Early diagnosis of breast cancer,the most common disease among women around the world,increases the chance of treatment and is highly important.Nuclear atypia grading in histopathological images plays an important rol...Early diagnosis of breast cancer,the most common disease among women around the world,increases the chance of treatment and is highly important.Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading ofbreast cancer.Grading images by pathologists is a time consuming and subjective task.Therefore,the existence of a computer-aided system for nuclear atypia grading is very useful and necessary;In this stud%two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed.A patch-based approach is introduced due to the large size of the histopathological images and restriction of the training data.In the proposed system I,the most important patches in the image are detected first and then a three-hidden-layer convolutional neural network(CNN)is designed and trained for feature extraction and to classify the patches individually.The proposed system II is based on a combination of the CNN for feature extraction and a two-layer Long short-term memoty(LSTM)network for classification.The LSTM network is utilised to consider all patches of an image simultaneously for image grading.The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature.展开更多
目的探究超声特征联合BRAFV600E基因对BethesdaⅢ类甲状腺结节良、恶性的诊断效能。方法本研究为回顾性队列研究,连续性纳入2020年1月至2024年12月在陆军军医大学第一附属医院超声科经超声引导下细针穿刺细胞学检查(ultrasound-guided f...目的探究超声特征联合BRAFV600E基因对BethesdaⅢ类甲状腺结节良、恶性的诊断效能。方法本研究为回顾性队列研究,连续性纳入2020年1月至2024年12月在陆军军医大学第一附属医院超声科经超声引导下细针穿刺细胞学检查(ultrasound-guided fine needle aspiration cytology,US-FNAC)诊断为BethesdaⅢ类、且最终经手术病理或重复US-FNAC确诊的甲状腺患者275例(278个结节),以手术病理或重复US-FNAC确诊的病理结果为标准将结节分为良性与恶性,计算年龄、性别、超声特征(9项)、美国放射学会甲状腺超声影像报告与数据系统(American College of Radiology Thyroid Imaging Reporting and Data System,ACR TI-RADS)评分、US-FNAC细胞核形态、BRAFV600E基因对BethesdaⅢ类结节的诊断效能,采用多因素Logistic回归模型分析超声特征及联合BRAFV600E基因对BethesdaⅢ类甲状腺结节良、恶性的诊断效能。结果患者年龄、结节边缘、结节纵横比、结节最大径、结节回声、Adler血流分级、ACR TI-RADS分类、US-FNAC细胞核形态以及BRAFV600E基因在甲状腺BethesdaⅢ类良、恶性结节中差异具有统计学意义(P均<0.05)。年龄≤52岁、BRAFV600E基因突变、细胞毛玻璃样核改变、颈部异常淋巴结是恶性结节的独立危险因素(OR值分别为7.444、108.218、5.389、13.351,P<0.05),回归模型的准确度、灵敏度、特异度、阳性预测值、阴性预测值分别为0.823、0.863、0.925、0.977、0.649,曲线下面积为0.955。结论超声特征联合BRAFV600E基因对甲状腺BethesdaⅢ类良恶性结节有较高诊断效能。展开更多
文摘Early diagnosis of breast cancer,the most common disease among women around the world,increases the chance of treatment and is highly important.Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading ofbreast cancer.Grading images by pathologists is a time consuming and subjective task.Therefore,the existence of a computer-aided system for nuclear atypia grading is very useful and necessary;In this stud%two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed.A patch-based approach is introduced due to the large size of the histopathological images and restriction of the training data.In the proposed system I,the most important patches in the image are detected first and then a three-hidden-layer convolutional neural network(CNN)is designed and trained for feature extraction and to classify the patches individually.The proposed system II is based on a combination of the CNN for feature extraction and a two-layer Long short-term memoty(LSTM)network for classification.The LSTM network is utilised to consider all patches of an image simultaneously for image grading.The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature.
文摘目的探究超声特征联合BRAFV600E基因对BethesdaⅢ类甲状腺结节良、恶性的诊断效能。方法本研究为回顾性队列研究,连续性纳入2020年1月至2024年12月在陆军军医大学第一附属医院超声科经超声引导下细针穿刺细胞学检查(ultrasound-guided fine needle aspiration cytology,US-FNAC)诊断为BethesdaⅢ类、且最终经手术病理或重复US-FNAC确诊的甲状腺患者275例(278个结节),以手术病理或重复US-FNAC确诊的病理结果为标准将结节分为良性与恶性,计算年龄、性别、超声特征(9项)、美国放射学会甲状腺超声影像报告与数据系统(American College of Radiology Thyroid Imaging Reporting and Data System,ACR TI-RADS)评分、US-FNAC细胞核形态、BRAFV600E基因对BethesdaⅢ类结节的诊断效能,采用多因素Logistic回归模型分析超声特征及联合BRAFV600E基因对BethesdaⅢ类甲状腺结节良、恶性的诊断效能。结果患者年龄、结节边缘、结节纵横比、结节最大径、结节回声、Adler血流分级、ACR TI-RADS分类、US-FNAC细胞核形态以及BRAFV600E基因在甲状腺BethesdaⅢ类良、恶性结节中差异具有统计学意义(P均<0.05)。年龄≤52岁、BRAFV600E基因突变、细胞毛玻璃样核改变、颈部异常淋巴结是恶性结节的独立危险因素(OR值分别为7.444、108.218、5.389、13.351,P<0.05),回归模型的准确度、灵敏度、特异度、阳性预测值、阴性预测值分别为0.823、0.863、0.925、0.977、0.649,曲线下面积为0.955。结论超声特征联合BRAFV600E基因对甲状腺BethesdaⅢ类良恶性结节有较高诊断效能。
