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Tabnak P, HajiEsmailPoor Z, Baradaran B, Pashazadeh F, Aghebati Maleki L. MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis. Acad Radiol 2024; 31:763-787. [PMID: 37925343 DOI: 10.1016/j.acra.2023.10.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/01/2023] [Accepted: 10/04/2023] [Indexed: 11/06/2023]
Abstract
RATIONALE AND OBJECTIVES The purpose of this systematic review and meta-analysis was to assess the quality and diagnostic accuracy of MRI-based radiomics for predicting Ki-67 expression in breast cancer. MATERIALS AND METHODS A systematic literature search was performed to find relevant studies published in different databases, including PubMed, Web of Science, and Embase up until March 10, 2023. All papers were independently evaluated for eligibility by two reviewers. Studies that matched research questions and provided sufficient data for quantitative synthesis were included in the systematic review and meta-analysis, respectively. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. The predictive value of MRI-based radiomics for Ki-67 antigen in patients with breast cancer was assessed using pooled sensitivity (SEN), specificity, and area under the curve (AUC). Meta-regression was performed to explore the cause of heterogeneity. Different covariates were used for subgroup analysis. RESULTS 31 studies were included in the systematic review; among them, 21 reported sufficient data for meta-analysis. 20 training cohorts and five validation cohorts were pooled separately. The pooled sensitivity, specificity, and AUC of MRI-based radiomics for predicting Ki-67 expression in training cohorts were 0.80 [95% CI, 0.73-0.86], 0.82 [95% CI, 0.78-0.86], and 0.88 [95%CI, 0.85-0.91], respectively. The corresponding values for validation cohorts were 0.81 [95% CI, 0.72-0.87], 0.73 [95% CI, 0.62-0.82], and 0.84 [95%CI, 0.80-0.87], respectively. Based on QUADAS-2, some risks of bias were detected for reference standard and flow and timing domains. However, the quality of the included article was acceptable. The mean RQS score of the included articles was close to 6, corresponding to 16.6% of the maximum possible score. Significant heterogeneity was observed in pooled sensitivity and specificity of training cohorts (I2 > 75%). We found that using deep learning radiomic methods, magnetic field strength (3 T vs. 1.5 T), scanner manufacturer, region of interest structure (2D vs. 3D), route of tissue sampling, Ki-67 cut-off, logistic regression for model construction, and LASSO for feature reduction as well as PyRadiomics software for feature extraction had a great impact on heterogeneity according to our joint model analysis. Diagnostic performance in studies that used deep learning-based radiomics and multiple MRI sequences (e.g., DWI+DCE) was slightly higher. In addition, radiomic features derived from DWI sequences performed better than contrast-enhanced sequences in terms of specificity and sensitivity. No publication bias was found based on Deeks' funnel plot. Sensitivity analysis showed that eliminating every study one by one does not impact overall results. CONCLUSION This meta-analysis showed that MRI-based radiomics has a good diagnostic accuracy in differentiating breast cancer patients with high Ki-67 expression from low-expressing groups. However, the sensitivity and specificity of these methods still do not surpass 90%, restricting them from being used as a supplement to current pathological assessments (e.g., biopsy or surgery) to predict Ki-67 expression accurately.
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Affiliation(s)
- Peyman Tabnak
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H.); Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Zanyar HajiEsmailPoor
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H.); Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Behzad Baradaran
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Fariba Pashazadeh
- Research Center for Evidence-Based Medicine, Iranian Evidence-Based Medicine (EBM) Centre: A Joanna Briggs Institute (JBI) Centre of Excellence, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (F.P.)
| | - Leili Aghebati Maleki
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.).
