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Ferro A, Bottosso M, Dieci MV, Scagliori E, Miglietta F, Aldegheri V, Bonanno L, Caumo F, Guarneri V, Griguolo G, Pasello G. Clinical applications of radiomics and deep learning in breast and lung cancer: A narrative literature review on current evidence and future perspectives. Crit Rev Oncol Hematol 2024; 203:104479. [PMID: 39151838 DOI: 10.1016/j.critrevonc.2024.104479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 07/22/2024] [Accepted: 08/10/2024] [Indexed: 08/19/2024] Open
Abstract
Radiomics, analysing quantitative features from medical imaging, has rapidly become an emerging field in translational oncology. Radiomics has been investigated in several neoplastic malignancies as it might allow for a non-invasive tumour characterization and for the identification of predictive and prognostic biomarkers. Over the last few years, evidence has been accumulating regarding potential clinical applications of machine learning in many crucial moments of cancer patients' history. However, the incorporation of radiomics in clinical decision-making process is still limited by low data reproducibility and study variability. Moreover, the need for prospective validations and standardizations is emerging. In this narrative review, we summarize current evidence regarding radiomic applications in high-incidence cancers (breast and lung) for screening, diagnosis, staging, treatment choice, response, and clinical outcome evaluation. We also discuss pro and cons of the radiomic approach, suggesting possible solutions to critical issues which might invalidate radiomics studies and propose future perspectives.
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Affiliation(s)
- Alessandra Ferro
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy
| | - Michele Bottosso
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy
| | - Maria Vittoria Dieci
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy.
| | - Elena Scagliori
- Radiology Unit, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy
| | - Federica Miglietta
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy
| | - Vittoria Aldegheri
- Radiology Unit, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy
| | - Laura Bonanno
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy
| | - Francesca Caumo
- Unit of Breast Radiology, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy
| | - Valentina Guarneri
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy
| | - Gaia Griguolo
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy
| | - Giulia Pasello
- Division of Medical Oncology 2, Veneto Institute of Oncology IOV - IRCCS, via Gattamelata 64, Padua 35128, Italy; Department of Surgery, Oncology and Gastroenterology, University of Padova, via Giustiniani 2, Padova 35128, Italy
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Qadir A, Singh N, Moe AAK, Cahoon G, Lye J, Chao M, Foroudi F, Uribe S. Potential of MRI in Assessing Treatment Response After Neoadjuvant Radiation Therapy Treatment in Breast Cancer Patients: A Scoping Review. Clin Breast Cancer 2024:S1526-8209(24)00136-8. [PMID: 38906720 DOI: 10.1016/j.clbc.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/07/2024] [Accepted: 05/26/2024] [Indexed: 06/23/2024]
Abstract
The objective of this scoping review is to evaluate the potential of Magnetic Resonance Imaging (MRI) and to determine which of the available MRI techniques reported in the literature are the most promising for assessing treatment response in breast cancer patients following neoadjuvant radiotherapy (NRT). Ovid Medline, Embase, CINAHL, and Cochrane databases were searched to identify relevant studies published from inception until March 13, 2023. After primary selection, 2 reviewers evaluated each study using a standardized data extraction template, guided by set inclusion and exclusion criteria. A total of 5 eligible studies were selected. The positive and negative predictive values for MRI predicting pathological complete response across the studies were 67% to 88% and 76% to 85%, respectively. MRI's potential in assessing postradiotherapy tumor sizes was greater for volume measurements than uni-dimensional longest diameter measurements; however, overestimation in surgical tumor sizes was observed. Apparent diffusion coefficient (ADC) values and Time to Enhance (TTE) was seen to increase post-NRT, with a notable difference between responders and nonresponders at 6 months, indicating a potential role in assessing treatment response. In conclusion, this review highlights tumor volume measurements, ADC, and TTE as promising MRI metrics for assessing treatment response post-NRT in breast cancer. However, further research with larger cohorts is needed to confirm their utility. If MRI can accurately identify responders from nonresponders to NRT, it could enable a more personalized and tailored treatment approach, potentially minimizing radiation therapy related toxicity and enhancing cosmetic outcomes.
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Affiliation(s)
- Ayyaz Qadir
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia.
| | - Nabita Singh
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia
| | - Aung Aung Kywe Moe
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia
| | - Glenn Cahoon
- Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Jessica Lye
- Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Michael Chao
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia; Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Farshad Foroudi
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia; Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Sergio Uribe
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia
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Zhang B, Yu Y, Mao Y, Wang H, Lv M, Su X, Wang Y, Li Z, Zhang Z, Bian T, Wang Q. Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer. Acad Radiol 2024; 31:800-811. [PMID: 37914627 DOI: 10.1016/j.acra.2023.10.004] [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: 08/07/2023] [Revised: 09/25/2023] [Accepted: 10/03/2023] [Indexed: 11/03/2023]
Abstract
RATIONALE AND OBJECTIVES To develop a MRI-based deep learning signature for predicting axillary response after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. MATERIALS AND METHODS We enrolled 327 BC patients with axillary lymph node (ALN) metastases receiving axillary operations after NAC. The deep learning features were extracted by ResNet34, which was pretrained by a large, well-annotated dataset from ImageNet. Then we identified deep learning radiomics on magnetic resonance imaging with dynamic contrast enhancement (DCE-MRI) in predicting axillary response after NAC in BC patients. RESULTS The extraction of 128 deep learning radiomics (DLR) features relied on the DCE-MRI for each patient. After the least absolute shrinkage and selection operator regression analysis, 13, 8, and 21 features remained from the pre-treatment, post-treatment, and combined DCE-MRI, respectively. The DLR signature established based on the combined DCE-MRI achieved good capacity in ALN response after NAC. The support vector machine achieved the best performance with an 0.99 area under the curve (AUC) of (95% confidence interval (CI), 0.98-1.00) and 0.83 (95% CI, 0.73-0.92) in the training and test sets, respectively. The LR model established with clinical parameters represented the best performance with 0.73 AUC (95% CI, 0.62-0.84), 0.73 sensitivity, 0.73 specificity, 0.63 PPV, and 0.81 NPV in the test set, respectively. Finally, the integration of radiomic signature and clinical signature resulted in establishing a predictive radiomic nomogram, with an AUC of 0.99 (95%CI, 0.99-1.00). CONCLUSION In conclusion, our current study constructed a predictive nomogram through the deep learning method, demonstrating favorable performance in the training and test cohort. The present prognostic model furnishes a precise and objective foundation for directing the surgical strategy toward ALN management in BC patients receiving NAC.
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Affiliation(s)
- Biyuan Zhang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, People's Republic of China (B.Z., Y.Y., H.W., Q.W.)
| | - Yimiao Yu
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, People's Republic of China (B.Z., Y.Y., H.W., Q.W.)
| | - Yan Mao
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, People's Republic of China (Y.M., M.L., Y.W., Z.L.)
| | - Haiji Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, People's Republic of China (B.Z., Y.Y., H.W., Q.W.)
| | - Meng Lv
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, People's Republic of China (Y.M., M.L., Y.W., Z.L.)
| | - Xiaohui Su
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, People's Republic of China (X.S., Z.Z., T.B.)
| | - Yongmei Wang
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, People's Republic of China (Y.M., M.L., Y.W., Z.L.)
| | - Zhenghao Li
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, People's Republic of China (Y.M., M.L., Y.W., Z.L.)
| | - Zaixian Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, People's Republic of China (X.S., Z.Z., T.B.)
| | - Tiantian Bian
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, People's Republic of China (X.S., Z.Z., T.B.)
| | - Qi Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, People's Republic of China (B.Z., Y.Y., H.W., Q.W.).
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Zhang L, Fan M, Li L. Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:13-24. [PMID: 38343210 DOI: 10.1007/s10278-023-00915-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 03/02/2024]
Abstract
Pharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration-time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was used to divide the whole tumor into three tumor subregions with different kinetic patterns. Radiomic features were calculated from the tumor subregion and whole tumor-based PK parameter maps. The optimal features determined by the fivefold cross-validation method were used to build random forest classifiers to predict molecular subtypes, Ki-67, and tumor grade. The diagnostic performance evaluated by the area under the receiver operating characteristic curve (AUC) was compared between the subregion and whole tumor-based PK parameters. The results showed that the DEC method obtained more accurate PK parameters than the Tofts method. Moreover, the results showed that the subregion-based Ktrans (best AUCs = 0.8319, 0.7032, 0.7132, 0.7490, 0.8074, and 0.6950) achieved a better diagnostic performance than the whole tumor-based Ktrans (AUCs = 0.8222, 0.6970, 0.6511, 0.7109, 0.7620, and 0.5894) for molecular subtypes, Ki-67, and tumor grade. These findings indicate that DEC-based Ktrans in the subregion has the potential to accurately predict molecular subtypes, Ki-67, and tumor grade.
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Affiliation(s)
- Liangliang Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
- School of Computer and Information, Anqing Normal University, Anqing, 246133, China
| | - Ming Fan
- Institute of Intelligent Biomedicine, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Lihua Li
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China.
- Institute of Intelligent Biomedicine, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
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Machireddy A, Thibault G, Loftis KG, Stoltz K, Bueno CE, Smith HR, Riesterer JL, Gray JW, Song X. Segmentation of cellular ultrastructures on sparsely labeled 3D electron microscopy images using deep learning. FRONTIERS IN BIOINFORMATICS 2023; 3:1308708. [PMID: 38162124 PMCID: PMC10754953 DOI: 10.3389/fbinf.2023.1308708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024] Open
Abstract
Focused ion beam-scanning electron microscopy (FIB-SEM) images can provide a detailed view of the cellular ultrastructure of tumor cells. A deeper understanding of their organization and interactions can shed light on cancer mechanisms and progression. However, the bottleneck in the analysis is the delineation of the cellular structures to enable quantitative measurements and analysis. We mitigated this limitation using deep learning to segment cells and subcellular ultrastructure in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. The ultrastructures, such as nuclei, nucleoli, mitochondria, endosomes, and lysosomes, are relatively better defined than their surroundings and can be segmented with high accuracy using a neural network trained with sparse manual labels. Cell segmentation, on the other hand, is much more challenging due to the lack of clear boundaries separating cells in the tissue. We adopted a multi-pronged approach combining detection, boundary propagation, and tracking for cell segmentation. Specifically, a neural network was employed to detect the intracellular space; optical flow was used to propagate cell boundaries across the z-stack from the nearest ground truth image in order to facilitate the separation of individual cells; finally, the filopodium-like protrusions were tracked to the main cells by calculating the intersection over union measure for all regions detected in consecutive images along z-stack and connecting regions with maximum overlap. The proposed cell segmentation methodology resulted in an average Dice score of 0.93. For nuclei, nucleoli, and mitochondria, the segmentation achieved Dice scores of 0.99, 0.98, and 0.86, respectively. The segmentation of FIB-SEM images will enable interpretative rendering and provide quantitative image features to be associated with relevant clinical variables.
