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Li S, Wang Y, Sun Y, Li D, Zhang Q, Ning Y, Lu Y, Wang W, Zhang H, Yang G. Both intra- and peri-tumoral radiomics signatures can be used to predict lymphatic vascular space invasion and lymphatic metastasis positive status from endometrial cancer MR imaging. Abdom Radiol (NY) 2024:10.1007/s00261-024-04432-3. [PMID: 38916618 DOI: 10.1007/s00261-024-04432-3] [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: 03/28/2024] [Revised: 05/18/2024] [Accepted: 05/25/2024] [Indexed: 06/26/2024]
Abstract
OBJECTIVES To identify lymphatic vascular space invasion (LVSI) and lymphatic node metastasis (LNM) status of endometrial cancer (EC) patients, using radiomics based on MRI images. METHODS Five hundred and ninety-eight EC patients between January 2015 and September 2020 from two institutions were retrospectively included. Tumoral regions on DWI, T1CE, and T2W images were manually outlined. Radiomics features were extracted from tumor region and peri-tumor region of different thicknesses. We established sub-models to select features from each smaller category. Using this method, we separately constructed radiomic signatures for intra-tumoral and peri-tumoral images using different sequences. We constructed intra-tumoral and peri-tumoral models by combining their features, and a multi-sequence model by combining logits. Models were trained with 397 patients and validated with 170 internal and 31 external patients. RESULTS For LVSI positive/LNM positive status identification, the multi-parameter MRI radiomics model achieved the area under curve (AUC) values of 0.771 (95%CI: [0.692-0.849])/0.801 (95%CI: [0.704, 0.898]) and 0.864 (95%CI: [0.728-1.000])/0.976 (95%CI: [0.919, 1.000]) in internal and external test cohorts, respectively. CONCLUSIONS Intra-tumoral and peri-tumoral radiomics signatures based on mpMRI can both be used to identify LVSI or LNM status in EC patients non-invasively. Further studies on LVSI and LNM should pay attention to both of them.
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Affiliation(s)
- Shengyong Li
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China
| | - Yida Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China
| | - Yiyang Sun
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China
| | - Dexuan Li
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China
| | - Qi Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China
| | - Yan Ning
- Department of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China
| | - Yuanyuan Lu
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Wenjing Wang
- Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, No.419 Fangxie Road, Shanghai, People's Republic of China.
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China.
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Ge W, Fan X, Zeng Y, Yang X, Zhou L, Zuo Z. Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer. Acad Radiol 2024:S1076-6332(24)00355-6. [PMID: 38876841 DOI: 10.1016/j.acra.2024.05.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/12/2024] [Accepted: 05/24/2024] [Indexed: 06/16/2024]
Abstract
RATIONALE AND OBJECTIVES Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC. MATERIALS AND METHODS In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, and dynamic contrast-enhanced MRI. Habitats-based spatial distributions were derived from the gross tumor volume (GTV) and gross tumor volume plus peritumoral volume (GPTV). GTV_habitats and GPTV_habitats were generated through sub-region segmentation, and their performances were compared. Subsequently, a combined nomogram was developed by integrating relevant spatial distributions with the identified MR morphological characteristics. Diagnostic performance was compared using receiver operating characteristic curve analysis and decision curve analysis. Statistical significance was set at p < 0.05. RESULTS GPTV_habitats exhibited superior performance compared to GTV_habitats. Consequently, the GPTV_habitats, diffusion-weighted imaging rim signs, and peritumoral edema were integrated to formulate the combined nomogram. This combined nomogram outperformed individual MR morphological characteristics and the GPTV_habitats index, achieving area under the curve values of 0.903 (0.847 -0.959), 0.770 (0.689 -0.852), and 0.843 (0.776 -0.910) in the training set and 0.931 (0.863 -0.999), 0.747 (0.613 -0.880), and 0.849 (0.759 -0.938) in the validation set. CONCLUSION The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC.
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Affiliation(s)
- Wu Ge
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Xiaohong Fan
- School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, Hunan province, PR China (X.F., Z.Z.).
| | - Ying Zeng
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Xiuqi Yang
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Lu Zhou
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Zhichao Zuo
- School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, Hunan province, PR China (X.F., Z.Z.).
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Yichu S, Fei L, Ying L, Youyou X. Potential of radiomics analysis and machine learning for predicting brain metastasis in newly diagnosed lung cancer patients. Clin Radiol 2024; 79:e807-e816. [PMID: 38395696 DOI: 10.1016/j.crad.2024.01.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 01/05/2024] [Accepted: 01/30/2024] [Indexed: 02/25/2024]
Abstract
AIM To explore the potential of utilising radiomics analysis and machine-learning models that incorporate intratumoural and peritumoural regions of interest (ROIs) for predicting brain metastasis (BM) in newly diagnosed lung cancer patients. MATERIALS AND METHODS The study comprised 183 lung cancer patients (training cohort: n=146; validation cohort: n=37) whose radiomics features were extracted from plain computed tomography (CT) images of the primary lesion. Four machine-learning algorithms (logistic regression [LR], support vector machine [SVM], k-nearest neighbour algorithm [KNN], and random forest [RF]) were employed to develop predictive models. Model diagnostic performance was assessed through receiver operating characteristic (ROC) analysis, and clinical utility was evaluated using decision curve analysis (DCA). Finally, the radiomics model's generalisation ability was further validated in the prediction of metachronous brain metastasis (MBM). RESULTS After feature screening, 22 radiomics features were identified as highly predictive, of which nine were derived from the peritumour region. All four machine-learning models demonstrated predictive capability, with SVM showing superior efficiency and robustness. The area under the ROC curve (AUC) of SVM was 0.918 in the training cohort and 0.901 in the validation cohort. DCA indicated the highest net benefit. Furthermore, the time-dependent ROC curve exhibited predictive efficacy for MBM occurrence across 1-, 2-, and 3-year follow-up periods, with all AUC values exceeding 0.7. CONCLUSION The optimal SVM model integrating intratumoural and peritumoural radiomics features was confirmed and defined as an imaging biomarker for predicting BM in newly diagnosed lung cancer patients, underscoring its potential to significantly impact clinical diagnosis and treatment.
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Affiliation(s)
- S Yichu
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/ Lianyungang Clinical College of Nanjing Medical University, Lianyungang City, Jiangsu Province, 222000, China
| | - L Fei
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/ Lianyungang Clinical College of Nanjing Medical University, Lianyungang City, Jiangsu Province, 222000, China
| | - L Ying
- Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang City, Jiangsu Province, 222000, China
| | - X Youyou
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/ Lianyungang Clinical College of Nanjing Medical University, Lianyungang City, Jiangsu Province, 222000, China.
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Wang Y, Zhao H, Fu P, Tian L, Su Y, Lyu Z, Gu W, Wang Y, Liu S, Wang X, Zheng H, Du J, Zhang R. Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis. Med Phys 2024. [PMID: 38801340 DOI: 10.1002/mp.17193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC). PURPOSE Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC. METHODS The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model. RESULTS The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility. CONCLUSIONS 18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.
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Affiliation(s)
- Yan Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Hongyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Peng Fu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Lin Tian
- Department of Pathology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yexin Su
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhehao Lyu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yang Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Shan Liu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xi Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Han Zheng
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jingjing Du
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Rui Zhang
- Department of Magnetic Resonance, The First Hospital of Qiqihar, Qiqihar, Heilongjiang, China
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Liu W, Wang W, Guo M, Zhang H. Tumor habitat and peritumoral region evolution-based imaging features to assess risk categorization of thymomas. Clin Radiol 2024:S0009-9260(24)00251-4. [PMID: 38862335 DOI: 10.1016/j.crad.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 06/13/2024]
Abstract
AIM To develop an aggregate model that integrated clinical data, habitat characteristics, and intratumoral and peritumoral features to assess the risk categorization of thymomas. MATERIALS AND METHODS We retrospectively analyzed 140 thymoma patients (70 low-risk and 70 high-risk), including pathological data. The patients were randomly divided into training cohort (n = 114) and test cohort (n = 26). The k-means clustering was utilized to partition the primary tumor into habitats based on intratumoral radiomic features, 6 distinct habitats were identified. By expanding the region of interest (ROI) mask, 2 peritumoral regions were obtained. Finally, 7 clinical characteristics, 3 habitat values, 20 radiomic features were utilized to develop an aggregated model, to predict the risk of thymoma. Shapley additive explanations (SHAP) interpretation was used for features importance ranking. The accuracy and area under curve (AUC) were used to analyze the performance of the models. RESULTS The aggregated model, which utilized the XGBoost classifier, demonstrated the best performance with an AUC of 0.811 and an accuracy of 0.769. In comparison, the radiomic model produced an AUC of 0.654 and an accuracy of 0.692. Additionally, the Intratumoral + peritumoral model exhibited an AUC of 0.728 and an accuracy of 0.769. CONCLUSION Our study establishes a novel tool to predict the risk of thymoma with a good performance. If prospectively validated, the model may refine thymoma patient selection for risk-adaptative therapy and improve prognosis.
