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Xu K, Hua M, Mai T, Ren X, Fang X, Wang C, Ge M, Qian H, Xu M, Zhang R. A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-023-00923-9. [PMID: 38424277 DOI: 10.1007/s10278-023-00923-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 03/02/2024]
Abstract
This study aims to develop an MRI-based radiomics model to assess the likelihood of recurrence in luminal B breast cancer. The study analyzed medical images and clinical data from 244 patients with luminal B breast cancer. Of 244 patients, 35 had experienced recurrence and 209 had not. The patients were randomly divided into the training set (51.5 ± 12.5 years old; n = 171) and the test set (51.7 ± 11.3 years old; n = 73) in a ratio of 7:3. The study employed univariate and multivariate Cox regression along with the least absolute shrinkage and selection operator (LASSO) regression methods to select radiomics features and calculate a risk score. A combined model was constructed by integrating the risk score with the clinical and pathological characteristics. The study identified two radiomics features (GLSZM and GLRLM) from DCE-MRI that were used to calculate a risk score. The AUCs were 0.860 and 0.868 in the training set and 0.816 and 0.714 in the testing set for 3- and 5-year recurrence risk, respectively. The combined model incorporating the risk score, pN, and endocrine therapy showed improved predictive power, with AUCs of 0.857 and 0.912 in the training set and 0.943 and 0.945 in the testing set for 3- and 5-year recurrence risk, respectively. The calibration curve of the combined model showed good consistency between predicted and measured values. Our study developed an MRI-based radiomics model that integrates clinical and radiomics features to assess the likelihood of recurrence in luminal B breast cancer. The model shows promise for improving clinical risk stratification and treatment decision-making.
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Affiliation(s)
- Kepei Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Meiqi Hua
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Ting Mai
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Xiaojing Ren
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Xiaozheng Fang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Chunjie Wang
- Department of Radiology, Hangzhou First People's Hospital, Zhejiang Province, Hangzhou, China
| | - Min Ge
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Hua Qian
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China.
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China.
| | - Ruixin Zhang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China.
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China.
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Wang L, Cong R, Chen Z, Li D, Feng B, Liang M, Wang S, Ma X, Zhao X. Determination of prognostic predictors in patients with solitary hepatocellular carcinoma: histogram analysis of multiparametric MRI. Abdom Radiol (NY) 2023; 48:3362-3372. [PMID: 37561148 DOI: 10.1007/s00261-023-04015-8] [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: 05/27/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023]
Abstract
PURPOSE To evaluate the histogram parameters of preoperative multiparametric magnetic resonance imaging (MRI) and clinical-radiological (CR) characteristics as prognostic predictors in patients with solitary hepatocellular carcinoma ≤ 5 cm and to determine the optimal time window for histogram analysis. METHODS We retrospectively included 151 patients who underwent preoperative MRI between January 2012 and December 2017. All patients were randomly separated into training and validation cohorts (n = 105 and 46). Eight whole-lesion histogram parameters were extracted from T2-weighted images, apparent diffusion coefficient maps, and dynamic contrast-enhanced images. Univariate and multivariate logistic regression analyses were performed to evaluate these histogram parameters and CR variables related to early recurrence (ER) and recurrence-free survival. A nomogram was derived from the clinical-radiological-histogram (CRH) model that incorporated these risk factors. Kaplan-Meier survival analysis was performed to evaluate the prognostic performance of the CRH model. RESULTS In total, 151 patients (male: female, 130: 21; median age, 54.46 ± 9.09 years) were evaluated. Multivariate logistic regression analysis revealed that the significant risk factors of ER were Mean Absolute Deviation and Minimum in the histogram analysis of the delayed phase images, as well as three important CR variables: albumin-bilirubin grade, microvascular invasion, and tumor size. The nomogram built by incorporating these risk factors showed satisfactory predictive ability in the training and validation cohorts with AUC values of 0.747 and 0.765, respectively. Furthermore, the prognostic nomogram can effectively classify patients into high- and low-risk groups (p < 0.05). CONCLUSION Multiparametric MRI-derived histogram parameters provide additional value in predicting patient prognosis. The CRH model may be a useful and noninvasive method for achieving prognostic stratification and personalized disease management.
