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Chen R, Zhou B, Liu W, Gan H, Liu X, Zhou L. Association of Pathological Features and Multiparametric MRI-Based Radiomics With TP53-Mutated Prostate Cancer. J Magn Reson Imaging 2024; 60:1134-1145. [PMID: 38153859 DOI: 10.1002/jmri.29186] [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: 07/11/2023] [Revised: 11/30/2023] [Accepted: 12/02/2023] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND TP53 mutations are associated with prostate cancer (PCa) prognosis and therapy. PURPOSE To develop TP53 mutation classification models for PCa using MRI radiomics and clinicopathological features. STUDY TYPE Retrospective. POPULATION 388 patients with PCa from two centers (Center 1: 281 patients; Center 2: 107 patients). Cases from Center 1 were randomly divided into training and internal validation sets (7:3). Cases from Center 2 were used for external validation. FIELD STRENGTH/SEQUENCE 3.0T/T2-weighted imaging, dynamic contrast-enhanced imaging, diffusion-weighted imaging. ASSESSMENT Each patient's index tumor lesion was manually delineated on the above MRI images. Five clinicopathological and 428 radiomics features were obtained from each lesion. Radiomics features were selected by least absolute shrinkage and selection operator and binary logistic regression (LR) analysis, while clinicopathological features were selected using Mann-Whitney U test. Radiomics models were constructed using LR, support vector machine (SVM), and random forest (RF) classifiers. Clinicopathological-radiomics combined models were constructed using the selected radiomics and clinicopathological features with the aforementioned classifiers. STATISTICAL TESTS Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis and area under the curve (AUC). P value <0.05 indicates statistically significant. RESULTS In the internal validation set, the radiomics model had an AUC of 0.74 with the RF classifier, which was significantly higher than LR (AUC = 0.61), but similar to SVM (AUC = 0.69; P = 0.422). For the combined model, the AUC of RF model was 0.84, which was significantly higher than LR (0.64), but similar to SVM (0.80; P = 0.548). Both the combined RF and combined SVM models showed significantly higher AUCs than the radiomics models. In the external validation set, the combined RF and combined SVM models showed AUCs of 0.83 and 0.82. DATA CONCLUSION Pathological-radiomics combined models with RF, SVM show the association of TP53 mutations and pathological-radiomics features of PCa. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ruchuan Chen
- Shanghai Institute of Medical imaging, Shanghai, China
- Department of Radiology, Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bingni Zhou
- Department of Radiology, Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Liu
- Department of Radiology, Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hualei Gan
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xiaohang Liu
- Department of Radiology, Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liangping Zhou
- Department of Radiology, Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Liu W, Chen W, Xia J, Lu Z, Fu Y, Li Y, Tan Z. Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer. BMC Med Imaging 2024; 24:91. [PMID: 38627678 PMCID: PMC11020672 DOI: 10.1186/s12880-024-01255-y] [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: 02/13/2023] [Accepted: 03/21/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics. METHODS Two cohorts from the Cancer Imaging Archive project were used, one as the training cohort (TCGA-Breast, n = 88) and one as the validation cohort (Breast-MRI-NACT Pilot, n = 57). Radiomics and deep learning features were extracted from preoperative DCE-MRI. After dual selection by principal components analysis (PCA) and relief methods, radiomics and deep learning models for predicting LNM were constructed by the random forest (RF) method. A post-fusion strategy was used to construct the DLR nomograms (DLRNs) for predicting LNM. The performance of the models was evaluated using the receiver operating characteristic (ROC) curve and Delong test. In the training cohort, transcriptome data were downloaded from the UCSC Xena online database, and biological pathways related to the DLR phenotypes were identified. Finally, hub genes were identified to obtain DLR gene expression (RadDeepGene) scores. RESULTS DLRNs were based on area under curve (AUC) evaluation (training cohort, AUC = 0.98; validation cohort, AUC = 0.87), which were higher than single radiomics models or GoogLeNet models. The Delong test (radiomics model, P = 0.04; GoogLeNet model, P = 0.01) also validated the above results in the training cohorts, but they were not statistically significant in the validation cohort. The GoogLeNet phenotypes were related to multiple classical tumor signaling pathways, characterizing the biological significance of immune response, signal transduction, and cell death. In all, 20 genes related to GoogLeNet phenotypes were identified, and the RadDeepGene score represented a high risk of LNM (odd ratio = 164.00, P < 0.001). CONCLUSIONS DLRNs combining radiomics and deep learning features of DCE-MRI images improved the preoperative prediction of LNM in breast cancer, and the potential biological characteristics of DLRN were identified through genomics.
