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Dong Y, Zhang Q, Chen H, Jin Y, Ji Z, Han H, Wang W. Radiomics of Multi-modality Ultrasound in Rabbit VX2 Liver Tumors: Differentiating Residual Tumors from Hyperemic Rim After Ablation. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00763-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Kaszak I, Witkowska-Piłaszewicz O, Domrazek K, Jurka P. The Novel Diagnostic Techniques and Biomarkers of Canine Mammary Tumors. Vet Sci 2022; 9:526. [PMID: 36288138 PMCID: PMC9610006 DOI: 10.3390/vetsci9100526] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/12/2022] [Accepted: 09/22/2022] [Indexed: 07/25/2023] Open
Abstract
Canine mammary tumors (CMTs) are considered a serious clinical problem in older bitches. Due to the high malignancy rate and poor prognosis, an early diagnosis is essential. This article is a summary of novel diagnostic techniques as well as the main biomarkers of CMTs. So far, CMTs are detected only when changes in mammary glands are clinically visible and surgical removal of the mass is the only recommended treatment. Proper diagnostics of CMT is especially important as they represent a very diverse group of tumors and therefore different treatment approaches may be required. Recently, new diagnostic options appeared, like a new cytological grading system of CMTs or B-mode ultrasound, the Doppler technique, contrast-enhanced ultrasound, and real-time elastography, which may be useful in pre-surgical evaluation. However, in order to detect malignancies before macroscopic changes are visible, evaluation of serum and tissue biomarkers should be considered. Among them, we distinguish markers of the cell cycle, proliferation, apoptosis, metastatic potential and prognosis, hormone receptors, inflammatory and more recent: metabolomic, gene expression, miRNA, and transcriptome sequencing markers. The use of a couple of the above-mentioned markers together seems to be the most useful for the early diagnosis of neoplastic diseases as well as to evaluate response to treatment, presence of tumor progression, or further prognosis. Molecular aspects of tumors seem to be crucial for proper understanding of tumorigenesis and the application of individual treatment options.
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Affiliation(s)
- Ilona Kaszak
- Laboratory of Small Animal Reproduction, Department of Small Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland
| | - Olga Witkowska-Piłaszewicz
- Department of Pathology and Veterinary Diagnostics, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland
| | - Kinga Domrazek
- Laboratory of Small Animal Reproduction, Department of Small Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland
| | - Piotr Jurka
- Laboratory of Small Animal Reproduction, Department of Small Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland
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Liu H, Cui G, Luo Y, Guo Y, Zhao L, Wang Y, Subasi A, Dogan S, Tuncer T. Artificial Intelligence-Based Breast Cancer Diagnosis Using Ultrasound Images and Grid-Based Deep Feature Generator. Int J Gen Med 2022; 15:2271-2282. [PMID: 35256855 PMCID: PMC8898057 DOI: 10.2147/ijgm.s347491] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/11/2022] [Indexed: 01/30/2023] Open
Abstract
Purpose Breast cancer is a prominent cancer type with high mortality. Early detection of breast cancer could serve to improve clinical outcomes. Ultrasonography is a digital imaging technique used to differentiate benign and malignant tumors. Several artificial intelligence techniques have been suggested in the literature for breast cancer detection using breast ultrasonography (BUS). Nowadays, particularly deep learning methods have been applied to biomedical images to achieve high classification performances. Patients and Methods This work presents a new deep feature generation technique for breast cancer detection using BUS images. The widely known 16 pre-trained CNN models have been used in this framework as feature generators. In the feature generation phase, the used input image is divided into rows and columns, and these deep feature generators (pre-trained models) have applied to each row and column. Therefore, this method is called a grid-based deep feature generator. The proposed grid-based deep feature generator can calculate the error value of each deep feature generator, and then it selects the best three feature vectors as a final feature vector. In the feature selection phase, iterative neighborhood component analysis (INCA) chooses 980 features as an optimal number of features. Finally, these features are classified by using a deep neural network (DNN). Results The developed grid-based deep feature generation-based image classification model reached 97.18% classification accuracy on the ultrasonic images for three classes, namely malignant, benign, and normal. Conclusion The findings obviously denoted that the proposed grid deep feature generator and INCA-based feature selection model successfully classified breast ultrasonic images.
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Affiliation(s)
- Haixia Liu
- Department of Ultrasound, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061000, People's Republic of China
| | - Guozhong Cui
- Department of Surgical Oncology, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061000, People's Republic of China
| | - Yi Luo
- Medical Statistics Room, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061000, People's Republic of China
| | - Yajie Guo
- Department of Ultrasound, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061000, People's Republic of China
| | - Lianli Zhao
- Department of Internal Medicine teaching and research group, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061000, China
| | - Yueheng Wang
- Department of Ultrasound, The Second Hospital of Hebei MedicalUniversity, Shijiazhuang, Hebei Province, 050000, People's Republic of China
| | - Abdulhamit Subasi
- Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, 20520, Finland.,Department of Computer Science, College of Engineering, Effat University, Jeddah, 21478, Saudi Arabia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, 23119, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, 23119, Turkey
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Zhang F, Li G, Jin L, Jia C, Shi Q, Wu R. Diagnostic value of Doppler imaging for malignant non-mass breast lesions: with different diagnostic criteria for older and younger women: first results. Clin Hemorheol Microcirc 2022; 81:123-134. [PMID: 35147531 DOI: 10.3233/ch-211371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVE To evaluate and optimize the additional diagnostic value of Doppler imaging for malignant NMLs detected by US. MATERIALS AND METHODS The characteristics of 233 NMLs in Doppler imaging were analyzed, and different Adler grades of intralesional vessels were selected as the diagnostic cutoffs on Doppler imaging: grade 1 in the full cohort and in women < 40 years, and grade 0 in women ≥40 years. The diagnostic performance of US and US + Doppler imaging were calculated and compared with that of mammography. RESULTS The AUC of US + Doppler was larger than that of US alone in each group (P < 0.001). In the full cohort, addition of Doppler imaging increased specificity of US, but decreased sensitivity. However, by use of different diagnostic cutoffs in the two subgroups, it was possible to achieve high sensitivity and specificity simultaneously, which were 100% and 75.8% in women < 40 years, 94.7% and 69.5% in women ≥40 years, respectively. The AUC + Doppler was comparable to that of mammography in the full cohort and in women ≥40 years. In women < 40 years, the AUC of the combination was larger than that of mammography (P < 0.001). CONCLUSION Doppler imaging, with different Adler grades used as cutoffs in older versus younger women, can improve the specificity of US for the diagnosis of malignant NMLs without losing sensitivity. In younger women, US + Doppler imaging may be better than mammography.
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Affiliation(s)
- Fan Zhang
- Department of Ultrasound, Shanghai General Hospital, Xin Song Jiang Road, Shanghai, China
| | - Gang Li
- Department of Ultrasound, Shanghai General Hospital, Xin Song Jiang Road, Shanghai, China
| | - Lifang Jin
- Department of Ultrasound, Shanghai General Hospital, Xin Song Jiang Road, Shanghai, China
| | - Chao Jia
- Department of Ultrasound, Shanghai General Hospital, Xin Song Jiang Road, Shanghai, China
| | - Qiusheng Shi
- Department of Ultrasound, Shanghai General Hospital, Xin Song Jiang Road, Shanghai, China
| | - Rong Wu
- Department of Ultrasound, Shanghai General Hospital, Xin Song Jiang Road, Shanghai, China
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Tang Y, Liang M, Tao L, Deng M, Li T. Machine learning-based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study. Quant Imaging Med Surg 2022; 12:1223-1234. [PMID: 35111618 DOI: 10.21037/qims-21-341] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 09/09/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Ultrasound is commonly used in breast cancer screening but lacks quantification ability and diagnostic power due to its low specificity, which can lead to overdiagnosis and unnecessary biopsies. This study evaluated the diagnostic efficacy and clinical utility of adding shear-wave elastography (SWE) to the screening of the Breast Imaging Reporting and Data System (BI-RADS) category 4 breast cancer. METHODS A machine learning-based diagnostic model was constructed using data retrospectively collected from 3 independent cohorts with features selected using lasso regression and support vector machine-recursive feature elimination algorithms. Propensity score matching (PSM) was used to preclude confounding baseline characteristics between malignant and benign lesions. A