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Affiliation(s)
- Archana Machireddy
- Program of Computer Science and Electrical Engineering, Oregon Health and Science University, Portland, OR, United States
| | - Guillaume Thibault
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Kevin G. Loftis
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
| | - Kevin Stoltz
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
| | - Cecilia E. Bueno
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
| | - Hannah R. Smith
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
| | - Jessica L. Riesterer
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Joe W. Gray
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Xubo Song
- Program of Computer Science and Electrical Engineering, Oregon Health and Science University, Portland, OR, United States
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States
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Zhang L, Fan M, Li L. Efficient estimation of pharmacokinetic parameters from breast dynamic contrast-enhanced MRI based on a convolutional neural network for predicting molecular subtypes. Phys Med Biol 2023; 68:245001. [PMID: 37983902 DOI: 10.1088/1361-6560/ad0e39] [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: 04/06/2023] [Accepted: 11/20/2023] [Indexed: 11/22/2023]
Abstract
Objective. Tracer kinetic models allow for estimating pharmacokinetic (PK) parameters, which are related to pathological characteristics, from breast dynamic contrast-enhanced magnetic resonance imaging. However, existing tracer kinetic models subject to inaccuracy are time-consuming for PK parameters estimation. This study aimed to accurately and efficiently estimate PK parameters for predicting molecular subtypes based on convolutional neural network (CNN).Approach. A CNN integrating global and local features (GL-CNN) was trained using synthetic data where known PK parameters map was used as the ground truth, and subsequently used to directly estimate PK parameters (volume transfer constantKtransand flux rate constantKep) map. The accuracy assessed by the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and concordance correlation coefficient (CCC) was compared between the GL-CNN and Tofts-based PK parameters in synthetic data. Radiomic features were calculated from the PK parameters map in 208 breast tumors. A random forest classifier was constructed to predict molecular subtypes using a discovery cohort (n= 144). The diagnostic performance evaluated on a validation cohort (n= 64) using the area under the receiver operating characteristic curve (AUC) was compared between the GL-CNN and Tofts-based PK parameters.Main results. The average PSNR (48.8884), SSIM (0.9995), and CCC (0.9995) between the GL-CNN-basedKtransmap and ground truth were significantly higher than those between the Tofts-basedKtransmap and ground truth. The GL-CNN-basedKtransobtained significantly better diagnostic performance (AUCs = 0.7658 and 0.8528) than the Tofts-basedKtransfor luminal B and HER2 tumors. The GL-CNN method accelerated the computation by speed approximately 79 times compared to the Tofts method for the whole breast of all patients.Significance. Our results indicate that the GL-CNN method can be used to accurately and efficiently estimate PK parameters for predicting molecular subtypes.
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Affiliation(s)
- Liangliang Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China
- School of Computer and Information, Anqing Normal University, Anqing, 246133, People's Republic of China
| | - Ming Fan
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China
| | - Lihua Li
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China
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Elsayed B, Alksas A, Shehata M, Mahmoud A, Zaky M, Alghandour R, Abdelwahab K, Abdelkhalek M, Ghazal M, Contractor S, El-Din Moustafa H, El-Baz A. Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review. Cancers (Basel) 2023; 15:5288. [PMID: 37958461 PMCID: PMC10648987 DOI: 10.3390/cancers15215288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.
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Affiliation(s)
- Basma Elsayed
- Biomedical Engineering Program, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Alksas
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Ali Mahmoud
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mona Zaky
- Diagnostic Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt;
| | - Reham Alghandour
- Medical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt;
| | - Khaled Abdelwahab
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohamed Abdelkhalek
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA;
| | | | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
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8
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Panthi B, Mohamed RM, Adrada BE, Boge M, Candelaria RP, Chen H, Hunt KK, Huo L, Hwang KP, Korkut A, Lane DL, Le-Petross HC, Leung JWT, Litton JK, Pashapoor S, Perez F, Son JB, Sun J, Thompson A, Tripathy D, Valero V, Wei P, White J, Xu Z, Yang W, Zhou Z, Yam C, Rauch GM, Ma J. Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer. Front Oncol 2023; 13:1264259. [PMID: 37941561 PMCID: PMC10628525 DOI: 10.3389/fonc.2023.1264259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Abstract
Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.
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Affiliation(s)
- Bikash Panthi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Rania M. Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Beatriz E. Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Koc University Hospital, Istanbul, Türkiye
| | - Rosalind P. Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Huiqin Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kelly K. Hunt
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lei Huo
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Deanna L. Lane
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Huong C. Le-Petross
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jessica W. T. Leung
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jennifer K. Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Sanaz Pashapoor
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Frances Perez
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Alastair Thompson
- Department of Surgery, Baylor College of Medicine, Houston, TX, United States
| | - Debu Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jason White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Zhan Xu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Wei Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Zijian Zhou
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Clinton Yam
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Gaiane M. Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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9
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Kiser K, Zhang J, Kim SG. Textural Features of Mouse Glioma Models Measured by Dynamic Contrast-Enhanced MR Images with 3D Isotropic Resolution. Tomography 2023; 9:721-735. [PMID: 37104129 PMCID: PMC10141208 DOI: 10.3390/tomography9020058] [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: 02/04/2023] [Revised: 03/20/2023] [Accepted: 03/23/2023] [Indexed: 04/28/2023] Open
Abstract
This paper investigates the effect of anisotropic resolution on the image textural features of pharmacokinetic (PK) parameters of a murine glioma model using dynamic contrast-enhanced (DCE) MR images acquired with an isotropic resolution at 7T with pre-contrast T1 mapping. The PK parameter maps of whole tumors at isotropic resolution were generated using the two-compartment exchange model combined with the three-site-two-exchange model. The textural features of these isotropic images were compared with those of simulated, thick-slice, anisotropic images to assess the influence of anisotropic voxel resolution on the textural features of tumors. The isotropic images and parameter maps captured distributions of high pixel intensity that were absent in the corresponding anisotropic images with thick slices. A significant difference was observed in 33% of the histogram and textural features extracted from anisotropic images and parameter maps, compared to those extracted from corresponding isotropic images. Anisotropic images in different orthogonal orientations demonstrated 42.1% of the histogram and textural features to be significantly different from those of isotropic images. This study demonstrates that the anisotropy of voxel resolution needs to be carefully considered when comparing the textual features of tumor PK parameters and contrast-enhanced images.
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Affiliation(s)
- Karl Kiser
- Department of Radiology, Weill Cornell Medical College, New York, NY 10065, USA
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10
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Thawani R, Gao L, Mohinani A, Tudorica A, Li X, Mitri Z, Huang W. Quantitative DCE-MRI prediction of breast cancer recurrence following neoadjuvant chemotherapy: a preliminary study. BMC Med Imaging 2022; 22:182. [PMID: 36266631 PMCID: PMC9585714 DOI: 10.1186/s12880-022-00908-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 09/15/2022] [Accepted: 09/30/2022] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Breast cancer patients treated with neoadjuvant chemotherapy (NACT) are at risk of recurrence depending on clinicopathological characteristics. This preliminary study aimed to investigate the predictive performances of quantitative dynamic contrast-enhanced (DCE) MRI parameters, alone and in combination with clinicopathological variables, for prediction of recurrence in patients treated with NACT. METHODS Forty-seven patients underwent pre- and post-NACT MRI exams including high spatiotemporal resolution DCE-MRI. The Shutter-Speed model was employed to perform pharmacokinetic analysis of the DCE-MRI data and estimate the Ktrans, ve, kep, and τi parameters. Univariable logistic regression was used to assess predictive accuracy for recurrence for each MRI metric, while Firth logistic regression was used to evaluate predictive performances for models with multi-clinicopathological variables and in combination with a single MRI metric or the first principal components of all MRI metrics. RESULTS Pre- and post-NACT DCE-MRI parameters performed better than tumor size measurement in prediction of recurrence, whether alone or in combination with clinicopathological variables. Combining post-NACT Ktrans with residual cancer burden and age showed the best improvement in predictive performance with ROC AUC = 0.965. CONCLUSION Accurate prediction of recurrence pre- and/or post-NACT through integration of imaging markers and clinicopathological variables may help improve clinical decision making in adjusting NACT and/or adjuvant treatment regimens to reduce the risk of recurrence and improve survival outcome.
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Affiliation(s)
- Rajat Thawani
- Division of Hematology and Oncology, Knight Cancer Institute, Oregon Health & Science University, Sam Jackson Park Road, OCH14110, 97239, Portland, OR, US.
| | - Lina Gao
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Ajay Mohinani
- Department of Internal Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Alina Tudorica
- Department of Radiology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Zahi Mitri
- Division of Hematology and Oncology, Knight Cancer Institute, Oregon Health & Science University, Sam Jackson Park Road, OCH14110, 97239, Portland, OR, US
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
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11
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Effect of Neoadjuvant Chemotherapy on Angiogenesis and Cell Proliferation of Breast Cancer Evaluated by Dynamic Enhanced Magnetic Resonance Imaging. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3156093. [PMID: 35915805 PMCID: PMC9338867 DOI: 10.1155/2022/3156093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/19/2022] [Accepted: 06/18/2022] [Indexed: 11/17/2022]
Abstract
Background. Breast cancer is the uncontrolled proliferation of breast epithelial cells under the action of various carcinogenic factors. The evaluation of early efficacy of neoadjuvant chemotherapy for breast cancer is helpful to change the treatment plan in time. On this basis, dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) was used to evaluate the effects of neoadjuvant chemotherapy on angiogenesis and cell proliferation in breast cancer. Objective. To evaluate the effect of neoadjuvant chemotherapy on angiogenesis and cell proliferation of breast cancer by dynamic enhanced DCE-MRI. Method. 80 breast cancer patients were divided into the routine chemotherapy group (3 cycles) and neoadjuvant chemotherapy groups (3 cycles) of 40 cases each from January 2018 to June 2021. Based on conventional imaging, DCE-MRI was performed with Intera Achieva 3.0 TMR superconducting MR scanner before and after treatment. The quantitative indexes, MRI parameters, cell proliferation expression, and DCE-MRI angiogenesis were compared between the two groups. Result. The inhibition rate, Vepost, Ktranspre, ADC, Bax, Alexi, and Aurora in the neoadjuvant chemotherapy group were significantly higher than those in the conventional chemotherapy group (
), while Kep, Ktrans, and Nek2 were significantly lower than those in the conventional chemotherapy group (
). Vepre (cm3), Ktranspre (ml/min/100 cm3), and Ve had no significant difference (
). Conclusion. The quantitative parameters, MRI parameters, proliferation, and expression of DCE-MRI in breast cancer patients with different chemotherapy regimens are quite different. They can be applied to the diagnosis of neoadjuvant chemotherapy in breast cancer patients with angiogenesis and cell proliferation.
