Prognostic Risk Stratification Research Articles

Radiomics-based predictive model for preoperative risk classification of gastrointestinal stromal tumors using multiparametric magnetic resonance imaging: aretrospective study.

The aim of this study was to develop and assess aradiomics model utilizing multiparametric magnetic resonance imaging (MRI) for the prediction of preoperative risk assessment in gastrointestinal stromal tumors (GISTs). An analysis was performed retrospectively on agroup of 121 patients who received ahistological diagnosis of GIST. They were then divided into two sets, with85 in the training set and 36 in the validation set through random partitioning. Radiomics features from five MRI sequences, totaling 600 per patient, were extracted and subjected to feature selection utilizing arandom forest algorithm. The discriminatory efficacy of the models was evaluated through receiver operating characteristic (ROC) and precision-recall (P-R) curve analyses. Model calibration was assessed via calibration curves. Subgroup analysis was performed on GISTs with apathological maximum diameter equal to or less than 5 cm. Furtherly, Kaplan-Meier (K-M) curves and log-rank tests were used to compare the differences in survival status among different groups. Cox regression analysis was employed to identify independent prognostic factors and to construct aprognostic prediction model. The clinical model (ModelC) displayed limited predictive efficacy in the context of GIST. Conversely, aradiomics model (ModelR) incorporating five parameters exhibited robust discriminative capabilities across both the training and validation sets, yielding area under the ROC curve (AUC) values of 0.893 (95% confidence interval [CI]: 0.807-0.949) and 0.855 (95% CI: 0.732-0.978), respectively. The F1max scores derived from the P‑R curves were 0.741 and 0.842 for the training and validation sets, respectively. Noteworthy was the exclusion of the two-dimensional tumor diameter and tumor location when constructing ahybrid model (ModelCR) that amalgamated radiomics and clinical features. ModelR demonstrated asubstantially enhanced discriminative capacity in the training set compared with ModelC (p < 0.005). The net reclassification improvement (NRI) corroborated the superior performance of ModelR over ModelC, thereby enhancing diagnostic accuracy and clinical applicability. Patients in the high-risk group had significantly worse recurrence-free survival (RFS, p < 0.001) and overall survival (OS, p = 0.004), and the radiomics signature is an independent risk factor for RFS. The extended model incorporating the radiomics signature outperformed the baseline model in terms of risk assessment accuracy (p < 0.001). Our investigation underscores the value of integrating radiomics analysis in conjunction with machine learning algorithms for prognostic risk stratification in GIST, presenting promising implications for informing clinical decision-making processes as well as optimizing management strategies.

Random survival forest algorithm for risk stratification and survival prediction in gastric neuroendocrine neoplasms.

This study aimed to construct and assess a machine-learning algorithm designed to forecast survival rates and risk stratification for patients with gastric neuroendocrine neoplasms (gNENs) after diagnosis. Data on patients with gNENs were extracted and randomly divided into training and validation sets using the Surveillance, Epidemiology, and End Results database. We developed a prediction model using 10 machine learning algorithms across 101 combinations to forecast cancer-related mortality in patients with gNENs, selecting the best model using the highest mean over a sequence of time-dependent area under the receiver operating characteristic (ROC) curve (AUC). The performance of the final model was assessed through time-dependent ROC curves for discrimination and calibration curves for calibration. The maximum selection rank method was used to determine the best prognostic risk score threshold for classifying patients into high- and low-risk groups. Afterward, Kaplan-Meier analysis and log-rank test were used to compare survival rates among these groups. Our study examined 775 patients with gNENs, dividing them into training and validation sets. A training set comprised 543 patients, with a median follow-up of 42 months and cumulative mortality rates of 40.0% at 1 year, 48.6% at 3 years, and 54.0% at 5 years. A validation set comprised 232 patients, with cumulative mortality rates of 29.1% at 1 year, 43.5% at 3 years, and 53.2% at 5 years. The optimal random survival forest (RSF) model (mtry = 4, node size = 5) achieved an AUC of 0.839 for survival prediction in the training set. Comprising 11 variables such as demographics, treatment details, tumor characteristics, T staging, N staging, and M staging, the RSF model revealed high predictive accuracy with AUCs of 0.92, 0.96, and 0.96 for 1-, 3-, and 5-year survival, respectively, which was consistently reflected in the validation set with AUCs of 0.88, 0.92, and 0.89, respectively. Moreover, patients were risk-stratified. Although our RSF model effectively stratified patients into different prognostic groups, it needs external validation to confirm its utility for noninvasive prognostic prediction and risk stratification in gNENs. Further research is required to verify its broader clinical applicability.

