| Autor: |
Lanyun Wang, Yi Ding, Wenjun Yang, Hao Wang, Jinjiang Shen, Weiyan Liu, Jingjing Xu, Ran Wei, Wenjuan Hu, Yaqiong Ge, Bei Zhang, Bin Song |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Frontiers in Oncology, Vol 12 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2234-943X |
| DOI: |
10.3389/fonc.2022.677803 |
| Popis: |
ObjectiveThe objective of this study is to develop a radiomics nomogram for the presurgical distinction of benign and malignant round-like solid tumors.MethodsThis retrospective trial enrolled patients with round-like tumors who had received preoperative digital mammography (DM) no sooner than 20 days prior to surgery. Breast tumors were segmented manually on DM images in order to extract radiomic features. Four machine learning classification models were constructed, and their corresponding areas under the receiver operating characteristic (ROC) curves (AUCs) for differential tumor diagnosis were calculated. The optimal classifier was then selected for the validation set. After this, predictive machine learning models that employed radiomic features and/or patient features were applied for tumor assessment. The models’ AUC, accuracy, negative (NPV) and positive (PPV) predictive values, sensitivity, and specificity were then derived.ResultsIn total 129 cases with benign and malignant tumors confirmed by pathological analysis were enrolled in the study, including 91 and 38 in the training and test sets, respectively. The DM images yielded 1,370 features per patient. For the machine learning models, the Least Absolute Shrinkage and Selection Operator for Gradient Boosting Classifier turned out to be the optimal classifier (AUC=0.87, 95% CI 0.76-0.99), and ROC curves for the radiomics nomogram and the DM-only model were statistically different (P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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