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12
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Pesapane F, Agazzi GM, Rotili A, Ferrari F, Cardillo A, Penco S, Dominelli V, D'Ecclesiis O, Vignati S, Raimondi S, Bozzini A, Pizzamiglio M, Petralia G, Nicosia L, Cassano E. Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients With MRI-Radiomics: A Systematic Review and Meta-analysis. Curr Probl Cancer 2022; 46:100883. [PMID: 35914383 DOI: 10.1016/j.currproblcancer.2022.100883] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 12/30/2022]
Abstract
We performed a systematic review and a meta-analysis of studies using MRI-radiomics for predicting the pathological complete response in breast cancer patients undergoing neoadjuvant therapy , and we evaluated their methodological quality using the radiomics-quality-score (RQS). Random effects meta-analysis was performed pooling area under the receiver operating characteristics curves. Publication-bias was assessed using the Egger's test and visually inspecting the funnel plot. Forty-three studies were included in the qualitative review and 34 in the meta-analysis. Summary area under the receiver operating characteristics curve was 0,78 (95%CI:0,74-0,81). Heterogeneity according to the I2 statistic was substantial (71%) and there was no evidence of publication bias (P-value = 0,2). The average RQS was 12,7 (range:-1-26), with an intra-class correlation coefficient of 0.93 (95%CI:0.61-0.97). Year of publication, field intensity and synthetic RQS score do not appear to be moderators of the effect (P-value = 0.36, P-value = 0.28 and P-value = 0.92, respectively). MRI-radiomics may predict response to neoadjuvant therapy in breast cancer patients but the heterogeneity of the current studies is still substantial.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | | | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Federica Ferrari
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Andrea Cardillo
- Radiology Department, Università degli studi di Torino, Turin, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Oriana D'Ecclesiis
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Silvano Vignati
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Anna Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Maria Pizzamiglio
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giuseppe Petralia
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy; Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
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13
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Herrero Vicent C, Tudela X, Moreno Ruiz P, Pedralva V, Jiménez Pastor A, Ahicart D, Rubio Novella S, Meneu I, Montes Albuixech Á, Santamaria MÁ, Fonfria M, Fuster-Matanzo A, Olmos Antón S, Martínez de Dueñas E. Machine Learning Models and Multiparametric Magnetic Resonance Imaging for the Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2022; 14:cancers14143508. [PMID: 35884572 PMCID: PMC9317428 DOI: 10.3390/cancers14143508] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/07/2022] [Accepted: 07/14/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Achieving pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) is crucial, as pCR is a surrogate marker for survival. However, only 10–30% of patients achieve it. It is therefore essential to develop tools that enable to accurately predict response. Recently, different studies have demonstrated the feasibility of applying machine learning approaches to non-invasively predict pCR before NAC administration from magnetic resonance imaging (MRI) data. Some of those models are based on radiomics, an emerging field that allows the automated extraction of clinically relevant information from radiologic images. However, the research is still at an early stage and further data are needed. Here, we determine whether the combination of imaging data (radiomic features and perfusion/diffusion imaging biomarkers) extracted from multiparametric MRIs and clinical variables can improve pCR prediction to NAC compared to models only including imaging or clinical data, potentially avoiding unnecessary treatment and delays to surgery. Abstract Background: Most breast cancer (BC) patients fail to achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). The aim of this study was to evaluate whether imaging features (perfusion/diffusion imaging biomarkers + radiomic features) extracted from pre-treatment multiparametric (mp)MRIs were able to predict, alone or in combination with clinical data, pCR to NAC. Methods: Patients with stage II-III BC receiving NAC and undergoing breast mpMRI were retrospectively evaluated. Imaging features were extracted from mpMRIs performed before NAC. Three different machine learning models based on imaging features, clinical data or imaging features + clinical data were trained to predict pCR. Confusion matrices and performance metrics were obtained to assess model performance. Statistical analyses were conducted to evaluate differences between responders and non-responders. Results: Fifty-eight patients (median [range] age, 52 [45–58] years) were included, of whom 12 showed pCR. The combined model improved pCR prediction compared to clinical and imaging models, yielding 91.5% of accuracy with no false positive cases and only 17% false negative results. Changes in different parameters between responders and non-responders suggested a possible increase in vascularity and reduced tumour heterogeneity in patients with pCR, with the percentile 25th of time-to-peak (TTP), a classical perfusion parameter, being able to discriminate both groups in a 75% of the cases. Conclusions: A combination of mpMRI-derived imaging features and clinical variables was able to successfully predict pCR to NAC. Specific patient profiles according to tumour vascularity and heterogeneity might explain pCR differences, where TTP could emerge as a putative surrogate marker for pCR.
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Affiliation(s)
- Carmen Herrero Vicent
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
- Correspondence:
| | - Xavier Tudela
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Paula Moreno Ruiz
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Víctor Pedralva
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Ana Jiménez Pastor
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Daniel Ahicart
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Silvia Rubio Novella
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Isabel Meneu
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Ángela Montes Albuixech
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Miguel Ángel Santamaria
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - María Fonfria
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Almudena Fuster-Matanzo
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Santiago Olmos Antón
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Eduardo Martínez de Dueñas
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
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14
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Lepola A, Arponen O, Okuma H, Holli-Helenius K, Junkkari H, Könönen M, Auvinen P, Sudah M, Sutela A, Vanninen R. Association between breast cancer's prognostic factors and 3D textural features of non-contrast-enhanced T1 weighted breast MRI. Br J Radiol 2022; 95:20210702. [PMID: 34826254 PMCID: PMC8822552 DOI: 10.1259/bjr.20210702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVES The aim of this exploratory study was to evaluate whether three-dimensional texture analysis (3D-TA) features of non-contrast-enhanced T1 weighted MRI associate with traditional prognostic factors and disease-free survival (DFS) of breast cancer. METHODS 3D-T1 weighted images from 78 patients with 81 malignant histopathologically verified breast lesions were retrospectively analysed using standard-size volumes of interest. Grey-level co-occurrence matrix (GLCM)-based features were selected for statistical analysis. In statistics the Mann-Whitney U and the Kruskal-Wallis tests, the Cox proportional hazards model and the Kaplan-Meier method were used. RESULTS Tumours with higher histological grade were significantly associated with higher contrast (1 voxel: p = 0.033, 2 voxels: p = 0.036). All the entropy parameters showed significant correlation with tumour grade (p = 0.015-0.050) but there were no statistically significant associations between other TA parameters and tumour grade. The Nottingham Prognostic Index (NPI) was correlated with contrast and sum entropy parameters. A higher sum variance TA parameter was a significant predictor of shorter DFS. CONCLUSION Texture parameters, assessed by 3D-TA from non-enhanced T1 weighted images, indicate tumour heterogeneity but have limited independent prognostic value. However, they are associated with tumour grade, NPI, and DFS. These parameters could be used as an adjunct to contrast-enhanced TA parameters. ADVANCES IN KNOWLEDGE 3D-TA of non-contrast enhanced T1 weighted breast MRI associates with tumour grade, NPI, and DFS. The use of non-contrast 3D-TA parameters in adjunct with contrast-enhanced 3D-TA parameters warrants further research.
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Affiliation(s)
| | | | | | | | | | - Mervi Könönen
- Department of Clinical Radiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
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15
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Choudhery S, Gomez-Cardona D, Favazza CP, Hoskin TL, Haddad TC, Goetz MP, Boughey JC. MRI Radiomics for Assessment of Molecular Subtype, Pathological Complete Response, and Residual Cancer Burden in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy. Acad Radiol 2022; 29 Suppl 1:S145-S154. [PMID: 33160859 PMCID: PMC8093323 DOI: 10.1016/j.acra.2020.10.020] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 10/11/2020] [Accepted: 10/16/2020] [Indexed: 01/03/2023]
Abstract
RATIONALE AND OBJECTIVES There are limited data on pretreatment imaging features that can predict response to neoadjuvant chemotherapy (NAC). To extract volumetric pretreatment MRI radiomics features and assess corresponding associations with breast cancer molecular subtypes, pathological complete response (pCR), and residual cancer burden (RCB) in patients treated with NAC. MATERIALS AND METHODS In this IRB-approved study, clinical and pretreatment MRI data from patients with biopsy-proven breast cancer who received NAC between September 2009 and July 2016 were retrospectively analyzed. Tumors were manually identified and semi-automatically segmented on first postcontrast images. Morphological and three-dimensional textural features were computed, including unfiltered and filtered image data, with spatial scaling factors (SSF) of 2, 4, and 6 mm. Wilcoxon rank-sum tests and area under the receiver operating characteristic curve were used for statistical analysis. RESULTS Two hundred and fifty nine patients with unilateral breast cancer, including 73 (28.2%) HER2+, 112 (43.2%) luminal, and 74 (28.6%) triple negative breast cancers (TNBC), were included. There was a significant difference in the median volume (p = 0.008), median longest axial tumor diameter (p = 0.009), and median longest volumetric diameter (p = 0.01) among tumor subtypes. There was also a significant difference in minimum signal intensity and entropy among the tumor subtypes with SSF = 4 mm (p = 0.009 and p = 0.02 respectively) and SSF = 6 mm (p = 0.007 and p < 0.001 respectively). Additionally, sphericity (p = 0.04) in HER2+ tumors and entropy with SSF = 2, 4, 6 mm (p = 0.004, 0.02, 0.047 respectively) in luminal tumors were significantly associated with pCR. Multiple features demonstrated significant association (p < 0.05) with pCR in TNBC and with RCB in luminal tumors and TNBC, with standard deviation of intensity with SSF = 6 mm achieving the highest AUC (AUC = 0.734) for pCR in TNBC. CONCLUSION MRI radiomics features are associated with different molecular subtypes of breast cancer, pCR, and RCB. These features may be noninvasive imaging biomarkers to identify cancer subtype and predict response to NAC.
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Affiliation(s)
| | | | | | - Tanya L Hoskin
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
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16
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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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17
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Li X, Guo H, Cong C, Liu H, Zhang C, Luo X, Zhong P, Shi H, Fang J, Wang Y. The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors. Front Oncol 2021; 11:745242. [PMID: 34858821 PMCID: PMC8631520 DOI: 10.3389/fonc.2021.745242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 10/18/2021] [Indexed: 12/03/2022] Open
Abstract
Purpose To explore the value of texture analysis (TA) based on dynamic contrast-enhanced MR (DCE-MR) images in the differential diagnosis of benign phyllode tumors (BPTs) and borderline/malignant phyllode tumors (BMPTs). Methods A total of 47 patients with histologically proven phyllode tumors (PTs) from November 2012 to March 2020, including 26 benign BPTs and 21 BMPTs, were enrolled in this retrospective study. The whole-tumor texture features based on DCE-MR images were calculated, and conventional imaging findings were evaluated according to the Breast Imaging Reporting and Data System (BI-RADS). The differences in the texture features and imaging findings between BPTs and BMPTs were compared; the variates with statistical significance were entered into logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of models from image-based analysis, TA, and the combination of these two approaches. Results Regarding texture features, three features of the histogram, two features of the gray-level co-occurrence matrix (GLCM), and three features of the run-length matrix (RLM) showed significant differences between the two groups (all p < 0.05). Regarding imaging findings, however, only cystic wall morphology showed significant differences between the two groups (p = 0.014). The areas under the ROC curve (AUCs) of image-based analysis, TA, and the combination of these two approaches were 0.687 (95% CI, 0.518–0.825, p = 0.014), 0.886 (95% CI, 0.760–0.960, p < 0.0001), and 0.894 (95% CI, 0.754–0.970, p < 0.0001), respectively. Conclusion TA based on DCE-MR images has potential in differentiating BPTs and BMPTs.