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Affiliation(s)
- Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Rong Cong
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Zhaowei Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Dengfeng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing, 100176, China
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Lu D, Long X, Fu W, Liu B, Zhou X, Sun S. Predictive value of machine learning for breast cancer recurrence: a systematic review and meta-analysis. J Cancer Res Clin Oncol 2023; 149:10659-10674. [PMID: 37302114 DOI: 10.1007/s00432-023-04967-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/02/2023] [Indexed: 06/13/2023]
Abstract
PURPOSE Recurrence of breast cancer leads to a high lifetime risk and a low 5 year survival rate. Researchers have used machine learning to predict the risk of recurrence in patients with breast cancer, but the predictive performance of machine learning remains controversial. Hence, this study aimed to explore the accuracy of machine learning in predicting breast cancer recurrence risk and aggregate predictive variables to provide guidance for the development of subsequent risk scoring systems. METHODS We searched Pubmed, EMBASE, Cochrane, and Web of Science. The risk of bias in the included studies was evaluated using prediction model risk of bias assessment tool (PROBAST). Meta-regression was adopted to explore whether there was a significant difference in the recurrence time by machine learning. RESULTS Thirty-four studies involving 67,560 subjects were included, among whom 8695 experienced breast cancer recurrence. The c-index of prediction models was 0.814 (95%CI 0.802-0.826) and 0.770 (95%CI 0.737-0.803) in the training and validation sets, respectively; the sensitivity and specificity were 0.69 (95% CI 0.64-0.74), 0.89 (95% CI 0.86-0.92) in the training, and 0.64 (95% CI 0.58-0.70), 0.88 (95% CI 0.82-0.92) in the validation, respectively. Age, histological grading, and lymph node status are the most commonly used variables in model construction. Attention should be paid to unhealthy lifestyles such as drinking, smoking and BMI as modeling variables. Risk prediction models based on machine learning have long-term monitoring value for breast cancer population, and subsequent studies should consider using large-sample and multi-center data to establish risk equations for verification. CONCLUSION Machine learning may be used as a predictive tool for breast cancer recurrence. Currently, there is a lack of effective and universally applicable machine learning models in clinical practice. We expect to incorporate multi-center studies in the future and attempt to develop tools for predicting breast cancer recurrence risk, so as to effectively identify populations at high risk of recurrence and develop personalized follow-up strategies and prognostic interventions to reduce the risk of recurrence.
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Affiliation(s)
- Dongmei Lu
- Radiology Department, Gansu Provincial Hospital, No. 204, Donggang West Road, Gansu, Lanzhou, China
| | - Xiaozhou Long
- Radiology Department, Gansu Provincial Hospital, No. 204, Donggang West Road, Gansu, Lanzhou, China
| | - Wenjie Fu
- Radiology Department, Gansu Provincial Hospital, No. 204, Donggang West Road, Gansu, Lanzhou, China
| | - Bo Liu
- Radiology Department, Gansu Provincial Hospital, No. 204, Donggang West Road, Gansu, Lanzhou, China
| | - Xing Zhou
- Radiology Department, Gansu Provincial Hospital, No. 204, Donggang West Road, Gansu, Lanzhou, China
| | - Shaoqin Sun
- Radiology Department, Gansu Provincial Hospital, No. 204, Donggang West Road, Gansu, Lanzhou, China.