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Affiliation(s)
- W Liu
- School of Health Management, China Medical University, Shenyang City, Liaoning Province, PR China.
| | - W Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang City, Liaoning Province, PR China.
| | - M Guo
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang City, Liaoning Province, PR China.
| | - H Zhang
- Department of Radiology, Liaoning Cancer Hospital and Institute, Shenyang City, Liaoning Province, PR China.
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Tan R, Sui C, Wang C, Zhu T. MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study. Front Oncol 2024; 14:1401977. [PMID: 38803534 PMCID: PMC11128562 DOI: 10.3389/fonc.2024.1401977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
Background Accurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade. Methods A total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice. Results The intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort. Conclusion This study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.
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Affiliation(s)
- Rui Tan
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Chunxiao Sui
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Chao Wang
- Department of Neurosurgery, Qilu Hospital of Shandong University Dezhou Hospital (Dezhou People’s Hospital), Shandong, China
| | - Tao Zhu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
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Jiang D, Qian Q, Yang X, Zeng Y, Liu H. Machine learning based on optimal VOI of multi-sequence MR images to predict lymphovascular invasion in invasive breast cancer. Heliyon 2024; 10:e29267. [PMID: 38623213 PMCID: PMC11016709 DOI: 10.1016/j.heliyon.2024.e29267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/24/2024] [Accepted: 04/03/2024] [Indexed: 04/17/2024] Open
Abstract
Objectives Lymphovascular invasion serves as a crucial prognostic indicator in invasive breast cancer, influencing treatment decisions. We aimed to develop a machine learning model utilizing optimal volumes of interest extracted from multisequence magnetic resonance images to predict lymphovascular invasion in patients with invasive breast cancer. Materials and methods This study comprised 191 patients postoperatively diagnosed with invasive breast cancer through multi-sequence magnetic resonance imaging. Independent predictors were identified through univariate and multivariate logistic regression analyses, culminating in the construction of a clinical model. Radiomic features were extracted from multi-sequence magnetic resonance imaging images across various volume of interest scales (-2 mm, entire, +2 mm, +4 mm, and +6 mm). Subsequently, various radiomic models were developed using machine learning model algorithms, including logistic regression, support vector machine, k-nearest neighbor, gradient boosting machine, classification and regression tree, and random forest. A hybrid model was then formulated, amalgamating optimal radiomic and clinical models. Results The area under the curve of the clinical model was 0.757. Among the radiomic models, the most efficient diagnosis was achieved by the k-nearest neighbor-based radiomics-volume of interest (+2 mm), resulting in an area under the curve of 0.780. The hybrid model, integrating the k-nearest neighbor-based radiomics-volume of interest (+2 mm), and the clinical model surpassed the individual clinical and radiomics models, exhibiting a superior area under the curve of 0.864. Conclusion Utilizing a hybrid approach integrating clinical data and multi-sequence magnetic resonance imaging-derived radiomics models based on the multiscale tumor region volume of interest (+2 mm) proved effective in determining lymphovascular invasion status in patients with invasive breast cancer. This innovative methodology may offer valuable insights for treatment planning and disease management.
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Affiliation(s)
- Dengke Jiang
- Department of Radiology, The Second Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, 410005, China
| | - Qiuqin Qian
- Department of Radiology, The Second Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, 410005, China
| | - Xiuqi Yang
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Ying Zeng
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Haibo Liu
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
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Zhou J, Yu X, Wu Q, Wu Y, Fu C, Wang Y, Hai M, Tan H, Wang M. Radiomics analysis of intratumoral and different peritumoral regions from multiparametric MRI for evaluating HER2 status of breast cancer: A comparative study. Heliyon 2024; 10:e28722. [PMID: 38623231 PMCID: PMC11016612 DOI: 10.1016/j.heliyon.2024.e28722] [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: 03/27/2023] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/17/2024] Open
Abstract
Purpose To investigate the potential of radiomics signatures (RSs) from intratumoral and peritumoral regions on multiparametric magnetic resonance imaging (MRI) to noninvasively evaluate HER2 status in breast cancer. Method In this retrospective study, 992 patients with pathologically confirmed breast cancers who underwent preoperative MRI were enrolled. The breast cancer lesions were segmented manually, and the intratumor region of interest (ROIIntra) was dilated by 2, 4, 6 and 8 mm (ROIPeri2mm, ROIPeri4mm, ROIPeri6mm, and ROIPeri8mm, respectively). Quantitative radiomics features were extracted from dynamic contrast-enhanced T1-weighted imaging (DCE-T1), fat-saturated T2-weighted imaging (T2) and diffusion-weighted imaging (DWI). A three-step procedure was performed for feature selection, and RSs were constructed using a support vector machine (SVM) to predict HER2 status. Result The best single-area RSs for predicting HER2 status were DCE_Peri4mm-RS, T2_Peri4mm-RS, and DWI_Peri4mm-RS, yielding areas under the curve (AUCs) of 0.716 (95% confidence interval (CI), 0.648-0.778), 0.706 (95% CI, 0.637-0.768), and 0.719 (95% CI, 0.651-0.780), respectively, in the test set. The optimal RSs combining intratumoral and peritumoral regions for evaluating HER2 status were DCE-T1_Intra + DCE_Peri4mm-RS, T2_Intra + T2_Peri6mm-RS and DWI_Intra + DWI_Peri4mm-RS, with AUCs of 0.752 (95% CI, 0.686-0.810), 0.754 (95% CI, 0.688-0.812) and 0.725 (95% CI, 0.657-0.786), respectively, in the test set. Combining three sequences in the ROIIntra, ROIPeri2mm, ROIPeri4mm, ROIPeri6mm and ROIPeri8mm areas, the optimal RS was DCE-T1_Peri4mm + T2_Peri4mm + DWI_Peri4mm-RS, achieving an AUC of 0.795 (95% CI, 0.733-0.849) in the test set. Conclusion This study systematically explored the influence of the intratumoral region, different peritumoral sizes and their combination in radiomics analysis for predicting HER2 status in breast cancer based on multiparametric MRI and found the optimal RS.
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Affiliation(s)
- Jing Zhou
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Xuan Yu
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging & United Imaging Intelligence (Beijing) Co., Ltd., Beijing, 100089, China
| | - Yaping Wu
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Cong Fu
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Yunxia Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Menglu Hai
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, Henan Province, China
| | - Hongna Tan
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
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Laskov V, Rothbauer D, Malikova H. Robustness of radiomic features in 123I-ioflupane-dopamine transporter single-photon emission computer tomography scan. PLoS One 2024; 19:e0301978. [PMID: 38603674 PMCID: PMC11008844 DOI: 10.1371/journal.pone.0301978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/26/2024] [Indexed: 04/13/2024] Open
Abstract
Radiomic features are usually used to predict target variables such as the absence or presence of a disease, treatment response, or time to symptom progression. One of the potential clinical applications is in patients with Parkinson's disease. Robust radiomic features for this specific imaging method have not yet been identified, which is necessary for proper feature selection. Thus, we are assessing the robustness of radiomic features in dopamine transporter imaging (DaT). For this study, we made an anthropomorphic head phantom with tissue heterogeneity using a personal 3D printer (polylactide 82% infill); the bone was subsequently reproduced with plaster. A surgical cotton ball with radiotracer (123I-ioflupane) was inserted. Scans were performed on the two-detector hybrid camera with acquisition parameters corresponding to international guidelines for DaT single photon emission tomography (SPECT). Reconstruction of SPECT was performed on a clinical workstation with iterative algorithms. Open-source LifeX software was used to extract 134 radiomic features. Statistical analysis was made in RStudio using the intraclass correlation coefficient (ICC) and coefficient of variation (COV). Overall, radiomic features in different reconstruction parameters showed a moderate reproducibility rate (ICC = 0.636, p <0.01). Assessment of ICC and COV within CT attenuation correction (CTAC) and non-attenuation correction (NAC) groups and within particular feature classes showed an excellent reproducibility rate (ICC > 0.9, p < 0.01), except for an intensity-based NAC group, where radiomic features showed a good repeatability rate (ICC = 0.893, p <0.01). By our results, CTAC becomes the main threat to feature stability. However, many radiomic features were sensitive to the selected reconstruction algorithm irrespectively to the attenuation correction. Radiomic features extracted from DaT-SPECT showed moderate to excellent reproducibility rates. These results make them suitable for clinical practice and human studies, but awareness of feature selection should be held, as some radiomic features are more robust than others.