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Romeo V, Kapetas P, Clauser P, Rasul S, Cuocolo R, Caruso M, Helbich TH, Baltzer PAT, Pinker K. Simultaneous 18F-FDG PET/MRI Radiomics and Machine Learning Analysis of the Primary Breast Tumor for the Preoperative Prediction of Axillary Lymph Node Status in Breast Cancer. Cancers (Basel) 2023; 15:5088. [PMID: 37894455 PMCID: PMC10604950 DOI: 10.3390/cancers15205088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/08/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
In this prospective study, 117 female patients (mean age = 53 years) with 127 histologically proven breast cancer lesions (lymph node (LN) positive = 85, LN negative = 42) underwent simultaneous 18F-FDG PET/MRI of the breast. Quantitative parameters were calculated from dynamic contrast-enhanced (DCE) imaging (tumor Mean Transit Time, Volume Distribution, Plasma Flow), diffusion-weighted imaging (DWI) (tumor ADCmean), and PET (tumor SUVmax, mean and minimum, SUVmean of ipsilateral breast parenchyma). Manual whole-lesion segmentation was also performed on DCE, T2-weighted, DWI, and PET images, and radiomic features were extracted. The dataset was divided into a training (70%) and a test set (30%). Multi-step feature selection was performed, and a support vector machine classifier was trained and tested for predicting axillary LN status. 13 radiomic features from DCE, DWI, T2-weighted, and PET images were selected for model building. The classifier obtained an accuracy of 79.8 (AUC = 0.798) in the training set and 78.6% (AUC = 0.839), with sensitivity and specificity of 67.9% and 100%, respectively, in the test set. A machine learning-based radiomics model comprising 18F-FDG PET/MRI radiomic features extracted from the primary breast cancer lesions allows high accuracy in non-invasive identification of axillary LN metastasis.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, 80138 Naples, Italy; (V.R.); (M.C.)
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
| | - Sazan Rasul
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria;
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, 84081 Baronissi, Italy;
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80131 Naples, Italy
| | - Martina Caruso
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, 80138 Naples, Italy; (V.R.); (M.C.)
| | - Thomas H. Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
- Department of Biomedical Imaging and Image-guided Therapy, Division of Structural Preclinical Imaging, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
| | - Pascal A. T. Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
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Wang M, Mei T, Gong Y. The quality and clinical translation of radiomics studies based on MRI for predicting Ki-67 levels in patients with breast cancer. Br J Radiol 2023; 96:20230172. [PMID: 37724784 PMCID: PMC10546437 DOI: 10.1259/bjr.20230172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/13/2023] [Accepted: 08/02/2023] [Indexed: 09/21/2023] Open
Abstract
OBJECTIVE To evaluate the methodological quality of radiomics literature predicting Ki-67 levels based on MRI in patients with breast cancer (BC) and to propose suggestions for clinical translation. METHODS In this review, we searched PubMed, Embase, and Web of Science for studies published on radiomics in patients with BC. We evaluated the methodological quality of the studies using the Radiomics Quality Score (RQS). The Cochrane Collaboration's software (RevMan 5.4), Meta-DiSc (v. 1.4) and IBM SPSS (v. 26.0) were used for all statistical analyses. RESULTS Eighteen studies met our inclusion criteria, and the average RQS was 10.17 (standard deviation [SD]: 3.54). None of these studies incorporated any of the following items: a phantom study on all scanners, cut-off analyses, prospective study, cost-effectiveness analysis, or open science and data. In the meta-analysis, it showed apparent diffusion coefficient (ADC) played a better role to predict Ki-67 level than dynamic contrast-enhanced (DCE) MRI in the radiomics, with the pooled area under the curve (AUC) of 0.969. CONCLUSION Ki-67 index is a common tumor biomarker with high clinical value. Radiomics is an ever-growing quantitative data-mining method helping predict tumor biomarkers from medical images. However, the quality of the reviewed studies evaluated by the RQS was not so satisfactory and there are ample opportunities for improvement. Open science and data, external validation, phantom study, publicly open radiomics database and standardization in the radiomics practice are what researchers should pay more attention to in the future. ADVANCES IN KNOWLEDGE The RQS tool considered the radiomics used to predict the Ki-67 level was of poor quality. ADC performed better than DCE in radiomic prediction. We propose some measures to facilitate the clinical translation of radiomics.
