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Sun P, Han J, Li M, Wang Z, Guo R, Zhang Y, Qian L, Ma J, Hu X. Ultrasound Spectral Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastatic in Breast Cancer. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024. [PMID: 39230251 DOI: 10.1002/jum.16564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 06/26/2024] [Accepted: 08/17/2024] [Indexed: 09/05/2024]
Abstract
OBJECTIVES To explore the clinical value of the nomogram based on ultrasound spectral combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer. METHODS We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively. RESULTS Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93). CONCLUSION Axillary ultrasound combined with Ki-67 and ultrasound spectral parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.
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Affiliation(s)
- Pengfei Sun
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jiaqi Han
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Min Li
- Clinical Epidemiology and EBM Unit, Beijing Friendship Hospital, Capital Medical University, Beijing Clinical Research Institute, Beijing, China
| | - Zhixiang Wang
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ruifang Guo
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yanning Zhang
- Department of Pathology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Linxue Qian
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jianguo Ma
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Xiangdong Hu
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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2
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Zhang M, Li X, Zhou P, Zhang P, Wang G, Lin X. Prediction value study of breast cancer tumor infiltrating lymphocyte levels based on ultrasound imaging radiomics. Front Oncol 2024; 14:1411261. [PMID: 38903726 PMCID: PMC11187250 DOI: 10.3389/fonc.2024.1411261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 05/24/2024] [Indexed: 06/22/2024] Open
Abstract
Objective Construct models based on grayscale ultrasound and radiomics and compare the efficacy of different models in preoperatively predicting the level of tumor-infiltrating lymphocytes in breast cancer. Materials and methods This study retrospectively collected clinical data and preoperative ultrasound images from 185 breast cancer patients confirmed by surgical pathology. Patients were randomly divided into a training set (n=111) and a testing set (n=74) using a 6:4 ratio. Based on a 10% threshold for tumor-infiltrating lymphocytes (TIL) levels, patients were classified into low-level and high-level groups. Radiomic features were extracted and selected using the training set. The evaluation included assessing the relationship between TIL levels and both radiomic features and grayscale ultrasound features. Subsequently, grayscale ultrasound models, radiomic models, and nomograms combining radiomics score (Rad-score) and grayscale ultrasound features were established. The predictive performance of different models was evaluated through receiver operating characteristic (ROC) analysis. Calibration curves assessed the fit of the nomograms, and decision curve analysis (DCA) evaluated the clinical effectiveness of the models. Results Univariate analyses and multivariate logistic regression analyses revealed that indistinct margin (P<0.001, Odds Ratio [OR]=0.214, 95% Confidence Interval [CI]: 0.103-1.026), posterior acoustic enhancement (P=0.027, OR=2.585, 95% CI: 1.116-5.987), and ipsilateral axillary lymph node enlargement (P=0.001, OR=4.214, 95% CI: 1.798-9.875) were independent predictive factors for high levels of TIL in breast cancer. In comparison to grayscale ultrasound model (Training set: Area under curve [AUC] 0.795; Testing set: AUC 0.720) and radiomics model (Training set: AUC 0.803; Testing set: AUC 0.759), the nomogram demonstrated superior discriminative ability on both the training (AUC 0.884) and testing (AUC 0.820) datasets. Calibration curves indicated high consistency between the nomogram model's predicted probability of breast cancer TIL levels and the actual occurrence probability. DCA revealed that the radiomics model and the nomogram model achieved higher clinical net benefits compared to the grayscale ultrasound model. Conclusion The nomogram based on preoperative ultrasound radiomics features exhibits robust predictive capacity for the non-invasive evaluation of breast cancer TIL levels, potentially providing a significant basis for individualized treatment decisions in breast cancer.
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Affiliation(s)
- Min Zhang
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Xuanyu Li
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Pin Zhou
- Department of Pathology, Taizhou Hospital of Zhejiang Province, Taizhou, Zhejiang, China
| | - Panpan Zhang
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Taizhou, Zhejiang, China
| | - Gang Wang
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Taizhou, Zhejiang, China
| | - Xianfang Lin
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Taizhou, Zhejiang, China
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Orhan K, Yazici G, Önder M, Evli C, Volkan-Yazici M, Kolsuz ME, Bağış N, Kafa N, Gönüldaş F. Development and Validation of an Ultrasonography-Based Machine Learning Model for Predicting Outcomes of Bruxism Treatments. Diagnostics (Basel) 2024; 14:1158. [PMID: 38893684 PMCID: PMC11172325 DOI: 10.3390/diagnostics14111158] [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: 04/24/2024] [Revised: 05/25/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND AND OBJECTIVES We aimed to develop a predictive model for the outcome of bruxism treatments using ultrasonography (USG)-based machine learning (ML) techniques. This study is a quantitative research study (predictive modeling study) in which different treatment methods applied to bruxism patients are evaluated through artificial intelligence. MATERIALS AND METHODS The study population comprised 102 participants with bruxism in three treatment groups: Manual therapy, Manual therapy and Kinesio Tape or Botulinum Toxin-A injection. USG imaging was performed on the masseter muscle to calculate muscle thickness, and pain thresholds were evaluated using an algometer. A radiomics platform was utilized to handle imaging and clinical data, as well as to perform a subsequent radiomics statistical analysis. RESULTS The area under the curve (AUC) values of all machine learning methods ranged from 0.772 to 0.986 for the training data and from 0.394 to 0.848 for the test data. The Support Vector Machine (SVM) led to excellent discrimination between bruxism and normal patients from USG images. Radiomics characteristics in pre-treatment ultrasound scans of patients, showing coarse and nonuniform muscles, were associated with a greater chance of less effective pain reduction outcomes. CONCLUSIONS This study has introduced a machine learning model using SVM analysis on ultrasound (USG) images for bruxism patients, which can detect masseter muscle changes on USG. Support Vector Machine regression analysis showed the combined ML models can also predict the outcome of the pain reduction.
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Affiliation(s)
- Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06560, Turkey; (K.O.); (M.E.K.)
- Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-059 Lublin, Poland
- Medical Design Application and Research Center (MEDITAM), Ankara University, Ankara 06000, Turkey
- Department of Oral Diagnostics, Faculty of Dendistry, Semmelweis University, 1088 Budapest, Hungary
| | - Gokhan Yazici
- Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Gazi University, Ankara 06490, Turkey; (G.Y.); (N.K.)
| | - Merve Önder
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06560, Turkey; (M.Ö.); (C.E.)
| | - Cengiz Evli
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06560, Turkey; (M.Ö.); (C.E.)
| | - Melek Volkan-Yazici
- Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Yuksek Ihtisas University, Ankara 06520, Turkey;
| | - Mehmet Eray Kolsuz
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06560, Turkey; (K.O.); (M.E.K.)
| | - Nilsun Bağış
- Department of Periodontology, Faculty of Dentistry, Ankara University, Ankara 06560, Turkey;
| | - Nihan Kafa
- Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Gazi University, Ankara 06490, Turkey; (G.Y.); (N.K.)
| | - Fehmi Gönüldaş
- Department of Prosthetic Dentistry, Faculty of Dentistry, Ankara University, Ankara 06500, Turkey
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Li X, Wu M, Wu M, Liu J, Song L, Wang J, Zhou J, Li S, Yang H, Zhang J, Cui X, Liu Z, Zeng F. A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer. Carcinogenesis 2024; 45:170-180. [PMID: 38195111 DOI: 10.1093/carcin/bgad098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/08/2023] [Accepted: 01/08/2024] [Indexed: 01/11/2024] Open
Abstract
Approximately 50% of colorectal cancer (CRC) patients would develop metastasis with poor prognosis, therefore, it is necessary to effectively predict metastasis in clinical treatment. In this study, we aimed to establish a machine-learning model for predicting metastasis in CRC patients by considering radiomics and transcriptomics simultaneously. Here, 1023 patients with CRC from three centers were collected and divided into five queues (Dazhou Central Hospital n = 517, Nanchong Central Hospital n = 120 and the Cancer Genome Atlas (TCGA) n = 386). A total of 854 radiomics features were extracted from tumor lesions on CT images, and 217 differentially expressed genes were obtained from non-metastasis and metastasis tumor tissues using RNA sequencing. Based on radiotranscriptomic (RT) analysis, a novel RT model was developed and verified through genetic algorithms (GA). Interleukin (IL)-26, a biomarker in RT model, was verified for its biological function in CRC metastasis. Furthermore, 15 radiomics variables were screened through stepwise regression, which was highly correlated with the IL26 expression level. Finally, a radiomics model (RA) was established by combining GA and stepwise regression analysis with radiomics features. The RA model exhibited favorable discriminatory ability and accuracy for metastasis prediction in two independent verification cohorts. We designed multicenter, multi-scale cohorts to construct and verify novel combined radiomics and genomics models for predicting metastasis in CRC. Overall, RT model and RA model might help clinicians in directing personalized diagnosis and therapeutic regimen selection for patients with CRC.
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Affiliation(s)
- Xue Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan 635000, China
| | - Meng Wu
- Department of Ultrasound, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Min Wu
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu 610041, China
| | - Jie Liu
- Department of General Surgery, Dazhou Central Hospital, Dazhou, Sichuan 635000, China
| | - Li Song
- Department of Clinical laboratory, Dazhou Central Hospital, Dazhou, Sichuan 635000, China
| | - Jiasi Wang
- Department of Clinical laboratory, Dazhou Central Hospital, Dazhou, Sichuan 635000, China
| | - Jun Zhou
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan 635000, China
| | - Shilin Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan 635000, China
| | - Hang Yang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan 635000, China
| | - Jun Zhang
- Department of General Surgery, Dazhou Central Hospital, Dazhou, Sichuan 635000, China
| | - Xinwu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Wuhan 430030, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100080, China
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan 635000, China
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Cai L, Sidey-Gibbons C, Nees J, Riedel F, Schaefgen B, Togawa R, Killinger K, Heil J, Pfob A, Golatta M. Ultrasound Radiomics Features to Identify Patients With Triple-Negative Breast Cancer: A Retrospective, Single-Center Study. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:467-478. [PMID: 38069582 DOI: 10.1002/jum.16377] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/04/2023] [Indexed: 02/08/2024]
Abstract
OBJECTIVES Patients with triple-negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis. METHODS We used single-center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST). We developed a logistic regression with an elastic net penalty algorithm using pretreatment ultrasound radiomics features in addition to patient and tumor variables to identify patients with TNBC. Findings were compared to the histopathologic evaluation of the biopsy specimen. The main outcome measure was the area under the curve (AUC). RESULTS We included 1161 patients, 813 in the development set and 348 in the validation set. Median age was 50.1 years and 24.4% (283 of 1161) had TNBC. The integrative model using radiomics and clinical information showed significantly better performance in identifying TNBC compared to the radiomics model (AUC: 0.71, 95% confidence interval [CI]: 0.65-0.76 versus 0.64, 95% CI: 0.57-0.71, P = .004). The five most important variables were cN status, shape surface volume ratio (SA:V), gray level co-occurrence matrix (GLCM) correlation, gray level dependence matrix (GLDM) dependence nonuniformity normalized, and age. Patients with TNBC were more often categorized as BI-RADS 4 than BI-RADS 5 compared to non-TNBC patients (P = .002). CONCLUSION A machine learning algorithm showed promising potential to identify patients with TNBC using ultrasound radiomics features and clinical information prior to histopathologic evaluation.
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Affiliation(s)
- Lie Cai
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Chris Sidey-Gibbons
- MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Juliane Nees
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Riedel
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Benedikt Schaefgen
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Riku Togawa
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Kristina Killinger
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Joerg Heil
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - André Pfob
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
- MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Golatta
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
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Majumder S, Katz S, Kontos D, Roshkovan L. State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation. BJR Open 2024; 6:tzad004. [PMID: 38352179 PMCID: PMC10860524 DOI: 10.1093/bjro/tzad004] [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: 02/15/2023] [Revised: 09/15/2023] [Accepted: 10/30/2023] [Indexed: 02/16/2024] Open
Abstract
Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.
