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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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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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Huang Y, Guo Y, Xiao Q, Liang S, Yu Q, Qian L, Zhou J, Le J, Pei Y, Wang L, Chang C, Chen S, Zhou S. Unraveling the Pivotal Network of Ultrasound and Somatic Mutations in Triple-Negative and Non-Triple-Negative Breast Cancer. BREAST CANCER (DOVE MEDICAL PRESS) 2023; 15:461-472. [PMID: 37456987 PMCID: PMC10349575 DOI: 10.2147/bctt.s408997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/01/2023] [Indexed: 07/18/2023]
Abstract
Purpose The emergence of genomic targeted therapy has improved the prospects of treatment for breast cancer (BC). However, genetic testing relies on invasive and sophisticated procedures. Patients and Methods Here, we performed ultrasound (US) and target sequencing to unravel the possible association between US radiomics features and somatic mutations in TNBC (n=83) and non-TNBC (n=83) patients. Least absolute shrinkage and selection operator (Lasso) were utilized to perform radiomic feature selection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was utilized to identify the signaling pathways associated with radiomic features. Results Thirteen differently represented radiomic features were identified in TNBC and non-TNBC, including tumor shape, textual, and intensity features. The US radiomic-gene pairs were differently exhibited between TNBC and non-TNBC. Further investigation with KEGG verified radiomic-pathway (ie, JAK-STAT, MAPK, Ras, Wnt, microRNAs in cancer, PI3K-Akt) associations in TNBC and non-TNBC. Conclusion The pivotal network provided the connections of US radiogenomic signature and target sequencing for non-invasive genetic assessment of precise BC treatment.
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Affiliation(s)
- Yunxia Huang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Yi Guo
- Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Qin Xiao
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, People’s Republic of China
| | - Shuyu Liang
- Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Qiang Yu
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Lang Qian
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Jin Zhou
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Jian Le
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Yuchen Pei
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Lei Wang
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Cai Chang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Sheng Chen
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Shichong Zhou
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
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Zhou J, Xie T, Shan H, Cheng G. HLA-DQA1 expression is associated with prognosis and predictable with radiomics in breast cancer. Radiat Oncol 2023; 18:117. [PMID: 37434241 DOI: 10.1186/s13014-023-02314-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 07/05/2023] [Indexed: 07/13/2023] Open
Abstract
BACKGROUND High HLA-DQA1 expression is associated with a better prognosis in many cancers. However, the association between HLA-DQA1 expression and prognosis of breast cancer and the noninvasive assessment of HLA-DQA1 expression are still unclear. This study aimed to reveal the association and investigate the potential of radiomics to predict HLA-DQA1 expression in breast cancer. METHODS In this retrospective study, transcriptome sequencing data, medical imaging data, clinical and follow-up data were downloaded from the TCIA ( https://www.cancerimagingarchive.net/ ) and TCGA ( https://portal.gdc.cancer.gov/ ) databases. The clinical characteristic differences between the high HLA-DQA1 expression group (HHD group) and the low HLA-DQA1 expression group were explored. Gene set enrichment analysis, Kaplan‒Meier survival analysis and Cox regression were performed. Then, 107 dynamic contrast-enhanced magnetic resonance imaging features were extracted, including size, shape and texture. Using recursive feature elimination and gradient boosting machine, a radiomics model was established to predict HLA-DQA1 expression. Receiver operating characteristic (ROC) curves, precision-recall curves, calibration curves, and decision curves were used for model evaluation. RESULTS The HHD group had better survival outcomes. The differentially expressed genes in the HHD group were significantly enriched in oxidative phosphorylation (OXPHOS) and estrogen response early and late signalling pathways. The radiomic score (RS) output from the model was associated with HLA-DQA1 expression. The area under the ROC curves (95% CI), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the radiomic model were 0.866 (0.775-0.956), 0.825, 0.939, 0.7, 0.775, and 0.913 in the training set and 0.780 (0.629-0.931), 0.659, 0.81, 0.5, 0.63, and 0.714 in the validation set, respectively, showing a good prediction effect. CONCLUSIONS High HLA-DQA1 expression is associated with a better prognosis in breast cancer. Quantitative radiomics as a noninvasive imaging biomarker has potential value for predicting HLA-DQA1 expression.