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Affiliation(s)
- Viktor Laskov
- Department of Radiology and Nuclear Medicine, Third Faculty of Medicine, Charles University and University Hospital Kralovske Vinohrady, Prague, Czech Republic
| | - David Rothbauer
- Department of Radiology and Nuclear Medicine, Third Faculty of Medicine, Charles University and University Hospital Kralovske Vinohrady, Prague, Czech Republic
| | - Hana Malikova
- Department of Radiology and Nuclear Medicine, Third Faculty of Medicine, Charles University and University Hospital Kralovske Vinohrady, Prague, Czech Republic
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Zhu Y, Ma Y, Zhai Z, Liu A, Wang Y, Zhang Y, Li H, Zhao M, Han P, Yin L, He N, Wu Y, Sechopoulos I, Ye Z, Caballo M. Radiomics in cone-beam breast CT for the prediction of axillary lymph node metastasis in breast cancer: a multi-center multi-device study. Eur Radiol 2024; 34:2576-2589. [PMID: 37782338 DOI: 10.1007/s00330-023-10256-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 07/09/2023] [Accepted: 07/30/2023] [Indexed: 10/03/2023]
Abstract
OBJECTIVES To develop a radiomics model in contrast-enhanced cone-beam breast CT (CE-CBBCT) for preoperative prediction of axillary lymph node (ALN) status and metastatic burden of breast cancer. METHODS Two hundred and seventy-four patients who underwent CE-CBBCT examination with two scanners between 2012 and 2021 from two institutions were enrolled. The primary tumor was annotated in each patient image, from which 1781 radiomics features were extracted with PyRadiomics. After feature selection, support vector machine models were developed to predict ALN status and metastatic burden. To avoid overfitting on a specific patient subset, 100 randomly stratified splits were made to assign the patients to either training/fine-tuning or test set. Area under the receiver operating characteristic curve (AUC) of these radiomics models was compared to those obtained when training the models only with clinical features and combined clinical-radiomics descriptors. Ground truth was established by histopathology. RESULTS One hundred and eighteen patients had ALN metastasis (N + (≥ 1)). Of these, 74 had low burden (N + (1~2)) and 44 high burden (N + (≥ 3)). The remaining 156 patients had none (N0). AUC values across the 100 test repeats in predicting ALN status (N0/N + (≥ 1)) were 0.75 ± 0.05 (0.67~0.93, radiomics model), 0.68 ± 0.07 (0.53~0.85, clinical model), and 0.74 ± 0.05 (0.67~0.88, combined model). For metastatic burden prediction (N + (1~2)/N + (≥ 3)), AUC values were 0.65 ± 0.10 (0.50~0.88, radiomics model), 0.55 ± 0.10 (0.40~0.80, clinical model), and 0.64 ± 0.09 (0.50~0.90, combined model), with all the ranges spanning 0.5. In both cases, the radiomics model was significantly better than the clinical model (both p < 0.01) and comparable with the combined model (p = 0.56 and 0.64). CONCLUSIONS Radiomics features of primary tumors could have potential in predicting ALN metastasis in CE-CBBCT imaging. CLINICAL RELEVANCE STATEMENT The findings support potential clinical use of radiomics for predicting axillary lymph node metastasis in breast cancer patients and addressing the limited axilla coverage of cone-beam breast CT. KEY POINTS • Contrast-enhanced cone-beam breast CT-based radiomics could have potential to predict N0 vs. N + (≥ 1) and, to a limited extent, N + (1~2) vs. N + (≥ 3) from primary tumor, and this could help address the limited axilla coverage, pending future verifications on larger cohorts. • The average AUC of radiomics and combined models was significantly higher than that of clinical models but showed no significant difference between themselves. • Radiomics features descriptive of tumor texture were found informative on axillary lymph node status, highlighting a higher heterogeneity for tumor with positive axillary lymph node.
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Affiliation(s)
- Yueqiang Zhu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Yue Ma
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Zhenzhen Zhai
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Mei-Hua-Dong Road, Xiangzhou District, Zhuhai, 519000, China
| | - Aidi Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Yafei Wang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Yuwei Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Haijie Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Mengran Zhao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Peng Han
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Lu Yin
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Ni He
- Department of Medical Imaging and Image-guided Therapy, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Dong-Feng-Dong Road, Yuexiu District, Guangzhou, 510060, China
| | - Yaopan Wu
- Department of Medical Imaging and Image-guided Therapy, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Dong-Feng-Dong Road, Yuexiu District, Guangzhou, 510060, China
| | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
- Dutch Expert Center for Screening (LRCB), PO Box 6873, Nijmegen, 6503 GJ, The Netherlands
- Technical Medicine Centre, University of Twente, PO Box 217, Enschede, 7500 AE, The Netherlands
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China.
| | - Marco Caballo
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
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Huang H, Chen H, Zheng D, Chen C, Wang Y, Xu L, Wang Y, He X, Yang Y, Li W. Habitat-based radiomics analysis for evaluating immediate response in colorectal cancer lung metastases treated by radiofrequency ablation. Cancer Imaging 2024; 24:44. [PMID: 38532520 DOI: 10.1186/s40644-024-00692-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/20/2024] [Indexed: 03/28/2024] Open
Abstract
PURPOSE To create radiomics signatures based on habitat to assess the instant response in lung metastases of colorectal cancer (CRC) after radiofrequency ablation (RFA). METHODS Between August 2016 and June 2019, we retrospectively included 515 lung metastases in 233 CRC patients who received RFA (412 in the training group and 103 in the test group). Multivariable analysis was performed to identify independent risk factors for developing the clinical model. Tumor and ablation regions of interest (ROI) were split into three spatial habitats through K-means clustering and dilated with 5 mm and 10 mm thicknesses. Radiomics signatures of intratumor, peritumor, and habitat were developed using the features extracted from intraoperative CT data. The performance of these signatures was primarily evaluated using the area under the receiver operating characteristics curve (AUC) via the DeLong test, calibration curves through the Hosmer-Lemeshow test, and decision curve analysis. RESULTS A total of 412 out of 515 metastases (80%) achieved complete response. Four clinical variables (cancer antigen 19-9, simultaneous systemic treatment, site of lung metastases, and electrode type) were utilized to construct the clinical model. The Habitat signature was combined with the Peri-5 signature, which achieved a higher AUC than the Peri-10 signature in the test set (0.825 vs. 0.816). The Habitat+Peri-5 signature notably surpassed the clinical and intratumor radiomics signatures (AUC: 0.870 in the test set; both, p < 0.05), displaying improved calibration and clinical practicality. CONCLUSIONS The habitat-based radiomics signature can offer precise predictions and valuable assistance to physicians in developing personalized treatment strategies.
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Affiliation(s)
- Haozhe Huang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Hong Chen
- Department of Medical Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Xuhui District, Shanghai, 200030, China
| | - Dezhong Zheng
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Science, 500 Yutian Road, Hongkou District, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing, 100049, China
| | - Chao Chen
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Ying Wang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Lichao Xu
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Yaohui Wang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Xinhong He
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Yuanyuan Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Science, 500 Yutian Road, Hongkou District, Shanghai, 200083, China.
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing, 100049, China.
| | - Wentao Li
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China.
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Zhao S, Li Y, Ning N, Liang H, Wu Y, Wu Q, Wang Z, Tian J, Yang J, Gao X, Liu A, Song Q, Zhang L. Association of peritumoral region features assessed on breast MRI and prognosis of breast cancer: a systematic review and meta-analysis. Eur Radiol 2024:10.1007/s00330-024-10612-y. [PMID: 38334760 DOI: 10.1007/s00330-024-10612-y] [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: 07/31/2023] [Revised: 12/03/2023] [Accepted: 01/01/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND Increasing attention has been given to the peritumoral region. However, conflicting findings have been reported regarding the relationship between peritumoral region features on MRI and the prognosis of breast cancer. PURPOSE To evaluate the relationship between peritumoral region features on MRI and prognosis of breast cancer. MATERIALS AND METHODS A retrospective meta-analysis of observational studies comparing either qualitative or quantitative assessments of peritumoral MRI features on breast cancer with poor prognosis and control subjects was performed for studies published till October 2022. Pooled odds ratios (ORs) or standardized mean differences and 95% confidence intervals (CIs) were estimated by using random-effects models. The heterogeneity across the studies was measured using the statistic I2. Sensitivity analyses were conducted to test this association according to different study characteristics. RESULTS Twenty-four studies comprising 1853 breast cancers of poor prognosis and 2590 control participants were included in the analysis. Peritumoral edema was associated with non-luminal breast cancers (OR=3.56; 95%CI: 2.17, 5.83; p=.000), high expression of the Ki-67 index (OR=3.70; 95%CI: 2.41, 5.70; p =.000), high histological grade (OR=5.85; 95%CI: 3.89, 8.80; p=.000), lymph node metastasis (OR=2.83; 95%CI: 1.71, 4.67; p=.000), negative expression of HR (OR=3.15; 95%CI: 2.03, 4.88; p=.000), and lymphovascular invasion (OR=1.72; 95%CI: 1.28, 2.30; p=.000). The adjacent vessel sign was associated with greater odds of breast cancer with poor prognosis (OR=2.02; 95%CI: 1.68, 2.44; p=.000). Additionally, breast cancers with poor prognosis had higher peritumor-tumor ADC ratio (SMD=0.67; 95%CI: 0.54, 0.79; p=.000) and peritumoral ADCmean (SMD=0.29; 95%CI: 0.15, 0.42; p=.000). A peritumoral region of 2-20 mm away from the margin of the tumor is recommended. CONCLUSION The presence of peritumoral edema and adjacent vessel signs, higher peritumor-tumor ADC ratio, and peritumoral ADCmean were significantly correlated with poor prognosis of breast cancer. CLINICAL RELEVANCE STATEMENT MRI features of the peritumoral region can be used as a non-invasive index for the prognostic evaluation of invasive breast cancer. KEY POINTS • Peritumoral edema was positively associated with non-luminal breast cancer, high expression of the Ki-67 index, high histological grade, lymph node metastasis, negative expression of HR, and lymphovascular invasion. • The adjacent vessel sign was associated with greater odds of breast cancers with poor prognosis. • Breast cancers with poor prognosis had higher peritumor-tumor ADC ratio and peritumoral ADCmean.