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Wu R, Luo J, Wan H, Zhang H, Yuan Y, Hu H, Feng J, Wen J, Wang Y, Li J, Liang Q, Gan F, Zhang G. Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database. PLoS One 2023; 18:e0280340. [PMID: 36701415 PMCID: PMC9879508 DOI: 10.1371/journal.pone.0280340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 12/26/2022] [Indexed: 01/27/2023] Open
Abstract
INTRODUCTION Many researchers used machine learning (ML) to predict the prognosis of breast cancer (BC) patients and noticed that the ML model had good individualized prediction performance. OBJECTIVE The cohort study was intended to establish a reliable data analysis model by comparing the performance of 10 common ML algorithms and the the traditional American Joint Committee on Cancer (AJCC) stage, and used this model in Web application development to provide a good individualized prediction for others. METHODS This study included 63145 BC patients from the Surveillance, Epidemiology, and End Results database. RESULTS Through the performance of the 10 ML algorithms and 7th AJCC stage in the optimal test set, we found that in terms of 5-year overall survival, multivariate adaptive regression splines (MARS) had the highest area under the curve (AUC) value (0.831) and F1-score (0.608), and both sensitivity (0.737) and specificity (0.772) were relatively high. Besides, MARS showed a highest AUC value (0.831, 95%confidence interval: 0.820-0.842) in comparison to the other ML algorithms and 7th AJCC stage (all P < 0.05). MARS, the best performing model, was selected for web application development (https://w12251393.shinyapps.io/app2/). CONCLUSIONS The comparative study of multiple forecasting models utilizing a large data noted that MARS based model achieved a much better performance compared to other ML algorithms and 7th AJCC stage in individualized estimation of survival of BC patients, which was very likely to be the next step towards precision medicine.
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Affiliation(s)
- Ruiyang Wu
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Jing Luo
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Hangyu Wan
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Haiyan Zhang
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Yewei Yuan
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Huihua Hu
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Jinyan Feng
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Jing Wen
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Yan Wang
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Junyan Li
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Qi Liang
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Fengjiao Gan
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Gang Zhang
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
- * E-mail:
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Fan M, Wu X, Yu J, Liu Y, Wang K, Xue T, Zeng T, Chen S, Li L. Multiparametric MRI radiomics fusion for predicting the response and shrinkage pattern to neoadjuvant chemotherapy in breast cancer. Front Oncol 2023; 13:1057841. [PMID: 37207135 PMCID: PMC10189126 DOI: 10.3389/fonc.2023.1057841] [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: 09/30/2022] [Accepted: 04/19/2023] [Indexed: 05/21/2023] Open
Abstract
Purpose During neoadjuvant chemotherapy (NACT), breast tumor morphological and vascular characteristics are usually changed. This study aimed to evaluate the tumor shrinkage pattern and response to NACT by preoperative multiparametric magnetic resonance imaging (MRI), including dynamic contrast-enhanced MRI (DCE-MRI), diffuse weighted imaging (DWI) and T2 weighted imaging (T2WI). Method In this retrospective analysis, female patients with unilateral unifocal primary breast cancer were included for predicting tumor pathologic/clinical response to NACT (n=216, development set, n=151 and validation set, n=65) and for discriminating the tumor concentric shrinkage (CS) pattern from the others (n=193; development set, n=135 and validation set, n=58). Radiomic features (n=102) of first-order statistical, morphological and textural features were calculated on tumors from the multiparametric MRI. Single- and multiparametric image-based features were assessed separately and were further combined to feed into a random forest-based predictive model. The predictive model was trained in the testing set and assessed on the testing dataset with an area under the curve (AUC). Molecular subtype information and radiomic features were fused to enhance the predictive performance. Results The DCE-MRI-based model showed higher performance (AUCs of 0.919, 0.830 and 0.825 for tumor pathologic response, clinical response and tumor shrinkage patterns, respectively) than either the T2WI or the ADC image-based model. An increased prediction performance was achieved by a model with multiparametric MRI radiomic feature fusion. Conclusions All these results demonstrated that multiparametric MRI features and their information fusion could be of important clinical value for the preoperative prediction of treatment response and shrinkage pattern.
