Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer
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| I publikationen: | BMC Medical Imaging vol. 24 (2024), p. 1 |
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| Huvudupphov: | |
| Övriga upphov: | , , , , |
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Springer Nature B.V.
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| Länkar: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 001 | 3091290575 | ||
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| 022 | |a 1471-2342 | ||
| 024 | 7 | |a 10.1186/s12880-024-01353-x |2 doi | |
| 035 | |a 3091290575 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 58449 |2 nlm | ||
| 100 | 1 | |a Wu, Linyong | |
| 245 | 1 | |a Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer | |
| 260 | |b Springer Nature B.V. |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundThe purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC).Methods50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality.ResultsSupervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype.ConclusionThe DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment. | |
| 653 | |a Datasets | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Supervised learning | ||
| 653 | |a Phenotypes | ||
| 653 | |a Unsupervised learning | ||
| 653 | |a Data systems | ||
| 653 | |a Image processing | ||
| 653 | |a Machine learning | ||
| 653 | |a Radiomics | ||
| 653 | |a Decision trees | ||
| 653 | |a Heterogeneity | ||
| 653 | |a Physicians | ||
| 653 | |a Clustering | ||
| 653 | |a Lymphatic system | ||
| 653 | |a Biopsy | ||
| 653 | |a Ultrasound | ||
| 653 | |a Information processing | ||
| 653 | |a Computers | ||
| 653 | |a Ultrasonic imaging | ||
| 653 | |a Decision making | ||
| 653 | |a Software | ||
| 653 | |a Womens health | ||
| 653 | |a Deep learning | ||
| 653 | |a Models | ||
| 653 | |a Visual discrimination learning | ||
| 653 | |a Clinical medicine | ||
| 653 | |a Nomograms | ||
| 653 | |a Cancer therapies | ||
| 653 | |a Mammography | ||
| 653 | |a Information systems | ||
| 653 | |a Work experience | ||
| 653 | |a Medical imaging | ||
| 653 | |a Breast cancer | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Mastitis | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Support vector machines | ||
| 653 | |a Ultrasonic testing | ||
| 653 | |a Neural networks | ||
| 653 | |a Visual discrimination | ||
| 653 | |a Vector quantization | ||
| 653 | |a Invasiveness | ||
| 700 | 1 | |a Li, Songhua | |
| 700 | 1 | |a Wu, Chaojun | |
| 700 | 1 | |a Wu, Shaofeng | |
| 700 | 1 | |a Lin, Yan | |
| 700 | 1 | |a Dayou Wei | |
| 773 | 0 | |t BMC Medical Imaging |g vol. 24 (2024), p. 1 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3091290575/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
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| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3091290575/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |