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Wu L, Li S, Wu C, Wu S, Lin Y, Wei D. Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer. BMC Med Imaging 2024; 24:189. [PMID: 39060962 PMCID: PMC11282842 DOI: 10.1186/s12880-024-01353-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 07/02/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC). METHODS 50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality. RESULTS Supervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype. CONCLUSION The DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment.
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Affiliation(s)
- Linyong Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Songhua Li
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Chaojun Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Shaofeng Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Yan Lin
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Dayou Wei
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China.
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Li W, Song Y, Qian X, Zhou L, Zhu H, Shen L, Dai Y, Dong F, Li Y. Radiomics analysis combining gray-scale ultrasound and mammography for differentiating breast adenosis from invasive ductal carcinoma. Front Oncol 2024; 14:1390342. [PMID: 39045562 PMCID: PMC11263089 DOI: 10.3389/fonc.2024.1390342] [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: 02/23/2024] [Accepted: 06/21/2024] [Indexed: 07/25/2024] Open
Abstract
Objectives To explore the utility of gray-scale ultrasound (GSUS) and mammography (MG) for radiomic analysis in distinguishing between breast adenosis and invasive ductal carcinoma (IDC). Methods Data from 147 female patients with pathologically confirmed breast lesions (breast adenosis: 61 patients; IDC: 86 patients) between January 2018 and December 2022 were retrospectively collected. A training cohort of 113 patients (breast adenosis: 50 patients; IDC: 63 patients) diagnosed from January 2018 to December 2021 and a time-independent test cohort of 34 patients (breast adenosis: 11 patients; IDC: 23 patients) diagnosed from January 2022 to December 2022 were included. Radiomic features of lesions were extracted from MG and GSUS images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most discriminant features, followed by logistic regression (LR) to construct clinical and radiomic models, as well as a combined model merging radiomic and clinical features. Model performance was assessed using receiver operating characteristic (ROC) analysis. Results In the training cohort, the area under the curve (AUC) for radiomic models based on MG features, GSUS features, and their combination were 0.974, 0.936, and 0.991, respectively. In the test cohort, the AUCs were 0.885, 0.876, and 0.949, respectively. The combined model, incorporating clinical and all radiomic features, and the MG plus GSUS radiomics model were found to exhibit significantly higher AUCs than the clinical model in both the training cohort and test cohort (p<0.05). No significant differences were observed between the combined model and the MG plus GSUS radiomics model in the training cohort and test cohort (p>0.05). Conclusion The effectiveness of radiomic features derived from GSUS and MG in distinguishing between breast adenosis and IDC is demonstrated. Superior discriminatory efficacy is shown by the combined model, integrating both modalities.
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Affiliation(s)
- Wen Li
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Department of Ultrasound, Huadong Sanatorium, Wuxi, Jiangsu, China
| | - Ying Song
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Xusheng Qian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Le Zhou
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Huihui Zhu
- Department of Ultrasound, Huadong Sanatorium, Wuxi, Jiangsu, China
| | - Long Shen
- Department of Radiology, Suzhou Xiangcheng District Second People’s Hospital, Suzhou, Jiangsu, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Fenglin Dong
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Yonggang Li
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, China
- National Clinical Research Center for Hematologic Diseases, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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Chu B, Chen Z, Wu X, Shi H, Jin X, Song B, Cui M, Zhao Y, Zhao Y, He Y, Wang H, Dong F. Photoactivated Gas-Generating Nanocontrast Agents for Long-Term Ultrasonic Imaging-Guided Combined Therapy of Tumors. ACS NANO 2024; 18:15590-15606. [PMID: 38847586 DOI: 10.1021/acsnano.4c01041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2024]
Abstract
To date, long-term and continuous ultrasonic imaging for guiding the puncture biopsy remains a challenge. In order to address this issue, a multimodality imaging and therapeutic method was developed in the present study to facilitate long-term ultrasonic and fluorescence imaging-guided precision diagnosis and combined therapy of tumors. In this regard, certain types of photoactivated gas-generating nanocontrast agents (PGNAs), capable of exhibiting both ultrasonic and fluorescence imaging ability along with photothermal and sonodynamic function, were designed and fabricated. The advantages of these fabricated PGNAs were then utilized against tumors in vivo, and high therapeutic efficacy was achieved through long-term ultrasonic imaging-guided treatment. In particular, the as-prepared multifunctional PGNAs were applied successfully for the fluorescence-based determination of patient tumor samples collected through puncture biopsy in clinics, and superior performance was observed compared to the clinically used SonoVue contrast agents that are incapable of specifically distinguishing the tumor in ex vivo tissues.
