Purpose: Metastatic rectal cancer (mRC) is a highly lethal and complex disease that demands a multidisciplinary treatment approach. However, the clinical effectiveness of radiotherapy (RT) for de novo mRC remains controversial and uncertain. Methods: This retrospective cohort study examined medical records from Kaohsiung Chang Gung Memorial Hospital for patients with histologically confirmed de novo mRC diagnosed between January 2015 and December 2020. All patients received standard systemic therapy and radical surgery when feasible. The primary outcome, overall survival (OS), was assessed using the Kaplan-Meier method. Multivariable analysis was performed using a Cox regression model. Results: Among 271 patients included in the analysis, 117 received RT and 154 did not. The median OS was significantly longer in the RT group compared with the non-RT group (27.8 months vs. 21.9 months; P=0.046). Multivariate analysis identified several independent predictors of OS: age ≥65 years (hazard ratio [HR], 1.69; 95% confidence interval [CI], 1.26–2.27; P=0.001), primary tumor resection (HR, 2.62; 95% CI, 1.90–3.61; P<0.001), M1b or M1c disease (HR, 1.97; 95% CI, 1.44–2.69; P<0.001), and receipt of RT (HR, 1.41; 95% CI, 1.02–1.94; P=0.036). Conclusion RT significantly improves OS in patients with mRC, underscoring its role in treatment strategies. These findings support its inclusion in therapeutic protocols and highlight the need for larger, multicenter trials to confirm and extend these results.

The global burden of hepatocellular carcinoma (HCC) has shifted from viral to nonviral etiologies.However, successful antiviral therapy does not fully eliminate the risk of HCC, underscoring the demand for more effective surveillance strategies. Current screening methods, such as semi-annual ultrasonography and the measurement of α-fetoprotein levels, offer suboptimal sensitivityfor early detection. A cost-effective, reliable surveillance approach remains an unmet need. The Barcelona Clinic Liver Cancer staging system provides a framework to guide HCC therapy; yet, some gray zone exists, particularly for patients with intermediate-stage disease. Although tyrosine kinase inhibitors and immunotherapies have transformed the therapeutic landscape, their efficacies vary among patients, highlighting the necessity for personalized treatment strategies.In response to these challenges, artificial intelligence (AI) approaches have emerged as transformative tools in healthcare. By processing complex, nonlinear relationships and uncovering hidden patterns in clinical data, AI methods offer capabilities beyond those of traditional statistical methods. Furthermore, AI-driven multi-omics analysis holds promise for identifying novel biomarkers, thereby advancing precision medicine for HCC patients. This review introduces the potential of AI applications in enhancing the diagnosis, treatment, and prognosis of HCC.

BACKGROUND: Non-invasive computed tomography angiography (CTA)-based fractional flow reserve (CT-FFR) could become a gatekeeper to invasive coronary angiography. Deep learning (DL)-based CT-FFR has shown promise when compared to invasive FFR. To evaluate the performance of a DL-based CT-FFR technique, DeepVessel FFR (DVFFR). METHODS: This retrospective study was designed for iScheMia Assessment based on a Retrospective, single-center Trial of CT-FFR (SMART). Patients suspected of stable coronary artery disease (CAD) and undergoing both CTA and invasive FFR examinations were consecutively selected from the Beijing Anzhen Hospital between January 1, 2016 to December 30, 2018. FFR obtained during invasive coronary angiography was used as the reference standard. DVFFR was calculated blindly using a DL-based CT-FFR approach that utilized the complete tree structure of the coronary arteries. RESULTS: Three hundred and thirty nine patients (60.5 ±10.0 years and 209 men) and 414 vessels with direct invasive FFR were included in the analysis. At per-vessel level, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of DVFFR were 94.7%, 88.6%, 90.8%, 82.7%, and 96.7%, respectively. The area under the receiver operating characteristics curve (AUC) was 0.95 for DVFFR and 0.56 for CTA-based assessment with a significant difference (P < 0.0001). At patient level, sensitivity, specificity, accuracy, PPV and NPV of DVFFR were 93.8%, 88.0%, 90.3%, 83.0%, and 95.8%, respectively. The computation for DVFFR was fast with the average time of 22.5 ± 1.9 s. CONCLUSIONS The results demonstrate that DVFFR was able to evaluate lesion hemodynamic significance accurately and effectively with improved diagnostic performance over CTA alone. Coronary artery disease (CAD) is a critical disease in which coronary artery luminal narrowing may result in myocardial ischemia. Early and effective assessment of myocardial ischemia is essential for optimal treatment planning so as to improve the quality of life and reduce medical costs.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.