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Affiliation(s)
- Wenci Liu
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Wubiao Chen
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Jun Xia
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Zhendong Lu
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Youwen Fu
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Yuange Li
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China
| | - Zhi Tan
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, 524001, Zhanjiang, Guangdong Province, P. R. China.
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Liang S, Xu S, Zhou S, Chang C, Shao Z, Wang Y, Chen S, Huang Y, Guo Y. IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes. J Genet Genomics 2024; 51:443-453. [PMID: 37783335 DOI: 10.1016/j.jgg.2023.09.010] [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: 09/04/2023] [Revised: 09/18/2023] [Accepted: 09/20/2023] [Indexed: 10/04/2023]
Abstract
Investigating correlations between radiomic and genomic profiling in breast cancer (BC) molecular subtypes is crucial for understanding disease mechanisms and providing personalized treatment. We present a well-designed radiogenomic framework image-gene-gene set (IMAGGS), which detects multi-way associations in BC subtypes by integrating radiomic and genomic features. Our dataset consists of 721 patients, each of whom has 12 ultrasound (US) images captured from different angles and gene mutation data. To better characterize tumor traits, 12 multi-angle US images are fused using two distinct strategies. Then, we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm, deviating from the prevalent one-to-one relationship pattern observed in previous studies. Key radiomic and genomic features are screened using these associations. In addition, gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC subtypes. We further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and diseases. Taken together, IMAGGS provides a comprehensive characterization for diseases by associating imaging, genes, and gene sets, paving the way for biological interpretation of radiomics and development of targeted therapy.
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Affiliation(s)
- Shuyu Liang
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China; The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200032, China
| | - Sicheng Xu
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai 200433, China
| | - Shichong Zhou
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Cai Chang
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Zhiming Shao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yuanyuan Wang
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China; The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200032, China
| | - Sheng Chen
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Yunxia Huang
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Yi Guo
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China; The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200032, China.
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Li K, Ji J, Li S, Yang M, Che Y, Xu Z, Zhang Y, Wang M, Fang Z, Luo L, Wu C, Lai X, Dong J, Zhang X, Zhao N, Liu Y, Wang W. Analysis of the Correlation and Prognostic Significance of Tertiary Lymphoid Structures in Breast Cancer: A Radiomics-Clinical Integration Approach. J Magn Reson Imaging 2024; 59:1206-1217. [PMID: 37526043 DOI: 10.1002/jmri.28900] [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: 03/07/2023] [Revised: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND Tertiary lymphoid structures (TLSs) are potential prognostic indicators. Radiomics may help reduce unnecessary invasive operations. PURPOSE To analyze the association between TLSs and prognosis, and to establish a nomogram model to evaluate the expression of TLSs in breast cancer (BC) patients. STUDY TYPE Retrospective. POPULATION Two hundred forty-two patients with localized primary BC (confirmed by surgery) were divided into BC + TLS group (N = 122) and BC - TLS group (N = 120). FIELD STRENGTH/SEQUENCE 3.0T; Caipirinha-Dixon-TWIST-volume interpolated breath-hold sequence for dynamic contrast-enhanced (DCE) MRI and inversion-recovery turbo spin echo sequence for T2-weighted imaging (T2WI). ASSESSMENT Three models for differentiating BC + TLS and BC - TLS were developed: 1) a clinical model, 2) a radiomics signature model, and 3) a combined clinical and radiomics (nomogram) model. The overall survival (OS), distant metastasis-free survival (DMFS), and disease-free survival (DFS) were compared to evaluate the prognostic value of TLSs. STATISTICAL TESTS LASSO algorithm and ANOVA were used to select highly correlated features. Clinical relevant variables were identified by multivariable logistic regression. Model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), and through decision curve analysis (DCA). The Kaplan-Meier method was used to calculate the survival rate. RESULTS The radiomics signature model (training: AUC 0.766; test: AUC 0.749) and the nomogram model (training: AUC 0.820; test: AUC 0.749) showed better validation performance than the clinical model. DCA showed that the nomogram model had a higher net benefit than the other models. The median follow-up time was 52 months. While there was no significant difference in 3-year OS (P = 0.22) between BC + TLS and BC - TLS patients, there were significant differences in 3-year DFS and 3-year DMFS between the two groups. DATA CONCLUSION The nomogram model performs well in distinguishing the presence or absence of TLS. BC + TLS patients had higher long-term disease control rates and better prognoses than those without TLS. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Kezhen Li
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, China
- Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China
| | - Juan Ji
