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Xie T, Gong J, Zhao Q, Wu C, Wu S, Peng W, Gu Y. Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer. BMC Med Imaging 2024; 24:136. [PMID: 38844842 PMCID: PMC11155097 DOI: 10.1186/s12880-024-01311-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/27/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND To develop and validate a peritumoral vascular and intratumoral radiomics model to improve pretreatment predictions for pathologic complete responses (pCRs) to neoadjuvant chemoradiotherapy (NAC) in patients with triple-negative breast cancer (TNBC). METHODS A total of 282 TNBC patients (93 in the primary cohort, 113 in the validation cohort, and 76 in The Cancer Imaging Archive [TCIA] cohort) were retrospectively included. The peritumoral vasculature on the maximum intensity projection (MIP) from pretreatment DCE-MRI was segmented by a Hessian matrix-based filter and then edited by a radiologist. Radiomics features were extracted from the tumor and peritumoral vasculature of the MIP images. The LASSO method was used for feature selection, and the k-nearest neighbor (k-NN) classifier was trained and validated to build a predictive model. The diagnostic performance was assessed using the ROC analysis. RESULTS One hundred of the 282 patient (35.5%) with TNBC achieved pCRs after NAC. In predicting pCRs, the combined peritumoral vascular and intratumoral model (fusion model) yields a maximum AUC of 0.82 (95% confidence interval [CI]: 0.75, 0.88) in the primary cohort, a maximum AUC of 0.67 (95% CI: 0.57, 0.76) in the internal validation cohort, and a maximum AUC of 0.65 (95% CI: 0.52, 0.78) in TCIA cohort. The fusion model showed improved performance over the intratumoral model and the peritumoral vascular model, but not significantly (p > 0.05). CONCLUSION This study suggested that combined peritumoral vascular and intratumoral radiomics model could provide a non-invasive tool to enable prediction of pCR in TNBC patients treated with NAC.
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Affiliation(s)
- Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, USA
| | - Siyu Wu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Frankhouser DE, Dietze E, Mahabal A, Seewaldt VL. Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging. FRONTIERS IN RADIOLOGY 2021; 1:735567. [PMID: 37492179 PMCID: PMC10364989 DOI: 10.3389/fradi.2021.735567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 11/11/2021] [Indexed: 07/27/2023]
Abstract
Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.
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Affiliation(s)
- David E. Frankhouser
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
| | - Eric Dietze
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
| | - Ashish Mahabal
- Department of Astronomy, Division of Physics, Mathematics, and Astronomy, California Institute of Technology (Caltech), Pasadena, CA, United States
| | - Victoria L. Seewaldt
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
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Min H, McClymont D, Chandra SS, Crozier S, Bradley AP. Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI. Biomed Phys Eng Express 2020; 6. [PMID: 35045404 DOI: 10.1088/2057-1976/abc45c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 10/23/2020] [Indexed: 11/11/2022]
Abstract
Previous studies on computer aided detection/diagnosis (CAD) in 4D breast magnetic resonance imaging (MRI) usually regard lesion detection, segmentation and characterization as separate tasks, and typically require users to manually select 2D MRI slices or regions of interest as the input. In this work, we present a breast MRI CAD system that can handle 4D multimodal breast MRI data, and integrate lesion detection, segmentation and characterization with no user intervention. The proposed CAD system consists of three major stages: region candidate generation, feature extraction and region candidate classification. Breast lesions are firstly extracted as region candidates using the novel 3D multiscale morphological sifting (MMS). The 3D MMS, which uses linear structuring elements to extract lesion-like patterns, can segment lesions from breast images accurately and efficiently. Analytical features are then extracted from all available 4D multimodal breast MRI sequences, including T1-, T2-weighted and DCE sequences, to represent the signal intensity, texture, morphological and enhancement kinetic characteristics of the region candidates. The region candidates are lastly classified as lesion or normal tissue by the random under-sampling boost (RUSboost), and as malignant or benign lesion by the random forest. Evaluated on a breast MRI dataset which contains a total of 117 cases with 141 biopsy-proven lesions (95 malignant and 46 benign lesions), the proposed system achieves a true positive rate (TPR) of 0.90 at 3.19 false positives per patient (FPP) for lesion detection and a TPR of 0.91 at a FPP of 2.95 for identifying malignant lesions without any user intervention. The average dice similarity index (DSI) is0.72±0.15for lesion segmentation. Compared with previously proposed lesion detection, detection-segmentation and detection-characterization systems evaluated on the same breast MRI dataset, the proposed CAD system achieves a favourable performance in breast lesion detection and characterization.