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Affiliation(s)
- JingYu Zhou
- Department of Radiology, Peking University Shenzhen Hospital, LianHua Road, Shenzhen, 518000, Guangdong, China
| | - TingTing Xie
- Department of Radiology, Peking University Shenzhen Hospital, LianHua Road, Shenzhen, 518000, Guangdong, China
| | - HuiMing Shan
- Department of Radiology, Peking University Shenzhen Hospital, LianHua Road, Shenzhen, 518000, Guangdong, China
| | - GuanXun Cheng
- Department of Radiology, Peking University Shenzhen Hospital, LianHua Road, Shenzhen, 518000, Guangdong, China.
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Thakur SB, Chakraborthy J, Pinker K. Editorial for "TP53 Mutation Estimation Based on Radiomics Analysis for Breast Cancer". J Magn Reson Imaging 2023; 57:1104-1105. [PMID: 35762927 DOI: 10.1002/jmri.28324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 06/10/2022] [Indexed: 11/11/2022] Open
Affiliation(s)
- Sunitha B Thakur
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jayasree Chakraborthy
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Sun K, Zhu H, Chai W, Yan F. TP53 Mutation Estimation Based on MRI Radiomics Analysis for Breast Cancer. J Magn Reson Imaging 2023; 57:1095-1103. [PMID: 35771720 DOI: 10.1002/jmri.28323] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/11/2022] [Accepted: 06/16/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Noninvasive detection of TP53 mutations is useful for the molecular stratification of breast cancer. PURPOSE To explore MRI radiomics features reflecting TP53 mutations in breast cancer and propose a classifier for detecting such mutations. STUDY TYPE Retrospective. POPULATION/SUBJECTS A total of 139 breast cancer patients with TP53 expression profiling (98 with TP53 mutations and 41 without TP53 mutations). FIELD STRENGTH/SEQUENCE 1.5 T, T1-weighted (T1W) DCE-MRI. ASSESSMENT Lesions were manually segmented using subtracted T1WI. A total of 944 radiomics features (including 744 wavelet-related features) and 7 clinicopathological features were extracted from each lesion. Principal component analysis and Pearson's correlation analysis were used to preprocess the features. Linear discriminant analysis, logistic regression (LR), support vector machine (SVM), and random forest (RF) were used as the classifiers. STATISTICAL TESTS Analysis of variance, Kruskal-Wallis and recursive features elimination were used to select features. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic accuracy. RESULTS For the radiomics model, the validation cohorts AUCs of the four classifiers ranged from 0.69 (RF) to 0.74 (LR), and LR (0.74) attained the highest AUCs. For the clinicopathological-radiomics combined model, the validation AUCs of the four classifiers ranged from 0.68 (RF) to 0.86 (SVM), and SVM (0.86) attained highest AUCs. In the subgroup analysis of triple-negative (TN) and luminal type breast cancer, RF achieved the highest AUCs (0.83 and 0.94). DATA CONCLUSION Clinicopathological-radiomics combined model with SVM could be used as noninvasive biomarkers for predicting TP53 mutations. RF was recommended for the detection of TP53 mutations in TN and luminal type breast cancer. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Kun Sun
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Hong Zhu
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Weimin Chai
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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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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Huang Y, Qiang Y, Jian L, Jin Z, Lang Q, Sheng C, Shichong Z, Cai C. Ultrasonic Features and Molecular Subtype Predict Somatic Mutations in TP53 and PIK3CA Genes in Breast Cancer. Acad Radiol 2022; 29:e261-e270. [PMID: 35450798 DOI: 10.1016/j.acra.2022.02.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 02/23/2022] [Accepted: 02/23/2022] [Indexed: 01/26/2023]
Abstract
RATIONALE AND OBJECTIVES To predict mutations in TP53 and PIK3CA genes in breast cancer using ultrasound (US) signatures and clinicopathology. MATERIALS AND METHODS In this study, we developed and trained a model in 386 breast cancer patients to predict TP53 and PIK3CA mutations. The clinicopathological and US characteristics (including two-dimensional and color Doppler US) were investigated. Statistically significant variables were used to build predictive models, then a combined model was developed using the multivariate logistic regression analysis. RESULTS Univariate and multivariate analyses revealed that calcifications on US was an independent predictor of TP53 mutation (p < 0.05), whereas diameter on US and US type were independent predictors of PIK3CA mutation in breast cancer (all p < 0.05). Meanwhile, Luminal B/Human epidermal growth factor receptor two-positive (HER2+), HER2+/estrogen receptor-negative (ER-), and triple-negative breast cancer (TNBC) subtypes were strong predictors of TP53 mutation (odds ratio [OR] = 3.13, 3.18, 3.44, respectively, all p < 0.05). HER2+/ER- and TNBC subtypes were negative predictors of PIK3CA mutation (OR = 0.223, 0.241, respectively, all p < 0.05). The areas under curves (AUCs) for PIK3CA mutation in the training set increased from 0.553-0.610 to 0.741 in the multivariate model that combined US features and molecular subtype, with a sensitivity and specificity of 80.6% and 58.7%, respectively. The application of the multivariate model in the validation set achieved acceptable discrimination (AUC = 0.715). For TP53 mutation, the AUC was 0.653. CONCLUSION US is a non-invasive modality to recognize the presence of TP53 and PIK3CA mutation. The models combined with US features and molecular subtype have implications for the practical application of predicting gene mutation for individual decision-making regarding treatment planning.