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Affiliation(s)
- Shweta Majumder
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Sharyn Katz
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Leonid Roshkovan
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
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7
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Xu M, Zeng S, Li F, Liu G. Utilizing grayscale ultrasound-based radiomics nomogram for preoperative identification of triple negative breast cancer. LA RADIOLOGIA MEDICA 2024; 129:29-37. [PMID: 37919521 DOI: 10.1007/s11547-023-01739-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 10/05/2023] [Indexed: 11/04/2023]
Abstract
PURPOSE This study aimed to develop a radiomics nomogram based on grayscale ultrasound (US) to distinguish triple-negative breast cancer (TNBC) from non-triple-negative breast cancer (NTNBC) prior to surgery. METHODS A retrospective analysis of 454 breast carcinoma patients confirmed by pathology was conducted, with 317 patients in the training dataset (59 with TNBC) and 137 patients in the validation dataset (27 with TNBC). Clinical information, conventional US features, and radiomics features were collected, and the Radscore model was constructed after feature selection. Independent risk factors were identified using univariate and multivariate logistic regression analysis. The nomogram model was assessed using the receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS Tumor shape, margin, and calcification were independent risk factors in the clinical prediction model. Additionally, 16 radiomics features were selected to construct the Radscore model out of a total of 474 extracted features. The radiomics nomogram model, which incorporated tumor shape, margin, calcification, and Radscore, achieved an AUC value of 0.837 in the training dataset and 0.813 in the validation dataset, outperforming both the Radscore and clinical models in terms of predictive performance. The significant improvement of NRI and IDI indicated that the Radscore may be useful biomarkers for TNBC. CONCLUSION The US-based radiomics nomogram showed satisfactory preoperative prediction of TNBC.
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Affiliation(s)
- Maolin Xu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Shue Zeng
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, 116 Zhuodaoquan South Road, Wuhan, 430079, China
| | - Fang Li
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, 116 Zhuodaoquan South Road, Wuhan, 430079, China.
| | - Guifeng Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China.
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Oh KE, Vasandani N, Anwar A. Radiomics to Differentiate Malignant and Benign Breast Lesions: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis. Cureus 2023; 15:e49015. [PMID: 38024014 PMCID: PMC10657146 DOI: 10.7759/cureus.49015] [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] [Accepted: 11/18/2023] [Indexed: 12/01/2023] Open
Abstract
Breast cancer is a prevalent global health concern, necessitating accurate diagnostic tools for effective management. Diagnostic imaging plays a pivotal role in breast cancer diagnosis, staging, treatment planning, and outcome evaluation. Radiomics is an emerging field of study in medical imaging that contains a broad set of computational methods to extract quantitative features from radiographic images. This can be utilized to guide diagnosis, treatment response, and prognosis in clinical settings. A systematic review was performed in concordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Quality was assessed using the radiomics quality score. Diagnostic sensitivity and specificity of radiomics analysis, with 95% confidence intervals (CIs), were included for meta-analysis. The area under the curve analysis was recorded. An extensive statistical analysis was performed following the Cochrane guidelines. Statistical significance was determined if p-values were less than 0.05. Statistical analyses were conducted using Review Manager (RevMan), Version 5.4.1. A total of 31 manuscripts involving 8,773 patients were included, with 17 contributing to the meta-analysis. The cohort comprised 56.2% malignant breast cancers and 43.8% benign breast lesions. MRI demonstrated a sensitivity of 0.91 (95% CI: 0.89-0.92) and a specificity of 0.84 (95% CI: 0.82-0.86) in differentiating between benign and malignant breast cancers. Mammography-based radiomic features predicted breast cancer subtype with a sensitivity of 0.79 (95% CI: 0.76-0.82) and a specificity of 0.81 (95% CI: 0.79-0.84). Ultrasound-based analysis yielded a sensitivity of 0.92 (95% CI: 0.90-0.94) and a specificity of 0.85 (95% CI: 0.83-0.88). Only one study reported the results of radiomic evaluation from CT, which had a sensitivity of 0.95 (95% CI: 0.88-0.99) and a specificity of 0.56 (95% CI: 0.45-0.67). Across different imaging modalities, radiomics exhibited robust diagnostic accuracy in differentiating benign and malignant breast lesions. The results underscore the potential of radiomic assessment as a minimally invasive alternative or adjunctive diagnostic tool for breast cancer. This is pioneering data that reports on a novel diagnostic approach that is understudied and underreported. However, due to study limitations, the complexity of this technology, and the need for future development, biopsy still remains the current gold standard method of determining breast cancer type.
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Affiliation(s)
- Ke En Oh
- Department of Surgery, University Hospital Galway, Galway, IRL
| | | | - Afiq Anwar
- Department of Surgery, University Hospital Galway, Galway, IRL
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Wang L, Liu J, Chen F, Li G, Wang J, Chan DSH, Wong CY, Wang W, Leung CH. A Switch-On Affinity-Based Iridium(III) Conjugate Probe for Imaging Mitochondrial Glutathione S-Transferase in Breast Cancer Cells. Bioconjug Chem 2023; 34:1727-1737. [PMID: 37750807 DOI: 10.1021/acs.bioconjchem.3c00267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Glutathione S-transferase is heterogeneously expressed in breast cancer cells and is therefore emerging as a potential diagnostic biomarker for studying the heterogeneity of breast cancers. However, available fluorescent probes for GSTs depend heavily on GSTs-catalyzed glutathione (GSH) nucleophilic substitution reactions, making them susceptible to interference by the high concentration of nucleophilic species in the cellular environment. Moreover, the functions of subcellular GSTs are generally overlooked due to the lack of suitable luminescence probes. Herein, we report a highly selective affinity-based luminescence probe 1 for GST in breast cancer cells through tethering a GST inhibitor, ethacrynic acid, to an iridium(III) complex. Compared to activity-based probes which require the use of GSH, this probe could image GST-pi in the mitochondria by directly adducting to GST-pi (or potentially GST-pi/GS) in living cells. Probe 1 possesses desirable photophysical properties including a lifetime of 911 ns, a Stokes shift of 343 nm, and high photostability. The "turn on" luminescence mode of the probe enables highly selective detection of the GST with a limit of detection of 1.01 μM, while its long emission lifetime allows sensitive detection in organic dye-spiked autofluorescence samples by a time-resolved mode. The probe was further applied to specifically and quantitatively visualize MDA-MB-231 cells via specific binding to mitochondrial GST, and could differentiate breast cell lines based on their expression levels of GST. To the best of our knowledge, this probe is the first affinity-based iridium(III) imaging probe for the subcellular GST. Our work provides a valuable tool for unmasking the diverse roles of a subcellular GST in living systems, as well as for studying the heterogeneity of breast cancers.
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Affiliation(s)
- Ling Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, 999078, China
| | - Jingqi Liu
- Institute of Medical Research, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shaanxi 710072, China
- Northwestern Polytechnical University Chongqing Technology Innovation Center, Chongqing 400000, China
| | - Feng Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, 999078, China
| | - Guodong Li
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, 999078, China
| | - Jing Wang
- Institute of Medical Research, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shaanxi 710072, China
- Northwestern Polytechnical University Chongqing Technology Innovation Center, Chongqing 400000, China
| | | | - Chun-Yuen Wong
- Department of Chemistry, City University of Hong Kong, Hong Kong, 999077, China
| | - Wanhe Wang
- Institute of Medical Research, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shaanxi 710072, China
- Northwestern Polytechnical University Chongqing Technology Innovation Center, Chongqing 400000, China
| | - Chung-Hang Leung
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, 999078, China
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Macao, 999078, China
- Macau Centre for Research and Development in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, 999078, China
- MoE Frontiers Science Center for Precision Oncology, University of Macau, Macao, 999078, China
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Zhou J, Liu C, Shi Z, Li X, Chang C, Zhi W, Zhou S. Application of ultrasound-based radiomics models of breast masses to predict invasive components of encapsulated papillary carcinoma. Quant Imaging Med Surg 2023; 13:6887-6898. [PMID: 37869304 PMCID: PMC10585530 DOI: 10.21037/qims-22-1069] [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: 12/01/2022] [Accepted: 09/01/2023] [Indexed: 10/24/2023]
Abstract
Background Axillary lymph node (ALN) metastasis is seen in encapsulated papillary carcinoma (EPC), mostly with an invasive component (INV). Radiomics can offer more information beyond subjective grayscale and color Doppler ultrasound (US) image interpretation. This study aimed to develop radiomics models for predicting an INV of EPC in the breast based on US images. Methods This study retrospectively enrolled 105 patients (107 masses) with a pathological diagnosis of EPC from January 2016 to April 2021, and all masses had preoperative US images. Of the 107 masses, 64 were randomized to a training set and 43 to a test set. US and clinical features were analyzed to identify features associated with INVs. Then, based on the manually segmented US images to obtain radiomics features, the models to predict INVs were built with 5 ensemble machine learning classifiers. We estimated the performance of the predictive models using accuracy, the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity. Results The mean age was 63.71 years (range, 31 to 85 years); the mean size of tumors was 23.40 mm (range, 9 to 120 mm). Among all clinical and US features, only shape was statistically different between EPC with INVs and those without (P<0.05). In this study, the models based on Random Under Sampling (RUS) Boost, Random Forest, XGBoost, AdaBoost, and Easy Ensemble methods had good performance, among which RUS Boost had the best performance with an AUC of 0.875 [95% confidence interval (CI): 0.750-0.974] in the test set. Conclusions Radiomics prediction models were effective in predicting the INV of EPC, whereas clinical and US features demonstrated relatively decreased predictive utility.
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Affiliation(s)
- Jin Zhou
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chaoxu Liu
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhaoting Shi
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiaokang Li
- Department of Electronic Engineering, Fudan University, Shanghai, China
| | - Cai Chang
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wenxiang Zhi
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shichong Zhou
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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11
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Ma Q, Wang J, Xu D, Zhu C, Qin J, Wu Y, Gao Y, Zhang C. Automatic Breast Volume Scanner and B-Ultrasound-Based Radiomics Nomogram for Clinician Management of BI-RADS 4A Lesions. Acad Radiol 2023; 30:1628-1637. [PMID: 36456445 DOI: 10.1016/j.acra.2022.11.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/17/2022] [Accepted: 11/01/2022] [Indexed: 11/29/2022]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a nomogram for predicting the risk of malignancy of breast imaging reporting and data system (BI-RADS) 4A lesions to reduce unnecessary invasive examinations. MATERIALS AND METHODS From January 2017 to July 2021, 190 cases of 4A lesions included in this study were divided into training and validation sets in a ratio of 8:2. Radiomics features were extracted from sonograms by Automatic Breast Volume Scanner (ABVS) and B-ultrasound. We constructed the radiomics model and calculated the rad-scores. Univariate and multivariate logistic regressions were used to assess demographics and lesion elastography values (virtual touch tissue image, shear wave velocity) and to develop clinical model. A clinical radiomics model was developed using rad-score and independent clinical factors, and a nomogram was plotted. Nomogram performance was evaluated using discrimination, calibration, and clinical utility. RESULTS The nomogram included rad-score, age, and elastography, and showed good calibration. In the training set, the area under the receiver operating characteristic curve (AUC) of the clinical radiomics model (0.900, 95% confidence interval (CI): 0.843-0.958) was superior to that of the radiomics model (0.860, 95% CI: 0.799-0.921) and clinical model (0.816, 95% CI: 0.735-0.958) (p = 0.024 and 0.008, respectively). The decision curve analysis showed that the clinical radiomics model had the highest net benefit in most threshold probability ranges. CONCLUSION ABVS and B-ultrasound-based radiomics nomograms have satisfactory performance in differentiating benign and malignant 4A lesions. This can help clinicians make an accurate diagnosis of 4A lesions and reduce unnecessary biopsy.
