Background/Aims: Biliary tract cancer (BTC) is a rare malignancy with poor prognosis. We investigated genomic determinants of clinical benefit from gemcitabine, cisplatin, and nab-paclitaxel (GAP) versus gemcitabine and cisplatin (GC) in advanced BTC. Methods: Clinical and genomic data using TruSight Oncology 500 were analyzed from patients treated with GAP (N=198) or GC (N=89) as first-line therapy. Results: With a median follow-up of 33.0 months, GAP modestly improved progression-free survival (PFS) (hazard ratio [HR] 0.764; 95% confidence interval [CI] 0.591–0.989) without significant overall survival (OS) difference compared to GC. Genomic profiling revealed frequent alterations in TP53 (35.2%), KRAS (16.4%), SMAD4 (10.5%), and TNFRSF14 (10.5%), involving RTK/RAS (44.3%), TP53 (41.8%), and PI3K (20.2%) pathways. Single-gene mutations did not predict treatment benefit. However, pathway-level analysis identified PI3K pathway activation as significantly associated with inferior PFS (HR 2.148; 95% CI 1.478–3.124) and OS (HR 2.096; 95% CI 1.413–3.109) in patients receiving GAP, an effect not observed with GC. Importantly, GAP conferred clinical benefit only in patients without PI3K pathway activation, while no survival advantage was seen in those with such alterations (Pinteraction=0.023 for PFS, Pinteraction=0.003 for OS). Similar results were obtained in the independent validation cohort treated with GAP (N=103) or GC (N=64) for BTC. Conclusions Genomic profiling using next-generation sequencing identified PI3K pathway activation as key molecular determinant that differentiates patient outcomes between GAP and GC treatments in advanced BTC.

Background/Aims: Combination immunotherapy, exemplified by atezolizumab plus bevacizumab, has become the standard of care for inoperable hepatocellular carcinoma (HCC). However, the lack of predictive biomarkers and limited understanding of response mechanisms remain a challenge. Methods: Using data from the IMbrave150plus cohort, we applied an immune signature score (ISS) predictor to stratify HCC patients treated with atezolizumab plus bevacizumab or with sorafenib alone into potential high and low response groups. By applying multiple statistical approaches including a Bayesian covariate prediction algorithm, we refined the signature to 10 key genes (ISS10) for clinical use while maintaining similar predictive power to the full model. We further validated ISS10 in an independent HCC cohort treated with nivolumab plus ipilimumab. Results: The study identified a significant association between the ISS and treatment response. Among patients classified as high responders, those treated with the atezolizumab plus bevacizumab combination exhibited improved overall and progression-free survival as well as better objective response rate compared to those treated with sorafenib. We also observed a significant correlation between ISS10 and response to nivolumab plus ipilimumab treatment. Analysis of immune cell subpopulations revealed distinct characteristics associated with ISS subtypes. In particular, the ISS10 high subtype displayed a more favorable immune environment with higher proportions of antitumor macrophages and activated T-cells, potentially explaining its better response. Conclusions Our study suggests that ISS and ISS10 are promising predictive biomarkers for enhanced therapeutic outcomes in HCC patients undergoing combination immunotherapy. These markers are crucial for refining patient stratification and personalized treatment approaches to advance the effectiveness of standard-of-care regimens.

Artificial intelligence (AI) is a field of computer science that equips machines with human-like intelligence and enables them to learn, reason, and solve problems when presented with data in various formats. Neurosurgery is often at the forefront of innovative and disruptive technologies, which have similarly altered the course of acute and chronic diseases. In diagnostic imaging, such as X-rays, computed tomography, and magnetic resonance imaging, AI is used to analyze images. The use of robots in the field of neurosurgery is also increasing. In neurointensive care units, AI is used to analyze data and provide care to critically ill patients. Moreover, AI can be used to predict a patient’s prognosis. Several AI applications have already been introduced in the field of neurosurgery, and many more are expected in the near future. Ultimately, it is our responsibility to keep pace with this evolution to provide meaningful outcomes and personalize each patient’s care. Rather than blindly relying on AI in the future, neurosurgeons should gain a thorough understanding of it and use it to enhance their patient care.

Purpose: There are clinical unmet needs in predicting therapeutic response and precise strategy for the patient with advanced biliary tract cancer (BTC). We aimed to identify genomic alterations predicting therapeutic response and resistance to gemcitabine and cisplatin (Gem/Cis)-based chemotherapy in advanced BTC. Materials and Methods: Genomic analysis of advanced BTC multi-institutional cohorts was performed using targeted panel sequencing. Genomic alterations were analyzed integrating patients’ clinicopathologic data, including clinical outcomes of Gem/Cis-based therapy. Significance of genetic alterations was validated using clinical next-generation sequencing (NGS) cohorts from public repositories and drug sensitivity data from cancer cell lines. Results: 193 BTC patients from three cancer centers were analyzed. Most frequent genomic alterations were TP53 (55.5%), KRAS (22.8%), ARID1A (10.4%) alterations, and ERBB2 amplification (9.8%). Among 177 patients with BTC receiving Gem/Cis-based chemotherapy, ARID1A alteration was the only independent predictive molecular marker of primary resistance showing disease progression for 1st-line chemotherapy in the multivariate regression model (odds ratio, 3.12; p=0.046). In addition, ARID1A alteration was significantly correlated with inferior progression-free survival on Gem/Cis-based chemotherapy in the overall patient population (p=0.033) and in patients with extrahepatic cholangiocarcinoma (CCA) (p=0.041). External validation using public repository NGS revealed that ARID1A mutation was a significant predictor for poor survival in BTC patients. Investigation of multi-OMICs drug sensitivity data from cancer cell lines revealed that cisplatin-resistance was exclusively observed in ARID1A mutant bile duct cancer cells. Conclusion Integrative analysis with genomic alterations and clinical outcomes of the first-line Gem/Cis-based chemotherapy in advanced BTC revealed that patients with ARID1Aalterations showed a significant worse clinical outcome, especially in extrahepatic CCA. Well-designed prospective studies are mandatory to validate the predictive role of ARID1Amutation.

