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Zhang L, Shen M, Zhang D, He X, Du Q, Liu N, Huang X. Radiomics Nomogram Based on Dual-Sequence MRI for Assessing Ki-67 Expression in Breast Cancer. J Magn Reson Imaging 2024; 60:1203-1212. [PMID: 38088478 DOI: 10.1002/jmri.29149] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Radiomics has been extensively applied in predicting Ki-67 in breast cancer (BC). However, this is often confined to the exploration of a single sequence, without considering the varying sensitivity and specificity among different sequences. PURPOSE To develop a nomogram based on dual-sequence MRI derived radiomic features combined with clinical characteristics for assessing Ki-67 expression in BC. STUDY TYPE Retrospective. POPULATION 227 females (average age, 51 years) with 233 lesions and pathologically confirmed BC, which were divided into the training set (n = 163) and test set (n = 70). FIELD STRENGTH/SEQUENCE 3.0-T, T1-weighted dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (EPI sequence). ASSESSMENT The regions of interest were manually delineated on ADC and DCE-MRI sequences. Three radiomics models of ADC, DCE-MRI, and dsMRI (combined ADC and DCE-MRI sequences) were constructed by logistic regression and the radiomics score (Radscore) of the best model was calculated. The correlation between Ki-67 expression and clinical characteristics such as receptor status, axillary lymph node (ALN) metastasis status, ADC value, and time signal intensity curve was analyzed, and the clinical model was established. The Radscore was combined with clinical predictors to construct a nomogram. STATISTICAL TESTS The independent sample t-test, Mann-Whitney U test, Chi-squared test, Interclass correlation coefficients (ICCs), single factor analysis, least absolute shrinkage and selection operator (LASSO), logistic regression, receiver operating characteristics, Delong test, Hosmer_Lemeshow test, calibration curve, decision curve. A P-value <0.05 was considered statistically significant. RESULTS In the test set, the prediction efficiency of the dsMRI model (AUC = 0.862) was higher than ADC model (AUC = 0.797) and DCE-MRI model (AUC = 0.755). With the inclusion of estrogen receptor (ER) and ALN metastasis, the nomogram displayed quality improvement (AUC = 0.876), which was superior to the clinical model (AUC = 0.787) and radiomics model. DATA CONCLUSION The nomogram based on dsMRI radiomic features and clinical characteristics may be able to assess Ki-67 expression in BC. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Li Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Mengyi Shen
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Dingyi Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xin He
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Qinglin Du
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Nian Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiaohua Huang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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Ba ZC, Zhang HX, Liu AY, Zhou XX, Liu L, Wang XY, Nanding A, Sang XQ, Kuai ZX. Combination of DCE-MRI and NME-DWI via Deep Neural Network for Predicting Breast Cancer Molecular Subtypes. Clin Breast Cancer 2024; 24:e417-e427. [PMID: 38555225 DOI: 10.1016/j.clbc.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND To explore whether the combination of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and nonmono-exponential (NME) model-based diffusion-weighted imaging (DWI) via deep neural network (DNN) can improve the prediction of breast cancer molecular subtypes compared to either imaging technique used alone. PATIENTS AND METHODS This prospective study examined 480 breast cancers in 475 patients undergoing DCE-MRI and NME-DWI at 3.0 T. Breast cancers were classified as follows: human epidermal growth factor receptor 2 enriched (HER2-enriched), luminal A, luminal B (HER2-), luminal B (HER2+), and triple-negative subtypes. A total of 20% cases were withheld as an independent test dataset, and the remaining cases were used to train DNN with an 80% to 20% training-validation split and 5-fold cross-validation. The diagnostic accuracies of DNN in 5-way subtype classification between the DCE-MRI, NME-DWI, and their combined multiparametric-MRI datasets were compared using analysis of variance with least significant difference posthoc test. Areas under the receiver-operating characteristic curves were calculated to assess the performances of DNN in binary subtype classification between the 3 datasets. RESULTS The 5-way classification accuracies of DNN on both DCE-MRI (0.71) and NME-DWI (0.64) were significantly lower (P < .05) than on multiparametric-MRI (0.76), while on DCE-MRI was significantly higher (P < .05) than on NME-DWI. The comparative results of binary classification between the 3 datasets were consistent with the 5-way classification. CONCLUSION The combination of DCE-MRI and NME-DWI via DNN achieved a significant improvement in breast cancer molecular subtype prediction compared to either imaging technique used alone. Additionally, DCE-MRI outperformed NME-DWI in differentiating subtypes.
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Affiliation(s)
- Zhi-Chang Ba
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Hong-Xia Zhang
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Ao-Yu Liu
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xin-Xiang Zhou
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Lu Liu
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xin-Yi Wang
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Abiyasi Nanding
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xi-Qiao Sang
- Division of Respiratory Disease, Fourth Affiliated Hospital of Harbin Medical University, Yiyuan street No.37, Nangang District, Harbin, China.
| | - Zi-Xiang Kuai
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China.