The impact of integrated genomic analysis on molecular classifications and prognostic risk stratification in endometrial cancer

The impact of integrated genomic analysis on molecular classifications and prognostic risk stratification in endometrial cancer

Circulating Amyloid beta 1-40 is independently associated with statin treatment and LDL cholesterol reduction: a prospective cohort study

Abstract Background Accumulating evidence suggests that circulating amyloid beta 1-40 (Aβ40), a pro-inflammatory and pro-atherogenic peptide, is associated with atherosclerotic cardiovascular disease (ASCVD), providing incremental prognostic value and risk stratification refinement in primary and secondary prevention settings. Existing evidence supports conflicting off-target effects of statins on plasma Aβ40. We aim to explore the impact of cardiovascular risk reduction through statin therapy on Aβ40 plasma levels in subjects with dyslipidemia and with/without ASCVD. Methods In this prospective study, patients with dyslipidemia who visited for the first time our lipid clinic for risk stratification and treatment plan assessment (n=146) were consecutively recruited. Αβ40 was measured in plasma samples by enzyme-linked immunosorbent assay at baseline and after a mean follow-up of 5.2 and 15 months. Patients in whom statin treatment was initiated or intensified in terms of dose or regimen (n= 68) were compared against patients not treated with statins or with unchanged statin intensity (n = 78). Statin intensity treatment was defined based on the European Society of Cardiology (ESC) guidelines. Results Increased Aβ40 plasma levels at baseline were associated with ASCVD presence, decreased glomerular filtration rate (GFR), and history of heart failure (p&amp;lt;0.05 for all). An increase in Aβ40 circulating levels between baseline and follow-up 1 was observed in patients in whom statin treatment was initiated or intensified (p=0.041). However, in patients not treated with statins or treated with unchanged statin intensity, an increase in Aβ40 circulating levels was observed between follow-up 1 and follow-up 2 (p=0.05, Figure 1A). These changes in Aβ40 were significantly different between the two groups during the short follow-up period (β=0.226, p for interaction =0.025) independently from age and changes in GFR, systolic blood pressure, and LDL-C. Additionally, patients who achieved a reduction in low-density lipoprotein cholesterol (LDL-C) ≥ 50% in follow-up 2 have unchanged Aβ40 levels during the long follow-up period, while an increase in Aβ40 was observed in the rest population (p=0.012, Figure 1B). These changes were significantly different between the two LDL-C groups independently of statin treatment and changes in high sensitive C-reactive protein (hs-CRP) (β= -0.231, p for interaction=0.035). Conclusion In a dyslipidemic population, statin initiation or intensification and LDL-C decrease showed a short-term increase and a long-term return to initial levels, respectively, in circulating Aβ40 levels. From a mechanistic point of view, our findings suggest that statins have an impact on Aβ40, possibly through an LDL-C-dependent and a non-LDL-C-dependent pathway. Further studies are needed to evaluate the utility of circulating Aβ40 as a novel biomarker in ASCVD risk reduction assessment.

