101
|
Shan YN, Xu W, Wang R, Wang W, Pang PP, Shen QJ. A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer. Front Oncol 2020; 10:1463. [PMID: 32983979 PMCID: PMC7483545 DOI: 10.3389/fonc.2020.01463] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 07/09/2020] [Indexed: 12/25/2022] Open
Abstract
Objective: To construct and validate a nomogram model integrating the magnetic resonance imaging (MRI) radiomic features and the kinetic curve pattern for detecting metastatic axillary lymph node (ALN) in invasive breast cancer preoperatively. Materials and Methods: A total of 145 ALNs from two institutions were classified into negative and positive groups according to the pathologic or surgical results. One hundred one ALNs from institution I were taken as the training cohort, and the other 44 ALNs from institution II were taken as the external validation cohort. The kinetic curve was computed using dynamic contrast-enhanced MRI software. The preprocessed images were used for radiomic feature extraction. The LASSO regression was applied to identify optimal radiomic features and construct the Radscore. A nomogram model was constructed combining the Radscore and the kinetic curve pattern. The discriminative performance was evaluated by receiver operating characteristic analysis and calibration curve. Results: Five optimal features were ultimately selected and contributed to the Radscore construction. The kinetic curve pattern was significantly different between negative and positive lymph nodes. The nomogram model showed a better performance in both training cohort [area under the curve (AUC) = 0.91, 95% CI = 0.83–0.96] and external validation cohort (AUC = 0.86, 95% CI = 0.72–0.94); the calibration curve indicated a better accuracy of the nomogram model for detecting metastatic ALN than either Radscore or kinetic curve pattern alone. Conclusion: A nomogram model integrated the Radscore and the kinetic curve pattern could serve as a biomarker for detecting metastatic ALN in patients with invasive breast cancer.
Collapse
Affiliation(s)
- Yan-Na Shan
- Department of Radiology, 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
| | - Rong Wang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Wang
- Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | | | - Qi-Jun Shen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| |
Collapse
|
102
|
Guo X, Liu Z, Sun C, Zhang L, Wang Y, Li Z, Shi J, Wu T, Cui H, Zhang J, Tian J, Tian J. Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer. EBioMedicine 2020; 60:103018. [PMID: 32980697 PMCID: PMC7519251 DOI: 10.1016/j.ebiom.2020.103018] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 09/07/2020] [Accepted: 09/08/2020] [Indexed: 12/24/2022] Open
Abstract
Background Completion axillary lymph node dissection is overtreatment for patients with sentinel lymph node (SLN) metastasis in whom the metastatic risk of residual non-SLN (NSLN) is low. However, the National Comprehensive Cancer Network panel posits that none of the previous studies has successfully identified such subset patients. Here, we develop a multicentre deep learning radiomics of ultrasonography model (DLRU) to predict the risk of SLN and NSLN metastasis. Methods In total, 937 eligible breast cancer patients with ultrasound images were enrolled from two hospitals as the training set (n = 542) and independent test set (n = 395) respectively. Using the images, we developed and validated a prediction model combined with deep learning radiomics and axillary ultrasound to sequentially identify the metastatic risk of SLN and NSLN, thereby, classifying patients to relevant axillary management groups. Findings In the test set, the DLRU yields the best performance in identifying patients with metastatic disease in SLNs (sensitivity=98.4%, 95% CI 96.6–100) and NSLNs (sensitivity=98.4%, 95% CI 95.6–99.9). The DLRU also accurately stratifies patients without metastasis in SLN or NSLN into the corresponding low-risk (LR)-SLN and high-risk (HR)-SLN&LR-NSLN category with the negative predictive value of 97% (95% CI 94.2–100) and 91.7% (95% CI 88.8–97.9), respectively. Moreover, compared with the current clinical management, DLRU appropriately assigned 51% (39.6%/77.4%) of overtreated patients in the entire study cohort into the LR group, perhaps avoiding overtreatment. Interpretation The performance of the DLRU indicates that it may offer a simple preoperative tool to promote personalized axillary management of breast cancer. Funding The National Nature Science Foundation of China; The National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; The Scientific research project of Heilongjiang Health Committee; The Postgraduate Research &Practice Innovation Program of Harbin Medical University.
Collapse
Affiliation(s)
- Xu Guo
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Caixia Sun
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China; Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University, Beijing, China
| | - Lei Zhang
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Ying Wang
- Department of general surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Ziyao Li
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jiaxin Shi
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Tong Wu
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Hao Cui
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jing Zhang
- Department of MRI Diagnosis, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shanxi, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University, Beijing, China.
| | - Jiawei Tian
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
| |
Collapse
|
103
|
Liu M, Mao N, Ma H, Dong J, Zhang K, Che K, Duan S, Zhang X, Shi Y, Xie H. Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer. Cancer Imaging 2020; 20:65. [PMID: 32933585 PMCID: PMC7493182 DOI: 10.1186/s40644-020-00342-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 09/02/2020] [Indexed: 12/13/2022] Open
Abstract
Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. Results Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. Conclusions The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.