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Affiliation(s)
- Yunxia Huang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Xuhui, Sanghai, 200032, China
| | - Yu Qiang
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Le Jian
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Xuhui, Sanghai, 200032, China
| | - Zhou Jin
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Xuhui, Sanghai, 200032, China
| | - Qian Lang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Xuhui, Sanghai, 200032, China
| | - Chen Sheng
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhou Shichong
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Xuhui, Sanghai, 200032, China.
| | - Chang Cai
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Xuhui, Sanghai, 200032, China
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Lai YC, Chen HH, Hsu JF, Hong YJ, Chiu TT, Chiou HJ. Evaluation of physician performance using a concurrent-read artificial intelligence system to support breast ultrasound interpretation. Breast 2022; 65:124-135. [PMID: 35944352 PMCID: PMC9379669 DOI: 10.1016/j.breast.2022.07.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 07/11/2022] [Accepted: 07/13/2022] [Indexed: 11/30/2022] Open
Abstract
Purpose The purpose of this study was to compare the diagnostic performance and the interpretation time of breast ultrasound examination between reading without and with the artificial intelligence (AI) system as a concurrent reading aid. Material and methods A fully crossed multi-reader and multi-case (MRMC) reader study was conducted. Sixteen participating physicians were recruited and retrospectively interpreted 172 breast ultrasound cases in two reading scenarios, once without and once with the AI system (BU-CAD™, TaiHao Medical Inc.) assistance for concurrent reading. Interpretations of any given case set with and without the AI system were separated by at least 5 weeks. These reading results were compared to the reference standard and the area under the LROC curve (AUCLROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for performance evaluations. The interpretation time was also compared between the unaided and aided scenarios. Results With the help of the AI system, the readers had higher diagnostic performance with an increase in the average AUCLROC from 0.7582 to 0.8294 with statistically significant. The sensitivity, specificity, PPV, and NPV were also improved from 95.77%, 24.07%, 44.18%, and 93.50%–98.17%, 30.67%, 46.91%, and 96.10%, respectively. Of these, the improvement in specificity reached statistical significance. The average interpretation time was significantly reduced by approximately 40% when the readers were assisted by the AI system. Conclusion The concurrent-read AI system improves the diagnostic performance in detecting and diagnosing breast lesions on breast ultrasound images. In addition, the interpretation time is effectively reduced for the interpreting physicians. A reader study was conducted to compare the breast ultrasound interpreting performance without and with the aid of AI system. The performance of breast ultrasound interpretation was improved by the AI system as a concurrent reading aid. The breast ultrasound interpretation time is significantly reduced by the AI system as a concurrent reading aid. The reproducibility experiments of the same lesion cropped by different rectangular proved the robustness of the AI system.