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Zhang N, Song Q, Liang H, Wang Z, Wu Q, Zhang H, Zhang L, Liu A, Wang H, Wang J, Lin L. Early prediction of pathological response to neoadjuvant chemotherapy of breast tumors: a comparative study using amide proton transfer-weighted, diffusion weighted and dynamic contrast enhanced MRI. Front Med (Lausanne) 2024; 11:1295478. [PMID: 38298813 PMCID: PMC10827983 DOI: 10.3389/fmed.2024.1295478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/05/2024] [Indexed: 02/02/2024] Open
Abstract
Objective To examine amide proton transfer-weighted (APTw) combined with diffusion weighed (DWI) and dynamic contrast enhanced (DCE) MRI for early prediction of pathological response to neoadjuvant chemotherapy in invasive breast cancer. Materials In this prospective study, 50 female breast cancer patients (49.58 ± 10.62 years old) administered neoadjuvant chemotherapy (NAC) were enrolled with MRI carried out both before NAC (T0) and at the end of the second cycle of NAC (T1). The patients were divided into 2 groups based on tumor response according to the Miller-Payne Grading (MPG) system. Group 1 included patients with a greater degree of decrease in major histologic responder (MHR, Miller-Payne G4-5), while group 2 included non-MHR cases (Miller-Payne G1-3). Traditional imaging protocols (T1 weighted, T2 weighted, diffusion weighted, and DCE-MRI) and APTw imaging were scanned for each subject before and after treatment. APTw value (APTw0 and APTw1), Dmax (maximum diameter, Dmax0 and Dmax1), V (3D tumor volume, V0 and V1), and ADC (apparent diffusion coefficient, ADC0 and ADC1) before and after treatment, as well as changes between the two times points (ΔAPT, ΔDmax, ΔV, ΔADC) for breast tumors were compared between the two groups. Results APT0 and APT1 values significantly differed between the two groups (p = 0.034 and 0.01). ΔAPTw values were significantly lower in non-MHR tumors compared with MHR tumors (p = 0.015). ΔDmax values were significantly higher in MHR tumors compared with non-MHR tumors (p = 0.005). ADC0 and ADC1 values were significantly higher in MHR tumors than in non-MHR tumors (p = 0.038 and 0.035). AUC (Dmax+DWI + APTw) = AUC (Dmax+APTw) > AUC (APTw) > AUC (Dmax+DWI) > AUC (Dmax). Conclusion APTw imaging along with change of tumor size showed a significant potential in early prediction of MHR for NAC treatment in breast cancer, which might allow timely regimen refinement before definitive surgical treatment.
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Affiliation(s)
- Nan Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qingwei Song
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Hongbing Liang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Zhuo Wang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Qi Wu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Haonan Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Lina Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Ailian Liu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Huali Wang
- Department of Pathology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Jiazheng Wang
- MSC Clinical and Technical Solutions, Philips Healthcare, Beijing, China
| | - Liangjie Lin
- MSC Clinical and Technical Solutions, Philips Healthcare, Beijing, China
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Debbi K, Habert P, Grob A, Loundou A, Siles P, Bartoli A, Jacquier A. Radiomics model to classify mammary masses using breast DCE-MRI compared to the BI-RADS classification performance. Insights Imaging 2023; 14:64. [PMID: 37052738 PMCID: PMC10102264 DOI: 10.1186/s13244-023-01404-x] [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: 10/14/2022] [Accepted: 01/29/2023] [Indexed: 04/14/2023] Open
Abstract
BACKGROUND Recent advanced in radiomics analysis could help to identify breast cancer among benign mammary masses. The aim was to create a radiomics signature using breast DCE-MRI extracted features to classify tumors and to compare the performances with the BI-RADS classification. MATERIAL AND METHODS From September 2017 to December 2019 images, exams and records from consecutive patients with mammary masses on breast DCE-MRI and available histology from one center were retrospectively reviewed (79 patients, 97 masses). Exclusion criterion was malignant uncertainty. The tumors were split in a train-set (70%) and a test-set (30%). From 14 kinetics maps, 89 radiomics features were extracted, for a total of 1246 features per tumor. Feature selection was made using Boruta algorithm, to train a random forest algorithm on the train-set. BI-RADS classification was recorded from two radiologists. RESULTS Seventy-seven patients were analyzed with 94 tumors, (71 malignant, 23 benign). Over 1246 features, 17 were selected from eight kinetic maps. On the test-set, the model reaches an AUC = 0.94 95 CI [0.85-1.00] and a specificity of 33% 95 CI [10-70]. There were 43/94 (46%) lesions BI-RADS4 (4a = 12/94 (13%); 4b = 9/94 (10%); and 4c = 22/94 (23%)). The BI-RADS score reached an AUC = 0.84 95 CI [0.73-0.95] and a specificity of 17% 95 CI [3-56]. There was no significant difference between the ROC curves for the model or the BI-RADS score (p = 0.19). CONCLUSION A radiomics signature from features extracted using breast DCE-MRI can reach an AUC of 0.94 on a test-set and could provide as good results as BI-RADS to classify mammary masses.
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Affiliation(s)
- Kawtar Debbi
- Service de Radiologie, La Timone Hôpital, 264 Rue Saint Pierre, 13005, Marseille, France
| | - Paul Habert
- Service de Radiologie, Hôpital Nord, Chemin des Bourrely, 13015, Marseille, France.
- LIIE, Aix Marseille Université, Marseille, France.