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Affiliation(s)
- Qianqing Ma
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, P R China
| | - Junli Wang
- Department of Ultrasound, The Second People's Hospital of WuHu, Wuhu, AH P R China
| | - Daojing Xu
- Department of Ultrasound, The Second People's Hospital of WuHu, Wuhu, AH P R China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, P R China
| | - Jing Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, P R China
| | - Yimin Wu
- Department of Ultrasound, The Second People's Hospital of WuHu, Wuhu, AH P R China
| | - Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, P R China
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, P R China.
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12
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Basara Akin I, Ozgul HA, Altay C, Guray Durak M, Aksoy SO, Sevinc AI, Secil M, Gulmez H, Balci P. Machine Learning-Based Ultrasound Texture Analysis in Differentiation of Benign Phyllodes Tumors from Borderline-Malignant Phyllodes Tumors. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2023; 44:318-326. [PMID: 34674218 DOI: 10.1055/a-1640-9508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
PURPOSE Phyllodes tumors (PTs) are uncommon fibroepithelial breast lesions that are classified as three different forms as benign phyllodes tumor (BPT), borderline phyllodes tumor (BoPT), and malignant phyllodes tumor (MPT). Conventional radiologic methods make only a limited contribution to exact diagnosis, and texture analysis data increase the diagnostic performance. In this study, we aimed to evaluate the contribution of texture analysis of US images (TAUI) of PTs in order to discriminate between BPTs and BoPTs-MPTs. METHODS The number of patients was 63 (41 BPTs, 12 BoPTs, and 10 MPTs). Patients were divided into two groups (Group 1-BPT, Group 2-BoPT/MPT). TAUI with LIFEx software was performed retrospectively. An independent machine learning approach, MATLAB R2020a (Math- Works, Natick, Massachusetts) was used with the dataset with p < 0.004. Two machine learning approaches were used to build prediction models for differentiating between Group 1 and Group 2. Receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic performance of statistically significant texture data between phyllodes subgroups. RESULTS In TAUI, 10 statistically significant second order texture values were identified as significant factors capable of differentiating among the two groups (p < 0.05). Both of the models of our dataset make a diagnostic contribution to the discrimination between BopTs-MPTs and BPTs. CONCLUSION In PTs, US is the main diagnostic method. Adding machine learning-based TAUI to conventional US findings can provide optimal diagnosis, thereby helping to choose the correct surgical method. Consequently, decreased local recurrence rates can be achieved.
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Affiliation(s)
- Isil Basara Akin
- Radiology, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
| | | | - Canan Altay
- Radiology, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
| | - Merih Guray Durak
- Pathology, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
| | | | - Ali Ibrahim Sevinc
- General Surgery, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
| | - Mustafa Secil
- Radiology, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
| | - Hakan Gulmez
- Family Medicine, İzmir Democracy University, Izmir, Turkey
| | - Pinar Balci
- Radiology, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
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Gong X, Li Q, Gu L, Chen C, Liu X, Zhang X, Wang B, Sun C, Yang D, Li L, Wang Y. Conventional ultrasound and contrast-enhanced ultrasound radiomics in breast cancer and molecular subtype diagnosis. Front Oncol 2023; 13:1158736. [PMID: 37287927 PMCID: PMC10242104 DOI: 10.3389/fonc.2023.1158736] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 05/11/2023] [Indexed: 06/09/2023] Open
Abstract
Objectives This study aimed to explore the value of conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) radiomics to diagnose breast cancer and predict its molecular subtype. Method A total of 170 lesions (121 malignant, 49 benign) were selected from March 2019 to January 2022. Malignant lesions were further divided into six categories of molecular subtype: (non-)Luminal A, (non-)Luminal B, (non-)human epidermal growth factor receptor 2 (HER2) overexpression, (non-)triple-negative breast cancer (TNBC), hormone receptor (HR) positivity/negativity, and HER2 positivity/negativity. Participants were examined using CUS and CEUS before surgery. Regions of interest images were manually segmented. The pyradiomics toolkit and the maximum relevance minimum redundancy algorithm were utilized to extract and select features, multivariate logistic regression models of CUS, CEUS, and CUS combined with CEUS radiomics were then constructed and evaluated by fivefold cross-validation. Results The accuracy of the CUS combined with CEUS model was superior to CUS model (85.4% vs. 81.3%, p<0.01). The accuracy of the CUS radiomics model in predicting the six categories of breast cancer is 68.2% (82/120), 69.3% (83/120), 83.7% (100/120), 86.7% (104/120), 73.5% (88/120), and 70.8% (85/120), respectively. In predicting breast cancer of Luminal A, HER2 overexpression, HR-positivity, and HER2 positivity, CEUS video improved the predictive performance of CUS radiomics model [accuracy=70.2% (84/120), 84.0% (101/120), 74.5% (89/120), and 72.5% (87/120), p<0.01]. Conclusion CUS radiomics has the potential to diagnose breast cancer and predict its molecular subtype. Moreover, CEUS video has auxiliary predictive value for CUS radiomics.
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Affiliation(s)
- Xuantong Gong
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qingfeng Li
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Lishuang Gu
- Department of Ultrasound, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Chen Chen
- Hangzhou Innovation Institute, Beihang University, Hangzhou, China
| | - Xuefeng Liu
- State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), Beihang University, Beijing, China
| | - Xuan Zhang
- Department of Ultrasound, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Bo Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Sun
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Di Yang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lin Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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14
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Cui H, Sun Y, Zhao D, Zhang X, Kong H, Hu N, Wang P, Zuo X, Fan W, Yao Y, Fu B, Tian J, Wu M, Gao Y, Ning S, Zhang L. Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions. J Transl Med 2023; 21:44. [PMID: 36694240 PMCID: PMC9875533 DOI: 10.1186/s12967-022-03840-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 12/19/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Human epidermal growth factor receptor 2 (HER2) overexpressed associated with poor prognosis in breast cancer and HER2 has been defined as a therapeutic target for breast cancer treatment. We aimed to explore the molecular biological information in ultrasound radiomic features (URFs) of HER2-positive breast cancer using radiogenomic analysis. Moreover, a radiomics model was developed to predict the status of HER2 in breast cancer. METHODS This retrospective study included 489 patients who were diagnosed with breast cancer. URFs were extracted from a radiomics analysis set using PyRadiomics. The correlations between differential URFs and HER2-related genes were calculated using Pearson correlation analysis. Functional enrichment of the identified URFs-correlated HER2 positive-specific genes was performed. Lastly, the radiomics model was developed based on the URF-module mined from auxiliary differential URFs to assess the HER2 status of breast cancer. RESULTS Eight differential URFs (p < 0.05) were identified among the 86 URFs extracted by Pyradiomics. 25 genes that were found to be the most closely associated with URFs. Then, the relevant biological functions of each differential URF were obtained through functional enrichment analysis. Among them, Zone Entropy is related to immune cell activity, which regulate the generation of calcification in breast cancer. The radiomics model based on the Logistic classifier and URF-module showed good discriminative ability (AUC = 0.80, 95% CI). CONCLUSION We searched for the URFs of HER2-positive breast cancer, and explored the underlying genes and biological functions of these URFs. Furthermore, the radiomics model based on the Logistic classifier and URF-module relatively accurately predicted the HER2 status in breast cancer.
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Affiliation(s)
- Hao Cui
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Yue Sun
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 China
| | - Dantong Zhao
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Xudong Zhang
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Hanqing Kong
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Nana Hu
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Panting Wang
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Xiaoxuan Zuo
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Wei Fan
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Yuan Yao
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Baiyang Fu
- grid.412463.60000 0004 1762 6325Department of Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Jiawei Tian
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Meixin Wu
- grid.412463.60000 0004 1762 6325Department of Clinical Medicine, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang, 150086 China
| | - Yue Gao
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 China
| | - Shangwei Ning
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 China
| | - Lei Zhang
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
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15
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Differential Diagnosis of DCIS and Fibroadenoma Based on Ultrasound Images: a Difference-Based Self-Supervised Approach. Interdiscip Sci 2023; 15:262-272. [PMID: 36656448 DOI: 10.1007/s12539-022-00547-7] [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: 09/05/2022] [Revised: 12/16/2022] [Accepted: 12/19/2022] [Indexed: 01/20/2023]
Abstract
Differentiation of ductal carcinoma in situ (DCIS, a precancerous lesion of the breast) from fibroadenoma (FA) using ultrasonography is significant for the early prevention of malignant breast tumors. Radiomics-based artificial intelligence (AI) can provide additional diagnostic information but usually requires extensive labeling efforts by clinicians with specialized knowledge. This study aims to investigate the feasibility of differentially diagnosing DCIS and FA using ultrasound radiomics-based AI techniques and further explore a novel approach that can reduce labeling efforts without sacrificing diagnostic performance. We included 461 DCIS and 651 FA patients, of whom 139 DCIS and 181 FA patients constituted a prospective test cohort. First, various feature engineering-based machine learning (FEML) and deep learning (DL) approaches were developed. Then, we designed a difference-based self-supervised (DSS) learning approach that only required FA samples to participate in training. The DSS approach consists of three steps: (1) pretraining a Bootstrap Your Own Latent (BYOL) model using FA images, (2) reconstructing images using the encoder and decoder of the pretrained model, and (3) distinguishing DCIS from FA based on the differences between the original and reconstructed images. The experimental results showed that the trained FEML and DL models achieved the highest AUC of 0.7935 (95% confidence interval, 0.7900-0.7969) on the prospective test cohort, indicating that the developed models are effective for assisting in differentiating DCIS from FA based on ultrasound images. Furthermore, the DSS model achieved an AUC of 0.8172 (95% confidence interval, 0.8124-0.8219), indicating that our model outperforms the conventional radiomics-based AI models and is more competitive.
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16
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Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images. Med Biol Eng Comput 2023; 61:567-578. [PMID: 36542320 PMCID: PMC9852203 DOI: 10.1007/s11517-022-02728-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022]
Abstract
To develop a deep-learning system for the automatic identification of triple-negative breast cancer (TNBC) solely from ultrasound images. A total of 145 patients and 831 images were retrospectively enrolled at Peking Union College Hospital from April 2018 to March 2019. Ultrasound images and clinical information were collected accordingly. Molecular subtypes were determined from immunohistochemical (IHC) results. A CNN with VGG-based architecture was then used to predict TNBC. The model's performance was evaluated using randomized k-fold stratified cross-validation. A t-SNE analysis and saliency maps were used for model visualization. TNBC was identified in 16 of 145 (11.03%) patients. One hundred fifteen (80%) patients, 15 (10%) patients, and 15 (10%) patients formed the train, validation, and test set respectively. The deep learning system exhibits good efficacy, with an AUC of 0.86 (95% CI: 0.64, 0.95), an accuracy of 85%, a sensitivity of 86%, a specificity of 86%, and an F1-score of 0.74. In addition, the internal representation features learned by the model showed clear differentiation across molecular subtype groups. Such a deep learning system can automatically predict triple-negative breast cancer preoperatively and accurately. It may help to get to more precise and comprehensive management.