Collapse
Affiliation(s)
- Meijie Liu
- School of Clinical Medicine, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000.,Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Jianjun Dong
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Kun Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | | | - Xuexi Zhang
- GE Healthcare, China, Shanghai, P. R. China, 200000
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000.
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000.
| |
Collapse
|
104
|
Gao J, Han F, Jin Y, Wang X, Zhang J. A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma. Front Oncol 2020; 10:1654. [PMID: 32974205 PMCID: PMC7482654 DOI: 10.3389/fonc.2020.01654] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 07/28/2020] [Indexed: 12/17/2022] Open
Abstract
PURPOSE To construct and verify a CT-based multidimensional nomogram for the evaluation of lymph node (LN) status in pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS We retrospectively assessed data from 172 patients with clinicopathologically confirmed PDAC surgically resected between February 2014 and November 2016. Patients were assigned to either a training cohort (n = 121) or a validation cohort (n = 51). We acquired radiomics features from the preoperative venous phase (VP) CT images. The maximum relevance-minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) methods were used to select the optimal features. We used multivariable logistic regression to construct a combined radiomics model for visualization in the form of a nomogram. Performance of the nomogram was evaluated by the receiver operating characteristic (ROC) curve approach, calibration testing, and analysis of clinical usefulness. RESULTS A Rad score consisting of 10 LN status-related radiomics features was found to be significantly associated with the actual LN status (P < 0.01). A nomogram that consisted of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status performed well in terms of predictive power in the training cohort (area under the curve, 0.92), and this was confirmed in the validation cohort (area under the curve, 0.95). The nomogram also performed well in the calibration test and decision curve analysis, demonstrating its potential clinical value. CONCLUSION A multidimensional radiomics nomogram consisting of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status may contribute to the non-invasive evaluation of LN status in PDAC patients.
Collapse
Affiliation(s)
| | | | | | | | - Jiawen Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
105
|
Jiang X, Zou X, Sun J, Zheng A, Su C. A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer. CONTRAST MEDIA & MOLECULAR IMAGING 2020; 2020:5418364. [PMID: 32922222 PMCID: PMC7468630 DOI: 10.1155/2020/5418364] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/06/2020] [Indexed: 02/05/2023]
Abstract
Objectives To develop and validate a radiomics-based nomogram with texture features from mammography for the prognostic prediction in patients with early-stage triple-negative breast cancer (TNBC). Methods The study included 200 consecutive patients with TNBC (training cohort: n = 133, validation cohort: n = 67). A total of 136 mammography-derived textural features were extracted, and LASSO (least absolute shrinkage and selection operator) was applied to select features for building the radiomics score (Rad-score). After univariate and multivariate logistic regression, a radiomics-based nomogram was constructed with independent prognostic factors. The discrimination and calibration power were assessed, and further the clinical applicability of the nomograms was evaluated. Results Among the 136 mammography-derived textural features, fourteen were used to build the Rad-score after LASSO regression. A radiomics nomogram that incorporates Rad-score and pN stage was constructed. This nomogram achieved a C-index of 0.873 (95% CI: 0.758-0.989) for predicting iDFS (invasive disease-free survival), which outperformed the clinical model. Moreover, it is feasible to stratify patients into high-risk and low-risk groups based on the optimal cut-off point of Rad-score. The validations of the nomogram confirmed favorable discrimination and considerable predictive efficiency. Conclusions The radiomics nomogram that incorporates Rad-score and pN stage exhibited favorable performance in the prediction of iDFS in patients with early-stage TNBCs.