- CERIMED, Aix Marseille Université, Marseille, France.
| | - Anaïs Grob
- Service de Radiologie, La Timone Hôpital, 264 Rue Saint Pierre, 13005, Marseille, France
| | - Anderson Loundou
- CEReSS UR3279-Health Service Research and Quality of Life Center, Aix-Marseille Université, Marseille, France
- Department of Public Health, Assistance Publique - Hôpitaux de Marseille, Marseille, France
| | - Pascale Siles
- Service de Radiologie, La Timone Hôpital, 264 Rue Saint Pierre, 13005, Marseille, France
| | - Axel Bartoli
- Service de Radiologie, La Timone Hôpital, 264 Rue Saint Pierre, 13005, Marseille, France
- UMR 7339, CNRS, CRMBM-CEMEREM (Centre de Résonance Magnétique Biologique et Médicale - Centre d'Exploration Métaboliques par Résonance Magnétique), Assistance Publique - Hôpitaux de Marseille, Aix-Marseille Université, 13385, Marseille, France
| | - Alexis Jacquier
- Service de Radiologie, La Timone Hôpital, 264 Rue Saint Pierre, 13005, Marseille, France
- UMR 7339, CNRS, CRMBM-CEMEREM (Centre de Résonance Magnétique Biologique et Médicale - Centre d'Exploration Métaboliques par Résonance Magnétique), Assistance Publique - Hôpitaux de Marseille, Aix-Marseille Université, 13385, Marseille, France
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Obuchowski NA, Huang E, deSouza NM, Raunig D, Delfino J, Buckler A, Hatt C, Wang X, Moskowitz C, Guimaraes A, Giger M, Hall TJ, Kinahan P, Pennello G. A Framework for Evaluating the Technical Performance of Multiparameter Quantitative Imaging Biomarkers (mp-QIBs). Acad Radiol 2023; 30:147-158. [PMID: 36180328 PMCID: PMC9825639 DOI: 10.1016/j.acra.2022.08.031] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/19/2022] [Accepted: 08/26/2022] [Indexed: 01/11/2023]
Abstract
Multiparameter quantitative imaging incorporates anatomical, functional, and/or behavioral biomarkers to characterize tissue, detect disease, identify phenotypes, define longitudinal change, or predict outcome. Multiple imaging parameters are sometimes considered separately but ideally are evaluated collectively. Often, they are transformed as Likert interpretations, ignoring the correlations of quantitative properties that may result in better reproducibility or outcome prediction. In this paper we present three use cases of multiparameter quantitative imaging: i) multidimensional descriptor, ii) phenotype classification, and iii) risk prediction. A fourth application based on data-driven markers from radiomics is also presented. We describe the technical performance characteristics and their metrics common to all use cases, and provide a structure for the development, estimation, and testing of multiparameter quantitative imaging. This paper serves as an overview for a series of individual articles on the four applications, providing the statistical framework for multiparameter imaging applications in medicine.
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Affiliation(s)
- Nancy A Obuchowski
- Quantitative Health Sciences /JJN3, Cleveland Clinic Foundation, 9500 Euclid Ave. Cleveland, OH 44195.
| | - Erich Huang
- Biometric Research Program, Division of Cancer Treatment and Diagnosis - National Cancer Institute, National Institutes of Health, Huang, Rockville, Maryland
| | - Nandita M deSouza
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom; European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology (ESR), Vienna, Austria
| | - David Raunig
- Data Science Institute, Takeda, Raunig, Hew Hope, PA
| | - Jana Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, Delfino, Silver Spring, Maryland
| | | | - Charles Hatt
- University of Michigan, Hatt, Radiology, University of Michigan, Ann Arbor, MI
| | - Xiaofeng Wang
- Quantitative Health Sciences, Cleveland Clinic Foundation, Wang, Cleveland, OH
| | - Chaya Moskowitz
- Memorial Sloan Kettering Cancer Institute, Moskowitz, NYC, NY
| | - Alexander Guimaraes
- Department of Radiology, Oregon Health and Science University, Guimaraes, Oregon, Portland
| | - Maryellen Giger
- Department of Radiology, University of Chicago, Giger, Chicago, IL
| | - Timothy J Hall
- Department of Medical Physics, University of Wisconsin, Hall, Madison, WI
| | | | - Gene Pennello
- Division of Biostatistics, Center for Devices and Radiological Health, FDA, Pennello, Silver Spring, Maryland
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Mumuni AN, Hasford F, Udeme NI, Dada MO, Awojoyogbe BO. A SWOT analysis of artificial intelligence in diagnostic imaging in the developing world: making a case for a paradigm shift. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2022-0121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Abstract
Diagnostic imaging (DI) refers to techniques and methods of creating images of the body’s internal parts and organs with or without the use of ionizing radiation, for purposes of diagnosing, monitoring and characterizing diseases. By default, DI equipment are technology based and in recent times, there has been widespread automation of DI operations in high-income countries while low and middle-income countries (LMICs) are yet to gain traction in automated DI. Advanced DI techniques employ artificial intelligence (AI) protocols to enable imaging equipment perceive data more accurately than humans do, and yet automatically or under expert evaluation, make clinical decisions such as diagnosis and characterization of diseases. In this narrative review, SWOT analysis is used to examine the strengths, weaknesses, opportunities and threats associated with the deployment of AI-based DI protocols in LMICs. Drawing from this analysis, a case is then made to justify the need for widespread AI applications in DI in resource-poor settings. Among other strengths discussed, AI-based DI systems could enhance accuracies in diagnosis, monitoring, characterization of diseases and offer efficient image acquisition, processing, segmentation and analysis procedures, but may have weaknesses regarding the need for big data, huge initial and maintenance costs, and inadequate technical expertise of professionals. They present opportunities for synthetic modality transfer, increased access to imaging services, and protocol optimization; and threats of input training data biases, lack of regulatory frameworks and perceived fear of job losses among DI professionals. The analysis showed that successful integration of AI in DI procedures could position LMICs towards achievement of universal health coverage by 2030/2035. LMICs will however have to learn from the experiences of advanced settings, train critical staff in relevant areas of AI and proceed to develop in-house AI systems with all relevant stakeholders onboard.
