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Meng H, Sun YF, Zhang Y, Yu YN, Wang J, Wang JN, Xue LY, Yin XP. Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:81-91. [PMID: 38343262 DOI: 10.1007/s10278-023-00936-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 03/02/2024]
Abstract
Endometrial carcinoma (EC) risk stratification prior to surgery is crucial for clinical treatment. In this study, we intend to evaluate the predictive value of radiomics models based on magnetic resonance imaging (MRI) for risk stratification and staging of early-stage EC. The study included 155 patients who underwent MRI examinations prior to surgery and were pathologically diagnosed with early-stage EC between January, 2020, and September, 2022. Three-dimensional radiomics features were extracted from segmented tumor images captured by MRI scans (including T2WI, CE-T1WI delayed phase, and ADC), with 1521 features extracted from each of the three modalities. Then, using five-fold cross-validation and a multilayer perceptron algorithm, these features were filtered using Pearson's correlation coefficient to develop a prediction model for risk stratification and staging of EC. The performance of each model was assessed by analyzing ROC curves and calculating the AUC, accuracy, sensitivity, and specificity. In terms of risk stratification, the CE-T1 sequence demonstrated the highest predictive accuracy of 0.858 ± 0.025 and an AUC of 0.878 ± 0.042 among the three sequences. However, combining all three sequences resulted in enhanced predictive accuracy, reaching 0.881 ± 0.040, with a corresponding increase in the AUC to 0.862 ± 0.069. In the context of staging, the utilization of a combination involving T2WI with CE-T1WI led to a notably elevated predictive accuracy of 0.956 ± 0.020, surpassing the accuracy achieved when employing any singular feature. Correspondingly, the AUC was 0.979 ± 0.022. When incorporating all three sequences concurrently, the predictive accuracy reached 0.956 ± 0.000, accompanied by an AUC of 0.986 ± 0.007. It is noteworthy that this level of accuracy surpassed that of the radiologist, which stood at 0.832. The MRI radiomics model has the potential to accurately predict the risk stratification and early staging of EC.
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Affiliation(s)
- Huan Meng
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Yu-Feng Sun
- College of Quality and Technical Supervision, Hebei University, Lianchi District, No. 180, Wusi East Road, Baoding, 071000, China
| | - Yu Zhang
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Ya-Nan Yu
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Jing Wang
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Jia-Ning Wang
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Lin-Yan Xue
- College of Quality and Technical Supervision, Hebei University, Lianchi District, No. 180, Wusi East Road, Baoding, 071000, China.
| | - Xiao-Ping Yin
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China.
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Li X, Xu S, Hao LW, Zhou XN. Value of Molybdenum Target X-Ray and High-Frequency Color Doppler Flow Imaging in Early Diagnosis of Breast Carcinoma: A Comparative Analysis. Cancer Manag Res 2023; 15:1155-1163. [PMID: 37868685 PMCID: PMC10588806 DOI: 10.2147/cmar.s412924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 09/21/2023] [Indexed: 10/24/2023] Open
Abstract
Background Breast carcinoma (BC) threatens the physical and mental health of women worldwide, and early diagnosis is important for improving patient outcomes and ensuring successful treatment. Purpose This research mainly aims to compare and analyze the value of molybdenum target X-ray and high-frequency color Doppler flow imaging (CDFI) in the early diagnosis of BC. Methods First, 102 patients with suspected early-stage BC (ESBC) admitted to Henan Provincial People's Hospital were examined by molybdenum target X-ray and CDFI. Based on the pathological findings, the diagnostic efficiency data of the two diagnostic modalities such as positive detection rate (PDR), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SEN), specificity (SPE), and accuracy (ACC), as well as imaging information like masses, microcalcifications (MCs), axillary lymph node (LN) metastases, and blood flow signal or vascular sign abnormalities were analyzed. Results CDFI contributed to higher PDR, PRV, NPV, SEN, and ACC than molybdenum target X-ray in ESBC diagnosis, but similar SPE. The combined diagnosis of molybdenum target X-ray plus CDFI contributed to even higher PDR, PRV, NPV, SEN, and ACC than molybdenum target X-ray alone and higher ACC than CDFI. Imaging inspection revealed that the number of cases of masses, axillary LN metastases, and abnormalities in blood flow signals or vascular signs detected by CDFI was significantly higher than that by molybdenum target X-ray, while the number of MCs was significantly lower. Conclusion Molybdenum target X-ray plus CDFI is more effective in the diagnosis of ESBC and plays a complementary role in imaging examination, which can synergistically improve the diagnostic ACC of ESBC and is worthy of clinical promotion.
