-to-cell cross talk has the advantage of being suitable for transfer/diffusion over relatively long distances. It would thus be expected to serve as a shuttling pad toward the development of advanced approaches that could enable very early detection of cancer and/or monitoring of metastasis and related cancer therapy.
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Affiliation(s)
- Mamatha Serasanambati
- Department of Chemical Engineering, Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel
| | - Yoav Y Broza
- Department of Chemical Engineering, Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel
| | - Hossam Haick
- Department of Chemical Engineering, Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel.,Russell Berries Nanotechnology Institute, Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel.,Technion Integrated Cancer Center, The Ruth and Bruce Rappaport Faculty of Medicine, 1-Efron St. Bat Galim, Haifa, 3525433, Israel
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14
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Sheth D, Giger ML. Artificial intelligence in the interpretation of breast cancer on MRI. J Magn Reson Imaging 2019; 51:1310-1324. [PMID: 31343790 DOI: 10.1002/jmri.26878] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 07/08/2019] [Indexed: 12/13/2022] Open
Abstract
Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, going beyond the current use in computer-aided detection to include diagnosis, prognosis, response to therapy, and risk assessment. The automated capabilities of AI offer the potential to enhance the diagnostic expertise of clinicians, including accurate demarcation of tumor volume, extraction of characteristic cancer phenotypes, translation of tumoral phenotype features to clinical genotype implications, and risk prediction. The combination of image-specific findings with the underlying genomic, pathologic, and clinical features is becoming of increasing value in breast cancer. The concurrent emergence of newer imaging techniques has provided radiologists with greater diagnostic tools and image datasets to analyze and interpret. Integrating an AI-based workflow within breast imaging enables the integration of multiple data streams into powerful multidisciplinary applications that may lead the path to personalized patient-specific medicine. In this article we describe the goals of AI in breast cancer imaging, in particular MRI, and review the literature as it relates to the current application, potential, and limitations in breast cancer. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1310-1324.
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Affiliation(s)
- Deepa Sheth
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Maryellen L Giger
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
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15
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Zhang L, Mohamed AA, Chai R, Guo Y, Zheng B, Wu S. Automated deep learning method for whole-breast segmentation in diffusion-weighted breast MRI. J Magn Reson Imaging 2019; 51:635-643. [PMID: 31301201 DOI: 10.1002/jmri.26860] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 06/26/2019] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Diffusion-weighted imaging (DWI) in MRI plays an increasingly important role in diagnostic applications and developing imaging biomarkers. Automated whole-breast segmentation is an important yet challenging step for quantitative breast imaging analysis. While methods have been developed on dynamic contrast-enhanced (DCE) MRI, automatic whole-breast segmentation in breast DWI MRI is still underdeveloped. PURPOSE To develop a deep/transfer learning-based segmentation approach for DWI MRI scans and conduct an extensive study assessment on four imaging datasets from both internal and external sources. STUDY TYPE Retrospective. SUBJECTS In all, 98 patients (144 MRI scans; 11,035 slices) of four different breast MRI datasets from two different institutions. FIELD STRENGTH/SEQUENCES 1.5T scanners with DCE sequence (Dataset 1 and Dataset 2) and DWI sequence. A 3.0T scanner with one external DWI sequence. ASSESSMENT Deep learning models (UNet and SegNet) and transfer learning were used as segmentation approaches. The main DCE Dataset (4,251 2D slices from 39 patients) was used for pre-training and internal validation, and an unseen DCE Dataset (431 2D slices from 20 patients) was used as an independent test dataset for evaluating the pre-trained DCE models. The main DWI Dataset (6,343 2D slices from 75 MRI scans of 29 patients) was used for transfer learning and internal validation, and an unseen DWI Dataset (10 2D slices from 10 patients) was used for independent evaluation to the fine-tuned models for DWI segmentation. Manual segmentations by three radiologists (>10-year experience) were used to establish the ground truth for assessment. The segmentation performance was measured using the Dice Coefficient (DC) for the agreement between manual expert radiologist's segmentation and algorithm-generated segmentation. STATISTICAL TESTS The mean value and standard deviation of the DCs were calculated to compare segmentation results from different deep learning models. RESULTS For the segmentation on the DCE MRI, the average DC of the UNet was 0.92 (cross-validation on the main DCE dataset) and 0.87 (external evaluation on the unseen DCE dataset), both higher than the performance of the SegNet. When segmenting the DWI images by the fine-tuned models, the average DC of the UNet was 0.85 (cross-validation on the main DWI dataset) and 0.72 (external evaluation on the unseen DWI dataset), both outperforming the SegNet on the same datasets. DATA CONCLUSION The internal and independent tests show that the deep/transfer learning models can achieve promising segmentation effects validated on DWI data from different institutions and scanner types. Our proposed approach may provide an automated toolkit to help computer-aided quantitative analyses of breast DWI images. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:635-643.
