Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE‐MRI in classifying benign from malignant lesions

Jiang, Zejun; Yin, Jiandong

doi:10.1002/jso.25901

Cited by 11 publications

(11 citation statements)

References 64 publications

(80 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To determine Ki‐67 status, the features from the SI slope map were most frequently selected to construct the peritumoral RS, while the features from the E initial map were most frequently extracted to generate the intratumoral and combined RSs. These findings are partially consistent with those of a study conducted by Jiang et al, which also showed that functional parametric maps derived from breast DCE‐MRI included important information to discriminate between malignant and benign lesions 37 …”

Section: Discussionsupporting

confidence: 92%

“…These findings are partially consistent with those of a study conducted by Jiang et al, which also showed that functional parametric maps derived from breast DCE-MRI included important information to discriminate between malignant and benign lesions. 37 For the features used in the RSs, most of the selected features were Gabor wavelet features, which can be used to comprehensively quantify tumor heterogeneity across different spatial scales at different directional orientations. 22,33 Two recent studies also showed that the Gabor wavelet features contain more detailed information about BC and were crucial components for construction of a radiomics model.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE‐MRI for Prediction of HER‐2 and Ki‐67 Status

Song

Yin

2021

Magnetic Resonance Imaging

Self Cite

View full text Add to dashboard Cite

Background Radiomics has been applied to breast magnetic resonance imaging (MRI) for gene status prediction. However, the features of peritumoral regions were not thoroughly investigated. Purpose To evaluate the use of intratumoral and peritumoral regions from functional parametric maps based on breast dynamic contrast‐enhanced MRI (DCE‐MRI) for prediction of HER‐2 and Ki‐67 status. Study Type Retrospective. Population A total of 351 female patients (average age, 51 years) with pathologically confirmed breast cancer were assigned to the training (n = 243) and validation (n = 108) cohorts. Field Strength/Sequence 3.0T, T1 gradient echo. Assessment Radiomic features were extracted from intratumoral and peritumoral regions on six functional parametric maps calculated using time‐intensity curves of DCE‐MRI. The intraclass correlation coefficients (ICCs) were used to determine the reproducibility of feature extraction. Based on the intratumoral, peritumoral, and combined intra‐ and peritumoral regions, three radiomics signatures (RSs) were built using the least absolute shrinkage and selection operator (LASSO) logistic regression model, respectively. Statistical Tests Wilcoxon rank‐sum test, minimum redundancy maximum relevance, LASSO, receiver operating characteristic curve (ROC) analysis, and DeLong test. Results The intratumoral and peritumoral RSs for prediction of HER‐2 and Ki‐67 status achieved areas under the ROC (AUCs) of 0.683 (95% confidence interval [CI], 0.574–0.793) and 0.690 (95% CI, 0.577–0.804), and 0.714 (95% CI, 0.616–0.812) and 0.692 (95% CI, 0.590–0.794) in the validation cohort, respectively. The combined RSs yielded AUCs of 0.713 (95% CI, 0.604–0.823) and 0.749 (95% CI, 0.656–0.841), respectively. There were no significant differences in prediction performance among intratumoral, peritumoral, and combined RSs. Most (69.7%) of the features had good agreement (ICCs >0.8). Data Conclusion Radiomic features of intratumoral and peritumoral regions on functional parametric maps based on breast DCE‐MRI had the potential to identify HER‐2 and Ki‐67 status. Level of Evidence: 3 Technical Efficacy Stage: 2

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE‐MRI for Prediction of HER‐2 and Ki‐67 Status

Song

Yin

2021

Magnetic Resonance Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…Selecting the malignant signs with the highest risk to construct a nomogram can help distinguish benign and malignant lesions and improve the diagnostic value. Among them, the TIC curve, ADC value, mass edge, calcification morphology, age, and SWEmax were identified as independent predictors of benign and malignant tumors, which are the same as the previous study (9)(10)(11). No matter from the multivariable logistic regression analysis or the visualization of the nomogram, it can be seen that the edge spiculated of the tumor is the most powerful indicator to predict the malignant tumor.…”

Section: Discussionsupporting

confidence: 78%

“…In addition to morphological characteristics, hemodynamic characteristics also play an important role in predicting benign and malignant masses. Jiang (10) believes that the TIC curve can objectively and accurately assess the dynamic enhancement characteristics of the diseased tissue, and has high specificity and sensitivity for the differential diagnosis of breast diseases. The risk of malignancy of the washout TIC curve is 11.23 times that of the non-enhanced or continuously rising TIC curve.…”

Section: Discussionmentioning

confidence: 99%

Downgrade BI-RADS 4A Patients Using Nomogram Based on Breast Magnetic Resonance Imaging, Ultrasound, and Mammography

et al. 2022

View full text Add to dashboard Cite

ObjectivesTo downgrade BI-RADS 4A patients by constructing a nomogram using R software.Materials and MethodsA total of 1,717 patients were retrospectively analyzed who underwent preoperative ultrasound, mammography, and magnetic resonance examinations in our hospital from August 2019 to September 2020, and a total of 458 patients of category BI-RADS 4A (mean age, 47 years; range 18–84 years; all women) were included. Multivariable logistic regression was used to screen out the independent influencing parameters that affect the benign and malignant tumors, and the nomogram was constructed by R language to downgrade BI-RADS 4A patients to eligible category.ResultsOf 458 BI-RADS 4A patients, 273 (59.6%) were degraded to category 3. The malignancy rate of these 273 lesions is 1.5% (4/273) (<2%), and the sensitivity reduced to 99.6%, the specificity increased from 4.41% to 45.3%, and the accuracy increased from 63.4% to 78.8%.ConclusionBy constructing a nomogram, some patients can be downgraded to avoid unnecessary biopsy.

show abstract

“…With its rich artistic expression, it plays an important role in the field of painting and exists in oil painting in a unique form. Through the expression form of its own unique attributes, it shows the aesthetic feeling of vision and form in oil paintings [ 7 ]. The texture of different materials can show the characteristics of artistic language and image on the picture.…”

Section: Methods Of Oil Painting Style Formation and Schemamentioning

confidence: 99%

Formation and Schema Analysis of Oil Painting Style Based on Texture and Color Texture Features under Few Shot

Zhao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Texture has strong expressiveness in picture art, and color texture features play an important role in composition. Together with texture, they can convey the artistic connotation of portrait, especially in oil painting. Therefore, in order to make the picture form oil painting style and oil painting schema, we need to study the texture and color texture in combination with the previous oil painting art images. But now, there are few samples of good oil paintings, so it is difficult to study the texture and color texture in oil paintings. Therefore, in order to form a unique artistic style of modern oil painting and promote the development of modern oil painting art, this paper studies the texture and color texture characteristics in the environment of few oil painting works. This paper establishes a model through deep neural network to extract the image incentive and color texture of oil painting art works, which provides guidance for promoting the development of oil painting art. The experiments in this paper show that the depth neural network has high definition for the extraction of texture and color texture of small sample oil painting images, which can reach more than 85%. It has high guiding significance for the research and creation of oil painting art.

show abstract

Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE‐MRI in classifying benign from malignant lesions

Cited by 11 publications

References 64 publications

Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE‐MRI for Prediction of HER‐2 and Ki‐67 Status

Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE‐MRI for Prediction of HER‐2 and Ki‐67 Status

Downgrade BI-RADS 4A Patients Using Nomogram Based on Breast Magnetic Resonance Imaging, Ultrasound, and Mammography

Formation and Schema Analysis of Oil Painting Style Based on Texture and Color Texture Features under Few Shot

Contact Info

Product

Resources

About