Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic images
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 imp...
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| Format: | Article |
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Elsevier
2025-02-01
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| Series: | Photoacoustics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213597924000922 |
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| author | Yixiao Lin Quing Zhu |
| author_facet | Yixiao Lin Quing Zhu |
| author_sort | Yixiao Lin |
| collection | DOAJ |
| description | 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 %. |
| format | Article |
| id | doaj-art-87b6f5c777a847ee9f5c56583aef7c55 |
| institution | Kabale University |
| issn | 2213-5979 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photoacoustics |
| spelling | doaj-art-87b6f5c777a847ee9f5c56583aef7c552025-01-17T04:49:31ZengElsevierPhotoacoustics2213-59792025-02-0141100675Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic imagesYixiao Lin0Quing Zhu1Biomedical Engineering Department, Washington University in St Louis, United StatesBiomedical Engineering Department, Washington University in St Louis, United States; Radiology Department, School of Medicine, Washington University in St Louis, United States; Corresponding author at: Biomedical Engineering Department, Washington University in St Louis, United States.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 %.http://www.sciencedirect.com/science/article/pii/S2213597924000922Ovarian-adnexal lesionsO-RADSCo-registered ultrasound-photoacoustic tomographyMultiparametric ultrasoundRadiomics |
| spellingShingle | Yixiao Lin Quing Zhu Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic images Photoacoustics Ovarian-adnexal lesions O-RADS Co-registered ultrasound-photoacoustic tomography Multiparametric ultrasound Radiomics |
| title | Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic images |
| title_full | Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic images |
| title_fullStr | Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic images |
| title_full_unstemmed | Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic images |
| title_short | Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic images |
| title_sort | classification and risk assessment of ovarian adnexal lesions using parametric and radiomic analysis of co registered ultrasound photoacoustic tomographic images |
| topic | Ovarian-adnexal lesions O-RADS Co-registered ultrasound-photoacoustic tomography Multiparametric ultrasound Radiomics |
| url | http://www.sciencedirect.com/science/article/pii/S2213597924000922 |
| work_keys_str_mv | AT yixiaolin classificationandriskassessmentofovarianadnexallesionsusingparametricandradiomicanalysisofcoregisteredultrasoundphotoacoustictomographicimages AT quingzhu classificationandriskassessmentofovarianadnexallesionsusingparametricandradiomicanalysisofcoregisteredultrasoundphotoacoustictomographicimages |