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Song Y, Ma S, Mao B, Xu K, Liu Y, Ma J, Jia J. Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT. Dentomaxillofac Radiol 2024; 53:316-324. [PMID: 38627247 PMCID: PMC11211686 DOI: 10.1093/dmfr/twae016] [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: 11/20/2023] [Revised: 01/03/2024] [Accepted: 04/11/2024] [Indexed: 06/29/2024] Open
Abstract
OBJECTIVES Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone-beam CT (CBCT) image features for the preoperative differential diagnosis of AME and OKC and compares ML algorithms to expert radiologists to validate performance. METHODS We retrospectively collected the data of 326 patients with AME and OKC, where all diagnoses were confirmed by histopathologic tests. A total of 348 features were selected to train six ML models for differential diagnosis by a 5-fold cross-validation. We then compared the performance of ML-based diagnoses to those of radiologists. RESULTS Among the six ML models, XGBoost was effective in distinguishing AME and OKC in CBCT images, with its classification performance outperforming the other models. The mean precision, recall, accuracy, F1-score, and area under the curve (AUC) were 0.900, 0.807, 0.843, 0.841, and 0.872, respectively. Compared to the diagnostics by radiologists, ML-based radiomic diagnostics performed better. CONCLUSIONS Radiomic-based ML algorithms allow CBCT images of AME and OKC to be distinguished accurately, facilitating the preoperative differential diagnosis of AME and OKC. ADVANCES IN KNOWLEDGE ML and radiomic approaches with high-resolution CBCT images provide new insights into the differential diagnosis of AME and OKC.
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Affiliation(s)
- Yang Song
- School of Medicine and Health Management, Huazhong University of Science & Technology, Hangkong Road, Wuhan, 430030, China
| | - Sirui Ma
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Luoyu Road, Wuhan, 430072, China
- Department of Oral and Maxillofacial-Head and Neck Oncology, School and Hospital of Stomatology, Wuhan University, Luoyu Road, Wuhan, 430072, China
| | - Bing Mao
- Zhengzhou University People's Hospital (Henan Provincial People's Hospital), Weiwu Road, Zhengzhou, 450003, China
| | - Kun Xu
- School of Medicine and Health Management, Huazhong University of Science & Technology, Hangkong Road, Wuhan, 430030, China
| | - Yuan Liu
- School of Medicine and Health Management, Huazhong University of Science & Technology, Hangkong Road, Wuhan, 430030, China
| | - Jingdong Ma
- School of Medicine and Health Management, Huazhong University of Science & Technology, Hangkong Road, Wuhan, 430030, China
| | - Jun Jia
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Luoyu Road, Wuhan, 430072, China
- Department of Oral and Maxillofacial-Head and Neck Oncology, School and Hospital of Stomatology, Wuhan University, Luoyu Road, Wuhan, 430072, China
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Wang H, Jia Q, Wang Y, Xue W, Jiang Q, Ning F, Wang J, Zhu Z, Tian L. Stacking learning based on micro-CT radiomics for outcome prediction in the early-stage of silica-induced pulmonary fibrosis model. Heliyon 2024; 10:e30651. [PMID: 38765063 PMCID: PMC11098827 DOI: 10.1016/j.heliyon.2024.e30651] [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: 11/16/2023] [Revised: 02/28/2024] [Accepted: 05/01/2024] [Indexed: 05/21/2024] Open
Abstract
Silicosis is a progressive pulmonary fibrosis disease caused by long-term inhalation of silica. The early diagnosis and timely implementation of intervention measures are crucial in preventing silicosis deterioration further. However, the lack of screening and diagnostic measures for early-stage silicosis remains a significant challenge. In this study, silicosis models of varying severity were established through a single exposure to silica with different doses (2.5mg/mice or 5mg/mice) and durations (4 weeks or 12 weeks). The diagnostic performance of computed tomography (CT) quantitative analysis was assessed using lung density biomarkers and the lung density distribution histogram, with a particular focus on non-aerated lung volume. Subsequently, we developed and evaluated a stacking learning model for early diagnosis of silicosis after extracting and selecting features from CT images. The CT quantitative analysis reveals that while the lung densitometric biomarkers and lung density distribution histogram, as traditional indicators, effectively differentiate severe fibrosis models, they are unable to distinguish early-stage silicosis. Furthermore, these findings remained consistent even when employing non-aerated areas, which is a more sensitive indicator. By establishing a radiomics stacking learning model based on non-aerated areas, we can achieve remarkable diagnostic performance to distinguish early-stage silicosis, which can provide a valuable tool for clinical assistant diagnosis. This study reveals the potential of using non-aerated lung areas as a region of interest in stacking learning for early diagnosis of silicosis, providing new insights into early detection of this disease.