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Affiliation(s)
- Siqi Zhao
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, No 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, People's Republic of China
| | - Yuanfei Li
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, No 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, People's Republic of China
| | - Ning Ning
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, No 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, People's Republic of China
| | - Hongbing Liang
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, No 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, People's Republic of China
| | - Yueqi Wu
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, No 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, People's Republic of China
| | - Qi Wu
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, No 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, People's Republic of China
| | - Zhuo Wang
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, No 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, People's Republic of China
| | - Jiahe Tian
- Zhongshan College of Dalian Medical University, No28 Aixian Road, Gaoxin District, Dalian, Liaoning, 116085, People's Republic of China
| | - Jie Yang
- School of Public Health, Dalian Medical University, Dalian, Liaoning Province, No. 9W. Lvshun South Road, Dalian, 116044, People's Republic of China
| | - Xue Gao
- Department of Pathology, First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Dalian, Liaoning, 116011, People's Republic of China
| | - Ailian Liu
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, No 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, People's Republic of China
| | - Qingwei Song
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, No 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, People's Republic of China
| | - Lina Zhang
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, No 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, People's Republic of China.
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Jiang W, Meng R, Cheng Y, Wang H, Han T, Qu N, Yu T, Hou Y, Xu S. Intra- and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer. J Magn Reson Imaging 2024; 59:613-625. [PMID: 37199241 DOI: 10.1002/jmri.28776] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Radiomics has been applied for assessing lymphovascular invasion (LVI) in patients with breast cancer. However, associations between features from peritumoral regions and the LVI status were not investigated. PURPOSE To investigate the value of intra- and peritumoral radiomics for assessing LVI, and to develop a nomogram to assist in making treatment decisions. STUDY TYPE Retrospective. POPULATION Three hundred and sixteen patients were enrolled from two centers and divided into training (N = 165), internal validation (N = 83), and external validation (N = 68) cohorts. FIELD STRENGTH/SEQUENCE 1.5 T and 3.0 T/dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI). ASSESSMENT Radiomics features were extracted and selected based on intra- and peritumoral breast regions in two magnetic resonance imaging (MRI) sequences to create the multiparametric MRI combined radiomics signature (RS-DCE plus DWI). The clinical model was built with MRI-axillary lymph nodes (MRI ALN), MRI-reported peritumoral edema (MPE), and apparent diffusion coefficient (ADC). The nomogram was constructed with RS-DCE plus DWI, MRI ALN, MPE, and ADC. STATISTICAL TESTS Intra- and interclass correlation coefficient analysis, Mann-Whitney U test, and least absolute shrinkage and selection operator regression were used for feature selection. Receiver operating characteristic and decision curve analyses were applied to compare performance of the RS-DCE plus DWI, clinical model, and nomogram. RESULTS A total of 10 features were found to be associated with LVI, 3 from intra- and 7 from peritumoral areas. The nomogram showed good performance in the training (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.884 vs. 0.695 vs. 0.870), internal validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.813 vs. 0.695 vs. 0.794), and external validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.862 vs. 0.601 vs. 0.849) cohorts. DATA CONCLUSION The constructed preoperative nomogram might effectively assess LVI. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Ruiqing Meng
- Department of Biomedical Engineering, China Medical University, Shenyang, China
| | - Yuan Cheng
- Department of Biomedical Engineering, China Medical University, Shenyang, China
| | - Haotian Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Tingting Han
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ning Qu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Shu Xu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
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Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, Yang H, Wang H, Zhang Y. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes. Biomark Res 2024; 12:12. [PMID: 38273398 PMCID: PMC10809593 DOI: 10.1186/s40364-024-00561-5] [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: 10/20/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. METHODS We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the 'Deep-RadScore,' a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. FINDINGS The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. INTERPRETATION This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.
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Affiliation(s)
- Xingping Zhang
- School of Medical Information Engineering, Gannan Medical University, 341000, Ganzhou, China
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Guijuan Zhang
- Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Jiao Yin
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, 110189, Shenyang, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China.
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia.
- Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China.
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Li F, Zhu TW, Lin M, Zhang XT, Zhang YL, Zhou AL, Huang DY. Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning. Acad Radiol 2024:S1076-6332(23)00714-6. [PMID: 38182442 DOI: 10.1016/j.acra.2023.12.036] [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: 11/16/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 01/07/2024]
Abstract
RATIONALE AND OBJECTIVES Traditional Ki-67 evaluation in breast cancer (BC) via core needle biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound system (ABUS) offers reproducibility but is constrained to morphological and echoic assessments. Radiomics and machine learning (ML) offer solutions, but their integration for improving Ki-67 predictive accuracy in BC remains unexplored. This study aims to enhance ABUS by integrating ML-assisted radiomics for Ki-67 prediction in BC, with a focus on both intratumoral and peritumoral regions. MATERIALS AND METHODS A retrospective analysis was conducted on 936 BC patients, split into training (n = 655) and testing (n = 281) cohorts. Radiomics features were extracted from intra- and peritumoral regions via ABUS. Feature selection involved Z-score normalization, intraclass correlation, Wilcoxon rank sum tests, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator logistic regression. ML classifiers were trained and optimized for enhanced predictive accuracy. The interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). RESULTS Of the 2632 radiomics features in each patient, 15 were significantly associated with Ki-67 levels. The support vector machine (SVM) was identified as the optimal classifier, with area under the receiver operating characteristic curve values of 0.868 (training) and 0.822 (testing). SHAP analysis indicated that five peritumoral and two intratumoral features, along with age and lymph node status, were key determinants in the predictive model. CONCLUSION Integrating ML with ABUS-based radiomics effectively enhances Ki-67 prediction in BC, demonstrating the SVM model's strong performance with both radiomics and clinical factors.
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Affiliation(s)
- Fang Li
- Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.)
| | - Tong-Wei Zhu
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Linhai, Zhejiang, China (T.Z.)
| | - Miao Lin
- Second Department of General Surgery, The People's Hospital of Yuhuan, Yuhuan, Zhejiang, China (M.L.)
| | - Xiao-Ting Zhang
- Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.)
| | - Ya-Li Zhang
- Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.)
| | - Ai-Li Zhou
- Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.)
| | - De-Yi Huang
- Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.).
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Xin P, Wang Q, Yan R, Chen Y, Zhu Y, Zhang E, Ren C, Lang N. Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model. Arthritis Res Ther 2023; 25:227. [PMID: 38001465 PMCID: PMC10668377 DOI: 10.1186/s13075-023-03193-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/13/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Identifying axial spondyloarthritis (axSpA) activity early and accurately is essential for treating physicians to adjust treatment plans and guide clinical decisions promptly. The current literature is mostly focused on axSpA diagnosis, and there has been thus far, no study that reported the use of a radiomics approach for differentiating axSpA disease activity. In this study, the aim was to develop a radiomics model for differentiating active from non-active axSpA based on fat-suppressed (FS) T2-weighted (T2w) magnetic resonance imaging (MRI) of sacroiliac joints. METHODS This retrospective study included 109 patients diagnosed with non-active axSpA (n = 68) and active axSpA (n = 41); patients were divided into training and testing cohorts at a ratio of 8:2. Radiomics features were extracted from 3.0 T sacroiliac MRI using two different heterogeneous regions of interest (ROIs, Circle and Facet). Various methods were used to select relevant and robust features, and different classifiers were used to build Circle-based, Facet-based, and a fusion prediction model. Their performance was compared using various statistical parameters. p < 0.05 is considered statistically significant. RESULTS For both Circle- and Facet-based models, 2284 radiomics features were extracted. The combined fusion ROI model accurately differentiated between active and non-active axSpA, with high accuracy (0.90 vs.0.81), sensitivity (0.90 vs. 0.75), and specificity (0.90 vs. 0.85) in both training and testing cohorts. CONCLUSION The multi-ROI fusion radiomics model developed in this study differentiated between active and non-active axSpA using sacroiliac FS T2w-MRI. The results suggest MRI-based radiomics of the SIJ can distinguish axSpA activity, which can improve the therapeutic result and patient prognosis. To our knowledge, this is the only study in the literature that used a radiomics approach to determine axSpA activity.