A practical nomogram and risk stratification system for predicting in-hospital mortality of patients with non-acute myocardial infarction related cardiogenic shock

Abstract Background The morbidity and mortality of Cardiogenic Shock(CS) remain high despite the advance in management these years1.As acute myocardial infarction (AMI) is considered to be the most common reason for cardiogenic shock,Non-AMI related CS is excluded from the majority of CS studies and remains largely understudied2. Considering its inherent complexity and hemodynamically multiplicity, it is needed to focusing on these patients to identify specific risk factors, which may be conducive to the promotion of critically CS patients with early decision and timely intervention. Purpose In this study,we sought to investigate the variables which are predictive of in hospital mortality in non AMI related CS patients, developed and validated a practical nomogram for risk quantification. Methods 1298 patients and 548 patients with cardiogenic shock from the MIMIC-IV (version 1.0) and MIMIC-III(version1.4) databaes were included in the study after excluding acute myocardial infarction. We used in-hospital death and 30 day survival as the end points. Lasson and logistic regression analysis were used to identify statistically significant predictors which were finally involved in the nomogram. The predictive performance of the nomogram was evaluated by Harrell’s concordance index (C-index) and calibration after comparing with commonly used ICU scores for in-hospital death.Lastly, we plotted Kaplan-Meier curves of three subgroup classified by the identified cutoff values of the nomogram to further assess the discriminative ability of the prognosis. Results The in-hospital mortality of patients with non-AMI related CS is about 36.6%.After the analysis of regression, age, heart rate, wbc, albumin, lactic acid, GCS score, urine volume, using vasopressor were identified as the most critical factors for the in-hospital death. Based on the these results, a nomogram for predicting in-hospital death was established.The calibration plot also shows good consistency between nomogram prediction and actual in-hospital mortality.In addition, compared with other scores, the AUC value of nomogram was 0.806 (95% CI = 0.799-0.831) in the training queue, 0.814 (95% CI = 0.771-0.852) in the internal validation queue and 0.730 (95% CI = 0.690-0.767) in the external validation set, which was significantly higher than other commonly used ICU scoring systems(SAPSII, APSIII and SOFA).Also, three risk subgroups were created using two boundary points (303 and 339) of the nomogram score and the survival curve shows that there were significant differences in the 30 day survival of each subgroup. Conclusion A prognostic nomogram and risk stratification system of non-AMI related CS were established and verified, which can provide a practical tool for individualized clinical management of patients.Workflow of the study.Nomogram for non-AMI related CS patients

The prognostic utility of troponin T levels in patients undergoing PCI during initial hospitalisation for STEMI

Abstract The prognostic utility of presentation or "peak" high-sensitivity troponin T (hsTnT) levels following ST-segment elevation myocardial infarction (STEMI) has been previously reported, but hsTnT, unlike other bio-markers of myocyte damage, have a 2nd or ‘plateau-phase’ of release which lasts over a week. We have reported correlations between plateau-phase hsTnT levels, and cardiac MRI determined infarct size, though whether these levels are more highly associated with late mortality than "peak" (or highest) levels, needs clarification. Methods Hence we determined late mortality and the combination of cardiovascular death, myocardial infarction and cerebrovascular accident [CD/MI/CVA] among patients with STEMI, and an HsTnT level &amp;gt;36 hours after symptom onset (plateau phase), who underwent percutaneous coronary intervention (PCI), either primary or pharmaco-invasive, during initial hospitalisation between October 2003 and December 2021. HsTnT levels and other potential prognostic factors were determined. Results Amongst 1955 patients, 119 patients (6%) had died by 2 years and 192 (10%) patients experienced CD/MI/Stroke. The highest quartile of plateau phase hsTnT levels (³ 3786 ng/L ng/L), were more highly associated with 2-year mortality, than the highest quartile of "peak" hsTnT levels (³ 7687 ng/L ng/L). The Figure shows Peak and plateau hs-TnT levels in quartile groups were assessed by rank test to obtain p-values. Patients lost to follow-up or with less than 2-years of follow up were considered censored. Peak quartile 1; hs-TnT £ 1611 ng/L, peak quartile 2; hs-TnT 1612 – 3900 ng/L, peak quartile 3; hs-TnT 3901 – 7686 ng/L; peak quartile 4; hs-TnT ³ 7687 ng/L. Plateau quartile 1; hs-TnT £ 1060 ng/L, plateau quartile 2; hs-TnT 1061 – 2135 ng/L; plateau quartile 3; hs-cTnT 2136 – 3785 ng/L. Log rank mortality analyses are shown in the Figure. Peak quartile 1; hs-TnT &amp;lt;1611 ng/L, peak quartile 2; hs-TnT 1612 – 3900 ng/L, peak quartile 3; hs-TnT 3901 – 7686 ng/L; peak quartile 4; hs-TnT ³ 7687 ng/L. Plateau quartile 1; hs-TnT £ 1060 ng/L, plateau quartile 2; hs-TnT 1061 – 2135 ng/L; plateau quartile 3; hs-cTnT 2136 – 3785 ng/L, plateau quartile 4; hs-TnT &amp;gt;3786 ng/L. Multivariate Cox regression analysis incorporating age, sex, smoking status, hypertension, renal function, cardiogenic shock and heart failure hospitalisation found plateau hs-TnT levels (HR, 3.564; 95% CI, 1.674 – 7.590; P = 0.001) were an independent predictor of all-cause mortality at 2-years post-STEMI, whereas peak hs-cTnT levels (HR, 1.965; 95% CI, 0.959 – 4.025; P = 0.065) were not. Conclusions Plateau phase hsTnT levels were of prognostic significance in STEMI patients treated with PCI. Thus, measurement of plateau hs-cTnT levels following STEMI could be an accurate, inexpensive method of long-term prognostic risk stratification.