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Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma. Diagnostics (Basel) 2022; 12:diagnostics12123130. [PMID: 36553137 PMCID: PMC9776855 DOI: 10.3390/diagnostics12123130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/02/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
(1) Objective: To evaluate the performance of ultrasound-based radiomics in the preoperative prediction of human epidermal growth factor receptor 2-positive (HER2+) and HER2- breast carcinoma. (2) Methods: Ultrasound images from 309 patients (86 HER2+ cases and 223 HER2- cases) were retrospectively analyzed, of which 216 patients belonged to the training set and 93 patients assigned to the time-independent validation set. The region of interest of the tumors was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analyses using the intra-class correlation coefficient (ICC), Mann-Whitney U test, and the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics score (Rad-score) for each patient was calculated through a linear combination of the nonzero coefficient features. The support vector machine (SVM), K nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction models based on the Rad-score. A clinical model based on significant clinical features was also established. In addition, the logistic regression method was used to integrate Rad-score and clinical features to generate the nomogram model. The leave-one-out cross validation (LOOCV) method was used to validate the reliability and stability of the model. (3) Results: Among the seven classifier models, the LR achieved the best performance in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.786, and was obtained as the Rad-score model, while the RF performed the worst. Tumor size showed a statistical difference between the HER2+ and HER2- groups (p = 0.028). The nomogram model had a slightly higher AUC than the Rad-score model (AUC, 0.788 vs. 0.786), but no statistical difference (Delong test, p = 0.919). The LOOCV method yielded a high median AUC of 0.790 in the validation set. (4) Conclusion: The Rad-score model performs best among the seven classifiers. The nomogram model based on Rad-score and tumor size has slightly better predictive performance than the Rad-score model, and it has the potential to be utilized as a routine modality for preoperatively determining HER2 status in BC patients non-invasively.
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Wu J, Ge L, Jin Y, Wang Y, Hu L, Xu D, Wang Z. Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma. Front Oncol 2022; 12:993466. [PMID: 36530981 PMCID: PMC9749858 DOI: 10.3389/fonc.2022.993466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/08/2022] [Indexed: 08/17/2023] Open
Abstract
INTRODUCTION The molecular subtype plays a significant role in breast carcinoma (BC), which is the main indicator to guide treatment and is closely associated with prognosis. The aim of this study was to investigate the feasibility and efficacy of an ultrasound-based radiomics nomogram in preoperatively discriminating the luminal from non-luminal type in patients with BC. METHODS A total of 264 BC patients who underwent routine ultrasound examination were enrolled in this study, of which 184 patients belonged to the training set and 80 patients to the test set. Breast tumors were delineated manually on the ultrasound images and then radiomics features were extracted. In the training set, the T test and least absolute shrinkage and selection operator (LASSO) were used for selecting features, and the radiomics score (Rad-score) for each patient was calculated. Based on the clinical risk features, Rad-score, and combined clinical risk features and Rad-score, three models were established, respectively. The performances of the models were validated with receiver operator characteristic (ROC) curve and decision curve analysis. RESULTS In all, 788 radiomics features per case were obtained from the ultrasound images. Through radiomics feature selection, 11 features were selected to constitute the Rad-score. The area under the ROC curve (AUC) of the Rad-score for predicting the luminal type was 0.828 in the training set and 0.786 in the test set. The nomogram comprising the Rad-score and US-reported tumor size showed AUCs of the training and test sets were 0.832 and 0.767, respectively, which were significantly higher than the AUCs of the clinical model in the training and test sets (0.691 and 0.526, respectively). However, there was no significant difference in predictive performance between the Rad-score and nomogram. CONCLUSION Both the Rad-score and nomogram can be applied as useful, noninvasive tools for preoperatively discriminating the luminal from non-luminal type in patients with BC. Furthermore, this study might provide a novel technique to evaluate molecular subtypes of BC.
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Affiliation(s)
- Jiangfeng Wu
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
| | - Lifang Ge
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
| | - Yun Jin
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
| | - Yunlai Wang
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
| | - Liyan Hu
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging and Interventional Therapy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Zhengping Wang
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
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Zhao Z, Hou S, Li S, Sheng D, Liu Q, Chang C, Chen J, Li J. Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:2267-2275. [PMID: 36055860 DOI: 10.1016/j.ultrasmedbio.2022.06.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/31/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Abstract
The aim of the work described here was to develop an ultrasound (US) image-based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the pre-operative US examination. A total of 479 breast lesions diagnosed as BI-RADS 4A in pre-operative US examination were enrolled. There were 362 benign lesions and 117 malignant lesions confirmed by postoperative pathology with a malignancy rate of 24.4%. US images were collected from the database server. They were then randomly divided into training and testing cohorts at a ratio of 4:1. To correctly classify malignant and benign tumors diagnosed as BI-RADS 4A in US, four deep learning models, including MobileNet, DenseNet121, Xception and Inception V3, were developed. The performance of deep learning models was compared using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Meanwhile, the robustness of the models was evaluated by five-fold cross-validation. Among the four models, the MobileNet model turned to be the optimal model with the best performance in classifying benign and malignant lesions among BI-RADS 4A breast lesions. The AUROC, accuracy, sensitivity, specificity, PPV and NPV of the optimal model in the testing cohort were 0.897, 0.913, 0.926, 0.899, 0.958 and 0.784, respectively. About 14.4% of patients were expected to be upgraded to BI-RADS 4B in US with the assistance of the MobileNet model. The deep learning model MobileNet can help to reduce the rate of malignancy among BI-RADS 4A breast lesions in pre-operative US examinations, which is valuable to clinicians in tailoring treatment for suspicious breast lesions identified on US.
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Affiliation(s)
- Zhijin Zhao
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Size Hou
- Department of Applied Mathematics, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Shuang Li
- International Business School Suzhou, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Danli Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qi Liu
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiangang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China.
| | - Jiawei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Wu J, Fang Q, Yao J, Ge L, Hu L, Wang Z, Jin G. Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma. Front Oncol 2022; 12:979358. [PMID: 36276108 PMCID: PMC9581085 DOI: 10.3389/fonc.2022.979358] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Objective The aim of this study was to develop and validate an ultrasound-based radiomics nomogram model by integrating the clinical risk factors and radiomics score (Rad-Score) to predict the Ki-67 status in patients with breast carcinoma. Methods Ultrasound images of 284 patients (196 high Ki-67 expression and 88 low Ki-67 expression) were retrospectively analyzed, of which 198 patients belonged to the training set and 86 patients to the test set. The region of interest of tumor was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analysis by using the independent sample t test and least absolute shrinkage and selection operator (LASSO) algorithm. The support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction model based on the selected features. The classifier with the highest AUC value was selected to convert the output of the results into the Rad-Score and was regarded as Rad-Score model. In addition, the logistic regression method was used to integrate Rad-Score and clinical risk factors to generate the nomogram model. The leave group out cross-validation (LGOCV) method was performed 200 times to verify the reliability and stability of the nomogram model. Results Six classifier models were established based on the 15 non-zero coefficient features. Among them, the LR classifier achieved the best performance in the test set, with the area under the receiver operating characteristic curve (AUC) value of 0.786, and was obtained as the Rad-Score model, while the XGB performed the worst (AUC, 0.615). In multivariate analysis, independent risk factor for high Ki-67 status was age (odds ratio [OR] = 0.97, p = 0.04). The nomogram model based on the age and Rad-Score had a slightly higher AUC than that of Rad-Score model (AUC, 0.808 vs. 0.798) in the test set, but no statistical difference (p = 0.144, DeLong test). The LGOCV yielded a median AUC of 0.793 in the test set. Conclusions This study proposed a convenient, clinically useful ultrasound radiomics nomogram model that can be used for the preoperative individualized prediction of the Ki-67 status in patients with BC.
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Affiliation(s)
- Jiangfeng Wu
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, China
- *Correspondence: Guilong Jin, ; Zhengping Wang, ; Liyan Hu, ; Lifang Ge,
| | - Qingqing Fang
- Department of Ultrasound, Tianxiang East Hospital, Yiwu, China
| | - Jincao Yao
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou, China
| | - Lifang Ge
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, China
- *Correspondence: Guilong Jin, ; Zhengping Wang, ; Liyan Hu, ; Lifang Ge,
| | - Liyan Hu
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, China
- *Correspondence: Guilong Jin, ; Zhengping Wang, ; Liyan Hu, ; Lifang Ge,
| | - Zhengping Wang
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, China
- *Correspondence: Guilong Jin, ; Zhengping Wang, ; Liyan Hu, ; Lifang Ge,
| | - Guilong Jin
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, China
- *Correspondence: Guilong Jin, ; Zhengping Wang, ; Liyan Hu, ; Lifang Ge,
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Lu Q, Chen W, Ji Y, Liu Y, Xue X. Ursolic Acid Enhances Cytotoxicity of Doxorubicin-Resistant Triple-Negative Breast Cancer Cells via ZEB1-AS1/miR-186-5p/ ABCC1 Axis. Cancer Biother Radiopharm 2022; 37:673-683. [PMID: 33493421 DOI: 10.1089/cbr.2020.4147] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Background: Triple-negative breast cancer (TNBC) is the most serious subtype of breast cancer (BC) and has been a great health threat to females. Although chemotherapeutic agent contributes a lot to TNBC treatment, drug resistance has been a great obstacle for chemotherapies. Ursolic acid (UA), a pentacyclic triterpenoid compound, was reported to reverse paclitaxel resistance in BC. However, whether UA could affect the resistance of TNBC cells to other drugs such as doxorubicin (DOX) remains to be discovered. Materials and Methods: MTT assay, EdU assay, colony formation assay, and flow cytometry analysis were implemented to detect the viability, proliferation, and apoptosis of DOX-resistant MDA-MB-468 and MDA-MB-436 cells with or without UA treatment. Mechanism assays including RIP, RNA pull-down, and luciferase reporter assays verified the interaction between RNAs. Results: UA treatment hindered the growth and mitigated the DOX resistance of DOX-resistant MDA-MB-468 and MDA-MB-436 cells. Mechanistically, multidrug resistance-associated protein 1 (ABCC1) expression was downregulated by UA treatment. MiR-186-5p was verified to target ABCC1. Further, UA-inhibited ZEB1-AS1 (zinc finger E-box binding homeobox 1 antisense RNA 1) was verified as a competitive endogenous RNA (ceRNA) to upregulate ABCC1 through sponging miR-186-5p. Importantly, UA treatment impaired the malignant phenotypes of DOX-resistant MDA-MB-468 and MDA-MB-436 cells through ZEB1-AS1/ABCC1 axis. Conclusion: UA promotes TNBC cell sensitivity to DOX through inactivating ZEB1-AS1/miR-186-5p/ABCC1 signaling.
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Affiliation(s)
- Qing Lu
- Department of Breast Surgery, Yueyang Hospital of Intergrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Weili Chen
- Department of Breast Surgery, Yueyang Hospital of Intergrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yajie Ji
- Department of Breast Surgery, Yueyang Hospital of Intergrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu Liu
- Department of Breast Surgery, Yueyang Hospital of Intergrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiaohong Xue
- Department of Breast Surgery, Yueyang Hospital of Intergrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Lan Z, Peng Y. Artificial intelligence diagnosis based on breast ultrasound imaging. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2022; 47:1009-1015. [PMID: 36097768 PMCID: PMC10950100 DOI: 10.11817/j.issn.1672-7347.2022.220110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Indexed: 06/15/2023]
Abstract
Breast cancer has now become the leading cancer in women. The development of breast ultrasound artificial intelligence (AI) diagnostic technology is conducive to promoting the precise diagnosis and treatment of breast cancer and alleviating the heavy medical burden due to the unbalanced regional development in China. In recent years, on the basis of improving diagnostic efficiency, AI technology has been continuously combined with various clinical application scenarios, thereby providing more comprehensive and reliable evidence-based suggestions for clinical decision-making. Although AI diagnostic technologies based on conventional breast ultrasound gray-scale images and cutting-edge technologies such as three-dimensional (3D) imaging and elastography have been developed to some extent, there are still technical pain points, diffusion difficulties and ethical dilemmas in the development of AI diagnostic technologies for breast ultrasound.
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Affiliation(s)
- Zihan Lan
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu 610000, China.
| | - Yulan Peng
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu 610000, China.