- Department of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Simin Li
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, China
- Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China
| | - Man Yang
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China
- Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yurou Che
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China
- Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhu Xu
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, China
- Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China
| | - Yiyao Zhang
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China
- Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Mei Wang
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China
- Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Zengyi Fang
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China
- Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Liping Luo
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China
- Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Chuan Wu
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin Lai
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Juan Dong
- Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, China
- Department of Chest, Meishan Cancer Hospital, Meishan, China
| | - Xinlan Zhang
- Department of Breast Surgery, Chengdu Women's and Children's Hospital, Chengdu, China
| | - Na Zhao
- Department of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yang Liu
- Department of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Weidong Wang
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, China
- Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China
- Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Lu L, Ma D, Xi Z. Coexpression of TP53, BIM, and PTEN Enhances the Therapeutic Efficacy of Non-Small-Cell Lung Cancer. Biomacromolecules 2024; 25:792-808. [PMID: 38237562 DOI: 10.1021/acs.biomac.3c00988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2024]
Abstract
For non-small-cell lung cancer (NSCLC), the ubiquitous occurrence of concurrent multiple genomic alterations poses challenges to single-gene therapy. To increase therapeutic efficacy, we used the branch-PCR method to develop a multigene nanovector, NP-TP53-BIM-PTEN, that carried three therapeutic gene expression cassettes for coexpression. NP-TP53-BIM-PTEN exhibited a uniform size of 104.8 ± 24.2 nm and high serum stability. In cell transfection tests, NP-TP53-BIM-PTEN could coexpress TP53, BIM, and PTEN in NCI-H1299 cells and induce cell apoptosis with a ratio of up to 94.9%. Furthermore, NP-TP53-BIM-PTEN also inhibited cell proliferation with a ratio of up to 42%. In a mouse model bearing an NCI-H1299 xenograft tumor, NP-TP53-BIM-PTEN exhibited a stronger inhibitory effect on the NCI-H1299 xenograft tumor than the other test vectors without any detectable side effects. These results exhibited the potential of NP-TP53-BIM-PTEN as an effective and safe multigene nanovector to enhance NSCLC therapy efficacy, which will provide a framework for genome therapy with multigene combinations.
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Affiliation(s)
- Liqing Lu
- State Key Laboratory of Elemento-Organic Chemistry and Department of Chemical Biology, National Engineering Research Center of Pesticide (Tianjin), College of Chemistry, Nankai University, Tianjin 300071, China
| | - Dejun Ma
- State Key Laboratory of Elemento-Organic Chemistry and Department of Chemical Biology, National Engineering Research Center of Pesticide (Tianjin), College of Chemistry, Nankai University, Tianjin 300071, China
| | - Zhen Xi
- State Key Laboratory of Elemento-Organic Chemistry and Department of Chemical Biology, National Engineering Research Center of Pesticide (Tianjin), College of Chemistry, Nankai University, Tianjin 300071, China
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Yao J, Zhou W, Xu S, Jia X, Zhou J, Chen X, Zhan W. Machine Learning-Based Breast Tumor Ultrasound Radiomics for Pre-operative Prediction of Axillary Sentinel Lymph Node Metastasis Burden in Early-Stage Invasive Breast Cancer. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:229-236. [PMID: 37951821 DOI: 10.1016/j.ultrasmedbio.2023.10.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 09/18/2023] [Accepted: 10/08/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVE The aim of the work described here was to assess the application of ultrasound (US) radiomics with machine learning (ML) classifiers to the prediction of axillary sentinel lymph node metastasis (SLNM) burden in early-stage invasive breast cancer (IBC). METHODS In this study, 278 early-stage IBC patients with at least one SLNM (195 in the training set and 83 in the test set) were studied at our institution. Pathologic SLNM burden was used as the reference standard. The US radiomics features of breast tumors were extracted by using 3D-Slicer and PyRadiomics software. Four ML classifiers-linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and decision tree (DT)-were used to construct radiomics models for the prediction of SLNM burden. The combined clinicopathologic-radiomics models were also assessed with respect to sensitivity, specificity, accuracy and areas under the curve (AUCs). RESULTS Among the US radiomics models, the SVM classifier achieved better predictive performance with an AUC of 0.920 compared with RF (AUC = 0.874), LDA (AUC = 0.835) and DT (AUC = 0.800) in the test set. The clinicopathologic model had low efficacy, with AUCs of 0.678 and 0.710 in the training and test sets, respectively. The combined clinicopathologic (C) factors and SVM classifier (C + SVM) model improved the predictive ability with an AUC of 0.934, sensitivity of 86.7%, specificity of 89.9% and accuracy of 91.0% in the test set. CONCLUSION ML-based US radiomics analysis, as a novel and promising predictive tool, is conducive to a precise clinical treatment strategy.