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Affiliation(s)
- Xiaoguang Li
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Hong Guo
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Chao Cong
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | | | - Chunlai Zhang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Xiangguo Luo
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Peng Zhong
- Department of Pathology, Daping Hospital, Army Medical University, Chongqing, China
| | - Hang Shi
- Department of Information, Daping Hospital, Army Medical University, Chongqing, China
| | - Jingqin Fang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Yi Wang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
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18
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Girot C, Volk A, Walczak C, Lassau N, Pitre-Champagnat S. New method for quantification of intratumoral heterogeneity: a feasibility study on K trans maps from preclinical DCE-MRI. MAGMA (NEW YORK, N.Y.) 2021; 34:845-857. [PMID: 34091826 DOI: 10.1007/s10334-021-00930-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 05/06/2021] [Accepted: 05/10/2021] [Indexed: 12/31/2022]
Abstract
OBJECT To develop new imaging biomarkers of therapeutic efficacy through the quantification of intratumoral microvascular heterogeneity. MATERIALS AND METHODS The described method was a combination of non-supervised clustering and extraction of intratumoral complexity features (ICF): number of non-connected objects, volume fraction. It was applied to a set of 3D DCE-MRI Ktrans maps acquired previously on tumor bearing mice prior to and on day 4 of anti-angiogenic treatment. Evolutions of ICF were compared to conventional summary statistics (CSS) and to heterogeneity related whole tumor texture features (TF) on treated (n = 9) and control (n = 6) mice. RESULTS Computed optimal number of clusters per tumor was 4. Several intratumoral features extracted from the clusters were able to monitor a therapy effect. Whereas no feature significantly changed for the control group, 6 features significantly changed for the treated group (4 ICF, 2 CSS). Among these, 5 also significantly differentiated the two groups (3 ICF, 2 CSS). TF failed in demonstrating differences within and between the two groups. DISCUSSION ICF are potential imaging biomarkers for anti-angiogenic therapy assessment. The presented method may be expected to have advantages with respect to texture analysis-based methods regarding interpretability of results and setup of standardized image analysis protocols.
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Affiliation(s)
- Charly Girot
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Gustave Roussy, 114 Rue Edouard Vaillant, 94805, Villejuif, France.
| | - Andreas Volk
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Gustave Roussy, 114 Rue Edouard Vaillant, 94805, Villejuif, France
| | - Christine Walczak
- Institut Curie, Inserm, Université Paris-Saclay, CNRS, 91405, Orsay, France
| | - Nathalie Lassau
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Gustave Roussy, 114 Rue Edouard Vaillant, 94805, Villejuif, France.,Département de Radiologie, Gustave Roussy, 94805, Villejuif, France
| | - Stéphanie Pitre-Champagnat
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Gustave Roussy, 114 Rue Edouard Vaillant, 94805, Villejuif, France
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Zhang L, Fan M, Wang S, Xu M, Li L. Radiomic Analysis of Pharmacokinetic Heterogeneity Within Tumor Based on the Unsupervised Decomposition of Dynamic Contrast-Enhanced MRI for Predicting Histological Characteristics of Breast Cancer. J Magn Reson Imaging 2021; 55:1636-1647. [PMID: 34773446 DOI: 10.1002/jmri.27993] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/31/2021] [Accepted: 11/02/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Breast tumor heterogeneity is associated with histological characteristics. However, pharmacokinetic (PK) heterogeneity within tumor might merit further exploration. PURPOSE To enhance the predictive power of molecular subtypes, Ki-67, and tumor grade by analyzing PK heterogeneity within tumor based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). STUDY TYPE Retrospective. POPULATION Two hundred and eight biopsy-proven breast cancer patients, randomly divided into a training cohort (N = 144) and a testing cohort (N = 64). FIELD STRENGTH/SEQUENCE T1 -weighted DCE-MRI at 3.0 T. ASSESSMENT A convex analysis of mixtures-compartmental modeling decomposition method was used to estimate the PK parameter (i.e., the volume transfer constant Ktrans ) in tumor subregions with distinct physiological kinetic patterns, including fast-flow kinetics, slow-flow kinetics, and plasma input. Radiomic features based on the PK parameter were calculated from each tumor subregion. STATISTICAL TESTS The training cohort was used to build random forest classifiers based on the optimal features determined by the 5-fold cross-validation method. The performance was assessed on the testing cohort using the area under the receiver operating characteristic curve (AUC). The AUCs derived from the tumor subregion-based PK parameter were compared with those of the original images of the entire tumor using the DeLong test. A P-value of <0.05 was considered statistically significant. RESULTS The tumor subregion-based PK parameter, which yielded the highest AUCs of 0.8782, 0.7568, 0.7019, 0.7963, 0.8080, and 0.7375 for luminal A, luminal B, basal-like, human epidermal growth factor receptor 2, Ki-67, and tumor grade, respectively, obtained better diagnostic performance than the original images in the entire tumor (highest AUCs = 0.8612, 0.6191, 0.5593, 0.7704, 0.7494, and 0.6261, respectively). In particular, statistically significant improvement in the diagnostic performance was obtained for luminal B. DATA CONCLUSION Radiomic analysis of PK heterogeneity within tumor can enhance the predictive performance of radiomic models compared with that of the entire tumor. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 3.
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Affiliation(s)
- Liangliang Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Ming Fan
- Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Shiwei Wang
- Department of Radiology, First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Lihua Li
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.,Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, China
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20
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Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med 2021; 127:39-56. [PMID: 34704213 DOI: 10.1007/s11547-021-01423-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/14/2021] [Indexed: 12/11/2022]
Abstract
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis for breast MRI, but ultrafast images, T2-weighted images, and diffusion-weighted images are also taken to improve the characteristics of the lesion. Such multiparametric MRI with numerous morphological and functional data poses new challenges to radiologists, and thus, new tools for reliable, reproducible, and high-volume quantitative assessments are warranted. In this context, radiomics, which is an emerging field of research involving the conversion of digital medical images into mineable data for clinical decision-making and outcome prediction, has been gaining ground in oncology. Recent development in artificial intelligence has promoted radiomics studies in various fields including breast cancer treatment and numerous studies have been conducted. However, radiomics has shown a translational gap in clinical practice, and many issues remain to be solved. In this review, we will outline the steps of radiomics workflow and investigate clinical application of radiomics focusing on breast MRI based on published literature, as well as current discussion about limitations and challenges in radiomics.
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Affiliation(s)
- Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
| | - Satoko Ishigaki
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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21
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Liu J, Pei Y, Zhang Y, Wu Y, Liu F, Gu S. Predicting the prognosis of hepatocellular carcinoma with the treatment of transcatheter arterial chemoembolization combined with microwave ablation using pretreatment MR imaging texture features. Abdom Radiol (NY) 2021; 46:3748-3757. [PMID: 33386449 PMCID: PMC8286952 DOI: 10.1007/s00261-020-02891-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 11/29/2020] [Accepted: 12/04/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To investigate the prognostic value of baseline magnetic resonance imaging (MRI) texture analysis of hepatocellular carcinoma (HCC) treated with transcatheter arterial chemoembolization (TACE) and microwave ablation (MWA). METHODS MRI was performed on 102 patients with HCC before receiving TACE combined with MWA in this retrospective study. The best 10 texture features were screened as a feature group for each MRI sequence by MaZda software using mutual information coefficient (MI), nonlinear discriminant analysis (NDA) and other methods. The optimal feature group with the lowest misdiagnosis rate was achieved on one MRI sequence between two groups dichotomized by 3-year survival, which was used to optimize the significant texture features with the optimal cutoff values. The Cox proportional hazards model was generated for the significant texture features and clinical variables to determine the independent predictors of overall survival (OS). The predictive performance of the model was further evaluated by the area under the ROC curve (AUC). Kaplan-Meier and log-rank tests were performed for disease-free survival (DFS) and Local recurrence-free survival (LRFS). RESULTS The optimal feature group with the lowest misdiagnosis rate of 8.82% was obtained on T2WI using MI combined with NDA feature analysis. For Cox proportional hazards regression models, the independent prognostic factors associated with OS were albumin (P = 0.047), BCLC stage (P = 0.001), Correlat(1,- 1)T2 (P = 0.01) and SumEntrp(3,0)T2 (P = 0.015), and the prediction efficiency of multivariate model is AUC = 0.876, 95%CI = 0.803-0.949. Kaplan-Meier analyses further demonstrated that BCLC (P < 0.001), Correlat(1,- 1)T2 (P = 0.023) and SumEntrp(3,0)T2 (P < 0.001) were associated with DFS, and BCLC (P = 0.007) related to LRFS. CONCLUSIONS MR imaging texture features may be used to predict the prognosis of HCC treated with TACE combined with MWA.
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Affiliation(s)
- Jun Liu
- Department of Interventional Therapy, Beijing Shijitan Hospital, Affiliated Hospital of Capital Medical University, Beijing, 100038 People’s Republic of China
| | - Yigang Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan People’s Republic of China
- Xiangya Hospital, Central South University, Changsha, 410008 Hunan People’s Republic of China
| | - Yu Zhang
- Department of Interventional Therapy, Beijing Shijitan Hospital, Affiliated Hospital of Capital Medical University, Beijing, 100038 People’s Republic of China
| | - Yifan Wu
- Department of Interventional Therapy, Beijing Shijitan Hospital, Affiliated Hospital of Capital Medical University, Beijing, 100038 People’s Republic of China
| | - Fuquan Liu
- Department of Interventional Therapy, Beijing Shijitan Hospital, Affiliated Hospital of Capital Medical University, Beijing, 100038 People’s Republic of China
| | - Shanzhi Gu
- Department of Interventional Therapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410006 Hunan People’s Republic of China
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22
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Li Q, Xiao Q, Li J, Wang Z, Wang H, Gu Y. Value of Machine Learning with Multiphases CE-MRI Radiomics for Early Prediction of Pathological Complete Response to Neoadjuvant Therapy in HER2-Positive Invasive Breast Cancer. Cancer Manag Res 2021; 13:5053-5062. [PMID: 34234550 PMCID: PMC8253937 DOI: 10.2147/cmar.s304547] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/04/2021] [Indexed: 12/15/2022] Open
Abstract
Background To assess the value of radiomics based on multiphases contrast-enhanced magnetic resonance imaging (CE-MRI) for early prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with human epithelial growth factor receptor 2 (HER2) positive invasive breast cancer. Methods A total of 127 patients with newly diagnosed primary HER2 positive invasive breast cancer underwent CE-MRI before NAT and performed surgery after NAT. Radiomic features were extracted from the 1st postcontrast CE-MRI phase (CE1) and multi-phases CE-MRI (CEm),respectively. With selected features using a forward stepwise regression, 23 machine learning classifiers based on CE1 and CEm were constructed respectively for differentiating pCR and non-pCR patients. The performances of classifiers were assessed and compared by their accuracy, sensitivity, specificity and AUC (area under curve). The optimal machine learning classification was used to discriminate pCR vs non-pCR in mass and non-mass groups, uni-focal and unilateral multi-focal groups, respectively. Results For the task of pCR classification, 6 radiomic features from CE1 and 6 from CEm were selected for the construction of machine learning models, respectively. The linear SVM based on CEm outperformed the logistic regression model using CE1 with an AUC of 0.84 versus 0.69. In mass and non-mass enhancement groups, the accuracy of linear SVM achieved 84% and 76%. Whereas in unifocal and unilateral multifocal cases, 79% and 75% accuracy were achieved by linear SVM. Conclusion Multiphases CE-MRI imaging may offer more heterogeneity information in the tumor and provide a non-invasive approach for early prediction of pCR to NAT in patients with HER2-positive invasive breast cancer.