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Liu J, Wang X, Hu M, Zheng Y, Zhu L, Wang W, Hu J, Zhou Z, Dai Y, Dong F. Development of an ultrasound-based radiomics nomogram to preoperatively predict Ki-67 expression level in patients with breast cancer. Front Oncol 2022; 12:963925. [PMID: 36046035 PMCID: PMC9421073 DOI: 10.3389/fonc.2022.963925] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveTo develop and validate a radiomics nomogram that could incorporate clinicopathological characteristics and ultrasound (US)-based radiomics signature to non-invasively predict Ki-67 expression level in patients with breast cancer (BC) preoperatively.MethodsA total of 328 breast lesions from 324 patients with BC who were pathologically confirmed in our hospital from June 2019 to October 2020 were included, and they were divided into high Ki-67 expression level group and low Ki-67 expression level group. Routine US and shear wave elastography (SWE) were performed for each lesion, and the ipsilateral axillary lymph nodes (ALNs) were scanned for abnormal changes. The datasets were randomly divided into training and validation cohorts with a ratio of 7:3. Correlation analysis and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomics features obtained from gray-scale US images of BC patients, and each radiomics score (Rad-score) was calculated. Afterwards, multivariate logistic regression analysis was used to establish a radiomics nomogram based on the radiomics signature and clinicopathological characteristics. The prediction performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA) using the results of immunohistochemistry as the gold standard.ResultsThe radiomics signature, consisted of eight selected radiomics features, achieved a nearly moderate prediction efficacy with AUC of 0.821 (95% CI:0.764-0.880) and 0.713 (95% CI:0.612-0.814) in the training and validation cohorts, respectively. The radiomics nomogram, incorporating maximum diameter of lesions, stiff rim sign, US-reported ALN status, and radiomics signature showed a promising performance for prediction of Ki-67 expression level, with AUC of 0.904 (95% CI:0.860-0.948) and 0.890 (95% CI:0.817-0.964) in the training and validation cohorts, respectively. The calibration curve and DCA indicated promising consistency and clinical applicability.ConclusionThe proposed US-based radiomics nomogram could be used to non-invasively predict Ki-67 expression level in BC patients preoperatively, and to assist clinicians in making reliable clinical decisions.
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Affiliation(s)
- Jinjin Liu
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xuchao Wang
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Mengshang Hu
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yan Zheng
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Lin Zhu
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Wei Wang
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jisu Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- *Correspondence: Zhiyong Zhou, ; Yakang Dai, ; Fenglin Dong,
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- *Correspondence: Zhiyong Zhou, ; Yakang Dai, ; Fenglin Dong,
| | - Fenglin Dong
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, China
- *Correspondence: Zhiyong Zhou, ; Yakang Dai, ; Fenglin Dong,
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Tong Y, Li J, Chen J, Hu C, Xu Z, Duan S, Wang X, Yu R, Cheng X. A Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperative Prediction of DNA Mismatch Repair Deficiency in Gastric Adenocarcinoma. Front Oncol 2022; 12:865548. [PMID: 35912185 PMCID: PMC9327646 DOI: 10.3389/fonc.2022.865548] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/26/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose To develop and validate a radiomics nomogram integrated with clinic-radiological features for preoperative prediction of DNA mismatch repair deficiency (dMMR) in gastric adenocarcinoma. Materials and Methods From March 2014 to August 2020, 161 patients with pathologically confirmed gastric adenocarcinoma were included from two centers (center 1 as the training and internal testing sets, n = 101; center 2 as the external testing sets, n = 60). All patients underwent preoperative contrast-enhanced computerized tomography (CT) examination. Radiomics features were extracted from portal-venous phase CT images. Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used to select features, and then radiomics signature was constructed using logistic regression analysis. A radiomics nomogram was built incorporating the radiomics signature and independent clinical predictors. The model performance was assessed using receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA). Results The radiomics signature, which was constructed using two selected features, was significantly associated with dMMR gastric adenocarcinoma in the training and internal testing sets (P < 0.05). The radiomics signature model showed a moderate discrimination ability with an area under the ROC curve (AUC) of 0.81 in the training set, which was confirmed with an AUC of 0.78 in the internal testing set. The radiomics nomogram consisting of the radiomics signature and clinical factors (age, sex, and location) showed excellent discrimination in the training, internal testing, and external testing sets with AUCs of 0.93, 0.82, and 0.83, respectively. Further, calibration curves and DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram. Conclusions The radiomics nomogram combining radiomics signature and clinical characteristics (age, sex, and location) may be used to individually predict dMMR of gastric adenocarcinoma.
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Affiliation(s)
- Yahan Tong
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Jiaying Li
- Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jieyu Chen
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Can Hu
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Zhiyuan Xu
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Shaofeng Duan
- Precision Health Institution, GE Healthcare, Shanghai, China
| | - Xiaojie Wang
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Risheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Xiangdong Cheng, ; Risheng Yu,
| | - Xiangdong Cheng
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- *Correspondence: Xiangdong Cheng, ; Risheng Yu,
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Yin HL, Jiang Y, Xu Z, Jia HH, Lin GW. Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions. J Cancer Res Clin Oncol 2022; 149:2575-2584. [PMID: 35771263 DOI: 10.1007/s00432-022-04142-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/13/2022] [Indexed: 02/05/2023]
Abstract
PURPOSE To investigate the value of the combined diagnosis of multiparametric MRI-based deep learning models to differentiate triple-negative breast cancer (TNBC) from fibroadenoma magnetic resonance Breast Imaging-Reporting and Data System category 4 (BI-RADS 4) lesions and to evaluate whether the combined diagnosis of these models could improve the diagnostic performance of radiologists. METHODS A total of 319 female patients with 319 pathologically confirmed BI-RADS 4 lesions were randomly divided into training, validation, and testing sets in this retrospective study. The three models were established based on contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and T2-weighted imaging using the training and validation sets. The artificial intelligence (AI) combination score was calculated according to the results of three models. The diagnostic performances of four radiologists with and without AI assistance were compared with the AI combination score on the testing set. The area under the curve (AUC), sensitivity, specificity, accuracy, and weighted kappa value were calculated to assess the performance. RESULTS The AI combination score yielded an excellent performance (AUC = 0.944) on the testing set. With AI assistance, the AUC for the diagnosis of junior radiologist 1 (JR1) increased from 0.833 to 0.885, and that for JR2 increased from 0.823 to 0.876. The AUCs of senior radiologist 1 (SR1) and SR2 slightly increased from 0.901 and 0.950 to 0.925 and 0.975 after AI assistance, respectively. CONCLUSION Combined diagnosis of multiparametric MRI-based deep learning models to differentiate TNBC from fibroadenoma magnetic resonance BI-RADS 4 lesions can achieve comparable performance to that of SRs and improve the diagnostic performance of JRs.
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Affiliation(s)
- Hao-Lin Yin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Jing'an District, 221# Yan'anxi Road, Shanghai, 200040, China
| | - Yu Jiang
- Department of Radiology, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan, China
| | - Zihan Xu
- Lung Cancer Center, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan, China
| | - Hui-Hui Jia
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Jing'an District, 221# Yan'anxi Road, Shanghai, 200040, China
| | - Guang-Wu Lin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Jing'an District, 221# Yan'anxi Road, Shanghai, 200040, China.
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Du Y, Zha HL, Wang H, Liu XP, Pan JZ, Du LW, Cai MJ, Zong M, Li CY. Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma. Br J Radiol 2022; 95:20210598. [PMID: 35138938 PMCID: PMC10993963 DOI: 10.1259/bjr.20210598] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 01/13/2022] [Accepted: 01/20/2022] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE This study aimed to develop a radiomics nomogram that incorporates radiomics, conventional ultrasound (US) and clinical features in order to differentiate triple-negative breast cancer (TNBC) from fibroadenoma. METHODS A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs, which underwent preoperative US examination, were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The radiomics features were extracted from the regions of interest of all lesions, which were delineated on the basis of preoperative US examination. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by using the receiver operator characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS The radiomics signature, composed of 12 selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975-0.997) and validation cohort (AUC 0.977, 95% CI, 0.953-1.000). The radiomics nomogram outperformed the Rad-score and clinical models (p < 0.05). The calibration curve and DCA demonstrated the good clinical utility of the combined radiomics nomogram. CONCLUSION The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models. ADVANCES IN KNOWLEDGE Recent advances in radiomics-based US are increasingly showing potential for improved diagnosis, assessment of therapeutic response and disease prediction in oncology. Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.
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Affiliation(s)
- Yu Du
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Hai-Ling Zha
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Hui Wang
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Xin-Pei Liu
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Jia-Zhen Pan
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Li-Wen Du
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Meng-Jun Cai
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Min Zong
- Department of Radiology, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Cui-Ying Li
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
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Niu S, Yu T, Cao Y, Dong Y, Luo Y, Jiang X. Digital breast tomosynthesis-based peritumoral radiomics approaches in the differentiation of benign and malignant breast lesions. Diagn Interv Radiol 2022; 28:217-225. [PMID: 35748203 PMCID: PMC9634934 DOI: 10.5152/dir.2022.20664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 02/24/2021] [Indexed: 11/22/2022]
Abstract
PURPOSE We aimed to evaluate digital breast tomosynthesis (DBT)-based radiomics in the differentiation of benign and malignant breast lesions in women. METHODS A total of 185 patients who underwent DBT scans were enrolled between December 2017 and June 2019. The features of handcrafted and deep learning-based radiomics were extracted from the tumoral and peritumoral regions with different radial dilation distances outside the tumor. A 3-step method was used to select discriminative features and develop the radiomics signature. Discriminative clinical factors were identified by univariate logistic regression. The clinical fac- tors with P < .05 were used to build a clinical model with multivariate logistic regression. The radiomics nomogram was developed by integrating the radiomics signature and discriminative clinical factors. Discriminative performance of the radiomics signature, clinical model, nomo- gram, and breast imaging reporting and data system assessment were evaluated and compared with the receiver operating characteristic and decision curves analysis (DCA). RESULTS A total of 2 handcrafted and 2 deep features were identified as the most discriminative features from the peritumoral regions with 2 mm dilation distances and used to develop the radiomics signature. The nomogram incorporating the radiomics signature, age, and menstruation status showed the best discriminative performance with area under the curve (AUC) values of 0.980 (95% CI, 0.960 to 1.000; sensitivity =0.970, specificity =0.946) in the training cohort and 0.985 (95% CI, 0.960 to 1.000; sensitivity = 0.909, specificity = 0.966) in the validation cohort. DCA con- firmed the potential clinical usefulness of our nomogram. CONCLUSION Our results illustrate that the radiomics nomogram integrating the DBT imaging features and clinical factors (age and menstruation status) can be considered as a useful tool in aiding the clinical diagnosis of breast cancer.
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Affiliation(s)
- Shuxian Niu
- Department of Biomedical Engineering, China Medical University, Shenyang, China
| | - Tao Yu
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yan Cao
- Department of Biomedical Engineering, China Medical University, Shenyang, China
| | - Yue Dong
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yahong Luo
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Xiran Jiang
- Department of Biomedical Engineering, China Medical University, Shenyang, China
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Zhang Q, Zhang Q, Liu T, Bao T, Li Q, Yang Y. Development and External Validation of a Simple-To-Use Dynamic Nomogram for Predicting Breast Malignancy Based on Ultrasound Morphometric Features: A Retrospective Multicenter Study. Front Oncol 2022; 12:868164. [PMID: 35463357 PMCID: PMC9021381 DOI: 10.3389/fonc.2022.868164] [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: 02/02/2022] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Background With advances in high-throughput computational mining techniques, various quantitative predictive models that are based on ultrasound have been developed. However, the lack of reproducibility and interpretability have hampered clinical use. In this study, we aimed at developing and validating an interpretable and simple-to-use US nomogram that is based on quantitative morphometric features for the prediction of breast malignancy. Methods Successive 917 patients with histologically confirmed breast lesions were included in this retrospective multicentric study and assigned to one training cohort and two external validation cohorts. Morphometric features were extracted from grayscale US images. After feature selection and validation of regression assumptions, a dynamic nomogram with a web-based calculator was developed. The performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness. Results Through feature selection, three morphometric features were identified as being the most optimal for predicting malignancy, and all regression assumptions of the prediction model were met. Combining all these predictors, the nomogram demonstrated a good discriminative performance in the training cohort and in the two external validation cohorts with AUCs of 0.885, 0.907, and 0.927, respectively. In addition, calibration and decision curves analyses showed good calibration and clinical usefulness. Conclusions By incorporating US morphometric features, we constructed an interpretable and easy-to-use dynamic nomogram for quantifying the probability of breast malignancy. The developed nomogram has good generalization abilities, which may fit into clinical practice and serve as a potential tool to guide personalized treatment. Our findings show that quantitative morphometric features from different ultrasound machines and systems can be used as imaging surrogate biomarkers for the development of robust and reproducible quantitative ultrasound dynamic models in breast cancer research.
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Affiliation(s)
- Qingling Zhang
- Depatment of Ultrasonography, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Qinglu Zhang
- Department of Ultrasonography, Shandong Provincial Third Hospital Affiliated to Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Taixia Liu
- Department of Ultrasonography, Linyi People's Hospital, Linyi, China
| | - Tingting Bao
- Depatment of Ultrasonography, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Qingqing Li
- Depatment of Ultrasonography, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - You Yang
- Depatment of Ultrasonography, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
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Xu M, Li F, Yu S, Zeng S, Weng G, Teng P, Yang H, Li X, Liu G. Value of Histogram of Gray-Scale Ultrasound Image in Differential Diagnosis of Small Triple Negative Breast Invasive Ductal Carcinoma and Fibroadenoma. Cancer Manag Res 2022; 14:1515-1524. [PMID: 35478712 PMCID: PMC9038159 DOI: 10.2147/cmar.s359986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/12/2022] [Indexed: 11/23/2022] Open
Abstract
Objective To investigate the value of gray-scale ultrasound (US) image histogram in the differential diagnosis between small (≤2.00 cm), oval, or round triple negative breast invasive ductal carcinoma (TN-IDC) and fibroadenoma (FA). Methods Fifty-five cases of triple negative breast invasive ductal carcinoma (TN-IDC group) and 57 cases of breast fibroadenoma (FA group) confirmed by pathology in Hubei cancer hospital from September 2017 to September 2021 were analyzed retrospectively. The gray-scale US images were analyzed by histogram analysis method, from which some parameters (including mean, variance, skewness, kurtosis and 1st, 10th, 50th, 90th and 99th percentile) can be obtained. Intraclass correlation coefficient (ICC) was used to evaluate the inter observer reliability of histogram parameters. Histogram parameters between the TN-IDC and FA groups were compared using independent Student’s t-test or Mann-Whitney U-test, respectively. In addition, the receiver operating characteristic (ROC) curve analysis was used for the significant parameters to calculate the differential diagnosis efficiency. Results All the histogram parameters showed excellent inter-reader consistency, with the ICC values ranged from 0.883 to 0.999. The mean value, 1st, 10th, 50th, 90th and 99th percentiles of TN-IDC group were significantly lower than those of FA group (P < 0.05). The area under ROC curve (AUC) values of mean and n percentiles were from 0.807 to 0.848. However, there were no significant differences in variance, skewness and kurtosis between the two groups (P > 0.05). Conclusion Histogram analysis of gray-scale US images can well distinguish small, oval, or round TN-IDC from FA.
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Affiliation(s)
- Maolin Xu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Fang Li
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Shaonan Yu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Shue Zeng
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Gaolong Weng
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Peihong Teng
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Huimin Yang
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Xuefeng Li
- Department of Anesthesiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
- Correspondence: Xuefeng Li, Department of Anesthesiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, People’s Republic of China, Email
| | - Guifeng Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
- Guifeng Liu, Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, People’s Republic of China, Email
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Lee SE, Han K, Youk JH, Lee JE, Hwang JY, Rho M, Yoon J, Kim EK, Yoon JH. Differing benefits of artificial intelligence-based computer-aided diagnosis (AI-CAD) for breast US according to workflow and experience level. Ultrasonography 2022; 41:718-727. [PMID: 35850498 PMCID: PMC9532201 DOI: 10.14366/usg.22014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/30/2022] [Indexed: 11/10/2022] Open
Abstract
Purpose This study evaluated how artificial intelligence-based computer-assisted diagnosis (AI-CAD) for breast ultrasonography (US) influences diagnostic performance and agreement between radiologists with varying experience levels in different workflows. Methods Images of 492 breast lesions (200 malignant and 292 benign masses) in 472 women taken from April 2017 to June 2018 were included. Six radiologists (three inexperienced [<1 year of experience] and three experienced [10-15 years of experience]) individually reviewed US images with and without the aid of AI-CAD, first sequentially and then simultaneously. Diagnostic performance and interobserver agreement were calculated and compared between radiologists and AI-CAD. Results After implementing AI-CAD, the specificity, positive predictive value (PPV), and accuracy significantly improved, regardless of experience and workflow (all P<0.001, respectively). The overall area under the receiver operating characteristic curve significantly increased in simultaneous reading, but only for inexperienced radiologists. The agreement for Breast Imaging Reporting and Database System (BI-RADS) descriptors generally increased when AI-CAD was used (κ=0.29-0.63 to 0.35-0.73). Inexperienced radiologists tended to concede to AI-CAD results more easily than experienced radiologists, especially in simultaneous reading (P<0.001). The conversion rates for final assessment changes from BI-RADS 2 or 3 to BI-RADS higher than 4a or vice versa were also significantly higher in simultaneous reading than sequential reading (overall, 15.8% and 6.2%, respectively; P<0.001) for both inexperienced and experienced radiologists. Conclusion Using AI-CAD to interpret breast US improved the specificity, PPV, and accuracy of radiologists regardless of experience level. AI-CAD may work better in simultaneous reading to improve diagnostic performance and agreement between radiologists, especially for inexperienced radiologists.
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Affiliation(s)
- Si Eun Lee
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Department of Radiology, Research Institute of Radiological Science, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Ji Hyun Youk
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jee Eun Lee
- Department of Radiology, Ewha Womans University College of Medicine, Seoul, Korea
| | - Ji-Young Hwang
- Department of Radiology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Miribi Rho
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jiyoung Yoon
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Eun-Kyung Kim
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Department of Radiology, Research Institute of Radiological Science, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Jung Hyun Yoon
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Correspondence to: Jung Hyun Yoon, MD, PhD, Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea Tel. +82-2-2228-7400 Fax. +82-2-2227-8337 E-mail:
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Li JW, Cao YC, Zhao ZJ, Shi ZT, Duan XQ, Chang C, Chen JG. Prediction for pathological and immunohistochemical characteristics of triple-negative invasive breast carcinomas: the performance comparison between quantitative and qualitative sonographic feature analysis. Eur Radiol 2022; 32:1590-1600. [PMID: 34519862 DOI: 10.1007/s00330-021-08224-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 06/28/2021] [Accepted: 07/15/2021] [Indexed: 12/01/2022]
Abstract
OBJECTIVE Sonographic features are associated with pathological and immunohistochemical characteristics of triple-negative breast cancer (TNBC). To predict the biological property of TNBC, the performance using quantitative high-throughput sonographic feature analysis was compared with that using qualitative feature assessment. METHODS We retrospectively reviewed ultrasound images, clinical, pathological, and immunohistochemical (IHC) data of 252 female TNBC patients. All patients were subgrouped according to the histological grade, Ki67 expression level, and human epidermal growth factor receptor 2 (HER2) score. Qualitative sonographic feature assessment included shape, margin, posterior acoustic pattern, and calcification referring to the Breast Imaging Reporting and Data System (BI-RADS). Quantitative sonographic features were acquired based on the computer-aided radiomics analysis. Breast cancer masses were manually segmented from the surrounding breast tissues. For each ultrasound image, 1688 radiomics features of 7 feature classes were extracted. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were used to determine the high-throughput radiomics features that were highly correlated to biological properties. The performance using both quantitative and qualitative sonographic features to predict biological properties of TNBC was represented by the area under the receiver operating characteristic curve (AUC). RESULTS In the qualitative assessment, regular tumor shape, no angular or spiculated margin, posterior acoustic enhancement, and no calcification were used as the independent sonographic features for TNBC. Using the combination of these four features to predict the histological grade, Ki67, HER2, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI), the AUC was 0.673, 0.680, 0.651, 0.587, and 0.566, respectively. The number of high-throughput features that closely correlated with biological properties was 34 for histological grade (AUC 0.942), 27 for Ki67 (AUC 0.732), 25 for HER2 (AUC 0.730), 34 for ALNM (AUC 0.804), and 34 for LVI (AUC 0.795). CONCLUSION High-throughput quantitative sonographic features are superior to traditional qualitative ultrasound features in predicting the biological behavior of TNBC. KEY POINTS • Sonographic appearances of TNBCs showed a great variety in accordance with its biological and clinical characteristics. • Both qualitative and quantitative sonographic features of TNBCs are associated with tumor biological characteristics. • The quantitative high-throughput feature analysis is superior to two-dimensional sonographic feature assessment in predicting tumor biological property.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No 270, Dong'an Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, No 270, Dong'an Road, Xuhui District, Shanghai, 200032, China
| | - Yu-Cheng Cao
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, #500 Dongchuan Rd., Shanghai, 200241, China
| | - Zhi-Jin Zhao
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No 270, Dong'an Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, No 270, Dong'an Road, Xuhui District, Shanghai, 200032, China
| | - Zhao-Ting Shi
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No 270, Dong'an Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, No 270, Dong'an Road, Xuhui District, Shanghai, 200032, China
| | - Xiao-Qian Duan
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, #500 Dongchuan Rd., Shanghai, 200241, China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No 270, Dong'an Road, Xuhui District, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, No 270, Dong'an Road, Xuhui District, Shanghai, 200032, China.
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, #500 Dongchuan Rd., Shanghai, 200241, China.
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Mireștean CC, Volovăț C, Iancu RI, Iancu DPT. Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease. J Clin Med 2022; 11:jcm11030616. [PMID: 35160069 PMCID: PMC8836903 DOI: 10.3390/jcm11030616] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/23/2022] [Accepted: 01/24/2022] [Indexed: 12/17/2022] Open
Abstract
In the last decade, the analysis of the medical images has evolved significantly, applications and tools capable to extract quantitative characteristics of the images beyond the discrimination capacity of the investigator's eye being developed. The applications of this new research field, called radiomics, presented an exponential growth with direct implications in the diagnosis and prediction of response to therapy. Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype with a severe prognosis, despite the aggressive multimodal treatments applied according to the guidelines. Radiomics has already proven the ability to differentiate TNBC from fibroadenoma. Radiomics features extracted from digital mammography may also distinguish between TNBC and non-TNBC. Recent research has identified three distinct subtypes of TNBC using IRM breast images voxel-level radiomics features (size/shape related features, texture features, sharpness). The correlation of these TNBC subtypes with the clinical response to neoadjuvant therapy may lead to the identification of biomarkers in order to guide the clinical decision. Furthermore, the variation of some radiomics features in the neoadjuvant settings provides a tool for the rapid evaluation of treatment efficacy. The association of radiomics features with already identified biomarkers can generate complex predictive and prognostic models. Standardization of image acquisition and also of radiomics feature extraction is required to validate this method in clinical practice.
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Affiliation(s)
- Camil Ciprian Mireștean
- Department of Oncology and Radiotherapy, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
- Department of Surgery, Railways Clinical Hospital, 700506 Iasi, Romania
| | - Constantin Volovăț
- Department of Medical Oncology-Radiotherapy, Faculty of Medicine, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (C.V.); (D.P.T.I.)
