• Journal of information and communication convergence engineering
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• v.23 no.4
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• pp.299-320
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• 2025

In breast cancer brain metastases (BCBM), the accurate evaluation of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) is essential for personalized treatment. However, tissue from brain lesions can only be obtained through invasive and risky stereotactic or surgical biopsy, which is rarely done in routine care. To address this, we developed a radiomics-based ensemble machine-learning model for the noninvasive prediction of ER, PR, and HER2 status. We leveraged The Cancer Imaging Archive BCBM-Radio Genomics dataset and retrospectively analyzed 165 patients with histologically confirmed BCBM by extracting radiomic features from T1-weighted post-contrast magnetic resonance imaging. After preprocessing the data with ADASYN for class balancing, correlation-based feature reduction, and Isolation Forest for outlier detection, we built an ensemble model that incorporated six algorithms (Random Forest, Extra Trees, Balanced Random Forest, XGBoost, LightGBM, and CatBoost) via ensemble voting. The resulting model achieved test accuracies of 82.1%, 83.0%, and 83.4% for ER, PR, and HER2, respectively, demonstrating its strong potential for noninvasive molecular prediction in BCBM.

• Journal of the Korean Society of Radiology
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• v.81 no.3
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• pp.632-643
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• 2020

Purpose To investigate the correlation between magnetic resonance (MR) image-based radiomics features and the genomic features of breast cancer by focusing on biomolecular intrinsic subtypes and gene expression profiles based on risk scores. Materials and Methods We used the publicly available datasets from the Cancer Genome Atlas and the Cancer Imaging Archive to extract the radiomics features of 122 breast cancers on MR images. Furthermore, PAM50 intrinsic subtypes were classified and their risk scores were determined from gene expression profiles. The relationship between radiomics features and biomolecular characteristics was analyzed. A penalized generalized regression analysis was performed to build prediction models. Results The PAM50 subtype demonstrated a statistically significant association with the maximum 2D diameter (p = 0.0189), degree of correlation (p = 0.0386), and inverse difference moment normalized (p = 0.0337). Among risk score systems, GGI and GENE70 shared 8 correlated radiomic features (p = 0.0008-0.0492) that were statistically significant. Although the maximum 2D diameter was most significantly correlated to both score systems (p = 0.0139, and p = 0.0008), the overall degree of correlation of the prediction models was weak with the highest correlation coefficient of GENE70 being 0.2171. Conclusion Maximum 2D diameter, degree of correlation, and inverse difference moment normalized demonstrated significant relationships with the PAM50 intrinsic subtypes along with gene expression profile-based risk scores such as GENE70, despite weak correlations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2480-2496
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• 2020

Considering that high-dose X-ray radiation during CT scans may bring potential risks to patients, in the medical imaging industry there has been increasing emphasis on low-dose CT. Due to complex statistical characteristics of noise found in low-dose CT images, many traditional methods are difficult to preserve structural details effectively while suppressing noise and artifacts. Inspired by the deep learning techniques, we propose a densely connected residual network (DCRN) for low-dose CT image noise cancelation, which combines the ideas of dense connection with residual learning. On one hand, dense connection maximizes information flow between layers in the network, which is beneficial to maintain structural details when denoising images. On the other hand, residual learning paired with batch normalization would allow for decreased training speed and better noise reduction performance in images. The experiments are performed on the 100 CT images selected from a public medical dataset-TCIA(The Cancer Imaging Archive). Compared with the other three competitive denoising algorithms, both subjective visual effect and objective evaluation indexes which include PSNR, RMSE, MAE and SSIM show that the proposed network can improve LDCT images quality more effectively while maintaining a low computational cost. In the objective evaluation indexes, the highest PSNR 33.67, RMSE 5.659, MAE 1.965 and SSIM 0.9434 are achieved by the proposed method. Especially for RMSE, compare with the best performing algorithm in the comparison algorithms, the proposed network increases it by 7 percentage points.