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Affiliation(s)
- Hongwei Wang
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, China
| | - Qiyue Jia
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, China
| | - Yan Wang
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, China
| | - Wenming Xue
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, China
| | - Qiyue Jiang
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, China
| | - Fuao Ning
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, China
| | - Jiaxin Wang
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, China
| | - Zhonghui Zhu
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, China
| | - Lin Tian
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, China
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Guo Y, Zhang H, Yuan L, Chen W, Zhao H, Yu QQ, Shi W. Machine learning and new insights for breast cancer diagnosis. J Int Med Res 2024; 52:3000605241237867. [PMID: 38663911 PMCID: PMC11047257 DOI: 10.1177/03000605241237867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 02/21/2024] [Indexed: 04/28/2024] Open
Abstract
Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.
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Affiliation(s)
- Ya Guo
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Heng Zhang
- Department of Laboratory Medicine, Shandong Daizhuang Hospital, Jining, Shandong Province, China
| | - Leilei Yuan
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Weidong Chen
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Haibo Zhao
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Qing-Qing Yu
- Phase I Clinical Research Centre, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Wenjie Shi
- Molecular and Experimental Surgery, University Clinic for General-, Visceral-, Vascular- and Trans-Plantation Surgery, Medical Faculty University Hospital Magdeburg, Otto-von Guericke University, Magdeburg, Germany
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Cheng Q, Lin H, Zhao J, Lu X, Wang Q. Application of machine learning-based multi-sequence MRI radiomics in diagnosing anterior cruciate ligament tears. J Orthop Surg Res 2024; 19:99. [PMID: 38297322 PMCID: PMC10829177 DOI: 10.1186/s13018-024-04602-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 01/28/2024] [Indexed: 02/02/2024] Open
Abstract
OBJECTIVE To compare the diagnostic power among various machine learning algorithms utilizing multi-sequence magnetic resonance imaging (MRI) radiomics in detecting anterior cruciate ligament (ACL) tears. Additionally, this research aimed to create and validate the optimal diagnostic model. METHODS In this retrospective analysis, 526 patients were included, comprising 178 individuals with ACL tears and 348 with a normal ACL. Radiomics features were derived from multi-sequence MRI scans, encompassing T1-weighted imaging and proton density (PD)-weighted imaging. The process of selecting the most reliable radiomics features involved using interclass correlation coefficient (ICC) testing, t tests, and the least absolute shrinkage and selection operator (LASSO) technique. After the feature selection process, five machine learning classifiers were created. These classifiers comprised logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP). A thorough performance evaluation was carried out, utilizing diverse metrics like the area under the receiver operating characteristic curve (ROC), specificity, accuracy, sensitivity positive predictive value, and negative predictive value. The classifier exhibiting the best performance was chosen. Subsequently, three models were developed: the PD model, the T1 model, and the combined model, all based on the optimal classifier. The diagnostic performance of these models was assessed by employing AUC values, calibration curves, and decision curve analysis. RESULTS Out of 2032 features, 48 features were selected. The SVM-based multi-sequence radiomics outperformed all others, achieving AUC values of 0.973 and 0.927, sensitivities of 0.933 and 0.857, and specificities of 0.930 and 0.829, in the training and validation cohorts, respectively. CONCLUSION The multi-sequence MRI radiomics model, which is based on machine learning, exhibits exceptional performance in diagnosing ACL tears. It provides valuable insights crucial for the diagnosis and treatment of knee joint injuries, serving as an accurate and objective supplementary diagnostic tool for clinical practitioners.