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Affiliation(s)
- Lei Zhang
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Aly A Mohamed
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Ruimei Chai
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.,Department of Radiology, First Hospital of China Medical University, Heping District, Shenyang, Liaoning, China
| | - Yuan Guo
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.,Department of Radiology, Second Affiliated Hospital of South China University of Technology, Guangzhou First People's Hospital, Guangzhou, China
| | - Bingjie Zheng
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.,Department of Radiology, Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Shandong Wu
- Departments of Radiology, Biomedical Informatics, Bioengineering, Intelligent Systems, and Clinical and Translational Science, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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16
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Liao GJ, Henze Bancroft LC, Strigel RM, Chitalia RD, Kontos D, Moy L, Partridge SC, Rahbar H. Background parenchymal enhancement on breast MRI: A comprehensive review. J Magn Reson Imaging 2019; 51:43-61. [PMID: 31004391 DOI: 10.1002/jmri.26762] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/09/2019] [Accepted: 04/09/2019] [Indexed: 12/22/2022] Open
Abstract
The degree of normal fibroglandular tissue that enhances on breast MRI, known as background parenchymal enhancement (BPE), was initially described as an incidental finding that could affect interpretation performance. While BPE is now established to be a physiologic phenomenon that is affected by both endogenous and exogenous hormone levels, evidence supporting the notion that BPE frequently masks breast cancers is limited. However, compelling data have emerged to suggest BPE is an independent marker of breast cancer risk and breast cancer treatment outcomes. Specifically, multiple studies have shown that elevated BPE levels, measured qualitatively or quantitatively, are associated with a greater risk of developing breast cancer. Evidence also suggests that BPE could be a predictor of neoadjuvant breast cancer treatment response and overall breast cancer treatment outcomes. These discoveries come at a time when breast cancer screening and treatment have moved toward an increased emphasis on targeted and individualized approaches, of which the identification of imaging features that can predict cancer diagnosis and treatment response is an increasingly recognized component. Historically, researchers have primarily studied quantitative tumor imaging features in pursuit of clinically useful biomarkers. However, the need to segment less well-defined areas of normal tissue for quantitative BPE measurements presents its own unique challenges. Furthermore, there is no consensus on the optimal timing on dynamic contrast-enhanced MRI for BPE quantitation. This article comprehensively reviews BPE with a particular focus on its potential to increase precision approaches to breast cancer risk assessment, diagnosis, and treatment. It also describes areas of needed future research, such as the applicability of BPE to women at average risk, the biological underpinnings of BPE, and the standardization of BPE characterization. Level of Evidence: 3 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:43-61.