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Affiliation(s)
- Min Wang
- Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Mei
- Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Youling Gong
- Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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Cho S, Joo B, Park M, Ahn SJ, Suh SH, Park YW, Ahn SS, Lee SK. A Radiomics-Based Model for Potentially More Accurate Identification of Subtypes of Breast Cancer Brain Metastases. Yonsei Med J 2023; 64:573-580. [PMID: 37634634 PMCID: PMC10462808 DOI: 10.3349/ymj.2023.0047] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/06/2023] [Accepted: 06/20/2023] [Indexed: 08/29/2023] Open
Abstract
PURPOSE Breast cancer brain metastases (BCBM) may involve subtypes that differ from the primary breast cancer lesion. This study aimed to develop a radiomics-based model that utilizes preoperative brain MRI for multiclass classification of BCBM subtypes and to investigate whether the model offers better prediction accuracy than the assumption that primary lesions and their BCBMs would be of the same subtype (non-conversion model) in an external validation set. MATERIALS AND METHODS The training and external validation sets each comprised 51 cases (102 cases total). Four machine learning classifiers combined with three feature selection methods were trained on radiomic features and primary lesion subtypes for prediction of the following four subtypes: 1) hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, 2) HR+/HER2+, 3) HR-/HER2+, and 4) triple-negative. After training, the performance of the radiomics-based model was compared to that of the non-conversion model in an external validation set using accuracy and F1-macro scores. RESULTS The rate of discrepant subtypes between primary lesions and their respective BCBMs were 25.5% (n=13 of 51) in the training set and 23.5% (n=12 of 51) in the external validation set. In the external validation set, the accuracy and F1-macro score of the radiomics-based model were significantly higher than those of the non-conversion model (0.902 vs. 0.765, p=0.004; 0.861 vs. 0.699, p=0.002). CONCLUSION Our radiomics-based model represents an incremental advance in the classification of BCBM subtypes, thereby facilitating a more appropriate personalized therapy.
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Affiliation(s)
- Seonghyeon Cho
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Bio Joo
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Jun Ahn
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sang Hyun Suh
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
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Orsini A, Diquigiovanni C, Bonora E. Omics Technologies Improving Breast Cancer Research and Diagnostics. Int J Mol Sci 2023; 24:12690. [PMID: 37628869 PMCID: PMC10454385 DOI: 10.3390/ijms241612690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.
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Affiliation(s)
| | - Chiara Diquigiovanni
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40131 Bologna, Italy; (A.O.); (E.B.)
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Fischer M, Küstner T, Pappa S, Niendorf T, Pischon T, Kröncke T, Bette S, Schramm S, Schmidt B, Haubold J, Nensa F, Nonnenmacher T, Palm V, Bamberg F, Kiefer L, Schick F, Yang B. Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study. BMC Med Imaging 2023; 23:104. [PMID: 37553619 PMCID: PMC10408104 DOI: 10.1186/s12880-023-01056-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/18/2023] [Indexed: 08/10/2023] Open
Abstract
In this work, we propose a processing pipeline for the extraction and identification of meaningful radiomics biomarkers in skeletal muscle tissue as displayed using Dixon-weighted MRI. Diverse and robust radiomics features can be identified that may be of aid in the accurate quantification e.g. varying degrees of sarcopenia in respective muscles of large cohorts. As such, the approach comprises the texture feature extraction from raw data based on well established approaches, such as a nnU-Net neural network and the Pyradiomics toolbox, a subsequent selection according to adequate conditions for the muscle tissue of the general population, and an importance-based ranking to further narrow the amount of meaningful features with respect to auxiliary targets. The performance was investigated with respect to the included auxiliary targets, namely age, body mass index (BMI), and fat fraction (FF). Four skeletal muscles with different fiber architecture were included: the mm. glutaei, m. psoas, as well as the extensors and adductors of the thigh. The selection allowed for a reduction from 1015 available texture features to 65 for age, 53 for BMI, and 36 for FF from the available fat/water contrast images considering all muscles jointly. Further, the dependence of the importance rankings calculated for the auxiliary targets on validation sets (in a cross-validation scheme) was investigated by boxplots. In addition, significant differences between subgroups of respective auxiliary targets as well as between both sexes were shown to be present within the ten lowest ranked features by means of Kruskal-Wallis H-tests and Mann-Whitney U-tests. The prediction performance for the selected features and the ranking scheme were verified on validation sets by a random forest based multi-class classification, with strong area under the curve (AUC) values of the receiver operator characteristic (ROC) of 73.03 ± 0.70 % and 73.63 ± 0.70 % for the water and fat images in age, 80.68 ± 0.30 % and 88.03 ± 0.89 % in BMI, as well as 98.36 ± 0.03 % and 98.52 ± 0.09 % in FF.
