Editorial for “Functional Tumor Volume by Fast Dynamic Contrast‐Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple‐Negative Breast Cancer”

Pineda, Federico

doi:10.1002/jmri.27669

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of increased peritumoral vascularity, triple-negative breast cancers exhibit a higher degree of invasiveness due to a greater degree of malignancy and therefore have a richer blood supply around the associated focal tissue [16]. Due to their more efficient tumor cell replication, patients with triple-negative breast tumors exhibit higher T2WI signals, accelerated phagocytosis of healthy cells, and the infiltration of lymphocytes [17]. The primary causes of circumferential intensification are a high rate of vascular system generation, a greater proportion of necrotic areas at the center of the tumor, and higher levels of fibrosis.…”

Section: Discussionmentioning

confidence: 99%

Construction of a risk prediction model based on the analysis of MRI imaging features for patients with triple-negative breast cancer and validation of its efficacy

2024

EJGO

View full text Add to dashboard Cite

This study is aimed at constructing a risk prediction model for patients with triplenegative breast cancer based on the feature analysis of Magnetic Resonance Imaging (MRI) and verifying the efficacy of the model. 150 patients admitted to our hospital, who had been diagnosed with breast cancer by immunohistochemistry were recruited as our study subjects. For each patient, we collated a range of clinical data (age, tumor size, menopausal status and family history of breast cancer), pathological findings (tumor pathological type and grading), and MRI imaging characteristics. Then patients with triple-negative breast cancer were compared to patients with non-triple-negative cancers. We created a risk prediction model for patients with triple-negative breast cancer after identifying risk variables for the disease using single-factor and multi-factor logistic regression analysis. The Hosmer and Lemeshow test was used to assess the goodnessof-fit of the risk prediction model and a Receiver Operating Characteristic (ROC) curve was plotted by SPSS to evaluate the predictive value of the risk prediction model. The results of single factor analysis based on MRI imaging characteristics showed that there were statistically significant differences between triple-negative breast cancer patients and non-triple-negative breast cancer patients in terms of clear boundaries, increased blood vessels around the tumor, T2-weighted imaging (T2WI) signals, and enhancement mode (p < 0.05). The statistical model for predicting triple-negative breast cancer was: P = 1/[1 + exp(6.055 − 2.802X 2 − 1.904X 3 − 2.120X 4 )]. The Hosmer and Lemeshow test was used to test the goodness-of-fit for the statistical model (χ 2 = 7.993, p = 0.434). ROC analysis showed that the area under the curve (AUC) was 0.916 and with a 95% confidence interval (CI) of 0.874-0.957.

show abstract

Section: Discussionmentioning

confidence: 99%

Construction of a risk prediction model based on the analysis of MRI imaging features for patients with triple-negative breast cancer and validation of its efficacy

2024

EJGO

View full text Add to dashboard Cite

show abstract

Editorial for “Functional Tumor Volume by Fast Dynamic Contrast‐Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple‐Negative Breast Cancer”

Cited by 1 publication

References 10 publications

Construction of a risk prediction model based on the analysis of MRI imaging features for patients with triple-negative breast cancer and validation of its efficacy

Construction of a risk prediction model based on the analysis of MRI imaging features for patients with triple-negative breast cancer and validation of its efficacy

Contact Info

Product

Resources

About