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Lin Z, Chen L, Wang Y, Zhang T, Huang P. Improving ultrasound diagnostic Precision for breast cancer and adenosis with modality-specific enhancement (MSE) - Breast Net. Cancer Lett 2024; 596:216977. [PMID: 38795759 DOI: 10.1016/j.canlet.2024.216977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 05/10/2024] [Accepted: 05/18/2024] [Indexed: 05/28/2024]
Abstract
Adenosis is a benign breast condition whose lesions can mimic breast carcinoma and is evaluated for malignancy with the Breast Imaging-Reporting and Data System (BI-RADS). We construct and validate the performance of modality-specific enhancement (MSE)-Breast Net based on multimodal ultrasound images and compare it to the BI-RADS in differentiating adenosis from breast cancer. A total of 179 patients with breast carcinoma and 229 patients with adenosis were included in this retrospective, two-institution study, then divided into a training cohort (institution I, n = 292) and a validation cohort (institution II, n = 116). In the training cohort, the final model had a significantly greater AUC (0.82; P < 0.05) than B-mode-based model (0.69, 95% CI [0.49-0.90]). In the validation cohort, the AUC of the final model was 0.81, greater than that of the BI-RADS (0.75, P < 0.05). The multimodal model outperformed the individual and bimodal models, reaching a significantly greater AUC of 0.87 (95% CI = 0.69-1.0) (P < 0.05). MSE-Breast Net, based on multimodal ultrasound images, exhibited better diagnostic performance than the BI-RADS in differentiating adenosis from breast cancer and may contribute to clinical diagnosis and treatment.
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Affiliation(s)
- Zimei Lin
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Libin Chen
- Department of Ultrasound in Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, 315201, China
| | - Yunzhong Wang
- Department of Ultrasound in Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, 315201, China
| | - Tao Zhang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China.
| | - Pintong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China; Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China; Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, 310053, 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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Su L, Xie Q, Chen J, Zhang Q, Li N, Hong C. Ultrasound-based comparative analysis and nomogram development for predicting triple-negative and non-triple-negative breast cancer: a 4-year institutional study in Quanzhou First Hospital. BMJ Open 2024; 14:e085340. [PMID: 38871659 DOI: 10.1136/bmjopen-2024-085340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/15/2024] Open
Abstract
OBJECTIVE The objective of this study was to compare ultrasound features and establish a predictive nomogram for distinguishing between triple-negative breast cancer (TNBC) and non-TNBC. DESIGN A retrospective cohort study. SETTING This study was conducted at Quanzhou First Hospital, a grade A tertiary hospital in Quanzhou, China, with the research data set covering the period from September 2019 to August 2023. PARTICIPANTS The study included a total of 205 female patients with confirmed TNBC and 574 female patients with non-TNBC, who were randomly divided into a training set and a validation set at a ratio of 7:3. MAIN OUTCOME MEASURES All patients underwent ultrasound examination and received a confirmatory pathological diagnosis. Nodules were classified according to the Breast Imaging-Reporting and Data System standard. Subsequently, the study conducted a comparative analysis of clinical characteristics and ultrasonic features. RESULTS A statistically significant difference was observed in multiple clinical and ultrasonic features between TNBC and non-TNBC. Specifically, in the logistic regression analysis conducted on the training set, indicators such as posterior echo, lesion size, presence of clinical symptoms, margin characteristics, internal blood flow signals, halo and microcalcification were found to be statistically significant (p<0.05). These significant indicators were then effectively incorporated into a static and dynamic nomogram model, demonstrating high predictive performance in distinguishing TNBC from non-TNBC. CONCLUSION The results of our study demonstrated that ultrasound features can be valuable in distinguishing between TNBC and non-TNBC. The presence of posterior echo, size, clinical symptoms, margin, internal flow, halo and microcalcification was identified as predictive factors for this differentiation. Microcalcification, hyperechoic halo, internal flow and clinical symptoms emerged as the strongest predictive factors, indicating their potential as reliable indicators for identifying TNBC and non-TNBC.