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Affiliation(s)
| | - Francis Hasford
- Department of Medical Physics , University of Ghana, Ghana Atomic Energy Commission , Accra , Ghana
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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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Belhaj Soulami K, Kaabouch N, Nabil Saidi M. Breast cancer: Classification of suspicious regions in digital mammograms based on capsule network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Xu A, Chu X, Zhang S, Zheng J, Shi D, Lv S, Li F, Weng X. Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study. Front Oncol 2022; 12:799232. [PMID: 35664741 PMCID: PMC9160981 DOI: 10.3389/fonc.2022.799232] [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: 11/04/2021] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Abstract
Objective To investigate the feasibility of radiomics in predicting molecular subtype of breast invasive ductal carcinoma (IDC) based on dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Methods A total of 303 cases with pathologically confirmed IDC from January 2018 to March 2021 were enrolled in this study, including 223 cases from Fudan University Shanghai Cancer Center (training/test set) and 80 cases from Shaoxing Central Hospital (validation set). All the cases were classified as HR+/Luminal, HER2-enriched, and TNBC according to immunohistochemistry. DCE-MRI original images were treated by semi-automated segmentation to initially extract original and wavelet-transformed radiomic features. The extended logistic regression with least absolute shrinkage and selection operator (LASSO) penalty was applied to identify the optimal radiomic features, which were then used to establish predictive models combined with significant clinical risk factors. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis were adopted to evaluate the effectiveness and clinical benefit of the models established. Results Of the 223 cases from Fudan University Shanghai Cancer Center, HR+/Luminal cancers were diagnosed in 116 cases (52.02%), HER2-enriched in 71 cases (31.84%), and TNBC in 36 cases (16.14%). Based on the training set, 788 radiomic features were extracted in total and 8 optimal features were further identified, including 2 first-order features, 1 gray-level run length matrix (GLRLM), 4 gray-level co-occurrence matrices (GLCM), and 1 3D shape feature. Three multi-class classification models were constructed by extended logistic regression: clinical model (age, menopause, tumor location, Ki-67, histological grade, and lymph node metastasis), radiomic model, and combined model. The macro-average areas under the ROC curve (macro-AUC) for the three models were 0.71, 0.81, and 0.84 in the training set, 0.73, 0.81, and 0.84 in the test set, and 0.76, 0.82, and 0.83 in the validation set, respectively. Conclusion The DCE-MRI-based radiomic features are significant biomarkers for distinguishing molecular subtypes of breast cancer noninvasively. Notably, the classification performance could be improved with the fusion analysis of multi-modal features.
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Affiliation(s)
- Aqiao Xu
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Xiufeng Chu
- Department of Surgical, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Shengjian Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jing Zheng
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Dabao Shi
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Shasha Lv
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Feng Li
- Department of Research Collaboration, Research & Development Center (R&D), Beijing Deepwise & League of Doctor of Philosophy (PHD) Technology Co., Ltd, Beijing, China
| | - Xiaobo Weng
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
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Yin XX, Hadjiloucas S, Zhang Y, Tian Z. MRI radiogenomics for intelligent diagnosis of breast tumors and accurate prediction of neoadjuvant chemotherapy responses-a review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106510. [PMID: 34852935 DOI: 10.1016/j.cmpb.2021.106510] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 11/01/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE This paper aims to overview multidimensional mining algorithms in relation to Magnetic Resonance Imaging (MRI) radiogenomics for computer aided detection and diagnosis of breast tumours. The work also aims to address a new problem in radiogenomics mining: how to combine structural radiomics information with non-structural genomics information for improving the accuracy and efficacy of Neoadjuvant Chemotherapy (NAC). METHODS This requires the automated extraction of parameters from non-structural breast radiomics data, and finding feature vectors with diagnostic value, which then are combined with genomics data. In order to address the problem of weakly labelled tumour images, a Generative Adiversarial Networks (GAN) based deep learning strategy is proposed for the classification of tumour types; this has significant potential for providing accurate real-time identification of tumorous regions from MRI scans. In order to efficiently integrate in a deep learning framework different features from radiogenomics datasets at multiple spatio-temporal resolutions, pyramid structured and multi-scale densely connected U-Nets are proposed. A bidirectional gated recurrent unit (BiGRU) combined with an attention based deep learning approach is also proposed. RESULTS The aim is to accurately predict NAC responses by combining imaging and genomic datasets. The approaches discussed incorporate some of the latest developments in of current signal processing and artificial intelligence and have significant potential in advancing and provide a development platform for future cutting-edge biomedical radiogenomics analysis. CONCLUSIONS The association of genotypic and phenotypic features is at the core of the emergent field of Precision Medicine. It makes use of advances in biomedical big data analysis, which enables the correlation between disease-associated phenotypic characteristics, genetics polymorphism and gene activation to be revealed.
