Toward Automated Pre-Biopsy Thyroid Cancer Risk Estimation in Ultrasound.
AMIA Annu Symp Proc
; 2017: 734-741, 2017.
Article
em En
| MEDLINE
| ID: mdl-29854139
ABSTRACT
We propose a computational framework for automated cancer risk estimation of thyroid nodules visualized in ultrasound (US) images. Our framework estimates the probability of nodule malignancy using random forests on a rich set of computational features. An expert radiologist annotated thyroid nodules in 93 biopsy-confirmed patients using semantic image descriptors derived from standardized lexicon. On our dataset, the AUC of the proposed method was 0.70, which was comparable to five baseline expert annotation-based classifiers with AUC values from 0.72 to 0.81. Moreover, the use of the framework for decision making on nodule biopsy could have spared five out of 46 benign nodule biopsies at no cost to the health of patients with malignancies. Our results confirm the feasibility of computer-aided tools for noninvasive malignancy risk estimation in patients with thyroid nodules that could help to decrease the number of unnecessary biopsies and surgeries.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Diagnóstico por Computador
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Ultrassonografia
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Nódulo da Glândula Tireoide
Tipo de estudo:
Diagnostic_studies
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Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article