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Affiliation(s)
- Binbin Chu
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou 215123 China
| | - Zhiming Chen
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China
| | - Xiaofeng Wu
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China
| | - Haoliang Shi
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou 215123 China
| | - Xiangbowen Jin
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou 215123 China
| | - Bin Song
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou 215123 China
| | - Mingyue Cui
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou 215123 China
| | - Yadan Zhao
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou 215123 China
| | - Yingying Zhao
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China
| | - Yao He
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou 215123 China
- Macao Translational Medicine Center, Macau University of Science and Technology, Taipa, 999078 Macau SAR, China
- Macao Institute of Materials Science and Engineering, Macau University of Science and Technology, Taipa, 999078 Macau SAR, China
| | - Houyu Wang
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou 215123 China
| | - Fenglin Dong
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China
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Wang Z, Hua L, Liu X, Chen X, Xue G. A hematological parameter-based model for distinguishing non-puerperal mastitis from invasive ductal carcinoma. Front Oncol 2023; 13:1295656. [PMID: 38152369 PMCID: PMC10751305 DOI: 10.3389/fonc.2023.1295656] [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: 09/17/2023] [Accepted: 11/29/2023] [Indexed: 12/29/2023] Open
Abstract
Purpose Non-puerperal mastitis (NPM) accounts for approximately 4-5% of all benign breast lesions. Ultrasound is the preferred method for screening breast diseases; however, similarities in imaging results can make it challenging to distinguish NPM from invasive ductal carcinoma (IDC). Our objective was to identify convenient and objective hematological markers to distinguish NPM from IDC. Methods We recruited 89 patients with NPM, 88 with IDC, and 86 with fibroadenoma (FA), and compared their laboratory data at the time of admission. LASSO regression, univariate logistic regression, and multivariate logistic regression were used to screen the parameters for construction of diagnostic models. Receiver operating characteristic curves, calibration curves, and decision curves were constructed to evaluate the accuracy of this model. Results We found significant differences in routine laboratory data between patients with NPM and IDC, and these indicators were candidate biomarkers for distinguishing between the two diseases. Additionally, we evaluated the ability of some classic hematological markers reported in previous studies to differentiate between NPM and IDC, and the results showed that these indicators are not ideal biomarkers. Furthermore, through rigorous LASSO and logistic regression, we selected age, white blood cell count, and thrombin time to construct a differential diagnostic model that exhibited a high level of discrimination, with an area under the curve of 0.912 in the training set and with 0.851 in the validation set. Furthermore, using the same selection method, we constructed a differential diagnostic model for NPM and FA, which also demonstrated good performance with an area under the curve of 0.862 in the training set and with 0.854 in the validation set. Both of these two models achieved AUCs higher than the AUCs of models built using machine learning methods such as random forest, decision tree, and SVM in both the training and validation sets. Conclusion Certain laboratory parameters on admission differed significantly between the NPM and IDC groups, and the constructed model was designated as a differential diagnostic marker. Our analysis showed that it has acceptable efficiency in distinguishing NPM from IDC and may be employed as an auxiliary diagnostic tool.
