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Singh A, Paruthy SB, Belsariya V, Chandra J N, Singh SK, Manivasagam SS, Choudhary S, Kumar MA, Khera D, Kuraria V. Revolutionizing Breast Healthcare: Harnessing the Role of Artificial Intelligence. Cureus 2023; 15:e50203. [PMID: 38192969 PMCID: PMC10772314 DOI: 10.7759/cureus.50203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2023] [Indexed: 01/10/2024] Open
Abstract
Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.
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Affiliation(s)
- Arun Singh
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Shivani B Paruthy
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Vivek Belsariya
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Nemi Chandra J
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Sunil Kumar Singh
- Surgical Oncology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | | | - Sushila Choudhary
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - M Anil Kumar
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Dhananjay Khera
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Vaibhav Kuraria
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
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Campana A, Gandomkar Z, Giannotti N, Reed W. The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review. J Med Radiat Sci 2023; 70:462-478. [PMID: 37534540 PMCID: PMC10715343 DOI: 10.1002/jmrs.709] [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: 02/28/2023] [Accepted: 07/16/2023] [Indexed: 08/04/2023] Open
Abstract
Radiomics is an emerging field that aims to extract and analyse a comprehensive set of quantitative features from medical images. This scoping review is focused on MRI-based radiomic features for the molecular profiling of breast tumours and the implications of this work for predicting patient outcomes. A thorough systematic literature search and outcome extraction were performed to identify relevant studies published in MEDLINE/PubMed (National Centre for Biotechnology Information), EMBASE and Scopus from 2015 onwards. The following information was retrieved from each article: study purpose, study design, extracted radiomic features, machine learning technique(s), sample size/characteristics, statistical result(s) and implications on patient outcomes. Based on the study purpose, four key themes were identified in the included 63 studies: tumour subtype classification (n = 35), pathologically complete response (pCR) prediction (n = 15), lymph node metastasis (LNM) detection (n = 7) and recurrence rate prediction (n = 6). In all four themes, reported accuracies widely varied among the studies, for example, area under receiver characteristics curve (AUC) for detecting LNM ranged from 0.72 to 0.91 and the AUC for predicting pCR ranged from 0.71 to 0.99. In all four themes, combining radiomic features with clinical data improved the predictive models. Preliminary results of this study showed radiomics potential to characterise the whole tumour heterogeneity, with clear implications for individual-targeted treatment. However, radiomics is still in the pre-clinical phase, currently with an insufficient number of large multicentre studies and those existing studies are often limited by insufficient methodological transparency and standardised workflow. Consequently, the clinical translation of existing studies is currently limited.
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Affiliation(s)
- Annalise Campana
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Ziba Gandomkar
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Nicola Giannotti
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Warren Reed
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
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3
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Lu D, Yan Y, Jiang M, Sun S, Jiang H, Lu Y, Zhang W, Zhou X. Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis. Front Oncol 2023; 13:1173090. [PMID: 37664048 PMCID: PMC10469000 DOI: 10.3389/fonc.2023.1173090] [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: 02/24/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
Purpose This study summarized the previously-published studies regarding the use of radiomics-based predictive models for the identification of breast cancer-associated prognostic factors, which can help clinical decision-making and follow-up strategy. Materials and methods This study has been pre-registered on PROSPERO. PubMed, Embase, Cochrane Library, and Web of Science were searched, from inception to April 23, 2022, for studies that used radiomics for prognostic prediction of breast cancer patients. Then the search was updated on July 18, 2023. Quality assessment was conducted using the Radiomics Quality Score, and meta-analysis was performed using R software. Results A total of 975 articles were retrieved, and 13 studies were included, involving 5014 participants and 35 prognostic models. Among the models, 20 models were radiomics-based and the other 15 were based on clinical or pathological information. The primary outcome was Disease-free Survival (DFS). The retrieved studies were screened using LASSO, and Cox Regression was applied for modeling. The mean RQS was 18. The c-index of radiomics-based models for DFS prediction was 0.763 (95%CI 0.718-0.810) in the training set and 0.702 (95%CI 0.637-0.774) in the validation set. The c-index of combination models was 0.807 (95%CI0.736-0.885) in the training set and 0.840 (95%CI 0.794-0.888) in the validation set. There was no significant change in the c-index of DFS at 1, 2, 3, and over 5 years of follow-up. Conclusion This study has proved that radiomics-based prognostic models are of great predictive performance for the prognosis of breast cancer patients. combination model shows significantly enhanced predictive performance. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022332392.