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Affiliation(s)
- Xiao-Xia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China.
| | - Sillas Hadjiloucas
- Department of Biomedical Engineering, The University of Reading, RG6 6AY, UK
| | - Yanchun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
| | - Zhihong Tian
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
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11
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Wang P, Nie P, Dang Y, Wang L, Zhu K, Wang H, Wang J, Liu R, Ren J, Feng J, Fan H, Yu J, Chen B. Synthesizing the First Phase of Dynamic Sequences of Breast MRI for Enhanced Lesion Identification. Front Oncol 2021; 11:792516. [PMID: 34950593 PMCID: PMC8689139 DOI: 10.3389/fonc.2021.792516] [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: 10/11/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
Objective To develop a deep learning model for synthesizing the first phases of dynamic (FP-Dyn) sequences to supplement the lack of information in unenhanced breast MRI examinations. Methods In total, 97 patients with breast MRI images were collected as the training set (n = 45), the validation set (n = 31), and the test set (n = 21), respectively. An enhance border lifelike synthesize (EDLS) model was developed in the training set and used to synthesize the FP-Dyn images from the T1WI images in the validation set. The peak signal-to-noise ratio (PSNR), structural similarity (SSIM), mean square error (MSE) and mean absolute error (MAE) of the synthesized images were measured. Moreover, three radiologists subjectively assessed image quality, respectively. The diagnostic value of the synthesized FP-Dyn sequences was further evaluated in the test set. Results The image synthesis performance in the EDLS model was superior to that in conventional models from the results of PSNR, SSIM, MSE, and MAE. Subjective results displayed a remarkable visual consistency between the synthesized and original FP-Dyn images. Moreover, by using a combination of synthesized FP-Dyn sequence and an unenhanced protocol, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MRI were 100%, 72.73%, 76.92%, and 100%, respectively, which had a similar diagnostic value to full MRI protocols. Conclusions The EDLS model could synthesize the realistic FP-Dyn sequence to supplement the lack of enhanced images. Compared with full MRI examinations, it thus provides a new approach for reducing examination time and cost, and avoids the use of contrast agents without influencing diagnostic accuracy.
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Affiliation(s)
- Pingping Wang
- Clinical Experimental Centre, Xi'an International Medical Center Hospital, Xi'an, China
| | - Pin Nie
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | - Yanli Dang
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | - Lifang Wang
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | - Kaiguo Zhu
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | - Hongyu Wang
- The School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China
| | - Jiawei Wang
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | - Rumei Liu
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | | | - Jun Feng
- The School of Information of Science and Technology, Northwest University, Xi'an, China
| | - Haiming Fan
- The School of Medicine, Northwest University, Xi'an, China
| | - Jun Yu
- Clinical Experimental Centre, Xi'an International Medical Center Hospital, Xi'an, China
| | - Baoying Chen
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
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12
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Song L, Li C, Yin J. Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer. Front Oncol 2021; 11:675160. [PMID: 34168994 PMCID: PMC8217832 DOI: 10.3389/fonc.2021.675160] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/14/2021] [Indexed: 12/29/2022] Open
Abstract
Objective To evaluate whether texture features derived from semiquantitative kinetic parameter maps based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can determine human epidermal growth factor receptor 2 (HER2) status of patients with breast cancer. Materials and Methods This study included 102 patients with histologically confirmed breast cancer, all of whom underwent preoperative breast DCE-MRI and were enrolled retrospectively. This cohort included 48 HER2-positive cases and 54 HER2-negative cases. Seven semiquantitative kinetic parameter maps were calculated on the lesion area. A total of 55 texture features were extracted from each kinetic parameter map. Patients were randomly divided into training (n = 72) and test (n = 30) sets. The least absolute shrinkage and selection operator (LASSO) was used to select features in the training set, and then, multivariate logistic regression analysis was conducted to establish the prediction models. The classification performance was evaluated by receiver operating characteristic (ROC) analysis. Results Among the seven prediction models, the model with features extracted from the early signal enhancement ratio (ESER) map yielded an area under the ROC curve (AUC) of 0.83 in the training set (sensitivity of 70.59%, specificity of 92.11%, and accuracy of 81.94%), and the highest AUC of 0.83 in the test set (sensitivity of 57.14%, specificity of 100.00%, and accuracy of 80.00%). The model with features extracted from the slope of signal intensity (SIslope) map yielded the highest AUC of 0.92 in the training set (sensitivity of 82.35%, specificity of 97.37%, and accuracy of 90.28%), and an AUC of 0.79 in the test set (sensitivity of 92.86%, specificity of 68.75%, and accuracy of 80.00%). Conclusions Texture features derived from kinetic parameter maps, calculated based on breast DCE-MRI, have the potential to be used as imaging biomarkers to distinguish HER2-positive and HER2-negative breast cancer.
