Automated Radiology-Pathology Module Correlation Using a Novel Report Matching Algorithm by Organ System

Radiology-pathology correlation is time-consuming and is not feasible in most clinical settings, with the notable exception of breast imaging. The purpose of this study was to determine if an automated radiology-pathology report pairing system could accurately match radiology and pathology reports,...

Full description

Saved in:
Bibliographic Details
Published inAcademic radiology Vol. 25; no. 5; p. 673
Main Authors Dane, Bari, Doshi, Ankur, Gfytopoulos, Soterios, Bhattacharji, Priya, Recht, Michael, Moore, William
Format Journal Article
LanguageEnglish
Published United States 01.05.2018
Subjects
Online AccessGet more information
ISSN1878-4046
DOI10.1016/j.acra.2017.11.009

Cover

Abstract Radiology-pathology correlation is time-consuming and is not feasible in most clinical settings, with the notable exception of breast imaging. The purpose of this study was to determine if an automated radiology-pathology report pairing system could accurately match radiology and pathology reports, thus creating a feedback loop allowing for more frequent and timely radiology-pathology correlation. An experienced radiologist created a matching matrix of radiology and pathology reports. These matching rules were then exported to a novel comprehensive radiology-pathology module. All distinct radiology-pathology pairings at our institution from January 1, 2016 to July 1, 2016 were included (n = 8999). The appropriateness of each radiology-pathology report pairing was scored as either "correlative" or "non-correlative." Pathology reports relating to anatomy imaged in the specific imaging study were deemed correlative, whereas pathology reports describing anatomy not imaged with the particular study were denoted non-correlative. Overall, there was 88.3% correlation (accuracy) of the radiology and pathology reports (n = 8999). Subset analysis demonstrated that computed tomography (CT) abdomen/pelvis, CT head/neck/face, CT chest, musculoskeletal CT (excluding spine), mammography, magnetic resonance imaging (MRI) abdomen/pelvis, MRI brain, musculoskeletal MRI (excluding spine), breast MRI, positron emission tomography (PET), breast ultrasound, and head/neck ultrasound all demonstrated greater than 91% correlation. When further stratified by imaging modality, CT, MRI, mammography, and PET demonstrated excellent correlation (greater than 96.3%). Ultrasound and non-PET nuclear medicine studies demonstrated poorer correlation (80%). There is excellent correlation of radiology imaging reports and appropriate pathology reports when matched by organ system. Rapid, appropriate radiology-pathology report pairings provide an excellent opportunity to close feedback loop to the interpreting radiologist.
AbstractList Radiology-pathology correlation is time-consuming and is not feasible in most clinical settings, with the notable exception of breast imaging. The purpose of this study was to determine if an automated radiology-pathology report pairing system could accurately match radiology and pathology reports, thus creating a feedback loop allowing for more frequent and timely radiology-pathology correlation. An experienced radiologist created a matching matrix of radiology and pathology reports. These matching rules were then exported to a novel comprehensive radiology-pathology module. All distinct radiology-pathology pairings at our institution from January 1, 2016 to July 1, 2016 were included (n = 