- Euroclinic Oncological Hospital, 700110 Iasi, Romania
| | - Roxana Irina Iancu
- Department of Oral Pathology, Faculty of Dentistry, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Clinical Laboratory Department, “St. Spiridon” Emergency Hospital, 700111 Iasi, Romania
- Correspondence: ; Tel.: +40-232-301-603
| | - Dragoș Petru Teodor Iancu
- Department of Medical Oncology-Radiotherapy, Faculty of Medicine, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (C.V.); (D.P.T.I.)
- Department of Radiotherapy, Regional Institute of Oncology, 700483 Iasi, Romania
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Magnuska ZA, Theek B, Darguzyte M, Palmowski M, Stickeler E, Schulz V, Kießling F. Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification. Cancers (Basel) 2022; 14:277. [PMID: 35053441 PMCID: PMC8773857 DOI: 10.3390/cancers14020277] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 12/30/2021] [Indexed: 02/04/2023] Open
Abstract
Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola-Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola-Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.
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Affiliation(s)
- Zuzanna Anna Magnuska
- Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; (Z.A.M.); (B.T.); (M.D.); (V.S.)
| | - Benjamin Theek
- Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; (Z.A.M.); (B.T.); (M.D.); (V.S.)
| | - Milita Darguzyte
- Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; (Z.A.M.); (B.T.); (M.D.); (V.S.)
| | - Moritz Palmowski
- Radiologie Baden-Baden, Beethovenstraße 2, 76530 Baden-Baden, Germany;
| | - Elmar Stickeler
- Department of Obstetrics and Gynecology, University Clinic Aachen, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany;
- Comprehensive Diagnostic Center Aachen, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany
| | - Volkmar Schulz
- Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; (Z.A.M.); (B.T.); (M.D.); (V.S.)
- Comprehensive Diagnostic Center Aachen, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany
- Physics Institute III B, RWTH Aachen University, 52074 Aachen, Germany
- Hyperion Hybrid Imaging Systems GmbH, 52074 Aachen, Germany
- Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, 28359 Bremen, Germany
| | - Fabian Kießling
- Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; (Z.A.M.); (B.T.); (M.D.); (V.S.)
- Comprehensive Diagnostic Center Aachen, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany
- Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, 28359 Bremen, Germany
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Zhang G, Ren Y, Xi X, Li D, Guo J, Li X, Tian C, Xu Z. LRSCnet: Local Reference Semantic Code learning for breast tumor classification in ultrasound images. Biomed Eng Online 2021; 20:127. [PMID: 34920726 PMCID: PMC8684265 DOI: 10.1186/s12938-021-00968-3] [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: 06/23/2021] [Accepted: 12/09/2021] [Indexed: 11/17/2022] Open
Abstract
Purpose This study proposed a novel Local Reference Semantic Code (LRSC) network for automatic breast ultrasound image classification with few labeled data. Methods In the proposed network, the local structure extractor is firstly developed to learn the local reference which describes common local characteristics of tumors. After that, a two-stage hierarchical encoder is developed to encode the local structures of lesion into the high-level semantic code. Based on the learned semantic code, the self-matching layer is proposed for the final classification. Results In the experiment, the proposed method outperformed traditional classification methods and AUC (Area Under Curve), ACC (Accuracy), Sen (Sensitivity), Spec (Specificity), PPV (Positive Predictive Values), and NPV(Negative Predictive Values) are 0.9540, 0.9776, 0.9629, 0.93, 0.9774 and 0.9090, respectively. In addition, the proposed method also improved matching speed. Conclusions LRSC-network is proposed for breast ultrasound images classification with few labeled data. In the proposed network, a two-stage hierarchical encoder is introduced to learn high-level semantic code. The learned code contains more effective high-level classification information and is simpler, leading to better generalization ability.
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Affiliation(s)
- Guang Zhang
- School of Software, Shandong University, Jinan, China.,Health Management, The First Affiliated Hospital of Shangdong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yanwei Ren
- School of Software, Shandong University, Jinan, China
| | - Xiaoming Xi
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China.
| | - Delin Li
- Health Management, The First Affiliated Hospital of Shangdong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Jie Guo
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Xiaofeng Li
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Cuihuan Tian
- School of Medicine, Shandong Universit, Jinan, China.,Health Management Center, QiLu Hospital of Shandong University, Jinan, China
| | - Zunyi Xu
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China.
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Dynamic contract-enhanced CT-based radiomics for differentiation of pancreatobiliary-type and intestinal-type periampullary carcinomas. Clin Radiol 2021; 77:e75-e83. [PMID: 34753589 DOI: 10.1016/j.crad.2021.09.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 09/09/2021] [Indexed: 11/21/2022]
Abstract
AIM To investigate whether computed tomography (CT) radiomics can differentiate pancreatobiliary-type from intestinal-type periampullary carcinomas. MATERIALS AND METHODS CT radiomics of 96 patients (54 pancreatobiliary type and 42 intestinal type) with surgically confirmed periampullary carcinoma were assessed retrospectively. Volumes of interest (VOIs) were delineated manually. Radiomic features were extracted from preoperative CT images. A single-phase model and combined-phase model were constructed. Five-fold cross-validation and five machine-learning algorithms were utilised for model construction. The diagnostic performance of the models was evaluated by receiver operating characteristic (ROC) curves, and indicators included area under the curve (AUC), accuracy, sensitivity, specificity, and precision. ROC curves were compared using DeLong's test. RESULTS A total of 788 features were extracted on each phase. After feature selection using least absolute shrinkage and selection operator (LASSO) algorithm, the number of selected optimal feature was 18 (plain scan), nine (arterial phase), two (venous phase), 23 (delayed phase), 15 (three enhanced phases), and 29 (all phases), respectively. For the single-phase model, the delayed-phase model using the logistic regression (LR) algorithm showed the best prediction performance with AUC, accuracy, sensitivity, specificity, and precision of 0.89, 0.83, 0.80, 0.88, and 0.93, respectively. Two combined-phase models showed better results than the single-phase models. The model of all phases using the LR algorithm showed the best prediction performance with AUC, accuracy, sensitivity, specificity, and precision of 0.96, 0.88, 0.90, 0.93, and 0.92, respectively. CONCLUSION Radiomic models based on preoperative CT images can differentiate pancreatobiliary-type from intestinal-type periampullary carcinomas, in particular, the model of all phases using the LR algorithm.
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Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. JOURNAL OF ONCOLOGY 2021; 2021:8615450. [PMID: 34671399 PMCID: PMC8523238 DOI: 10.1155/2021/8615450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022]
Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
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Bi L, Liu Y, Xu J, Wang X, Zhang T, Li K, Duan M, Huang C, Meng X, Huang Z. A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas. Front Oncol 2021; 11:632176. [PMID: 34395237 PMCID: PMC8358686 DOI: 10.3389/fonc.2021.632176] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 07/09/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose To establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas. Materials and Methods A total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation. Results The radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p < 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram. Conclusion Our CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.
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Affiliation(s)
- Lei Bi
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.,Department of Radiology, Linyi People's Hospital, Linyi, China
| | - Yubo Liu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Tong Zhang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Kaiguo Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Mingguang Duan
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Xiangjiao Meng
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhaoqin Huang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
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Chen Q, Xia J, Zhang J. Identify the triple-negative and non-triple-negative breast cancer by using texture features of medicale ultrasonic image: A STROBE-compliant study. Medicine (Baltimore) 2021; 100:e25878. [PMID: 34087829 PMCID: PMC8183753 DOI: 10.1097/md.0000000000025878] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 04/17/2021] [Indexed: 01/04/2023] Open
Abstract
The study aimed to explore the value of ultrasound (US) texture analysis in the differential diagnosis of triple-negative breast cancer (TNBC) and non-TNBC.Retrospective analysis was done on 93 patients with breast cancer (35 patients with TNBC and 38 patients with non-TNBC) who were admitted to Taizhou people's hospital from July 2015 to June 2019. All lesions were pathologically proven at surgery. US images of all patients were collected. Texture analysis of US images was performed using MaZda software package. The differences between textural features in TNBC and non-TNBC were assessed. Receiver operating characteristic curve analysis was used to compare the diagnostic performance of textural parameters showing significant difference.Five optimal texture feature parameters were extracted from gray level run-length matrix, including gray level non-uniformity (GLNU) in horizontal direction, vertical gray level non-uniformity, GLNU in the 45 degree direction, run length non-uniformity in 135 degree direction, GLNU in the 135 degree direction. All these texture parameters were statistically higher in TNBC than in non-TNBC (P <.05). Receiver operating characteristic curve analysis indicated that at a threshold of 268.9068, GLNU in horizontal direction exhibited best diagnostic performance for differentiating TNBC from non-TNBC. Logistic regression model established based on all these parameters showed a sensitivity of 69.3%, specificity of 91.4% and area under the curve of 0.834.US texture features were significantly different between TNBC and non-TNBC, US texture analysis can be used for preliminary differentiation of TNBC from non-TNBC.
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Affiliation(s)
| | | | - Jun Zhang
- Department of Nuclear Medicine, Taizhou people's Hospital affiliated to Medical College of Yangzhou University Taizhou, China
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Romeo V, Cuocolo R, Apolito R, Stanzione A, Ventimiglia A, Vitale A, Verde F, Accurso A, Amitrano M, Insabato L, Gencarelli A, Buonocore R, Argenzio MR, Cascone AM, Imbriaco M, Maurea S, Brunetti A. Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions. Eur Radiol 2021; 31:9511-9519. [PMID: 34018057 PMCID: PMC8589755 DOI: 10.1007/s00330-021-08009-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 04/06/2021] [Accepted: 04/22/2021] [Indexed: 01/28/2023]
Abstract
Objectives We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML’s accuracy with that of a breast radiologist, and verify if the radiologist’s performance is improved by using ML. Methods Our retrospective study included patients from two institutions. A total of 135 lesions from Institution 1 were used to train and test the ML model with cross-validation. Radiomic features were extracted from manually annotated images and underwent a multistep feature selection process. Not reproducible, low variance, and highly intercorrelated features were removed from the dataset. Then, 66 lesions from Institution 2 were used as an external test set for ML and to assess the performance of a radiologist without and with the aid of ML, using McNemar’s test. Results After feature selection, 10 of the 520 features extracted were employed to train a random forest algorithm. Its accuracy in the training set was 82% (standard deviation, SD, ± 6%), with an AUC of 0.90 (SD ± 0.06), while the performance on the test set was 82% (95% confidence intervals (CI) = 70–90%) with an AUC of 0.82 (95% CI = 0.70–0.93). It resulted in being significantly better than the baseline reference (p = 0.0098), but not different from the radiologist (79.4%, p = 0.815). The radiologist’s performance improved when using ML (80.2%), but not significantly (p = 0.508). Conclusions A radiomic analysis combined with ML showed promising results to differentiate benign from malignant breast lesions on ultrasound images. Key Points • Machine learning showed good accuracy in discriminating benign from malignant breast lesions • The machine learning classifier’s performance was comparable to that of a breast radiologist • The radiologist’s accuracy improved with machine learning, but not significantly Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08009-2.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", Naples, Italy.,Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
| | - Roberta Apolito
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy.
| | - Antonio Ventimiglia
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Annalisa Vitale
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Antonello Accurso
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Michele Amitrano
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Luigi Insabato
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Annarita Gencarelli
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Roberta Buonocore
- Department of Radiology, A.O.U. San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | | | - Anna Maria Cascone
- Department of Radiology, A.O.U. San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
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Jiang M, Li CL, Chen RX, Tang SC, Lv WZ, Luo XM, Chuan ZR, Jin CY, Liao JT, Cui XW, Dietrich CF. Management of breast lesions seen on US images: dual-model radiomics including shear-wave elastography may match performance of expert radiologists. Eur J Radiol 2021; 141:109781. [PMID: 34029933 DOI: 10.1016/j.ejrad.2021.109781] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/28/2021] [Accepted: 05/17/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE To develop a nomogram incorporating B-mode ultrasound (BMUS) and shear-wave elastography (SWE) radiomics to predict malignant status of breast lesions seen on US non-invasively. METHODS Data on 278 consecutive patients from Hospital #1 (training cohort) and 123 cases from Hospital #2 (external validation cohort) referred for breast US with subsequent histopathologic analysis between May 2017 and October 2019 were retrospectively collected. Using their BMUS and SWE images, we built a radiomics nomogram to improve radiology workflow for management of breast lesions. The performance of the algorithm was compared with a consensus of three ACR BI-RADS committee experts and four individual radiologists, all of whom interpreted breast US images in clinical practice. RESULTS Twelve features from BMUS and three from SWE were selected finally to construct the respective radiomic signature. The nomogram based on the dual-modal US radiomics achieved good diagnostic performance in the training (AUC 0.96; 95% confidence intervals [CI], 0.94-0.98) and the validation set (AUC 0.92; 95% CI, 0.87-0.97). For the 123 test lesions, the algorithm achieved 105 of 123 (85%) accuracy, comparable to the expert consensus (104 of 123 [85%], P = 0.86) and four individual radiologists (93, 99, 95 and 97 of 123, with P value of 0.05, 0.31, 0.10 and 0.18 respectively). Furthermore, the model also performed well in the BI-RADS 4 and 5 categories. CONCLUSIONS Performance of a dual-model US radiomics nomogram based on SWE for breast lesion classification may comparable to that of expert radiologists who used ACR BI-RADS guideline.