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Affiliation(s)
- Xia Li
- Health Management Discipline of Henan Provincial People’s Hospital, Zhengzhou, Henan Province, 450000, People’s Republic of China
| | - Shuang Xu
- Health Management Discipline of Henan Provincial People’s Hospital, Zhengzhou, Henan Province, 450000, People’s Republic of China
| | - Liu-Wei Hao
- Health Management Discipline of Henan Provincial People’s Hospital, Zhengzhou, Henan Province, 450000, People’s Republic of China
| | - Xiao-Ning Zhou
- Health Management Discipline of Henan Provincial People’s Hospital, Zhengzhou, Henan Province, 450000, People’s Republic of China
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Detection of Breast Cancer Lump and BRCA1/2 Genetic Mutation under Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9591781. [PMID: 36172325 PMCID: PMC9512604 DOI: 10.1155/2022/9591781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/16/2022] [Accepted: 08/23/2022] [Indexed: 11/17/2022]
Abstract
To diagnose and cure breast cancer early, thus reducing the mortality of patients with breast cancer, a method was provided to judge threshold of image segmentation by wavelet transform (WT). It was used to obtain information about the general area of breast lumps by making a rough segmentation of the suspected area of the lump on mammogram. The boundary signal of the lump was obtained by region growth calculation or contour model of local activity. Meanwhile, multiplex polymerase chain reaction (mPCR) and mPCR-next-generation sequencing (mPCR-NGS) were used to detect BRCA1/2 genome. Sanger test was used for newly high virulent mutations to verify the correctness of mutagenic sites. The results were compared with the information marked by experts in the database. According to Daubechies wavelet coefficients, the average measurement accuracy was 92.9% and the average false positive rate of each image was 86%. According to mPCR-NGS, there was no pathogenic mutation in the 7 patients with high-risk BRCA1/2 genetic mutations. Single nucleotide polymorphism (SNP) in nonsynonymous coding region was detected, which was consistent with the Sanger test results. This method effectively isolated the lump area of human mammogram, and mPCR-NGS had high specificity and sensitivity in detecting BRCA1/2 genetic mutation sites. Compared with traditional Sanger test and target sequence capture test, it also had such advantages as easy operation, short duration, and low cost of consumables, which was worthy of further promotion and adoption.
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Effect of Neoadjuvant Chemotherapy on Angiogenesis and Cell Proliferation of Breast Cancer Evaluated by Dynamic Enhanced Magnetic Resonance Imaging. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3156093. [PMID: 35915805 PMCID: PMC9338867 DOI: 10.1155/2022/3156093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/19/2022] [Accepted: 06/18/2022] [Indexed: 11/17/2022]
Abstract
Background. Breast cancer is the uncontrolled proliferation of breast epithelial cells under the action of various carcinogenic factors. The evaluation of early efficacy of neoadjuvant chemotherapy for breast cancer is helpful to change the treatment plan in time. On this basis, dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) was used to evaluate the effects of neoadjuvant chemotherapy on angiogenesis and cell proliferation in breast cancer. Objective. To evaluate the effect of neoadjuvant chemotherapy on angiogenesis and cell proliferation of breast cancer by dynamic enhanced DCE-MRI. Method. 80 breast cancer patients were divided into the routine chemotherapy group (3 cycles) and neoadjuvant chemotherapy groups (3 cycles) of 40 cases each from January 2018 to June 2021. Based on conventional imaging, DCE-MRI was performed with Intera Achieva 3.0 TMR superconducting MR scanner before and after treatment. The quantitative indexes, MRI parameters, cell proliferation expression, and DCE-MRI angiogenesis were compared between the two groups. Result. The inhibition rate, Vepost, Ktranspre, ADC, Bax, Alexi, and Aurora in the neoadjuvant chemotherapy group were significantly higher than those in the conventional chemotherapy group (
), while Kep, Ktrans, and Nek2 were significantly lower than those in the conventional chemotherapy group (
). Vepre (cm3), Ktranspre (ml/min/100 cm3), and Ve had no significant difference (
). Conclusion. The quantitative parameters, MRI parameters, proliferation, and expression of DCE-MRI in breast cancer patients with different chemotherapy regimens are quite different. They can be applied to the diagnosis of neoadjuvant chemotherapy in breast cancer patients with angiogenesis and cell proliferation.