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Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Xilin Wu
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Jiadong Yu
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Yueyue Liu
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Kailang Wang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Tailong Xue
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Tieyong Zeng
- Department of Mathematics, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Shujun Chen
- Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- *Correspondence: Shujun Chen, ; Lihua Li,
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
- *Correspondence: Shujun Chen, ; Lihua Li,
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Chen Y, Tang W, Liu W, Li R, Wang Q, Shen X, Gong J, Gu Y, Peng W. Multiparametric
MR
Imaging Radiomics Signatures for Assessing the Recurrence Risk of
ER
+/
HER2
− Breast Cancer Quantified With 21‐Gene Recurrence Score. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Yang Chen
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Wei Tang
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Wei Liu
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Ruimin Li
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Qifeng Wang
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
- Department of Pathology Fudan University Shanghai Cancer Center Shanghai China
| | - Xigang Shen
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Jing Gong
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Yajia Gu
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Weijun Peng
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
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Mazo C, Aura C, Rahman A, Gallagher WM, Mooney C. Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review. J Pers Med 2022; 12:jpm12091496. [PMID: 36143281 PMCID: PMC9500690 DOI: 10.3390/jpm12091496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 12/31/2022] Open
Abstract
Breast cancer is the most common disease among women, with over 2.1 million new diagnoses each year worldwide. About 30% of patients initially presenting with early stage disease have a recurrence of cancer within 10 years. Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment. Artificial intelligence strategies that can predict the risk of recurrence of breast cancer could help breast cancer clinicians avoid ineffective overtreatment. Despite its significance, most breast cancer recurrence datasets are insufficiently large, not publicly available, or imbalanced, making these studies more difficult. This systematic review investigates the role of artificial intelligence in the prediction of breast cancer recurrence. We summarise common techniques, features, training and testing methodologies, metrics, and discuss current challenges relating to implementation in clinical practice. We systematically reviewed works published between 1 January 2011 and 1 November 2021 using the methodology of Kitchenham and Charter. We leveraged Springer, Google Scholar, PubMed, and IEEE search engines. This review found three areas that require further work. First, there is no agreement on artificial intelligence methodologies, feature predictors, or assessment metrics. Second, issues such as sampling strategies, missing data, and class imbalance problems are rarely addressed or discussed. Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment. We conclude that predicting breast cancer recurrence remains an open problem despite the use of artificial intelligence.
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Affiliation(s)
- Claudia Mazo
- UCD School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Claudia Aura
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Arman Rahman
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, D04 V1W8 Dublin, Ireland
| | - William M. Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Catherine Mooney
- UCD School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland
- Correspondence:
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Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy. Cancers (Basel) 2022; 14:cancers14163848. [PMID: 36010844 PMCID: PMC9405765 DOI: 10.3390/cancers14163848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/29/2022] [Accepted: 08/04/2022] [Indexed: 11/17/2022] Open
Abstract
In current clinical practice, it is difficult to predict whether a patient receiving neoadjuvant chemotherapy (NAC) for breast cancer is likely to encounter recurrence after treatment and have the cancer recur locally in the breast or in other areas of the body. We explore the use of clinical history, immunohistochemical markers, and multiparametric magnetic resonance imaging (DCE, ADC, Dixon) to predict the risk of post-treatment recurrence within five years. We performed a retrospective study on a cohort of 1738 patients from Institut Curie and analyzed the data using classical machine learning, image processing, and deep learning. Our results demonstrate the ability to predict recurrence prior to NAC treatment initiation using each modality alone, and the possible improvement achieved by combining the modalities. When evaluated on holdout data, the multimodal model achieved an AUC of 0.75 (CI: 0.70, 0.80) and 0.57 specificity at 0.90 sensitivity. We then stratified the data based on known prognostic biomarkers. We found that our models can provide accurate recurrence predictions (AUC > 0.89) for specific groups of women under 50 years old with poor prognoses. A version of our method won second place at the BMMR2 Challenge, with a very small margin from being first, and was a standout from the other challenge entries.
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Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med 2021; 127:39-56. [PMID: 34704213 DOI: 10.1007/s11547-021-01423-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/14/2021] [Indexed: 12/11/2022]
Abstract
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis for breast MRI, but ultrafast images, T2-weighted images, and diffusion-weighted images are also taken to improve the characteristics of the lesion. Such multiparametric MRI with numerous morphological and functional data poses new challenges to radiologists, and thus, new tools for reliable, reproducible, and high-volume quantitative assessments are warranted. In this context, radiomics, which is an emerging field of research involving the conversion of digital medical images into mineable data for clinical decision-making and outcome prediction, has been gaining ground in oncology. Recent development in artificial intelligence has promoted radiomics studies in various fields including breast cancer treatment and numerous studies have been conducted. However, radiomics has shown a translational gap in clinical practice, and many issues remain to be solved. In this review, we will outline the steps of radiomics workflow and investigate clinical application of radiomics focusing on breast MRI based on published literature, as well as current discussion about limitations and challenges in radiomics.
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Affiliation(s)
- Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
| | - Satoko Ishigaki
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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