Exercise stress testing recovery kinetics: the forgotten phase

Abstract Background Impaired exercise capacity is a cardinal feature of heart failure (HF). Peak oxygen uptake (pVO2) and ventilatory efficiency (VE/VCO2 slope) during exercise are well-consolidated prognostic markers in patients with HF. A previous study suggested that abnormally prolonged VO2 recovery, specifically the failure to decrease VO2 from peak exercise by 10.5 mL/kg/min at 180 seconds of the recovery phase, predicts adverse outcomes in HF. However, it is unknown to what extent the recovery of VEqCO2, a marker of ventilation/perfusion mismatch, adds prognostic value to the already existing parameters. Purpose The aim of this study was to characterize the functional and prognostic significance of VEqCO2 kinetics following peak exercise in individuals with HF. Methods This retrospective single-center study included consecutive adult patients with chronic (&amp;gt;3 months) and stable HF with left ventricular ejection fraction (LVEF) &amp;lt;50% who underwent cardiopulmonary exercise testing (CPET) between 2015 and 2020. Following maximal exercise, patients recovered over a 3-minute period (walking 2km/h for 1 minute and sittng passively for 2 minutes). VO2 and VEqCO2 kinetics during recovery, described as the absolute difference to peak VO2 (difVO2) and VEqCO2 (difVEqCO2), were measured at 30, 60, 120, and 180 seconds. Outcomes were assessed as a composite of cardiovascular (CV) death, urgent cardiac transplant, or left ventricular assist device (LVAD) implantation at 1 year. Results A total of 238 patients were included (mean age 58 ± 12 years; 82% males; mean LVEF 34 ± 10%; 68% with ischemic HF; 75% in NYHA class II-III; mean pVO2 18 ± 6 mL/kg/min; mean VE/VCO2 slope 41 ± 12). During the 1-year follow-up, 19 patients met the endpoint (8 CV deaths, 8 urgent heart transplants, and 3 LVAD). ROC curve analysis showed that the best correlation between difVEqCO2 and outcomes was apparent at difVEqCO2 of 180 seconds (AUC 0.83) with a cut-off value of 1 (sensitivity 93%, specificity 58%). These patients, who had a decrease in VEqCO2 (difVEqCO2 &amp;gt;1) at 180 seconds, were significantly older (p=0.02), had a lower LVEF (p&amp;lt;0.001), and higher NYHA (p&amp;lt;0.001). They were at an increased risk of cardiovascular events compared to those with an increase in VEqCO2 (difVEqCO2 &amp;lt;1) at 180 seconds (event-free survival at 1 year 86% vs. 98%, log-rank p&amp;lt;0.001). In multivariate analysis, the statistical association between difVEqCO2 and outcomes remained significant, even after adjusting for difVO2 at 180 seconds (HR 4.315, CI95% (1.349-13.804), p=0.014). Conclusion Abnormal VEqCO2 kinetics in the recovery period of CPET is an easily recognizable non-invasively derived measurement that predicts outcomes in HF. VEqCO2 may complement the evaluation of VO2 and other parameters during recovery to further improve prognostic risk stratification in HF.

