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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] [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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Lo Gullo R, Marcus E, Huayanay J, Eskreis-Winkler S, Thakur S, Teuwen J, Pinker K. Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction. Invest Radiol 2024; 59:230-242. [PMID: 37493391 PMCID: PMC10818006 DOI: 10.1097/rli.0000000000001010] [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] [Indexed: 07/27/2023]
Abstract
ABSTRACT Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Jorge Huayanay
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Radiology, National Institute of Neoplastic Diseases, Lima, Peru
| | - Sarah Eskreis-Winkler
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Sunitha Thakur
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jonas Teuwen
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
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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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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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Patkulkar P, Subbalakshmi AR, Jolly MK, Sinharay S. Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis. ACS OMEGA 2023; 8:6126-6138. [PMID: 36844580 PMCID: PMC9948167 DOI: 10.1021/acsomega.2c06659] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 01/05/2023] [Indexed: 05/14/2023]
Abstract
Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics, and spatial transcriptomics allow recording of patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multiscale dynamics of its evolution. Here, we review the latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed burgeoning growth in the recent past in terms of mapping heterogeneity within tumor cell types as well as the stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor and a more systematic investigation of the implications of heterogeneity for patient outcomes.
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Sherminie LPG, Jayatilake ML. Fractal Dimension Analysis of Pixel Dynamic Contrast Enhanced-Magnetic Resonance Imaging Pharmacokinetic Parameters for Discrimination of Benign and Malignant Breast Lesions. JCO Clin Cancer Inform 2023; 7:e2200101. [PMID: 36745858 DOI: 10.1200/cci.22.00101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
PURPOSE Breast cancer is the most frequent cancer in women worldwide. However, its diagnosis mostly depends on visual examination of radiologic images, leading to an overdiagnosis with substantial costs. Therefore, a quantitative approach such as dynamic contrast enhanced (DCE)-magnetic resonance imaging (MRI) through pharmacokinetic (PK) modeling is required for reliable analysis. As PK parameters lack information on parameter heterogeneity, texture-based analysis is required to quantify PK parameter heterogeneity. Therefore, this study focused on determining the usefulness of fractal dimension (FD) as a potential imaging biomarker of tumor heterogeneity for discriminating benign and malignant breast lesions. METHODS Parametric maps for PK parameters, extravasation rate of contrast agent from blood plasma to extravascular extracellular space (Ktrans) and volume fraction of extravascular extracellular space (ve), were generated for the regions of interest (ROIs) under the standard model using 18 lesions. Then, tumor ROI and pixel DCE-MRI time-course data were analyzed to extract pixel values of Ktrans and ve. For each ROI, FD values of Ktrans and ve were computed using the blanket method. RESULTS The FD values of Ktrans for benign and malignant lesions varied from 2.96 to 3.49 and from 2.37 to 3.16, respectively, whereas FD values of ve for benign and malignant lesions varied from 3.01 to 5.15 and 2.42 to 3.44, respectively. There were significant differences in FD values derived from Ktrans parametric maps (P = .0053) and ve parametric maps (P = .0271) between benign and malignant lesions according to the statistical analysis. CONCLUSION Incorporating texture heterogeneity changes in breast lesions captured by FD with quantitative DCE-MRI parameters generated under the standard model is a potential marker for prediction of malignant lesions.