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Affiliation(s)
- Marc Fischer
- Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), University Hospital Tübingen, Tübingen, Germany.
| | - Sofia Pappa
- Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
| | - Tobias Pischon
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
| | - Thomas Kröncke
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany
- Centre for Advanced Analytics and Predictive Sciences (CAAPS), University Augsburg, Augsburg, Germany
| | - Stefanie Bette
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany
| | - Sara Schramm
- Institute for Medical Informatics, Biometry and Epidemiology, Essen University Hospital, Essen, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology, Essen University Hospital, Essen, Germany
| | | | | | | | | | | | - Lena Kiefer
- Department of Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Fritz Schick
- Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Bin Yang
- Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
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Huang Y, Yao Z, Li L, Mao R, Huang W, Hu Z, Hu Y, Wang Y, Guo R, Tang X, Yang L, Wang Y, Luo R, Yu J, Zhou J. Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers. EBioMedicine 2023; 94:104706. [PMID: 37478528 PMCID: PMC10393555 DOI: 10.1016/j.ebiom.2023.104706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatment decisions making. However, the molecular immunohistochemical subtypes based on biopsy specimens are not always consistent with final results based on surgical specimens due to the high intra-tumoral heterogeneity. Given that, we aimed to develop and validate a deep learning radiopathomics (DLRP) model to preoperatively distinguish between luminal and non-luminal breast cancers at early stages based on preoperative ultrasound (US) images, and hematoxylin and eosin (H&E)-stained biopsy slides. METHODS This multicentre study included three cohorts from a prospective study conducted by our team and registered on the Chinese Clinical Trial Registry (ChiCTR1900027497). Between January 2019 and August 2021, 1809 US images and 603 H&E-stained whole slide images (WSIs) from 603 patients with early-stage breast cancers were obtained. A Resnet18 model pre-trained on ImageNet and a multi-instance learning based attention model were used to extract the features of US and WSIs, respectively. An US-guided Co-Attention module (UCA) was designed for feature fusion of US and WSIs. The DLRP model was constructed based on these three feature sets including deep learning US feature, deep learning WSIs feature and UCA-fused feature from a training cohort (1467 US images and 489 WSIs from 489 patients). The DLRP model's diagnostic performance was validated in an internal validation cohort (342 US images and 114 WSIs from 114 patients) and an external test cohort (270 US images and 90 WSIs from 90 patients). We also compared diagnostic efficacy of the DLRP model with that of deep learning radiomics model and deep learning pathomics model in the external test cohort. FINDINGS The DLRP yielded high performance with area under the curve (AUC) values of 0.929 (95% CI 0.865-0.968) in the internal validation cohort, and 0.900 (95% CI 0.819-0.953) in the external test cohort. The DLRP also outperformed deep learning radiomics model based on US images only (AUC 0.815 [0.719-0.889], p = 0.027) and deep learning pathomics model based on WSIs only (AUC 0.802 [0.704-0.878], p = 0.013) in the external test cohort. INTERPRETATION The DLRP can effectively distinguish between luminal and non-luminal breast cancers at early stages before surgery based on pretherapeutic US images and biopsy H&E-stained WSIs, providing a tool to facilitate treatment decision making in early-stage breast cancers. FUNDING Natural Science Foundation of Guangdong Province (No. 2023A1515011564), and National Natural Science Foundation of China (No. 91959127; No. 81971631).
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Affiliation(s)
- Yini Huang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zhao Yao
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Lingling Li
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Rushuang Mao
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Weijun Huang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Foshan First People's Hospital, Foshan, Guangdong, China
| | - Zhengming Hu
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Yixin Hu
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yun Wang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Ruohan Guo
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xiaofeng Tang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Liang Yang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Rongzhen Luo
- Department of Pathology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China.
