Application of the Kaiser score to increase diagnostic accuracy in equivocal lesions on diagnostic mammograms referred for MR mammography.


Journal

European journal of radiology
ISSN: 1872-7727
Titre abrégé: Eur J Radiol
Pays: Ireland
ID NLM: 8106411

Informations de publication

Date de publication:
Jan 2021
Historique:
received: 08 10 2020
revised: 29 10 2020
accepted: 09 11 2020
pubmed: 9 12 2020
medline: 15 4 2021
entrez: 8 12 2020
Statut: ppublish

Résumé

We aimed to interpret MR mammography (MRM) using the Kaiser scores for equivocal or inconclusive lesions on mammography (MG). Retrospective IRB-approved evaluation of 3623 MG for which MRM was deployed as a problem-solving tool, after inclusion-exclusion criteria were met. Three readers with different levels of experience assigned a final score from 1 to 11 based on the previously established tree classification system. Area under the curve (AUC) derived from receiver operating characteristic (ROC) analysis was used to determine the overall diagnostic performance for all lesions and separately for mass and non-mass enhancement. Sensitivity, specificity, and likelihood ratio values were obtained at different cut-off values of >4, > 5, and > 8 to rule in and rule out malignancy. Histopathology of 183 mass and 133 non-mass enhancement (NME) lesions show benign etiology in 95 and malignant in 221. The AUC was 0.796 [0.851 for mass and 0.715 for NME]. Applying the Kaiser score upgraded 202 lesions with correct prediction in 77 %, and downgraded 28 lesions with correct prediction in 60.8 %. Using a score <5 instead of <4 to rule out malignancy improved our diagnostic ability to correctly identify 100 % benign lesions. Applying Kaiser score correctly downgraded 60.8 % (17/28) lesions; thus avoiding biopsies in these. Using a high cut-off value>8 to rule-in malignancy, we correctly identified 59.7 % of lesions with 80 % specificity and positive likelihood ratio of 3. The Kaiser score has clinical translation benefits when used as a problem-solving tool for inconclusive MG findings.

Identifiants

pubmed: 33290973
pii: S0720-048X(20)30603-3
doi: 10.1016/j.ejrad.2020.109413
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

109413

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Auteurs

Ankush Jajodia (A)

Department of Radiology, Rajiv Gandhi Cancer Institute and Research Centre, Delhi, India. Electronic address: ankushjaj@gmail.com.

Geetika Sindhwani (G)

Department of Radiology, Rajiv Gandhi Cancer Institute and Research Centre, Delhi, India.

Sunil Pasricha (S)

Department of Histopathology, Rajiv Gandhi Cancer Institute, Delhi, India.

Helmut Prosch (H)

Department of Biomedical Imaging and Image-guided Therapy, University of Vienna, Vienna, Austria.

Sunil Puri (S)

Department of Radiology, Rajiv Gandhi Cancer Institute and Research Centre, Delhi, India.

Ajay Dewan (A)

Department of Surgical Oncology, Rajiv Gandhi Cancer Institute, Delhi, India.

Ullas Batra (U)

Department of Medical Oncology, Rajiv Gandhi Cancer Institute and Research Centre, Delhi, India.

Dinesh Chandra Doval (DC)

Department of Medical Oncology, Rajiv Gandhi Cancer Institute and Research Centre, Delhi, India.

Anurag Mehta (A)

Department of Laboratory & Transfusion Services and Director Research, Rajiv Gandhi Cancer Institute, Delhi, India.

Arvind K Chaturvedi (AK)

Department of Radiology, Rajiv Gandhi Cancer Institute and Research Centre, Delhi, India.

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Classifications MeSH