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Affiliation(s)
- Qin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Qin Xiao
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Jianwei Li
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.,Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Zhe Wang
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, People's Republic of China.,Human Phenome Institute, Fudan University, Shanghai, People's Republic of China
| | - He Wang
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, People's Republic of China.,Human Phenome Institute, Fudan University, Shanghai, People's Republic of China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
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Moyya PD, Asaithambi M. Radiomics- Quantitative Biomarker Analysis for Breast Cancer Diagnosis and Prediction: A Review. Curr Med Imaging 2021; 18:3-17. [PMID: 33655872 DOI: 10.2174/1573405617666210303102526] [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: 10/28/2020] [Revised: 01/06/2021] [Accepted: 01/14/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Cancer of the breast has become a global problem for women's health. Though concerns regarding early detection and accurate diagnosis were raised, an effort is required for precision medicine as well as personalized treatment. In the past years, the area of medicinal imaging has seen an unprecedented growth that leads to an advancement of radiomics, which provides countless quantitative biomarkers extracted from modern diagnostic images, including a detailed tumor characterization of breast malignancy. DISCUSSION In this research, we presented the methodology and implementation of radiomics, together with its future trends and challenges by the basis of published papers. Radiomics could distinguish between malignant from benign tumors, predict prognostic factors, molecular subtypes of breast carcinoma, treatment response to neoadjuvant chemotherapy (NAC), and recurrence survival. The incorporation of quantitative knowledge with clinical, histopathological and genomic information will enable physicians to afford customized care of treatment for patients with breast cancer. CONCLUSION Our research was intended to help physicians and radiologists learn fundamental knowledge about radiomics and also to work collaboratively with researchers to explore evidence for further usage in clinical practice.
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Affiliation(s)
- Priscilla Dinkar Moyya
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu-632014. India
| | - Mythili Asaithambi
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu-632014. India
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24
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Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions. Eur Radiol 2021; 31:4848-4859. [PMID: 33404696 DOI: 10.1007/s00330-020-07519-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 09/27/2020] [Accepted: 11/13/2020] [Indexed: 10/22/2022]
Abstract
PURPOSE To assess the diagnostic performance of radiomic analysis using high temporal resolution (HTR)-dynamic contrast enhancement (DCE) MR sequences compared to BI-RADS analysis to distinguish benign from malignant breast lesions. MATERIALS AND METHODS We retrospectively analyzed data from consecutive women who underwent breast MRI including HTR-DCE MR sequencing for abnormal enhancing lesions and who had subsequent pathological analysis at our tertiary center. Semi-quantitative enhancement parameters and textural features were extracted. Temporal change across each phase of textural features in HTR-DCE MR sequences was calculated and called "kinetic textural parameters." Statistical analysis by LASSO logistic regression and cross validation was performed to build a model. The diagnostic performance of the radiomic model was compared to the results of BI-RADS MR score analysis. RESULTS We included 117 women with a mean age of 54 years (28-88). Of the 174 lesions analyzed, 75 were benign and 99 malignant. Seven semi-quantitative enhancement parameters and 57 textural features were extracted. Regression analysis selected 15 significant variables in a radiomic model (called "malignant probability score") which displayed an AUC = 0.876 (sensitivity = 0.98, specificity = 0.52, accuracy = 0.78). The performance of the malignant probability score to distinguish benign from malignant breast lesions (AUC = 0.876, 95%CI 0.825-0.925) was significantly better than that of BI-RADS analysis (AUC = 0.831, 95%CI 0.769-0.892). The radiomic model significantly reduced false positives (42%) with the same number of missed cancers (n = 2). CONCLUSION A radiomic model including kinetic textural features extracted from an HTR-DCE MR sequence improves diagnostic performance over BI-RADS analysis. KEY POINTS • Radiomic analysis using HTR-DCE is of better diagnostic performance (AUC = 0.876) than conventional breast MRI reading with BI-RADS (AUC = 0.831) (p < 0.001). • A radiomic malignant probability score under 19.5% gives a negative predictive value of 100% while a malignant probability score over 81% gives a positive predictive value of 100%. • Kinetic textural features extracted from HTR-DCE-MRI have a major role to play in distinguishing benign from malignant breast lesions.
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25
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Saint Martin MJ, Orlhac F, Akl P, Khalid F, Nioche C, Buvat I, Malhaire C, Frouin F. A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 34:355-366. [PMID: 33180226 DOI: 10.1007/s10334-020-00892-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/27/2020] [Accepted: 10/23/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies. MATERIALS AND METHODS T1, T2, and T1-DCE MR images of two breast phantoms were acquired using two scanners and three dual breast coils. Images were retrospectively corrected for bias field inhomogeneity and further normalised using Z score or histogram matching. Extracted radiomic features were harmonised between coils by the ComBat method. The whole pipeline was assessed qualitatively and quantitatively using statistical comparisons on two series of radiomic feature values computed in the gel mimicking the normal breast tissue or in dense lesions. RESULTS Intra and inter-acquisition variabilities were strongly reduced by the standardisation pipeline. Harmonisation by ComBat lowered the percentage of radiomic features significantly different between the three coils from 87% after bias field correction and MR normalisation to 3% in the gel, while preserving or improving performance of lesion classification in the phantoms. DISCUSSION A dedicated standardisation pipeline was developed to reduce variabilities in breast MRI, which paves the way for robust multi-scanner radiomic studies but needs to be assessed on patient data.
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Affiliation(s)
- Marie-Judith Saint Martin
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France.
| | - Fanny Orlhac
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Pia Akl
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
- HCL, Radiologie du Groupement Hospitalier Est, Hôpital Femme Mère Enfant, Unité Fonctionnelle: Imagerie de la Femme, 3 Quai des Célestins, 69002, Lyon, France
- Institut Curie, Service de Radiodiagnostic, 26 rue d'Ulm, 75005, Paris, France
| | - Fahad Khalid
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Christophe Nioche
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Irène Buvat
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Caroline Malhaire
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
- Institut Curie, Service de Radiodiagnostic, 26 rue d'Ulm, 75005, Paris, France
| | - Frédérique Frouin
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
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Zhang Z, He D, Song Y, Yan Z, Wang X, Shao J, Hou Z. Exploring the Inter-voxel Information in Pharmacokinetic Maps for Cervical Carcinoma Prediction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1477-1480. [PMID: 33018270 DOI: 10.1109/embc44109.2020.9176233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Physiological parameters can be estimated from dynamic contrast enhanced magnetic resonance imaging (DCEMRI) data using pharmacokinetic models. This work evaluates the performance of various pharmacokinetic models through a retrospective study on cervix cancer, including two generalized kinetic models and three 2-compartment exchange models (2CXMs). In the current clinical practice, region of interest (ROI) is treated as a whole and the models are assessed by their top pharmacokinetic parameters. We explore the intervoxel relationship in the pharmacokinetic parameter maps and demonstrate that, for those insignificant parameters, texture descriptors can largely improve their discriminative power. Multi-parametric classifiers are developed to fuse the information carried by physiological parameters and the descriptors. Assessed merely by the top parameter, the DP (distributed parameter) model is the best one with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.80; by combining multiple pharmacokinetic parameters, the ExTofts model is the winner with an AUC of 0.837. Finally, the classifier of the AATH (adiabatic approximation to the tissue homogeneity) model build on combined features achieves an AUC of 0.92.Clinical Relevance - Using data from 36 cervical cancer patients and 17 normal subjects, this work quantitatively compared the various pharmacokinetic models and provided recommendations for model selection in cervical cancer diagnosis.
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Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies. Future Sci OA 2020; 6:FSO624. [PMID: 33235811 PMCID: PMC7668124 DOI: 10.2144/fsoa-2020-0073] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Aim: We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT). Materials & methods: QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders. Results: Single-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively. Conclusion: QUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment. Patients with head and neck cancer are often treated with radiation, which usually spans over 6–7 weeks. The response is usually measured 3 months after treatment completion. In this study, we had performed ultrasound scans from the patient’s neck node during radiation treatment (after 24 h, 1 and 4 weeks). Artificial intelligence was used to interpret the ultrasound imaging and predict the response to radiation at the end of 3 months. The scans obtained after the first week were able to predict the treatment response with reasonable accuracy (86%).
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DiCenzo D, Quiaoit K, Fatima K, Bhardwaj D, Sannachi L, Gangeh M, Sadeghi‐Naini A, Dasgupta A, Kolios MC, Trudeau M, Gandhi S, Eisen A, Wright F, Look Hong N, Sahgal A, Stanisz G, Brezden C, Dinniwell R, Tran WT, Yang W, Curpen B, Czarnota GJ. Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi-institutional study. Cancer Med 2020; 9:5798-5806. [PMID: 32602222 PMCID: PMC7433820 DOI: 10.1002/cam4.3255] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 05/02/2020] [Accepted: 06/04/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND This study was conducted in order to develop a model for predicting response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) using pretreatment quantitative ultrasound (QUS) radiomics. METHODS This was a multicenter study involving four sites across North America, and appropriate approval was obtained from the individual ethics committees. Eighty-two patients with LABC were included for final analysis. Primary tumors were scanned using a clinical ultrasound system before NAC was started. The tumors were contoured, and radiofrequency data were acquired and processed from whole tumor regions of interest. QUS spectral parameters were derived from the normalized power spectrum, and texture analysis was performed based on six QUS features using a gray level co-occurrence matrix. Patients were divided into responder or nonresponder classes based on their clinical-pathological response. Classification analysis was performed using machine learning algorithms, which were trained to optimize classification accuracy. Cross-validation was performed using a leave-one-out cross-validation method. RESULTS Based on the clinical outcomes of NAC treatment, there were 48 responders and 34 nonresponders. A K-nearest neighbors (K-NN) approach resulted in the best classifier performance, with a sensitivity of 91%, a specificity of 83%, and an accuracy of 87%. CONCLUSION QUS-based radiomics can predict response to NAC based on pretreatment features with acceptable accuracy.
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Quiaoit K, DiCenzo D, Fatima K, Bhardwaj D, Sannachi L, Gangeh M, Sadeghi-Naini A, Dasgupta A, Kolios MC, Trudeau M, Gandhi S, Eisen A, Wright F, Look-Hong N, Sahgal A, Stanisz G, Brezden C, Dinniwell R, Tran WT, Yang W, Curpen B, Czarnota GJ. Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results. PLoS One 2020; 15:e0236182. [PMID: 32716959 PMCID: PMC7384762 DOI: 10.1371/journal.pone.0236182] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 06/30/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Neoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting. METHODS Fifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA), and Princess Margaret Cancer Centre (Toronto, Canada)). QUS data were collected before starting NAC and subsequently at weeks 1 and 4 during chemotherapy. Spectral tumour parametric maps were generated, and textural features determined using grey-level co-occurrence matrices. Patients were divided into two groups based on their pathological outcomes following surgery: responders and non-responders. Machine learning algorithms using Fisher's linear discriminant (FLD), K-nearest neighbour (K-NN), and support vector machine (SVM-RBF) were used to generate response classification models. RESULTS Thirty-six patients were classified as responders and twenty-three as non-responders. Among all the models, SVM-RBF had the highest accuracy of 81% at both weeks 1 and week 4 with area under curve (AUC) values of 0.87 each. The inclusion of week 1 and 4 features led to an improvement of the classifier models, with the accuracy and AUC from baseline features only being 76% and 0.68, respectively. CONCLUSION QUS data obtained during NAC reflect the ongoing treatment-related changes during chemotherapy and can lead to better classifier performances in predicting the ultimate pathologic response to treatment compared to baseline features alone.