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
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Kovačević L, Štajduhar A, Stemberger K, Korša L, Marušić Z, Prutki M. Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study. J Pers Med 2023; 13:1150. [PMID: 37511763 PMCID: PMC10381456 DOI: 10.3390/jpm13071150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
This study aimed to explore the potential of multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for classifying breast cancer surrogate subtypes. This retrospective study analyzed 360 breast cancers from 319 patients who underwent pretreatment DCE-MRI between January 2015 and January 2019. The cohort consisted of 33 triple-negative, 26 human epidermal growth factor receptor 2 (HER2)-positive, 109 luminal A-like, 144 luminal B-like HER2-negative, and 48 luminal B-like HER2-positive lesions. A total of 1781 radiomic features were extracted from manually segmented breast cancers in each DCE-MRI sequence. The model was internally validated and selected using ten times repeated five-fold cross-validation on the primary cohort, with further evaluation using a validation cohort. The most successful models were logistic regression models applied to the third post-contrast subtraction images. These models exhibited the highest area under the curve (AUC) for discriminating between luminal A like vs. others (AUC: 0.78), luminal B-like HER2 negative vs. others (AUC: 0.57), luminal B-like HER2 positive vs. others (AUC: 0.60), HER2 positive vs. others (AUC: 0.81), and triple negative vs. others (AUC: 0.83). In conclusion, the radiomic features extracted from multi-phase DCE-MRI are promising for discriminating between breast cancer subtypes. The best-performing models relied on tissue changes observed during the mid-stage of the imaging process.
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Affiliation(s)
- Lucija Kovačević
- Clinical Department of Diagnostic and Interventional Radiology, University Hospital Centre Zagreb, Kispaticeva 12, 10000 Zagreb, Croatia; (L.K.); (M.P.)
| | - Andrija Štajduhar
- Department for Medical Statistics, Epidemiology and Medical Informatics School of Medicine, University of Zagreb, Salata 12, 10000 Zagreb, Croatia
| | - Karlo Stemberger
- Clinical Department of Diagnostic and Interventional Radiology, University Hospital Centre Zagreb, Kispaticeva 12, 10000 Zagreb, Croatia; (L.K.); (M.P.)
| | - Lea Korša
- Clinical Department of Pathology and Cytology, University Hospital Centre Zagreb, Kispaticeva 12, 10000 Zagreb, Croatia
| | - Zlatko Marušić
- Clinical Department of Pathology and Cytology, University Hospital Centre Zagreb, Kispaticeva 12, 10000 Zagreb, Croatia
| | - Maja Prutki
- Clinical Department of Diagnostic and Interventional Radiology, University Hospital Centre Zagreb, Kispaticeva 12, 10000 Zagreb, Croatia; (L.K.); (M.P.)
- School of Medicine, University of Zagreb, Salata 3, 10000 Zagreb, Croatia
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9
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Faraz K, Dauce G, Bouhamama A, Leporq B, Sasaki H, Bito Y, Beuf O, Pilleul F. Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches. J Pers Med 2023; 13:1062. [PMID: 37511674 PMCID: PMC10382057 DOI: 10.3390/jpm13071062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/24/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, and IDC vs. ILC classification tasks. The best results were obtained for the ER+ vs. ER- and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers.