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Affiliation(s)
- Peijin Xin
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Qizheng Wang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Ruixin Yan
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yongye Chen
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yupeng Zhu
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Enlong Zhang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Cui Ren
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China.
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China.
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Wang J, Gao W, Lu M, Yao X, Yang D. Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features. Front Oncol 2023; 13:1290313. [PMID: 38044998 PMCID: PMC10691503 DOI: 10.3389/fonc.2023.1290313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/02/2023] [Indexed: 12/05/2023] Open
Abstract
Background Traditional immunohistochemistry assessment of Ki-67 in breast cancer (BC) via core needle biopsy is invasive, inaccurate, and nonrepeatable. While machine learning (ML) provides a promising alternative, its effectiveness depends on extensive data. Although the current mainstream MRI-centered radiomics offers sufficient data, its unsuitability for repeated examinations, along with limited accessibility and an intratumoral focus, constrain the application of predictive models in evaluating Ki-67 levels. Objective This study aims to explore ultrasound (US) image-based radiomics, incorporating both intra- and peritumoral features, to develop an interpretable ML model for predicting Ki-67 expression in BC patients. Methods A retrospective analysis was conducted on 263 BC patients, divided into training and external validation cohorts. From intratumoral and peritumoral regions of interest (ROIs) in US images, 849 distinctive radiomics features per ROI were derived. These features underwent systematic selection to analyze Ki-67 expression relationships. Four ML models-logistic regression, random forests, support vector machine (SVM), and extreme gradient boosting-were formulated and internally validated to identify the optimal predictive model. External validation was executed to ascertain the robustness of the optimal model, followed by employing Shapley Additive Explanations (SHAP) to reveal the significant features of the model. Results Among 231 selected BC patients, 67.5% exhibited high Ki-67 expression, with consistency observed across both training and validation cohorts as well as other clinical characteristics. Of the 1698 radiomics features identified, 15 were significantly correlated with Ki-67 expression. The SVM model, utilizing combined ROI, demonstrated the highest accuracy [area under the receiver operating characteristic curve (AUROC): 0.88], making it the most suitable for predicting Ki-67 expression. External validation sustained an AUROC of 0.82, affirming the model's robustness above a 40% threshold. SHAP analysis identified five influential features from intra- and peritumoral ROIs, offering insight into individual prediction. Conclusion This study emphasized the potential of SVM model using radiomics features from both intra- and peritumoral US images, for predicting elevated Ki-67 levels in BC patients. The model exhibited strong performance in validations, indicating its promise as a noninvasive tool to enable personalized decision-making in BC care.
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Li H, Chen XL, Liu H, Liu YS, Li ZL, Pang MH, Pu H. MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study. Eur Radiol 2023; 33:7561-7572. [PMID: 37160427 DOI: 10.1007/s00330-023-09723-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 03/07/2023] [Accepted: 03/20/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVE To build T2WI-based multiregional radiomics for predicting tumor deposit (TD) and prognosis in patients with resectable rectal cancer. MATERIALS AND METHODS A total of 208 patients with pathologically confirmed rectal cancer from two hospitals were prospectively enrolled. Intra- and peritumoral features were extracted separately from T2WI images and the least absolute shrinkage and selection operator was used to screen the most valuable radiomics features. Clinical-radiomics nomogram was developed by radiomics signatures and the most predictive clinical parameters. Prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis. RESULTS For TD, the area under the receiver operating characteristic curve (AUC) for intratumoral radiomics model was 0.956, 0.823, and 0.860 in the training cohort, test cohort, and external validation cohort, respectively. AUC for the peritumoral radiomics model was 0.929, 0.906, and 0.773 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for combined intra- and peritumoral radiomics model was 0.976, 0.918, and 0.874 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for clinical-radiomics nomogram was 0.989, 0.777, and 0.870 in the training cohort, test cohort, and external validation cohort, respectively. The prognostic model constructed by combining intra- and peritumoral radiomics signature score (radscore)-based TD and MRI-reported lymph nodes metastasis (LNM) indicated good performance for predicting 3-year RFS, with AUC of 0.824, 0.865, and 0.738 in the training cohort, test cohort and external validation cohort, respectively. CONCLUSION Combined intra- and peritumoral radiomics model showed good performance for predicting TD. Combining intra- and peritumoral radscore-based TD and MRI-reported LNM indicated the recurrence risk. CLINICAL RELEVANCE STATEMENT Combined intra- and peritumoral radiomics model could help accurately predict tumor deposits. Combining this predictive model-based tumor deposits with MRI-reported lymph node metastasis was associated with relapse risk of rectal cancer after surgery. KEY POINTS • Combined intra- and peritumoral radiomics model provided better diagnostic performance than that of intratumoral and peritumoral radiomics model alone for predicting TD in rectal cancer. • The predictive performance of the clinical-radiomics nomogram was not improved compared with the combined intra- and peritumoral radiomics model for predicting TD. • The prognostic model constructed by combining intra- and peritumoral radscore-based TD and MRI-reported LNM showed good performance for assessing 3-year RFS.
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Affiliation(s)
- Hang Li
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Xiao-Li Chen
- Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, Chengdu, 610000, China
| | | | - Yi-Sha Liu
- Department of Pathology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Zhen-Lin Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ming-Hui Pang
- Department of Gastrointestinal Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Hong Pu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China.
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Bian X, Du S, Yue Z, Gao S, Zhao R, Huang G, Guo L, Peng C, Zhang L. Potential Antihuman Epidermal Growth Factor Receptor 2 Target Therapy Beneficiaries: The Role of MRI-Based Radiomics in Distinguishing Human Epidermal Growth Factor Receptor 2-Low Status of Breast Cancer. J Magn Reson Imaging 2023; 58:1603-1614. [PMID: 36763035 DOI: 10.1002/jmri.28628] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/21/2023] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Multiparametric MRI radiomics could distinguish human epidermal growth factor receptor 2 (HER2)-positive from HER2-negative breast cancers. However, its value for further distinguishing HER2-low from HER2-negative breast cancers has not been investigated. PURPOSE To investigate whether multiparametric MRI-based radiomics can distinguish HER2-positive from HER2-negative breast cancers (task 1) and HER2-low from HER2-negative breast cancers (task 2). STUDY TYPE Retrospective. POPULATION Task 1: 310 operable breast cancer patients from center 1 (97 HER2-positive and 213 HER2-negative); task 2: 213 HER2-negative patients (108 HER2-low and 105 HER2-zero); 59 patients from center 2 (16 HER2-positive, 27 HER2-low and 16 HER2-zero) for external validation. FIELD STRENGTH/SEQUENCE A 3.0 T/T1-weighted contrast-enhanced imaging (T1CE), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC). ASSESSMENT Patients in center 1 were assigned to a training and internal validation cohort at a 2:1 ratio. Intratumoral and peritumoral features were extracted from T1CE and ADC. After dimensionality reduction, the radiomics signatures (RS) of two tasks were developed using features from T1CE (RS-T1CE), ADC (RS-ADC) alone and T1CE + ADC combination (RS-Com). STATISTICAL TESTS Mann-Whitney U tests, the least absolute shrinkage and selection operator, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). RESULTS For task 1, RS-ADC yielded higher area under the ROC curve (AUC) in the training, internal, and external validation of 0.767/0.725/0.746 than RS-T1CE (AUC = 0.733/0.674/0.641). For task 2, RS-T1CE yielded higher AUC of 0.765/0.755/0.678 than RS-ADC (AUC = 0.706/0.608/0.630). For both of task 1 and task 2, RS-Com achieved the best performance with AUC of 0.793/0.778/0.760 and 0.820/0.776/0.711, respectively, and obtained higher clinical benefit in DCA compared with RS-T1CE and RS-ADC. The calibration curves of all RS demonstrated a good fitness. DATA CONCLUSION Multiparametric MRI radiomics could noninvasively and robustly distinguish HER2-positive from HER2-negative breast cancers and further distinguish HER2-low from HER2-negative breast cancers. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Xiaoqian Bian
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Siyao Du
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Zhibin Yue
- School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Si Gao
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Ruimeng Zhao
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Guoliang Huang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Liangcun Guo
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Can Peng
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Lina Zhang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
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Duan Y, Chen X, Li W, Li S, Zhang C. Multimodal radiomics and nomogram-based prediction of axillary lymph node metastasis in breast cancer: An analysis considering optimal peritumoral region. JOURNAL OF CLINICAL ULTRASOUND : JCU 2023; 51:1231-1241. [PMID: 37410710 DOI: 10.1002/jcu.23520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/25/2023] [Accepted: 06/27/2023] [Indexed: 07/08/2023]
Abstract
PURPOSE To explore the optimal peri-tumoral regions on ultrasound (US) images and investigate the performance of multimodal radiomics for predicting axillary lymph node metastasis (ALNM). METHODS This retrospective study included 326 patients (training cohort: n = 162, internal validation cohort: n = 74, external validation cohort: n = 90). Intra-tumoral region of interests (ROIs) were delineated on US and digital mammography (DM) images. Peri-tumoral ROI (PTR) on US images were gained by dilating actual 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 3.5 mm radius surrounding the tumor. Support vector machine (SVM) method was used to calculate the importance of radiomics features and to pick the 10 most important. Recursive feature elimination-SVM was used to evaluate the efficacy of models with different feature numbers used. RESULTS The PTR0.5mm yielded a maximum AUC of 0.802 (95% confidence interval (CI): 0.676-0.901) within the validation cohort using SVM classifier. The multimodal radiomics (intra-tumoral US and DM and US-based PTR0.5mm radiomics model) achieved the highest predictive ability (AUC = 0.888/0.844/0.835 and 95% CI = 0.829-0.936/0.741-0.929/0.752-0.896 for training/internal validation/external validation cohort, respectively). CONCLUSION The PTR0.5mm could be the optimal area for predicting ALNM. A favorable predictive accuracy for predicting ALNM was achieved using multimodal radiomics and its based nomogram.