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Affiliation(s)
- Karina Quiaoit
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Daniel DiCenzo
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Kashuf Fatima
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Divya Bhardwaj
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Lakshmanan Sannachi
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Mehrdad Gangeh
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Ali Sadeghi-Naini
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada
| | - Archya Dasgupta
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Maureen Trudeau
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Sonal Gandhi
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Andrea Eisen
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Frances Wright
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Surgery, University of Toronto, Toronto, Canada
| | - Nicole Look-Hong
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Surgery, University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Greg Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Christine Brezden
- Department of Medical Oncology, Saint Michael's Hospital, University of Toronto, Toronto, Canada
| | - Robert Dinniwell
- Department of Radiation Oncology, Princess Margaret Hospital, University Health Network, Toronto, Canada
- Department of Radiation Oncology, London Health Sciences Centre, London, Canada
- Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - William T. Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Wei Yang
- Department of Diagnostic Radiology, University of Texas, M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Gregory J. Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada
- Department of Physics, Ryerson University, Toronto, Canada
- * E-mail:
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Orlando A, Dimarco M, Cannella R, Bartolotta TV. Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art. Artif Intell Med Imaging 2020; 1:6-18. [DOI: 10.35711/aimi.v1.i1.6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/17/2020] [Accepted: 06/19/2020] [Indexed: 02/06/2023] Open
Abstract
Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI.
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Affiliation(s)
- Alessia Orlando
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Mariangela Dimarco
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Roberto Cannella
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Tommaso Vincenzo Bartolotta
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
- Department of Radiology, Fondazione Istituto Giuseppe Giglio, Ct.da Pietrapollastra, Palermo 90015, Italy
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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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Preliminary study on discriminating HER2 2+ amplification status of breast cancers based on texture features semi-automatically derived from pre-, post-contrast, and subtraction images of DCE-MRI. PLoS One 2020; 15:e0234800. [PMID: 32555662 PMCID: PMC7299320 DOI: 10.1371/journal.pone.0234800] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 06/02/2020] [Indexed: 01/10/2023] Open
Abstract
Objective To investigate whether texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with human epidermal growth factor receptor type 2 (HER2) 2+ status of breast cancer. Materials and methods 92 MRI cases including 52 HER2 2+ positive and 40 negative patients confirmed by fluorescence in situ hybridization were retrospectively selected. The lesion area was semi-automatically delineated, and a total of 488 texture features were respectively extracted from precontrast, postcontrast, and subtraction images. The Student’s t-test or Mann-Whitney U test was performed to identify statistically significant features between different HER2 2+ amplification groups. Least absolute shrinkage and selection operator (LASSO) was used to search for the optimal feature subsets. Three machine learning classifiers, logistic regression analysis (LRA), quadratic discriminant analysis (QDA), and support vector machine (SVM), were used with a leave-one-out cross validation method to establish the classification models of HER2 2+ status. Classification performance was evaluated by receiver operating characteristic (ROC) analysis. Results Based on the texture analysis with SVM model, the areas under the ROC curve (AUCs) were 0.890 for subtraction images, 0.736 for postcontrast images, and 0.672 for precontrast images, respectively. For LRA model, the AUCs were 0.884, 0.733, and 0.623, respectively. For QDA model, the AUCs were 0.831, 0.726, and 0.568, respectively. LRA and the SVM model with subtraction images reached significantly better performance than the QDA model (P = 0.0227 and P = 0.0088, respectively). Conclusion Texture features of breast cancer extracted from DCE-MRI are associated with HER2 2+ status. Additional studies are necessary to confirm the present preliminary findings.
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Pan S, Ding Z, Zhang L, Ruan M, Shan Y, Deng M, Pang P, Shen Q. A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian Cystadenomas. Front Oncol 2020; 10:895. [PMID: 32547958 PMCID: PMC7277787 DOI: 10.3389/fonc.2020.00895] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 05/06/2020] [Indexed: 12/26/2022] Open
Abstract
Objective: To construct and validate a combined Nomogram model based on radiomic and semantic features to preoperatively classify serous and mucinous pathological types in patients with ovarian cystadenoma. Methods: A total of 103 patients with pathology-confirmed ovarian cystadenoma who underwent CT examination were collected from two institutions. All cases divided into training cohort (N = 73) and external validation cohort (N = 30). The CT semantic features were identified by two abdominal radiologists. The preprocessed initial CT images were used for CT radiomic features extraction. The LASSO regression were applied to identify optimal radiomic features and construct the Radscore. A Nomogram model was constructed combining the Radscore and the optimal semantic feature. The model performance was evaluated by ROC analysis, calibration curve and decision curve analysis (DCA). Result: Five optimal features were ultimately selected and contributed to the Radscore construction. Unilocular/multilocular identification was significant difference from semantic features. The Nomogram model showed a better performance in both training cohort (AUC = 0.94, 95%CI 0.86–0.98) and external validation cohort (AUC = 0.92, 95%CI 0.76–0.98). The calibration curve and DCA analysis indicated a better accuracy of the Nomogram model for classification than either Radscore or the loculus alone. Conclusion: The Nomogram model combined radiomic and semantic features could be used as imaging biomarker for classification of serous and mucinous types of ovarian cystadenomas.
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Affiliation(s)
- Shushu Pan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lexing Zhang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mei Ruan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yanna Shan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Meixiang Deng
- Department of Radiology, Women's Hospital School of Medicine Zhejiang University, Hangzhou, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Qijun Shen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Sutton EJ, Onishi N, Fehr DA, Dashevsky BZ, Sadinski M, Pinker K, Martinez DF, Brogi E, Braunstein L, Razavi P, El-Tamer M, Sacchini V, Deasy JO, Morris EA, Veeraraghavan H. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy. Breast Cancer Res 2020; 22:57. [PMID: 32466777 PMCID: PMC7254668 DOI: 10.1186/s13058-020-01291-w] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 05/10/2020] [Indexed: 12/13/2022] Open
Abstract
Background For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and validate a radiomics classifier that classifies breast cancer pCR post-NAC on MRI prior to surgery. Methods This retrospective study included women treated with NAC for breast cancer from 2014 to 2016 with (1) pre- and post-NAC breast MRI and (2) post-NAC surgical pathology report assessing response. Automated radiomics analysis of pre- and post-NAC breast MRI involved image segmentation, radiomics feature extraction, feature pre-filtering, and classifier building through recursive feature elimination random forest (RFE-RF) machine learning. The RFE-RF classifier was trained with nested five-fold cross-validation using (a) radiomics only (model 1) and (b) radiomics and molecular subtype (model 2). Class imbalance was addressed using the synthetic minority oversampling technique. Results Two hundred seventy-three women with 278 invasive breast cancers were included; the training set consisted of 222 cancers (61 pCR, 161 no-pCR; mean age 51.8 years, SD 11.8), and the independent test set consisted of 56 cancers (13 pCR, 43 no-pCR; mean age 51.3 years, SD 11.8). There was no significant difference in pCR or molecular subtype between the training and test sets. Model 1 achieved a cross-validation AUROC of 0.72 (95% CI 0.64, 0.79) and a similarly accurate (P = 0.1) AUROC of 0.83 (95% CI 0.71, 0.94) in both the training and test sets. Model 2 achieved a cross-validation AUROC of 0.80 (95% CI 0.72, 0.87) and a similar (P = 0.9) AUROC of 0.78 (95% CI 0.62, 0.94) in both the training and test sets. Conclusions This study validated a radiomics classifier combining radiomics with molecular subtypes that accurately classifies pCR on MRI post-NAC.
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Affiliation(s)
- Elizabeth J Sutton
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Natsuko Onishi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Duc A Fehr
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Brittany Z Dashevsky
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meredith Sadinski
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Danny F Martinez
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Edi Brogi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lior Braunstein
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pedram Razavi
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mahmoud El-Tamer
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Virgilio Sacchini
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elizabeth A Morris
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Comparison of Model-Free and Model-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Parameters for Predicting Breast Cancers' Response to Neoadjuvant Chemotherapy. J Comput Assist Tomogr 2020; 44:269-274. [PMID: 32195807 DOI: 10.1097/rct.0000000000001001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To prospectively compare the performance of model-based and model-free dynamic contrast-enhanced (DCE) pharmacokinetic parameters in monitoring breast cancers' early response to neoadjuvant chemotherapy (NACT). METHODS Sixty patients, with 61 pathology-proven breast cancers, were examined using DCE magnetic resonance imaging before, after the first cycle, and after full cycles of NACT. Both model-based (Ktrans and others) and model-free parameters, mainly time-intensity curve (TIC), were measured. According to Miller-Payne grading, patients were divided into response and nonresponse group. Mann-Whitney U test, Fisher exact test, multivariate logistic regression, and receiver operating characteristic curve were used in analysis. RESULTS After the first cycle, among all the parameters, Ktrans and TIC were strongly associated with tumors' early response. There was no significant difference between the areas under receiver operating characteristic curve of Ktrans and TIC (0.768, 0.852, respectively). CONCLUSIONS Model-based and model-free DCE parameters, especially Ktrans and TIC, have similar performance in predicting the efficacy of NACT for breast cancers.
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Zhou J, Lu J, Gao C, Zeng J, Zhou C, Lai X, Cai W, Xu M. Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI. BMC Cancer 2020; 20:100. [PMID: 32024483 PMCID: PMC7003343 DOI: 10.1186/s12885-020-6523-2] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/07/2020] [Indexed: 02/06/2023] Open
Abstract
Background The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC). Methods Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1) + (2), Model III: group (3), Model IV: group (1) + (3), Model V: group (2) + (3), and Model VI: group (1) + (2) + (3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC). Results The AUCs of the six models for the prediction of pCR were 0.816 ± 0.033 (Model I), 0.823 ± 0.020 (Model II), 0.888 ± 0.025 (Model III), 0.876 ± 0.015 (Model IV), 0.885 ± 0.030 (Model V), and 0.874 ± 0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance. Conclusions Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.
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Affiliation(s)
- Jiali Zhou
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.,Ningbo First Hospital, Ningbo, China
| | - Jinghui Lu
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St., 400C, Boston, MA, 02114, USA
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jingjing Zeng
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Changyu Zhou
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaobo Lai
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Wenli Cai
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St., 400C, Boston, MA, 02114, USA.
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China. .,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
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Fusco R, Granata V, Maio F, Sansone M, Petrillo A. Textural radiomic features and time-intensity curve data analysis by dynamic contrast-enhanced MRI for early prediction of breast cancer therapy response: preliminary data. Eur Radiol Exp 2020; 4:8. [PMID: 32026095 PMCID: PMC7002809 DOI: 10.1186/s41747-019-0141-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 12/05/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND To investigate the potential of semiquantitative time-intensity curve parameters compared to textural radiomic features on arterial phase images by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for early prediction of breast cancer neoadjuvant therapy response. METHODS A retrospective study of 45 patients subjected to DCE-MRI by public datasets containing examination performed prior to the start of treatment and after the treatment first cycle ('QIN Breast DCE-MRI' and 'QIN-Breast') was performed. In total, 11 semiquantitative parameters and 50 texture features were extracted. Non-parametric test, receiver operating characteristic analysis with area under the curve (ROC-AUC), Spearman correlation coefficient, and Kruskal-Wallis test with Bonferroni correction were applied. RESULTS Fifteen patients with pathological complete response (pCR) and 30 patients with non-pCR were analysed. Significant differences in median values between pCR patients and non-pCR patients were found for entropy, long-run emphasis, and busyness among the textural features, for maximum signal difference, washout slope, washin slope, and standardised index of shape among the dynamic semiquantitative parameters. The standardised index of shape had the best results with a ROC-AUC of 0.93 to differentiate pCR versus non-pCR patients. CONCLUSIONS The standardised index of shape could become a clinical tool to differentiate, in the early stages of treatment, responding to non-responding patients.
