Preoperative Prediction Nomogram Based on Integrated Profiling for Glioblastoma Multiforme in Glioma Patients

A nomogram of inflammatory indexes for preoperatively predicting the risk of lymph node metastasis in colorectal cancer

PURPOSE

To investigate the independent risk factors associated with the development of lymph node metastasis (LNM) in patients with colorectal cancer (CRC), focusing on preoperative systemic inflammatory indicators, and to construct a corresponding risk predictive model.

MATERIALS AND METHODS

The clinical data of 241 patients with CRC who underwent surgery after the first diagnosis between January 2012 and December 2017 at our hospital were reviewed. A best logistic regression model was constructed by Lasso regression for multivariate analysis, from which a Nomogram was derived. Using bootstrap to conduct internal validation. The model's predictive performance and clinical practicability were evaluated using the receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis (DCA). External validation was conducted using retrospective data from 170 patients who underwent surgery between January 2020 and May 2022 at another hospital.

RESULTS

Cross-validation indicated smoking history, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), fibrinogen-albumin ratio (FAR), and fecal occult blood (FOB) as variables with non-zero coefficients. These factors were included in the logistic regression, and multivariate analysis confirmed that smoking history, NLR, LMR, FAR, and FOB were independent risk factors (P < 0.05). The ROC and calibration curve of the original model and external validation indicated strong predictive power of the model. DCA suggested the model's favorable clinical utility.

CONCLUSIONS

The model constructed in this study has robust predictive performance and clinical utility for the preoperative determination of CRC LMN, offering significant for clinical decision-making in patients with CRC.

Surgical outcomes of glioblastoma multiforme in low and middle-income countries: current state and future directions

Glioblastoma (GBM) is a highly aggressive and deadly brain tumor. The challenges in managing GBM in low- and middle-income countries (LMICs) have been underexplored. This review provides a review of surgical management techniques, challenges, outcomes, and future directions for GBM treatment in LMICs. A search of academic databases yielded studies from various LMICs, focusing on surgical management techniques and their outcomes. The data were analyzed in the context of socio-economic, cultural, and infrastructural factors. Comparative analyses were performed to highlight disparities between LMICs and high-income countries. GBM management in LMICs faces multi-faceted challenges, including healthcare infrastructure deficiencies, delayed diagnosis, high treatment costs, cultural beliefs, and limited research funding. This adversely affects patient outcomes and survival rates. Surgical excision followed by radiation and chemotherapy remains the standard of care, but LMICs have not significantly benefited from recent advancements in GBM management. Intraoperative neurosurgery ultrasound is identified as an affordable and practical alternative for LMICs. Patient outcomes following GBM surgery in LMICs vary widely, making early detection challenging. Cultural sensitivity and ethical considerations are crucial factors in improving healthcare practices. Surgical management of GBM in LMICs is hindered by complex challenges that require multi-faceted interventions. By addressing socio-economic, cultural, and infrastructural factors, LMICs can improve GBM care and outcomes. Raising awareness and advocating for change are crucial steps in this process.

Practical Nomograms and Risk Stratification System for Predicting the Overall and Cancer-specific Survival in Patients with Anaplastic Astrocytoma

OBJECTIVE

Anaplastic astrocytoma (AA) is an uncommon primary brain tumor with highly variable clinical outcomes. Our study aimed to develop practical tools for clinical decision-making in a population-based cohort study.

METHODS

Data from 2997 patients diagnosed with AA between 2004 and 2015 were retrospectively extracted from the Surveillance, Epidemiology, and End Results database. The Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analyses were applied to select factors and establish prognostic nomograms. The discriminatory ability of these nomogram models was evaluated using the concordance index and receiver operating characteristic curve. Risk stratifications were established based on the nomograms.

RESULTS

Selected 2997 AA patients were distributed into the training cohort (70%, 2097) and the validation cohort (30%, 900). Age, household income, tumor site, extension, surgery, radiotherapy, and chemotherapy were identified as independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS). In the training cohort, our nomograms for OS and CSS exhibited good predictive accuracy with concordance index values of 0.752 (95% CI: 0.741-0.764) and 0.753 (95% CI: 0.741-0.765), respectively. Calibration and decision curve analyses curves showed that the nomograms demonstrated considerable consistency and satisfactory clinical utilities. With the establishment of nomograms, we stratified AA patients into high- and low-risk groups, and constructed risk stratification systems for OS and CSS.

CONCLUSIONS

We constructed two predictive nomograms and risk classification systems to effectively predict the OS and CSS rates in AA patients. These models were internally validated with considerable accuracy and reliability and might be helpful in future clinical practices.

A nomogram for predicting residual low back pain after percutaneous kyphoplasty in osteoporotic vertebral compression fractures

To establish a risk prediction model for residual low back pain after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fractures. We used retrospective data for model construction and evaluated the model using internal validation and temporal external validation and finally concluded that the model had good predictive performance.

