Background: s/Aims: Given the high mortality associated with gallbladder cancer (GBC), the efficacy of adjuvant therapy (AT) remains controversial. We audited our data over an 11-year period to assess the impact of AT. Methods: This study included all patients who underwent curative resection for GBC from 2007 to 2017. Analyses were conducted of clinicopathological characteristics, surgical details, and postoperative therapeutic records. The benefits of adjuvant chemotherapy (CT) or chemoradiotherapy (CTRT) were evaluated against surgery alone using SPSS version 20 for statistical analysis. Results: The median age of patients (n = 142) was 50 years. The median overall survival (OS) was 93, 34, and 30 months with CT, CTRT, and surgery alone respectively (p = 0.612). Multivariate analysis indicated that only disease stage and microscopically involved margins significantly impacted OS and disease-free survival (DFS). CT showed increased effectiveness across all prognostic subsets, except for stage 4 and margin-positive resections. Following propensity score matching, median DFS and OS were higher in the CT group than in the CTRT group, although the differences were not statistically significant (p > 0.05). Conclusions Radically resected GBC patients appear to benefit more from adjuvant CT, while CTRT should be reserved for cases with high-risk features.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Background: s/Aims: Given the high mortality associated with gallbladder cancer (GBC), the efficacy of adjuvant therapy (AT) remains controversial. We audited our data over an 11-year period to assess the impact of AT. Methods: This study included all patients who underwent curative resection for GBC from 2007 to 2017. Analyses were conducted of clinicopathological characteristics, surgical details, and postoperative therapeutic records. The benefits of adjuvant chemotherapy (CT) or chemoradiotherapy (CTRT) were evaluated against surgery alone using SPSS version 20 for statistical analysis. Results: The median age of patients (n = 142) was 50 years. The median overall survival (OS) was 93, 34, and 30 months with CT, CTRT, and surgery alone respectively (p = 0.612). Multivariate analysis indicated that only disease stage and microscopically involved margins significantly impacted OS and disease-free survival (DFS). CT showed increased effectiveness across all prognostic subsets, except for stage 4 and margin-positive resections. Following propensity score matching, median DFS and OS were higher in the CT group than in the CTRT group, although the differences were not statistically significant (p > 0.05). Conclusions Radically resected GBC patients appear to benefit more from adjuvant CT, while CTRT should be reserved for cases with high-risk features.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Background: s/Aims: Given the high mortality associated with gallbladder cancer (GBC), the efficacy of adjuvant therapy (AT) remains controversial. We audited our data over an 11-year period to assess the impact of AT. Methods: This study included all patients who underwent curative resection for GBC from 2007 to 2017. Analyses were conducted of clinicopathological characteristics, surgical details, and postoperative therapeutic records. The benefits of adjuvant chemotherapy (CT) or chemoradiotherapy (CTRT) were evaluated against surgery alone using SPSS version 20 for statistical analysis. Results: The median age of patients (n = 142) was 50 years. The median overall survival (OS) was 93, 34, and 30 months with CT, CTRT, and surgery alone respectively (p = 0.612). Multivariate analysis indicated that only disease stage and microscopically involved margins significantly impacted OS and disease-free survival (DFS). CT showed increased effectiveness across all prognostic subsets, except for stage 4 and margin-positive resections. Following propensity score matching, median DFS and OS were higher in the CT group than in the CTRT group, although the differences were not statistically significant (p > 0.05). Conclusions Radically resected GBC patients appear to benefit more from adjuvant CT, while CTRT should be reserved for cases with high-risk features.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.