BMC medical imaging · 2021
Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wu Linyong, Zhao Yujia, Lin Peng, Qin Hui, Liu Yichen, Wan Da, Li Xin, He Yun, Yang Hong
Аннотация
BACKGROUND: The molecular biomarkers of breast ductal carcinoma in situ (DCIS) have important guiding significance for individualized precision treatment. This study was intended to explore the significance of radiomics based on ultrasound images to predict the expression of molecular biomarkers of mass type of DCIS.
METHODS: 116 patients with mass type of DCIS were included in this retrospective study. The radiomics features were extracted based on ultrasound images. According to the ratio of 7:3, the data sets of molecular biomarkers were split into training set and test set. The radiomics models were developed to predict the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki67, p16, and p53 by using combination of multiple feature selection and classifiers. The predictive performance of the models were evaluated using the area under the curve (AUC) of the receiver operating curve.
RESULTS: The investigators extracted 5234 radiomics features from ultrasound images. 12, 23, 41, 51, 31 and 23 features were important for constructing the models. The radiomics scores were significantly (P < 0.05) in each molecular marker expression of mass type of DCIS. The radiomics models showed predictive performance with AUC greater than 0.7 in the training set and test set: ER (0.94 and 0.84), PR (0.90 and 0.78), HER2 (0.94 and 0.74), Ki67 (0.95 and 0.86), p16 (0.96 and 0.78), and p53 (0.95 and 0.74), respectively.
CONCLUSION: Ultrasonic-based radiomics analysis provided a noninvasive preoperative method for predicting the expression of molecular markers of mass type of DCIS with good accuracy.
METHODS: 116 patients with mass type of DCIS were included in this retrospective study. The radiomics features were extracted based on ultrasound images. According to the ratio of 7:3, the data sets of molecular biomarkers were split into training set and test set. The radiomics models were developed to predict the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki67, p16, and p53 by using combination of multiple feature selection and classifiers. The predictive performance of the models were evaluated using the area under the curve (AUC) of the receiver operating curve.
RESULTS: The investigators extracted 5234 radiomics features from ultrasound images. 12, 23, 41, 51, 31 and 23 features were important for constructing the models. The radiomics scores were significantly (P < 0.05) in each molecular marker expression of mass type of DCIS. The radiomics models showed predictive performance with AUC greater than 0.7 in the training set and test set: ER (0.94 and 0.84), PR (0.90 and 0.78), HER2 (0.94 and 0.74), Ki67 (0.95 and 0.86), p16 (0.96 and 0.78), and p53 (0.95 and 0.74), respectively.
CONCLUSION: Ultrasonic-based radiomics analysis provided a noninvasive preoperative method for predicting the expression of molecular markers of mass type of DCIS with good accuracy.
Предметные рубрики MeSH
AdultAgedAged, 80 and overBiomarkers, TumorBreast NeoplasmsCarcinoma, Intraductal, NoninfiltratingCyclin-Dependent Kinase Inhibitor p16FemaleHumansKi-67 AntigenMiddle AgedErb-b2 Receptor Tyrosine KinasesReceptors, EstrogenReceptors, ProgesteroneRetrospective StudiesTumor Suppressor Protein p53Ultrasonography