Development and internal validation of a conventional ultrasound-based nomogram for predicting malignant nonmasslike breast lesions

Lin, Xiao; Zhuang, Shulian; Yang, Shuang; Lai, Danhui; Chen, Miao; Zhang, Jianxing

doi:10.21037/qims-22-378

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…Compared with IDC, the size of SA nodules is smaller, and SA does not clinically manifest symptoms, thereby complicating palpation and preoperative ultrasonic diagnosis. Therefore, this ultrasound-based comparative study between SA and IDC enabled the construction of a nomogram to assist ultrasound physicians in not only further analyzing the value of various ultrasonic features in SA diagnosis but also calculating the total score on the nomogram to predict the risk of SA (17)(18)(19). Our research indicated that age, nodule size, menopausal status, presence of clinical symptoms, palpability of lesions, margins, internal echo, CDFI grading, and RI could be used as predictive factors for distinguishing between SA and IDC.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, microcalcification is an essential manifestation of mammography. Earlier studies have reported that 20-40% of SA lesions exhibit microcalcification, and the morphology and distribution of microcalcification are comparable to malignant tumors (5,10,19). In a breast ultrasound examination, SA lesions with microcalcification are more susceptible to being classified by ultrasound physicians as BI-RADS level 4 or higher.…”

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confidence: 97%

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A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram

Li,

Wei,

Pang

et al. 2023

Front. Oncol.

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ObjectiveTo analyze the clinical and ultrasonic characteristics of breast sclerosing adenosis (SA) and invasive ductal carcinoma (IDC), and construct a predictive nomogram for SA.Materials and methodsA total of 865 patients were recruited at the Second Hospital of Shandong University from January 2016 to November 2022. All patients underwent routine breast ultrasound examinations before surgery, and the diagnosis was confirmed by histopathological examination following the operation. Ultrasonic features were recorded using the Breast Imaging Data and Reporting System (BI-RADS). Of the 865 patients, 203 (252 nodules) were diagnosed as SA and 662 (731 nodules) as IDC. They were randomly divided into a training set and a validation set at a ratio of 6:4. Lastly, the difference in clinical characteristics and ultrasonic features were comparatively analyzed.ResultThere was a statistically significant difference in multiple clinical and ultrasonic features between SA and IDC (P<0.05). As age and lesion size increased, the probability of SA significantly decreased, with a cut-off value of 36 years old and 10 mm, respectively. In the logistic regression analysis of the training set, age, nodule size, menopausal status, clinical symptoms, palpability of lesions, margins, internal echo, color Doppler flow imaging (CDFI) grading, and resistance index (RI) were statistically significant (P<0.05). These indicators were included in the static and dynamic nomogram model, which showed high predictive performance, calibration and clinical value in both the training and validation sets.ConclusionSA should be suspected in asymptomatic young women, especially those younger than 36 years of age, who present with small-size lesions (especially less than 10 mm) with distinct margins, homogeneous internal echo, and lack of blood supply. The nomogram model can provide a more convenient tool for clinicians.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 97%

A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram

Li,

Wei,

Pang

et al. 2023

Front. Oncol.

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“…No gold-standard approach is currently available for the calculation of the sample size requirements of risk prediction models. However, it is widely accepted that at least 10 events per candidate variable for the logistic regression analysis are needed for the derivation of a risk prediction model ( 13 ). As 22 candidate variables were included in the multivariable regression analysis, at least 220 lesions were required for this study.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a prediction model for the diagnosis of breast cancer based on clinical and ultrasonic features