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Affiliation(s)
- Liyang Su
- Department of Ultrasonography, Quanzhou First Hospital, Quanzhou, Fujian, China
| | - Qiaojie Xie
- Department of Ultrasonography, Quanzhou First Hospital, Quanzhou, Fujian, China
| | - Jiaohong Chen
- Department of Ultrasonography, Quanzhou First Hospital, Quanzhou, Fujian, China
| | - Qingquan Zhang
- Department of Ultrasonography, Quanzhou First Hospital, Quanzhou, Fujian, China
| | - Nian Li
- Department of Ultrasonography, Quanzhou First Hospital, Quanzhou, Fujian, China
| | - Chuntian Hong
- Department of Ultrasonography, Quanzhou First Hospital, Quanzhou, Fujian, China
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Dong WH, Wu G, Zhao N, Zhang J. Development and Validation of a Nomogram for Predicting Breast Malignancy in Male Patients Based on Clinical and Ultrasound Features. Curr Radiopharm 2024; 17:266-275. [PMID: 38288830 DOI: 10.2174/0118744710274400231219060149] [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: 08/22/2023] [Revised: 11/16/2023] [Accepted: 11/21/2023] [Indexed: 07/23/2024]
Abstract
OBJECTIVE This study aimed to construct a nomogram based on clinical and ultrasound (US) features to predict breast malignancy in males. METHODS The medical records between August, 2021 and February, 2023 were retrospectively collected from the database. Patients included in this study were randomly divided into training and validation sets in a 7:3 ratio. The models for predicting the risk of malignancy in male patients with breast lesions were virtualized by the nomograms. RESULTS Among the 71 enrolled patients, 50 were grouped into the training set, while 21 were grouped into the validation set. After the multivariate analysis was done, pain, BI-RADS category, and elastography score were identified as the predictors for malignancy risk and were selected to generate the nomogram. The C-index was 0.931 for the model. Concordance between predictions and observations was detected by calibration curves and was found to be good in this study. The model achieved a net benefit across all threshold probabilities, which was shown by the decision curve analysis (DCA) curve. CONCLUSION We successfully constructed a nomogram to evaluate the risk of breast malignancy in males using clinical and US features, including pain, BI-RADS category, and elastography score, which yielded good predictive performance.
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Affiliation(s)
- Wei-Hong Dong
- Department of Ultrasound, Henan Provincial People's Hospital, Zhengzhou, 450000, Henan Province, China
| | - Gang Wu
- Department of Ultrasound, Henan Provincial People's Hospital, Zhengzhou, 450000, Henan Province, China
| | - Nan Zhao
- Department of Ultrasound, Henan Provincial People's Hospital, Zhengzhou, 450000, Henan Province, China
| | - Juan Zhang
- Department of Ultrasound, Henan Provincial People's Hospital, Zhengzhou, 450000, Henan Province, China
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Li Y, Wei XL, Pang KK, Ni PJ, Wu M, Xiao J, Zhang LL, Zhang FX. A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram. Front Oncol 2023; 13:1276524. [PMID: 37936612 PMCID: PMC10627161 DOI: 10.3389/fonc.2023.1276524] [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: 08/12/2023] [Accepted: 10/09/2023] [Indexed: 11/09/2023] Open
Abstract
Objective To analyze the clinical and ultrasonic characteristics of breast sclerosing adenosis (SA) and invasive ductal carcinoma (IDC), and construct a predictive nomogram for SA. Materials and methods A total of 865 patients were recruited at the Second Hospital of Shandong University from January 2016 to November 2022. All patients underwent routine breast ultrasound examinations before surgery, and the diagnosis was confirmed by histopathological examination following the operation. Ultrasonic features were recorded using the Breast Imaging Data and Reporting System (BI-RADS). Of the 865 patients, 203 (252 nodules) were diagnosed as SA and 662 (731 nodules) as IDC. They were randomly divided into a training set and a validation set at a ratio of 6:4. Lastly, the difference in clinical characteristics and ultrasonic features were comparatively analyzed. Result There was a statistically significant difference in multiple clinical and ultrasonic features between SA and IDC (P<0.05). As age and lesion size increased, the probability of SA significantly decreased, with a cut-off value of 36 years old and 10 mm, respectively. In the logistic regression analysis of the training set, age, nodule size, menopausal status, clinical symptoms, palpability of lesions, margins, internal echo, color Doppler flow imaging (CDFI) grading, and resistance index (RI) were statistically significant (P<0.05). These indicators were included in the static and dynamic nomogram model, which showed high predictive performance, calibration and clinical value in both the training and validation sets. Conclusion SA should be suspected in asymptomatic young women, especially those younger than 36 years of age, who present with small-size lesions (especially less than 10 mm) with distinct margins, homogeneous internal echo, and lack of blood supply. The nomogram model can provide a more convenient tool for clinicians.