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Affiliation(s)
- Yayang Duan
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaobo Chen
- Department of Radiology, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wanyan Li
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Siyao Li
- Department of Ultrasound, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Ding Y, Yang F, Han M, Li C, Wang Y, Xu X, Zhao M, Zhao M, Yue M, Deng H, Yang H, Yao J, Liu Y. Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning. NPJ Breast Cancer 2023; 9:58. [PMID: 37443117 DOI: 10.1038/s41523-023-00562-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The objective of our study is to develop a deep learning model based on clinicopathological data and digital pathological image of core needle biopsy specimens for predicting breast cancer lymph node metastasis. We collected 3701 patients from the Fourth Hospital of Hebei Medical University and 190 patients from four medical centers in Hebei Province. Integrating clinicopathological data and image features build multi-modal and multi-instance (MMMI) deep learning model to obtain the final prediction. For predicting with or without lymph node metastasis, the AUC was 0.770, 0.709, 0.809 based on the clinicopathological features, WSI and MMMI, respectively. For predicting four classification of lymph node status (no metastasis, isolated tumor cells (ITCs), micrometastasis, and macrometastasis), the prediction based on clinicopathological features, WSI and MMMI were compared. The AUC for no metastasis was 0.770, 0.709, 0.809, respectively; ITCs were 0.619, 0.531, 0.634, respectively; micrometastasis were 0.636, 0.617, 0.691, respectively; and macrometastasis were 0.748, 0.691, 0.758, respectively. The MMMI model achieved the highest prediction accuracy. For prediction of different molecular types of breast cancer, MMMI demonstrated a better prediction accuracy for any type of lymph node status, especially in the molecular type of triple negative breast cancer (TNBC). In the external validation sets, MMMI also showed better prediction accuracy in the four classification, with AUC of 0.725, 0.757, 0.525, and 0.708, respectively. Finally, we developed a breast cancer lymph node metastasis prediction model based on a MMMI model. Through all cases tests, the results showed that the overall prediction ability was high.
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Affiliation(s)
- Yan Ding
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | - Fan Yang
- AI Lab, Tencent, 518057, Shenzhen, China
| | - Mengxue Han
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | - Chunhui Li
- Department of Pathology, Chengde Medical University Affiliated Hospital, 067000, Chengde, Hebei, China
| | - Yanan Wang
- Department of Pathology, Affiliated Hospital of Hebei University, 071000, Baoding, Hebei, China
| | - Xin Xu
- Department of Pathology, Xingtai People's Hospital, 054000, Xingtai, Hebei, China
| | - Min Zhao
- Department of Pathology, First Hospital of Qinhuangdao, 066000, Qinhuangdao, Hebei, China
| | - Meng Zhao
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | - Meng Yue
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | - Huiyan Deng
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | - Huichai Yang
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | | | - Yueping Liu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China.
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Li H, Chen XL, Liu H, Lu T, Li ZL. MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer. Front Oncol 2023; 12:1087882. [PMID: 36686763 PMCID: PMC9846353 DOI: 10.3389/fonc.2022.1087882] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/06/2022] [Indexed: 01/06/2023] Open
Abstract
Purpose To establish and evaluate multiregional T2-weighted imaging (T2WI)-based clinical-radiomics model for predicting lymph node metastasis (LNM) and prognosis in patients with resectable rectal cancer. Methods A total of 346 patients with pathologically confirmed rectal cancer from two hospitals between January 2019 and December 2021 were prospectively enrolled. Intra- and peritumoral features were extracted separately, and least absolute shrinkage and selection operator regression was applied for feature selection. Radiomics signatures were built using the selected features from different regions. The clinical-radiomic nomogram was developed by combining the intratumoral and peritumoral radiomics signatures score (radscore) and the most predictive clinical parameters. The diagnostic performances of the nomogram and clinical model were evaluated using the area under the receiver operating characteristic curve (AUC). The prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis. Results The intratumoral radscore (radscore 1) included four features, the peritumoral radscore (radscore 2) included five features, and the combined intratumoral and peritumoural radscore (radscore 3) included ten features. The AUCs for radscore 3 were higher than that of radscore 1 in training cohort (0.77 vs. 0.71, P=0.182) and internal validation cohort (0.76 vs. 0.64, P=0.041). The AUCs for radscore 3 were higher than that of radscore 2 in training cohort (0.77 vs. 0.74, P=0.215) and internal validation cohort (0.76 vs. 0.68, P=0.083). A clinical-radiomic nomogram showed a higher AUC compared with the clinical model in training cohort (0.84 vs. 0.67, P<0.001) and internal validation cohort (0.78 vs. 0.64, P=0.038) but not in external validation (0.72 vs. 0.76, P=0.164). Multivariate Cox analysis showed MRI-reported extramural vascular invasion (EMVI) (HR=1.099, 95%CI: 0.462-2.616; P=0.031) and clinical-radiomic nomogram-based LNM (HR=2.232, 95%CI:1.238-7.439; P=0.017) were independent risk factors for assessing 3-year RFS. Combined clinical-radiomic nomogram based LNM and MRI-reported EMVI showed good performance in training cohort (AUC=0.748), internal validation cohort (AUC=0.706) and external validation (AUC=0.688) for predicting 3-year RFS. Conclusion A clinical-radiomics nomogram exhibits good performance for predicting preoperative LNM. Combined clinical-radiomic nomogram based LNM and MRI-reported EMVI showed clinical potential for assessing 3-year RFS.
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Affiliation(s)
- Hang Li
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Xiao-li Chen
- Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, Chengdu, China
| | | | - Tao Lu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China,*Correspondence: Tao Lu, ; Zhen-lin Li,
| | - Zhen-lin Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China,*Correspondence: Tao Lu, ; Zhen-lin Li,
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Cheng Y, Xu S, Wang H, Wang X, Niu S, Luo Y, Zhao N. Intra- and peri-tumoral radiomics for predicting the sentinel lymph node metastasis in breast cancer based on preoperative mammography and MRI. Front Oncol 2022; 12:1047572. [PMID: 36578933 PMCID: PMC9792138 DOI: 10.3389/fonc.2022.1047572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 11/21/2022] [Indexed: 12/14/2022] Open
Abstract
Purpose This study aims to investigate values of intra- and peri-tumoral regions in the mammography and magnetic resonance imaging (MRI) image for prediction of sentinel lymph node metastasis (SLNM) in invasive breast cancer (BC). Methods This study included 208 patients with invasive BC between Spe. 2017 and Apr. 2021. All patients underwent preoperative digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI) scans. Radiomics features were extracted from manually outlined intratumoral regions, and automatically dilated peritumoral tumor regions in each modality. The least absolute shrinkage and selection operator (LASSO) regression was used to select key features from each region to develop radiomics signatures (RSs). Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity and negative predictive value (NPV) were calculated to evaluate performance of the RSs. Results Intra- and peri-tumoral regions of BC can provide complementary information on the SLN status. In each modality, the Com-RSs derived from combined intra- and peri-tumoral regions always yielded higher AUCs than the Intra-RSs or Peri-RSs. A total of 10 and 11 features were identified as the most important predictors from mammography (DM plus DBT) and MRI (DCE-MRI plus DWI), respectively. The DCE-MRI plus DWI generated higher AUCs compared with DM plus DBT in the training (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.897 vs. 0.846) and validation (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.826 vs. 0.786) cohort. Conclusions Radiomics features from intra- and peri-tumoral regions can provide complementary information to identify the SLNM in both mammography and MRI. The DCE-MRI plus DWI generated lower specificity, but higher AUC, accuracy, sensitivity and negative predictive value compared with DM plus DBT.
