目的探讨不同病理医师间乳腺导管原位癌(ductal carcinoma in situ,DCIS)分级的一致性,明确计算机定量辅助对提高乳腺DCIS分级一致性的价值。方法收集四川大学华西医院存档的453例乳腺DCIS样本,每例选取一张具有代表性的HE切片。由三位...目的探讨不同病理医师间乳腺导管原位癌(ductal carcinoma in situ,DCIS)分级的一致性,明确计算机定量辅助对提高乳腺DCIS分级一致性的价值。方法收集四川大学华西医院存档的453例乳腺DCIS样本,每例选取一张具有代表性的HE切片。由三位病理医师分别按常规工作状态、培训交流后、计算机定量辅助三个阶段对所有乳腺DICS病例独立进行低、中、高级别分级。采用Fleiss Kappa值对不同病理医师间的诊断一致性进行分析。结果常规工作状态下,三位病理医师对乳腺DCIS诊断一致性较低(Kappa=0.360),特别是中核级乳腺DCIS(Kappa=0.181);经统一诊断标准培训后,三位医师诊断的一致性略有提升(Kappa=0.362),其中中核级乳腺DCIS诊断的Kappa=0.227;经计算机定量辅助测量肿瘤细胞核直径,三位医师诊断的一致性明显升高(Kappa=0.638),特别是对中核级乳腺DCIS的诊断(Kappa=0.550)。结论计算机定量辅助方法能够提高乳腺DCIS诊断的一致性。展开更多
Computer-aided diagnosis(CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance im...Computer-aided diagnosis(CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging(MRI), image features from T2-weighted images(T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone(PZ) and central gland(CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features(10/12) had significant difference(P<0.01) between PCa and non-PCa in the PZ, while only five features(sum average, minimum value, standard deviation, 10 th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2 w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.展开更多
文摘目的探讨不同病理医师间乳腺导管原位癌(ductal carcinoma in situ,DCIS)分级的一致性,明确计算机定量辅助对提高乳腺DCIS分级一致性的价值。方法收集四川大学华西医院存档的453例乳腺DCIS样本,每例选取一张具有代表性的HE切片。由三位病理医师分别按常规工作状态、培训交流后、计算机定量辅助三个阶段对所有乳腺DICS病例独立进行低、中、高级别分级。采用Fleiss Kappa值对不同病理医师间的诊断一致性进行分析。结果常规工作状态下,三位病理医师对乳腺DCIS诊断一致性较低(Kappa=0.360),特别是中核级乳腺DCIS(Kappa=0.181);经统一诊断标准培训后,三位医师诊断的一致性略有提升(Kappa=0.362),其中中核级乳腺DCIS诊断的Kappa=0.227;经计算机定量辅助测量肿瘤细胞核直径,三位医师诊断的一致性明显升高(Kappa=0.638),特别是对中核级乳腺DCIS的诊断(Kappa=0.550)。结论计算机定量辅助方法能够提高乳腺DCIS诊断的一致性。
文摘Computer-aided diagnosis(CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging(MRI), image features from T2-weighted images(T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone(PZ) and central gland(CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features(10/12) had significant difference(P<0.01) between PCa and non-PCa in the PZ, while only five features(sum average, minimum value, standard deviation, 10 th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2 w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.