Normal reference values of left atrioventricular coupling index on two-dimensional echocardiography

Abstract Background From a physiological background, there is close relationship between left atrial (LA) and left ventricular (LV) expressed as coupling. The assessment of LA/LV coupling may reflect LV dysfunction and differentiate between normal and abnormal LV function. Aim of study The study is designed to sample normal subjects to provide the normal reference values of LA/LV coupling index (LACI) according to age and sex using 2-dimensional echocardiography (2DE). Methods 550 healthy subjects (51.9±17.8 years, 56% males) were included with good 2DE image quality enabled measurement of end-diastolic volumes of both LA and LV. LACI was defined as the ratio between LA to LV end-diastolic volumes. Results An off-line analysis of apical 4-chamber and 2-chamber views to obtain the 2DE measurements was performed within short time (1.5± 0.3 min). Measurement of LACI was obtained in all individuals. Subjects were divided into 3 age groups (G I: 18-40 years, G II: 41-65 years, and GIII: &amp;gt;65 years). As shown in table 1, the normal LACI values in the whole population was 0.17±0.08 with significantly higher in females (0.178±0.09) than males (0.15±0.06); P&amp;lt;0.02. The LACI values tends to be higher in females than males in the subdivided 3 age groups but not statistically significant. In both sexes, increasing age was associated with significant reduction of the mean LVEDV (P&amp;lt;0.0001), significant increase of the mean LAEDV (P= 0.004), and significant increase of LACI (P&amp;lt; 0.0001); especially considering the differences between the youngest and oldest age groups. Conclusions Measurement of LACI is feasible and easily obtainable using 2DE. Age and sex should be considered when defining normal reference values for LACI. The standardization of normal LACI values will help the future studies to assess the changes of these normal values in various cardiac abnormalities for prognostic risk stratification.

The clinical prognostic risk stratification system for HIV infected hepatocellular carcinoma

BackgroundPatients with human immunodeficiency virus (HIV) are more susceptible to liver cancer because of their compromised immune system. There is no specific prognostic model for HIV-infected hepatocellular carcinoma (HCC) patients. MethodsClinical data of 85 patients with HIV-infected HCC was divided into a 7:3 ratio for training and internal validation sets, while the data of 23 patients with HIV-infected HCC was served as the external validation set. Data of 275 HIV-negative HCC patients was considered as external HIV-negative validation set. Variables associated with overall survival (OS) in the training set were used to develop the HIV-infected HCC prognosis (HIHP) model. The model was tested in the internal and external validation sets. The predictive accuracy of the model was assessed with conventional HIV-negative HCC prognostic scoring systems. ResultsIn the training set, variables independently associated with OS in multivariable analysis were organ involvement and tumor number. The HIHP model demonstrated a significant association with OS in the training set, with a median OS of 13 months for low risk, 7 months for medium risk, and 3 months for high risk (p < 0.001). The HIHP model showed a significant association with OS, and exhibited greater discriminative abilities compared to conventional HIV-negative HCC prognostic models both in the internal and external validation sets. In the external HIV-negative validation set, the HIHP model did not show better discrimination than conventional HIV-negative HCC scores. ConclusionThe new model presented in the work provided a more accurate prognostic prediction of OS in HIV-infected HCC patients. However, the model is not applicable to patients with HIV-negative HCC.