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Affiliation(s)
- Yi-Chen Lai
- Comprehensive Breast Health Center, Taipei Veterans General Hospital, No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, 11217, Taiwan, ROC; School of Medicine, National Yang Ming Chiao Tung University, No.155, Sec. 2, Linong St., Beitou District, Taipei, 112304, Taiwan, ROC.
| | - Hong-Hao Chen
- TaiHao Medical Inc., 6F.-1, No.100, Sec. 2, Heping E. Rd., Da'an District, Taipei, 10663, Taiwan, ROC.
| | - Jen-Feng Hsu
- TaiHao Medical Inc., 6F.-1, No.100, Sec. 2, Heping E. Rd., Da'an District, Taipei, 10663, Taiwan, ROC; Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Da'an District, Taipei, 10617, Taiwan, ROC.
| | - Yi-Jun Hong
- TaiHao Medical Inc., 6F.-1, No.100, Sec. 2, Heping E. Rd., Da'an District, Taipei, 10663, Taiwan, ROC.
| | - Ting-Ting Chiu
- TaiHao Medical Inc., 6F.-1, No.100, Sec. 2, Heping E. Rd., Da'an District, Taipei, 10663, Taiwan, ROC.
| | - Hong-Jen Chiou
- School of Medicine, National Yang Ming Chiao Tung University, No.155, Sec. 2, Linong St., Beitou District, Taipei, 112304, Taiwan, ROC; Department of Radiology, Taipei Veterans General Hospital, No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, 11217, Taiwan, ROC.
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Shen WQ, Guo Y, Ru WE, Li C, Zhang GC, Liao N, Du GQ. Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image. Front Oncol 2022; 12:850515. [PMID: 35719907 PMCID: PMC9204315 DOI: 10.3389/fonc.2022.850515] [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: 01/07/2022] [Accepted: 04/11/2022] [Indexed: 11/30/2022] Open
Abstract
Background The detection of phosphatidylinositol-3 kinase catalytic alpha (PIK3CA) gene mutations in breast cancer is a key step to design personalizing an optimal treatment strategy. Traditional genetic testing methods are invasive and time-consuming. It is urgent to find a non-invasive method to estimate the PIK3CA mutation status. Ultrasound (US), one of the most common methods for breast cancer screening, has the advantages of being non-invasive, fast imaging, and inexpensive. In this study, we propose to develop a deep convolutional neural network (DCNN) to identify PIK3CA mutations in breast cancer based on US images. Materials and Methods We retrospectively collected 312 patients with pathologically confirmed breast cancer who underwent genetic testing. All US images (n=800) of breast cancer patients were collected and divided into the training set (n=600) and test set (n=200). A DCNN-Improved Residual Network (ImResNet) was designed to identify the PIK3CA mutations. We also compared the ImResNet model with the original ResNet50 model, classical machine learning models, and other deep learning models. Results The proposed ImResNet model has the ability to identify PIK3CA mutations in breast cancer based on US images. Notably, our ImResNet model outperforms the original ResNet50, DenseNet201, Xception, MobileNetv2, and two machine learning models (SVM and KNN), with an average area under the curve (AUC) of 0.775. Moreover, the overall accuracy, average precision, recall rate, and F1-score of the ImResNet model achieved 74.50%, 74.17%, 73.35%, and 73.76%, respectively. All of these measures were significantly higher than other models. Conclusion The ImResNet model gives an encouraging performance in predicting PIK3CA mutations based on breast US images, providing a new method for noninvasive gene prediction. In addition, this model could provide the basis for clinical adjustments and precision treatment.