Collapse
Affiliation(s)
- Xian Jiang
- Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, China
| | - Xiuhe Zou
- Department of Thyroid Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Sun
- Department of Integrated Chinese and Western Medicine, Qingdao Central Hospital, Qingdao University, Qingdao, Shandong, China
| | - Aiping Zheng
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Chao Su
- State Key Laboratory of Biotherapy and Cancer Center/Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
106
|
Wahab RA, Lewis K, Vijapura C, Zhang B, Lee SJ, Brown A, Mahoney MC. Textural Characteristics of Biopsy-proven Metastatic Axillary Nodes on Preoperative Breast MRI in Breast Cancer Patients: A Feasibility Study. JOURNAL OF BREAST IMAGING 2020; 2:361-371. [PMID: 38424965 DOI: 10.1093/jbi/wbaa038] [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: 11/23/2019] [Indexed: 03/02/2024]
Abstract
OBJECTIVE To determine the diagnostic accuracy of MRI textural analysis (TA) to differentiate malignant from benign axillary lymph nodes in patients with breast cancer. METHODS This was an institutional review board-approved retrospective study of axillary lymph nodes in women with breast cancer that underwent ultrasound-guided biopsy and contrast-enhanced (CE) breast MRI from January 2015 to December 2018. TA of axillary lymph nodes was performed on 3D dynamic CE T1-weighted fat-suppressed, 3D delayed CE T1-weighted fat-suppressed, and T2-weighted fat-suppressed MRI sequences. Quantitative parameters used to measure TA were compared with pathologic diagnoses. Areas under the curve (AUC) were calculated using receiver operating characteristic curve analysis to distinguish between malignant and benign lymph nodes. RESULTS Twenty-three biopsy-proven malignant lymph nodes and 24 benign lymph nodes were analyzed. The delayed CE T1-weighted fat-suppressed sequence had the greatest ability to differentiate malignant from benign outcome at all spatial scaling factors, with the highest AUC (0.84-0.93), sensitivity (0.78 [18/23] to 0.87 [20/23]), and specificity (0.76 [18/24] to 0.88 [21/24]). Kurtosis on the 3D delayed CE T1-weighted fat-suppressed sequence was the most prominent TA parameter differentiating malignant from benign lymph nodes (P < 0.0001). CONCLUSION This study suggests that MRI TA could be helpful in distinguishing malignant from benign axillary lymph nodes. Kurtosis has the greatest potential on 3D delayed CE T1-weighted fat-suppressed sequences to distinguish malignant and benign lymph nodes.
Collapse
Affiliation(s)
- Rifat A Wahab
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
| | - Kyle Lewis
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
| | - Charmi Vijapura
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
| | - Bin Zhang
- Cincinnati Children's Hospital Medical Center, Division of Biostatistics and Epidemiology, Cincinnati, OH
| | - Su-Ju Lee
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
| | - Ann Brown
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
| | - Mary C Mahoney
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
| |
Collapse
|
107
|
Orlando A, Dimarco M, Cannella R, Bartolotta TV. Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art. Artif Intell Med Imaging 2020; 1:6-18. [DOI: 10.35711/aimi.v1.i1.6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/17/2020] [Accepted: 06/19/2020] [Indexed: 02/06/2023] Open
Abstract
Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI.
Collapse
Affiliation(s)
- Alessia Orlando
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Mariangela Dimarco
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Roberto Cannella
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Tommaso Vincenzo Bartolotta
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
- Department of Radiology, Fondazione Istituto Giuseppe Giglio, Ct.da Pietrapollastra, Palermo 90015, Italy
| |
Collapse
|
108
|
Yang J, Wu Q, Xu L, Wang Z, Su K, Liu R, Yen EA, Liu S, Qin J, Rong Y, Lu Y, Niu T. Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer. Radiother Oncol 2020; 150:89-96. [PMID: 32531334 DOI: 10.1016/j.radonc.2020.06.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 05/31/2020] [Accepted: 06/02/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND To develop and validate a radiomics method via integrating tumor and lymph node radiomics for the preoperative prediction of lymph node (LN) status in gastric cancer (GC). MATERIALS AND METHODS We retrospectively collected 170 contrast-enhanced abdominal CT images from GC patients. Five times repeated random hold-out experiment was employed. Tumor and nodal radiomics features were extracted from each individual tumor and LN respectively, and then multi-step feature selection was performed. The optimal tumor and nodal features were selected using Pearson correlation analysis and sequential forward floating selection (SFFS) algorithm. After feature fusion, the SFFS algorithm was used to develop radiomics signatures. The performance of the radiomics signatures developed based on logistic regression classifier was further analyzed and compared using the area under the receiver operating characteristic curve (AUC). RESULTS The AUC values, reported as mean ± standard deviation, were 0.9319 ± 0.0129 and 0.8546 ± 0.0261 for the training and validation cohorts respectively. The radiomic signatures could predict LN status, especially in T2-stage, diffuse-type and moderately/well differentiated GC. After integrating clinicopathologic information, the radiomic-clinicopathologic model (training cohort, 0.9432 ± 0.0129; validation cohort, 0.8764 ± 0.0322) showed a better discrimination capability than other radiomics models and clinicopathologic model. The radiomic-clinicopathologic model also showed superior performance to the gastroenterologist' decision in all experiments, and outperformed the radiologist in some experiments. CONCLUSION Our proposed method presented good predictive performance and great potential for predicting LNM in GC. As a noninvasive preoperative prediction tool, it can be helpful for guiding the prognosis and treatment decision-making in GC patients.