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Affiliation(s)
- Jiejie Yao
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Zhou
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shangyan Xu
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaohong Jia
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianqiao Zhou
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaosong Chen
- Department of Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Guo R, Yu Y, Huang Y, Lin M, Liao Y, Hu Y, Li Q, Peng C, Zhou J. A nomogram model combining ultrasound-based radiomics features and clinicopathological factors to identify germline BRCA1/2 mutation in invasive breast cancer patients. Heliyon 2024; 10:e23383. [PMID: 38169922 PMCID: PMC10758804 DOI: 10.1016/j.heliyon.2023.e23383] [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: 08/29/2023] [Revised: 09/18/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Objective BRCA1/2 status is a key to personalized therapy for invasive breast cancer patients. This study aimed to explore the association between ultrasound radiomics features and germline BRCA1/2 mutation in patients with invasive breast cancer. Materials and methods In this retrospective study, 100 lesions in 92 BRCA1/2-mutated patients and 390 lesions in 357 non-BRCA1/2-mutated patients were included and randomly assigned as training and validation datasets in a ratio of 7:3. Gray-scale ultrasound images of the largest plane of the lesions were used for feature extraction. Maximum relevance minimum redundancy (mRMR) algorithm and multivariate logistic least absolute shrinkage and selection operator (LASSO) regression were used to select features. The multivariate logistic regression method was used to construct predictive models based on clinicopathological factors, radiomics features, or a combination of them. Results In the clinical model, age at first diagnosis, family history of BRCA1/2-related malignancies, HER2 status, and Ki-67 level were found to be independent predictors for BRCA1/2 mutation. In the radiomics model, 10 significant features were selected from the 1032 radiomics features extracted from US images. The AUCs of the radiomics model were not inferior to those of the clinical model in both training dataset [0.712 (95% CI, 0.647-0.776) vs 0.768 (95% CI, 0.704-0.835); p = 0.429] and validation dataset [0.705 (95% CI, 0.597-0.808) vs 0.723 (95% CI, 0.625-0.828); p = 0.820]. The AUCs of the nomogram model combining clinical and radiomics features were 0.804 (95% CI, 0.748-0.861) in the training dataset and 0.811 (95% CI, 0.724-0.894) in the validation dataset, which were proved significantly higher than those of the clinical model alone by DeLong's test (p = 0.041; p = 0.007). To be noted, the negative predictive values (NPVs) of the nomogram model reached a favorable 0.93 in both datasets. Conclusion This machine nomogram model combining ultrasound-based radiomics and clinical features exhibited a promising performance in identifying germline BRCA1/2 mutation in patients with invasive breast cancer and may help avoid unnecessary gene tests in clinical practice.