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Affiliation(s)
- Lirong Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chunli Li
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Jiang M, Han L, Sun H, Li J, Bao N, Li H, Zhou S, Yu T. Cross-modality image feature fusion diagnosis in breast cancer. Phys Med Biol 2021; 66. [PMID: 33784653 DOI: 10.1088/1361-6560/abf38b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 03/30/2021] [Indexed: 01/22/2023]
Abstract
Considering the complementarity of mammography and breast MRI, the research of feature fusion diagnosis based on cross-modality images was explored to improve the accuracy of breast cancer diagnosis. 201 patients with both mammography and breast MRI were collected retrospectively, including 117 cases of benign lesions and 84 cases of malignant ones. Two feature optimization strategies of sequential floating forward selection (SFFS), SFFS-1 and SFFS-2, were defined based on the sequential floating forward selection method. Each strategy was used to analyze the diagnostic performance of single-modality images and then to study the feature fusion diagnosis of cross-modality images. Three feature fusion approaches were compared: optimizing MRI features and then fusing those of mammography; optimizing mammography features and then fusing those of MRI; selecting the effective features from the whole feature set (mammography and MRI). Support vector machine, Naive Bayes, and K-nearest neighbor were employed as the classifiers and were finally integrated to get better performance. The average accuracy and area under the ROC curve (AUC) of MRI (88.56%, 0.9 for SFFS-1, 88.39%, 0.89 for SFFS-2) were better than mammography (84.25%, 0.84 for SFFS-1, 80.43%, 0.80 for SFFS-2). Furthermore, compared with a single modality, the average accuracy and AUC of cross-modality feature fusion can improve from 85.40% and 0.86 to 89.66% and 0.91. Classifier integration improved the accuracy and AUC from 90.49%, 0.92 to 92.37%, and 0.97. Cross-modality image feature fusion can achieve better diagnosis performance than a single modality. Feature selection strategy SFFS-1 has better efficiency than SFFS-2. Classifier integration can further improve diagnostic accuracy.
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Affiliation(s)
- Mingkuan Jiang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, People's Republic of China
| | - Lu Han
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Hang Sun
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, People's Republic of China
| | - Jing Li
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, People's Republic of China
| | - Nan Bao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, People's Republic of China
| | - Hong Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, People's Republic of China
| | - Shi Zhou
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, People's Republic of China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
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Granata V, Fusco R, Avallone A, De Stefano A, Ottaiano A, Sbordone C, Brunese L, Izzo F, Petrillo A. Radiomics-Derived Data by Contrast Enhanced Magnetic Resonance in RAS Mutations Detection in Colorectal Liver Metastases. Cancers (Basel) 2021; 13:cancers13030453. [PMID: 33504085 PMCID: PMC7865653 DOI: 10.3390/cancers13030453] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 01/07/2021] [Accepted: 01/21/2021] [Indexed: 12/21/2022] Open
Abstract
Simple Summary In the present study, we assessed the association of RAS mutation status and radiomics derived data by Contrast Enhanced Magnetic Resonance Imaging (CE-MRI) in liver metastases by CRC. We performed the evaluation extracting by CE-MRI both texture and morphological metrics in a 3D setting. We demonstrated that radiomics with texture parameters could add value to qualitative assessment of MR studies and with better results compared to morphological metrics, providing individualized evaluation of CRLM. Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status. Abstract Purpose: To assess the association of RAS mutation status and radiomics-derived data by Contrast Enhanced-Magnetic Resonance Imaging (CE-MRI) in liver metastases. Materials and Methods: 76 patients (36 women and 40 men; 59 years of mean age and 36–80 years as range) were included in this retrospective study. Texture metrics and parameters based on lesion morphology were calculated. Per-patient univariate and multivariate analysis were made. Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analysis, pattern recognition approaches with features selection approaches were considered. Results: Significant results were obtained for texture features while morphological parameters had not significant results to classify RAS mutation. The results showed that using a univariate analysis was not possible to discriminate accurately the RAS mutation status. Instead, considering a multivariate analysis and classification approaches, a KNN exclusively with texture parameters as predictors reached the best results (AUC of 0.84 and an accuracy of 76.9% with 90.0% of sensitivity and 67.8% of specificity on training set and an accuracy of 87.5% with 91.7% of sensitivity and 83.3% of specificity on external validation cohort). Conclusions: Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status.
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Affiliation(s)
- Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, Via Mariano Semmola, 80121 Naples, Italy; (V.G.); (A.P.)
| | - Roberta Fusco
- Radiology Division, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, Via Mariano Semmola, 80121 Naples, Italy; (V.G.); (A.P.)
- Correspondence: ; Tel.: +39-081590714; Fax: +39-0815903825
| | - Antonio Avallone
- Abdominal Oncology Division, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, Via Mariano Semmola, 80121 Naples, Italy; (A.A.); (A.D.S.); (A.O.)
| | - Alfonso De Stefano
- Abdominal Oncology Division, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, Via Mariano Semmola, 80121 Naples, Italy; (A.A.); (A.D.S.); (A.O.)
| | - Alessandro Ottaiano
- Abdominal Oncology Division, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, Via Mariano Semmola, 80121 Naples, Italy; (A.A.); (A.D.S.); (A.O.)
| | - Carolina Sbordone
- Radiology Division, Università degli Studi del Molise, Via Francesco De Sanctis, 86100 Campobasso, Italy; (C.S.); (L.B.)
| | - Luca Brunese
- Radiology Division, Università degli Studi del Molise, Via Francesco De Sanctis, 86100 Campobasso, Italy; (C.S.); (L.B.)
| | - Francesco Izzo
- Hepatobiliary Surgical Oncology Division, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, Via Mariano Semmola, 80121 Naples, Italy;
| | - Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, Via Mariano Semmola, 80121 Naples, Italy; (V.G.); (A.P.)