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Affiliation(s)
- Dongmei Lu
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yuke Yan
- The Second Department of General Surgery, Gansu Provincial Hospital, Lanzhou, China
| | - Min Jiang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Shaoqin Sun
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Haifeng Jiang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yashan Lu
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Wenwen Zhang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Xing Zhou
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
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Jia T, Lv Q, Cai X, Ge S, Sang S, Zhang B, Yu C, Deng S. Radiomic signatures based on pretreatment 18F-FDG PET/CT, combined with clinicopathological characteristics, as early prognostic biomarkers among patients with invasive breast cancer. Front Oncol 2023; 13:1210125. [PMID: 37576897 PMCID: PMC10415070 DOI: 10.3389/fonc.2023.1210125] [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: 04/21/2023] [Accepted: 07/06/2023] [Indexed: 08/15/2023] Open
Abstract
Purpose The aim of this study was to investigate the predictive role of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in the prognostic risk stratification of patients with invasive breast cancer (IBC). To achieve this, we developed a clinicopathologic-radiomic-based model (C-R model) and established a nomogram that could be utilized in clinical practice. Methods We retrospectively enrolled a total of 91 patients who underwent preoperative 18F-FDG PET/CT and randomly divided them into training (n=63) and testing cohorts (n=28). Radiomic signatures (RSs) were identified using the least absolute shrinkage and selection operator (LASSO) regression algorithm and used to compute the radiomic score (Rad-score). Patients were assigned to high- and low-risk groups based on the optimal cut-off value of the receiver operating characteristic (ROC) curve analysis for both Rad-score and clinicopathological risk factors. Univariate and multivariate Cox regression analyses were performed to determine the association between these variables and progression-free survival (PFS) or overall survival (OS). We then plotted a nomogram integrating all these factors to validate the predictive performance of survival status. Results The Rad-score, age, clinical M stage, and minimum standardized uptake value (SUVmin) were identified as independent prognostic factors for predicting PFS, while only Rad-score, age, and clinical M stage were found to be prognostic factors for OS in the training cohort. In the testing cohort, the C-R model showed superior performance compared to single clinical or radiomic models. The concordance index (C-index) values for the C-R model, clinical model, and radiomic model were 0.816, 0.772, and 0.647 for predicting PFS, and 0.882, 0.824, and 0.754 for OS, respectively. Furthermore, decision curve analysis (DCA) and calibration curves demonstrated that the C-R model had a good ability for both clinical net benefit and application. Conclusion The combination of clinicopathological risks and baseline PET/CT-derived Rad-score could be used to evaluate the prognosis in patients with IBC. The predictive nomogram based on the C-R model further enhanced individualized estimation and allowed for more accurate prediction of patient outcomes.
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Affiliation(s)
- Tongtong Jia
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qingfu Lv
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaowei Cai
- Department of Nuclear Medicine, The Affiliated Suqian First People’s Hospital of Nanjing Medical University, Suqian, China
| | - Shushan Ge
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shibiao Sang
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Bin Zhang
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chunjing Yu
- Department of Nuclear Medicine, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Shengming Deng
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
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Cè M, Caloro E, Pellegrino ME, Basile M, Sorce A, Fazzini D, Oliva G, Cellina M. Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2022; 3:795-816. [PMID: 36654817 PMCID: PMC9834285 DOI: 10.37349/etat.2022.00113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/28/2022] [Indexed: 12/28/2022] Open
Abstract
The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.
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Affiliation(s)
- Maurizio Cè
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Elena Caloro
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Maria E. Pellegrino
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Mariachiara Basile
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Adriana Sorce
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | | | - Giancarlo Oliva
- Department of Radiology, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
| | - Michaela Cellina
- Department of Radiology, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
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6
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Ma M, Gan L, Liu Y, Jiang Y, Xin L, Liu Y, Qin N, Cheng Y, Liu Q, Xu L, Zhang Y, Wang X, Zhang X, Ye J, Wang X. Radiomics features based on automatic segmented MRI images: Prognostic biomarkers for triple-negative breast cancer treated with neoadjuvant chemotherapy. Eur J Radiol 2021; 146:110095. [PMID: 34890936 DOI: 10.1016/j.ejrad.2021.110095] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 11/12/2021] [Accepted: 11/30/2021] [Indexed: 12/31/2022]
Abstract