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Affiliation(s)
| | | | - Yini Huang
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, 510060, PR China
| | - Min Lin
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, 510060, PR China
| | - Ying Liao
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, 510060, PR China
| | - Yixin Hu
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, 510060, PR China
| | - Qing Li
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, 510060, PR China
| | - Chuan Peng
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, 510060, PR China
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, 510060, PR China
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Fan ZC, Zhang L, Yang GQ, Li S, Guo JT, Bai JJ, Wang B, Li Y, Wang L, Wang XC. MRI radiomics for predicting poor disease-free survival in muscle invasive bladder cancer: the results of the retrospective cohort study. Abdom Radiol (NY) 2024; 49:151-162. [PMID: 37804424 DOI: 10.1007/s00261-023-04028-3] [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: 06/02/2023] [Revised: 08/09/2023] [Accepted: 08/14/2023] [Indexed: 10/09/2023]
Abstract
OBJECTIVES To develop an MRI radiomic nomogram capable of identifying muscle invasive bladder cancer (MIBC) patients with high-risk molecular characteristics related to poor 2-year disease-free survival (DFS). METHODS We performed a retrospective analysis of DNA sequencing data, prognostic information, and radiomics features from 91 MIBC patients at stages T2-T4aN0M0 without history of immunotherapy. To identify risk stratification, we employed Cox regression based on TP53 mutation status and tumor mutational burden (TMB) level. Radiomics signatures were selected using the least absolute shrinkage and selection operator (LASSO) to construct a nomogram based on logistic regression for predicting the stratification in the training cohort. The predictive performance of the nomogram was assessed in the testing cohort using receiver operator curve (ROC), Hosmer-Lemeshow (HL) test, clinical impact curve (CIC), and decision curve analysis (DCA). RESULTS Among 91 participants, the mean TMB value was 3.3 mut/Mb, with 60 participants having TP53 mutations. Patients with TP53 mutations and a below-average TMB value were identified as high risk and had a significantly poor 2-year DFS (hazard ratio = 4.36, 95% CI 1.82-10.44, P < 0.001). LASSO identified five radiomics signatures that correlated with the risk stratification. In the testing cohort, the nomogram achieved an area under the ROC curve of 0.909 (95% CI 0.789-0.991) and an accuracy of 0.889 (95% CI 0.708-0.977). CONCLUSION The molecular risk stratification based on TP53 mutation status combined with TMB level is strongly associated with DFS in MIBC. Radiomics signatures can effectively predict this stratification and provide valuable information to clinical decision-making.
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Affiliation(s)
- Zhi-Chang Fan
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Lu Zhang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Guo-Qiang Yang
- Department of Radiology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Shuo Li
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Jun-Ting Guo
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Jing-Jing Bai
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Bin Wang
- Department of Radiology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Yan Li
- Department of Radiology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Le Wang
- Department of Radiology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Xiao-Chun Wang
- Department of Radiology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China.
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Lin Z, Gu W, Guo Q, Xiao M, Li R, Deng L, Li Y, Cui Y, Li H, Qiang J. Multisequence MRI-based radiomics model for predicting POLE mutation status in patients with endometrial cancer. Br J Radiol 2023; 96:20221063. [PMID: 37660398 PMCID: PMC10607390 DOI: 10.1259/bjr.20221063] [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: 11/12/2022] [Revised: 07/19/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVES Preoperative identification of POLE mutation status would help tailor the surgical procedure and adjuvant treatment strategy. This study aimed to explore the feasibility of developing a radiomics model to pre-operatively predict the pathogenic POLE mutation status in patients with EC. METHODS The retrospective study involved 138 patients with histopathologically confirmed EC (35 POLE-mutant vs 103 non-POLE-mutant). After selecting relevant features with a series of steps, three radiomics signatures were built based on axial fat-saturation T2WI, DWI, and CE-T1WI images, respectively. Then, two radiomics models which integrated features from T2WI + DWI and T2WI + DWI+CE-T1WI were further developed using multivariate logistic regression. The performance of the radiomics model was evaluated from discrimination, calibration, and clinical utility aspects. RESULTS Among all the models, radiomics model2 (RM2), which integrated features from all three sequences, showed the best performance, with AUCs of 0.885 (95%CI: 0.828-0.942) and 0.810 (95%CI: 0.653-0.967) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses indicated that RM2 had improvement in predicting POLE mutation status when compared with the single-sequence-based signatures and the radiomics model1 (RM1). The calibration curve, decision curve analysis, and clinical impact curve suggested favourable calibration and clinical utility of RM2. CONCLUSIONS The RM2, fusing features from three sequences, could be a potential tool for the non-invasive preoperative identification of patients with POLE-mutant EC, which is helpful for developing individualized therapeutic strategies. ADVANCES IN KNOWLEDGE This study developed a potential surrogate of POLE sequencing, which is cost-efficient and non-invasive.
