Predicting Upgrades in High-Risk Breast Lesions with a Radiomics Model
Author Information
Author(s): Liao Tingting, Yang Yuting, Lin Xiaohui, Ouyang Rushan, Deng Yaohong, Ma Jie
Primary Institution: Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University
Hypothesis
Can a nomogram combining intratumoral and peritumoral radiomics predict the pathological upgrade of high-risk breast lesions?
Conclusion
The nomogram effectively predicts postoperative pathological upgrades in high-risk breast lesions, aiding in clinical decision-making.
Supporting Evidence
- The nomogram achieved an AUC of 0.914 in the training set and 0.867 in the validation set.
- Specificity and accuracy were 85.1% and 82.3% during training, and 66.7% and 76.2% during validation.
- Multivariate analysis identified maximum lesion diameter, Ki-67, and background parenchymal enhancement as independent predictors.
Takeaway
This study created a tool that helps doctors figure out if certain breast lumps might turn into cancer, so they can avoid unnecessary surgeries.
Methodology
The study used a retrospective design with 138 patients, dividing them into training and validation sets, and developed a nomogram based on radiomics features from MRI.
Potential Biases
Selection bias may affect the generalizability of the findings.
Limitations
The study is limited by its single-center design and the potential for selection bias.
Participant Demographics
All participants were female, aged 25 to 83 years, with various types of high-risk breast lesions.
Statistical Information
P-Value
<0.001
Confidence Interval
95% CI for AUC values reported
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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