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Affiliation(s)
- Yuan Cheng
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Shu Xu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Haotian Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Shuxian Niu
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Nannan Zhao
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China,*Correspondence: Nannan Zhao,
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The potential of predictive and prognostic breast MRI (P2-bMRI). Eur Radiol Exp 2022; 6:42. [PMID: 35989400 PMCID: PMC9393116 DOI: 10.1186/s41747-022-00291-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 06/08/2022] [Indexed: 11/10/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.
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Zhang S, Wang X, Yang Z, Zhu Y, Zhao N, Li Y, He J, Sun H, Xie Z. Intra- and Peritumoral Radiomics Model Based on Early DCE-MRI for Preoperative Prediction of Molecular Subtypes in Invasive Ductal Breast Carcinoma: A Multitask Machine Learning Study. Front Oncol 2022; 12:905551. [PMID: 35814460 PMCID: PMC9263840 DOI: 10.3389/fonc.2022.905551] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 05/23/2022] [Indexed: 12/29/2022] Open
Abstract
Purpose The aim of this study is to investigate radiomics features extracted from the optimal peritumoral region and the intratumoral area on the early phase of dynamic contrast-enhanced MRI (DCE-MRI) for predicting molecular subtypes of invasive ductal breast carcinoma (IDBC). Methods A total of 422 IDBC patients with immunohistochemical and fluorescence in situ hybridization results from two hospitals (Center 1: 327 cases, Center 2: 95 cases) who underwent preoperative DCE-MRI were retrospectively enrolled. After image preprocessing, radiomic features were extracted from the intratumoral area and four peritumoral regions on DCE-MRI from two centers, and selected the optimal peritumoral region. Based on the intratumoral, peritumoral radiomics features, and clinical–radiological characteristics, five radiomics models were constructed through support vector machine (SVM) in multiple classification tasks related to molecular subtypes and visualized by nomogram. The performance of radiomics models was evaluated by receiver operating characteristic curves, confusion matrix, calibration curves, and decision curve analysis. Results A 6-mm peritumoral size was defined the optimal peritumoral region in classification tasks of hormone receptor (HR)-positive vs others, triple-negative breast cancer (TNBC) vs others, and HR-positive vs human epidermal growth factor receptor 2 (HER2)-enriched vs TNBC, and 8 mm was applied in HER2-enriched vs others. The combined clinical–radiological and radiomics models in three binary classification tasks (HR-positive vs others, HER2-enriched vs others, TNBC vs others) obtained optimal performance with AUCs of 0.838, 0.848, and 0.930 in the training cohort, respectively; 0.827, 0.813, and 0.879 in the internal test cohort, respectively; and 0.791, 0.707, and 0.852 in the external test cohort, respectively. Conclusion Radiomics features in the intratumoral and peritumoral regions of IDBC on DCE-MRI had a potential to predict the HR-positive, HER2-enriched, and TNBC molecular subtypes preoperatively.
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Affiliation(s)
- Shuhai Zhang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Xiaolei Wang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Zhao Yang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Yun Zhu
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Nannan Zhao
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Yang Li
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Jie He
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haitao Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Haitao Sun, ; Zongyu Xie,
| | - Zongyu Xie
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
- *Correspondence: Haitao Sun, ; Zongyu Xie,
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Ma M, Jiang Y, Qin N, Zhang X, Zhang Y, Wang X, Wang X. A Radiomics Model for Preoperative Predicting Sentinel Lymph Node Metastasis in Breast Cancer Based on Dynamic Contrast-Enhanced MRI. Front Oncol 2022; 12:884599. [PMID: 35734587 PMCID: PMC9207247 DOI: 10.3389/fonc.2022.884599] [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: 02/26/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To develop a radiomics model based on preoperative dynamic contrast-enhanced MRI (DCE-MRI) to identify sentinel lymph node (SLN) metastasis in breast cancer (BC) patients. Materials and Methods The MRI images and clinicopathological data of 142 female primary BC patients from January 2017 to December 2018 were included in this study. The patients were randomly divided into the training and testing cohorts at a ratio of 7:3. Four types of radiomics models were built: 1) a radiomics model based on the region of interest (ROI) of breast tumor; 2) a radiomics model based on the ROI of intra- and peri-breast tumor; 3) a radiomics model based on the ROI of axillary lymph node (ALN); 4) a radiomics model based on the ROI of ALN and breast tumor. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to assess the performance of the three radiomics models. The technique for order of preference by similarity to ideal solution (TOPSIS) through decision matrix analysis was used to select the best model. Results Models 1, 2, 3, and 4 yielded AUCs of 0.977, 0.999, 0.882, and 1.000 in the training set and 0.699, 0.817, 0.906, and 0.696 in the testing set, respectively, in terms of predicting SLN metastasis. Model 3 had the highest AUC in the testing cohort, and only the difference from Model 1 was statistically significant (p = 0.022). DCA showed that Model 3 yielded a greater net benefit to predict SLN metastasis than the other three models in the testing cohort. The best model analyzed by TOPSIS was Model 3, and the method's names for normalization, dimensionality reduction, feature selection, and classification are mean, principal component analysis (PCA), ANOVA, and support vector machine (SVM), respectively. Conclusion ALN radiomics feature extraction on DCE-MRI is a potential method to evaluate SLN status in BC patients.
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Affiliation(s)
- Mingming Ma
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yuan Jiang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Naishan Qin
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yaofeng Zhang
- Beijing Smart Tree Medical Technology Co., Ltd., Beijing, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co., Ltd., Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
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Xu H, Liu J, Chen Z, Wang C, Liu Y, Wang M, Zhou P, Luo H, Ren J. Intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI for preoperative prediction of intraductal component in invasive breast cancer. Eur Radiol 2022; 32:4845-4856. [DOI: 10.1007/s00330-022-08539-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 12/01/2021] [Accepted: 12/22/2021] [Indexed: 12/11/2022]
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MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status. Acad Radiol 2022; 29 Suppl 1:S126-S134. [PMID: 34876340 DOI: 10.1016/j.acra.2021.10.026] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/19/2021] [Accepted: 10/27/2021] [Indexed: 12/27/2022]
Abstract
RATIONALE AND OBJECTIVES In patients with breast cancer (BC), lymphovascular invasion (LVI) status is considered an important prognostic factor. We aimed to develop machine learning (ML)-based radiomics models for the prediction of LVI status in patients with BC, using preoperative MRI images. MATERIALS AND METHODS This retrospective study included patients with BC with known LVI status and preoperative MRI. The dataset was split into training and unseen testing sets by stratified sampling with a 2:1 ratio. 2D and 3D radiomic features were extracted from contrast-enhanced T1 weighted images (C+T1W) and apparent diffusion coefficient (ADC) maps. The reliability of the features was assessed with two radiologists' segmentation data. Dimension reduction was done with reliability analysis, multi-collinearity analysis, removal of low-variance features, and feature selection. ML models were created with base, tuned, and boosted random forest algorithms. RESULT A total of 128 lesions (LVI-positive, 76; LVI-negative, 52) were included. The best model performance was achieved with tunning and boosting model based on 3D ADC maps and selected four radiomic features. The area under the curve and accuracy were 0.726 and 63.5% in the training data, 0.732 and 76.7% in the test data, respectively. The overall sensitivity and positive predictive values were 68% and 69.6% in the training data, 84.6% and 78.6% in the test data, respectively. CONCLUSION ML and radiomics based on 3D segmentation of ADC maps can be used to predict LVI status in BC, with satisfying performance.
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Shi J, Dong Y, Jiang W, Qin F, Wang X, Cui L, Liu Y, Jin Y, Luo Y, Jiang X. MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study. Magn Reson Imaging 2021; 88:1-8. [PMID: 34968703 DOI: 10.1016/j.mri.2021.12.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/29/2021] [Accepted: 12/22/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE To evaluate intra- and preitumoral radiomics on the contrast-enhanced T1-weighted (CE-T1) and T2-weighted (T2W) MRI for predicting the LNM, and develop a nomogram for potential clinical uses. METHODS We enrolled 169 cervical cancer cases who underwent CE-T1 and T2W MR scans from two hospitals between Dec. 2015 and Sep. 2021. Intra- and peritumoral features were extracted separately and selected by the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures were built using the selected features from different regions. Clinical parameters were evaluated by statistical analysis. The nomogram was developed combining the multi-regional radiomics signature and the most predictive clinical parameters. RESULTS Five radiomics features were finally selected from the peritumoral regions with 1 and 3 mm distances in the CE-T1 and T2W MRI, respectively. The nomogram incorporating multi-regional combined radiomics signature, MR-reported LN status and tumor diameter achieved the highest AUCs in the training (nomogram vs. combined radiomics signature vs. clinical model, 0.891 vs. 0.830 vs. 0.812), internal validation (nomogram vs. combined radiomics signature vs. clinical model, 0.863 vs. 0.853 vs. 0.816) and external validation (nomogram vs. combined radiomics signature vs. clinical model, 0.804 vs. 0.701 vs. 0.787) cohort. DCA suggested good clinical usefulness of our developed models. CONCLUSION The current work suggested clinical potential for intra- and peritumoral radiomics with multi-modal MRI for preoperative predicting LNM.