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Affiliation(s)
- Lahanda Purage G Sherminie
- Department of Nuclear Science, Faculty of Science, University of Colombo, Colombo, Sri Lanka.,Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Mohan L Jayatilake
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
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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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Li WZ, Wu G, Li TS, Dai GM, Liao YT, Yang QY, Chen F, Huang WY. Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma. Front Oncol 2022; 12:955866. [PMID: 36338711 PMCID: PMC9627984 DOI: 10.3389/fonc.2022.955866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 09/05/2022] [Indexed: 08/30/2023] Open
Abstract
To establish a multidimensional nomogram model for predicting progression-free survival (PFS) and risk stratification in patients with advanced nasopharyngeal carcinoma (NPC). This retrospective cross-sectional study included 156 patients with advanced NPC who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Radiomic features were extracted from the efflux rate constant (Ktrans ) and extracellular extravascular volume (Ve ) mapping derived from DCE-MRI. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied for feature selection. The Radscore was constructed using the selected features with their respective weights in the LASSO Cox regression analysis. A nomogram model combining the Radscore and clinical factors was built using multivariate Cox regression analysis. The C-index was used to assess the discrimination power of the Radscore and nomogram. The Kaplan-Meier method was used for survival analysis. Of the 360 radiomic features, 28 were selected (7, 6, and 15 features extracted from Ktrans , Ve, and Ktrans +Ve images, respectively). The combined Radscore k trans +Ve (C-index, 0.703, 95% confidence interval [CI]: 0.571-0.836) showed higher efficacy in predicting the prognosis of advanced NPC than Radscore k trans (C-index, 0.693; 95% CI, 0.560-0.826) and Radscore Ve (C-index, 0.614; 95% CI, 0.481-0.746) did. Multivariable Cox regression analysis revealed clinical stage, T stage, and treatment with nimotuzumab as risk factors for PFS. The nomogram established by Radscore k trans +Ve and risk factors (C-index, 0.732; 95% CI: 0.599-0.864) was better than Radscore k trans +Ve in predicting PFS in patients with advanced NPC. A lower Radscore k trans +Ve (HR 3.5584, 95% CI 2.1341-5.933), lower clinical stage (hazard ratio [HR] 1.5982, 95% CI 0.5262-4.854), lower T stage (HR 1.4365, 95% CI 0.6745-3.060), and nimotuzumab (NTZ) treatment (HR 0.7879, 95% CI 0.4899-1.267) were associated with longer PFS. Kaplan-Meier analysis showed a lower PFS in the high-risk group than in the low-risk group (p<0.0001). The nomogram based on combined pretreatment DCE-MRI radiomics features, NTZ, and clinicopathological risk factors may be considered as a noninvasive imaging marker for predicting individual PFS in patients with advanced NPC.
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Affiliation(s)
- Wen-zhu Li
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Tian-sheng Li
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Gan-mian Dai
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Yu-ting Liao
- Department of Pharmaceutical Diagnostics, GE Healthcare, Guangzhou, China
| | - Qian-yu Yang
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Wei-yuan Huang
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
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Ren Z, Pineda FD, Howard FM, Hill E, Szasz T, Safi R, Medved M, Nanda R, Yankeelov TE, Abe H, Karczmar GS. Differences Between Ipsilateral and Contralateral Early Parenchymal Enhancement Kinetics Predict Response of Breast Cancer to Neoadjuvant Therapy. Acad Radiol 2022; 29:1469-1479. [PMID: 35351365 DOI: 10.1016/j.acra.2022.02.008] [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: 10/25/2021] [Revised: 02/02/2022] [Accepted: 02/08/2022] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES To determine whether kinetics measured with ultrafast dynamic contrast-enhanced magnetic resonance imaging in tumor and normal parenchyma pre- and post-neoadjuvant therapy (NAT) can predict the response of breast cancer to NAT. MATERIALS AND METHODS Twenty-four patients with histologically confirmed invasive breast cancer were enrolled. They were scanned with ultrafast dynamic contrast-enhanced magnetic resonance imaging (3-7 seconds/frame) pre- and post-NAT. Four kinetic parameters were calculated in the segmented tumors, and ipsi- and contra-lateral normal parenchyma: (1) tumor (tSE30) or background parenchymal relative enhancement at 30 seconds (BPE30), (2) maximum relative enhancement slope (MaxSlope), (3) bolus arrival time (BAT), and (4) area under relative signal enhancement curve for the initial 30 seconds (AUC30). The tumor kinetics and the differences between ipsi- and contra-lateral parenchymal kinetics were compared for patients achieving pathologic complete response (pCR) vs those who had residual disease after NAT. The chi-squared test and two-sided t-test were used for baseline demographics. The Wilcoxon rank sum test and one-way analysis of variance were used for differential responses to therapy. RESULTS Patients with similar pre-NAT mean BPE30, median BAT and mean AUC30 in the ipsi- and contralateral normal parenchyma were more likely to achieve pCR following NAT (p < 0.02). Patients classified as having residual cancer burden (RCB) II after NAT showed higher post-NAT tSE30 and tumor AUC30 and higher post-NAT MaxSlope in ipsilateral normal parenchyma compared to those classified as RCB I or pCR (p < 0.05). CONCLUSION Bilateral asymmetry in normal parenchyma could predict treatment outcome prior to NAT. Post-NAT tumor kinetics could evaluate the aggressiveness of residual tumor.
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Affiliation(s)
- Zhen Ren
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Federico D Pineda
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Frederick M Howard
- Section of Hematology and Oncology, Department of Medicine, The University of Chicago, Chicago, Illinois
| | - Elle Hill
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Teodora Szasz
- Research Computing Center, The University of Chicago, Chicago, Illinois
| | - Rabia Safi
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Milica Medved
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Rita Nanda
- Section of Hematology and Oncology, Department of Medicine, The University of Chicago, Chicago, Illinois
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, Texas; Department of Oncology, The University of Texas at Austin, Austin, Texas; Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Texas; Department of Imaging Physics, MD Anderson Cancer Center, Houston, Texas
| | - Hiroyuki Abe
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Gregory S Karczmar
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637.
