Assessment Of Prognosis In Patients Research Articles

Basement Membrane-Associated lncRNA Risk Model Predicts Prognosis and Guides Clinical Treatment in Clear Cell Renal Cell Carcinoma.

The basement membrane (BM) affects the invasion and growth of malignant tumors. The role and mechanism of BM-associated lncRNAs in clear cell renal cell carcinoma (ccRCC) are unknown. In this study, we identified biomarkers of ccRCC and developed a risk model to assess patient prognosis. We downloaded transcripts and clinical data from the Cancer Genome Atlas (TCGA). Differential analysis, co-expression analysis, Cox regression analysis, and lasso regression were used to identify BM-associated prognostic lncRNAs and create a risk prediction model. We evaluated and validated the accuracy of the model using multiple methods and constructed a nomogram to predict the prognosis of ccRCC. GO, KEGG, and immunity analyses were used to explore differences in biological function. We constructed a risk model containing six BM-associated lncRNAs (LINC02154, IGFL2-AS1, NFE4, AC112715.1, AC092535.5, and AC105105.3). The risk model has higher diagnostic efficiency compared to clinical characteristics and can be used to forecast patient prognoses. We used renal cancer cells and tissue microarrays to verify the expression of lncRNAs in the risk model. We found that knocking down LINC02154 and AC112715.1 could inhibit the invasion ability of renal cancer cells. The risk model based on BM-associated lncRNAs can well predict ccRCC and guide clinical treatment.

Multi-parameter MRI based radiomics nomogram for predicting telomerase reverse transcriptase promoter mutation and prognosis in glioblastoma.

To investigate the clinical utility of multi-parameter MRI-based radiomics nomogram for predicting telomerase reverse transcriptase (TERT) promoter mutation status and prognosis in adult glioblastoma (GBM). We retrospectively analyzed MRI and pathological data of 152 GBM patients. A total of 2,832 radiomics features were extracted and filtered from preoperative MRI images. A radiomics nomogram was created on the basis of radiomics signature (rad-score) and clinical traits. The performance of the nomogram in TERT mutation identification was assessed using receiver operating characteristic (ROC) curve, calibration curves, and clinical decision curves. Pathologically confirmed TERT mutations and risk score-based TERT mutations were employed to assess patient prognosis, respectively. The random forest (RF) algorithm outperformed the other two algorithms, yielding the best diagnostic efficacy in differentiating TERT mutations, with area under the curve (AUC) values of 0.892 (95% CI: 0.828-0.956) and 0.824 (95% CI: 0.677-0.971) in the training set and validation sets, respectively. Furthermore, the predictive power of the radiomics nomogram constructed with the rad-score and clinical variables reached 0.916 (95%CI: 0.864, 0.968) in the training set and 0.880 (95%CI: 0.743, 1) in the validation set. Calibration curve and decision curve analysis findings further uphold the clinical application value of the radiomics nomogram. The overall survival of the high-risk subgroup was significantly shorter than that of the low-risk subgroup, which was consistent with the results of the pathologically confirmed TERT mutation group. The radiomics nomogram could non-invasively provide promising insights for predicting TERT mutations and prognosis in GBM patients with excellent identification and calibration abilities.

Nomograms Based on MRI Radiomics for Differential Diagnosis and Predicting BRAFV600E Expression in Pleomorphic Xanthoastrocytoma and Ganglioglioma