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Affiliation(s)
- Meng Jiang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Chang-Li Li
- Department of Geratology, Hubei Provincial Hospital of Integrated Chinese and Western medicine, 11 Lingjiaohu Avenue, Wuhan, 430015, China
| | - Rui-Xue Chen
- Department of Medical Ultrasound, Wuchang Hospital, Wuhan, 430030, China
| | - Shi-Chu Tang
- Department of Medical Ultrasound, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China
| | - Wen-Zhi Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, 430030, China
| | - Xiao-Mao Luo
- Deaprtment of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China
| | - Zhi-Rui Chuan
- Deaprtment of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China
| | - Chao-Ying Jin
- Department of Medical Ultrasound, Taizhou Hospital of Zhejiang Province, Taizhou, 317000, China
| | - Jin-Tang Liao
- Department of Ultrasound Imaging, Xiangya Hospital, Central South University, Changsha, 410013, China.
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Christoph F Dietrich
- Department of Internal Medicine, Hirslanden Clinic, Schänzlihalde 11, 3013, Bern, Switzerland
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Tomaszewski MR, Gillies RJ. The Biological Meaning of Radiomic Features. Radiology 2021; 298:505-516. [PMID: 33399513 PMCID: PMC7924519 DOI: 10.1148/radiol.2021202553] [Citation(s) in RCA: 226] [Impact Index Per Article: 75.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 07/30/2020] [Accepted: 08/17/2020] [Indexed: 02/06/2023]
Abstract
An earlier incorrect version appeared online. This article was corrected on February 10, 2021.
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Affiliation(s)
- Michal R. Tomaszewski
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
| | - Robert J. Gillies
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
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Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys 2021; 47:e185-e202. [PMID: 32418336 DOI: 10.1002/mp.13678] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/22/2019] [Accepted: 06/13/2019] [Indexed: 12/12/2022] Open
Abstract
Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three-dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open-source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics-based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, 33081, Italy
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
| | | | - Martin Vallières
- Medical Physics Unit, McGill University, Montreal, QC, Canada.,Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Arvind Rao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48103, USA
| | - Olivier Morin
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Sarah A Mattonen
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
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Goyal S. An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging. REPORTS IN MEDICAL IMAGING 2021. [DOI: 10.2147/rmi.s295205] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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Tao W, Lu M, Zhou X, Montemezzi S, Bai G, Yue Y, Li X, Zhao L, Zhou C, Lu G. Machine Learning Based on Multi-Parametric MRI to Predict Risk of Breast Cancer. Front Oncol 2021; 11:570747. [PMID: 33718131 PMCID: PMC7952867 DOI: 10.3389/fonc.2021.570747] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 01/18/2021] [Indexed: 01/22/2023] Open
Abstract
Purpose Machine learning (ML) can extract high-throughput features of images to predict disease. This study aimed to develop nomogram of multi-parametric MRI (mpMRI) ML model to predict the risk of breast cancer. Methods The mpMRI included non-enhanced and enhanced T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), K trans, K ep, V e, and V p. Regions of interest were annotated in an enhanced T1WI map and mapped to other maps in every slice. 1,132 features and top-10 principal components were extracted from every parameter map. Single-parametric and multi-parametric ML models were constructed via 10 rounds of five-fold cross-validation. The model with the highest area under the curve (AUC) was considered as the optimal model and validated by calibration curve and decision curve. Nomogram was built with the optimal ML model and patients' characteristics. Results This study involved 144 malignant lesions and 66 benign lesions. The average age of patients with benign and malignant lesions was 42.5 years old and 50.8 years old, respectively, which were statistically different. The sixth and fourth principal components of K trans had more importance than others. The AUCs of K trans, K ep, V e and V p, non-enhanced T1WI, enhanced T1WI, T2WI, and ADC models were 0.86, 0.81, 0.81, 0.83, 0.79, 0.81, 0.84, and 0.83 respectively. The model with an AUC of 0.90 was considered as the optimal model which was validated by calibration curve and decision curve. Nomogram for the prediction of breast cancer was built with the optimal ML models and patient age. Conclusion Nomogram could improve the ability of breast cancer prediction preoperatively.
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Affiliation(s)
- Weijing Tao
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.,Department of Nuclear Medicine, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Mengjie Lu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xiaoyu Zhou
- Faculty of Mechanical Electronic and Information Engineering, Jiangsu Vocational College of Finance and Economics, Huai'an, China
| | - Stefania Montemezzi
- Radiology Unit, Department of Pathology and Diagnostics, Azienda Ospedaliera Universitaria Integrata-Verona, Verona, Italy
| | - Genji Bai
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Yangming Yue
- Deepwise AI Laboratory, Deepwise Inc., Beijing, China
| | - Xiuli Li
- Deepwise AI Laboratory, Deepwise Inc., Beijing, China
| | - Lun Zhao
- Deepwise AI Laboratory, Deepwise Inc., Beijing, China
| | - Changsheng Zhou
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
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Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma. Sci Rep 2021; 11:4679. [PMID: 33633160 PMCID: PMC7907098 DOI: 10.1038/s41598-021-84048-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 02/11/2021] [Indexed: 01/06/2023] Open
Abstract
The purpose of this study was to examine differences in texture features between olfactory neuroblastoma (ONB) and sinonasal squamous cell carcinoma (SCC) on contrast-enhanced CT (CECT) images, and to evaluate the predictive accuracy of texture analysis compared to radiologists’ interpretations. Forty-three patients with pathologically-diagnosed primary nasal and paranasal tumor (17 ONB and 26 SCC) were included. We extracted 42 texture features from tumor regions on CECT images obtained before treatment. In univariate analysis, each texture features were compared, with adjustment for multiple comparisons. In multivariate analysis, the elastic net was used to select useful texture features and to construct a texture-based prediction model with leave-one-out cross-validation. The prediction accuracy was compared with two radiologists’ visual interpretations. In univariate analysis, significant differences were observed for 28 of 42 texture features between ONB and SCC, with areas under the receiver operating characteristic curve between 0.68 and 0.91 (median: 0.80). In multivariate analysis, the elastic net model selected 18 texture features that contributed to differentiation. It tended to show slightly higher predictive accuracy than radiologists’ interpretations (86% and 74%, respectively; P = 0.096). In conclusion, several texture features contributed to differentiation of ONB from SCC, and the texture-based prediction model was considered useful.
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Artificial Intelligence and the Medical Physicist: Welcome to the Machine. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041691] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.
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Dietzel M, Clauser P, Kapetas P, Schulz-Wendtland R, Baltzer PAT. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. ROFO-FORTSCHR RONTG 2021; 193:898-908. [PMID: 33535260 DOI: 10.1055/a-1346-0095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. METHODS AND RESULTS This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. CONCLUSION Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. KEY POINTS · In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review.. CITATION FORMAT · Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908.
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Affiliation(s)
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | | | - Pascal Andreas Thomas Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
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Vara G, Rustici A, Sechi A, Mosconi C, Lucidi V, Golfieri R. Texture Analysis on Ultrasound: The Effect of Time Gain Compensation on Histogram Metrics and Gray-Level Matrices. J Med Phys 2021; 45:249-255. [PMID: 33953501 PMCID: PMC8074715 DOI: 10.4103/jmp.jmp_82_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 12/11/2020] [Accepted: 12/13/2020] [Indexed: 11/17/2022] Open
Abstract
Aims: Texture analysis (TA) is becoming an increasingly used tool in radiological research. Some papers have been published on its use in ultrasound (US), but the way in which the machine settings affect the features has not yet been fully explored. With this research, we analyze how the time gain compensation (TGC) influences the features of the gray-level matrices in the abdominal US setting. Subjects and Methods: We analyzed the images acquired from the hepatorenal acoustic window of a healthy 29-year-old volunteer acquired with different TGC settings. TA was carried out using the LifeX software. Results: Several both 1st and 2nd order gray-level matrices features showed a strong correlation with TGC settings. Conclusions: TGC settings must be accounted for when carrying out further TA studies.
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Affiliation(s)
- Giulio Vara
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Arianna Rustici
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Andrea Sechi
- Department of Specialized, Clinical and Experimental Medicine, Division of Dermatology, University of Bologna, Bologna, Italy
| | - Cristina Mosconi
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Vincenzo Lucidi
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Rita Golfieri
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
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Yi CMB, Xia LMB, Run WMD, Xiaona LMM, Jieyu ZMD, Yan CMM, Huali CMB, Yongbin LMM, Zhicheng LMD, Zhengming HMD, Desheng SMD. Ultrasound Radiomics in Breast Cancer - A Literature Review. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY 2021. [DOI: 10.37015/audt.2021.200052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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Caballo M, Pangallo DR, Sanderink W, Hernandez AM, Lyu SH, Molinari F, Boone JM, Mann RM, Sechopoulos I. Multi-marker quantitative radiomics for mass characterization in dedicated breast CT imaging. Med Phys 2020; 48:313-328. [PMID: 33232521 PMCID: PMC7898616 DOI: 10.1002/mp.14610] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 09/07/2020] [Accepted: 11/16/2020] [Indexed: 12/11/2022] Open
Abstract
Purpose To develop and evaluate the diagnostic performance of an algorithm for multi‐marker radiomic‐based classification of breast masses in dedicated breast computed tomography (bCT) images. Methods Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well‐established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single‐feature‐based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple‐step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant). Results The majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80–0.96). Conclusions The proposed multi‐marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.
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Affiliation(s)
- Marco Caballo
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Domenico R Pangallo
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands.,Biolab, Department of Electronics and Telecommunication, Politecnico di Torino, Torino, 10129, Italy
| | - Wendelien Sanderink
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Andrew M Hernandez
- Department of Radiology, University of California Davis, Sacramento, CA, 95817, USA
| | - Su Hyun Lyu
- Department of Biomedical Engineering, University of California Davis, Sacramento, CA, 95817, USA
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecommunication, Politecnico di Torino, Torino, 10129, Italy
| | - John M Boone
- Department of Radiology, University of California Davis, Sacramento, CA, 95817, USA.,Department of Biomedical Engineering, University of California Davis, Sacramento, CA, 95817, USA
| | - Ritse M Mann
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands.,Dutch Expert Center for Screening (LRCB), PO Box 6873, Nijmegen, 6503 GJ, The Netherlands
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