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Quantitative ultrasound image analysis of axillary lymph nodes to differentiate malignancy from reactive benign changes due to COVID-19 vaccination.
Coronado-Gutiérrez, David; Ganau, Sergi; Bargalló, Xavier; Úbeda, Belén; Porta, Marta; Sanfeliu, Esther; Burgos-Artizzu, Xavier P.
  • Coronado-Gutiérrez D; Transmural Biotech S. L., Barcelona, Spain; BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Clínic de Barcelona (University of Barcelona) and Hospital Sant Joan de Deu, Barcelona, Spain. Electronic address: david.coronado@transmuralbiotech.com.
  • Ganau S; Radiology Department, Hospital Clinic de Barcelona (University of Barcelona), Barcelona, Spain.
  • Bargalló X; Radiology Department, Hospital Clinic de Barcelona (University of Barcelona), Barcelona, Spain.
  • Úbeda B; Radiology Department, Hospital Clinic de Barcelona (University of Barcelona), Barcelona, Spain.
  • Porta M; Radiology Department, Hospital Clinic de Barcelona (University of Barcelona), Barcelona, Spain.
  • Sanfeliu E; Radiology Department, Hospital Clinic de Barcelona (University of Barcelona), Barcelona, Spain.
  • Burgos-Artizzu XP; Transmural Biotech S. L., Barcelona, Spain; BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Clínic de Barcelona (University of Barcelona) and Hospital Sant Joan de Deu, Barcelona, Spain.
Eur J Radiol ; 154: 110438, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1914325
ABSTRACT

PURPOSE:

The aim of this study is to assess the potential of quantitative image analysis and machine learning techniques to differentiate between malignant lymph nodes and benign lymph nodes affected by reactive changes due to COVID-19 vaccination.

METHOD:

In this institutional review board-approved retrospective study, we improved our previously published artificial intelligence model, by retraining it with newly collected images and testing its performance on images containing benign lymph nodes affected by COVID-19 vaccination. All the images were acquired and selected by specialized breast-imaging radiologists and the nature of each node (benign or malignant) was assessed through a strict clinical protocol using ultrasound-guided biopsies.

RESULTS:

A total of 180 new images from 154 different patients were recruited 71 images (10 cases and 61 controls) were used to retrain the old model and 109 images (36 cases and 73 controls) were used to evaluate its performance. The achieved accuracy of the proposed method was 92.7% with 77.8% sensitivity and 100% specificity at the optimal cut-off point. In comparison, the visual node inspection made by radiologists from ultrasound images reached 69.7% accuracy with 41.7% sensitivity and 83.6% specificity.

CONCLUSIONS:

The results obtained in this study show the potential of the proposed techniques to differentiate between malignant lymph nodes and benign nodes affected by reactive changes due to COVID-19 vaccination. These techniques could be useful to non-invasively diagnose lymph node status in patients with suspicious reactive nodes, although larger multicenter studies are needed to confirm and validate the results.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Breast Neoplasms / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Female / Humans Language: English Journal: Eur J Radiol Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Breast Neoplasms / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Female / Humans Language: English Journal: Eur J Radiol Year: 2022 Document Type: Article