This study was designed to investigate the value of nomograms based on MRI radiomics and clinical semantic features in identifying pleomorphic xanthoastrocytoma (PXA) and ganglioglioma (GG) as well as predicting BRAFV600E expression. This study included 265 patients histologically diagnosed with PXA (n=113) and GG (n=152). T1WI, T2WI, and CET1 sequences were utilized to extract radiomics features. Univariate analysis, Spearman correlation analysis, and the least absolute shrinkage and selection operator were used for dimensionality reduction and feature selection. Following this, logistic regression was utilized to establish the radiomics model. Univariate and multivariate analyses of clinical semantic features were applied, and clinical models were constructed. The nomograms were established by merging radiomics and clinical features. Furthermore, ROC curve analysis was used for examining the model performance, whereas the decision curve analysis (DCA) examined the clinical utility of the nomograms. Nomograms achieved the best predictive efficacy compared to clinical and radiomics models alone. Concerning the differentiation between PXA and GG, the area under the curve (AUC) values of the nomogram were 0.879 (0.828-0.930) and 0.887 (0.805-0.969) for the training and testing cohorts, respectively. For predicting BRAFV600E expression, the AUC values of the nomogram were 0.873 (0.811-0.936) and 0.851 (0.740-0.963) for the training and testing cohorts, respectively. DCA confirmed the clinical utility of the nomograms. Nomograms based on radiomics and clinical semantic features were noninvasive tools for differential diagnosis of PXA and GG and predicting BRAFV600E expression, which may be helpful for assessing patient prognosis and developing individualized treatment strategies.

Remnant liver function is associated with long-term survival in patients with hepatocellular carcinoma undergoing hepatectomy

It is important to assess the prognosis and intervene before and after surgery in patients with hepatocellular carcinoma. This study aims to elucidate the association of outcomes and residual liver function after hepatectomy. A total of 176 patients who underwent the initial resection for hepatocellular carcinoma between January 2011 and March 2021 at Jichi Medical University were included. Hepatic clearance of the remnant liver was measured using 99mTc-galactosyl serum albumin scintigraphy. The log-rank test was used to analyze survival using the Kaplan–Meier method. Hazard ratios (HR) and 95% confidence intervals (CI) for overall survival were calculated using Cox’s proportional hazard model. In multivariate analysis, microvascular invasion, intraoperative blood loss, and hepatic clearance of the remnant liver were independently associated with overall survival. Hepatic clearance of the remnant liver was independently associated with recurrence free survival. This is the first report to show that lower residual liver function is associated with shorter survival in patients with hepatocellular carcinoma undergoing hepatectomy. Preoperative determination of remnant liver function may allow assessment of prognosis in patients planned to undergo resection of hepatocellular carcinoma. Preservation of liver functional reserve may be crucial for improved long-term outcomes after hepatectomy.

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence.

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

Typing and modeling of hepatocellular carcinoma based on disulfidptosis-related amino acid metabolism genes for predicting prognosis and guiding individualized treatment.

Hepatocellular carcinoma (HCC) is the most common type of cancer worldwide and is a major public health problem in the 21st century. Disulfidopathy, a novel cystine-associated programmed cell death, plays complex roles in various tumors. However, the relationship between disulfidoptosis and prognosis in patients with HCC remains unclear. This study aimed to explore the relationship between disulfideptosis and the prognosis of liver cancer and to develop a prognostic model based on amino acid metabolism and disulfideptosis genes. We downloaded the clinicopathological information and gene expression data of patients with HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and classified them into different molecular subtypes based on the expression patterns of disulfidoptosis-associated amino acid metabolism genes (DRAGs). Patients were then classified into different gene subtypes using the differential genes between the molecular subtypes, and the predictive value of staging was assessed using survival and clinicopathological analyses. Subsequently, risk prognosis models were constructed based on Cox regression analysis to assess patient prognosis, receiver operating characteristic (ROC) curves, somatic mutations, microsatellite instability, tumor microenvironment, and sensitivity to antitumor therapeutic agents. Patients were classified into two subtypes based on differential DRAGs gene expression, with cluster B having a better survival outcome than cluster A. Three gene subtypes were identified based on the differential genes between the two DRAGs molecular subtypes. The patients in cluster B had the best prognosis, whereas those in cluster C had the worst prognosis. The heat map showed better consistency in the patient subtypes obtained using both typing methods. We screened six valuable genes and constructed a prognostic signature. By scoring, we found that patients in the low-risk group had a better prognosis, higher immune scores, and more abundant immune-related pathways compared to the high-risk group, which was consistent with the tumor subtype results. In conclusion, we developed a prognostic signature of disulfidptosis-related amino acid metabolism genes to assist clinicians in predicting the survival of patients with HCC and provide a reference value for targeted therapy and immunotherapy for HCC.