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Affiliation(s)
- Qi Cheng
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Haoran Lin
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Jie Zhao
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Xiao Lu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Qiang Wang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China.
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Fusco R, Granata V, Simonetti I, Setola SV, Iasevoli MAD, Tovecci F, Lamanna CMP, Izzo F, Pecori B, Petrillo A. An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies. Curr Oncol 2024; 31:403-424. [PMID: 38248112 PMCID: PMC10814313 DOI: 10.3390/curroncol31010027] [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: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy;
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Maria Assunta Daniela Iasevoli
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Filippo Tovecci
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Ciro Michele Paolo Lamanna
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Biagio Pecori
- Division of Radiation Protection and Innovative Technology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Cai Z, Wong LM, Wong YH, Lee HL, Li KY, So TY. Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading. Cancers (Basel) 2023; 15:5459. [PMID: 38001719 PMCID: PMC10670283 DOI: 10.3390/cancers15225459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/07/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics for meningioma grading on Magnetic Resonance Imaging (MRI). METHODS This study recruited 160 consecutive patients with pathologically proven meningioma (129 low-grade (WHO grade I) tumors; 31 high-grade (WHO grade II and III) tumors) with preoperative multisequence MRI imaging. A dual-level augmentation strategy combining IA and FA was applied and evaluated in 100 repetitions in 3-, 5-, and 10-fold cross-validation. RESULTS The best area under the receiver operating characteristics curve of our method in 100 repetitions was ≥0.78 in all cross-validations. The corresponding cross-validation sensitivities (cross-validation specificity) were 0.72 (0.69), 0.76 (0.71), and 0.63 (0.82) in 3-, 5-, and 10-fold cross-validation, respectively. The proposed method achieved significantly better performance and distribution of results, outperforming single-level augmentation (IA or FA) or no augmentation in each cross-validation. CONCLUSIONS The dual-level augmentation strategy using IA and FA significantly improves the performance of the radiomics model for meningioma grading on MRI, allowing better radiomics-based preoperative stratification and individualized treatment.
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Affiliation(s)
| | | | | | | | | | - Tiffany Y. So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China; (Z.C.); (L.M.W.); (Y.H.W.); (H.-l.L.); (K.-y.L.)
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Deng XY, Cao PW, Nan SM, Pan YP, Yu C, Pan T, Dai G. Differentiation Between Phyllodes Tumors and Fibroadenomas of Breast Using Mammography-based Machine Learning Methods: A Preliminary Study. Clin Breast Cancer 2023; 23:729-736. [PMID: 37481337 DOI: 10.1016/j.clbc.2023.07.002] [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: 06/06/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/24/2023]
Abstract
OBJECTIVE To investigate the diagnostic performance of a mammography-based radiomics model for distinguishing phyllodes tumors (PTs) from fibroadenomas (FAs) of the breast. MATERIALS AND METHODS A total of 156 patients were retrospectively included (75 with PTs, 81 with FAs) and divided into training and validation groups at a ratio of 7:3. Radiomics features were extracted from craniocaudal and mediolateral oblique images. The least absolute shrinkage and selection operator (LASSO) algorithm and principal component analysis (PCA) were performed to select features. Three machine learning classifiers, including logistic regression (LR), K-nearest neighbor classifier (KNN) and support vector machine (SVM), were implemented in the radiomics model, imaging model and combined model. Receiver operating characteristic curves, area under the curve (AUC), sensitivity and specificity were computed. RESULTS Among 1084 features, the LASSO algorithm selected 17 features, and PCA further selected 6 features. Three machine learning classifiers yielded the same AUC of 0.935 in the validation group for the radiomics model. In the imaging model, KNN yielded the highest accuracy rate of 89.4% and AUC of 0.947 in the validation set. For the combined model, the SVM classifier reached the highest AUC of 0.918 with an accuracy rate of 86.2%, sensitivity of 83.9%, and specificity of 89.4% in the training group. In the validation group, LR yielded the highest AUC of 0.973. The combined model had a relatively higher AUC than the radiomics model or imaging model, especially in the validation group. CONCLUSIONS Mammography-based radiomics features demonstrate good diagnostic performance for discriminating PTs from FAs.