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Affiliation(s)
- Roberta Fusco
- Radiology Division, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, Naples, Italy.
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, Naples, Italy
| | - Francesca Maio
- Radiology Division, Universita' Degli Stui di Napoli Federico II, Via Pansini, Naples, Italy
| | - Mario Sansone
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples Federico II, Via Claudio, Naples, Italy
| | - Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, Naples, Italy
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Machireddy A, Thibault G, Tudorica A, Afzal A, Mishal M, Kemmer K, Naik A, Troxell M, Goranson E, Oh K, Roy N, Jafarian N, Holtorf M, Huang W, Song X. Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps. ACTA ACUST UNITED AC 2020; 5:90-98. [PMID: 30854446 PMCID: PMC6403033 DOI: 10.18383/j.tom.2018.00046] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (Ktrans), in the training and testing sets, respectively. The differences in AUC were statistically significant (P < .05) for several parametric maps. Thus, multiresolution analysis that decomposes the texture at various spatial-frequency scales may more accurately capture changes in tumor vascular heterogeneity as measured by DCE-MRI, and therefore provide better early prediction of NACT response.
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Affiliation(s)
| | | | | | - Aneela Afzal
- Oregon Health and Science University, Portland, OR
| | - May Mishal
- Oregon Health and Science University, Portland, OR
| | | | - Arpana Naik
- Oregon Health and Science University, Portland, OR
| | | | | | - Karen Oh
- Oregon Health and Science University, Portland, OR
| | - Nicole Roy
- Oregon Health and Science University, Portland, OR
| | | | | | - Wei Huang
- Oregon Health and Science University, Portland, OR
| | - Xubo Song
- Oregon Health and Science University, Portland, OR
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Shao J, Zhang Z, Liu H, Song Y, Yan Z, Wang X, Hou Z. DCE-MRI pharmacokinetic parameter maps for cervical carcinoma prediction. Comput Biol Med 2020; 118:103634. [PMID: 32174312 DOI: 10.1016/j.compbiomed.2020.103634] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 01/15/2020] [Accepted: 01/27/2020] [Indexed: 12/30/2022]
Abstract
Pharmacokinetic parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time course data enable the physio-biological interpretation of tissue angiogenesis. This study aims to develop machine learning approaches for cervical carcinoma prediction based on pharmacokinetic parameters. The performance of individual parameters was assessed in terms of their efficacy in differentiating cancerous tissue from normal cervix tissue. The effect of combining parameters was evaluated using the following two approaches: the first approach was based on support vector machines (SVMs) to combine the parameters from one pharmacokinetic model or across several models; the second approach was based on a novel method called APITL (artificial pharmacokinetic images for transfer learning), which was designed to fully utilize the comprehensive pharmacokinetic information acquired from DCE-MRI data. A "winner-takes-all" strategy was employed to consolidate the slice-wise prediction into subject-wise prediction. Experiments were carried out with a dataset comprising 36 patients with cervical cancer and 17 healthy subjects. The results demonstrated that parameter Ve, representing volume fraction of the extracellular extravascular space (EES), attained high discriminative power regardless of the pharmacokinetic model used for estimation. An approximately 10% improvement in the accuracy was achieved with the SVM approach. The APITL method further outperformed SVM and attained a subject-wise prediction accuracy of 94.3%. Our experiment demonstrated that APITL could predict cervical carcinoma with high accuracy and had potential in clinical applications.
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Affiliation(s)
| | - Zhuo Zhang
- Institute for Infocomm Research, Singapore.
| | | | - Ying Song
- Institute for Infocomm Research, Singapore
| | - Zhihan Yan
- The Second Affiliated Hospital of Wenzhou Medical University, China
| | - Xue Wang
- The Second Affiliated Hospital of Wenzhou Medical University, China
| | - Zujun Hou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China
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Texture Analysis of Dynamic Contrast-Enhanced MRI in Evaluating Pathologic Complete Response (pCR) of Mass-Like Breast Cancer after Neoadjuvant Therapy. JOURNAL OF ONCOLOGY 2019; 2019:4731532. [PMID: 31949430 PMCID: PMC6944972 DOI: 10.1155/2019/4731532] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 11/28/2019] [Accepted: 12/09/2019] [Indexed: 01/09/2023]
Abstract
Objectives MRI is the standard imaging method in evaluating treatment response of breast cancer after neoadjuvant therapy (NAT), while identification of pathologic complete response (pCR) remains challenging. Texture analysis (TA) on post-NAT dynamic contrast-enhanced (DCE) MRI was explored to assess the existence of pCR in mass-like cancer. Materials and Methods A primary cohort of 112 consecutive patients (40 pCR and 72 non-pCR) with mass-like breast cancers who received preoperative NAT were retrospectively enrolled. On post-NAT MRI, volumes of the residual-enhanced areas and TA first-order features (19 for each sequence) of the corresponding areas were achieved for both early- and late-phase DCE using an in-house radiomics software. Groups were divided according to the operational pathology. Receiver operating characteristic curves and binary logistic regression analysis were used to select features and achieve a predicting formula. Overall diagnostic abilities were compared between TA and radiologists' subjective judgments. Validation was performed on a time-independent cohort of 39 consecutive patients. Results TA features with high consistency (Cronbach's alpha >0.9) between 2 observers showed significant differences between pCR and non-pCR groups. Logistic regression using features selected by ROC curves generated a synthesized formula containing 3 variables (volume of residual enhancement, entropy, and robust mean absolute deviation from early-phase) to yield AUC = 0.81, higher than that of using radiologists' subjective judgment (AUC = 0.72), and entropy was an independent risk factor (P < 0.001). Accuracy and sensitivity for identifying pCR were 83.93% and 70.00%. AUC of the validation cohort was 0.80. Conclusions TA may help to improve the diagnostic ability of post-NAT MRI in identifying pCR in mass-like breast cancer. Entropy, as a first-order feature to depict residual tumor heterogeneity, is an important factor.
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Eun NL, Kang D, Son EJ, Park JS, Youk JH, Kim JA, Gweon HM. Texture Analysis with 3.0-T MRI for Association of Response to Neoadjuvant Chemotherapy in Breast Cancer. Radiology 2019; 294:31-41. [PMID: 31769740 DOI: 10.1148/radiol.2019182718] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background Previous studies have suggested that texture analysis is a promising tool in the diagnosis, characterization, and assessment of treatment response in various cancer types. Therefore, application of texture analysis may be helpful for early prediction of pathologic response in breast cancer. Purpose To investigate whether texture analysis of features from MRI is associated with pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. Materials and Methods This retrospective study included 136 women (mean age, 47.9 years; range, 31-70 years) who underwent NAC and subsequent surgery for breast cancer between January 2012 and August 2017. Patients were monitored with 3.0-T MRI before (pretreatment) and after (midtreatment) three or four cycles of NAC. Texture analysis was performed at pre- and midtreatment T2-weighted MRI, contrast material-enhanced T1-weighted MRI, diffusion-weighted MRI, and apparent diffusion coefficient (ADC) mapping by using commercial software. A random forest method was applied to build a predictive model for classifying those with pCR with use of texture parameters. Diagnostic performance for predicting pCR was assessed and compared with that of six other machine learning classifiers (adaptive boosting, decision tree, k-nearest neighbor, linear support vector machine, naive Bayes, and linear discriminant analysis) by using the Wald test and DeLong method. Results Forty of the 136 patients (29%) achieved pCR after NAC. In the prediction of pCR, the random forest classifier showed the lowest diagnostic performance with pretreatment ADC (area under the receiver operating characteristic curve [AUC], 0.53; 95% confidence interval: 0.44, 0.61) and the highest diagnostic performance with midtreatment contrast-enhanced T1-weighted MRI (AUC, 0.82; 95% confidence interval: 0.74, 0.88) among pre- and midtreatment T2-weighted MRI, contrast-enhanced T1-weighted MRI, diffusion-weighted MRI, and ADC mapping. Conclusion Texture parameters using a random forest method of contrast-enhanced T1-weighted MRI at midtreatment of neoadjuvant chemotherapy were valuable and associated with pathologic complete response in breast cancer. © RSNA, 2019 Online supplemental material is available for this article.
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Affiliation(s)
- Na Lae Eun
- From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)
| | - Daesung Kang
- From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)
| | - Eun Ju Son
- From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)
| | - Jeong Seon Park
- From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)
| | - Ji Hyun Youk
- From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)
| | - Jeong-Ah Kim
- From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)
| | - Hye Mi Gweon
- From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)
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Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review. Eur J Radiol 2019; 121:108736. [PMID: 31734639 DOI: 10.1016/j.ejrad.2019.108736] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 09/26/2019] [Accepted: 10/31/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE MRI-based tumor response prediction to neoadjuvant systemic therapy (NST) in breast cancer patients is increasingly being studied using radiomics with outcomes that appear to be promising. The aim of this study is to systematically review the current literature and reflect on its quality. METHODS PubMed and EMBASE databases were systematically searched for studies investigating MRI-based radiomics for tumor response prediction. Abstracts were screened by two reviewers independently. The quality of the radiomics workflow of eligible studies was assessed using the Radiomics Quality Score (RQS). An overview of the methodologies used in steps of the radiomics workflow and current results are presented. RESULTS Sixteen studies were included with cohort sizes ranging from 35 to 414 patients. The RQS scores varied from 0 % to 41.2 %. Methodologies in the radiomics workflow varied greatly, especially region of interest segmentation, features selection, and model development with heterogeneous outcomes as a result. Seven studies applied univariate analysis and nine studies applied multivariate analysis. Most studies performed their analysis on the pretreatment dynamic contrast-enhanced T1-weighted sequence. Entropy was the best performing individual feature with AUC values ranging from 0.83 to 0.85. The best performing multivariate prediction model, based on logistic regression analysis, scored a validation AUC of 0.94. CONCLUSION This systematic review revealed large methodological heterogeneity for each step of the MRI-based radiomics workflow, consequently, the (overall promising) results are difficult to compare. Consensus for standardization of MRI-based radiomics workflow for tumor response prediction to NST in breast cancer patients is needed to further improve research.