Connectomics as a prognostic tool of functional outcome in glioma surgery of the supplementary motor area: illustrative case.

The supplementary motor area (SMA) is essential in facilitating the commencement and coordination of complex self-initiated movements. Its complex functional connectivity poses a great risk for postoperative neurological deterioration. SMA syndrome can occur after tumor resection and comprises hemiakinesia and akinetic mutism (often, but unpredictably temporary). Although awake surgery is preferred for mapping and monitoring eloquent areas, connectomics is emerging as a novel technique to tailor neurosurgical approaches and predict functional prognosis, as illustrated in this case. The authors report on a patient presenting with recurrent oligodendroglioma after subtotal resection 7 years earlier. After extensive neuropsychological and neuroradiological assessment (including connectomics), awake surgery was indicated. No intraoperative deficits were recorded; however, the patient presented with postoperative right-sided akinesia and mutism. Postoperative neuroimaging demonstrated the connectome overlapping the preoperative one, and indeed, neurological symptoms resolved after 3 days. Comparison of the pre- and postoperative connectome can be used to objectively evaluate surgical outcomes and assess patient prognosis. To the best of the authors' knowledge, this is the first case demonstrating the feasibility of quantitative functional connectivity analysis as a prognostic tool for neurological improvement after surgery. A better understanding of brain networks is instrumental for improving diagnosis, prognosis, and treatment of neuro-oncological patients.

Retracted: Analysis of the Application Value of Echocardiography Combined with CK-MB, Alb, and CysC in the Prognosis Assessment of Patients with Chronic HF.

[This retracts the article DOI: 10.1155/2022/3986646.].

Identifying immune infiltration by deep learning to assess the prognosis of patients with hepatocellular carcinoma.

The treatment situation for hepatocellular carcinoma remains critical. The use of deep learning algorithms to assess immune infiltration is a promising new diagnostic tool. Patient data and whole slide images (WSIs) were obtained for the Xijing Hospital (XJH) cohort and TCGA cohort. We wrote programs using Visual studio 2022 with C# language to segment the WSI into tiles. Pathologists classified the tiles and later trained deep learning models using the ResNet 101V2 network via ML.NET with the TensorFlow framework. Model performance was evaluated using AccuracyMicro versus AccuracyMacro. Model performance was examined using ROC curves versus PR curves. The percentage of immune infiltration was calculated using the R package survminer to calculate the intergroup cutoff, and the Kaplan‒Meier method was used to plot the overall survival curve of patients. Cox regression was used to determine whether the percentage of immune infiltration was an independent risk factor for prognosis. A nomogram was constructed, and its accuracy was verified using time-dependent ROC curves with calibration curves. The CIBERSORT algorithm was used to assess immune infiltration between groups. Gene Ontology was used to explore the pathways of differentially expressed genes. There were 100 WSIs and 165,293 tiles in the training set. The final deep learning models had an AccuracyMicro of 97.46% and an AccuracyMacro of 82.28%. The AUCs of the ROC curves on both the training and validation sets exceeded 0.95. The areas under the classification PR curves exceeded 0.85, except that of the TLS on the validation set, which might have had poor results (0.713) due to too few samples. There was a significant difference in OS between the TIL classification groups (p < 0.001), while there was no significant difference in OS between the TLS groups (p = 0.294). Cox regression showed that TIL percentage was an independent risk factor for prognosis in HCC patients (p = 0.015). The AUCs according to the nomogram were 0.714, 0.690, and 0.676 for the 1-year, 2-year, and 5-year AUCs in the TCGA cohort and 0.756, 0.797, and 0.883 in the XJH cohort, respectively. There were significant differences in the levels of infiltration of seven immune cell types between the two groups of samples, and gene ontology showed that the differentially expressed genes between the groups were immune related. Their expression levels of PD-1 and CTLA4 were also significantly different. We constructed and tested a deep learning model that evaluates the immune infiltration of liver cancer tissue in HCC patients. Our findings demonstrate the value of the model in assessing patient prognosis, immune infiltration and immune checkpoint expression levels.

