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.

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.

Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common hereditary kidney diseases. In addition to renal involvement, vascular complications including intracranial arterial, aortic aneurysms and dissections are common in these patients. We report the case of a 35-year-old male patient with ADPKD who presented with hematuria and was diagnosed with two intrarenal arterial pseudoaneurysms. Endovascular embolization using coils was performed to resolve these symptoms. Vascular complications are often encountered in patients with ADPKD; hence, sufficient clinical suspicion and timely diagnosis can help manage the disease. The most common causes of hematuria in ADPKD patients are cyst hemorrhage or infection; however, vascular aneurysms should also be considered a possibility.

Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common hereditary kidney diseases. In addition to renal involvement, vascular complications including intracranial arterial, aortic aneurysms and dissections are common in these patients. We report the case of a 35-year-old male patient with ADPKD who presented with hematuria and was diagnosed with two intrarenal arterial pseudoaneurysms. Endovascular embolization using coils was performed to resolve these symptoms. Vascular complications are often encountered in patients with ADPKD; hence, sufficient clinical suspicion and timely diagnosis can help manage the disease. The most common causes of hematuria in ADPKD patients are cyst hemorrhage or infection; however, vascular aneurysms should also be considered a possibility.

Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common hereditary kidney diseases. In addition to renal involvement, vascular complications including intracranial arterial, aortic aneurysms and dissections are common in these patients. We report the case of a 35-year-old male patient with ADPKD who presented with hematuria and was diagnosed with two intrarenal arterial pseudoaneurysms. Endovascular embolization using coils was performed to resolve these symptoms. Vascular complications are often encountered in patients with ADPKD; hence, sufficient clinical suspicion and timely diagnosis can help manage the disease. The most common causes of hematuria in ADPKD patients are cyst hemorrhage or infection; however, vascular aneurysms should also be considered a possibility.

Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common hereditary kidney diseases. In addition to renal involvement, vascular complications including intracranial arterial, aortic aneurysms and dissections are common in these patients. We report the case of a 35-year-old male patient with ADPKD who presented with hematuria and was diagnosed with two intrarenal arterial pseudoaneurysms. Endovascular embolization using coils was performed to resolve these symptoms. Vascular complications are often encountered in patients with ADPKD; hence, sufficient clinical suspicion and timely diagnosis can help manage the disease. The most common causes of hematuria in ADPKD patients are cyst hemorrhage or infection; however, vascular aneurysms should also be considered a possibility.

Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common hereditary kidney diseases. In addition to renal involvement, vascular complications including intracranial arterial, aortic aneurysms and dissections are common in these patients. We report the case of a 35-year-old male patient with ADPKD who presented with hematuria and was diagnosed with two intrarenal arterial pseudoaneurysms. Endovascular embolization using coils was performed to resolve these symptoms. Vascular complications are often encountered in patients with ADPKD; hence, sufficient clinical suspicion and timely diagnosis can help manage the disease. The most common causes of hematuria in ADPKD patients are cyst hemorrhage or infection; however, vascular aneurysms should also be considered a possibility.

Gastrointestinal mucormycosis is a rare disease with a significant mortality rate, even when promptly diagnosed and treated. An unusual complication was observed in India during the second wave of coronavirus disease 2019 (COVID-19). Two incidences of gastric mucormycosis were found. A 53-year-old male patient with a history of COVID-19 one month earlier came into the intensive care unit. After admission, the patient developed hematemesis, which was initially treated with blood transfusions and digital subtraction angiography embolization. Esophagogastroduodenoscopy (EGD) revealed a large ulcer with a clot in the stomach. During an exploratory laparotomy, the proximal stomach was necrotic. Histopathological examination confirmed mucormycosis. The patient was started on antifungals, but despite rigorous therapy, the patient died on the tenth postoperative day. Another patient, an 82-year-old male with a history of COVID-19, arrived with hematemesis two weeks earlier and was treated conservatively. EGD revealed a large white-based ulcer with abundant slough along the larger curvature of the body. Mucormycosis was verified by biopsy.He was treated with amphotericin B and isavuconazole. He was discharged after two weeks in a stable condition. Despite quick detection and aggressive treatment, the prognosis is poor. In the second case, prompt diagnosis and treatment saved the patient’s life.