Background/Aims: Programmed death-ligand 1 (PD-L1) expression in tumor cells is associated with a poor biliary tract cancer (BTC) prognosis; tumor-infiltrating immune cells in the tumor microenvironment are associated with a better prognosis. The effect of PD-L1 expression on immune cells on survival is unclear. We investigated the relationship between PD-L1 expression in immune cells and BTC prognosis. Methods: PD-L1 expression was evaluated using an anti-PD-L1 22C3 mouse monoclonal primary antibody, and its relationships with clinical characteristics and prognosis were analyzed using the Cox proportional hazard model to investigate the prognostic performance of PD-L1 in BTC. Results: Among 144 analyzed cases, patients with positive PD-L1 expression in tumor cells and negative PD-L1 expression in immune cells showed poorer overall survival rates than those exhibiting other expressions (tumor cells: hazard ratio [HR]=1.023, p<0.001; immune cells: HR=0.983, p=0.021). PD-L1 expression in tumor cells was an independent predictor of poor overall survival (HR=1.024, p<0.001). In contrast, PD-L1 expression in immune cells was a predictive marker of good prognosis (HR=0.983, p=0.018). Conclusions PD-L1 expression in immune cells may be used as an independent factor to evaluate the prognosis of patients with BTC.

Background: Positron emission tomography (PET) viability scan is used to determine whether patients with a myocardial scar on single-photon emission computed tomography (SPECT) may need revascularization. However, the clinical utility of revascularization decision-making guided by PET viability imaging has not been proven yet. The purpose of this study was to investigate the impact of PET to determine revascularization on clinical outcomes. Methods: Between September 2012 and May 2021, 53 patients (37 males; mean age = 64 ± 11 years) with a myocardial scar on MIBI SPECT who underwent PET viability test were analyzed in this study. The primary outcome was a temporal change in echocardiographic findings.The secondary outcome was all-cause mortality. Results: Viable myocardium was presented by PET imaging in 29 (54.7%) patients.Revascularization was performed in 26 (49.1%) patients, including 18 (34.0%) with percutaneous coronary intervention (PCI) and 8 (15.1%) with coronary artery bypass grafting.There were significant improvements in echocardiographic findings in the revascularization group and the viable myocardium group. All-cause mortality was significantly lower in the revascularization group than in the medical therapy-alone group (19.2% vs. 44.4%, log-rank P = 0.002) irrespective of viable (21.4% vs. 46.7%, log-rank P = 0.025) or non-viable myocardium (16.7% vs. 41.7%, log-rank P = 0.046). All-cause mortality was significantly lower in the PCI group than in the medical therapy-alone group (11.1% vs. 44.4%, log-rank P < 0.001). Conclusion Revascularization improved left ventricular systolic function and survival of patients with a myocardial scar on SPECT scans, irrespective of myocardial viability on PET scans.

Purpose: With an increasing number of anterior cervical discectomy and fusion (ACDF) being conducted for degenerative cervical disc disease, there is a rising interest in the related quality of management and healthcare costs. Unplanned readmission after ACDF affects both the quality of management and medical expenses. This meta-analysis was performed to evaluate the risk factors of unplanned readmission after ACDF to improve the quality of management and prevent increase in healthcare costs. Materials and Methods: We searched the databases of PubMed, EMBASE, Web of Science, and Cochrane Library to identify eligible studies using the searching terms, “readmission” and “ACDF.” A total of 10 studies were included. Results: Among the demographic risk factors, older age [weighted mean difference (WMD), 3.93; 95% confidence interval (CI), 2.30–5.56; p<0.001], male [odds ratio (OR), 1.23; 95% CI, 1.10–1.36; p<0.001], and private insurance (OR, 0.34; 95% CI, 0.17–0.69;p<0.001) were significantly associated with unplanned readmission. Among patient characteristics, hypertension (HTN) (OR, 2.14; 95% CI, 1.41–3.25; p<0.001), diabetes mellitus (DM) (OR, 1.59; 95% CI, 1.20–2.11; p=0.001), coronary artery disease (CAD) (OR, 2.87; 95% CI, 2.13–3.86; p<0.001), American Society of Anesthesiologists (ASA) physical status grade >2 (OR, 2.13; 95% CI, 1.68–2.72; p<0.001), and anxiety and depression (OR, 1.39; 95% CI, 1.29–1.51; p<0.001) were significantly associated with unplanned readmission. Among the perioperative factors, pulmonary complications (OR, 22.52; 95% CI, 7.21–70.41; p<0.001) was significantly associated with unplanned readmission. Conclusion Male, older age, HTN, DM, CAD, ASA grade >2, anxiety and depression, pulmonary complications were significantly associated with an increased occurrence of unplanned readmission after ACDF.