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Affiliation(s)
- Geraldine J Liao
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA.,Department of Radiology, Virginia Mason Medical Center, Seattle, Washington, USA
| | | | - Roberta M Strigel
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA.,Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA.,Carbone Cancer Center, University of Wisconsin, Madison, Wisconsin, USA
| | - Rhea D Chitalia
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Linda Moy
- Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Habib Rahbar
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA
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17
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Braman N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch BN, Vulchi M, Turk P, Bera K, Abraham J, Sikov WM, Somlo G, Harris LN, Gilmore H, Plecha D, Varadan V, Madabhushi A. Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer. JAMA Netw Open 2019; 2:e192561. [PMID: 31002322 PMCID: PMC6481453 DOI: 10.1001/jamanetworkopen.2019.2561] [Citation(s) in RCA: 193] [Impact Index Per Article: 38.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
IMPORTANCE There has been significant recent interest in understanding the utility of quantitative imaging to delineate breast cancer intrinsic biological factors and therapeutic response. No clinically accepted biomarkers are as yet available for estimation of response to human epidermal growth factor receptor 2 (currently known as ERBB2, but referred to as HER2 in this study)-targeted therapy in breast cancer. OBJECTIVE To determine whether imaging signatures on clinical breast magnetic resonance imaging (MRI) could noninvasively characterize HER2-positive tumor biological factors and estimate response to HER2-targeted neoadjuvant therapy. DESIGN, SETTING, AND PARTICIPANTS In a retrospective diagnostic study encompassing 209 patients with breast cancer, textural imaging features extracted within the tumor and annular peritumoral tissue regions on MRI were examined as a means to identify increasingly granular breast cancer subgroups relevant to therapeutic approach and response. First, among a cohort of 117 patients who received an MRI prior to neoadjuvant chemotherapy (NAC) at a single institution from April 27, 2012, through September 4, 2015, imaging features that distinguished HER2+ tumors from other receptor subtypes were identified. Next, among a cohort of 42 patients with HER2+ breast cancers with available MRI and RNaseq data accumulated from a multicenter, preoperative clinical trial (BrUOG 211B), a signature of the response-associated HER2-enriched (HER2-E) molecular subtype within HER2+ tumors (n = 42) was identified. The association of this signature with pathologic complete response was explored in 2 patient cohorts from different institutions, where all patients received HER2-targeted NAC (n = 28, n = 50). Finally, the association between significant peritumoral features and lymphocyte distribution was explored in patients within the BrUOG 211B trial who had corresponding biopsy hematoxylin-eosin-stained slide images. Data analysis was conducted from January 15, 2017, to February 14, 2019. MAIN OUTCOMES AND MEASURES Evaluation of imaging signatures by the area under the receiver operating characteristic curve (AUC) in identifying HER2+ molecular subtypes and distinguishing pathologic complete response (ypT0/is) to NAC with HER2-targeting. RESULTS In the 209 patients included (mean [SD] age, 51.1 [11.7] years), features from the peritumoral regions better discriminated HER2-E tumors (maximum AUC, 0.85; 95% CI, 0.79-0.90; 9-12 mm from the tumor) compared with intratumoral features (AUC, 0.76; 95% CI, 0.69-0.84). A classifier combining peritumoral and intratumoral features identified the HER2-E subtype (AUC, 0.89; 95% CI, 0.84-0.93) and was significantly associated with response to HER2-targeted therapy in both validation cohorts (AUC, 0.80; 95% CI, 0.61-0.98 and AUC, 0.69; 95% CI, 0.53-0.84). Features from the 0- to 3-mm peritumoral region were significantly associated with the density of tumor-infiltrating lymphocytes (R2 = 0.57; 95% CI, 0.39-0.75; P = .002). CONCLUSIONS AND RELEVANCE A combination of peritumoral and intratumoral characteristics appears to identify intrinsic molecular subtypes of HER2+ breast cancers from imaging, offering insights into immune response within the peritumoral environment and suggesting potential benefit for treatment guidance.