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Affiliation(s)
- Wen-Qian Shen
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Department of Ultrasound, The Second Affifiliated Hospital of Harbin Medical University, Harbin, China
| | - Yanhui Guo
- Department of Computer Science, University of Illinois Springfield, Springfield, IL, United States
| | - Wan-Er Ru
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,College of Medicine, Shantou University, Shantou, China
| | - Cheukfai Li
- Department of Breast Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Guo-Chun Zhang
- Department of Breast Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Ning Liao
- Department of Breast Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Guo-Qing Du
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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11
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Gao J, Chen X, Li X, Miao F, Fang W, Li B, Qian X, Lin X. Differentiating TP53 Mutation Status in Pancreatic Ductal Adenocarcinoma Using Multiparametric MRI-Derived Radiomics. Front Oncol 2021; 11:632130. [PMID: 34079753 PMCID: PMC8165316 DOI: 10.3389/fonc.2021.632130] [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: 11/22/2020] [Accepted: 04/27/2021] [Indexed: 12/18/2022] Open
Abstract
Objectives This study assessed the preoperative prediction of TP53 status based on multiparametric magnetic resonance imaging (mpMRI) radiomics extracted from two-dimensional (2D) and 3D images. Methods 57 patients with pancreatic cancer who underwent preoperative MRI were included. The diagnosis and TP53 gene test were based on resections. Of the 57 patients included 37 mutated TP53 genes and the remaining 20 had wild-type TP53 genes. Two radiologists performed manual tumour segmentation on seven different MRI image acquisition sequences per patient, including multi-phase [pre-contrast, late arterial phase (ap), portal venous phase, and delayed phase] dynamic contrast enhanced (DCE) T1-weighted imaging, T2-weighted imaging (T2WI), Diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC). PyRadiomics-package was used to generate 558 two-dimensional (2D) and 994 three-dimensional (3D) image features. Models were constructed by support vector machine (SVM) for differentiating TP53 status and DX score method were used for feature selection. The evaluation of the model performance included area under the curve (AUC), accuracy, calibration curves, and decision curve analysis. Results The 3D ADC-ap-DWI-T2WI model with 11 selected features yielded the best performance for differentiating TP53 status, with accuracy = 0.91 and AUC = 0.96. The model showed the good calibration. The decision curve analysis indicated that the radiomics model had clinical utility. Conclusions A non-invasive and quantitative mpMRI-based radiomics model can accurately predict TP53 mutation status in pancreatic cancer patients and contribute to the precision treatment.
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Affiliation(s)
- Jing Gao
- Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiahan Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xudong Li
- Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Department of Nuclear Medicine, Qingdao Municipal Hospital, Qingdao, China
| | - Fei Miao
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Weihuan Fang
- Department of Radiology, Ruijin Hospital North, Shanghai, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaohua Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaozhu Lin
- Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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12
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Lin P, Liu WK, Li X, Wan D, Qin H, Li Q, Chen G, He Y, Yang H. MRI-based radiogenomics analysis for predicting genetic alterations in oncogenic signalling pathways in invasive breast carcinoma. Clin Radiol 2020; 75:561.e1-561.e11. [PMID: 32183997 DOI: 10.1016/j.crad.2020.02.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 02/18/2020] [Indexed: 01/23/2023]
Abstract
AIM To investigate the effect of radiomics in the assessment of alterations in canonical cancer pathways in breast cancer. MATERIALS AND METHODS Eighty-eight biopsy-proven breast cancer cases were included in the present study. Radiomics features were extracted from T1-weighted sagittal dynamic contrast-enhanced magnetic resonance imaging (MRI) images. Radiomics signatures were developed to predict genetic alterations in the cell cycle, Myc, PI3K, RTK/RAS, and p53 signalling pathways by using hypothesis testing combined with least absolute shrinkage and selection operator (LASSO) regression analysis. The predictive powers of the models were examined by the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS A total of 5,234 radiomics features were obtained from MRI images based on the tumour region of interest. Hypothesis tests screened 250, 229, 156, 785, and 319 radiomics features that were differentially displayed between cell cycle, Myc, PI3K, RTK/RAS, and p53 alterations and no alteration status. According to the LASSO algorithm, 11, 12, 12, 15, and 13 features were identified for the construction of the radiomics signatures to predict cell cycle, Myc, PI3K, RTK/RAS, and p53 alterations, with AUC values of 0.933, 0.926, 0.956, 0.940, and 0.886, respectively. The cell cycle radiomics score correlated closely with the RTK/RAS and p53 radiomics scores. These signatures were also dysregulated in patients with different oestrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 statuses. CONCLUSION MRI-based radiogenomics analysis exhibits excellent performance in predicting genetic pathways alterations, thus providing a novel approach for non-invasively obtaining genetic-level molecular characteristics of tumours.
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Affiliation(s)
- P Lin
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - W K Liu
- Department of Ultrasound, Affiliated Union Hospital of Fujian Medical University, Fuzhou, Fu Jian 350000, China
| | - X Li
- GE Healthcare, Shanghai, China
| | - D Wan
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - H Qin
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - Q Li
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - G Chen
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - Y He
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - H Yang
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
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