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Affiliation(s)
- Hang Min
- School of Information Technology and Electrical Engineering, University of Queensland, Australia
| | - Darryl McClymont
- School of Information Technology and Electrical Engineering, University of Queensland, Australia
| | - Shekhar S Chandra
- School of Information Technology and Electrical Engineering, University of Queensland, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Australia
| | - Andrew P Bradley
- Science and Engineering Faculty, Queensland University of Technology, Australia
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Vairavan R, Abdullah O, Retnasamy PB, Sauli Z, Shahimin MM, Retnasamy V. A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection. Curr Med Imaging 2020; 15:85-121. [PMID: 31975658 DOI: 10.2174/1573405613666170912115617] [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] [Received: 03/27/2017] [Revised: 08/27/2017] [Accepted: 08/29/2017] [Indexed: 12/22/2022]
Abstract
BACKGROUND Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival. DISCUSSION This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection. CONCLUSION This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.
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Affiliation(s)
- Rajendaran Vairavan
- School of Microelectronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
| | - Othman Abdullah
- Hospital Sultan Abdul Halim, 08000 Sg. Petani, Kedah, Malaysia
| | | | - Zaliman Sauli
- School of Microelectronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
| | - Mukhzeer Mohamad Shahimin
- Department of Electrical and Electronic Engineering, Faculty of Engineering, National Defence University of Malaysia (UPNM), Kem Sungai Besi, 57000 Kuala Lumpur, Malaysia
| | - Vithyacharan Retnasamy
- School of Microelectronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
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Xu W, Chen X, Deng F, Zhang J, Zhang W, Tang J. Predictors of Neoadjuvant Chemotherapy Response in Breast Cancer: A Review. Onco Targets Ther 2020; 13:5887-5899. [PMID: 32606799 PMCID: PMC7320215 DOI: 10.2147/ott.s253056] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 05/18/2020] [Indexed: 12/17/2022] Open
Abstract
Neoadjuvant chemotherapy (NAC) largely increases operative chances and improves prognosis of the local advanced breast cancer patients. However, no specific means have been invented to predict the therapy responses of patients receiving NAC. Therefore, we focus on the alterations of tumor tissue-related microenvironments such as stromal tumor-infiltrating lymphocytes status, cyclin-dependent kinase expression, non-coding RNA transcription or other small molecular changes, in order to detect potentially predicted biomarkers which reflect the therapeutic efficacy of NAC in different subtypes of breast cancer. Further, possible mechanisms are also discussed to discover feasible treatment targets. Thus, these findings will be helpful to promote the prognosis of breast cancer patients who received NAC and summarized in this review.
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Affiliation(s)
- Weilin Xu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
| | - Xiu Chen
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
| | - Fei Deng
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
| | - Jian Zhang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
| | - Wei Zhang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
| | - Jinhai Tang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
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Wu C, Pineda F, Hormuth DA, Karczmar GS, Yankeelov TE. Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors. Magn Reson Med 2019; 81:2147-2160. [PMID: 30368906 PMCID: PMC6347496 DOI: 10.1002/mrm.27529] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2018] [Revised: 08/22/2018] [Accepted: 08/22/2018] [Indexed: 12/30/2022]
Abstract
PURPOSE We propose a novel methodology to integrate morphological and functional information of tumor-associated vessels to assist in the diagnosis of suspicious breast lesions. THEORY AND METHODS Ultrafast, fast, and high spatial resolution DCE-MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE-MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, Ktrans (volume transfer coefficient), and vp (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability. RESULTS A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, Ktrans , and vp showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91). CONCLUSION This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.