Collapse
Affiliation(s)
- Jing Yang
- Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China; Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Qingyao Wu
- The Affiliated Hospital of Qingdao University, China
| | - Lei Xu
- Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China; Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Zijie Wang
- The Affiliated Hospital of Qingdao University, China
| | - Kefan Su
- The Affiliated Hospital of Qingdao University, China
| | - Ruiqing Liu
- The Affiliated Hospital of Qingdao University, China
| | - Eric Alexander Yen
- Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China; Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Shunli Liu
- The Affiliated Hospital of Qingdao University, China
| | - Jiale Qin
- Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Cancer Center, Sacramento, USA
| | - Yun Lu
- The Affiliated Hospital of Qingdao University, China.
| | - Tianye Niu
- Nuclear & Radiological Engineering and Medical Physics Programs, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, USA.
| |
Collapse
|
109
|
Tan H, Wu Y, Bao F, Zhou J, Wan J, Tian J, Lin Y, Wang M. Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer. Br J Radiol 2020; 93:20191019. [PMID: 32401540 PMCID: PMC7336077 DOI: 10.1259/bjr.20191019] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To establish a radiomics nomogram by integrating clinical risk factors and radiomics features extracted from digital mammography (MG) images for pre-operative prediction of axillary lymph node (ALN) metastasis in breast cancer. METHODS 216 patients with breast cancer lesions confirmed by surgical excision pathology were divided into the primary cohort (n = 144) and validation cohort (n = 72). Radiomics features were extracted from craniocaudal (CC) view of mammograms, and radiomics features selection were performed using the methods of ANOVA F-value and least absolute shrinkage and selection operator; then a radiomics signature was constructed with the method of support vector machine. Multivariate logistic regression analysis was used to establish a radiomics nomogram based on the combination of radiomics signature and clinical factors. The C-index and calibration curves were derived based on the regression analysis both in the primary and validation cohorts. RESULTS 95 of 216 patients were confirmed with ALN metastasis by pathology, and 52 cases were diagnosed as ALN metastasis based on MG-reported criteria. The sensitivity, specificity, accuracy and AUC (area under the receiver operating characteristic curve of MG-reported criteria were 42.7%, 90.8%, 24.1% and 0.666 (95% confidence interval: 0.591-0.741]. The radiomics nomogram, comprising progesterone receptor status, molecular subtype and radiomics signature, showed good calibration and better favorite performance for the metastatic ALN detection (AUC 0.883 and 0.863 in the primary and validation cohorts) than each independent clinical features (AUC 0.707 and 0.657 in the primary and validation cohorts) and radiomics signature (AUC 0.876 and 0.862 in the primary and validation cohorts). CONCLUSION The MG-based radiomics nomogram could be used as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to assist clinicians for pre-operative decision-making. ADVANCES IN KNOWLEDGE ALN status remains among the most important breast cancer prognostic factors and is essential for making treatment decisions. However, the value of detecting metastatic ALN by MG is very limited. The studies on pre-operative ALN metastasis prediction using the method of MG-based radiomics in breast cancer are very few. Therefore, we studied whether MG-based radiomics nomogram could be used as a predictive biomarker for the detection of metastatic ALN.
Collapse
Affiliation(s)
- Hongna Tan
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, Henan, China, 450003
| | - Yaping Wu
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, Henan, China, 450003
| | - Fengchang Bao
- Department of Hematology, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Henan, China, 450003
| | - Jing Zhou
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, Henan, China, 450003
| | - Jianzhong Wan
- Collaborative Innovation Center for Internet Healthcare & School of Software, Zhengzhou University, Zhengzhou, Henan, China, 450052
| | - Jie Tian
- Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
| | - Yusong Lin
- Collaborative Innovation Center for Internet Healthcare & School of Software, Zhengzhou University, Zhengzhou, Henan, China, 450052
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, Henan, China, 450003
| |
Collapse
|
110
|
Chang JM, Leung JWT, Moy L, Ha SM, Moon WK. Axillary Nodal Evaluation in Breast Cancer: State of the Art. Radiology 2020; 295:500-515. [PMID: 32315268 DOI: 10.1148/radiol.2020192534] [Citation(s) in RCA: 134] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Axillary lymph node (LN) metastasis is the most important predictor of overall recurrence and survival in patients with breast cancer, and accurate assessment of axillary LN involvement is an essential component in staging breast cancer. Axillary management in patients with breast cancer has become much less invasive and individualized with the introduction of sentinel LN biopsy (SLNB). Emerging evidence indicates that axillary LN dissection may be avoided in selected patients with node-positive as well as node-negative cancer. Thus, assessment of nodal disease burden to guide multidisciplinary treatment decision making is now considered to be a critical role of axillary imaging and can be achieved with axillary US, MRI, and US-guided biopsy. For the node-positive patients treated with neoadjuvant chemotherapy, restaging of the axilla with US and MRI and targeted axillary dissection in addition to SLNB is highly recommended to minimize the false-negative rate of SLNB. Efforts continue to develop prediction models that incorporate imaging features to predict nodal disease burden and to select proper candidates for SLNB. As methods of axillary nodal evaluation evolve, breast radiologists and surgeons must work closely to maximize the potential role of imaging and to provide the most optimized treatment for patients.