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Affiliation(s)
- Yuan Li
- Department of Ultrasound, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Xiu-liang Wei
- Department of Ultrasound, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Kun-kun Pang
- Department of Ultrasound, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Ping-juan Ni
- Department of Ultrasound, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Mei Wu
- Department of Ultrasound, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Juan Xiao
- Center of Evidence-Based Medicine, Institute of Medical Sciences, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Lu-lu Zhang
- Department of Pathology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Fei-xue Zhang
- Department of Ultrasound, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
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Huang Q, Nong W, Tang X, Gao Y. An ultrasound-based radiomics model to distinguish between sclerosing adenosis and invasive ductal carcinoma. Front Oncol 2023; 13:1090617. [PMID: 36959807 PMCID: PMC10028189 DOI: 10.3389/fonc.2023.1090617] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 02/17/2023] [Indexed: 03/09/2023] Open
Abstract
Objectives We aimed to develop an ultrasound-based radiomics model to distinguish between sclerosing adenosis (SA) and invasive ductal carcinoma (IDC) to avoid misdiagnosis and unnecessary biopsies. Methods From January 2020 to March 2022, 345 cases of SA or IDC that were pathologically confirmed were included in the study. All participants underwent pre-surgical ultrasound (US), from which clinical information and ultrasound images were collected. The patients from the study population were randomly divided into a training cohort (n = 208) and a validation cohort (n = 137). The US images were imported into MaZda software (Version 4.2.6.0) to delineate the region of interest (ROI) and extract features. Intragroup correlation coefficient (ICC) was used to evaluate the consistency of the extracted features. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation were performed to obtain the radiomics score of the features. Based on univariate and multivariate logistic regression analyses, a model was developed. 56 cases from April 2022 to December 2022 were included for independent validation of the model. The diagnostic performance of the model and the radiomics scores were evaluated by performing the receiver operating characteristic (ROC) analysis. The calibration curve and decision curve analysis (DCA) were used for calibration and evaluation. Leave-One-Out Cross-Validation (LOOCV) was used for the stability of the model. Results Three predictors were selected to develop the model, including radiomics score, palpable mass and BI-RADS. In the training cohort, validation cohort and independent validation cohort, AUC of the model and radiomics score were 0.978 and 0.907, 0.946 and 0.886, 0.951 and 0.779, respectively. The model showed a statistically significant difference compared with the radiomics score (p<0.05). The Kappa value of the model was 0.79 based on LOOCV. The Brier score, calibration curve, and DCA showed the model had a good calibration and clinical usefulness. Conclusions The model based on radiomics, ultrasonic features, and clinical manifestations can be used to distinguish SA from IDC, which showed good stability and diagnostic performance. The model can be considered a potential candidate diagnostic tool for breast lesions and can contribute to effective clinical diagnosis.
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Affiliation(s)
| | | | | | - Yong Gao
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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Li C, Zhang H, Chen J, Shao S, Li X, Yao M, Zheng Y, Wu R, Shi J. Deep learning radiomics of ultrasonography for differentiating sclerosing adenosis from breast cancer. Clin Hemorheol Microcirc 2022:CH221608. [PMID: 36373313 DOI: 10.3233/ch-221608] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
OBJECTIVES: The purpose of our study is to present a method combining radiomics with deep learning and clinical data for improved differential diagnosis of sclerosing adenosis (SA)and breast cancer (BC). METHODS: A total of 97 patients with SA and 100 patients with BC were included in this study. The best model for classification was selected from among four different convolutional neural network (CNN) models, including Vgg16, Resnet18, Resnet50, and Desenet121. The intra-/inter-class correlation coefficient and least absolute shrinkage and selection operator method were used for radiomics feature selection. The clinical features selected were patient age and nodule size. The overall accuracy, sensitivity, specificity, Youden index, positive predictive value, negative predictive value, and area under curve (AUC) value were calculated for comparison of diagnostic efficacy. RESULTS: All the CNN models combined with radiomics and clinical data were significantly superior to CNN models only. The Desenet121+radiomics+clinical data model showed the best classification performance with an accuracy of 86.80%, sensitivity of 87.60%, specificity of 86.20% and AUC of 0.915, which was better than that of the CNN model only, which had an accuracy of 85.23%, sensitivity of 85.48%, specificity of 85.02%, and AUC of 0.870. In comparison, the diagnostic accuracy, sensitivity, specificity, and AUC value for breast radiologists were 72.08%, 100%, 43.30%, and 0.716, respectively. CONCLUSIONS: A combination of the CNN-radiomics model and clinical data could be a helpful auxiliary diagnostic tool for distinguishing between SA and BC.