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Affiliation(s)
- Nathaniel Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jon Whitney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Salendra Singh
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Maryam Etesami
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - David D. B. Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katherine Gallagher
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - B. Nicolas Bloch
- Department of Radiology, Boston Medical Center, Boston, Massachusetts
- Department of Radiology, Boston University School of Medicine, Boston, Massachusetts
| | - Manasa Vulchi
- Department of Hematology and Medical Oncology, The Cleveland Clinic, Cleveland, Ohio
| | - Paulette Turk
- Department of Diagnostic Radiology, The Cleveland Clinic, Cleveland, Ohio
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jame Abraham
- Department of Hematology and Medical Oncology, The Cleveland Clinic, Cleveland, Ohio
| | - William M. Sikov
- Program in Women’s Oncology, Women and Infants Hospital, Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - George Somlo
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, California
| | - Lyndsay N. Harris
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
- National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Hannah Gilmore
- Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Donna Plecha
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Vinay Varadan
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio
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18
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Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer. Eur Radiol 2019; 29:4456-4467. [PMID: 30617495 DOI: 10.1007/s00330-018-5891-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 10/02/2018] [Accepted: 11/13/2018] [Indexed: 10/27/2022]
Abstract
OBJECTIVES This study aimed to predict the molecular subtypes of breast cancer via intratumoural and peritumoural radiomic analysis with subregion identification based on the decomposition of contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS The study included 211 women with histopathologically confirmed breast cancer. We utilised a completely unsupervised convex analysis of mixtures (CAM) method by unmixing dynamic imaging series from heterogeneous tissues. Each tumour and the surrounding parenchyma were thus decomposed into multiple subregions, representing different vascular characterisations, from which radiomic features were extracted. A random forest model was trained and tested using a leave-one-out cross-validation (LOOCV) method to predict breast cancer subtypes. The predictive models from tumoural and peritumoural subregions were fused for classification. RESULTS Tumour and peritumour DCE-MR images were decomposed into three compartments, representing plasma input, fast-flow kinetics, and slow-flow kinetics. The tumour subregion related to fast-flow kinetics showed the best performance among the subregions for differentiating between patients with four molecular subtypes (area under the receiver operating characteristic curve (AUC) = 0.832), exhibiting an AUC value significantly (p < 0.0001) higher than that obtained with the entire tumour (AUC = 0.719). When the tumour- and parenchyma-based predictive models were fused, the performance, measured as the AUC, increased to 0.897; this value was significantly higher than that obtained with other tumour partition methods. CONCLUSIONS Radiomic analysis of intratumoural and peritumoural heterogeneity based on the decomposition of image time-series signals has the potential to more accurately identify tumour kinetic features and serve as a valuable clinical marker to enhance the prediction of breast cancer subtypes. KEY POINTS • Decomposition of image time-series signals has the potential to more accurately identify tumour kinetic features. • Fusion of intratumoural- and peritumoural-based predictive models improves the prediction of breast cancer subtypes.
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19
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Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:7417126. [PMID: 30344618 PMCID: PMC6174735 DOI: 10.1155/2018/7417126] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 07/24/2018] [Accepted: 09/04/2018] [Indexed: 01/17/2023]
Abstract
Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. Furthermore, the combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics that increases diagnostic accuracy towards individualized therapy planning. More importantly, radiomics can now be complemented by the emerging deep learning techniques for further process automation and correlation with other clinical data which facilitate the monitoring of treatment response, as well as the prediction of patient's outcome, by means of unravelling of the complex underlying pathophysiological mechanisms which are reflected in tissue phenotype. The scope of this review is to provide applications and limitations of radiomics towards the development of clinical decision support systems for breast cancer diagnosis and prognosis.
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20
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Histopathologic characteristics of background parenchymal enhancement (BPE) on breast MRI. Breast Cancer Res Treat 2018; 172:487-496. [PMID: 30140962 DOI: 10.1007/s10549-018-4916-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 08/03/2018] [Indexed: 10/28/2022]
Abstract
PURPOSE Breast fibroglandular tissue (FGT), as visualized on a mammogram (mammographic density, MD), is one of the strongest known risk factors for breast cancer. FGT is also visible on breast MRI, and increased background parenchymal enhancement (BPE) in the FGT has been identified as potentially a major breast cancer risk factor. The aim of this exploratory study was to examine the biologic basis of BPE. METHODS We examined the unaffected contra-lateral breast of 80 breast cancer patients undergoing a prophylactic mastectomy before any treatment other than surgery of their breast cancer. BPE was classified on the BI-RADS scale (minimal/mild/moderate/marked). Slides were stained for microvessel density (MVD), CD34 (another measure of endothelial density), glandular tissue within the FGT and VEGF. Spearman correlations were used to evaluate the associations between BPE and these pathologic variables. RESULTS In pre-menopausal patients, BPE was highly correlated with MVD, CD34 and glandular concentration within the FGT, and the pathologic variables were themselves highly correlated. The expression of VEGF was effectively confined to terminal duct lobular unit (TDLU) epithelium. The same relationships of the four pathologic variables with BPE were seen in post-menopausal patients, but the relationships were much weaker and not statistically significant. CONCLUSION The strong correlation of BPE and MVD together with the high correlation of MVD with glandular concentration seen in pre-menopausal patients indicates that increased breast cancer risk associated with BPE in pre-menopausal women is likely to result from its association with increased concentration of glandular tissue in the FGT. The effective confinement of VEGF expression to the TDLUs shows that the signal for MVD growth arises directly from the glandular tissue. Further studies are needed to understand the basis of BPE in post-menopausal women.
