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A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI.
European Radiology Experimental ( IF 3.7 ) Pub Date : 2020-01-28 , DOI: 10.1186/s41747-019-0131-4 Natascha C D'Amico 1, 2 , Enzo Grossi 3 , Giovanni Valbusa 3 , Francesca Rigiroli 4 , Bernardo Colombo 1 , Massimo Buscema 5 , Deborah Fazzini 1 , Marco Ali 1 , Ala Malasevschi 1 , Gianpaolo Cornalba 1 , Sergio Papa 1
中文翻译:
一种在乳房MRI上区分恶性和良性灶的机器学习方法。
更新日期:2020-01-28
European Radiology Experimental ( IF 3.7 ) Pub Date : 2020-01-28 , DOI: 10.1186/s41747-019-0131-4 Natascha C D'Amico 1, 2 , Enzo Grossi 3 , Giovanni Valbusa 3 , Francesca Rigiroli 4 , Bernardo Colombo 1 , Massimo Buscema 5 , Deborah Fazzini 1 , Marco Ali 1 , Ala Malasevschi 1 , Gianpaolo Cornalba 1 , Sergio Papa 1
Affiliation
Background
Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature.Methods
Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method (“training with input selection and testing”) was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs).Results
A k-nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87–100%), a specificity of 37/41 (90%, 95% CI 77–97%), and an accuracy of 64/68 (94%, 95% CI 86–98%).Conclusion
This preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI.中文翻译:
一种在乳房MRI上区分恶性和良性灶的机器学习方法。