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Gu WQ, Cai SM, Liu WD, Zhang Q, Shi Y, Du LJ. Combined molybdenum target X-ray and magnetic resonance imaging examinations improve breast cancer diagnostic efficacy. World J Clin Cases 2022; 10:485-491. [PMID: 35097073 PMCID: PMC8771396 DOI: 10.12998/wjcc.v10.i2.485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/19/2021] [Accepted: 12/03/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Early-stage breast cancer patients often lack specific clinical manifestations, making diagnosis difficult. Molybdenum target X-ray and magnetic resonance imaging (MRI) examinations both have their own advantages. Thus, a combined examination methodology may improve early breast cancer diagnoses.
AIM To explore the combined diagnostic efficacy of molybdenum target X-ray and MRI examinations in breast cancer.
METHODS Patients diagnosed with breast cancer at our hospital from March 2019 to April 2021 were recruited, as were the same number of patients during the same period with benign breast tumors. Both groups underwent molybdenum target X-ray and MRI examinations, and diagnoses were given based on each exam. The single (i.e., X-ray or MRI) and combined (i.e., using both methods) diagnoses were counted, and the MRI-related examination parameters (e.g., T-wave peak, peak and early enhancement rates, and apparent diffusion coefficient) were compared between the groups.
RESULTS In total, 63 breast cancer patients and 63 benign breast tumor patients were recruited. MRI detected 53 breast cancer cases and 61 benign breast tumor cases. Molybdenum target X-ray detected 50 breast cancer cases and 60 benign breast tumor cases. The combined methodology detected 61 breast cancer cases and 61 benign breast tumor cases. The sensitivity (96.83%) and accuracy (96.83%) of the combined methodology were higher than single-method MRI (84.13% and 90.48%, respectively) and molybdenum target X-ray (79.37% and 87.30%, respectively) (P < 0.05). The combined methodology specificity (96.83%) did not differ from single-method MRI (96.83%) or molybdenum target X-ray (95.24%) (P > 0.05). The T-wave peak (169.43 ± 32.05) and apparent diffusion coefficient (1.01 ± 0.23) were lower in the breast cancer group than in the benign tumor group (228.86 ± 46.51 and 1.41 ± 0.35, respectively). However, the peak enhancement rate (1.08 ± 0.24) and early enhancement rate (1.07 ± 0.26) were significantly higher in the breast cancer group than in the benign tumor group (0.83 ± 0.19 and 0.75 ± 0.19, respectively) (P < 0.05).
CONCLUSION Combined molybdenum target X-ray and MRI examinations for diagnosing breast cancer improved the diagnostic sensitivity and accuracy, minimizing the missed- and misdiagnoses risks and promoting timely treatment intervention.