Collapse
Affiliation(s)
- Jung Min Chang
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Jessica W T Leung
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Linda Moy
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Su Min Ha
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Woo Kyung Moon
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| |
Collapse
|
111
|
Lee SE, Sim Y, Kim S, Kim EK. Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer. Ultrasonography 2020; 40:93-102. [PMID: 32623841 PMCID: PMC7758097 DOI: 10.14366/usg.20026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 04/01/2020] [Indexed: 12/29/2022] Open
Abstract
PURPOSE The purpose of this study was to evaluate the predictive performance of ultrasonography (US)-based radiomics for axillary lymph node metastasis and to compare it with that of a clinicopathologic model. METHODS A total of 496 patients (mean age, 52.5±10.9 years) who underwent breast cancer surgery between January 2014 and December 2014 were included in this study. Among them, 306 patients who underwent surgery between January 2014 and August 2014 were enrolled as a training cohort, and 190 patients who underwent surgery between September 2014 and December 2014 were enrolled as a validation cohort. To predict axillary lymph node metastasis in breast cancer, we developed a preoperative clinicopathologic model using multivariable logistic regression and constructed a radiomics model using 23 radiomic features selected via least absolute shrinkage and selection operator regression. RESULTS In the training cohort, the areas under the curve (AUC) were 0.760, 0.812, and 0.858 for the clinicopathologic, radiomics, and combined models, respectively. In the validation cohort, the AUCs were 0.708, 0.831, and 0.810, respectively. The combined model showed significantly better diagnostic performance than the clinicopathologic model. CONCLUSION A radiomics model based on the US features of primary breast cancers showed additional value when combined with a clinicopathologic model to predict axillary lymph node metastasis.
Collapse
Affiliation(s)
- Si Eun Lee
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Yongsik Sim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sungwon Kim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Eun-Kyung Kim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| |
Collapse
|
112
|
Jin X, Ai Y, Zhang J, Zhu H, Jin J, Teng Y, Chen B, Xie C. Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images. Eur Radiol 2020; 30:4117-4124. [PMID: 32078013 DOI: 10.1007/s00330-020-06692-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/18/2019] [Accepted: 01/30/2020] [Indexed: 12/27/2022]
Abstract
OBJECTIVE To investigate the feasibility of a noninvasive detection of lymph node metastasis (LNM) for early-stage cervical cancer (ECC) patients with radiomics methods based on the textural features from ultrasound images. METHODS One hundred seventy-two ECC patients between January 2014 and September 2018 with pathologically confirmed lymph node status (LNS) and preoperative ultrasound images were retrospectively reviewed. Regions of interest (ROIs) were delineated by a senior radiologist in the ultrasound images. LIFEx was applied to extract textural features for radiomics study. Least absolute shrinkage and selection operator (LASSO) regression was applied for dimension reduction and for selection of key features. A multivariable logistic regression analysis was adopted to build the radiomics signature. The Mann-Whitney U test was applied to investigate the correlation between radiomics and LNS for both training and validation cohorts. Receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of the radiomics prediction models. RESULTS A total of 152 radiomics features were extracted from ultrasound images, in which 6 features were significantly associated with LNS (p < 0.05). The radiomics signatures demonstrated a good discrimination between patients with LNM and non-LNM groups. The best radiomics performance model achieved an area under the curve (AUC) of 0.79 (95% confidence interval (CI), 0.71-0.88) in the training cohort and 0.77 (95% CI, 0.65-0.88) in the validation cohort. CONCLUSIONS The feasibility of radiomics features from ultrasound images for the prediction of LNM in ECC was investigated. This noninvasive prediction method may be used to facilitate preoperative identification of LNS in patients with ECC. KEY POINTS • Few studied had investigated the feasibility of radiomics based on ultrasound images for cervical cancer, even though it is the most common practice for gynecological cancer diagnosis and treatment. • The radiomics signatures based on ultrasound images demonstrated a good discrimination between patients with and without lymph node metastasis with an area under the curve (AUC) of 0.79 and 0.77 in the training and validation cohorts, respectively. • The radiomics model based on preoperative ultrasound images has the potential ability to predict lymph node status noninvasively in patients with early-state cervical cancer, so as to reduce the impact of invasive examination and to optimize the treatment choices.