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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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11
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Ke ZR, Chen W, Li MX, Wu S, Jin LT, Wang TJ. Added value of systemic inflammation markers for monitoring response to neoadjuvant chemotherapy in breast cancer patients. World J Clin Cases 2022; 10:3389-3400. [PMID: 35611192 PMCID: PMC9048567 DOI: 10.12998/wjcc.v10.i11.3389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/23/2021] [Accepted: 02/27/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Complete response after neoadjuvant chemotherapy (rNACT) elevates the surgical outcomes of patients with breast cancer, however, non-rNACT have a higher risk of death and recurrence.
AIM To establish novel machine learning (ML)-based predictive models for predicting probability of rNACT in breast cancer patients who intends to receive NACT.
METHODS A retrospective analysis of 487 breast cancer patients who underwent mastectomy or breast-conserving surgery and axillary lymph node dissection following neoadjuvant chemotherapy at the Hubei Cancer Hospital between January 1, 2013, and October 1, 2021. The study cohort was divided into internal training and testing datasets in a 70:30 ratio for further analysis. A total of twenty-four variables were included to develop predictive models for rNACT by multiple ML-based algorithms. A feature selection approach was used to identify optimal predictive factors. These models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance.
RESULTS Analysis identified several significant differences between the rNACT and non-rNACT groups, including total cholesterol, low-density lipoprotein, neutrophil-to-lymphocyte ratio, body mass index, platelet count, albumin-to-globulin ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. The areas under the curve of the six models ranged from 0.81 to 0.96. Some ML-based models performed better than models using conventional statistical methods in both ROC curves. The support vector machine (SVM) model with twelve variables introduced was identified as the best predictive model.
CONCLUSION By incorporating pretreatment serum lipids and serum inflammation markers, it is feasible to develop ML-based models for the preoperative prediction of rNACT and therefore facilitate the choice of treatment, particularly the SVM, which can improve the prediction of rNACT in patients with breast cancer.
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Affiliation(s)
- Zi-Rui Ke
- Department of Breast Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Provincial Clinical Research Center for Breast Cancer, Wuhan 430079, Hubei Province, China
| | - Wei Chen
- Department of Breast Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Provincial Clinical Research Center for Breast Cancer, Wuhan 430079, Hubei Province, China
| | - Man-Xiu Li
- Department of Breast Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Provincial Clinical Research Center for Breast Cancer, Wuhan 430079, Hubei Province, China
| | - Shun Wu
- Department of Breast Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Provincial Clinical Research Center for Breast Cancer, Wuhan 430079, Hubei Province, China
| | - Li-Ting Jin
- Department of Breast Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Provincial Clinical Research Center for Breast Cancer, Wuhan 430079, Hubei Province, China
| | - Tie-Jun Wang
- Department of Breast Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Provincial Clinical Research Center for Breast Cancer, Wuhan 430079, Hubei Province, China
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12
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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: 30] [Impact Index Per Article: 15.0] [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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13
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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: 6] [Impact Index Per Article: 2.0] [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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14
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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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15
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Reig B, Lewin AA, Du L, Heacock L, Toth HK, Heller SL, Gao Y, Moy L. Breast MRI for Evaluation of Response to Neoadjuvant Therapy. Radiographics 2021; 41:665-679. [PMID: 33939542 DOI: 10.1148/rg.2021200134] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Neoadjuvant therapy is increasingly being used to treat early-stage triple-negative and human epidermal growth factor 2-overexpressing breast cancers, as well as locally advanced and inflammatory breast cancers. The rationales for neoadjuvant therapy are to shrink tumor size and potentially decrease the extent of surgery, to serve as an in vivo test of response to therapy, and to reveal prognostic information for the patient. MRI is the most accurate modality to demonstrate response to therapy and to help ensure accurate presurgical planning. Changes in lesion diameter, volume, and enhancement are used to predict complete response, partial response, or nonresponse to therapy. However, residual disease may be overestimated or underestimated at MRI. Fibrosis, necrotic tumors, and residual benign masses may be causes of overestimation of residual disease. Nonmass lesions, invasive lobular carcinoma, hormone receptor-positive tumors, nonconcentric shrinkage patterns, the use of antiangiogenic therapy, and late-enhancing foci may be causes of underestimation of residual disease. In patients with known axillary lymph node metastasis, neoadjuvant therapy may be followed by targeted axillary dissection to avoid the potential morbidity associated with an axillary lymph node dissection. Diffusion-weighted imaging, radiomics, machine learning, and deep learning methods are under investigation to improve MRI accuracy in predicting treatment response.©RSNA, 2021.