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Affiliation(s)
- Wen-Quan Gu
- Department of Radiology, Punan Hospital, Shanghai 200126, China
| | - Sun-Mei Cai
- Department of Radiology, Punan Hospital, Shanghai 200126, China
| | - Wei-Dong Liu
- Department of Radiology, Punan Hospital, Shanghai 200126, China
| | - Qi Zhang
- Department of Radiology, Punan Hospital, Shanghai 200126, China
| | - Ying Shi
- Department of Radiology, Punan Hospital, Shanghai 200126, China
| | - Li-Juan Du
- Department of Radiology, Punan Hospital, Shanghai 200126, China
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Wang P, Nie P, Dang Y, Wang L, Zhu K, Wang H, Wang J, Liu R, Ren J, Feng J, Fan H, Yu J, Chen B. Synthesizing the First Phase of Dynamic Sequences of Breast MRI for Enhanced Lesion Identification. Front Oncol 2021; 11:792516. [PMID: 34950593 PMCID: PMC8689139 DOI: 10.3389/fonc.2021.792516] [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: 10/11/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
Objective To develop a deep learning model for synthesizing the first phases of dynamic (FP-Dyn) sequences to supplement the lack of information in unenhanced breast MRI examinations. Methods In total, 97 patients with breast MRI images were collected as the training set (n = 45), the validation set (n = 31), and the test set (n = 21), respectively. An enhance border lifelike synthesize (EDLS) model was developed in the training set and used to synthesize the FP-Dyn images from the T1WI images in the validation set. The peak signal-to-noise ratio (PSNR), structural similarity (SSIM), mean square error (MSE) and mean absolute error (MAE) of the synthesized images were measured. Moreover, three radiologists subjectively assessed image quality, respectively. The diagnostic value of the synthesized FP-Dyn sequences was further evaluated in the test set. Results The image synthesis performance in the EDLS model was superior to that in conventional models from the results of PSNR, SSIM, MSE, and MAE. Subjective results displayed a remarkable visual consistency between the synthesized and original FP-Dyn images. Moreover, by using a combination of synthesized FP-Dyn sequence and an unenhanced protocol, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MRI were 100%, 72.73%, 76.92%, and 100%, respectively, which had a similar diagnostic value to full MRI protocols. Conclusions The EDLS model could synthesize the realistic FP-Dyn sequence to supplement the lack of enhanced images. Compared with full MRI examinations, it thus provides a new approach for reducing examination time and cost, and avoids the use of contrast agents without influencing diagnostic accuracy.
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Affiliation(s)
- Pingping Wang
- Clinical Experimental Centre, Xi'an International Medical Center Hospital, Xi'an, China
| | - Pin Nie
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | - Yanli Dang
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | - Lifang Wang
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | - Kaiguo Zhu
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | - Hongyu Wang
- The School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China
| | - Jiawei Wang
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | - Rumei Liu
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
| | | | - Jun Feng
- The School of Information of Science and Technology, Northwest University, Xi'an, China
| | - Haiming Fan
- The School of Medicine, Northwest University, Xi'an, China
| | - Jun Yu
- Clinical Experimental Centre, Xi'an International Medical Center Hospital, Xi'an, China
| | - Baoying Chen
- Imaging Diagnosis and Treatment Center, Xi'an International Medical Center Hospital, Xi'an, China
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Shao Y, Zhang YX, Chen HH, Lu SS, Zhang SC, Zhang JX. Advances in the application of artificial intelligence in solid tumor imaging. Artif Intell Cancer 2021; 2:12-24. [DOI: 10.35713/aic.v2.i2.12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/02/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Early diagnosis and timely treatment are crucial in reducing cancer-related mortality. Artificial intelligence (AI) has greatly relieved clinical workloads and changed the current medical workflows. We searched for recent studies, reports and reviews referring to AI and solid tumors; many reviews have summarized AI applications in the diagnosis and treatment of a single tumor type. We herein systematically review the advances of AI application in multiple solid tumors including esophagus, stomach, intestine, breast, thyroid, prostate, lung, liver, cervix, pancreas and kidney with a specific focus on the continual improvement on model performance in imaging practice.
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Affiliation(s)
- Ying Shao
- Department of Laboratory Medicine, People Hospital of Jiangying, Jiangying 214400, Jiangsu Province, China
| | - Yu-Xuan Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Huan-Huan Chen
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Shan-Shan Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Shi-Chang Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Jie-Xin Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
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