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Affiliation(s)
- Chunxiao Li
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Huili Zhang
- School of Communication and Information Engineering, Shanghai University, Baoshan District, Shanghai, China
| | - Jing Chen
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Sihui Shao
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Xin Li
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Minghua Yao
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Yi Zheng
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Rong Wu
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Jun Shi
- School of Communication and Information Engineering, Shanghai University, Baoshan District, Shanghai, China
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Ruan M, Ding Z, Shan Y, Pan S, Shao C, Xu W, Zhen T, Pang P, Shen Q. Radiomics Based on DCE-MRI Improved Diagnostic Performance Compared to BI-RADS Analysis in Identifying Sclerosing Adenosis of the Breast. Front Oncol 2022; 12:888141. [PMID: 35646630 PMCID: PMC9133496 DOI: 10.3389/fonc.2022.888141] [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: 03/02/2022] [Accepted: 04/12/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose Sclerosing adenosis (SA) is a benign lesion that could mimic breast carcinoma and be evaluated as malignancy by Breast Imaging-Reporting and Data System (BI-RADS) analysis. We aimed to construct and validate the performance of radiomic model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) compared to BI-RADS analysis to identify SA. Methods Sixty-seven patients with invasive ductal carcinoma (IDC) and 58 patients with SA were included in this retrospective study from two institutions. The 125 patients were divided into a training cohort (n= 88) from institution I and a validation cohort from institution II (n=37). Dynamic contrast-enhanced sequences including one pre-contrast and five dynamic post-contrast series were obtained for all cases with different 3T scanners. Single-phase enhancement, multi-phase enhancement, and dynamic radiomic features were extracted from DCE-MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation was performed to build the radscore of each single-phase enhancement and the final model combined multi-phase and dynamic radiomic features. The diagnostic performance of radiomics was evaluated by receiver operating characteristic (ROC) analysis and compared to the performance of BI-RADS analysis. The classification performance was tested using external validation. Results In the training cohort, the AUCs of BI-RADS analysis were 0.71 (95%CI [0.60, 0.80]), 0.78 (95%CI [0.67, 0.86]), and 0.80 (95%CI [0.70, 0.88]), respectively. In single-phase analysis, the second enhanced phase radiomic signature achieved the highest AUC of 0.88 (95%CI [0.79, 0.94]) in distinguishing SA from IDC. Nine multi-phase radiomic features and two dynamic radiomic features showed the best predictive ability for final model building. The final model improved the AUC to 0.92 (95%CI [0.84, 0.97]), and showed statistically significant differences with BI-RADS analysis (p<0.05 for all). In the validation cohort, the AUC of the final model was 0.90 (95%CI [0.75, 0.97]), which was higher than all BI-RADS analyses and showed statistically significant differences with one of the BI-RADS analysis observers (p = 0.03). Conclusions Radiomics based on DCE-MRI could show better diagnostic performance compared to BI-RADS analysis in differentiating SA from IDC, which may contribute to clinical diagnosis and treatment.
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Affiliation(s)
- Mei Ruan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yanna Shan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shushu Pan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chang Shao
- Department of Pathology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wen Xu
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Zhen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Qijun Shen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 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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Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions. Diagnostics (Basel) 2022; 12:diagnostics12010172. [PMID: 35054339 PMCID: PMC8774686 DOI: 10.3390/diagnostics12010172] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/07/2022] [Accepted: 01/10/2022] [Indexed: 01/22/2023] Open
Abstract
Improving the assessment of breast imaging reporting and data system (BI-RADS) 4 lesions and reducing unnecessary biopsies are urgent clinical issues. In this prospective study, a radiomic nomogram based on the automated breast volume scanner (ABVS) was constructed to identify benign and malignant BI-RADS 4 lesions and evaluate its value in reducing unnecessary biopsies. A total of 223 histologically confirmed BI-RADS 4 lesions were enrolled and assigned to the training and validation cohorts. A radiomic score was generated from the axial, sagittal, and coronal ABVS images. Combining the radiomic score and clinical-ultrasound factors, a radiomic nomogram was developed by multivariate logistic regression analysis. The nomogram integrating the radiomic score, lesion size, and BI-RADS 4 subcategories showed good discrimination between malignant and benign BI-RADS 4 lesions in the training (AUC, 0.959) and validation (AUC, 0.925) cohorts. Moreover, 42.5% of unnecessary biopsies would be reduced by using the nomogram, but nine (4%) malignant BI-RADS 4 lesions were unfortunately missed, of which 4A (77.8%) and small-sized (<10 mm) lesions (66.7%) accounted for the majority. The ABVS radiomics nomogram may be a potential tool to reduce unnecessary biopsies of BI-RADS 4 lesions, but its ability to detect small BI-RADS 4A lesions needs to be improved.
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