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Affiliation(s)
- Chengyue Wu
- Department of Biomedical Engineering, The University of Texas at Austin, Texas 78712
| | - Federico Pineda
- Department of Radiology The University of Chicago, Chicago, Illinois 60637
| | - David A. Hormuth
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Texas 78712
| | | | - Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Texas 78712,Department of Diagnostic Medicine, The University of Texas at Austin, Texas 78712,Department of Oncology The University of Texas at Austin, Texas 78712,Institute for Computational and Engineering Sciences, The University of Texas at Austin, Texas 78712
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Breast Contrast Enhanced MR Imaging: Semi-Automatic Detection of Vascular Map and Predominant Feeding Vessel. PLoS One 2016; 11:e0161691. [PMID: 27571255 PMCID: PMC5003359 DOI: 10.1371/journal.pone.0161691] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 08/10/2016] [Indexed: 11/19/2022] Open
Abstract
Purpose To obtain breast vascular map and to assess correlation between predominant feeding vessel and tumor location with a semi-automatic method compared to conventional radiologic reading. Methods 148 malignant and 75 benign breast lesions were included. All patients underwent bilateral MR imaging. Written informed consent was obtained from the patients before MRI. The local ethics committee granted approval for this study. Semi-automatic breast vascular map and predominant vessel detection was performed on MRI, for each patient. Semi-automatic detection (depending on grey levels threshold manually chosen by radiologist) was compared with results of two expert radiologists; inter-observer variability and reliability of semi-automatic approach were assessed. Results Anatomic analysis of breast lesions revealed that 20% of patients had masses in internal half, 50% in external half and the 30% in subareolar/central area. As regards the 44 tumors in internal half, based on radiologic consensus, 40 demonstrated a predominant feeding vessel (61% were supplied by internal thoracic vessels, 14% by lateral thoracic vessels, 16% by both thoracic vessels and 9% had no predominant feeding vessel—p<0.01), based on semi-automatic detection, 38 tumors demonstrated a predominant feeding vessel (66% were supplied by internal thoracic vessels, 11% by lateral thoracic vessels, 9% by both thoracic vessels and 14% had no predominant feeding vessel—p<0.01). As regards the 111 tumors in external half, based on radiologic consensus, 91 demonstrated a predominant feeding vessel (25% were supplied by internal thoracic vessels, 39% by lateral thoracic vessels, 18% by both thoracic vessels and 18% had no predominant feeding vessel—p<0.01), based on semi-automatic detection, 94 demonstrated a predominant feeding vessel (27% were supplied by internal thoracic vessels, 45% by lateral thoracic vessels, 4% by both thoracic vessels and 24% had no predominant feeding vessel—p<0.01). An excellent agreement between two radiologic assessments (k = 0.81) and between radiologic consensus and semi-automatic assessment (k = 0.80) was found to identify origin of predominant feeding vessel. An excellent reliability for semi-automatic assessment (Cronbach's alpha = 0.96) was reported. Conclusions Predominant feeding vessel location was correlated with breast lesion location: internal thoracic artery supplied the highest proportion of breasts with tumor in internal half and lateral thoracic artery supplied the highest proportion of breasts with lateral tumor.
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Wu LA, Chang RF, Huang CS, Lu YS, Chen HH, Chen JY, Chang YC. Evaluation of the treatment response to neoadjuvant chemotherapy in locally advanced breast cancer using combined magnetic resonance vascular maps and apparent diffusion coefficient. J Magn Reson Imaging 2015; 42:1407-20. [PMID: 25875904 DOI: 10.1002/jmri.24915] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Accepted: 03/31/2015] [Indexed: 12/12/2022] Open
Abstract
PURPOSE To evaluate the treatment response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy using magnetic resonance (MR) vascular maps and apparent diffusion coefficient (ADC) at 3T. Materials and Methods Thirty-one patients with LABC who underwent breast MR studies before, after the first course, and after completing neoadjuvant chemotherapy were enrolled. Vascular morphology was retrieved via Hessian matrix and the voxels of the vessels and volume of vessels were measured automatically. Whole tumor mean ADC values were calculated. Clinical responders were defined as >50% tumor reduction in the final MR studies. Pathologically complete responders were also recorded. RESULTS There were 21 clinical responders and 10 nonresponders. Compared to the nonresponders after the first course, the responders were characterized by more vascular reduction of the breast lesion and decreased bilateral vascular discrepancy (voxels and volume), and increments in the ADC value and ADC percentage of the lesions (all P < 0.05). There were three pathological complete responders who showed more apparent early vascular reduction of the lesion breast (voxels and volume) and increments in the ADC value than others (P = 0.02, 0.01 and 0.02, respectively). CONCLUSION The early changes of MR vascular maps and ADC are associated with the final treatment response of LABC.
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Affiliation(s)
- Li-An Wu
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.,Department of Medical Imaging, Taipei City Hospital, Heping, Branch, Taipei, Taiwan
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yen-Shen Lu
- Department of Oncology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Hong-Hao Chen
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Jo-Yu Chen
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
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