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Affiliation(s)
- Beatriu Reig
- From the Department of Radiology (B.R., A.A.L., L.H., H.K.T., S.L.H., Y.G., L.M.), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (L.M.), and Center for Advanced Imaging Innovation and Research (CAI2R) (L.M.), New York University Grossman School of Medicine, 160 E 34th St, New York, NY 10016; and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (L.D.)
| | - Alana A Lewin
- From the Department of Radiology (B.R., A.A.L., L.H., H.K.T., S.L.H., Y.G., L.M.), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (L.M.), and Center for Advanced Imaging Innovation and Research (CAI2R) (L.M.), New York University Grossman School of Medicine, 160 E 34th St, New York, NY 10016; and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (L.D.)
| | - Linda Du
- From the Department of Radiology (B.R., A.A.L., L.H., H.K.T., S.L.H., Y.G., L.M.), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (L.M.), and Center for Advanced Imaging Innovation and Research (CAI2R) (L.M.), New York University Grossman School of Medicine, 160 E 34th St, New York, NY 10016; and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (L.D.)
| | - Laura Heacock
- From the Department of Radiology (B.R., A.A.L., L.H., H.K.T., S.L.H., Y.G., L.M.), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (L.M.), and Center for Advanced Imaging Innovation and Research (CAI2R) (L.M.), New York University Grossman School of Medicine, 160 E 34th St, New York, NY 10016; and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (L.D.)
| | - Hildegard K Toth
- From the Department of Radiology (B.R., A.A.L., L.H., H.K.T., S.L.H., Y.G., L.M.), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (L.M.), and Center for Advanced Imaging Innovation and Research (CAI2R) (L.M.), New York University Grossman School of Medicine, 160 E 34th St, New York, NY 10016; and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (L.D.)
| | - Samantha L Heller
- From the Department of Radiology (B.R., A.A.L., L.H., H.K.T., S.L.H., Y.G., L.M.), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (L.M.), and Center for Advanced Imaging Innovation and Research (CAI2R) (L.M.), New York University Grossman School of Medicine, 160 E 34th St, New York, NY 10016; and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (L.D.)
| | - Yiming Gao
- From the Department of Radiology (B.R., A.A.L., L.H., H.K.T., S.L.H., Y.G., L.M.), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (L.M.), and Center for Advanced Imaging Innovation and Research (CAI2R) (L.M.), New York University Grossman School of Medicine, 160 E 34th St, New York, NY 10016; and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (L.D.)
| | - Linda Moy
- From the Department of Radiology (B.R., A.A.L., L.H., H.K.T., S.L.H., Y.G., L.M.), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (L.M.), and Center for Advanced Imaging Innovation and Research (CAI2R) (L.M.), New York University Grossman School of Medicine, 160 E 34th St, New York, NY 10016; and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (L.D.)
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16
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Fukushima A, Sugimoto M, Hiwa S, Hiroyasu T. Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles. BMC Bioinformatics 2021; 22:132. [PMID: 33736614 PMCID: PMC7977599 DOI: 10.1186/s12859-021-04052-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 02/28/2021] [Indexed: 12/14/2022] Open
Abstract
Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04052-4.
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Affiliation(s)
- Arika Fukushima
- Graduate School of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto, 610-0321, Japan
| | - Masahiro Sugimoto
- Research and Development Center for Minimally Invasive Therapies, Institute of Medical Science, Tokyo Medical University, Shinjuku, Tokyo, 160-8402, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
| | - Satoru Hiwa
- Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto, 610-0321, Japan
| | - Tomoyuki Hiroyasu
- Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto, 610-0321, Japan.
