AIM:To automate breast cancer diagnosis and to study the inter-observer and intra-observer variations in the manual evaluations.METHODS:Breast tissue specimens from sixty cases were stained separately for estrogen rec...AIM:To automate breast cancer diagnosis and to study the inter-observer and intra-observer variations in the manual evaluations.METHODS:Breast tissue specimens from sixty cases were stained separately for estrogen receptor(ER),progesterone receptor(PR)and human epidermal growth factor receptor-2(HER-2/neu).All cases were assessed by manual grading as well as image analysis.The manual grading was performed by an experienced expert pathologist.To study inter-observer and intra-observer variations,we obtained readings from another pathologist as the second observer from a different laboratory who has a little less experience than the first observer.We also took a second reading from the second observer to study intra-observer variations.Image analysis was carried out using in-house developed software(TissueQuant).A comparison of the results from image analysis and manual scoring of ER,PR and HER-2/neu was also carried out.RESULTS:The performance of the automated analysis in the case of ER,PR and HER-2/neu expressions was compared with the manual evaluations.The performance of the automated system was found to correlate well with the manual evaluations.The inter-observer variations were measured using Spearman correlation coefficient r and 95%confidence interval.In the case of ER expression,Spearman correlation r=0.53,in the case of PR expression,r=0.63,and in the case of HER-2/neu expression,r=0.68.Similarly,intra-observer variations were also measured.In the case of ER,PR and HER-2/neu expressions,r=0.46,0.66 and 0.70,respectively.CONCLUSION:The automation of breast cancer diagnosis from immunohistochemically stained specimens is very useful for providing objective and repeatable evaluations.展开更多
目的:探讨自动乳腺容积成像技术(automated breast volume scanner, ABVS)与常规手持超声(handheld ultrasound,HHUS)对乳腺BI-RADS 3～5类病变的分类评估价值。方法:回顾性分析我院2016年10月至2017年6月期间对109个乳腺病灶进行自动...目的:探讨自动乳腺容积成像技术(automated breast volume scanner, ABVS)与常规手持超声(handheld ultrasound,HHUS)对乳腺BI-RADS 3～5类病变的分类评估价值。方法:回顾性分析我院2016年10月至2017年6月期间对109个乳腺病灶进行自动乳腺容积成像技术与常规手持超声的影像资料,基于乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)分别对病变进行分类评定,以病理诊断为标准,将BI-RADS分类为3、4A、4B、4C、5类的良性病变分别评为5、4、3、2、1分。将BI-RADS分类为3、4A、4B、4C、5类的恶性病变分别评为1、2、3、4、5分。结果:92例患者109个乳腺病灶,80个恶性病灶,29个良性病灶。29个良性病灶包括纤维腺瘤12例、腺病瘤7例、复杂性囊肿3例、浆细胞性乳腺炎2例、肉芽肿性乳腺炎1例、导管内乳头状瘤1例、良性叶状肿瘤1例、小叶增生性腺病并放射状瘢痕1例、硬化性腺病1例;ABVS评价为5、4、3、2、1分的分别为24、1、2、2、0个,HHUS评价为5、4、3、2、1分的分别为19、8、1、1、0个。80个恶性病变包括导管内原位癌2例、非浸润性癌5例、早期浸润癌10例、浸润性非特殊癌39例、浸润性特殊癌24例;ABVS评价为5、4、3、2、1分的分别为17、35、22、6、0个,HHUS评价为5、4、3、2、1分的分别为12、25、31、10、2个。将ABVS与HHUS对乳腺病变的BI-RADS分类评分进行对比分析,Z=-3.069,P=0.002,差异有统计学意义。结论:ABVS能准确的对乳腺BI-RADS 3～5类病变进行分类评估,且ABVS较HHUS具有整体性强、可重复率高、操作者依赖性小、不易漏诊等优点,因而可以作为一种乳腺BI-RADS分类评估的常规检查方法。展开更多
文摘AIM:To automate breast cancer diagnosis and to study the inter-observer and intra-observer variations in the manual evaluations.METHODS:Breast tissue specimens from sixty cases were stained separately for estrogen receptor(ER),progesterone receptor(PR)and human epidermal growth factor receptor-2(HER-2/neu).All cases were assessed by manual grading as well as image analysis.The manual grading was performed by an experienced expert pathologist.To study inter-observer and intra-observer variations,we obtained readings from another pathologist as the second observer from a different laboratory who has a little less experience than the first observer.We also took a second reading from the second observer to study intra-observer variations.Image analysis was carried out using in-house developed software(TissueQuant).A comparison of the results from image analysis and manual scoring of ER,PR and HER-2/neu was also carried out.RESULTS:The performance of the automated analysis in the case of ER,PR and HER-2/neu expressions was compared with the manual evaluations.The performance of the automated system was found to correlate well with the manual evaluations.The inter-observer variations were measured using Spearman correlation coefficient r and 95%confidence interval.In the case of ER expression,Spearman correlation r=0.53,in the case of PR expression,r=0.63,and in the case of HER-2/neu expression,r=0.68.Similarly,intra-observer variations were also measured.In the case of ER,PR and HER-2/neu expressions,r=0.46,0.66 and 0.70,respectively.CONCLUSION:The automation of breast cancer diagnosis from immunohistochemically stained specimens is very useful for providing objective and repeatable evaluations.
文摘目的:探讨自动乳腺容积成像技术(automated breast volume scanner, ABVS)与常规手持超声(handheld ultrasound,HHUS)对乳腺BI-RADS 3～5类病变的分类评估价值。方法:回顾性分析我院2016年10月至2017年6月期间对109个乳腺病灶进行自动乳腺容积成像技术与常规手持超声的影像资料,基于乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)分别对病变进行分类评定,以病理诊断为标准,将BI-RADS分类为3、4A、4B、4C、5类的良性病变分别评为5、4、3、2、1分。将BI-RADS分类为3、4A、4B、4C、5类的恶性病变分别评为1、2、3、4、5分。结果:92例患者109个乳腺病灶,80个恶性病灶,29个良性病灶。29个良性病灶包括纤维腺瘤12例、腺病瘤7例、复杂性囊肿3例、浆细胞性乳腺炎2例、肉芽肿性乳腺炎1例、导管内乳头状瘤1例、良性叶状肿瘤1例、小叶增生性腺病并放射状瘢痕1例、硬化性腺病1例;ABVS评价为5、4、3、2、1分的分别为24、1、2、2、0个,HHUS评价为5、4、3、2、1分的分别为19、8、1、1、0个。80个恶性病变包括导管内原位癌2例、非浸润性癌5例、早期浸润癌10例、浸润性非特殊癌39例、浸润性特殊癌24例;ABVS评价为5、4、3、2、1分的分别为17、35、22、6、0个,HHUS评价为5、4、3、2、1分的分别为12、25、31、10、2个。将ABVS与HHUS对乳腺病变的BI-RADS分类评分进行对比分析,Z=-3.069,P=0.002,差异有统计学意义。结论:ABVS能准确的对乳腺BI-RADS 3～5类病变进行分类评估,且ABVS较HHUS具有整体性强、可重复率高、操作者依赖性小、不易漏诊等优点,因而可以作为一种乳腺BI-RADS分类评估的常规检查方法。
