| Autor: |
Yixiao Lin, Quing Zhu |
| Jazyk: |
angličtina |
| Rok vydání: |
2025 |
| Předmět: |
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| Zdroj: |
Photoacoustics, Vol 41, Iss , Pp 100675- (2025) |
| Druh dokumentu: |
article |
| ISSN: |
2213-5979 |
| DOI: |
10.1016/j.pacs.2024.100675 |
| Popis: |
Ovarian-adnexal lesions are conventionally assessed with ultrasound (US) under the guidance of the Ovarian-Adnexal Reporting and Data System (O-RADS). However, the low specificity of O-RADS results in many unnecessary surgeries. Here, we use co-registered US and photoacoustic tomography (PAT) to improve the diagnostic accuracy of O-RADS. Physics-based parametric algorithms for US and PAT were developed to estimate the acoustic and photoacoustic properties of 93 ovarian lesions. Additionally, statistics-based radiomic algorithms were applied to quantify differences in the lesion texture on US-PAT images. A machine learning model (US-PAT KNN model) was developed based on an optimized subset of eight US and PAT imaging features to classify a lesion as either cancer, one of four subtypes of benign lesions, or a normal ovary. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.969 and a balanced six-class classification accuracy of 86.0 %. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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