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Affiliation(s)
- Xue-Ying Deng
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
| | - Pei-Wei Cao
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Shuai-Ming Nan
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yue-Peng Pan
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Chang Yu
- Department of Pathology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Ting Pan
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Gang Dai
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
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Shi Z, Ma Y, Ma X, Jin A, Zhou J, Li N, Sheng D, Chang C, Chen J, Li J. Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115099. [PMID: 37299826 DOI: 10.3390/s23115099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/14/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023]
Abstract
The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy.
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Affiliation(s)
- Zhaoting Shi
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Yebo Ma
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, No. 500, Dongchuan Road, Shanghai 200241, China
| | - Xiaowen Ma
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Anqi Jin
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Jin Zhou
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Na Li
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Danli Sheng
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Cai Chang
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Jiangang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, No. 500, Dongchuan Road, Shanghai 200241, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, No. 1200, Cailun Road, Pudong District, Shanghai 201203, China
| | - Jiawei Li
- Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Xuhui District, Shanghai 200032, China
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10
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Chen K, Xu J, Wang W, Jiang R, Zhang H, Wang X, Cao J, Fang M. Clinical outcomes and biomarkers of phyllodes tumors of the breast: A single-center retrospective study. Cancer Med 2023. [PMID: 37081723 DOI: 10.1002/cam4.5849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/11/2023] [Accepted: 03/12/2023] [Indexed: 04/22/2023] Open
Abstract
PURPOSE Phyllodes tumors (PTs) are rare neoplasms with a certain risk of recurrence and/or metastasis. In clinical practice, there is a lack of high-quality clinical studies and unified guidelines to guide the treatment. MATERIALS AND METHODS All malignant and recurrence/metastasis PTs were retrospectively collected, which were diagnosed from 2008 to 2022. RESULTS A total of 82 patients were enrolled, including 69 malignant and 13 borderline tumors. 96.3% (79/82) received surgical treatment. During a median follow-up of 55.5 months, 20 patients (20/82, 24.4%) had distant metastasis (DM), while 32 (32/82, 39.0%) had local recurrence (LR). Univariate analysis showed the survival of PTs was associated with surgical methods (p < 0.001), tumor size (p = 0.026), and biological behavior (p = 0.017), but not age at diagnosis. In relapsed borderline PTs, we did not find deaths due to disease progression. Patients with DM were all malignant PTs, with disease-progression occurring within 3 years in more than 80% of patients. Among salvage treatments, the combination of antiangiogenic drugs improved the prognosis to some extent, with a significant increase in mPFS (2.77 vs. 1.53 months), but no significant statistical results were obtained (p = 0.168). Lactate dehydrogenase (LDH) was an independent predictor of the prognosis for malignant PTs (p = 0.001, HR = 1.203, 95%CI, 1.082-1.336). CONCLUSION Borderline PTs rarely metastasize, and even if LR occurs, surgical resection can lead to long-term survival. In metastatic phyllodes tumors (MPT), systemic therapy is not effective, but antiangiogenic drugs may prolong survival. LDH is an independent prognostic factor for malignant PTs to identify high-risk tumors.
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Affiliation(s)
- Keyu Chen
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Jiaojiao Xu
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Wei Wang
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Wenzhou Medical University, Wenzhou, China
| | - Ruiyuan Jiang
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Huanping Zhang
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Wenzhou Medical University, Wenzhou, China
| | - Xiaojia Wang
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
| | - Jun Cao
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Department of rare and head and neck oncology, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Cancer Hospital of University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
| | - Meiyu Fang
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Department of rare and head and neck oncology, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Cancer Hospital of University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
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11
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Mammography-based radiomics analysis and imaging features for predicting the malignant risk of phyllodes tumours of the breast. Clin Radiol 2023; 78:e386-e392. [PMID: 36868973 DOI: 10.1016/j.crad.2023.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 02/21/2023]
Abstract
AIM To determine whether the mammography (MG)-based radiomics analysis and MG/ultrasound (US) imaging features could predict the malignant risk of phyllodes tumours (PTs) of the breast. MATERIALS AND METHODS Seventy-five patients with PTs were included retrospectively (39 with benign PTs, 36 with borderline/malignant PTs) and divided into thetraining (n=52) and validation groups (n=23). The clinical information, MG and US imaging characteristics, and histogram features were extracted from craniocaudal (CC) and mediolateral oblique (MLO) images. The lesion region of interest (ROI) and perilesional ROI were delineated. Multivariate logistic regression analysis was performed to determine the malignant factors of PTs. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC), sensitivity, and specificity were calculated. RESULTS There was no significant difference found in the clinical or MG/US features between benign and borderline/malignant PTs. In the lesion ROI, variance in the CC view and mean and variance in the MLO view were independent predictors. The AUC was 0.942, sensitivity and specificity were 96.3% and 92%, respectively, in the training group. In the validation group, the AUC was 0.879, the sensitivity was 91.7%, and the specificity was 81.8%. In the perilesional ROI, the AUCs were 0.904 and 0.939, sensitivities were 88.9% and 91.7%, and the specificities were 92% and 90.9% in the training and validation groups, respectively. CONCLUSIONS MG-based radiomic features could predict the risk of malignancy of patients with PTs and may be used as a potential tool to differentiate benign and borderline/malignant PTs.
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12
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Do QN. Editorial for "Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors". J Magn Reson Imaging 2023; 57:646-647. [PMID: 35661474 PMCID: PMC9720037 DOI: 10.1002/jmri.28285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 05/12/2022] [Indexed: 02/03/2023] Open
Affiliation(s)
- Quyen N. Do
- The Department of Radiology UT Southwestern Medical Center Dallas Texas USA
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13
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Giacobbe G, Granata V, Trovato P, Fusco R, Simonetti I, De Muzio F, Cutolo C, Palumbo P, Borgheresi A, Flammia F, Cozzi D, Gabelloni M, Grassi F, Miele V, Barile A, Giovagnoni A, Gandolfo N. Gender Medicine in Clinical Radiology Practice. J Pers Med 2023; 13:jpm13020223. [PMID: 36836457 PMCID: PMC9966684 DOI: 10.3390/jpm13020223] [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: 12/24/2022] [Revised: 01/18/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023] Open
Abstract
Gender Medicine is rapidly emerging as a branch of medicine that studies how many diseases common to men and women differ in terms of prevention, clinical manifestations, diagnostic-therapeutic approach, prognosis, and psychological and social impact. Nowadays, the presentation and identification of many pathological conditions pose unique diagnostic challenges. However, women have always been paradoxically underestimated in epidemiological studies, drug trials, as well as clinical trials, so many clinical conditions affecting the female population are often underestimated and/or delayed and may result in inadequate clinical management. Knowing and valuing these differences in healthcare, thus taking into account individual variability, will make it possible to ensure that each individual receives the best care through the personalization of therapies, the guarantee of diagnostic-therapeutic pathways declined according to gender, as well as through the promotion of gender-specific prevention initiatives. This article aims to assess potential gender differences in clinical-radiological practice extracted from the literature and their impact on health and healthcare. Indeed, in this context, radiomics and radiogenomics are rapidly emerging as new frontiers of imaging in precision medicine. The development of clinical practice support tools supported by artificial intelligence allows through quantitative analysis to characterize tissues noninvasively with the ultimate goal of extracting directly from images indications of disease aggressiveness, prognosis, and therapeutic response. The integration of quantitative data with gene expression and patient clinical data, with the help of structured reporting as well, will in the near future give rise to decision support models for clinical practice that will hopefully improve diagnostic accuracy and prognostic power as well as ensure a more advanced level of precision medicine.
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Affiliation(s)
- Giuliana Giacobbe
- General and Emergency Radiology Department, “Antonio Cardarelli” Hospital, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Piero Trovato
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Correspondence:
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy
| | - Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Federica Flammia
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, 56126 Pisa, Italy
| | - Francesca Grassi
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, 80138 Naples, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Antonio Barile
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, 67100 L’Aquila, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
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14
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Sansone M, Fusco R, Grassi F, Gatta G, Belfiore MP, Angelone F, Ricciardi C, Ponsiglione AM, Amato F, Galdiero R, Grassi R, Granata V, Grassi R. Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography. Curr Oncol 2023; 30:839-853. [PMID: 36661713 PMCID: PMC9858566 DOI: 10.3390/curroncol30010064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/31/2022] [Accepted: 01/06/2023] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND breast cancer (BC) is the world's most prevalent cancer in the female population, with 2.3 million new cases diagnosed worldwide in 2020. The great efforts made to set screening campaigns, early detection programs, and increasingly targeted treatments led to significant improvement in patients' survival. The Full-Field Digital Mammograph (FFDM) is considered the gold standard method for the early diagnosis of BC. From several previous studies, it has emerged that breast density (BD) is a risk factor in the development of BC, affecting the periodicity of screening plans present today at an international level. OBJECTIVE in this study, the focus is the development of mammographic image processing techniques that allow the extraction of indicators derived from textural patterns of the mammary parenchyma indicative of BD risk factors. METHODS a total of 168 patients were enrolled in the internal training and test set while a total of 51 patients were enrolled to compose the external validation cohort. Different Machine Learning (ML) techniques have been employed to classify breasts based on the values of the tissue density. Textural features were extracted only from breast parenchyma with which to train classifiers, thanks to the aid of ML algorithms. RESULTS the accuracy of different tested classifiers varied between 74.15% and 93.55%. The best results were reached by a Support Vector Machine (accuracy of 93.55% and a percentage of true positives and negatives equal to TPP = 94.44% and TNP = 92.31%). The best accuracy was not influenced by the choice of the features selection approach. Considering the external validation cohort, the SVM, as the best classifier with the 7 features selected by a wrapper method, showed an accuracy of 0.95, a sensitivity of 0.96, and a specificity of 0.90. CONCLUSIONS our preliminary results showed that the Radiomics analysis and ML approach allow us to objectively identify BD.
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Affiliation(s)
- Mario Sansone
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
| | - Francesca Grassi
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Gianluca Gatta
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Francesca Angelone
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Carlo Ricciardi
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Alfonso Maria Ponsiglione
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Francesco Amato
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Grassi
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberto Grassi
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
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Ece B, Aydın S. Imaging of fibroadenoma: Be careful with imaging follow-up. World J Clin Cases 2022; 10:9176-9179. [PMID: 36157665 PMCID: PMC9477063 DOI: 10.12998/wjcc.v10.i25.9176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/10/2022] [Accepted: 07/29/2022] [Indexed: 02/05/2023] Open
Abstract
The present letter to the editor is related to the study titled, “Preoperational diagnosis and management of breast ductal carcinoma in situ arising within fibroadenoma: Two case reports.” Fibroadenoma is the most common benign mass lesion in young females. Based on this study showing that malignancy can develop on fibroadenomas, we want to emphasize that careful sonographic follow-up of fibroadenomas should be done and that each lesion should be followed carefully and separately in cases with multiple fibroadenomas. Additionally, we want to emphasize the critical role of sonographic examination in diagnosing fibroadenoma, the importance of correctly defining benign and malignant sonographic findings, and which lesions should be followed up sonographically and which lesions should be evaluated histopathologically.
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Affiliation(s)
- Bunyamin Ece
- Department of Radiology, Kastamonu University, Kastamonu 37150, Turkey
| | - Sonay Aydın
- Department of Radiology, Erzincan University, Erzincan 24142, Turkey
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