Radiomic features of Pk-DCE MRI parameters based on the extensive Tofts model in application of breast cancer

Zhou, Xiaoyu; Gao, Feng; Duan, Shaofeng; Zhang, Lianmei; Lu, Yan; Zhou, Junyi; Bai, Genji; Tao, Weijing

doi:10.1007/s13246-020-00852-9

Cited by 15 publications

(13 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The imaging modalities studied were PWI ( n = 9) [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ], conventional MRI ( n = 2) [ 63 , 64 ], DWI ( n = 1) [ 65 ], DWI+PWI [ 66 ], Digital Breast Tomography (DBT, n = 1) [ 67 ], PET/CT ( n = 1) [ 68 ] and standard 2D digital mammography (DMG, n = 1) [ 69 ]. Most studies focused on invasive breast cancer ( n = 13) [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 63 , 64 , 65 , 66 , 67 , 69 ], while three studies investigated non-invasive breast cancer types [ 61 , 62 , 68 ]. HER-2 was the most frequently investigated biomarker ( n = 10), and all studies showed some degree of linkage between HER-2 status and radiomic features (AUC = 0.65–0.95) [ 54 , 56 , 57 , 58 , 59 , 60 , 62 , 64 , 68 , 69 ].…”

Section: Resultsmentioning

confidence: 99%

“…Most studies focused on invasive breast cancer ( n = 13) [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 63 , 64 , 65 , 66 , 67 , 69 ], while three studies investigated non-invasive breast cancer types [ 61 , 62 , 68 ]. HER-2 was the most frequently investigated biomarker ( n = 10), and all studies showed some degree of linkage between HER-2 status and radiomic features (AUC = 0.65–0.95) [ 54 , 56 , 57 , 58 , 59 , 60 , 62 , 64 , 68 , 69 ]. Eight studies examined Ki-67, and all except one study found a significant association to radiomics features (AUC = 0.70–0.81) [ 56 , 57 , 62 , 63 , 65 , 66 , 67 , 68 ].…”

Section: Resultsmentioning

confidence: 99%

“…HER-2 was the most frequently investigated biomarker ( n = 10), and all studies showed some degree of linkage between HER-2 status and radiomic features (AUC = 0.65–0.95) [ 54 , 56 , 57 , 58 , 59 , 60 , 62 , 64 , 68 , 69 ]. Eight studies examined Ki-67, and all except one study found a significant association to radiomics features (AUC = 0.70–0.81) [ 56 , 57 , 62 , 63 , 65 , 66 , 67 , 68 ]. One study investigated TP-53 status ( n = 1) (AUC = 0.88) [ 55 ].…”

Section: Resultsmentioning

confidence: 99%

“…Ten studies focused on HER-2 status prediction based on radiomic features derived from PWI ( n = 7) [ 54 , 57 , 58 , 59 , 60 , 61 , 62 ], DMG ( n = 1) [ 69 ], PET/CT ( n = 1) [ 68 ], and conventional MRI ( n = 1) [ 64 ]. The term HER-2 positivity was used equivalently to an immunohistochemistry (IHC) result of +3 in most studies, while HER-2 negativity corresponded to an IHC score of 0 or +1.…”

Section: Resultsmentioning

confidence: 99%

“…Three studies investigated the relationship between Ki-67 expression levels and PWI radiomics (AUC = 0.74–0.81) [ 56 , 57 , 62 ], with two of them employing pharmacokinetic radiomic features. Authors in two of the three studies suggested that high Ki-67 expressing tumors are associated with higher intra-tumoral heterogeneity.…”

Section: Resultsmentioning

confidence: 99%

See 4 more Smart Citations

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints

Saint-Esteven

Vuong

Tschanz

et al. 2021

Cancers

View full text Add to dashboard Cite

Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints

Saint-Esteven

Vuong

Tschanz

et al. 2021

Cancers

View full text Add to dashboard Cite

show abstract

Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions

et al. 2021

View full text Add to dashboard Cite

PURPOSE:To assess the diagnostic performance of radiomic analysis using High Temporal Resolution (HTR)-Dynamic Contrast Enhancement (DCE) MR sequences compared to BI-RADS analysis to distinguish benign from malignant breast lesions. MATERIALS AND METHODS:We retrospectively analyzed data from consecutive women who underwent breast MRI including HTR-DCE MR sequencing for abnormal enhancing lesions and who had subsequent pathological analysis at our tertiary center. Semiquantitative enhancement parameters and textural features were extracted. Temporal change across each phase of textural features in HTR-DCE MR sequences were calculated and called "kinetic textural parameters". Statistical analysis by LASSO-logistic regression and cross validation were performed to build a model. The diagnostic performance of the radiomic model was compared to the results of BI-RADS MR score analysis. RESULTS:We included 117 women with a mean age of 54 years (28-88). Of the 174 lesions analyzed 75 were benign and 99 malignant. Seven semi-quantitative enhancement parameters and 57 textural features were extracted. Regression analysis selected 15 significant variables in a radiomic model (called "malignant probability score") which displayed an AUC=0.876 (sensitivity=0.98, specificity=0.52, accuracy=0.78). The performance of the malignant probability score to distinguish benign from malignant breast lesions (AUC=0.876, 95%CI 0.825-0.925) was significantly better than that of BI-RADS analysis (AUC=0.831, 95%CI 0.769-0.892). The radiomic model significantly reduced false positives (42%) with the same number of missed cancers (n=2) 2 CONCLUSION: A radiomic model including kinetic textural features extracted from an HTR-DCE MR sequence improves diagnostic performance over BI-RADS analysis.

show abstract

Survival analysis of breast cancer patients using machine learning models

Kirubha

Precious

2023

Multimed Tools Appl

View full text Add to dashboard Cite

Radiomic features of Pk-DCE MRI parameters based on the extensive Tofts model in application of breast cancer

Cited by 15 publications

References 32 publications

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints

Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions

Survival analysis of breast cancer patients using machine learning models

Contact Info

Product

Resources

About