背景与目的:腺泡状软组织肉瘤罕见,其影像学表现至今尚未见系统研究,本文旨在探讨腺泡状软组织肉瘤(alveolar soft part sarcomas,ASPS)的临床特征和影像学表现,以提高诊断的准确率。方法:回顾性分析10例经病理证实的腺泡状软组织肉瘤...背景与目的:腺泡状软组织肉瘤罕见,其影像学表现至今尚未见系统研究,本文旨在探讨腺泡状软组织肉瘤(alveolar soft part sarcomas,ASPS)的临床特征和影像学表现,以提高诊断的准确率。方法:回顾性分析10例经病理证实的腺泡状软组织肉瘤的临床特征和影像学表现,所有患者(术前或活检前)均行X线、CT或MR检查。其中,9例行X线平片检查,9例行CT检查,6例行MRI检查。所有切除或活检组织均行HE染色,5例患者有比较完整的免疫组化结果。结果:ASPS发病年龄较轻,80%(8/10)为30岁以下。多表现为无痛性肿块。3例就诊时已有肺转移。70%(7/10)发生于下肢深部软组织以及臀部。另3例分别位于胸壁、颈部及眼眶内。ASPS的CT表现为软组织肿块影,增强后呈明显不均匀强化。MRI表现为T1WI等或略高信号,T2WI高信号,肿瘤内外可见血管流空信号,增强后肿瘤呈不均匀明显强化。镜下ASPS是由嗜伊红色的大多边形上皮样细胞组成,呈特征性的器官样或腺泡状排列,腺泡之间为衬覆单层扁平内皮细胞的裂隙状或血窦样毛细血管网。免疫组化显示3例神经元特异性烯醇化酶(NSE)阳性,两例抗淀粉酶消化染色(PAS)阳性,1例MyoD1横纹肌特异肌调节蛋白阳性(胞质染色),1例Desmin结蛋白阳性。结论:ASPS虽然是少见软组织肉瘤,但影像学很有特点,结合临床、影像和病理表现是诊断的关键。展开更多
In many fields, particularly that of health, the diagnosis of diseases is a very difficult task to carry out. Therefore, early detection of diseases using artificial intelligence tools can be of paramount importance i...In many fields, particularly that of health, the diagnosis of diseases is a very difficult task to carry out. Therefore, early detection of diseases using artificial intelligence tools can be of paramount importance in the medical field. In this study, we proposed an intelligent system capable of performing diagnoses for radiologists. The support system is designed to evaluate mammographic images, thereby classifying normal and abnormal patients. The proposed method (DiagBC for Breast Cancer Diagnosis) combines two (2) intelligent unsupervised learning algorithms (the C-Means clustering algorithm and the Gaussian Mixture Model) for the segmentation of medical images and an algorithm for supervised learning (a modified DenseNet) for the diagnosis of breast images. Ultimately, a prototype of the proposed system was implemented for the Magori Polyclinic in Niamey (Niger) making it possible to diagnose (or classify) breast cancer into two (2) classes: the normal class and the abnormal class.展开更多
文摘背景与目的:腺泡状软组织肉瘤罕见,其影像学表现至今尚未见系统研究,本文旨在探讨腺泡状软组织肉瘤(alveolar soft part sarcomas,ASPS)的临床特征和影像学表现,以提高诊断的准确率。方法:回顾性分析10例经病理证实的腺泡状软组织肉瘤的临床特征和影像学表现,所有患者(术前或活检前)均行X线、CT或MR检查。其中,9例行X线平片检查,9例行CT检查,6例行MRI检查。所有切除或活检组织均行HE染色,5例患者有比较完整的免疫组化结果。结果:ASPS发病年龄较轻,80%(8/10)为30岁以下。多表现为无痛性肿块。3例就诊时已有肺转移。70%(7/10)发生于下肢深部软组织以及臀部。另3例分别位于胸壁、颈部及眼眶内。ASPS的CT表现为软组织肿块影,增强后呈明显不均匀强化。MRI表现为T1WI等或略高信号,T2WI高信号,肿瘤内外可见血管流空信号,增强后肿瘤呈不均匀明显强化。镜下ASPS是由嗜伊红色的大多边形上皮样细胞组成,呈特征性的器官样或腺泡状排列,腺泡之间为衬覆单层扁平内皮细胞的裂隙状或血窦样毛细血管网。免疫组化显示3例神经元特异性烯醇化酶(NSE)阳性,两例抗淀粉酶消化染色(PAS)阳性,1例MyoD1横纹肌特异肌调节蛋白阳性(胞质染色),1例Desmin结蛋白阳性。结论:ASPS虽然是少见软组织肉瘤,但影像学很有特点,结合临床、影像和病理表现是诊断的关键。
文摘In many fields, particularly that of health, the diagnosis of diseases is a very difficult task to carry out. Therefore, early detection of diseases using artificial intelligence tools can be of paramount importance in the medical field. In this study, we proposed an intelligent system capable of performing diagnoses for radiologists. The support system is designed to evaluate mammographic images, thereby classifying normal and abnormal patients. The proposed method (DiagBC for Breast Cancer Diagnosis) combines two (2) intelligent unsupervised learning algorithms (the C-Means clustering algorithm and the Gaussian Mixture Model) for the segmentation of medical images and an algorithm for supervised learning (a modified DenseNet) for the diagnosis of breast images. Ultimately, a prototype of the proposed system was implemented for the Magori Polyclinic in Niamey (Niger) making it possible to diagnose (or classify) breast cancer into two (2) classes: the normal class and the abnormal class.