PURPOSE To establish radiomics prediction models based on automatic segmented magnetic resonance imaging (MRI) for predicting the systemic recurrence of triple-negative breast cancer (TNBC) in patients after neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS A total of 147 patients with TNBC who underwent NAC between January 2009 and December 2018 were enrolled in this study. Clinicopathologic data were collected, and the differences between the recurrent and nonrecurrent patients were analyzed by univariate and multivariate analyses. Patients were randomly divided into training and testing sets. The training set consisted of 104 patients (recurrence: 22, nonrecurrence: 82), and the testing set consisted of 43 patients (recurrence: 9, nonrecurrence: 34). To establish the radiomics prediction model, we used a deep learning segmentation model to automatically segment tumor areas on dynamiccontrast-enhanced-MRI images of pre- and post-NAC magnetic resonance examinations. Radiomics features were then extracted from the tumor areas. Three MRI radiomics models were developed in the training set: a radiomics model based on pre-NAC MRI features (model 1), a radiomics model based on post-NAC MRI features (model 2), and a radiomics model based on both pre- and post-NAC MRI features (model 3). A clinical model for predicting systemic recurrence was built in the training set using independent clinical prediction factors. Receiver operating characteristic curve analysis was used to evaluate the performance of the radiomics and clinical models. RESULTS The clinical model yielded areas under the curve (AUCs) of 0.747 in the training set and 0.737 in the testing set in terms of predicting systemic recurrence. Models 1, 2, and 3 yielded AUCs of 0.879, 0.91, and 0.963 in the training set and 0.814, 0.802, and 0.933 in the testing set, respectively, in terms of predicting systemic recurrence. All of the radiomics models had achieved higher AUCs than the clinical model in the testing set. DeLong test was used to compare the AUCs between the models and indicated that the predictive performance of model 3 was better than the clinical model, and the difference was statistically significant (p < 0.05). CONCLUSION The radiomics models built based on the combination of pre- and post-NAC MRI features showed good performance in predicting whether patients with TNBC will have systemic recurrence within 3 years post-NAC. This can help us non-invasively identify which patients are at high risk of recurrence post-NAC, so that we can strengthen follow-up and treatment of these patients. Then the prognosis of these patients might be improved.
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Affiliation(s)
- Mingming Ma
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Liangyu Gan
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Yinhua Liu
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Yuan Jiang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Ling Xin
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Yi Liu
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Naishan Qin
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yuanjia Cheng
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Qian Liu
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Ling Xu
- Breast Disease Center, 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
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Jingming Ye
- Breast Disease Center, Peking University First Hospital, Beijing, China.
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China.
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Xia B, Wang H, Wang Z, Qian Z, Xiao Q, Liu Y, Shao Z, Zhou S, Chai W, You C, Gu Y. A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy. Front Genet 2021; 12:783513. [PMID: 34868273 PMCID: PMC8632946 DOI: 10.3389/fgene.2021.783513] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 10/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background: To investigate whether the radiomics signature (Rad-score) of DCE-MRI images obtained in triple-negative breast cancer (TNBC) patients before neoadjuvant chemotherapy (NAC) is associated with disease-free survival (DFS). Develop and validate an intuitive nomogram based on radiomics signatures, MRI findings, and clinicopathological variables to predict DFS. Methods: Patients (n = 150) from two hospitals who received NAC from August 2011 to May 2017 were diagnosed with TNBC by pathological biopsy, and follow-up through May 2020 was retrospectively analysed. Patients from one hospital (n = 109) were used as the training group, and patients from the other hospital (n = 41) were used as the validation group. ROIs were drawn on 1.5 T MRI T1W enhancement images of the whole volume of the tumour obtained with a 3D slicer. Radiomics signatures predicting DFS were identified, optimal cut-off value for Rad-score was determined, and the associations between DFS and radiomics signatures, MRI findings, and clinicopathological variables were analysed. A nomogram was developed and validated for individualized DFS estimation. Results: The median follow-up time was 53.5 months, and 45 of 150 (30.0%) patients experienced recurrence and metastasis. The optimum cut-off value of the Rad-score was 0.2528, which stratified patients into high- and low-risk groups for DFS in the training group (p<0.001) and was validated in the external validation group. Multivariate analysis identified three independent indicators: multifocal/centric disease status, pCR status, and Rad-score. A nomogram based on these factors showed discriminatory ability, the C-index of the model was 0.834 (95% CI, 0.761-0.907) and 0.868 (95% CI, 0.787-949) in the training and the validation groups, respectively, which is better than clinicoradiological nomogram(training group: C-index = 0.726, 95% CI = 0.709-0.743; validation group: C-index = 0.774,95% CI = 0.743-0.805). Conclusion: The Rad-score derived from preoperative MRI features is an independent biomarker for DFS prediction in patients with TNBC to NAC, and the combined radiomics nomogram improved individualized DFS estimation.
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Affiliation(s)
- Bingqing Xia
- International Peace Maternity and Child Health Hospital, Shanghai, China.,Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zhe Wang
- Shanghai United Imaging Medical Technology Co., Ltd., Shanghai, China
| | - Zhaoxia Qian
- International Peace Maternity and Child Health Hospital, Shanghai, China
| | - Qin Xiao
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yin Liu
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhimin Shao
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shuling Zhou
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Weimin Chai
- Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chao You
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yajia Gu
- Shanghai Cancer Center, Fudan University, Shanghai, China
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Li H, Chen L, Zeng H, Liao Q, Ji J, Ma X. Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma. Front Oncol 2021; 11:636451. [PMID: 34646756 PMCID: PMC8504715 DOI: 10.3389/fonc.2021.636451] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 08/31/2021] [Indexed: 02/05/2023] Open
Abstract
Background Colon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD. Methods We downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF). Results There were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group. Conclusions These results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.
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Affiliation(s)
- Hui Li
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Linyan Chen
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Hao Zeng
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Qimeng Liao
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Jianrui Ji
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
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9
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Li Q, Xiao Q, Li J, Wang Z, Wang H, Gu Y. Value of Machine Learning with Multiphases CE-MRI Radiomics for Early Prediction of Pathological Complete Response to Neoadjuvant Therapy in HER2-Positive Invasive Breast Cancer. Cancer Manag Res 2021; 13:5053-5062. [PMID: 34234550 PMCID: PMC8253937 DOI: 10.2147/cmar.s304547] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/04/2021] [Indexed: 12/15/2022] Open
Abstract
Background To assess the value of radiomics based on multiphases contrast-enhanced magnetic resonance imaging (CE-MRI) for early prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with human epithelial growth factor receptor 2 (HER2) positive invasive breast cancer. Methods A total of 127 patients with newly diagnosed primary HER2 positive invasive breast cancer underwent CE-MRI before NAT and performed surgery after NAT. Radiomic features were extracted from the 1st postcontrast CE-MRI phase (CE1) and multi-phases CE-MRI (CEm),respectively. With selected features using a forward stepwise regression, 23 machine learning classifiers based on CE1 and CEm were constructed respectively for differentiating pCR and non-pCR patients. The performances of classifiers were assessed and compared by their accuracy, sensitivity, specificity and AUC (area under curve). The optimal machine learning classification was used to discriminate pCR vs non-pCR in mass and non-mass groups, uni-focal and unilateral multi-focal groups, respectively. Results For the task of pCR classification, 6 radiomic features from CE1 and 6 from CEm were selected for the construction of machine learning models, respectively. The linear SVM based on CEm outperformed the logistic regression model using CE1 with an AUC of 0.84 versus 0.69. In mass and non-mass enhancement groups, the accuracy of linear SVM achieved 84% and 76%. Whereas in unifocal and unilateral multifocal cases, 79% and 75% accuracy were achieved by linear SVM. Conclusion Multiphases CE-MRI imaging may offer more heterogeneity information in the tumor and provide a non-invasive approach for early prediction of pCR to NAT in patients with HER2-positive invasive breast cancer.
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Affiliation(s)
- Qin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Qin Xiao
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Jianwei Li
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.,Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Zhe Wang
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, People's Republic of China.,Human Phenome Institute, Fudan University, Shanghai, People's Republic of China
| | - He Wang
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, People's Republic of China.,Human Phenome Institute, Fudan University, Shanghai, People's Republic of China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
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An MRI-Based Radiomic Prognostic Index Predicts Poor Outcome and Specific Genetic Alterations in Endometrial Cancer. J Clin Med 2021; 10:jcm10030538. [PMID: 33540589 PMCID: PMC7867221 DOI: 10.3390/jcm10030538] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/25/2021] [Accepted: 01/29/2021] [Indexed: 12/14/2022] Open
Abstract
Integrative tumor characterization linking radiomic profiles to corresponding gene expression profiles has the potential to identify specific genetic alterations based on non-invasive radiomic profiling in cancer. The aim of this study was to develop and validate a radiomic prognostic index (RPI) based on preoperative magnetic resonance imaging (MRI) and assess possible associations between the RPI and gene expression profiles in endometrial cancer patients. Tumor texture features were extracted from preoperative 2D MRI in 177 endometrial cancer patients. The RPI was developed using least absolute shrinkage and selection operator (LASSO) Cox regression in a study cohort (n = 95) and validated in an MRI validation cohort (n = 82). Transcriptional alterations associated with the RPI were investigated in the study cohort. Potential prognostic markers were further explored for validation in an mRNA validation cohort (n = 161). The RPI included four tumor texture features, and a high RPI was significantly associated with poor disease-specific survival in both the study cohort (p < 0.001) and the MRI validation cohort (p = 0.030). The association between RPI and gene expression profiles revealed 46 significantly differentially expressed genes in patients with a high RPI versus a low RPI (p < 0.001). The most differentially expressed genes, COMP and DMBT1, were significantly associated with disease-specific survival in both the study cohort and the mRNA validation cohort. In conclusion, a high RPI score predicts poor outcome and is associated with specific gene expression profiles in endometrial cancer patients. The promising link between radiomic tumor profiles and molecular alterations may aid in developing refined prognostication and targeted treatment strategies in endometrial cancer.
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