The Experts below are selected from a list of 200721 Experts worldwide ranked by ideXlab platform
Hesham F. A. Hamed - One of the best experts on this subject based on the ideXlab platform.
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A Review on Brain Tumor Diagnosis from MRI Images : Practical Implications, Key Achievements, and Lessons Learned
Magnetic resonance imaging, 2019Co-Authors: Mahmoud Khaled Abd-ellah, Ali Ismail Awad, Ashraf A. M. Khalaf, Hesham F. A. HamedAbstract:Abstract The successful early Diagnosis of brain Tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early Tumor Diagnosis. Computer-aided brain Tumor Diagnosis from MRI images consists of Tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain Tumor Diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain Tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain Tumor Diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three Diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.
Li Hai-xia - One of the best experts on this subject based on the ideXlab platform.
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Application of Determination of Serum TSGF to Tumor Diagnosis and Observation of Curative Effect
Henan Journal of Oncology, 2000Co-Authors: Li Hai-xiaAbstract:Objective Study the significance of determination of serum TSGF in Tumor Diagnosis and observation of curative effect.Methods We have measured serum TSGF level with colorimetry in 33 normal people, 209 malignant Tumor patients, 15 carcinoid patients and some patients who were treated.Results (1)The serum TSGF level of malignant Tumor patinets was significantly higher than that of normal people and carcinoid patients. The positive rate of the three groups was 80.9%, 3.0%, 6.7% respectively. (2) The serum TSGF level decreased after treatment. The sensibility and specificity were 80.9% and 93.6%.Conclusion Determination of serum TSGF concentration is helpful in early Diagnosis of malignant Tumor, observeation of curative effect and prognosis.
Yue Sun - One of the best experts on this subject based on the ideXlab platform.
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rhomboidal pt ii metallacycle based nir ii theranostic nanoprobe for Tumor Diagnosis and image guided therapy
Proceedings of the National Academy of Sciences of the United States of America, 2019Co-Authors: Yue Sun, Feng Ding, Zhixuan Zhou, Yibei Zhan, Guangfu Yang, Yao Sun, Peter J StangAbstract:Fluorescent theranostics probes at the second near-IR region (NIR-II; 1.0–1.7 µm) are in high demand for precise theranostics that minimize autofluorescence, reduce photon scattering, and improve the penetration depth. Herein, we designed and synthesized an NIR-II theranostic nanoprobe 1 that incorporates a Pt(II) metallacycle 2 and an organic molecular dye 3 into DSPE-mPEG5000 (1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycol)-5000]). This design endows 1 with good photostability and passive targeting ability. Our studies show that 1 accurately diagnoses cancer with high resolution and selectively delivers the Pt(II) metallacycle to Tumor regions via an enhanced permeability and retention effect. In vivo studies reveal that 1 efficiently inhibits the growth of Tumor with minimal side effects. At the same time, improved fluorescent imaging quality and signal-to-noise ratios are shown due to the long emission wavelengths. These studies demonstrate that 1 is a potential theranostic platform for Tumor Diagnosis and treatment in the NIR-II region.
Lingxin Chen - One of the best experts on this subject based on the ideXlab platform.
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Fluorescent chemical probes for accurate Tumor Diagnosis and targeting therapy
Chemical Society reviews, 2017Co-Authors: Gao Min, Jaebum Choo, Lingxin ChenAbstract:Surgical resection of solid Tumors is currently the gold standard and preferred therapeutic strategy for cancer. Chemotherapy drugs also make a significant contribution by inhibiting the rapid growth of Tumor cells and these two approaches are often combined to enhance treatment efficacy. However, surgery and chemotherapy inevitably lead to severe side effects and high systemic toxicity, which in turn results in poor prognosis. Precision medicine has promoted the development of treatment modalities that are developed to specifically target and kill Tumor cells. Advances in in vivo medical imaging for visualizing Tumor lesions can aid Diagnosis, facilitate surgical resection, investigate therapeutic efficacy, and improve prognosis. In particular, the modality of fluorescence imaging has high specificity and sensitivity and has been utilized for medical imaging. Therefore, there are great opportunities for chemists and physicians to conceive, synthesize, and exploit new chemical probes that can image Tumors and release chemotherapy drugs in vivo. This review focuses on small molecular ligand-targeted fluorescent imaging probes and fluorescent theranostics, including their design strategies and applications in clinical Tumor treatment. The progress in chemical probes described here suggests that fluorescence imaging is a vital and rapidly developing field for interventional surgical imaging, as well as Tumor Diagnosis and therapy.
Mahmoud Khaled Abd-ellah - One of the best experts on this subject based on the ideXlab platform.
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A Review on Brain Tumor Diagnosis from MRI Images : Practical Implications, Key Achievements, and Lessons Learned
Magnetic resonance imaging, 2019Co-Authors: Mahmoud Khaled Abd-ellah, Ali Ismail Awad, Ashraf A. M. Khalaf, Hesham F. A. HamedAbstract:Abstract The successful early Diagnosis of brain Tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early Tumor Diagnosis. Computer-aided brain Tumor Diagnosis from MRI images consists of Tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain Tumor Diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain Tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain Tumor Diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three Diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.