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17
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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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18
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Zhou X, Fan X, Mustafi D, Pineda F, Markiewicz E, Zamora M, Sheth D, Olopade OI, Oto A, Karczmar GS. Comparison of DCE-MRI of murine model cancers with a low dose and high dose of contrast agent. Phys Med 2021; 81:31-39. [PMID: 33373779 DOI: 10.1016/j.ejmp.2020.11.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/27/2020] [Accepted: 11/19/2020] [Indexed: 02/08/2023] Open
Abstract
There are increasing concerns regarding intracellular accumulation of gadolinium (Gd) after multiple dynamic contrast enhanced (DCE) MRI scans. We investigated whether a low dose (LD) of Gd-based contrast agent is as effective as a high dose (HD) for quantitative analysis of DCE-MRI data, and evaluated the use of a split dose protocol to obtain new diagnostic parameters. Female C3H mice (n = 6) were injected with mammary carcinoma cells in the hind leg. MRI experiments were performed on 9.4 T scanner. DCE-MRI data were acquired with 1.5 s temporal resolution before and after a LD (0.04 mmol/kg), then again after 30 min followed by a HD (0.2 mmol/kg) bolus injection of Omniscan. The standard Tofts model was used to extract physiological parameters (Ktrans and ve) with the arterial input function derived from muscle reference tissue. In addition, an empirical mathematical model was used to characterize maximum contrast agent uptake (A), contrast agent uptake rate (α) and washout rate (β and γ). There were moderate to strong correlations (r = 0.69-0.97, p < 0001) for parameters Ktrans, ve, A, α and β from LD versus HD data. On average, tumor parameters obtained from LD data were significantly larger (p < 0.05) than those from HD data. The parameter ratios, Ktrans, ve, A and α calculated from the LD data divided by the HD data, were all significantly larger than 1.0 (p < 0.003) for tumor. T2* changes following contrast agent injection affected parameters calculated from HD data, but this was not the case for LD data. The results suggest that quantitative analysis of LD data may be at least as effective for cancer characterization as quantitative analysis of HD data. In addition, the combination of parameters from two different doses may provide useful diagnostic information.
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Affiliation(s)
- Xueyan Zhou
- School of Technology, Harbin University, Harbin, China; Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Xiaobing Fan
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Devkumar Mustafi
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Federico Pineda
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Erica Markiewicz
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Marta Zamora
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Deepa Sheth
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | | | - Aytekin Oto
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Gregory S Karczmar
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States.
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Abstract
The National Cancer Institute's Quantitative Imaging Network (QIN) has thrived over the past 12 years with an emphasis on the development of image-based decision support software tools for improving measurements of imaging metrics. An overarching goal has been to develop advanced tools that could be translated into clinical trials to provide for improved prediction of response to therapeutic interventions. This article provides an overview of the successes in development and translation of new algorithms into the clinical workflow by the many research teams of the Quantitative Imaging Network.
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Chhetri A, Li X, Rispoli JV. Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer. Front Med (Lausanne) 2020; 7:175. [PMID: 32478083 PMCID: PMC7235971 DOI: 10.3389/fmed.2020.00175] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 04/15/2020] [Indexed: 01/10/2023] Open
Abstract
Breast cancer is the most commonly diagnosed cancer among women worldwide, and early detection remains a principal factor for improved patient outcomes and reduced mortality. Clinically, magnetic resonance imaging (MRI) techniques are routinely used in determining benign and malignant tumor phenotypes and for monitoring treatment outcomes. Static MRI techniques enable superior structural contrast between adipose and fibroglandular tissues, while dynamic MRI techniques can elucidate functional characteristics of malignant tumors. The preferred clinical procedure-dynamic contrast-enhanced MRI-illuminates the hypervascularity of breast tumors through a gadolinium-based contrast agent; however, accumulation of the potentially toxic contrast agent remains a major limitation of the technique, propelling MRI research toward finding an alternative, noninvasive method. Three such techniques are magnetic resonance spectroscopy, chemical exchange saturation transfer, and non-contrast diffusion weighted imaging. These methods shed light on underlying chemical composition, provide snapshots of tissue metabolism, and more pronouncedly characterize microstructural heterogeneity. This review article outlines the present state of clinical MRI for breast cancer and examines several research techniques that demonstrate capacity for clinical translation. Ultimately, multi-parametric MRI-incorporating one or more of these emerging methods-presently holds the best potential to afford improved specificity and deliver excellent accuracy to clinics for the prediction, detection, and monitoring of breast cancer.
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Affiliation(s)
- Apekshya Chhetri
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
- Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States
| | - Xin Li
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Joseph V. Rispoli
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
- Center for Cancer Research, Purdue University, West Lafayette, IN, United States
- School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN, United States
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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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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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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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Reig B, Heacock L, Geras KJ, Moy L. Machine learning in breast MRI. J Magn Reson Imaging 2019; 52:998-1018. [PMID: 31276247 DOI: 10.1002/jmri.26852] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 06/18/2019] [Accepted: 06/19/2019] [Indexed: 12/13/2022] Open
Abstract
Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.
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Affiliation(s)
- Beatriu Reig
- The Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Laura Heacock
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Krzysztof J Geras
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2 R), New York University School of Medicine, New York, New York, USA
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