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Affiliation(s)
- Khuram Faraz
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Grégoire Dauce
- FUJIFILM Healthcare France S.A.S., 69800 Saint-Priest, France
| | - Amine Bouhamama
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
- Department of Radiology, Centre Léon Bérard, 69008 Lyon, France
| | - Benjamin Leporq
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Hajime Sasaki
- FUJIFILM Healthcare France S.A.S., 69800 Saint-Priest, France
- FUJIFILM Healthcare Corporation, Tokyo 107-0052, Japan
| | | | - Olivier Beuf
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Frank Pilleul
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
- Department of Radiology, Centre Léon Bérard, 69008 Lyon, France
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Zhong J, Lu J, Zhang G, Mao S, Chen H, Yin Q, Hu Y, Xing Y, Ding D, Ge X, Zhang H, Yao W. An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation. Insights Imaging 2023; 14:111. [PMID: 37336830 DOI: 10.1186/s13244-023-01437-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/14/2023] [Indexed: 06/21/2023] Open
Abstract
OBJECTIVE To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level. METHODS A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated. RESULTS We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation. CONCLUSIONS The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence. CLINICAL RELEVANCE STATEMENT The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Junjie Lu
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Guangcheng Zhang
- Department of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Shiqi Mao
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Haoda Chen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qian Yin
- Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
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Pereslucha AM, Wenger DM, Morris MF, Aydi ZB. Invasive Lobular Carcinoma: A Review of Imaging Modalities with Special Focus on Pathology Concordance. Healthcare (Basel) 2023; 11:healthcare11050746. [PMID: 36900751 PMCID: PMC10000992 DOI: 10.3390/healthcare11050746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023] Open
Abstract
Invasive lobular cancer (ILC) is the second most common type of breast cancer. It is characterized by a unique growth pattern making it difficult to detect on conventional breast imaging. ILC can be multicentric, multifocal, and bilateral, with a high likelihood of incomplete excision after breast-conserving surgery. We reviewed the conventional as well as newly emerging imaging modalities for detecting and determining the extent of ILC- and compared the main advantages of MRI vs. contrast-enhanced mammogram (CEM). Our review of the literature finds that MRI and CEM clearly surpass conventional breast imaging in terms of sensitivity, specificity, ipsilateral and contralateral cancer detection, concordance, and estimation of tumor size for ILC. Both MRI and CEM have each been shown to enhance surgical outcomes in patients with newly diagnosed ILC that had one of these imaging modalities added to their preoperative workup.
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Affiliation(s)
- Alicia M Pereslucha
- Department of Surgery, University of Arizona College of Medicine-Phoenix, Phoenix, AZ 85006, USA
| | - Danielle M Wenger
- College of Medicine-Phoenix, University of Arizona, Phoenix, AZ 85004, USA
| | - Michael F Morris
- Division of Diagnostic Imaging, Banner MD Anderson Cancer Center, Phoenix, AZ 85006, USA
- Department of Radiology, Banner University Medical Center-Phoenix, Phoenix, AZ 85006, USA
| | - Zeynep Bostanci Aydi
- Department of Surgery, University of Arizona College of Medicine-Phoenix, Phoenix, AZ 85006, USA
- Department of Surgical Oncology, Banner MD Anderson Cancer Center, Phoenix, AZ 85006, USA
- Correspondence:
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12
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Spadarella G, Stanzione A, Akinci D'Antonoli T, Andreychenko A, Fanni SC, Ugga L, Kotter E, Cuocolo R. Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol 2023; 33:1884-1894. [PMID: 36282312 PMCID: PMC9935718 DOI: 10.1007/s00330-022-09187-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/31/2022] [Accepted: 09/19/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The main aim of the present systematic review was a comprehensive overview of the Radiomics Quality Score (RQS)-based systematic reviews to highlight common issues and challenges of radiomics research application and evaluate the relationship between RQS and review features. METHODS The literature search was performed on multiple medical literature archives according to PRISMA guidelines for systematic reviews that reported radiomic quality assessment through the RQS. Reported scores were converted to a 0-100% scale. The Mann-Whitney and Kruskal-Wallis tests were used to compare RQS scores and review features. RESULTS The literature research yielded 345 articles, from which 44 systematic reviews were finally included in the analysis. Overall, the median of RQS was 21.00% (IQR = 11.50). No significant differences of RQS were observed in subgroup analyses according to targets (oncological/not oncological target, neuroradiology/body imaging focus and one imaging technique/more than one imaging technique, characterization/prognosis/detection/other). CONCLUSIONS Our review did not reveal a significant difference of quality of radiomic articles reported in systematic reviews, divided in different subgroups. Furthermore, low overall methodological quality of radiomics research was found independent of specific application domains. While the RQS can serve as a reference tool to improve future study designs, future research should also be aimed at improving its reliability and developing new tools to meet an ever-evolving research space. KEY POINTS • Radiomics is a promising high-throughput method that may generate novel imaging biomarkers to improve clinical decision-making process, but it is an inherently complex analysis and often lacks reproducibility and generalizability. • The Radiomics Quality Score serves a necessary role as the de facto reference tool for assessing radiomics studies. • External auditing of radiomics studies, in addition to the standard peer-review process, is valuable to highlight common limitations and provide insights to improve future study designs and practical applicability of the radiomics models.