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15
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Attallah O, Anwar F, Ghanem NM, Ismail MA. Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images. PeerJ Comput Sci 2021; 7:e493. [PMID: 33987459 PMCID: PMC8093954 DOI: 10.7717/peerj-cs.493] [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: 11/09/2020] [Accepted: 03/26/2021] [Indexed: 05/06/2023]
Abstract
Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic diagnosis of BC. Most related studies were based on individual deep learning methods. Also, studies did not examine the influence of fusing features from multiple CNNs and handcrafted features. In addition, related studies did not investigate the best combination of fused features that influence the performance of the CADx. Therefore, Histo-CADx is based on two stages of fusion. The first fusion stage involves the investigation of the impact of fusing several deep learning (DL) techniques with handcrafted feature extraction methods using the auto-encoder DL method. This stage also examines and searches for a suitable set of fused features that could improve the performance of Histo-CADx. The second fusion stage constructs a multiple classifier system (MCS) for fusing outputs from three classifiers, to further improve the accuracy of the proposed Histo-CADx. The performance of Histo-CADx is evaluated using two public datasets; specifically, the BreakHis and the ICIAR 2018 datasets. The results from the analysis of both datasets verified that the two fusion stages of Histo-CADx successfully improved the accuracy of the CADx compared to CADx constructed with individual features. Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. Moreover, the results after the two fusion stages confirmed that Histo-CADx is reliable and has the capacity of classifying BC more accurately compared to other latest studies. Consequently, it can be used by pathologists to help them in the accurate diagnosis of BC. In addition, it can decrease the time and effort needed by medical experts during the examination.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Alexandria, Egypt
| | - Fatma Anwar
- Computer and Systems Engineering Department, Alexandria University, Alexandria, Egypt
| | - Nagia M. Ghanem
- Computer and Systems Engineering Department, Alexandria University, Alexandria, Egypt
| | - Mohamed A. Ismail
- Computer and Systems Engineering Department, Alexandria University, Alexandria, Egypt
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Pathak P, Jalal AS, Rai R. Breast Cancer Image Classification: A Review. Curr Med Imaging 2020; 17:720-740. [PMID: 33371857 DOI: 10.2174/0929867328666201228125208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/23/2020] [Accepted: 10/14/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Breast cancer represents uncontrolled breast cell growth. Breast cancer is the most diagnosed cancer in women worldwide. Early detection of breast cancer improves the chances of survival and increases treatment options. There are various methods for screening breast cancer, such as mammogram, ultrasound, computed tomography and Magnetic Resonance Imaging (MRI). MRI is gaining prominence as an alternative screening tool for early detection and breast cancer diagnosis. Nevertheless, MRI can hardly be examined without the use of a Computer-Aided Diagnosis (CAD) framework, due to the vast amount of data. OBJECTIVE This paper aims to cover the approaches used in the CAD system for the detection of breast cancer. METHODS In this paper, the methods used in CAD systems are categories into two classes: the conventional approach and artificial intelligence (AI) approach. RESULTS The conventional approach covers the basic steps of image processing, such as preprocessing, segmentation, feature extraction and classification. The AI approach covers the various convolutional and deep learning networks used for diagnosis. CONCLUSION This review discusses some of the core concepts used in breast cancer and presents a comprehensive review of efforts in the past to address this problem.
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Affiliation(s)
- Pooja Pathak
- Department of Mathematics, GLA University, Mathura, India
| | - Anand Singh Jalal
- Department of Computer Engineering & Applications, GLA University, Mathura, India
| | - Ritu Rai
- Department of Computer Engineering & Applications, GLA University, Mathura, India
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Fusco R, Granata V, Petrillo A. Introduction to Special Issue of Radiology and Imaging of Cancer. Cancers (Basel) 2020; 12:E2665. [PMID: 32961946 PMCID: PMC7565136 DOI: 10.3390/cancers12092665] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 09/15/2020] [Indexed: 12/12/2022] Open
Abstract
The increase in knowledge in oncology and the possibility of creating personalized medicine by selecting a more appropriate therapy related to the different tumor subtypes, as well as the management of patients with cancer within a multidisciplinary team has improved the clinical outcomes [...].
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Affiliation(s)
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, Via Mariano Semmola, 80131 Naples, Italy; (R.F.); (A.P.)