Collapse
Affiliation(s)
- Xiance Jin
- Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
| | - Yao Ai
- Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
| | - Ji Zhang
- Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
| | - Haiyan Zhu
- Department of Gynecology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
- Department of Gynecology, Shanghai First Maternal and Infant Hospital, Tongji University School of Medicine, Shanghai, 200126, People's Republic of China
| | - Juebin Jin
- Department of Medical Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
| | - Yinyan Teng
- Department of Ultrasound imaging, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
| | - Bin Chen
- Department of Ultrasound imaging, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China.
| | - Congying Xie
- Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China.
| |
Collapse
|
113
|
Chen L, Wang Y, Zhao K, Wang Y, He X. Postoperative Nomogram for Predicting Cancer-Specific and Overall Survival among Patients with Medullary Thyroid Cancer. Int J Endocrinol 2020; 2020:8888677. [PMID: 33299412 PMCID: PMC7704131 DOI: 10.1155/2020/8888677] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 11/04/2020] [Accepted: 11/07/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Medullary thyroid carcinoma (MTC) accounts for 1%-2% of thyroid cancer in the United States based on the latest Surveillance, Epidemiology, and End Results (SEER) data, and this study aimed to construct a comprehensive predictive nomogram based on various clinical variables in MTC patients who underwent total thyroidectomy and neck lymph nodes dissection. METHODS Data regarding 1,237 MTC patients who underwent total thyroidectomy and neck lymph nodes dissection from 2004 to 2015 were obtained from the SEER database. Univariate and multivariate Cox regression analyses were used to screen for meaningful independent predictors. These independent factors were used to construct a nomogram model, a survival prognostication tool for 3- and 5-year overall survival, and cancer-specific survival among these MTC patients. RESULT A total of 1,237 patients enrolled from the SEER database were randomly divided into the training group (n = 867) and the test group (n = 370). Univariate and multivariate Cox regression analyses were used to identify meaningful independent prognostic factors (P < 0.05). Tumor size, age, metastasis status, and LNR were selected as independent predictors of overall survival (OS) and cancer-specific survival (CSS). Finally, two nomograms were developed, and the predicted C-index of overall survival (OS) and cancer-specific survival (CSS) rate in the training group was 0.828 and 0.904, respectively. The predicted C-index of overall survival (OS) and cancer-specific survival (CSS) rate in the test group was 0.813 and 0.828. CONCLUSION Nomograms constructed by using various clinical variables can make more comprehensive and accurate predictions for MTC patients who underwent total thyroidectomy and neck lymph nodes. These predictive nomograms help identify postoperative high-risk MTC patients and facilitate patient counseling on clinical prognosis and follow-up.
Collapse
Affiliation(s)
- Li Chen
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Yizeng Wang
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Ke Zhao
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Yuyun Wang
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Xianghui He
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| |
Collapse
|
114
|
Ye DM, Wang HT, Yu T. The Application of Radiomics in Breast MRI: A Review. Technol Cancer Res Treat 2020; 19:1533033820916191. [PMID: 32347167 PMCID: PMC7225803 DOI: 10.1177/1533033820916191] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 01/21/2020] [Accepted: 02/27/2020] [Indexed: 12/13/2022] Open
Abstract
Breast cancer has been a worldwide burden of women's health. Although concerns have been raised for early diagnosis and timely treatment, the efforts are still needed for precision medicine and individualized treatment. Radiomics is a new technology with immense potential to obtain mineable data to provide rich information about the diagnosis and prognosis of breast cancer. In our study, we introduced the workflow and application of radiomics as well as its outlook and challenges based on published studies. Radiomics has the potential ability to differentiate between malignant and benign breast lesions, predict axillary lymph node status, molecular subtypes of breast cancer, tumor response to chemotherapy, and survival outcomes. Our study aimed to help clinicians and radiologists to know the basic information of radiomics and encourage cooperation with scientists to mine data for better application in clinical practice.
Collapse
Affiliation(s)
- Dong-Man Ye
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning Province, People’s Republic of China
| | - Hao-Tian Wang
- Dalian Medical University, The First Clinical College, Dalian, Liaoning Province, People’s Republic of China
| | - Tao Yu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning Province, People’s Republic of China
| |
Collapse
|
115
|
Chen W, Wang S, Dong D, Gao X, Zhou K, Li J, Lv B, Li H, Wu X, Fang M, Tian J, Xu M. Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics. Front Oncol 2019; 9:1265. [PMID: 31824847 PMCID: PMC6883384 DOI: 10.3389/fonc.2019.01265] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 11/01/2019] [Indexed: 12/14/2022] Open
Abstract
Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients. Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0 T magnetic resonance imaging (MRI) examination. The dataset was allocated to a training cohort (n = 71) and an internal validation cohort (n = 47) from one center along with an external validation cohort (n = 28) from another. A summary of 1,305 radiomic features were extracted per patient. The least absolute shrinkage and selection operator (LASSO) logistic regression and learning vector quantization (LVQ) methods with cross-validations were adopted to select significant features in a radiomic signature. Combining the radiomic signature and independent clinical factors, a radiomic nomogram was established. The MRI-reported N staging and the MRI-derived model were built for comparison. Model performance was evaluated considering receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Results: A two-feature radiomic signature was found significantly associated with LNM (p < 0.01, training and internal validation cohorts). A radiomic nomogram was established by incorporating the clinical minimum apparent diffusion coefficient (ADC) and MRI-reported N staging. The radiomic nomogram showed a favorable classification ability with an area under ROC curve of 0.850 [95% confidence interval (CI), 0.758–0.942] in the training cohort, which was then confirmed with an AUC of 0.857 (95% CI, 0.714–1.000) in internal validation cohort and 0.878 (95% CI, 0.696–1.000) in external validation cohort. Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, and 0.851 in internal validation cohort, and 0.714, 0.952, and 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. DCA demonstrated good clinical use of radiomic nomogram. Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.