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21
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Ke D, Yang R, Jing L. Combined diagnosis of breast cancer in the early stage by MRI and detection of gene expression. Exp Ther Med 2018; 16:467-472. [PMID: 30112019 PMCID: PMC6090468 DOI: 10.3892/etm.2018.6242] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 01/26/2018] [Indexed: 11/28/2022] Open
Abstract
Breast cancer is one of the most common cancer types in humans. Magnetic resonance imaging (MRI) is an efficient method for the detection of human breast cancer. However, the efficacy of MRI in detecting breast cancer in the early stage requires to be improved. The present study investigated the diagnostic efficacy of a combination of MRI and detection of gene expression in patients with breast cancer in the early stage. The gene expression levels of Ki-67, BCL11A, FOXC1, HOXD13, PCDHGB7 and her-2 were used as an auxiliary diagnostic index for patients with breast cancer in the early stage. Higher expression levels of TPA and C2erbB22 were observed in tumor tissue obtained from diagnostic biopsy and determined by immunohistochemistry, which indicated a higher risk of breast cancer in a total of 84 participants. Diagnostic data revealed that combination MRI and detection of gene expression had a significantly higher diagnostic rate (66/84) in diagnosing breast cancer in an early stage compared with either MRI (78/360) or detection of gene expression (72/84; P<0.01). It was indicated that the combination of MRI and detection of gene expression had a higher diagnostic rate (94.5%) than either MRI (81.4%) or detection of gene expression (75.5%). Histological analysis confirmed the diagnosis determined by MRI and detection of gene expression. These results suggest that the combination of MRI and detection of gene expression may be a potential diagnostic method for assessing patients with early-stage breast cancer.
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Affiliation(s)
- Dena Ke
- Radiology Department, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, P.R. China
| | - Rong Yang
- Radiology Department, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, P.R. China
| | - Lina Jing
- Radiology Department, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, P.R. China
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22
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Pujara AC, Mikheev A, Rusinek H, Gao Y, Chhor C, Pysarenko K, Rallapalli H, Walczyk J, Moccaldi M, Babb JS, Melsaether AN. Comparison between qualitative and quantitative assessment of background parenchymal enhancement on breast MRI. J Magn Reson Imaging 2017; 47:1685-1691. [PMID: 29140576 DOI: 10.1002/jmri.25895] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 10/28/2017] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Potential clinical implications of the level of background parenchymal enhancement (BPE) on breast MRI are increasing. Currently, BPE is typically evaluated subjectively. Tests of concordance between subjective BPE assessment and computer-assisted quantified BPE have not been reported. PURPOSE OR HYPOTHESIS To compare subjective radiologist assessment of BPE with objective quantified parenchymal enhancement (QPE). STUDY TYPE Cross-sectional observational study. POPULATION Between 7/24/2015 and 11/27/2015, 104 sequential patients (ages 23 - 81 years, mean 49 years) without breast cancer underwent breast MRI and were included in this study. FIELD STRENGTH/SEQUENCE 3T; fat suppressed axial T2, axial T1, and axial fat suppressed T1 before and after intravenous contrast. ASSESSMENT Four breast imagers graded BPE at 90 and 180 s after contrast injection on a 4-point scale (a-d). Fibroglandular tissue masks were generated using a phantom-validated segmentation algorithm, and were co-registered to pre- and postcontrast fat suppressed images to define the region of interest. QPE was calculated. STATISTICAL TESTS Receiver operating characteristic (ROC) analyses and kappa coefficients (k) were used to compare subjective BPE with QPE. RESULTS ROC analyses indicated that subjective BPE at 90 s was best predicted by quantified QPE ≤20.2 = a, 20.3-25.2 = b, 25.3-50.0 = c, >50.0 = d, and at 180 s by quantified QPE ≤ 32.2 = a, 32.3-38.3 = b, 38.4-74.5 = c, >74.5 = d. Agreement between subjective BPE and QPE was slight to fair at 90 s (k = 0.20-0.36) and 180 s (k = 0.19-0.28). At higher levels of QPE, agreement between subjective BPE and QPE significantly decreased for all four radiologists at 90 s (P ≤ 0.004) and for three of four radiologists at 180 s (P ≤ 0.004). DATA CONCLUSION Radiologists were less consistent with QPE as QPE increased. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1685-1691.