Risk Stratification in Pulmonary Arterial Hypertension, Update and Perspectives

Risk stratification in pulmonary arterial hypertension (PAH) is crucial in assessing patient prognosis. It serves a prominent role in everyday patient care and can be determined using several validated risk assessment scores worldwide. The recently published 2022 European Society of Cardiology (ESC)/European Respiratory Society (ERS) guidelines underline the importance of risk stratification not only at baseline but also during follow-up. Achieving a low-risk status has now become the therapeutic goal, emphasising the importance of personalised therapy. The application of these guidelines is also important in determining the timing for lung transplantation referral. In this review, we summarise the most relevant prognostic factors of PAH as well as the parameters used in PAH risk scores and their evolution in the guidelines over the last decade. Finally, we describe the central role that risk stratification plays in the current guidelines not only in European countries but also in Asian countries.

Assessment of the diagnostic value and prognosis of different detection markers in endocervical adenocarcinoma

Objective: To study the diagnostic value of different detection markers in histological categories of endocervical adenocarcinoma (ECA), and their assessment of patient prognosis. Methods: A retrospective study of 54 patients with ECA in the Cancer Hospital, Chinese Academy of Medical Sciences from 2005-2010 were performed. The cases of ECA were classified into two categories, namely human papillomavirus-associated adenocarcinoma (HPVA) and non-human papillomavirus-associated adenocarcinoma (NHPVA), based on the 2018 international endocervical adenocarcinoma criteria and classification (IECC). To detect HR-HPV DNA and HR-HPV E6/E7 mRNA in all patients, we used whole tissue section PCR (WTS-PCR) and HPV E6/E7 mRNA in situ hybridization (ISH) techniques, respectively. Additionally, we performed Laser microdissection PCR (LCM-PCR) on 15 randomly selected HR-HPV DNA-positive cases to confirm the accuracy of the above two assays in identifying ECA lesions. Receiver operating characteristic (ROC) curves were used to analyze the efficacy of markers to identify HPVA and NHPVA. Univariate and multifactorial Cox proportional risk model regression analyses were performed for factors influencing ECA patients' prognoses. Results: Of the 54 patients with ECA, 30 were HPVA and 24 were NHPVA. A total of 96.7% (29/30) of HPVA patients were positive for HR-HPV DNA and 63.3% (19/30) for HR-HPV E6/E7 mRNA, and 33.3% (8/24) of NHPVA patients were positive for HR-HPV DNA and HR-HPV E6/E7 mRNA was not detected (0/24), and the differences were statistically significant (P<0.001). LCM-PCR showed that five patients were positive for HR-HPV DNA in the area of glandular epithelial lesions and others were negative, which was in good agreement with the E6/E7 mRNA ISH assay (Kappa=0.842, P=0.001). Analysis of the ROC results showed that the AUC of HR-HPV DNA, HR-HPV E6/E7 mRNA, and p16 to identify HPVA and NHPVA were 0.817, 0.817, and 0.692, respectively, with sensitivities of 96.7%, 63.3%, and 80.0% and specificities of 66.7%, 100.0%, and 58.3%, respectively. HR-HPV DNA identified HPVA and NHPVA with higher AUC than p16 (P=0.044). The difference in survival rates between HR-HPV DNA (WTS-PCR assay) positive and negative patients was not statistically significant (P=0.156), while the difference in survival rates between HR-HPV E6/E7 mRNA positive and negative patients, and p16 positive and negative patients were statistically significant (both P<0.05). Multifactorial Cox regression analysis showed that International Federation of Obstetrics and Gynecology (FIGO) staging (HR=19.875, 95% CI: 1.526-258.833) and parametrial involvement (HR=14.032, 95% CI: 1.281-153.761) were independent factors influencing the prognosis of patients with ECA. Conclusions: HR-HPV E6/E7 mRNA is more reflective of HPV infection in ECA tissue. The efficacy of HR-HPV E6/E7 mRNA and HR-HPV DNA (WTS-PCR assay) in identifying HPVA and NHPVA is similar, with higher sensitivity of HR-HPV DNA and higher specificity of HR-HPV E6/E7 mRNA. HR-HPV DNA is more effective than p16 in identifying HPVA and NHPVA. HPV E6/E7 mRNA and p16 positive ECA patients have better survival rates than negative.

Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework.

Bladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response. The expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts. In the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy. Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment.

Analysis of the prognostic significance and potential mechanisms of lncRNAs related to m6A methylation in laryngeal cancer

ABSTRACT Objective to investigate the prognostic significance and potential mechanism analysis of m6A methylation-associated lncRNAs in laryngeal cancer. Methods based on the expression of m6A-associated lncRNAs, the samples were divided into two clusters and least absolute value and selection operator (LASSO) regression analysis was performed to build and validate prognostic models. In addition, the relationships between risk scores, clusters, arginine synthase (SMS), tumor microenvironment, clinicopathological features, immune infiltration, immune checkpoints, and tumor mutation burden were analyzed. Finally, the relationship between SMS and m6A-associated IncRNAs was analyzed and SMS-associated pathways were enriched by gene set enrichment analysis (GSEA). Results a total of 95 lncRNAs were associated with the expression of 22 m6A methylation regulators in laryngeal cancer, 14 of which were prognostic lncRNAs. These lncRNAs were divided into two clusters and evaluated. Clinicopathological features did not show significant differences. However, the two clusters differed significantly in terms of naive B cells, memory B cells, naive CD4 T cells, T helper cells and immune score. lASSO regression analysis showed that risk score was a significant predictor of progression-free survival. Conclusion low expression of m6A-related lncRNAs involved in laryngeal cancer development in laryngeal cancer tissues can be used as an indicator to diagnose patients with laryngeal cancer, reduce patient prognosis, be an independent risk factor affecting patient prognosis and be able to assess patient prognosis.

Assessing the Predictive Power of NEWS2 Score in Vaccinated and Non-Vaccinated COVID-19 Inpatients at Omar BONGO ONDIMBA Armed Forces Training Hospital

Background: Since the onset of the COVID-19 pandemic, hospitalized patients have been evaluated through clinical examinations and paraclinical tests to inform initial management. The primary aim of this study was to use the NEWS2 score to assess the prognosis of vaccinated and unvaccinated COVID-19 inpatients in the medical department of HIA OBO. Patients and Methods: This retrospective, descriptive, monocentric assessment was carried out for a period of 15 months (September 1, 2021 to January 30, 2022) with a total of 219 patients included. Results: The most represented age group in the study was 40-64 years (43%), with a median age of 60 years. Non-vaccinated patients accounted for 90% of the study population. Comorbidities were present in 71% of patients and mainly consisted of arterial hypertension (40%), diabetes (17.8%), heart disease (8.25%), and COPD (2.7%). The most commonly observed clinical symptoms were asthenia and fever in 72.47% of patients, followed by cough in 65.13%. The NEWS2 score, which was used to assess the prognosis of COVID-19 inpatients, was high in 16% of cases, moderate in 76% of cases, and low in 1% of cases. Conclusion: The results of our study indicate similarities in clinical forms of COVID-19 between unvaccinated and vaccinated patients. Additionally, the NEWS2 score was a valuable tool for assessing patient prognosis upon admission and optimizing therapeutic management.

Expression and clinical significance of plasma methylated SEPT 9 gene in patients with primary liver cancer

