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.

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.

OBJECTIVES: To investigate the clinical characteristics and prognosis of chronic disseminated candidiasis (CDC) in children with acute leukemia (AL) following chemotherapy. METHODS: A retrospective analysis was conducted on children diagnosed with CDC (including confirmed, clinically diagnosed, and suspected cases) after AL chemotherapy from January 2015 to December 2023 at Fujian Medical University Union Hospital, Zhangzhou Municipal Hospital, and Quanzhou First Hospital Affiliated to Fujian Medical University. Clinical characteristics and prognosis were analyzed. RESULTS: The incidence of CDC in children with AL following chemotherapy was 1.92% (32/1 668). Among the children with acute lymphoblastic leukemia, the incidence of CDC in the high-risk group was significantly higher than in the low-risk group (P=0.002). All patients presented with fever unresponsive to antibiotics during the neutropenic period, with 81% (26/32) involving the liver. C-reactive protein (CRP) levels were significantly elevated (≥50 mg/L) in 97% (31/32) of the patients. The efficacy of combined therapy with liposomal amphotericin B and caspofungin or posaconazole for CDC was 66% (19/29), higher than with caspofungin (9%, 2/22) or liposomal amphotericin B (18%, 2/11) monotherapy. The overall cure rate was 72% (23/32). The proportion of patients with CRP ≥50 mg/L and/or a positive β-D-glucan test for more than 2 weeks and breakthrough infections during caspofungin treatment was significantly higher in the treatment failure group compared to the successful treatment group (P<0.05). CONCLUSIONS CDC in children with AL after chemotherapy may be associated with prolonged neutropenia due to intensive chemotherapy. Combination antifungal regimens based on liposomal amphotericin B have a higher cure rate, while persistently high CRP levels and positive β-D-glucan tests may indicate poor prognosis.
Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Infant ; Male ; Antifungal Agents/therapeutic use* ; Candidiasis/diagnosis* ; Chronic Disease ; Leukemia/complications* ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/complications* ; Prognosis ; Retrospective Studies

OBJECTIVES: To evaluate the clinical value of CD4⁺ cell percentage (CD4⁺%) and NK cell percentage (NK%) in predicting treatment outcomes in children with aplastic anemia (AA), providing a reference for precise diagnosis and treatment. METHODS: This retrospective study analyzed the clinical data of AA children treated with cyclosporine A at the First Affiliated Hospital of Xinjiang Medical University from January 2019 to April 2024. The study involved 48 AA children as the observation group and 50 children undergoing medical check-ups during the same period as the control group. Lymphocyte subset data were collected from both groups to analyze differences and their relationship with treatment efficacy. Based on hematological responses, the observation group was divided into an effective group of 18 patients (HR group, including complete and partial remission) and an ineffective group of 30 patients (NHR group, including non-remission). RESULTS: Univariate analysis showed that NK% in the observation group was significantly lower than that in the control group (P<0.05). The observation group was followed up for 3 months. The HR group had a lower CD4⁺% than the NHR group (P=0.018) and a higher NK% than the NHR group (P=0.029). Multivariate logistic regression analysis indicated that a high CD4⁺% was a risk factor for poor treatment efficacy (OR=1.062), whereas a high NK% was a protective factor (OR=0.820). The area under the curve for the prediction of HR in pediatric AA by combining CD4⁺% and NK% was 0.812. CONCLUSIONS A higher CD4⁺% at diagnosis is a predictor of poor treatment response, whereas a higher NK% is associated with better outcomes.
Humans ; Anemia, Aplastic/blood* ; Male ; Female ; Killer Cells, Natural ; Child ; Retrospective Studies ; Child, Preschool ; Prognosis ; Adolescent ; CD4-Positive T-Lymphocytes ; Infant

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.

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.

Objective:To evaluate the efficacy of acute T-cell lymphoblastic leukemia(T-ALL)in children and explore the prognostic risk factors.Methods:The clinical data of 127 newly diagnosed children with T-ALL admitted to five hospitals in Fujian province from April 2011 to December 2020 were retrospectively analyzed,and compared with children with newly diagnosed acute precursor B-cell lymphoblastic leukemia(B-ALL)in the same period.Kaplan-Meier analysis was used to evaluate the overall survival(OS)and event-free survival(EFS),and COX proportional hazard regression model was used to evaluate the prognostic factors.Among 116 children with T-ALL who received standard treatment,78 cases received the Chinese Childhood Leukemia Collaborative Group(CCLG)-ALL 2008 protocol(CCLG-ALL 2008 group),and 38 cases received the China Childhood Cancer Collaborative Group(CCCG)-ALL 2015 protocol(CCCG-ALL 2015 group).The efficacy and serious adverse event(SAE)incidence of the two groups were compared.Results:Proportion of male,age ≥ 10 years old,white blood cell count(WBC)≥ 50 × 109/L,central nervous system leukemia,minimal residual disease(MRD)≥ 1％during induction therapy,and MRD ≥ 0.01％at the end of induction in T-ALL children were significantly higher than those in B-ALL children(P＜0.05).The expected 10-year EFS and OS of T-ALL were 59.7％and 66.0％,respectively,which were significantly lower than those of B-ALL(P＜0.001).COX analysis showed that WBC ≥ 100 x 109/L at initial diagnosis and failure to achieve complete remission(CR)after induction were independent risk factors for poor prognosis.Compared with CCLG-ALL 2008 group,CCCG-ALL 2015 group had lower incidence of infection-related SAE(15.8％vs 34.6％,P=0.042),but higher EFS and OS(73.9％vs 57.2％,PEFS=0.090;86.5％vs 62.3％,PoS=0.023).Conclusions:The prognosis of children with T-ALL is worse than children with B-ALL.WBC ≥ 100 × 109/L at initial diagnosis and non-CR after induction(especially mediastinal mass has not disappeared)are the risk factors for poor prognosis.CCCG-ALL 2015 regimen may reduce infection-related SAE and improve efficacy.

Objective:To analyze the related factors of treatment failure in children with acute lymphoblastic leukemia (ALL)in real-world.Methods:The clinical data of 1414 newly diagnosed children with ALL admitted to five hospital in Fujian province from April 2011 to December 2020 were retrospectively analyzed.Treatment failure was defined as relapse,non-relapse death,and secondary tumor.Results:Following-up for median time 49.7 (0.1-136. 9)months,there were 269 cases (19.0％)treatment failure,including 140 cases (52.0％)relapse,and 129 cases (48.0％)non-relapse death.Cox univariate and multivariate analysis showed that white WBC≥50 ×109/L at newly diagnosis,acute T-cell lymphoblastic leukemia (T-ALL),BCR-ABL1,KMT2A-rearrangement and poor early treatment response were independent risk factor for treatment failure (all HR>1.000,P<0.05).The 5-year OS of 140 relapsed ALL patients was only 23.8％,with a significantly worse prognosis for very early relapse (relapse time within 18 months of diagnosis).Among 129 patients died from non-relapse death,71 cases (26.4％)were died from treatment-related complications,56 cases (20.8％)died from treatment abandonment,and 2 cases (0.7％)died from disease progression.Among them,treatment-related death were significantly correlated with chemotherapy intensity,while treatment abandonment were mainly related to economic factors.Conclusion:The treatment failure of children with ALL in our province is still relatively high,with relapse being the main cause of treatment failure,while treatment related death and treatment abandonment caused by economic factors are the main causes of non-relapse related death.