8999). The appropriateness of each radiology-pathology report pairing was scored as either "correlative" or "non-correlative." Pathology reports relating to anatomy imaged in the specific imaging study were deemed correlative, whereas pathology reports describing anatomy not imaged with the particular study were denoted non-correlative. Overall, there was 88.3% correlation (accuracy) of the radiology and pathology reports (n = 8999). Subset analysis demonstrated that computed tomography (CT) abdomen/pelvis, CT head/neck/face, CT chest, musculoskeletal CT (excluding spine), mammography, magnetic resonance imaging (MRI) abdomen/pelvis, MRI brain, musculoskeletal MRI (excluding spine), breast MRI, positron emission tomography (PET), breast ultrasound, and head/neck ultrasound all demonstrated greater than 91% correlation. When further stratified by imaging modality, CT, MRI, mammography, and PET demonstrated excellent correlation (greater than 96.3%). Ultrasound and non-PET nuclear medicine studies demonstrated poorer correlation (80%). There is excellent correlation of radiology imaging reports and appropriate pathology reports when matched by organ system. Rapid, appropriate radiology-pathology report pairings provide an excellent opportunity to close feedback loop to the interpreting radiologist.
Author Dane, Bari
Doshi, Ankur
Bhattacharji, Priya
Moore, William
Gfytopoulos, Soterios
Recht, Michael
Author_xml – sequence: 1
  givenname: Bari
  surname: Dane
  fullname: Dane, Bari
  organization: Department of Radiology, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016
– sequence: 2
  givenname: Ankur
  surname: Doshi
  fullname: Doshi, Ankur
  organization: Department of Radiology, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016
– sequence: 3
  givenname: Soterios
  surname: Gfytopoulos
  fullname: Gfytopoulos, Soterios
  organization: Department of Radiology, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016
– sequence: 4
  givenname: Priya
  surname: Bhattacharji
  fullname: Bhattacharji, Priya
  organization: Department of Radiology, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016
– sequence: 5
  givenname: Michael
  surname: Recht
  fullname: Recht, Michael
  organization: Department of Radiology, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016
– sequence: 6
  givenname: William
  surname: Moore
  fullname: Moore, William
  email: William.Moore@nyumc.org
  organization: Department of Radiology, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016. Electronic address: William.Moore@nyumc.org
BackLink https://www.ncbi.nlm.nih.gov/pubmed/29373209$$D View this record in MEDLINE/PubMed
BookMark eNo1j8lqwzAURUVpaYb2B7oo-gG7T4NlaxlCJ0ibkqbr8GzJQ7CtICsF_33TaXUv58CFOyPnvestITcMYgZM3e1jLDzGHFgaMxYD6DMyZVmaRRKkmpDZMOwBWKIycUkmXItUcNBTUi-OwXUYrKEbNI1rXTVGbxjqn0ZfnDm2li6d97bF0LiefgxNX1Gkr-7TtnRjD84H-oKhqL_5oq2cb0Ld0Xyka19hT9_HIdjuilyU2A72-i_nZPtwv10-Rav14_NysYoKCWmI8lJII0SBUnEpJZjcKJ5arotScaaznAmrNJMnnuBJp1AmidEFoDlh5HNy-zt7OOadNbuDbzr04-7_Mf8CKpFakg
CitedBy_id crossref_primary_10_1016_j_jacr_2023_04_010
crossref_primary_10_1016_j_jacr_2019_03_001
crossref_primary_10_1016_j_jacr_2019_05_010
crossref_primary_10_1002_uog_23595
crossref_primary_10_1016_j_acra_2020_12_006
crossref_primary_10_1016_j_jacr_2019_05_007
crossref_primary_10_1148_radiol_2021204405
crossref_primary_10_1148_rg_2018180037
crossref_primary_10_1016_j_clinimag_2020_04_031
crossref_primary_10_1016_j_acra_2023_06_007
crossref_primary_10_1016_j_jacr_2022_12_015
crossref_primary_10_1016_j_jacr_2020_05_036