Objective: To investigate the expression and clinical significance of plasma methylated SEPT9 (mSEPT9) gene in patients with primary liver cancer. Methods: 393 cases who visited our hospital from May 2016 to October 2018 were selected. Among them, 75 cases were in the primary liver cancer (PLC) group, 50 cases were in the liver cirrhosis (LC) group, and 268 cases were in the healthy control group (HC). The three groups' positive rates of mSEPT9 expression in the peripheral plasma were detected by the polymerase chain reaction (PCR) fluorescent probe method. The correlational clinical features of liver cancer were analyzed. At the same time, the electrochemiluminescence detection method was used to compare the AFP positive rate. Statistical analysis was conducted using chi-square tests or continuity-corrected chi-square tests. Results: 367 cases actually had valid samples. There were 64, 42, and 64 cases in the liver cancer group, cirrhosis group, and healthy control group, respectively. Among them, 34 cases of liver cancer were verified from pathological tissues. The positive rate of plasma mSEPT9 was significantly higher in the liver cancer group than that in the liver cirrhosis and healthy control groups [76.6% (49/64), 35.7% (15/42), and 3.8% (10/261), respectively], and the differences were statistically significant (χ (2) = 176.017, P < 0.001). The sensitivity of plasma mSEPT9 detection (76.6%) was significantly better in liver cancer (76.6%) than that of AFP patients (54.7%), and the difference was statistically significant (χ (2) = 6.788, P < 0.01). Compared with the single detection, the sensitivity and specificity of plasma mSEPT9 combined with AFP were significantly improved (89.7% vs. 96.3%, respectively). Patients with liver cancer aged≥50 years, with clinical stage II or above, and those with pathological signs of moderate to low differentiation had higher levels of plasma mSEPT9 positive expression, and the differences were statistically significant (χ (2) = 6.41, 9.279, 6.332, P < 0.05). During the follow-up period, the survival time of liver cancer patients with positive plasma mSEPT9 expression was significantly shorter than that of those with negative expression (310 ± 26 days vs. 487 ± 59 days, respectively), with statistically significant differences (Log Rank P = 0.039). Conclusion: In China, the positive rate of plasma mSEPT9 detection in liver cancer patients is higher than that of AFP in relation to age, clinical stage, and degree of tissue differentiation; additionally, it has certain survival predictive values. As a result, detecting this gene has important clinical significance and potential clinical application value in the non-invasive diagnosis and prognosis assessment of patients with primary liver cancer.

Neutrophil to Lymphocyte Ratio as a Predictor of Postoperative Outcomes in Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

(1) Introduction: Traumatic brain injury (TBI) is a leading cause of injury and mortality worldwide, carrying an estimated cost of $38 billion in the United States alone. Neutrophil to lymphocyte ratio (NLR) has been investigated as a standardized biomarker that can be used to predict outcomes of TBI. The aim of this review was to determine the prognostic utility of NLR among patients admitted for TBI. (2) Methods: A literature search was conducted in PubMed, Scopus, and Web of Science in November 2022 to retrieve articles regarding the use of neutrophil to lymphocyte ratio (NLR) as a prognostic measure in traumatic brain injury (TBI) patients. Inclusion criteria included studies reporting outcomes of TBI patients with associated NLR values. Exclusion criteria were studies reporting only non-primary data, those insufficiently disaggregated to extract NLR data, and non-English or cadaveric studies. The Newcastle-Ottawa Scale was utilized to assess for the presence of bias in included studies. (3) Results: Following the final study selection 19 articles were included for quantitative and qualitative analysis. The average age was 46.25 years. Of the 7750 patients, 73% were male. Average GCS at presentation was 10.51. There was no significant difference in the NLR between surgical vs. non-surgical cohorts (SMD 2.41 95% CI -1.82 to 6.63, p = 0.264). There was no significant difference in the NLR between bleeding vs. non-bleeding cohorts (SMD 4.84 95% CI -0.26 to 9.93, p = 0.0627). There was a significant increase in the NLR between favorable vs. non-favorable cohorts (SMD 1.31 95% CI 0.33 to 2.29, p = 0.0090). (4) Conclusions: Our study found that NLR was only significantly predictive for adverse outcomes in TBI patients and not surgical treatment or intracranial hemorrhage, making it nonetheless an affordable alternative for physicians to assess patient prognosis.

