Background:Targeted magnetic resonance(MR)with ultrasound(US)fusion-guided biopsy has been shown to improve detection of prostate cancer.The implementation of this approach requires integration of skills from radiolog...Background:Targeted magnetic resonance(MR)with ultrasound(US)fusion-guided biopsy has been shown to improve detection of prostate cancer.The implementation of this approach requires integration of skills from radiologists and urologists.Objective methods for assessment of learning curves,such as cumulative sum(CUSUM)analysis,may be helpful in identifying the presence and duration of a learning curve.The aim of this study is to determine the learning curve for MR/US fusion-guided biopsy in detecting clinically significant prostate cancer using CUSUM analysis.Materials and methods:Retrospective analysis was performed in this institutional review board-approved study.Two urologists implemented an MR/US fusion-guided prostate biopsy program between March 2015 and September 2017.The primary outcome measure was cancer detection rate(CDR)stratified by Prostate Imaging Reporting and Data System(PI-RADS)scores assigned on the MR imaging.Cumulative sum analysis quantified actual cancer detection versus a predetermined target satisfactory CDR of MR/US fusion biopsies in a sequential case-by-case basis.For this analysis,satisfactory performance was defined as>80%CDR in patients with Pl-RADS 5,>50%in PI-RADS 4,and<20%in Pl-RADS 1-3.Results:Complete data were available for MR/US fusion-guided biopsies performed on 107 patients.The CUSUM learning curve analysis demonstrated intermittent underperformance until approximately 50 cases.After this inflection point,there was consistently good performance,evidence that no further learning curve was being encountered.Conclusions:At a new center implementing MR/US fusion-guided prostate biopsy,the learning curve was approximately 50 cases before a consistently high performance for prostate cancer detection.展开更多
Objectives:This study presents a method combining a one-class classifier and laser-induced breakdown spectrometry(LIBS)to quickly identify healthy Tegillarca granosa(T.granosa).Materials and Methods:The sum of ranking...Objectives:This study presents a method combining a one-class classifier and laser-induced breakdown spectrometry(LIBS)to quickly identify healthy Tegillarca granosa(T.granosa).Materials and Methods:The sum of ranking differences(SRD)was used to fuse multiple anomaly detection metrics to build the one-class classifier,which was only trained with healthy T.granosa.The one-class classifier can identify healthy T.granosa to exclude non-healthy T.granosa.The proposed method calculated multiple anomaly detection metrics and standardized them to obtain a fusion matrix.Based on the fusion matrix,the samples were ranked by SRD and those ranked lowest and below the threshold were considered to be unhealthy.Results:Multiple anomaly detection metrics were fused by the SRD algorithm and tested on each band,and the final fusion model achieved an accuracy rate of 98.46%,a sensitivity of 100%,and a specificity of 80%.The remaining three single classification models obtained the following results:the SVDD model achieved an accuracy rate of 87.69%,a sensitivity of 90%,and a specificity of 60%;the OCSVM model achieved an accuracy rate of 80%,a sensitivity of 76.67%,and a specificity of 60%;and the DD-SIMCA model achieved an accuracy rate of 95.38%,a sensitivity of 98.33%,and a specificity of 60%.Conclusions:The experimental results showed that the proposed method achieved better results than the traditional one-class classification methods with a single metric.Therefore,the fusion method effectively improves the performance of traditional one-class classifiers when using LIBS to quickly identify healthy substances(healthy T.granosa).展开更多
文摘Background:Targeted magnetic resonance(MR)with ultrasound(US)fusion-guided biopsy has been shown to improve detection of prostate cancer.The implementation of this approach requires integration of skills from radiologists and urologists.Objective methods for assessment of learning curves,such as cumulative sum(CUSUM)analysis,may be helpful in identifying the presence and duration of a learning curve.The aim of this study is to determine the learning curve for MR/US fusion-guided biopsy in detecting clinically significant prostate cancer using CUSUM analysis.Materials and methods:Retrospective analysis was performed in this institutional review board-approved study.Two urologists implemented an MR/US fusion-guided prostate biopsy program between March 2015 and September 2017.The primary outcome measure was cancer detection rate(CDR)stratified by Prostate Imaging Reporting and Data System(PI-RADS)scores assigned on the MR imaging.Cumulative sum analysis quantified actual cancer detection versus a predetermined target satisfactory CDR of MR/US fusion biopsies in a sequential case-by-case basis.For this analysis,satisfactory performance was defined as>80%CDR in patients with Pl-RADS 5,>50%in PI-RADS 4,and<20%in Pl-RADS 1-3.Results:Complete data were available for MR/US fusion-guided biopsies performed on 107 patients.The CUSUM learning curve analysis demonstrated intermittent underperformance until approximately 50 cases.After this inflection point,there was consistently good performance,evidence that no further learning curve was being encountered.Conclusions:At a new center implementing MR/US fusion-guided prostate biopsy,the learning curve was approximately 50 cases before a consistently high performance for prostate cancer detection.
基金The authors would like to acknowledge the financial support provided by the Natural Science Foundation of Zhejiang(No.LY21C200001)China,the National Natural Science Foundation of China(Nos.62105245 and 61805180)the Wenzhou Science and Technology Bureau General Project(Nos.S2020011 and G20200044),China。
文摘Objectives:This study presents a method combining a one-class classifier and laser-induced breakdown spectrometry(LIBS)to quickly identify healthy Tegillarca granosa(T.granosa).Materials and Methods:The sum of ranking differences(SRD)was used to fuse multiple anomaly detection metrics to build the one-class classifier,which was only trained with healthy T.granosa.The one-class classifier can identify healthy T.granosa to exclude non-healthy T.granosa.The proposed method calculated multiple anomaly detection metrics and standardized them to obtain a fusion matrix.Based on the fusion matrix,the samples were ranked by SRD and those ranked lowest and below the threshold were considered to be unhealthy.Results:Multiple anomaly detection metrics were fused by the SRD algorithm and tested on each band,and the final fusion model achieved an accuracy rate of 98.46%,a sensitivity of 100%,and a specificity of 80%.The remaining three single classification models obtained the following results:the SVDD model achieved an accuracy rate of 87.69%,a sensitivity of 90%,and a specificity of 60%;the OCSVM model achieved an accuracy rate of 80%,a sensitivity of 76.67%,and a specificity of 60%;and the DD-SIMCA model achieved an accuracy rate of 95.38%,a sensitivity of 98.33%,and a specificity of 60%.Conclusions:The experimental results showed that the proposed method achieved better results than the traditional one-class classification methods with a single metric.Therefore,the fusion method effectively improves the performance of traditional one-class classifiers when using LIBS to quickly identify healthy substances(healthy T.granosa).