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Affiliation(s)
- Jiaxin Shi
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang 110122, PR China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, PR China
| | - Wenyan Jiang
- Scientific Research and Academic Department, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, PR China
| | - Fengying Qin
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, PR China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, PR China
| | - Linpeng Cui
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang 110122, PR China
| | - Yan Liu
- The Affiliated Reproductive Hospital of China Medical University, Liaoning Research Institute of Family Planning, Shenyang 110031, PR China
| | - Ying Jin
- The Affiliated Reproductive Hospital of China Medical University, Liaoning Research Institute of Family Planning, Shenyang 110031, PR China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, PR China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang 110122, PR China.
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Gan L, Ma M, Liu Y, Liu Q, Xin L, Cheng Y, Xu L, Qin N, Jiang Y, Zhang X, Wang X, Ye J. A Clinical–Radiomics Model for Predicting Axillary Pathologic Complete Response in Breast Cancer With Axillary Lymph Node Metastases. Front Oncol 2021; 11:786346. [PMID: 34993145 PMCID: PMC8724774 DOI: 10.3389/fonc.2021.786346] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/29/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose To develop a clinical–radiomics model based on radiomics features extracted from MRI and clinicopathologic factors for predicting the axillary pathologic complete response (apCR) in breast cancer (BC) patients with axillary lymph node (ALN) metastases. Materials and Methods The MR images and clinicopathologic data of 248 eligible invasive BC patients at the Peking University First Hospital from January 2013 to December 2020 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and the presence of ALN metastases was confirmed through cytology pre-NAC. The data from January 2013 to December 2018 were randomly divided into the training and validation sets in a ratio of 7:3, and the data from January 2019 to December 2020 served as the independent testing set. The following three types of prediction models were investigated in this study. 1) A clinical model: the model was built by independently predicting clinicopathologic factors through logistic regression. 2) Radiomics models: we used an automatic segmentation model based on deep learning to segment the axillary areas, visible ALNs, and breast tumors on post-NAC dynamic contrast-enhanced MRI. Radiomics features were then extracted from the region of interest (ROI). Radiomics models were built based on different ROIs or their combination. 3) A clinical–radiomics model: it was built by integrating radiomics signature and independent predictive clinical factors by logistic regression. All models were assessed using a receiver operating characteristic curve analysis and by calculating the area under the curve (AUC). Results The clinical model yielded AUC values of 0.759, 0.787, and 0.771 in the training, validation, and testing sets, respectively. The radiomics model based on the combination of MRI features of breast tumors and visible ALNs yielded the best AUC values of 0.894, 0.811, and 0.806 in the training, validation, and testing sets, respectively. The clinical–radiomics model yielded AUC values of 0.924, 0.851, and 0.878 in the training, validation, and testing sets, respectively, for predicting apCR. Conclusion We developed a clinical–radiomics model by integrating radiomics signature and clinical factors to predict apCR in BC patients with ALN metastases post-NAC. It may help the clinicians to screen out apCR patients to avoid lymph node dissection.
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Affiliation(s)
- Liangyu Gan
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Mingming Ma
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yinhua Liu
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Qian Liu
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Ling Xin
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Yuanjia Cheng
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Ling Xu
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Naishan Qin
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yuan Jiang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
- *Correspondence: Xiaoying Wang, ; Jingming Ye,
| | - Jingming Ye
- Breast Disease Center, Peking University First Hospital, Beijing, China
- *Correspondence: Xiaoying Wang, ; Jingming Ye,
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Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 2021; 19:132-146. [PMID: 34663898 DOI: 10.1038/s41571-021-00560-7] [Citation(s) in RCA: 230] [Impact Index Per Article: 76.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2021] [Indexed: 12/14/2022]
Abstract
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.
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Affiliation(s)
- Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Nathaniel Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Tempus Labs, Chicago, IL, USA
| | - Amit Gupta
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. .,Louis Stokes Cleveland Veterans Medical Center, Cleveland, OH, USA.
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Features from MRI texture analysis associated with survival outcomes in triple-negative breast cancer patients. Breast Cancer 2021; 29:164-173. [PMID: 34529241 DOI: 10.1007/s12282-021-01294-1] [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: 02/12/2021] [Accepted: 09/13/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE The purpose of the study is to evaluate the associations between intratumoral or peritumoral textural features derived from pretreatment magnetic resonance imaging (MRI) and recurrence-free survival (RFS) in triple-negative breast cancer (TNBC) patients. METHODS Forty-three patients with TNBC who underwent preoperative MRI between February 2008 and March 2014 were included. We performed two-dimensional texture analysis on the intratumoral or peritumoral region of interest (ROI) on axial of T2-weighted image (T2WI), dynamic contrast-enhanced (DCE)-MRI and DCE-MRI subtraction images. We also analyzed histopathological data. Cox proportional hazards models were used to investigate associations with survival outcomes. RESULTS Twelve of the 43 patients (27.9%) had recurrence disease, at a median of 32.5 months follow-up (1.4-61.5 months). In univariate analysis, nine texture features in T2WI and DCE-MRI subtraction images were significantly associated with RFS. In multivariate analysis, intratumoral difference entropy in DCE-MRI subtraction images in the initial phase (hazard ratio 11.71; 95% confidence interval (CI) [1.41, 97.00]; p value 0.023) and, peritumoral difference variance in DCE-MRI subtraction images in the delayed phase (hazard ratio 9.60; 95% CI [1.98, 46.51]; p value 0.005), were both independently associated with RFS. Moreover, multivariate analysis revealed the presence of lymphovascular invasion as independently associated with RFS (hazard ratio 8.13; 95% CI [2.16, 30.30]; p value 0.002). CONCLUSIONS At pretreatment MRI, an intratumoral and peritumoral quantitative approach using texture analysis has the potential to serve as a prognostic marker in patients with TNBC.
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Wang S, Sun Y, Li R, Mao N, Li Q, Jiang T, Chen Q, Duan S, Xie H, Gu Y. Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions. Eur Radiol 2021; 32:639-649. [PMID: 34189600 DOI: 10.1007/s00330-021-08134-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 05/16/2021] [Accepted: 06/01/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To conduct perilesional region radiomics analysis of contrast-enhanced mammography (CEM) images to differentiate benign and malignant breast lesions. METHODS AND MATERIALS This retrospective study included patients who underwent CEM from November 2017 to February 2020. Lesion contours were manually delineated. Perilesional regions were automatically obtained. Seven regions of interest (ROIs) were obtained for each lesion, including the lesion ROI, annular perilesional ROIs (1 mm, 3 mm, 5 mm), and lesion + perilesional ROIs (1 mm, 3 mm, 5 mm). Overall, 4,098 radiomics features were extracted from each ROI. Datasets were divided into training and testing sets (1:1). Seven classification models using features from the seven ROIs were constructed using LASSO regression. Model performance was assessed by the AUC with 95% CI. RESULTS Overall, 190 women with 223 breast lesions (101 benign; 122 malignant) were enrolled. In the testing set, the annular perilesional ROI of 3-mm model showed the highest AUC of 0.930 (95% CI: 0.882-0.977), followed by the annular perilesional ROI of 1 mm model (AUC = 0.929; 95% CI: 0.881-0.978) and the lesion ROI model (AUC = 0.909; 95% CI: 0.857-0.961). A new model was generated by combining the predicted probabilities of the lesion ROI and annular perilesional ROI of 3-mm models, which achieved a higher AUC in the testing set (AUC = 0.940). CONCLUSIONS Annular perilesional radiomics analysis of CEM images is useful for diagnosing breast cancers. Adding annular perilesional information to the radiomics model built on the lesion information may improve the diagnostic performance. KEY POINTS • Radiomics analysis of the annular perilesional region of 3 mm in CEM images may provide valuable information for the differential diagnosis of benign and malignant breast lesions. • The radiomics information from the lesion region and the annular perilesional region may be complementary. Combining the predicted probabilities of the models constructed by the features from the two regions may improve the diagnostic performance of radiomics models.
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Affiliation(s)
- Simin Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Yuqi Sun
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, 200032, China
| | - Ruimin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Shandong, 264000, China
| | - Qin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Tingting Jiang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Qianqian Chen
- GE Healthcare China, No. 1 Huatuo Road, Shanghai, 210000, China
| | - Shaofeng Duan
- GE Healthcare China, No. 1 Huatuo Road, Shanghai, 210000, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Shandong, 264000, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
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