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Affiliation(s)
- Gaia Spadarella
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
| | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland
| | - Anna Andreychenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | | | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Elmar Kotter
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
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13
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Ming W, Li F, Zhu Y, Bai Y, Gu W, Liu Y, Sun X, Liu X, Liu H. Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique. Comput Biol Med 2022; 150:106147. [PMID: 36201887 DOI: 10.1016/j.compbiomed.2022.106147] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/06/2022] [Accepted: 09/24/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND The recent development of artificial intelligence (AI) technologies coupled with medical imaging data has gained considerable attention, and offers a non-invasive approach for cancer diagnosis and prognosis. In this context, improved breast cancer (BC) molecular characteristics assessment models are foreseen to enable personalized strategies with better clinical outcomes compared to existing screening strategies. And it is a promising approach to developing models for hormone receptors (HR) and subtypes of BC patients from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. METHODS In this institutional review board-approved study, 174 BC patients with both DCE-MRI and RNA-seq data in the local database were analyzed. Slice images from tumor lesions and multi-scale peri-tumor regions were used as model inputs, and five representative pre-trained transfer learning (TF) networks, such as Inception-v3 and Xception, were employed to establish prediction models. A comprehensive analysis was performed using five-fold cross-validation to avoid overfitting, and accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) to evaluate model performance. RESULTS Xception achieved the superior results when using solely tumor regions, with highest AUROCs of 0.844 (95% CI: [0.841, 0.847]) and 0.784 (95% CI: [0.781, 0.788]) for estrogen receptor (ER) and progesterone receptor (PR), respectively, and best ACC of 0.467 (95% CI: [0.462, 0.470]) for PAM50 subtypes. A significant improvement in the model performance was observed when images of the peri-tumor region were included, with optimal results achieved using images of the tumor and the 10 mm peri-tumor regions. Xception-based TF models performed most effectively in predicting ER and PR statuses, with the AUROCs were 0.942 (95% CI: [0.940, 0.944]) and 0.920 (95% CI: [0.917, 0.922]), respectively, whereas for PAM50 subtypes, the Inception-v3-based network yielded the highest ACC as 0.742 (95% CI: [0.738, 0.746]). CONCLUSIONS Transfer learning analysis based on DCE-MRI data of tumor and peri-tumor regions was helpful to the non-invasive assessment of molecular characteristics of BC.
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Affiliation(s)
- Wenlong Ming
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Fuyu Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Yanhui Zhu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China
| | - Yunfei Bai
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Wanjun Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China; Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, PR China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Xiaoan Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China.
| | - Hongde Liu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China.
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14
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Stanzione A, Verde F, Cuocolo R, Romeo V, Paolo Mainenti P, Brunetti A, Maurea S. Placenta Accreta Spectrum Disorders and Radiomics: Systematic review and quality appraisal. Eur J Radiol 2022; 155:110497. [PMID: 36030661 DOI: 10.1016/j.ejrad.2022.110497] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/13/2022] [Accepted: 08/18/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE Ultrasound and magnetic resonance imaging are the imaging modalities of choice for placenta accrete spectrum (PAS) disorders assessment. Radiomics could further increase the value of medical images and allow to overcome the limitations linked to their visual assessment. Aim of this systematic review was to identify and appraise the methodological quality of radiomics studies focused PAS disorders applications. METHOD Three online databases (PubMed, Scopus and Web of Science) were searched to identify original research articles on human subjects published in English. For the qualitative synthesis of results, data regarding study design (e.g., retrospective or prospective), purpose, patient population (e.g., sample size), imaging modalities and radiomics pipelines (e.g., segmentation and feature extraction strategy) were collected. The appraisal of methodological quality was performed using the Radiomics Quality Score (RQS). RESULTS 10 articles were finally included and analyzed. All were retrospective and MRI-powered. The majority included more than 100 patients (6/10). Four were prognostic (focused on either the prediction of bleeding volume or the prediction of needed management) while six diagnostic (PAS vs not PAS classification) studies. The median RQS was 8, with maximum and minimum respectively equal to 17/36 and - 6/36. Major methodological concerns were the lack of feature stability to multiple segmentation testing and poor data openness. CONCLUSIONS Radiomics studies focused on PAS disorders showed a heterogeneous methodological quality, overall lower than desirable. Furthermore, many relevant research questions remain unexplored. More robust investigations are needed to foster advancements in the field and possibly clinical translation.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy; Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Research Council, Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
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15
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Ming W, Zhu Y, Bai Y, Gu W, Li F, Hu Z, Xia T, Dai Z, Yu X, Li H, Gu Y, Yuan S, Zhang R, Li H, Zhu W, Ding J, Sun X, Liu Y, Liu H, Liu X. Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer. Front Oncol 2022; 12:943326. [PMID: 35965527 PMCID: PMC9366134 DOI: 10.3389/fonc.2022.943326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/29/2022] [Indexed: 11/17/2022] Open
Abstract
Background To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001). Conclusions Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.