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Affiliation(s)
- Akshat C Pujara
- Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Artem Mikheev
- Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Henry Rusinek
- Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Yiming Gao
- Department of Radiology, New York University School of Medicine, New York, New York, USA.,Breast Imaging Section, New York University School of Medicine, New York, New York, USA
| | - Chloe Chhor
- Department of Radiology, New York University School of Medicine, New York, New York, USA.,Breast Imaging Section, New York University School of Medicine, New York, New York, USA
| | - Kristine Pysarenko
- Department of Radiology, New York University School of Medicine, New York, New York, USA.,Breast Imaging Section, New York University School of Medicine, New York, New York, USA
| | - Harikrishna Rallapalli
- Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Jerzy Walczyk
- Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Melanie Moccaldi
- Department of Radiology, New York University School of Medicine, New York, New York, USA.,Perlmutter Cancer Center, New York University School of Medicine, New York, New York, USA
| | - James S Babb
- Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Amy N Melsaether
- Department of Radiology, New York University School of Medicine, New York, New York, USA.,Breast Imaging Section, New York University School of Medicine, New York, New York, USA
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23
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van der Velden BHM, Elias SG, Bismeijer T, Loo CE, Viergever MA, Wessels LFA, Gilhuijs KGA. Complementary Value of Contralateral Parenchymal Enhancement on DCE-MRI to Prognostic Models and Molecular Assays in High-risk ER +/HER2 - Breast Cancer. Clin Cancer Res 2017; 23:6505-6515. [PMID: 28790119 DOI: 10.1158/1078-0432.ccr-17-0176] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 04/05/2017] [Accepted: 07/27/2017] [Indexed: 11/16/2022]
Abstract
Purpose: To determine whether markers of healthy breast stroma are able to select a subgroup of patients at low risk of death or metastasis from patients considered at high risk according to routine markers of the tumor.Experimental Design: Patients with ER+/HER2- breast cancer were consecutively included for retrospective analysis. The contralateral parenchyma was segmented automatically on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), where upon the average of the top-10% late enhancement was calculated. This contralateral parenchymal enhancement (CPE) was analyzed with respect to routine prognostic models and molecular assays (Nottingham Prognostic Index, Dutch clinical chemotherapy-selection guidelines, 70-gene signature, and 21-gene recurrence score). CPE was split in tertiles and tested for overall and distant disease-free survival. CPE was adjusted for patient and tumor characteristics, as well as systemic therapy, using inverse probability weighting (IPW). Subanalyses were performed in patients at high risk according to prognostic models and molecular assays.Results: Four-hundred-and-fifteen patients were included, constituting the same group in which the association between CPE and survival was discovered. Median follow-up was 85 months, 34/415(8%) patients succumbed. After IPW-adjustment for patient and tumor characteristics, patients with high CPE had significantly better overall survival than those with low CPE in groups at high risk according to the Nottingham Prognostic Index [HR (95% CI): 0.08 (0.00-0.40), P < 0.001]; Dutch clinical guidelines [HR (95% CI): 0.22 (0.00-0.81), P = 0.021]; and 21-gene recurrence score [HR (95% CI): 0.14 (0.00-0.84), P = 0.030]. One group showed a trend [70-gene signature: HR (95% CI): 0.25 (0.00-1.02), P = 0.054].Conclusions: In patients at high risk based on the tumor, subgroups at relatively low risk were identified using pretreatment enhancement of the stroma on breast DCE-MRI. Clin Cancer Res; 23(21); 6505-15. ©2017 AACR.
