Dataset Information

Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors.

ABSTRACT:

Background

Early diagnosis is crucial to the treatment of breast cancer, but conventional imaging detection is challenging. Radiomics has the potential to improve early diagnostic efficacy in a noninvasive manner. This study examined whether integrating computed tomography (CT) radiomics information based on ultrasound (US) models can improve the efficacy of breast cancer prediction.

Methods

We retrospectively analyzed 420 patients with pathologically confirmed benign or malignant breast tumors. Clinical data and examination images were collected, and the population was divided into training (n=294) and validation (n=126) groups at a ratio of 7:3. The region of interest (ROI) was manually segmented along the tumor boundary using MaZda software, and the features of each ROI was extracted. After dimension reduction and screening, the best features were retained. Subsequently, random forest (RF), support vector machines, and K-nearest neighbor classifiers were used to establish prediction models in an US and combined-methods group.

Results

Finally, 8 of the 379 features were retained in the US group. Random forest was found to be the best model, and the area under the curve (AUC) of the training and validation groups was 0.90 [95% confidence interval (CI): 0.852-0.942] and 0.85 (95% CI: 0.775-0.930), respectively. Meanwhile, 12 of the 750 features were retained in the combined group. In this regard, random forest proved to be the best model, and the AUC of the training and validation group was 0.95 (95% CI: 0.918-0.981) and 0.92 (95% CI: 0.866-0.969), respectively. The calibration curve showed a good fit of the model. The decision curve showed that the clinical net benefit of the combined group was far greater than that of any single examination, and the prediction model of the combined group exhibited a degree of practical clinical value.

Conclusions

The combined model based on US and CT images has potential application value in the prognostic prediction of benign and malignant breast diseases.

SUBMITTER: Qin J

PROVIDER: S-EPMC10894355 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors.

Qin Jing J Qin Xiachuan X Duan Yayang Y Xie Yuchen Y Zhou Yuanyuan Y Zhang Chaoxue C

Translational cancer research 20240115 1

<h4>Background</h4>Early diagnosis is crucial to the treatment of breast cancer, but conventional imaging detection is challenging. Radiomics has the potential to improve early diagnostic efficacy in a noninvasive manner. This study examined whether integrating computed tomography (CT) radiomics information based on ultrasound (US) models can improve the efficacy of breast cancer prediction.<h4>Methods</h4>We retrospectively analyzed 420 patients with pathologically confirmed benign or malignant ...[more]

PMID: 38410225

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Dataset Information

Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors.

Background

Methods

Results

Conclusions

Publications

Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors.

Similar Datasets

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