decision curve analysis (DCA) was used to evaluate the clinical benefit of the diagnostic model in identifying high-risk tumor patients for intervention while simultaneously avoiding overtreatment of low-risk patients with integrative evaluation using a net benefit value and treatment reduction rate. RESULTS In our training center, a total of 122 patients were enrolled, and 577 breast tumors were collected. The comparison between malignant and benign lesions revealed significant differences in patient age, tumor size, resistance index (RI), and elasticity values. The maximum elasticity value (Emax) was identified as an independent diagnostic feature and was included in the diagnostic model. The combination of Emax with BI-RADS category 4 demonstrated a significantly better diagnostic efficacy than the BI-RADS category alone [BI-RADS+Emax: AUC =0.908, 95% confidence interval (CI): 0.842-0.974; BI-RADS: AUC =0.862, 95% CI: 0.784-0.94; P=0.024] and significantly increased the clinical benefit for patients and policy makers by effectively reducing overdiagnosis and biopsy rates. In the BI-RADS category 4A subgroup, adding Emax to breast cancer screening benefited patients and showed a greater absolute benefit than did the BI-RADS category alone when used for patients with a higher probability of cancer (>0.403), demonstrating a 50% overtreatment reduction. CONCLUSIONS Adding Emax to BI-RADS category 4 breast cancer screening using SWE significantly reduced overdiagnosis and biopsy rates compared with the BI-RADS category alone, especially for BI-RADS 4A patients.
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Affiliation(s)
- Yi Tang
- Department of Medical Technology, Guangdong Key Laboratory of Traditional Chinese Medicine Research and Development, Guangdong Second Hospital of Traditional Chinese Medicine, Guangzhou, China.,Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Minjie Liang
- Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Li Tao
- Department of Obstetrics and Gynecology, The First Affiliated Hospital, Anhui Medical University, Hefei, China
| | - Minjun Deng
- Department of Medical Technology, Guangdong Key Laboratory of Traditional Chinese Medicine Research and Development, Guangdong Second Hospital of Traditional Chinese Medicine, Guangzhou, China
| | - Tianfu Li
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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Cai Y, Gao K, Peng B, Xu Z, Peng J, Li J, Chen X, Zeng S, Hu K, Yan Y. Alantolactone: A Natural Plant Extract as a Potential Therapeutic Agent for Cancer. Front Pharmacol 2021; 12:781033. [PMID: 34899346 PMCID: PMC8664235 DOI: 10.3389/fphar.2021.781033] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/03/2021] [Indexed: 02/05/2023] Open
Abstract
Alantolactone (ALT) is a natural compound extracted from Chinese traditional medicine Inula helenium L. with therapeutic potential in the treatment of various diseases. Recently, in vitro and in vivo studies have indicated cytotoxic effects of ALT on various cancers, including liver cancer, colorectal cancer, breast cancer, etc. The inhibitory effects of ALT depend on several cancer-associated signaling pathways and abnormal regulatory factors in cancer cells. Moreover, emerging studies have reported several promising strategies to enhance the oral bioavailability of ALT, such as combining ALT with other herbs and using ALT-entrapped nanostructured carriers. In this review, studies on the anti-tumor roles of ALT are mainly summarized, and the underlying molecular mechanisms of ALT exerting anticancer effects on cells investigated in animal-based studies are also discussed.
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Affiliation(s)
- Yuan Cai
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Kewa Gao
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Bi Peng
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhijie Xu
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Pathology, Xiangya Changde Hospital, Changde, China
| | - Jinwu Peng
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China.,Department of Pathology, Xiangya Changde Hospital, Changde, China
| | - Juanni Li
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Xi Chen
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China
| | - Shuangshuang Zeng
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China
| | - Kuan Hu
- Department of Hepatobiliary Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Yuanliang Yan
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China
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Athamnah SI, Oglat AA, Fohely F. Diagnostice breast elastography estimation from doppler imaging using central difference (CD) and least-squares (LS) algorithms. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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