Plasmatic BRAF-V600E allele fraction as a prognostic factor in metastatic colorectal cancer treated with BRAF combinatorial treatments

Combination of a BRAF inhibitor (BRAFi) and an anti-epidermal growth factor receptor (EGFR), with or without a MEK inhibitor (MEKi), improves survival in BRAF-V600E-mutant metastatic colorectal cancer (mCRC) over standard chemotherapy. However, responses are heterogeneous and there are no available biomarkers to assess patient prognosis or guide doublet- or triplet-based regimens. In order to better characterize the clinical heterogeneity observed, we assessed the prognostic and predictive role of the plasmatic BRAF allele fraction (AF) for these combinations. A prospective discovery cohort including 47 BRAF-V600E-mutant patients treated with BRAFi-anti-EGFR ± MEKi in clinical trials and real-world practice was evaluated. Results were validated in an independent multicenter cohort (n= 29). Plasmatic BRAF-V600E AF cut-off at baseline was defined in the discovery cohort with droplet digital PCR (ddPCR). All patients had tissue-confirmed BRAF-V600E mutations. Patients with high AF have major frequency of liver metastases and more metastatic sites. In the discovery cohort, median progression-free survival (PFS) and overall survival (OS) were 4.4 and 10.1 months, respectively. Patients with high BRAF AF (≥2%, n= 23) showed worse PFS [hazard ratio (HR) 2.97, 95% confidence interval (CI)1.55-5.69; P= 0.001] and worse OS (HR 3.28, 95% CI 1.58-6.81; P= 0.001) than low-BRAF AF patients (<2%, n= 24). In the multivariable analysis, BRAF AF levels maintained independent significance. In the validation cohort, high BRAF AF was associated with worse PFS (HR 3.83, 95% CI 1.60-9.17; P= 0.002) and a trend toward worse OS was observed (HR 1.86, 95% CI 0.80-4.34; P= 0.15). An exploratory analysis of predictive value showed that high-BRAF AF patients (n= 35) benefited more from triplet therapy than low-BRAF AF patients (n= 41; PFS and OS interaction tests, P < 0.01). Plasmatic BRAF AF determined by ddPCR is a reliable surrogate of tumor burden and aggressiveness in BRAF-V600E-mutant mCRC treated with a BRAFi plus an anti-EGFR with or without a MEKi and identifies patients who may benefit from treatment intensification. Our results warrant further validation of plasmatic BRAF AF to refine clinical stratification and guide treatment strategies.

Development and validation of a cancer cachexia risk score for digestive tract cancer patients before abdominal surgery

Cancer cachexia is prevalent in digestive tract cancer patients and has significant impacts on prognosis; it is vital to identify individuals who are at risk of cancer cachexia to allow for appropriate evaluation and treatment. This study evaluated whether digestive tract cancer patients with a risk of cancer cachexia and who had a risk of adverse survival could be identified before abdominal surgery. This large-scale cohort study involved patients who underwent abdominal surgery between January 2015 and December 2020 to treat digestive tract cancer. Participants were allocated to the development cohort, the validation cohort, or the application cohort. Univariate and multivariate analyses of the development cohort were performed to detect distinct risk variables for cancer cachexia to create a cancer cachexia risk score. The performance of the risk score across all the three cohorts was assessed through calculating the area under the receiver operating characteristic curve (AUC), as well as calibration and decision curves. We tested how well the score predicted survival outcomes in the application cohort. A total of 16264 patients (median 64years of age; 65.9% male) were included, with 8743 in the development cohort, 5828 in the validation cohort, and 1693 in the application cohort. Seven variables were identified as independent predictive factors and were included in the cancer cachexia risk score: cancer site, cancer stage, time from symptom onset to hospitalization, appetite loss, body mass index, skeletal muscle index, and neutrophil-lymphocyte ratio. The risk score predicting cancer cachexia owns a good discrimination, with the mean AUC of 0.760(P<0.001) in the development cohort, 0.743 (P<0.001) in the validation cohort, and 0.751 (P<0.001) in the application cohort, respectively, and had an excellent calibration (all P>0.05). The decision curve analysis revealed net benefits of the risk score across a range of risk thresholds in the three cohorts. In the application cohort, compared with the high-risk group, the low-risk group experienced significantly longer overall survival [hazard ratio (HR) 2.887, P<0.001] as well as relapse-free survival (HR 1.482, P=0.01). The cancer cachexia risk score constructed and validated demonstrated good performance in identifying those digestive tract cancer patients before abdominal surgery at a higher risk of cancer cachexia and unfavourable survival. This risk score can help clinicians to enhance their capabilities to screen for cancer cachexia, assess patient prognosis, and strengthen early decision-making on targeted approaches to attune cancer cachexia for digestive tract cancer patients before abdominal surgery.

