Cargando…
Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis
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, suc...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337819/ https://www.ncbi.nlm.nih.gov/pubmed/37448610 http://dx.doi.org/10.1097/CU9.0000000000000116 |
_version_ | 1785071497532932096 |
---|---|
author | Xu, Linhan Ye, Nancy Yating Lee, Adrianna Chopra, Jasleen Naslund, Michael Wong-You-Cheong, Jade Wnorowski, Amelia Siddiqui, Mohummad Minhaj |
author_facet | Xu, Linhan Ye, Nancy Yating Lee, Adrianna Chopra, Jasleen Naslund, Michael Wong-You-Cheong, Jade Wnorowski, Amelia Siddiqui, Mohummad Minhaj |
author_sort | Xu, Linhan |
collection | PubMed |
description | 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 PI-RADS 5, >50% in PI-RADS 4, and <20% in PI-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. |
format | Online Article Text |
id | pubmed-10337819 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-103378192023-09-01 Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis Xu, Linhan Ye, Nancy Yating Lee, Adrianna Chopra, Jasleen Naslund, Michael Wong-You-Cheong, Jade Wnorowski, Amelia Siddiqui, Mohummad Minhaj Curr Urol Special Topic - Advances in Prostate Cancer Therapy 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 PI-RADS 5, >50% in PI-RADS 4, and <20% in PI-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. Lippincott Williams & Wilkins 2023-09 2022-08-22 /pmc/articles/PMC10337819/ /pubmed/37448610 http://dx.doi.org/10.1097/CU9.0000000000000116 Text en Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Special Topic - Advances in Prostate Cancer Therapy Xu, Linhan Ye, Nancy Yating Lee, Adrianna Chopra, Jasleen Naslund, Michael Wong-You-Cheong, Jade Wnorowski, Amelia Siddiqui, Mohummad Minhaj Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis |
title | Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis |
title_full | Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis |
title_fullStr | Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis |
title_full_unstemmed | Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis |
title_short | Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis |
title_sort | learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis |
topic | Special Topic - Advances in Prostate Cancer Therapy |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337819/ https://www.ncbi.nlm.nih.gov/pubmed/37448610 http://dx.doi.org/10.1097/CU9.0000000000000116 |
work_keys_str_mv | AT xulinhan learningcurveformagneticresonanceimagingultrasoundfusionprostatebiopsyindetectingprostatecancerusingcumulativesumanalysis AT yenancyyating learningcurveformagneticresonanceimagingultrasoundfusionprostatebiopsyindetectingprostatecancerusingcumulativesumanalysis AT leeadrianna learningcurveformagneticresonanceimagingultrasoundfusionprostatebiopsyindetectingprostatecancerusingcumulativesumanalysis AT choprajasleen learningcurveformagneticresonanceimagingultrasoundfusionprostatebiopsyindetectingprostatecancerusingcumulativesumanalysis AT naslundmichael learningcurveformagneticresonanceimagingultrasoundfusionprostatebiopsyindetectingprostatecancerusingcumulativesumanalysis AT wongyoucheongjade learningcurveformagneticresonanceimagingultrasoundfusionprostatebiopsyindetectingprostatecancerusingcumulativesumanalysis AT wnorowskiamelia learningcurveformagneticresonanceimagingultrasoundfusionprostatebiopsyindetectingprostatecancerusingcumulativesumanalysis AT siddiquimohummadminhaj learningcurveformagneticresonanceimagingultrasoundfusionprostatebiopsyindetectingprostatecancerusingcumulativesumanalysis |