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Affiliation(s)
| | - Sjoerd G Elias
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tycho Bismeijer
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Claudette E Loo
- Department of Radiology, the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Max A Viergever
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Kenneth G A Gilhuijs
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands.
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24
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Wu S, Zuley ML, Berg WA, Kurland BF, Jankowitz RC, Sumkin JH, Gur D. DCE-MRI Background Parenchymal Enhancement Quantified from an Early versus Delayed Post-contrast Sequence: Association with Breast Cancer Presence. Sci Rep 2017; 7:2115. [PMID: 28522877 PMCID: PMC5437095 DOI: 10.1038/s41598-017-02341-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 04/10/2017] [Indexed: 12/23/2022] Open
Abstract
We investigated automated quantitative measures of background parenchymal enhancement (BPE) derived from an early versus delayed post-contrast sequence in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for association with breast cancer presence in a case-control study. DCE-MRIs were retrospectively analyzed for 51 cancer cases and 51 controls with biopsy-proven benign lesions, matched by age and year-of-MRI. BPE was quantified using fully-automated validated computer algorithms, separately from three sequential DCE-MRI post-contrast-subtracted sequences (SUB1, SUB2, and SUB3). The association of BPE computed from the three SUBs and other known factors with breast cancer were assessed in terms of odds ratio (OR) and area under the receiver operating characteristic curve (AUC). The OR of breast cancer for the percentage BPE measure (BPE%) quantified from SUB1 was 3.5 (95% Confidence Interval: 1.3, 9.8; p = 0.015) for 20% increments. Slightly lower and statistically significant ORs were also obtained for BPE quantified from SUB2 and SUB3. There was no significant difference (p > 0.2) in AUC for BPE quantified from the three post-contrast sequences and their combination. Our study showed that quantitative measures of BPE are associated with breast cancer presence and the association was similar across three breast DCE-MRI post-contrast sequences.
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Affiliation(s)
- Shandong Wu
- Departments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
| | - Margarita L Zuley
- Departments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.,Magee-Womens Hospital of University of Pittsburgh Medical Center, 300 Halket St, Pittsburgh, PA, 15213, USA
| | - Wendie A Berg
- Departments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.,Magee-Womens Hospital of University of Pittsburgh Medical Center, 300 Halket St, Pittsburgh, PA, 15213, USA
| | - Brenda F Kurland
- University of Pittsburgh Cancer Institute, Department of Biostatistics, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Rachel C Jankowitz
- Magee-Womens Hospital of University of Pittsburgh Medical Center, 300 Halket St, Pittsburgh, PA, 15213, USA.,Department of Medicine, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Jules H Sumkin
- Departments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.,Magee-Womens Hospital of University of Pittsburgh Medical Center, 300 Halket St, Pittsburgh, PA, 15213, USA
| | - David Gur
- Departments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
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25
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Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Plecha D, Madabhushi A. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 2017; 19:57. [PMID: 28521821 PMCID: PMC5437672 DOI: 10.1186/s13058-017-0846-1] [Citation(s) in RCA: 391] [Impact Index Per Article: 55.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Accepted: 04/25/2017] [Indexed: 12/26/2022] Open
Abstract
Background In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Methods A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2−) and triple-negative or HER2+ (TN/HER2+) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance. Results Among all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR+, HER2− group using DLDA and 0.93 ± 0.018 within the TN/HER2+ group using a naive Bayes classifier. In HR+, HER2− breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2+ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors. Conclusions Through a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes. Electronic supplementary material The online version of this article (doi:10.1186/s13058-017-0846-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nathaniel M Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
| | - Maryam Etesami
- University Hospitals Case Medical Center, Cleveland, OH, 44106, USA
| | - Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | | | - Hannah Gilmore
- University Hospitals Case Medical Center, Cleveland, OH, 44106, USA
| | - Pallavi Tiwari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Donna Plecha
- University Hospitals Case Medical Center, Cleveland, OH, 44106, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
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