crossref_primary_10_1007_s00256_020_03616_4
crossref_primary_10_1186_s12931_020_01370_8
ContentType Journal Article
Copyright Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
Copyright_xml – notice: Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
DBID NPM
DOI 10.1016/j.acra.2017.11.009
DatabaseName PubMed
DatabaseTitle PubMed
DatabaseTitleList PubMed
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
DeliveryMethod no_fulltext_linktorsrc
Discipline Medicine
EISSN 1878-4046
ExternalDocumentID 29373209
Genre Journal Article
GroupedDBID ---
--K
.1-
.FO
.GJ
0R~
1B1
1P~
23M
4.4
457
53G
5GY
5RE
5VS
AAEDT
AAEDW
AALRI
AAQFI
AAQXK
AAWTL
AAXUO
ABJNI
ABMAC
ABWVN
ACGFS
ACRPL
ADBBV
ADMUD
ADNMO
AENEX
AEVXI
AFCTW
AFFNX
AFJKZ
AFRHN
AFTJW
AITUG
AJUYK
ALMA_UNASSIGNED_HOLDINGS
AMRAJ
ASPBG
AVWKF
AZFZN
BELOY
C5W
CS3
EBS
EFJIC
EJD
F5P
FDB
FEDTE
FGOYB
G-Q
HVGLF
HZ~
IHE
J1W
KOM
M41
MO0
NPM
NQ-
O9-
OI~
OU0
P2P
R2-
ROL
RPZ
SEL
SES
SEW
SJN
SSZ
UHS
XH2
Z5R
ZGI
ZXP
ID FETCH-LOGICAL-c407t-bf34d33ca4624440dbd627e29cf62198b13e6914dbd5a40d70f55d9c0ad691a2
IngestDate Thu Apr 03 07:05:26 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 5
Keywords Radiology pathology correlation
radiology education
concordance
Language English
License Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c407t-bf34d33ca4624440dbd627e29cf62198b13e6914dbd5a40d70f55d9c0ad691a2
PMID 29373209
ParticipantIDs pubmed_primary_29373209
PublicationCentury 2000
PublicationDate 2018-05-00
PublicationDateYYYYMMDD 2018-05-01
PublicationDate_xml – month: 05
  year: 2018
  text: 2018-05-00
PublicationDecade 2010
PublicationPlace United States
PublicationPlace_xml – name: United States
PublicationTitle Academic radiology
PublicationTitleAlternate Acad Radiol
PublicationYear 2018
SSID ssj0015683
Score 2.2898533
Snippet Radiology-pathology correlation is time-consuming and is not feasible in most clinical settings, with the notable exception of breast imaging. The purpose of...
SourceID pubmed
SourceType Index Database
StartPage 673
Title Automated Radiology-Pathology Module Correlation Using a Novel Report Matching Algorithm by Organ System
URI https://www.ncbi.nlm.nih.gov/pubmed/29373209
Volume 25
hasFullText
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3da9swEBdpB2Mvo_v-Rg97Cw6ybEvOYza2lUFC2TLoW5EseWmXRsGTB9k_0X-5p484Juug24sJJyIb3S_nu8vd7xB6W7KqKhzRLU8FTfKiVElZZ1miyjTVilRj6mcdTmfs-Fv--bQ4HQyuelVLrZWj6veNfSX_o1WQgV5dl-w_aLbbFATwGfQLV9AwXG-l40lrDXic4DN-ESr0nSQnwi4Cr9LUqHbpptI1Tax4G4YCATGcmV96GZ3v4RSssc9DTZbfTXNuF5fOJ_VNmpHQvO_BdgX1zfaWu2R3yI6-g_C7k5mffmrwcLL60XZ1wJ_qjTVr0y5Djd9X4wijzS5hvxDWCtcPduG_e9Kcb0Q_PZGWu2LAkQ4mtYQ4NScx0Rhtbmh2jtgqegaUhcEmfxj2kGO4AJA1ji0q5SPHveqZFWxP0-tLr2rwYXhGb7O6R7a9XTpAB5y7SSAzl_yJf0oVrMxi31UoEdx_GMcsHTfYi1K8tzI_QvdjmIEnATMP0ECvHqK701hI8QgtOujgG6CDA3RwDzrYQwcL7KGDA3TwFjq4gw6WG-yhgwN0HqP5xw_z98dJHLqRVBDb20TWWa6yrBI5A88vJ0oqRrmm46pm8HYrZZppNk5zkBcCljmpi0KNKyIUiAV9gg5XZqWfIZzlkgpCFBVC50rxknFGJKGSEzgkrZ6jp-GIztaBWOVse3gv_rryEt3bgewVulPDL1m_BrfQyjdeWdeoS2Td
linkProvider National Library of Medicine
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Automated+Radiology-Pathology+Module+Correlation+Using+a+Novel+Report+Matching+Algorithm+by+Organ+System&rft.jtitle=Academic+radiology&rft.au=Dane%2C+Bari&rft.au=Doshi%2C+Ankur&rft.au=Gfytopoulos%2C+Soterios&rft.au=Bhattacharji%2C+Priya&rft.date=2018-05-01&rft.eissn=1878-4046&rft.volume=25&rft.issue=5&rft.spage=673&rft_id=info:doi/10.1016%2Fj.acra.2017.11.009&rft_id=info%3Apmid%2F29373209&rft_id=info%3Apmid%2F29373209&rft.externalDocID=29373209