Assessing the variability and correlation between SUV and ADC parameters of head and neck cancers derived from simultaneous PET/MRI: A single-center study.

Intratumoral heterogeneity is associated with poor outcomes in head and neck cancer (HNC) patients owing to chemoradiotherapy resistance. [18 F]-FDG positron emission tomography (PET) / Magnetic Resonance Imaging (MRI) provides spatial information about tumor mass, allowing intratumor heterogeneity assessment through histogram analysis. However, variability in quantitative PET/MRI parameter measurements could influence their reliability in assessing patient prognosis. Therefore, to use standardized uptake value (SUV) and apparent diffusion coefficient (ADC) parameters for assessing tumor response, this study aimed to measure SUV and ADC's variability and assess their relationship in HNC. First, ADC variability was measured in an in-house diffusion phantom and in five healthy volunteers. The SUV variability was only measured with the NEMA phantom using a clinical imaging protocol. Furthermore, simultaneous PET/MRI data of 11 HNC patients were retrospectively collected from the National Cyclotron and PET center in Chulabhorn Hospital. Tumor contours were manually drawn from PET images by an experienced nuclear medicine radiologist before tumor volume segmentation. Next, SUV and ADC's histogram were used to extract statistic variables of ADC and SUV: mean, median, min, max, skewness, kurtosis, and 5th , 10th , 25th , 50th , 75th , 90th , and 95th percentiles. Finally, the correlation between the statistic variables of ADC and SUV, as well as Metabolic Tumor volume and Total Lesion Glycolysis parameters was assessed using Pearson's correlation. This pilot study showed that both parameters' maximum coefficient of variation was 13.9% and 9.8% in the phantom and in vivo, respectively. Furthermore, we found a strong and negative correlation between SUVmax and ADVmed (r=-0.75, P=0.01). The SUV and ADC obtained by simultaneous PET/MRI can be potentially used as an imaging biomarker for assessing intratumoral heterogeneity in patients with HNC. The low variability and relationship between SUV and ADC could allow multimodal prediction of tumor response in future studies.

Single-cell analysis of myeloid cells in HPV+ tonsillar cancer.

The incidence of human papillomavirus-positive (HPV+) tonsillar cancer has been sharply rising during the last decades. Myeloid cells represent an appropriate therapeutic target due to their proximity to virus-infected tumor cells, and their ability to orchestrate antigen-specific immunity, within the tonsil. However, the interrelationship of steady-state and inflammatory myeloid cell subsets, and their impact on patient survival remains unexplored. Here, we used single-cell RNA-sequencing to map the myeloid compartment in HPV+ tonsillar cancer. We observed an expansion of the myeloid compartment in HPV+ tonsillar cancer, accompanied by interferon-induced cellular responses both in dendritic cells (DCs) and monocyte-macrophages. Our analysis unveiled the existence of four DC lineages, two macrophage polarization processes, and their sequential maturation profiles. Within the DC lineages, we described a balance shift in the frequency of progenitor and mature cDC favoring the cDC1 lineage in detriment of cDC2s. Furthermore, we observed that all DC lineages apart from DC5s matured into a common activated DC transcriptional program involving upregulation of interferon-inducible genes. In turn, the monocyte-macrophage lineage was subjected to early monocyte polarization events, which give rise to either interferon-activated or CXCL-producing macrophages, the latter enriched in advanced tumor stages. We validated the existence of most of the single-cell RNA-seq clusters using 26-plex flow cytometry, and described a positive impact of cDC1 and interferon-activated DCs and macrophages on patient survival using gene signature scoring. The current study contributes to the understanding of myeloid ontogeny and dynamics in HPV-driven tonsillar cancer, and highlights myeloid biomarkers that can be used to assess patient prognosis.