Collapse
Affiliation(s)
- Wujie Chen
- First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.,Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Siwen Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xuning Gao
- First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Kefeng Zhou
- First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jiaying Li
- First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Bin Lv
- First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.,Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Hailin Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xiangjun Wu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
| | - Maosheng Xu
- First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| |
Collapse
|
116
|
Tagliafico AS, Piana M, Schenone D, Lai R, Massone AM, Houssami N. Overview of radiomics in breast cancer diagnosis and prognostication. Breast 2019; 49:74-80. [PMID: 31739125 PMCID: PMC7375670 DOI: 10.1016/j.breast.2019.10.018] [Citation(s) in RCA: 135] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 10/29/2019] [Accepted: 10/30/2019] [Indexed: 12/11/2022] Open
Abstract
Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity and suboptimal positive predictive power of radiology screening and diagnostic approaches, respectively; b) invasiveness of biopsy with discomfort for women undergoing diagnostic tests; c) long turnaround time for recall tests. In the screening setting, radiology sensitivity is suboptimal, and when a suspicious lesion is detected and a biopsy is recommended, the positive predictive value of radiology is modest. Recent technological advances in medical imaging, especially in the field of artificial intelligence applied to image analysis, hold promise in addressing clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. Radiomics include feature extraction from clinical images; these features are related to tumor size, shape, intensity, and texture, collectively providing comprehensive tumor characterization, the so-called radiomics signature of the tumor. Radiomics is based on the hypothesis that extracted quantitative data derives from mechanisms occurring at genetic and molecular levels. In this article we focus on the role and potential of radiomics in breast cancer diagnosis and prognostication.
Collapse
Affiliation(s)
- Alberto Stefano Tagliafico
- Department of Health Sciences, University of Genoa, Genoa, Italy; Ospedale Policlinico San Martino, Genoa, Italy.
| | - Michele Piana
- Dipartimento di Matematica, Università di Genova, Genova, Italy; CNR - SPIN, Genova, Italy
| | | | | | - Anna Maria Massone
- Dipartimento di Matematica, Università di Genova, Genova, Italy; CNR - SPIN, Genova, Italy
| | - Nehmat Houssami
- Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, NSW, Australia
| |
Collapse
|
117
|
Marino MA, Avendano D, Zapata P, Riedl CC, Pinker K. Lymph Node Imaging in Patients with Primary Breast Cancer: Concurrent Diagnostic Tools. Oncologist 2019; 25:e231-e242. [PMID: 32043792 PMCID: PMC7011661 DOI: 10.1634/theoncologist.2019-0427] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 08/12/2019] [Indexed: 12/26/2022] Open
Abstract
The detection of lymph node metastasis affects the management of patients with primary breast cancer significantly in terms of staging, treatment, and prognosis. The main goal for the radiologist is to determine and detect the presence of metastatic disease in nonpalpable axillary lymph nodes with a positive predictive value that is high enough to initially select patients for upfront axillary lymph node dissection. Features that are suggestive of axillary adenopathy may be seen with different imaging modalities, but ultrasound is the method of choice for evaluating axillary lymph nodes and for performing image-guided lymph node interventions. This review aims to provide a comprehensive overview of the available imaging modalities for lymph node assessment in patients diagnosed with primary breast cancer. IMPLICATIONS FOR PRACTICE: The detection of lymph node metastasis affects the management of patients with primary breast cancer. The main goal for the radiologist is to detect lymph node metastasis in patients to allow for the selection of patients who should undergo upfront axillary lymph node dissection. Features that are suggestive of axillary adenopathy may be seen with mammography, computed tomography, and magnetic resonance imaging, but ultrasonography is the imaging modality of choice for evaluating axillary lymph nodes. A normal axillary lymph node is characterized by a reniform shape, a maximal cortical thickness of 3 mm without focal bulging, smooth margins, and, depending on size, a discernable central fatty hilum.