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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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Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps. Radiol Med 2019; 125:109-116. [PMID: 31696388 DOI: 10.1007/s11547-019-01100-1] [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: 07/05/2019] [Accepted: 10/24/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE The purpose of this study is to develop a radiomics model for predicting the Ki-67 proliferation index in patients with invasive ductal breast cancer through magnetic resonance imaging (MRI) preoperatively. MATERIALS AND METHODS A total of 128 patients who were clinicopathologically diagnosed with invasive ductal breast cancer were recruited. This cohort included 32 negative Ki67 expression (Ki67 proliferation index < 14%) and 96 cases with positive Ki67 expression (Ki67 proliferation index ≥ 14%). All patients had undergone diffusion-weighted imaging (DWI) MRI before surgery on a 3.0T MRI scanner. Radiomics features were extracted from apparent diffusion coefficient (ADC) maps which were obtained by DWI-MRI from patients with invasive ductal breast cancer. 80% of the patients were divided into training set to build radiomics model, and the rest into test set to evaluate its performance. The least absolute shrinkage and selection operator (LASSO) was used to select radiomics features, and then, the logistic regression (LR) model was established using fivefold cross-validation to predict the Ki-67 index. The performance was evaluated by receiver-operating characteristic (ROC) analysis, accuracy, sensitivity and specificity. RESULTS Quantitative imaging features (n = 1029) were extracted from ADC maps, and 11 features were selected to construct the LR model. Good identification ability was exhibited by the ADC-based radiomics model, with areas under the ROC (AUC) values of 0.75 ± 0.08, accuracy of 0.71 in training set and 0.72, 0.70 in test set. CONCLUSIONS The ADC-based radiomics model is a feasible predictor for the Ki-67 index in patients with invasive ductal breast cancer. Therefore, we proposed that three-dimensional imaging features from ADC maps could be used as candidate biomarker for preoperative prediction the Ki-67 index noninvasively.
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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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Fusco R, Vallone P, Filice S, Granata V, Petrosino T, Rubulotta MR, Setola SV, Maio F, Raiano C, Raiano N, Siani C, Di Bonito M, Sansone M, Botti G, Petrillo A. Radiomic features analysis by digital breast tomosynthesis and contrast-enhanced dual-energy mammography to detect malignant breast lesions. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101568] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Li H, Sun H, Liu S, Zhang W, Arukalam FM, Ma H, Qian W. Assessing the performance of benign and malignant breast lesion classification with bilateral TIC differentiation and other effective features in DCE-MRI. J Magn Reson Imaging 2019; 50:465-473. [DOI: 10.1002/jmri.26646] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 12/25/2018] [Accepted: 12/27/2018] [Indexed: 11/08/2022] Open
Affiliation(s)
- Hong Li
- Sino-Dutch Biomedical and Information Engineering School; Northeastern University; Shenyang China
| | - Hang Sun
- Sino-Dutch Biomedical and Information Engineering School; Northeastern University; Shenyang China
| | - Siqi Liu
- Sino-Dutch Biomedical and Information Engineering School; Northeastern University; Shenyang China
| | - Wei Zhang
- Shengjing Hospital of China Medical University; Shenyang China
| | - Felicity Mmaezi Arukalam
- Sino-Dutch Biomedical and Information Engineering School; Northeastern University; Shenyang China
| | - He Ma
- Sino-Dutch Biomedical and Information Engineering School; Northeastern University; Shenyang China
| | - Wei Qian
- Sino-Dutch Biomedical and Information Engineering School; Northeastern University; Shenyang China
- Department of Electrical and Computer Engineering; University of Texas; El Paso Texas USA
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The Value of 16-cm Wide-Detector Computed Tomography in Coronary Computed Tomography Angiography for Patients With High Heart Rate Variability. J Comput Assist Tomogr 2018; 42:906-911. [PMID: 30119063 DOI: 10.1097/rct.0000000000000787] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The aim of this study was to investigate radiation dose and image quality of coronary computed tomography (CT) angiography (CCTA) for patients with high heart rate variability (HRv) using 16-cm wide-detector CT scanner. METHODS One hundred sixty-six patients with uncontrolled heart rate underwent CCTA on a 16-cm wide-detector CT system and were divided into 2 groups based on their HRv for analysis: group A (n = 95, HRv ≤10 beats/min [bpm]) and group B (n = 71, HRv >10 bpm). Images in both groups were reconstructed with motion correction algorithm. Subjective and objective image qualities were analyzed. RESULTS There were no significant differences in age, body mass index, and heart rate (68.1 ± 11.4 vs 67.6 ± 12.3 bpm) between the 2 groups (P > 0.05). However, group B had significantly higher HRv than group A (33.5 ± 24.4 vs 7.8 ± 1.2 bpm, P < 0.001). All images were acceptable for clinical diagnosis. Compared with group A, image quality scores in group B decreased slightly (4.1 ± 0.5 vs 4.0 ± 0.6). However, the difference was not statistically significant. The mean effective doses were both relatively low at 2.2 ± 1.1 mSv in group A and 2.6 ± 1.4 mSv in group B. CONCLUSIONS Single-heartbeat free-breathing CCTA can be performed for patients with high HRv using 16-cm wide-detector CT scanner to achieve diagnostic image quality with low radiation dose.
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Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2018; 2:35. [PMID: 30353365 PMCID: PMC6199205 DOI: 10.1186/s41747-018-0061-6] [Citation(s) in RCA: 310] [Impact Index Per Article: 51.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 07/31/2018] [Indexed: 02/08/2023] Open
Abstract
One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as “machine/deep learning” and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100–150 per year in 2007–2008 to 700–800 per year in 2016–2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6–9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient’s values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.
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Affiliation(s)
- Filippo Pesapane
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Marina Codari
- Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy.
| | - Francesco Sardanelli
- Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy.,Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy
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