He¹,

Lu²,

Li³

2023

Gland Surg

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Background The aim of this study was to develop a simple and effective prediction model for calculating the probability of breast cancer by selecting clinical and sonographic features associated with breast cancer. Methods A total of 402 lesions from 304 adult females from the ultrasound department of of PLA General Hospital from March 1 st , 2020 to April 1 st , 2021, were prospectively collected as the development group. The validation group included 121 lesions from 98 patients in our physical examination center from April 1 st , 2021 to March 1 st , 2022. Least absolute shrinkage and selection operator (LASSO) was applied to select clinical and ultrasonic variables, and R language was applied to build a web version of the interactive dynamic column line graph. The prediction model was validated by the validation group and the Breast Imaging Reporting and Data System (BI-RADS) categories. Calibration, differentiation and effectiveness were evaluated by R 2 , receiver operating characteristic (ROC) and decision curve analysis (DCA), respectively. Results One hundred and seventy-nine malignant lesions and 223 benign lesions were included in the development group after exclusion and follow-up, whereas 62 malignant lesions and 59 benign lesions were enrolled in the validation group. Age, bloody nipple discharge, irregular shape, irregular border, heterogeneous echo, microcalcification, attenuation effects, decreased echo in surrounding tissues, lesions in ducts, abnormal lymph node morphology, nourishing vessel and nourishing vessel’s resistance index (RI) greater than 0.70 were selected as independent risk factors. There was no significant difference in the area under the curve (AUC) of the development group between the prediction model and the BI-RADS category (0.959 vs. 0.953, P>0.05), and so as the validation group (0.952 vs. 0.932, P>0.05). For the prediction model, R 2 of the development and validation group was 0.78 and 0.72. The DCA showed that the net benefits (NB) of the development group were higher than that of the validation group (0–100% vs. 0–90%). Conclusions A prediction model was developed with the clinical and ultrasonic features for the precise and intuitive probability of breast cancer. This could provide a reliable reference for further examination.

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“…All images included at least 2 orthogonal planes (radial and antiradial planes or transverse and longitudinal planes). According to the ACR BI-RADS fifth edition classification criteria and a previous study (15), all images were analyzed retrospectively by two breast radiologists (reader 1 with 10 years' experience and reader 2 with 5 years' experience). The radiologists strictly recorded 15 ultrasonic features, as follows: orientation, shape, echogenic pattern, margin, posterior features, calcifications, vascularity grade, vascularity distribution, background echotexture of parenchyma (BEP), anteroposterior thickness of breast parenchyma (TBP), anteroposterior thickness ratio of breast parenchyma to tissue before pectoralis fascia (RPT), anteroposterior thickness ratio of breast parenchyma to mammary fat (RPF), lymph node metastasis, tumor size, and BI-RADS category.…”

Section: Clinical Characteristic Acquisition Ultrasonic Image Acquisi...mentioning

confidence: 99%

Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning

Liang¹,

Shen²,

Wang³

et al. 2023

Quant Imaging Med Surg

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Background: The American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) used with ultrasonography cannot guide the individual management of solid breast tumors, but preoperative core biopsy categories (CBCs) can. We aimed to use machine learning to analyze clinical and ultrasonic features for predicting CBCs and to aid in the development of a new ultrasound (US) imaging reporting system for solid tumors of the breast.Methods: This retrospective study included women with solid breast tumors who underwent US-guided core needle biopsy from March 1, 2019, to December 31, 2019. All patients were randomly assigned to a training or validation cohort (7:3 ratio). CBC was predicted using 5 machine learning models: random forest (RF), support vector machine (SVM), k-nearest-neighbor (KNN), multilayer perceptron (MLP), and ridge regression (RR). In the validation cohort, the area under the curve (AUC) and accuracy were ascertained for every algorithm. Based on AUC values, the optimal algorithm was determined, and the features' importance was depicted.Results: A total of 1,082 female patients were included (age range, 12-96 years; mean age ± standard deviation, 42.22±13.37 years). The proportion of the 4 CBCs was 4% (44/1,185) for the B1 group, 60% (714/1,185) for the B2 group, 5% (57/1,185) for the B3 group, and 31% (370/1,185) for the B5 group. In the validation cohort, AUCs of the optimal algorithm constructed RF were 0.78, 0.88, 0.64, and 0.92 for B1, B2, B3, and B5, respectively, with an accuracy of 0.82.Conclusions: Machine learning could strongly predict CBC, particularly in B2 and B5 categories of solid breast tumors, with RF being the optimal machine learning model.

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Development and internal validation of a conventional ultrasound-based nomogram for predicting malignant nonmasslike breast lesions

Cited by 6 publications

References 33 publications

A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram

A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram

Development and validation of a prediction model for the diagnosis of breast cancer based on clinical and ultrasonic features

Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning

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