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Affiliation(s)
| | - Weiyong Gu
- Department of Pathology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | | | - Meiling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | | | - Lin Deng
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Ying Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | | | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
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Ghosh A, Yekeler E, Teixeira SR, Dalal D, States L. Role of MRI radiomics for the prediction of MYCN amplification in neuroblastomas. Eur Radiol 2023; 33:6726-6735. [PMID: 37178203 DOI: 10.1007/s00330-023-09628-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 02/18/2023] [Accepted: 02/26/2023] [Indexed: 05/15/2023]
Abstract
OBJECTIVES We evaluate MR radiomics and develop machine learning-based classifiers to predict MYCN amplification in neuroblastomas. METHODS A total of 120 patients with neuroblastomas and baseline MR imaging examination available were identified of whom 74 (mean age ± standard deviation [SD] of 6 years and 2 months ± 4 years and 9 months; 43 females and 31 males, 14 MYCN amplified) underwent imaging at our institution. This was therefore used to develop radiomics models. The model was tested in a cohort of children with the same diagnosis but imaged elsewhere (n = 46, mean age ± SD: 5 years 11 months ± 3 years 9 months, 26 females and 14 MYCN amplified). Whole tumour volumes of interest were adopted to extract first-order histogram and second-order radiomics features. Interclass correlation coefficient and maximum relevance and minimum redundancy algorithm were applied for feature selection. Logistic regression, support vector machine, and random forest were employed as the classifiers. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic accuracy of the classifiers on the external test set. RESULTS The logistic regression model and the random forest both showed an AUC of 0.75. The support vector machine classifier obtained an AUC of 0.78 on the test set with a sensitivity of 64% and a specificity of 72%. CONCLUSION The study provides preliminary retrospective evidence demonstrating the feasibility of MRI radiomics in predicting MYCN amplification in neuroblastomas. Future studies are needed to explore the correlation between other imaging features and genetic markers and to develop multiclass predictive models. KEY POINTS • MYCN amplification in neuroblastomas is an important determinant of disease prognosis. • Radiomics analysis of pre-treatment MR examinations can be used to predict MYCN amplification in neuroblastomas. • Radiomics machine learning models showed good generalisability to external test set, demonstrating reproducibility of the computational models.
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Affiliation(s)
- Adarsh Ghosh
- Department of Radiology, Cincinnati Children's Hospital and Medical Centre, Cincinnati, OH, USA.
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Ensar Yekeler
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sara Reis Teixeira
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Deepa Dalal
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lisa States
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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Sun K, Zhu H, Chai W, Yan F. Multimodality MRI radiomics analysis of TP53 mutations in triple negative breast cancer. Front Oncol 2023; 13:1153261. [PMID: 37064157 PMCID: PMC10090452 DOI: 10.3389/fonc.2023.1153261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 03/16/2023] [Indexed: 03/31/2023] Open
Abstract
ObjectivesTo explore the value of T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) radiomics features reflecting TP53 mutations in patients with triple negative breast cancer (TNBC).Study designThis retrospective study enrolled 91 patients with TNBC with TP53 testing (64 patients in the training cohort and 27 patients in the validation cohort). A total of 2832 radiomics features were extracted from the first phase of dynamic contrast-enhanced T1WI, T2WI and ADC maps. Analysis of variance (ANOVA) and the Kruskal-Wallis-test were used for feature selection. Then, linear discriminant analysis (LDA), multilayer perceptron (MLP), logistic regression (LR), LR with LASSO, decision tree (DT), naïve Bayes (NB), random forest (RF), and support vector machine (SVM) models were used for classification.ResultsThe validation AUCs of the eight classifiers ranged from 0.74 (NB) to 0.85 (SVM). SVM attained the highest AUC (0.85) and diagnostic accuracy (0.82) of all tested models. The top 3 ranking features in the SVM model were T1-square-first order-skewness (coefficient: 1.735), T2-wavelet-LHH-GLCM-joint energy, and T2-wavelet-LHH-GLCM-inverse difference moment (coefficient: -0.654, -0.634).ConclusionsRadiomics-based analysis with the SVM model is recommended for the detection of TP53 mutations in TNBC. Furthermore, T1WI- and T2WI-related features could be used as noninvasive biomarkers for predicting TP53 mutations.
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A Variable-Clustering-Based Feature Selection to Improve Positive and Negative Discrimination of P53 Protein in Colorectal Cancer Patients. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9261713. [DOI: 10.1155/2022/9261713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 11/18/2022]
Abstract
P53 protein tumor suppressor gene plays a guiding role in the treatment and prognosis of colorectal cancer (CRC). This paper aimed at proposing a feature selection method based on variable clustering to improve positive and negative discrimination of P53 protein in CRC patients. In this approach, we cluster the preprocessed dataset with variables, and then find the features with the largest information value (IV) for each cluster to form a feature subset. We call this method as IV_Cluster. In the actual medical data test, compared with the information value feature selection method, the accuracy of the 10-fold cross-validation logistic regression model increased by 4.4%, 2.0%, and 5.8%, and Kappa values increased by 21.8%, 8.6%, and 22.4%, respectively, under 5, 10, and 15 feature sets.
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