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Affiliation(s)
- Zhichun Wang
- Department of Breast Surgery, Jiujiang NO.1 People’s Hospital, Jiujiang, Jiangxi, China
| | - Lin Hua
- Department of Clinical Laboratory, Jiujiang NO.1 People’s Hospital, Jiujiang, Jiangxi, China
| | - Xiaofeng Liu
- Department of Clinical Laboratory, Jiujiang NO.1 People’s Hospital, Jiujiang, Jiangxi, China
| | - Xueli Chen
- Department of Clinical Laboratory, Jiujiang NO.1 People’s Hospital, Jiujiang, Jiangxi, China
| | - Guohui Xue
- Department of Clinical Laboratory, Jiujiang NO.1 People’s Hospital, Jiujiang, Jiangxi, China
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Ma Q, Lu X, Qin X, Xu X, Fan M, Duan Y, Tu Z, Zhu J, Wang J, Zhang C. A sonogram radiomics model for differentiating granulomatous lobular mastitis from invasive breast cancer: a multicenter study. LA RADIOLOGIA MEDICA 2023; 128:1206-1216. [PMID: 37597127 DOI: 10.1007/s11547-023-01694-7] [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: 04/04/2023] [Accepted: 07/28/2023] [Indexed: 08/21/2023]
Abstract
PURPOSE To construct a nomogram based on sonogram features and radiomics features to differentiate granulomatous lobular mastitis (GLM) from invasive breast cancer (IBC). MATERIALS AND METHODS A retrospective collection of 213 GLMs and 472 IBCs from three centers was divided into a training set, an internal validation set, and an external validation set. A radiomics model was built based on radiomics features, and the RAD score of the lesion was calculated. The sonogram radiomics model was constructed using ultrasound features and RAD scores. Finally, the diagnostic efficacy of the three sonographers with different levels of experience before and after combining the RAD score was assessed in the external validation set. RESULTS The RAD score, lesion diameter, orientation, echogenicity, and tubular extension showed significant differences in GLM and IBC (p < 0.05). The sonogram radiomics model based on these factors achieved optimal performance, and its area under the curve (AUC) was 0.907, 0.872, and 0.888 in the training, internal, and external validation sets, respectively. The AUCs before and after combining the RAD scores were 0.714, 0.750, and 0.830 and 0.834, 0.853, and 0.878, respectively, for sonographers with different levels of experience. The diagnostic efficacy was comparable for all sonographers when combined with the RAD score (p > 0.05). CONCLUSION Radiomics features effectively enhance the ability of sonographers to discriminate between GLM and IBC and reduce interobserver variation. The nomogram combining ultrasound features and radiomics features show promising diagnostic efficacy and can be used to identify GLM and IBC in a noninvasive approach.
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Affiliation(s)
- Qianqing Ma
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China
| | - Xiaofeng Lu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China
| | - Xiachuan Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China
- Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University Nan Chong), Sichuan, China
| | - Xiangyi Xu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China
| | - Min Fan
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yayang Duan
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China
| | - Zhengzheng Tu
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China
| | - Jianhui Zhu
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China
| | - Junli Wang
- Department of Ultrasound, The Second People's Hospital of Wuhu, No.259 Jiuhuashan Road, Jinghu District, Wuhu, 241001, Anhui, China.
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China.
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Chu B, Chen Z, Shi H, Wu X, Wang H, Dong F, He Y. Fluorescence, ultrasonic and photoacoustic imaging for analysis and diagnosis of diseases. Chem Commun (Camb) 2023; 59:2399-2412. [PMID: 36744435 DOI: 10.1039/d2cc06654h] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Biomedical imaging technology, which allows us to peer deeply within living subjects and visually explore the delivery and distribution of agents in living things, is producing tremendous opportunities for the early diagnosis and precise therapy of diseases. In this feature article, based on reviewing the latest representative examples of progress together with our recent efforts in the bioimaging field, we intend to introduce three typical kinds of non-invasive imaging technologies, i.e., fluorescence, ultrasonic and photoacoustic imaging, in which optical and/or acoustic signals are employed for analyzing various diseases. In particular, fluorescence imaging possesses a series of outstanding advantages, such as high temporal resolution, as well as rapid and sensitive feedback. Hence, in the first section, we will introduce the latest studies on developing novel fluorescence imaging methods for imaging bacterial infections, cancer and lymph node metastasis in a long-term and real-time manner. However, the issues of imaging penetration depth induced by photon scattering and light attenuation of biological tissue limit their widespread in vivo imaging applications. Taking advantage of the excellect penetration depth of acoustic signals, ultrasonic imaging has been widely applied for determining the location, size and shape of organs, identifying normal and abnormal tissues, as well as confirming the edges of lesions in hospitals. Thus, in the second section, we will briefly summarize recent advances in ultrasonic imaging techniques for diagnosing diseases in deep tissues. Nevertheless, the absence of lesion targeting and dependency on a professional technician may lead to the possibility of false-positive diagnosis. By combining the merits of both optical and acoustic signals, newly-developed photoacoustic imaging, simultaneously featuring higher temporal and spatial resolution with good sensitivity, as well as deeper penetration depth, is discussed in the third secretion. In the final part, we further discuss the major challenges and prospects for developing imaging technology for accurate disease diagnosis. We believe that these non-invasive imaging technologies will introduce a new perspective for the precise diagnosis of various diseases in the future.
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Affiliation(s)
- Binbin Chu
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China.
| | - Zhiming Chen
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China.
| | - Haoliang Shi
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China.
| | - Xiaofeng Wu
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China.
| | - Houyu Wang
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China.
| | - Fenglin Dong
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China.
| | - Yao He
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China.
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