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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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Kontopodis E, Venianaki M, Manikis GC, Nikiforaki K, Salvetti O, Papadaki E, Papadakis GZ, Karantanas AH, Marias K. Investigating the Role of Model-Based and Model-Free Imaging Biomarkers as Early Predictors of Neoadjuvant Breast Cancer Therapy Outcome. IEEE J Biomed Health Inform 2019; 23:1834-1843. [DOI: 10.1109/jbhi.2019.2895459] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Liu S, Wen L, Hou J, Nie S, Zhou J, Cao F, Lu Q, Qin Y, Fu Y, Yu X. Predicting the pathological response to chemoradiotherapy of non-mucinous rectal cancer using pretreatment texture features based on intravoxel incoherent motion diffusion-weighted imaging. Abdom Radiol (NY) 2019; 44:2689-2698. [PMID: 31030244 DOI: 10.1007/s00261-019-02032-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVES To investigate the performance of the mean parametric values and texture features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) on identifying pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). METHODS Pretreatment IVIM-DWI was performed on 41 LARC patients receiving nCRT in this prospective study. The values of IVIM-DWI parameters (apparent diffusion coefficient, ADC; pure diffusion coefficient, D; pseudo-diffusion coefficient, D* and perfusion fraction, f), the first-order, and gray-level co-occurrence matrix (GLCM) texture features were compared between the pCR (n = 9) and non-pathological responder (non-pCR, n = 32) groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine the efficiency for identifying pCR. RESULTS The values of IVIM-DWI parameters and first-order texture features did not show significant differences between the pCR and non-pCR groups. The pCR group had lower Contrast and DifVarnc values extracted from the ADC, D, and D* maps, respectively, as well as lower CorrelatD value. Higher CorrelatD*, Correlatf, SumAvergADC, and SumAvergD values were observed in the pCR group. The area under the ROC curve (AUC) values for the individual predictors in univariate analysis ranged from 0.698 to 0.837, with sensitivities from 43.75% to 87.50% and specificities from 66.67 to 100.00%. In multivariate analysis, CorrelatD* (P < 0.001), DifVarncADC (P = 0.024), and DifVarncD (P < 0.001) were the independent predictors to pCR, with an AUC of 0.986, a sensitivity of 93.75%, and a specificity of 100.00%. CONCLUSION Pretreatment GLCM analysis based on IVIM-DWI may be a potential approach to identify the pathological response of LARC.
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Affiliation(s)
- Siye Liu
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China
| | - Lu Wen
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China
| | - Jing Hou
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China
| | - Shaolin Nie
- Department of Colorectal Surgery, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410006, Hunan, People's Republic of China
| | - Jumei Zhou
- Department of Radiotherapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410006, Hunan, People's Republic of China
| | - Fang Cao
- Department of Pathology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410006, Hunan, People's Republic of China
| | - Qiang Lu
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China
| | - Yuhui Qin
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China
| | - Yi Fu
- Department of Medical Service, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410006, Hunan, People's Republic of China
| | - Xiaoping Yu
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China.
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Jun W, Cong W, Xianxin X, Daqing J. Meta-Analysis of Quantitative Dynamic Contrast-Enhanced MRI for the Assessment of Neoadjuvant Chemotherapy in Breast Cancer. Am Surg 2019. [DOI: 10.1177/000313481908500630] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The purpose of this meta-analysis was to determine the value of quantitative dynamic contrast-enhanced (DCE) MRI (DCE-MRI) in evaluating the response of breast cancer to neoadjuvant chemotherapy (NAC). PubMed, Embase, and Cochrane Library databases (from building to July 31, 2018) were searched to collect articles about the therapeutic evaluation of NAC using the quantitative DCE-MRI in patients with breast cancer. The sensitivities and specificities of quantitative DCE-MRI in the evaluation of NAC for breast cancer were extracted from the articles. Meta-DiSc1.4 was applied to evaluate the efficacy of the sensitivity and specificity; forest figure and summary receiver operating characteristics (SROC) were created. A total of 356 articles were enrolled in this study, including 739 cases in total, in which 218 cases were effective and the other 521 cases were ineffective to NAC, considering the pathological results as the gold standard. The sensitivity and specificity in the included 14 articles of quantitative DCE-MRI ( Ktrans, kep, and ve) in comprehensively evaluating NAC for breast cancer were 84 per cent (95% confidence interval (CI): 78–88%) and 83 per cent (95% CI: 79–86%), respectively. The area under SROC was 0.899 (95% CI: 0.867–0.943). The sensitivity and specificity in the three articles of Ktrans evaluating NAC for breast cancer were 84.1 per cent (95% CI: 71.0–92.1%) and 81.3 per cent (95% CI: 70.5%-88.5%), respectively. The area under SROC was 0.899 (95% CI: 0.834–0.962). Our study confirmed that the quantitative DCE-MRI is able to monitor NAC treatment for breast cancer because of its high sensitivity and specificity. However, there is a high degree of heterogeneity in published studies, highlighting the lack of standardization in the field.
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Affiliation(s)
- Wei Jun
- Department of Breast Surgery, Cancer Hospital of China Medical University, Shenyang, China
| | - Wang Cong
- Department of Breast Surgery, Cancer Hospital of China Medical University, Shenyang, China
| | - Xie Xianxin
- Department of Breast Surgery, Cancer Hospital of China Medical University, Shenyang, China
| | - Jiang Daqing
- Department of Breast Surgery, Cancer Hospital of China Medical University, Shenyang, China
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Chitalia RD, Kontos D. Role of texture analysis in breast MRI as a cancer biomarker: A review. J Magn Reson Imaging 2018; 49:927-938. [PMID: 30390383 DOI: 10.1002/jmri.26556] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 10/11/2018] [Accepted: 10/12/2018] [Indexed: 12/26/2022] Open
Abstract
Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis for breast cancer. MRI can provide a whole-tumor sampling of disease burden and is widely utilized in clinical care. Texture analysis can provide a localized description of breast cancer, with particular emphasis on quantifying breast lesion heterogeneity. The object of this review is to provide an overview of texture analysis applications towards breast cancer diagnosis, prognosis, and treatment response evaluation and review the role of image-based texture features as noninvasive prognostic and predictive biomarkers. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:927-938.
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Affiliation(s)
- Rhea D Chitalia
- Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Nagasaka K, Satake H, Ishigaki S, Kawai H, Naganawa S. Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer. Breast Cancer 2018; 26:113-124. [PMID: 30069785 DOI: 10.1007/s12282-018-0899-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 07/26/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND Breast cancer heterogeneity influences poor prognoses thorough therapy resistance. This study quantitatively evaluated intratumoral heterogeneity through a histogram analysis of dynamic contrast-enhanced MRI (DCE-MRI) pharmacokinetic parameters, and determined correlations with prognostic factors and molecular subtypes. METHODS We retrospectively investigated 101 invasive ductal breast cancers from 99 women who underwent preoperative DCE-MRI between July 2012 and November 2014. Pharmacokinetic parameters (Ktrans, kep, and ve) were obtained by the Tofts model. For each parameter, the mean, standard deviation, coefficient of variation, skewness, and kurtosis values of tumor were calculated, and prognostic factors and subtypes associations were assessed. RESULTS The mean of ve was lower in cancers with high Ki-67 than in cancers with low Ki-67 (P = 0.002). The coefficient of variation of ve was higher in cancers with estrogen receptor negativity than in cancers with estrogen receptor positivity (P < 0.001). The coefficient of variation of ve was also higher in cancers with high Ki-67 than in cancers with low Ki-67 (P < 0.001). The skewness of ve was higher in cancers with high nuclear grade than in cancers with low nuclear grade (P = 0.006). Triple-negative cancers showed higher ve coefficient of variation than did those with luminal A (P < 0.001) and B (P = 0.006). CONCLUSIONS Various ve parameters correlated with breast cancer prognostic factors and molecular subtypes.
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Affiliation(s)
- Ken Nagasaka
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan.
| | - Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Satoko Ishigaki
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Hisashi Kawai
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
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Klein J, Lam WW, Czarnota GJ, Stanisz GJ. Chemical exchange saturation transfer MRI to assess cell death in breast cancer xenografts at 7T. Oncotarget 2018; 9:31490-31501. [PMID: 30140385 PMCID: PMC6101146 DOI: 10.18632/oncotarget.25844] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 07/12/2018] [Indexed: 12/23/2022] Open
Abstract
Purpose Detecting cell death and predicting tumor response early in a course of chemotherapy could help optimize treatment regimens and improve clinical outcomes. Chemical exchange saturation transfer (CEST) MRI was investigated in vivo to study properties that may be able to detect cancer death. Results Using a magnetization transfer ratio (MTR) cutoff of 0.12 at 1.8 ppm was able to differentiate between viable tumor and cell death regions. Comparison of MTR values at this frequency showed significant differences (p < 0.0001) between viable tumor and cell death regions, matching patterns seen on histology. Using this cutoff, the mean increase in cell death index (± standard error of the mean) after chemotherapy was 4 ± 4%, 10% ± 7%, 10 ± 8%, and 4 ± 9% at 4, 8, 12, and 24 h, respectively. Conclusions CEST MRI can detect cell death in MDA-231 xenografts but further work is needed to characterize the clinical applications of this finding. Maximum response to chemotherapy occurred at 8-12 h after chemotherapy injection in this in vivo tumor model. Materials and Methods Breast cancer xenografts (MDA-MB-231) were scanned using 7 T MRI before and after chemotherapy. As a measure of CEST effect at 0.5 µT saturation amplitude, MTR values at frequency offsets of 1.8 and -3.3 ppm were evaluated. CEST signals after chemotherapy treatment were compared to cell-death histopathology of tumors.
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Affiliation(s)
- Jonathan Klein
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Wilfred W Lam
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Gregory J Czarnota
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Greg J Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Neurosurgery and Pediatric Neurosurgery, Medical University of Lublin, Lublin, Poland
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Qin Y, Yu X, Hou J, Hu Y, Li F, Wen L, Lu Q, Fu Y, Liu S. Predicting chemoradiotherapy response of nasopharyngeal carcinoma using texture features based on intravoxel incoherent motion diffusion-weighted imaging. Medicine (Baltimore) 2018; 97:e11676. [PMID: 30045324 PMCID: PMC6078652 DOI: 10.1097/md.0000000000011676] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The aim of the study was to investigative the utility of gray-level co-occurrence matrix (GLCM) texture analysis based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the early response to chemoradiotherapy for nasopharyngeal carcinoma (NPC).Baseline IVIM-DWI was performed on 81 patients with NPC receiving chemoradiotherapy in a prospective nested case-control study. The patients were categorized into the residue (n = 11) and nonresidue (n = 70) groups, according to whether there was local residual lesion or not at the end of chemoradiotherapy. The pretreatment tumor volume and the values of IVIM-DWI parameters (apparent diffusion coefficient [ADC], D, D, and f) and GLCM features based on IVIM-DWI were compared between the 2 groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine significant indicator of treatment response.The nonresidue group had lower tumor volume, ADC, D, CorrelatADC, CorrelatD, InvDfMomADC, InvDfMomD and InvDfMomD values, together with higher ContrastD, Contrastf, SumAvergADC, SumAvergD, and SumAvergD values, than the residue group (all P < .05). Based on ROC curve in univariate analysis, the area under the curve (AUC) values for individual GLCM features in the prediction of the treatment response ranged from 0.635 to 0.879, with sensitivities from 54.55% to 100.00% and specificities from 52.86% to 85.71%. Multivariate logistic regression analysis demonstrated D (P = .026), InvDfMomADC (P = .033) and SumAvergD (P = .015) as the independent predictors for identifying NPC without residue, with an AUC value of 0.977, a sensitivity of 90.91% and a specificity of 95.71%.Pretreatment GLCM features based on IVIM-DWI, especially on the diffusion-related maps, may have the potential to predict the early response to chemoradiotherapy for NPC.
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Affiliation(s)
| | | | - Jing Hou
- Department of Diagnostic Radiology
| | | | | | - Lu Wen
- Department of Diagnostic Radiology
| | - Qiang Lu
- Department of Diagnostic Radiology
| | - Yi Fu
- Department of Medical Service, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University and Hunan Cancer Hospital, Changsha, Hunan, China
| | - Siye Liu
- Department of Diagnostic Radiology
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