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Fu Y, Zhou J, Li J. Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis. PLoS One 2024; 19:e0303669. [PMID: 38820391 PMCID: PMC11142607 DOI: 10.1371/journal.pone.0303669] [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: 02/22/2024] [Accepted: 04/29/2024] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods. OBJECTIVE This meta-analysis investigates the diagnostic accuracy of ultrasound-based radiomics as a novel approach to predicting these markers. METHODS A comprehensive search of PubMed, EMBASE, and Web of Science databases was conducted to identify studies evaluating ultrasound-based radiomics in BC. Inclusion criteria encompassed research on HER2, Ki67, PR, and ER as key molecular markers. Quality assessment using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) was performed. The data extraction step was performed systematically. RESULTS Our meta-analysis quantifies the diagnostic accuracy of ultrasound-based radiomics with a sensitivity and specificity of 0.76 and 0.78 for predicting HER2, 0.80, and 0.76 for Ki67 biomarkers. Studies did not provide sufficient data for quantitative PR and ER prediction analysis. The overall quality of studies based on the RQS tool was moderate. The QUADAS-2 evaluation showed that the studies had an unclear risk of bias regarding the flow and timing domain. CONCLUSION Our analysis indicated that AI models have a promising accuracy for predicting key molecular biomarkers' status in BC patients. We performed the quantitative analysis for HER2 and Ki67 biomarkers which yielded a moderate to high accuracy. However, studies did not provide adequate data for meta-analysis of ER and PR prediction accuracy of developed models. The overall quality of the studies was acceptable. In future research, studies need to report the results thoroughly. Also, we suggest more prospective studies from different centers.
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Affiliation(s)
- Yuxia Fu
- Department of Ultrasound, Dianjiang People’s Hospital of Chongqing, Chongqing, China
| | - Jialin Zhou
- Department of Ultrasound, Dianjiang People’s Hospital of Chongqing, Chongqing, China
| | - Junfeng Li
- Department of Oncology, Dianjiang People’s Hospital of Chongqing, Chongqing, China
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Qin J, Qin X, Duan Y, Xie Y, Zhou Y, Zhang C. Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors. Transl Cancer Res 2024; 13:317-329. [PMID: 38410225 PMCID: PMC10894355 DOI: 10.21037/tcr-23-1042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 10/08/2023] [Indexed: 02/28/2024]
Abstract
Background Early diagnosis is crucial to the treatment of breast cancer, but conventional imaging detection is challenging. Radiomics has the potential to improve early diagnostic efficacy in a noninvasive manner. This study examined whether integrating computed tomography (CT) radiomics information based on ultrasound (US) models can improve the efficacy of breast cancer prediction. Methods We retrospectively analyzed 420 patients with pathologically confirmed benign or malignant breast tumors. Clinical data and examination images were collected, and the population was divided into training (n=294) and validation (n=126) groups at a ratio of 7:3. The region of interest (ROI) was manually segmented along the tumor boundary using MaZda software, and the features of each ROI was extracted. After dimension reduction and screening, the best features were retained. Subsequently, random forest (RF), support vector machines, and K-nearest neighbor classifiers were used to establish prediction models in an US and combined-methods group. Results Finally, 8 of the 379 features were retained in the US group. Random forest was found to be the best model, and the area under the curve (AUC) of the training and validation groups was 0.90 [95% confidence interval (CI): 0.852-0.942] and 0.85 (95% CI: 0.775-0.930), respectively. Meanwhile, 12 of the 750 features were retained in the combined group. In this regard, random forest proved to be the best model, and the AUC of the training and validation group was 0.95 (95% CI: 0.918-0.981) and 0.92 (95% CI: 0.866-0.969), respectively. The calibration curve showed a good fit of the model. The decision curve showed that the clinical net benefit of the combined group was far greater than that of any single examination, and the prediction model of the combined group exhibited a degree of practical clinical value. Conclusions The combined model based on US and CT images has potential application value in the prognostic prediction of benign and malignant breast diseases.
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Affiliation(s)
- Jing Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiachuan Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yayang Duan
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yuchen Xie
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yuanyuan Zhou
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Mărginean L, Filep RC, Suciu BA, Jovin TG, Ștefan PA, Lupean RA, Arbănași EM, Arbănași EM, Opriș DR, Timm AN, Vodă R, Vunvulea V. Textural Analysis of the Hyperdense Artery Sign in Patients with Acute Ischemic Stroke Predicts the Outcome of Thrombectomy. J Cardiovasc Dev Dis 2023; 10:359. [PMID: 37754788 PMCID: PMC10532176 DOI: 10.3390/jcdd10090359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/03/2023] [Accepted: 08/17/2023] [Indexed: 09/28/2023] Open
Abstract
Textural analysis is pivotal in augmenting the diagnosis and outcomes of endovascular procedures for stroke patients. Due to the detection of changes imperceptible to the human eye, this type of analysis can potentially aid in deciding the optimal type of endovascular treatment. We included 40 patients who suffered from acute ischemic stroke caused by large vessel occlusion, and calculated 130 different textural features based on the non-enhanced CT scan using an open-source software (3D Slicer). Using chi-squared and Mann-Whitney tests and receiver operating characteristics analysis, we identified a total of 21 different textural parameters capable of predicting the outcome of thrombectomy (quantified as the mTICI score), with variable sensitivity (50-97.9%) and specificity (64.6-99.4%) rates. In conclusion, CT-based radiomics features are potential factors that can predict the outcome of thrombectomy in patients suffering from acute ischemic stroke, aiding in the decision between aspiration, mechanical, or combined thrombectomy procedure.
