Journal of Yeungnam Medical Science

Original article

Biomedical Engineering
Gene expression-based machine learning model for diagnosis, prognosis, and treatment response prediction in hepatocellular carcinoma: a retrospective study: Tan Thinh Nguyen, Thanh Dat Nguyen, Phu Qui Le Nguyen, Phuong Thi Bui, Minh Nam Nguyen; J Yeungnam Med Sci. 2026;43:21. Published online March 4, 2026; DOI: https://doi.org/10.12701/jyms.2026.43.21 [Epub ahead of print]

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Abstract PDF: Background
Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, largely because of challenges in early diagnosis and the limited sensitivity of conventional biomarkers. Therefore, reliable molecular tools for early detection, prognostic stratification, and individualized treatment predictions are urgently required.
Methods
This retrospective study analyzed publicly available gene expression datasets. Candidate biomarkers were identified from the GSE14520 cohort using a multistep screening workflow that integrated differential expression analysis, diagnostic performance, and prognostic relevance. A 10-gene diagnostic model was constructed using least absolute shrinkage and selection operator logistic regression and subsequently validated across multiple independent cohorts. Survival outcomes were evaluated using the Kaplan-Meier analysis and treatment responses to sorafenib and transarterial chemoembolization (TACE) were assessed using receiver operating characteristic analysis.
Results
A 10-gene signature (TOP2A, CDK1, CYP3A4, MASP2, EPHX2, HAO1, RACGAP1, GLYAT, ADH1B, and CYP4A11) was established. The model demonstrated robust internal performance and consistent accuracy across external validation cohorts (area under the curve [AUC], >0.9). This signature effectively identified early-stage HCC and distinguished malignancy from cirrhosis. High-risk scores were significantly associated with poor overall survival and recurrence-free survival (p<0.05). Furthermore, the model could predict treatment sensitivity, with higher risk scores associated with better outcomes for sorafenib (AUC, 0.791), whereas lower risk scores correlated with an improved response to TACE (AUC, 0.768).
Conclusion
Our gene expression-based machine learning model provides a robust tool for HCC diagnosis, prognosis, and treatment response prediction, with potential as a supportive system for personalized clinical decision-making.

Original Article

Radiation Oncology
Evaluation of Treatment Response Using Diffusion-Weighted MRI in Metastatic Spines.: Jang Jin Lee, Sei One Shin; Yeungnam Univ J Med. 2001;18(1):30-38. Published online June 30, 2001; DOI: https://doi.org/10.12701/yujm.2001.18.1.30

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Abstract PDF: BACKGROUND
The purpose of this study was to evaluated the usefulness of diffusion-weighted magnatic resonance imaging for monitoring the response to radiation therapy in metastatic bone marrow of the spines. MATERIALS AND METHOD: Twenty-one patients with metastatic bone marrow of the spine were examined with MRI. Diffusion-weighted and spin-echo MRI were performed in 10 patients before and after radiation therapy with or without systematic chemotherapy, and performed in 11 patiemts after radiation therapy alone. Follow up spin-echo and diffusion-weighted MRI were obtained at 1 to 6 months after radiation therapy according to patients' condition. The diffusion-weighted imaging sequence was based on reversed fast imaging with steady-state precession(PSIF). Signal intensity changes of the metastatic bone marrows before and after radiation therapy on conventional spin-echo sequence MRI and diffusion-weighted MRI were evaluated. Bone marrow contrast ratios and signal-to-noise ratio before and after radiation therapy of diffusion-weighted MRI were analyzed. RESULTS: All metastatic bone marrow of the spinal bodies were hyperintense to normal bone marrow of the spinal bodies on pretreatment diffusion-weighted MRI and positive bone marrow contrast ratio(p<0.001). and hypointense to normal spinal bodies on posttreatment diffusion-weighted MRI and negative bone marrow contrast ratio(p<0.001). The signal to noise ratio after treatment decreased comparing with those of pretreatment. Decreased signal intensity of the metastatic bone marrows on diffusion-weighted MRI began to be observed at average more than one month after the initiation of the radiation therapy. CONCLUSION: tThese results suggest that diffusion-weighted MRI would be an excellent method for monitoring the response to therapy of metastatic bone marrow of the spinal bodies. However, must be investigated in a larger series of patients with longer follow up period.

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