Purpose: The optimal sequencing of targeted therapies and the role of primary tumor resection (PTR) in KRAS wild-type metastatic colorectal cancer (mCRC) remain unclear. This study compared survival outcomes in patients treated with first-line cetuximab plus FOLFIRI (folinic acid, 5-fluorouracil, and irinotecan) versus bevacizumab plus FOLFIRI, followed by second-line oxaliplatin-based chemotherapy and later-line trifluridine/tipiracil or regorafenib. Methods: This retrospective cohort study used Taiwan’s National Health Insurance Research Database and the Taiwan Cancer Registry. Patients diagnosed with mCRC between 2013 and 2019 were included if they received first-line cetuximab or bevacizumab plus FOLFIRI, followed by later-line trifluridine/tipiracil or regorafenib. Patients were stratified by PTR status. Primary endpoints were overall survival and survival during trifluridine/tipiracil or regorafenib treatment. Secondary endpoints included time to treatment discontinuation (TTD) and TTD during trifluridine/tipiracil or regorafenib therapy. Stabilized inverse probability of treatment weighting was used for adjustment. Results: Among 559 patients, 278 were assigned to the non-PTR group and 281 to the PTR group. In the non-PTR group, the cetuximab cohort demonstrated significantly longer survival during trifluridine/tipiracil or regorafenib therapy (6.2 months vs. 4.9 months; hazard ratio [HR], 0.72) and longer TTD1 (the interval between initiation of first-line therapy and the start of second-line chemotherapy; 11.8 months vs. 9.5 months; HR, 0.67) than the bevacizumab cohort. Survival differences between regimens were less pronounced among patients who underwent PTR. Conclusion First-line cetuximab plus FOLFIRI may confer a survival advantage over bevacizumab in patients with KRAS wild-type mCRC without PTR, including during later-line therapy with trifluridine/tipiracil or regorafenib, whereas bevacizumab appears to provide more consistent benefits in those with PTR.

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.

Background: Delivering a poor prognosis to patients and their families is critically challenging in pediatric populations. The application of palliative care (PC) provides a bridge between accepting the occurrence of mortality and offering lifelong support.However, little is known about the specifics of PC. This study aims to explore the unmet need for PC in pediatric populations. Methods: We retrospectively reviewed the medical records of mortality cases in the Department of Pediatric Hematology and Oncology at Chang Gung Memorial Hospital. Statistical tests, including Chi-square and Student’s t-tests, were applied to determine the differences between early and late intervention groups in terms of the timing of PC introduction. Results: During the study period, 41 patients were included. Their median age was 11.8 years (IQR, 7.6-15.9). The majority of the disease statuses were refractory or relapsing (R/R). The incidence of memento application was significantly higher in the early intervention group (47.6% vs. 10%, P=0.0081). Vital signs variations tended to be end-of-life (EoL) indicators in this study. Conclusion The early introduction of PC encourages families to accompany their beloved child. EoL signs in the pediatric population include vital sign variations. With the presence of relevant EoL signs, clinical physicians can apply PC earlier to meet the needs.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background: Delivering a poor prognosis to patients and their families is critically challenging in pediatric populations. The application of palliative care (PC) provides a bridge between accepting the occurrence of mortality and offering lifelong support.However, little is known about the specifics of PC. This study aims to explore the unmet need for PC in pediatric populations. Methods: We retrospectively reviewed the medical records of mortality cases in the Department of Pediatric Hematology and Oncology at Chang Gung Memorial Hospital. Statistical tests, including Chi-square and Student’s t-tests, were applied to determine the differences between early and late intervention groups in terms of the timing of PC introduction. Results: During the study period, 41 patients were included. Their median age was 11.8 years (IQR, 7.6-15.9). The majority of the disease statuses were refractory or relapsing (R/R). The incidence of memento application was significantly higher in the early intervention group (47.6% vs. 10%, P=0.0081). Vital signs variations tended to be end-of-life (EoL) indicators in this study. Conclusion The early introduction of PC encourages families to accompany their beloved child. EoL signs in the pediatric population include vital sign variations. With the presence of relevant EoL signs, clinical physicians can apply PC earlier to meet the needs.

INTRODUCTION: Outcomes after SARS-CoV-2 Omicron infection in patients with heart failure (HF) and ischaemic heart disease (IHD) remain poorly defined. METHOD: In a highly vaccinated cohort of adult Singapore citizens and permanent residents, we used Cox proportional hazards models (adjusted for sociodemographic variables and comorbidities) to compare the risks of Omicron infection, COVID-19- related hospitalisation, and severe COVID-19 between indivi-duals with HF or IHD and matched controls without these conditions. RESULTS: From national databases, we identified 15,426 HF patients matched 1:∼3 to 41,221 controls, and 110,442 IHD patients matched 1:∼2 to 223,843 controls. Over 80% of HF and IHD patients had received at least 3 vaccine doses. During the Omicron-predominant period, both HF and IHD cohorts demonstrated higher adjusted risks of COVID-19 hospitalisation compared with matched controls (HF: adjusted hazard ratio [aHR] 1.77, 95% confidence interval [CI] 1.65-1.90; IHD: aHR 1.21, 95% CI 1.17-1.26). Among those with at least 1 HF-or IHD-related admission in the prior year, hospitalisation risk was further elevated (HF: aHR 1.27, 95% CI 1.13-1.42; IHD: aHR 1.11, 95% CI 1.01-1.23). Receipt of ≥3 vaccine doses was associated with substantially lower risk of severe COVID-19 versus only 2 doses (HF: aHR 0.35, 95% CI 0.28-0.43; IHD: aHR 0.27, 95% CI 0.23-0.32). A fourth dose conferred additional reductions in infection and adverse outcomes, though CIs for infection overlapped with those for 3 doses. CONCLUSION During Omicron predominance, HF and IHD patients experienced greater risk of COVID-19 hospitalisation and severe COVID-19 versus matched controls. Booster vaccinations attenuated these risks. Individuals with recent HF/IHD admissions should be prioritised for receipt of booster vaccine doses.
Humans ; COVID-19/complications* ; Male ; Heart Failure/complications* ; Myocardial Ischemia/complications* ; Female ; Middle Aged ; Hospitalization/statistics & numerical data* ; Aged ; COVID-19 Vaccines/administration & dosage* ; Singapore/epidemiology* ; SARS-CoV-2 ; Proportional Hazards Models ; Adult ; Case-Control Studies ; Vaccination/statistics & numerical data*

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.