Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers
For ultrasound imaging of thyroid nodules, medical guidelines are all based on findings of sonographic features to provide clinicians management recommendations. Due to the recent development of artificial intelligence and machine learning (AI/ML) technologies, there have been computer-assisted dete...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/10/7/1513 |
| _version_ | 1797407372302352384 |
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| author | Hao-Chih Tai Kuen-Yuan Chen Ming-Hsun Wu King-Jen Chang Chiung-Nien Chen Argon Chen |
| author_facet | Hao-Chih Tai Kuen-Yuan Chen Ming-Hsun Wu King-Jen Chang Chiung-Nien Chen Argon Chen |
| author_sort | Hao-Chih Tai |
| collection | DOAJ |
| description | For ultrasound imaging of thyroid nodules, medical guidelines are all based on findings of sonographic features to provide clinicians management recommendations. Due to the recent development of artificial intelligence and machine learning (AI/ML) technologies, there have been computer-assisted detection (CAD) software devices available for clinical use to detect and quantify the sonographic features of thyroid nodules. This study is to validate the accuracy of the computerized sonographic features (CSF) by a CAD software device, namely, AmCAD-UT, and then to assess how the reading performance of clinicians (readers) can be improved providing the computerized features. The feature detection accuracy is tested against the ground truth established by a panel of thyroid specialists and a multiple-reader multiple-case (MRMC) study is performed to assess the sequential reading performance with the assistance of the CSF. Five computerized features, including anechoic area, hyperechoic foci, hypoechoic pattern, heterogeneous texture, and indistinct margin, were tested, with AUCs ranging from 0.888~0.946, 0.825~0.913, 0.812~0.847, 0.627~0.77, and 0.676~0.766, respectively. With the five CSFs, the sequential reading performance of 18 clinicians is found significantly improved, with the AUC increasing from 0.720 without CSF to 0.776 with CSF. Our studies show that the computerized features are consistent with the clinicians’ findings and provide additional value in assisting sonographic diagnosis. |
| first_indexed | 2024-03-09T03:40:27Z |
| format | Article |
| id | doaj.art-6f2b5548765a4e56b5fc1c3c8a977208 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-09T03:40:27Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-6f2b5548765a4e56b5fc1c3c8a9772082023-12-03T14:41:17ZengMDPI AGBiomedicines2227-90592022-06-01107151310.3390/biomedicines10071513Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid CancersHao-Chih Tai0Kuen-Yuan Chen1Ming-Hsun Wu2King-Jen Chang3Chiung-Nien Chen4Argon Chen5Department of Surgery, National Taiwan University Hospital and College of Medicine, Taipei 100225, TaiwanDepartment of Surgery, National Taiwan University Hospital and College of Medicine, Taipei 100225, TaiwanDepartment of Surgery, National Taiwan University Hospital and College of Medicine, Taipei 100225, TaiwanDepartment of Surgery, National Taiwan University Hospital and College of Medicine, Taipei 100225, TaiwanDepartment of Surgery, National Taiwan University Hospital and College of Medicine, Taipei 100225, TaiwanGraduate Institute of Industrial Engineering, National Taiwan University, Taipei 106216, TaiwanFor ultrasound imaging of thyroid nodules, medical guidelines are all based on findings of sonographic features to provide clinicians management recommendations. Due to the recent development of artificial intelligence and machine learning (AI/ML) technologies, there have been computer-assisted detection (CAD) software devices available for clinical use to detect and quantify the sonographic features of thyroid nodules. This study is to validate the accuracy of the computerized sonographic features (CSF) by a CAD software device, namely, AmCAD-UT, and then to assess how the reading performance of clinicians (readers) can be improved providing the computerized features. The feature detection accuracy is tested against the ground truth established by a panel of thyroid specialists and a multiple-reader multiple-case (MRMC) study is performed to assess the sequential reading performance with the assistance of the CSF. Five computerized features, including anechoic area, hyperechoic foci, hypoechoic pattern, heterogeneous texture, and indistinct margin, were tested, with AUCs ranging from 0.888~0.946, 0.825~0.913, 0.812~0.847, 0.627~0.77, and 0.676~0.766, respectively. With the five CSFs, the sequential reading performance of 18 clinicians is found significantly improved, with the AUC increasing from 0.720 without CSF to 0.776 with CSF. Our studies show that the computerized features are consistent with the clinicians’ findings and provide additional value in assisting sonographic diagnosis.https://www.mdpi.com/2227-9059/10/7/1513thyroid nodulessonographic featurescomputer-assisted detectionMRMC study |
| spellingShingle | Hao-Chih Tai Kuen-Yuan Chen Ming-Hsun Wu King-Jen Chang Chiung-Nien Chen Argon Chen Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers Biomedicines thyroid nodules sonographic features computer-assisted detection MRMC study |
| title | Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers |
| title_full | Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers |
| title_fullStr | Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers |
| title_full_unstemmed | Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers |
| title_short | Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers |
| title_sort | assessing detection accuracy of computerized sonographic features and computer assisted reading performance in differentiating thyroid cancers |
| topic | thyroid nodules sonographic features computer-assisted detection MRMC study |
| url | https://www.mdpi.com/2227-9059/10/7/1513 |
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