Collapse
Affiliation(s)
- Maria Adele Marino
- Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino, University of MessinaMessinaItaly
| | - Daly Avendano
- Department of Breast Imaging, Breast Cancer Center TecSalud, Instituto Tecnológico de Estudios Superiores (ITESM) MonterreyNuevo LeonMexico
| | - Pedro Zapata
- Department of Radiology, San Felipe de Jesus HospitalMonterreyNuevo LeonMexico
| | - Christopher C. Riedl
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Katja Pinker
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
- Molecular and Gender Imaging Service, Department of Biomedical Imaging and Image‐guided Therapy, Medical University of ViennaViennaAustria
| |
Collapse
|
118
|
Yu FH, Wang JX, Ye XH, Deng J, Hang J, Yang B. Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer. Eur J Radiol 2019; 119:108658. [PMID: 31521878 DOI: 10.1016/j.ejrad.2019.108658] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 08/20/2019] [Accepted: 09/01/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE To establish a radiomics nomogram integrating clinical factors and radiomics features from ultrasound for the preoperative diagnosis axillary lymph node (ALN) status in patients with early-stage invasive breast cancer (EIBC). MATERIALS AND METHODS Between September 2016 and December 2018, four hundred twenty-six ultrasound manually segmented images of patients with EIBC were enrolled in our retrospective study, which were divided into a primary cohort (n = 300) and a validation cohort (n = 126). A radiomics signature was built with the least absolute shrinkage and selection operator (LASSO) algorithm in the primary cohort. Multivariable logistic regression analysis was used to establish a radiomics nomogram model based on radiomics signature and clinical variables. The performance of nomogram was quantified with respect to discrimination and calibration. The radiomics model was further evaluated in the internal validation cohort. RESULTS The radiomics signature, consisted of fourteen selected ALN-status-related features, achieved moderate prediction efficacy with an area under the curve (AUC) of 0.78 and 0.71 in the primary and validation cohorts respectively. The radiomics nomogram, comprising tumor size, US-reported LN status and radiomics signature, showed good calibration and favorite performance for ALN detection (AUC 0.84 and 0.81 in the primary and validation cohort). The decision curve which was demonstrated the radiomics nomogram displayed good clinical utility. CONCLUSION The radiomics nomogram could hold promise as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to develop more effective preoperative decision-making.
Collapse
Affiliation(s)
- Fei-Hong Yu
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jian-Xiang Wang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xin-Hua Ye
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Deng
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Hang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Bin Yang
- Department of Ultrasound, Jinling Clinical Medical College, Nanjing Medical University, Nanjing, China.
| |
Collapse
|
119
|
Lu W, Zhong L, Dong D, Fang M, Dai Q, Leng S, Zhang L, Sun W, Tian J, Zheng J, Jin Y. Radiomic analysis for preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma. Eur J Radiol 2019; 118:231-238. [PMID: 31439247 DOI: 10.1016/j.ejrad.2019.07.018] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 06/08/2019] [Accepted: 07/16/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE Cervical lymph node (LN) metastasis of papillary thyroid carcinoma (PTC) is critical for treatment and prognosis. We explored the feasibility of using radiomics to preoperatively predict cervical LN metastasis in PTC patients. METHOD Total 221 PTC patients (training cohort: n = 154; validation cohort: n = 67; divided randomly at the ratio of 7:3) were enrolled and divided into 2 groups based on LN pathologic diagnosis (N0: n = 118; N1a and N1b: n = 88 and 15, respectively). We extracted 546 radiomic features from non-contrast and venous contrast-enhanced computed tomography (CT) images. We selected 8 groups of candidate feature sets by minimum redundancy maximum relevance (mRMR), and obtained 8 radiomic sub-signatures by support vector machine (SVM) to construct the radiomic signature. Incorporating the radiomic signature, CT-reported cervical LN status and clinical risk factors, a nomogram was constructed using multivariable logistic regression. The nomogram's calibration, discrimination, and clinical utility were assessed. RESULTS The radiomic signature was associated significantly with cervical LN status (p < 0.01 for both training and validation cohorts). The radiomic signature showed better predictive performance than any radiomic sub-signatures devised by SVM. Addition of radiomic signature to the nomogram improved the predictive value (area under the curve (AUC), 0.807 to 0.867) in the training cohort; this was confirmed in an independent validation cohort (AUC, 0.795 to 0.822). Good agreement was observed using calibration curves in both cohorts. Decision curve analysis demonstrated the radiomic nomogram was worthy of clinical application. CONCLUSIONS Our radiomic nomogram improved the preoperative prediction of cervical LN metastasis in PTC patients.
Collapse
Affiliation(s)
- Wei Lu
- Department of Medical Imaging, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, Zhejiang, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Lianzhen Zhong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Mengjie Fang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Qi Dai
- Department of Medical Imaging, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, Zhejiang, China.
| | - Shaoyi Leng
- Department of Medical Imaging, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, Zhejiang, China.
| | - Liwen Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Wei Sun
- Department of Medical Imaging, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, Zhejiang, China.
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China.
| | - Jianjun Zheng
- Department of Medical Imaging, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, Zhejiang, China.
| | - Yinhua Jin
- Department of Medical Imaging, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, Zhejiang, China.
| |
Collapse
|