INTRODUCTION

The cause of residual low back pain in patients with osteoporotic vertebral compression fractures (OVCFs) after PKP remains highly controversial, and our goal was to investigate the most likely cause and to develop a novel nomogram for the prediction of residual low back pain and to evaluate the predictive performance of the model.

METHODS

The clinical data of 281 patients with OVCFs who underwent PKP at our hospital from July 2019 to July 2020 were reviewed. The optimal logistic regression model was determined by lasso regression for multivariate analysis, thus constructing a nomogram. Bootstrap was used to perfomance the internal validation; receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to assess the predictive performance and clinical utility of the model, respectively. Temporal external validation of the model was also performed using retrospective data from 126 patients who underwent PKP at our hospital from January 2021 to October 2021.

RESULTS

Lasso regression cross-validation showed that the variables with non-zero coefficients were the number of surgical vertebrae, preoperative bone mineral density (pre-BMD), smoking history, thoracolumbar fascia injury (TLFI), intraoperative facet joint injury (FJI), and postoperative incomplete cementing of the fracture line (ICFL). The above factors were included in the multivariate analysis and showed that the pre-BMD, smoking history, TLFI, FJI, and ICFL were independent risk factors for residual low back pain (P < 0.05). The ROC and calibration curve of the original model and temporal external validation indicated a good predictive power of the model. The DCA curve suggested that the model has good clinical practicability.

CONCLUSION

The risk prediction model has good predictive performance and clinical practicability, which can provide a certain basis for clinical decision-making in patients with OVCFs.

Development of a Nomogram for Predicting Asymptomatic Coronary Artery Disease in Patients with Ischemic Stroke

BACKGROUND

Coronary artery stenosis (CAS) ≥50% often coexists in patients with ischemic stroke, which leads to a significant increase in the occurrence of major vascular events after stroke. This study aimed to develop a nomogram for diagnosing the presence of ≥50% asymptomatic CAS in patients with ischemic stroke.

METHODS

A primary cohort was established that included 275 non-cardioembolic ischemic stroke patients who were admitted from January 2011 to April 2013 to a teaching hospital in southern China. The preoperative data were used to construct two models by the best subset regression and the forward stepwise regression methods, and a nomogram between these models was established. The assessment of the nomogram was carried out by discrimination and calibration in an internal cohort.

RESULTS

Out of the two models, model 1 contained eight clinical-related variables and exhibited the lowest Akaike Information Criterion value (322.26) and highest concordance index 0.716 (95% CI, 0.654-0.778). The nomogram showed good calibration and significant clinical benefit according to calibration curves and the decision curve analysis.

CONCLUSION

The nomogram, composed of age, sex, NIHSS score on admission, hypertension history, fast glucose level, HDL cholesterol level, LDL cholesterol level, and presence of ≥50% cervicocephalic artery stenosis, can be used for prediction of ≥50% asymptomatic coronary artery disease (CAD). Further studies are needed to validate the effectiveness of this nomogram in other populations.

Based on clinical Ki-67 expression and serum infiltrating lymphocytes related nomogram for predicting the diagnosis of glioma-grading

Background

Compelling evidence indicates that elevated peripheral serum lymphocytes are associated with a favorable prognosis in various cancers. However, the association between serum lymphocytes and glioma is contradictory. In this study, a nomogram was established to predict the diagnosis of glioma-grading through Ki-67 expression and serum lymphocytes.

Methods

We performed a retrospective analysis of 239 patients diagnosed with LGG and 178 patients with HGG. Immunohistochemistry was used to determine the Ki-67 expression. Following multivariate logistic regression analysis, a nomogram was established and used to identify the most related factors associated with HGG. The consistency index (C-index), decision curve analysis (DCA), and a calibration curve were used to validate the model.

Results

The number of LGG patients with more IDH1/2 mutations and 1p19q co-deletion was greater than that of HGG patients. The multivariate logistic analysis identified Ki-67 expression, serum lymphocyte count, and serum albumin (ALU) as independent risk factors associated with HGG, and these factors were included in a nomogram in the training cohort. In the validation cohort, the nomogram demonstrated good calibration and high consistency (C-index = 0.794). The Spearman correlation analysis revealed a significant association between HGG and serum lymphocyte count (r = −0.238, P &lt;0.001), ALU (r = −0.232, P &lt;0.001), and Ki-67 expression (r = 0.457, P &lt;0.001). Furthermore, the Ki-67 expression was negatively correlated with the serum lymphocyte count (r = −0.244, P &lt;0.05). LGG patients had lower Ki-67 expression and higher serum lymphocytes compared with HGG patients, and a combination of these two variables was significantly higher in HGG patients.

Conclusion

The constructed nomogram is capable of predicting the diagnosis of glioma-grade. A decrease in the level of serum lymphocyte count and increased Ki-67 expression in HGG patients indicate that their immunological function is diminished and the tumor is more aggressive.