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Affiliation(s)
- Lucian Mărginean
- Radiology and Medical Imaging, Clinical Sciences Department, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania;
- Interventional Radiology Department, Târgu Mureș County Emergency Clinical Hospital, 540136 Targu Mures, Romania
| | - Rares Cristian Filep
- Interventional Radiology Department, Târgu Mureș County Emergency Clinical Hospital, 540136 Targu Mures, Romania
| | - Bogdan Andrei Suciu
- Department of Anatomy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Tudor G. Jovin
- Cooper Neurological Institute, Cherry Hill, NJ 08002, USA;
| | - Paul-Andrei Ștefan
- Anatomy and Embryology, Morphological Sciences Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Victor Babeș, 400012 Cluj-Napoca, Romania
- Radiology and Imaging Department, County Emergency Hospital, Clinicilor Street, Number 3–5, 400006 Cluj-Napoca, Romania
| | - Roxana-Adelina Lupean
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, Number 4, 400349 Cluj-Napoca, Romania;
| | - Eliza Mihaela Arbănași
- Faculty of Pharmacy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Emil Marian Arbănași
- Clinic of Vascular Surgery, Mures County Emergency Hospital, 540136 Targu Mures, Romania
- Center for Advanced Medical and Pharmaceutical Research, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Diana Roxana Opriș
- Emergency Institute for Cardiovascular Diseases and Transplantation (IUBCVT) of Târgu Mureș, 540136 Targu Mures, Romania
| | - Alexander Niklas Timm
- Department of Anatomy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Rareș Vodă
- Department of Anatomy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Vlad Vunvulea
- Department of Anatomy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
- Radiology and Medical Imaging Laboratory, Târgu Mureș County Emergency Clinical Hospital, 540136 Targu Mures, Romania
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Wen Y, Guo D, Zhang J, Liu X, Liu T, Li L, Jiang S, Wu D, Jiang H. Clinical photoacoustic/ultrasound dual-modal imaging: Current status and future trends. Front Physiol 2022; 13:1036621. [PMID: 36388111 PMCID: PMC9651137 DOI: 10.3389/fphys.2022.1036621] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 10/05/2022] [Indexed: 08/24/2023] Open
Abstract
Photoacoustic tomography (PAT) is an emerging biomedical imaging modality that combines optical and ultrasonic imaging, providing overlapping fields of view. This hybrid approach allows for a natural integration of PAT and ultrasound (US) imaging in a single platform. Due to the similarities in signal acquisition and processing, the combination of PAT and US imaging creates a new hybrid imaging for novel clinical applications. Over the recent years, particular attention is paid to the development of PAT/US dual-modal systems highlighting mutual benefits in clinical cases, with an aim of substantially improving the specificity and sensitivity for diagnosis of diseases. The demonstrated feasibility and accuracy in these efforts open an avenue of translating PAT/US imaging to practical clinical applications. In this review, the current PAT/US dual-modal imaging systems are discussed in detail, and their promising clinical applications are presented and compared systematically. Finally, this review describes the potential impacts of these combined systems in the coming future.
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Affiliation(s)
- Yanting Wen
- Department of Ultrasound Imaging, The Fifth People’s Hospital of Chengdu, Chengdu, China
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Dan Guo
- Department of Ultrasound Imaging, The Fifth People’s Hospital of Chengdu, Chengdu, China
| | - Jing Zhang
- Department of Ultrasound Imaging, The Fifth People’s Hospital of Chengdu, Chengdu, China
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xiaotian Liu
- Department of Ultrasound Imaging, The Fifth People’s Hospital of Chengdu, Chengdu, China
| | - Ting Liu
- Department of Ultrasound Imaging, The Fifth People’s Hospital of Chengdu, Chengdu, China
| | - Lu Li
- Department of Ultrasound Imaging, The Fifth People’s Hospital of Chengdu, Chengdu, China
| | - Shixie Jiang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Dan Wu
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Huabei Jiang
- Department of Medical Engineering, University of South Florida, Tampa, FL, United States
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