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Affiliation(s)
- Wenlong Ming
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yanhui Zhu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yunfei Bai
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Wanjun Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China
| | - Fuyu Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Zixi Hu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Tiansong Xia
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zuolei Dai
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiafei Yu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Huamei Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yu Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Shaoxun Yuan
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Rongxin Zhang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Haitao Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Wenyong Zhu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Jianing Ding
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
| | - Hongde Liu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
| | - Xiaoan Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Yun Liu, ; Hongde Liu, ; Xiaoan Liu,
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16
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Sha Y, Chen J. MRI-based radiomics for the diagnosis of triple-negative breast cancer: a meta-analysis. Clin Radiol 2022; 77:655-663. [DOI: 10.1016/j.crad.2022.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 04/21/2022] [Indexed: 11/03/2022]
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17
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Wang F, Wang D, Xu Y, Jiang H, Liu Y, Zhang J. Potential of the Non-Contrast-Enhanced Chest CT Radiomics to Distinguish Molecular Subtypes of Breast Cancer: A Retrospective Study. Front Oncol 2022; 12:848726. [PMID: 35387125 PMCID: PMC8979294 DOI: 10.3389/fonc.2022.848726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 02/14/2022] [Indexed: 11/30/2022] Open
Abstract
Objectives The molecular subtype plays an important role in breast cancer, which is the main reference to guide treatment and is closely related to prognosis. The objective of this study was to explore the potential of the non-contrast-enhanced chest CT-based radiomics to predict breast cancer molecular subtypes non-invasively. Methods A total of 300 breast cancer patients (153 luminal types and 147 non-luminal types) who underwent routine chest CT examination were included in the study, of which 220 cases belonged to the training set and 80 cases to the time-independent test set. Identification of the molecular subtypes is based on immunohistochemical staining of postoperative tissue samples. The region of interest (ROI) of breast masses was delineated on the continuous slices of CT images. Forty-two models to predict the luminal type of breast cancer were established by the combination of six feature screening methods and seven machine learning classifiers; 5-fold cross-validation (cv) was used for internal validation. Finally, the optimal model was selected for external validation on the independent test set. In addition, we also took advantage of SHapley Additive exPlanations (SHAP) values to make explanations of the machine learning model. Results During internal validation, the area under the curve (AUC) values for different models ranged from 0.599 to 0.842, and the accuracy ranged from 0.540 to 0.775. Eventually, the LASSO_SVM combination was selected as the final model, which included 9 radiomics features. The AUC, accuracy, sensitivity, and specificity of the model to distinguish luminal from the non-luminal type were 0.842 [95% CI: 0.728−0.957], 0.773, 0.818, and 0.773 in the training set and 0.757 [95% CI: 0.640–0.866], 0.713, 0.767, and 0.676 in the test set. Conclusion The radiomics based on chest CT may provide a new idea for the identification of breast cancer molecular subtypes.
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Affiliation(s)
- Fei Wang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Dandan Wang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ye Xu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yang Liu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Jinfeng Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
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Castaldo R, Garbino N, Cavaliere C, Incoronato M, Basso L, Cuocolo R, Pace L, Salvatore M, Franzese M, Nicolai E. A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study. Diagnostics (Basel) 2022; 12:499. [PMID: 35204589 PMCID: PMC8871349 DOI: 10.3390/diagnostics12020499] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/08/2022] [Accepted: 02/12/2022] [Indexed: 01/27/2023] Open
Abstract
Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman’s correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis.
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