Diagnosis and Risk Stratification of Acute Myeloid Leukemia, Myelodysplasia -Related

To analyze the clinical and genetic characteristics of acute myeloid leukemia, myelodysplasia-related (AML-MR) patients and evaluate their prognostic risk stratification, to guide clinical treatment decisions and improve understanding of the biological characteristics and disease progression. The study analyzed cellular and molecular genetic information of 307 AML-MR patients, diagnosed based on clinical history, bone marrow morphology, cytogenetics, and molecular genetic abnormalities. The risk stratification followed the 2022 ELN guidelines. 57 cases (18.6%) met the AML-MR diagnostic criteria based on morphology and clinical history, 110 cases (37.2%) met the AML-MR diagnostic criteria based on cytogenetic results, and 210 cases (74.5%) met the AML-MR diagnostic criteria based on molecular testing results. Among different type of mutations, ASXL1 mutation was the most frequent, followed by SRSF2 and BCOR mutations. Except for 2 cases with incomplete data that could not be classified, 263 (86.2%) of the 305 patients were classified as poor prognosis, 20 (6.6%) were classified as good prognosis group, and 22 (7.2%) were classified as intermediate prognosis group. Molecular genetic information plays a crucial role in diagnosing AML-MR, highlighting the importance of genetics in diagnosis and prognosis. Most AML-MR patients fall into poor prognosis categories, necessitating early intensive and targeted therapy for better survival outcomes.

Integrated analysis of radiomics, RNA, and clinicopathologic phenotype reveals biological basis of prognostic risk stratification in colorectal cancer

Integrated analysis of radiomics, RNA, and clinicopathologic phenotype reveals biological basis of prognostic risk stratification in colorectal cancer

Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning.

Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle complexities of phenotypic information. We trained a weakly supervised deep learning model on data from 640 CRC patients in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial dataset and validated it using data from 522 CRC patients in the cancer genome atlas (TCGA) dataset. We created the colorectal cancer risk score (CRCRS) to assess patient prognosis, visualized the pathological phenotype of the risk score using Grad-CAM, and employed multiomics data from the TCGA CRC cohort to investigate the potential biological mechanisms underlying the risk score. The overall survival analysis revealed that the CRCRS served as an independent prognostic indicator for both the PLCO cohort (p < 0.001) and the TCGA cohort (p < 0.001), with its predictive efficacy remaining unaffected by the clinical staging system. Additionally, satisfactory chemotherapeutic benefits were observed in stage II/III CRC patients with high CRCRS but not in those with low CRCRS. A pathomics nomogram constructed by integrating the CRCRS with the tumor-node-metastasis (TNM) staging system enhanced prognostic prediction accuracy compared with using the TNM staging system alone. Noteworthy features of the risk score were identified, such as immature tumor mesenchyme, disorganized gland structures, small clusters of cancer cells associated with unfavorable prognosis, and infiltrating inflammatory cells associated with favorable prognosis. The TCGA multiomics data revealed potential correlations between the CRCRS and the activation of energy production and metabolic pathways, the tumor immune microenvironment, and genetic mutations in APC, SMAD2, EEF1AKMT4, EPG5, and TANC1. In summary, our deep learning algorithm identified the CRCRS as a prognostic indicator in CRC, providing a significant approach for prognostic risk stratification and tailoring precise treatment strategies for individual patients.

Spatial Cancer-Immune Phenotypes Predict Shorter Recurrence-Free Survival in the No Specific Molecular Profile Molecular Subtype of Endometrial Carcinoma

Compartmentation of the immune response into 3 main spatial cancer-immune phenotypes (SCIs) – inflamed, excluded, and desert – has been proposed as the main predictor of response to immune checkpoint inhibitors in solid tumors. The objective of the study was to define and characterize the SCI in a consecutive series of 213 endometrial carcinomas (ECs) by correlating it with molecular subtypes, clinicopathologic features, and prognosis. Immunohistochemistry (IHC) and next-generation sequencing were used to assign surrogate molecular EC subtypes: POLE mutant (POLE), mismatch repair deficient (MMRd), TP53 mutant (p53abn), and no specific molecular profile (NSMP). Immune cell markers (CD20, CD3, CD8, CD68, PD-L1) were assessed by IHC on whole sections and quantified by digital image analysis to define the 3 SCIs. ECs were stratified into 4 molecular subtypes: 17 (8.0%) POLE, 68 (31.9%) MMRd, 42 (19.7%) p53abn, and 86 (40.4%) NSMP. SCI determination showed 105 (49.3%) inflamed, 62 (29.1%) desert, and 46 (25.6%) excluded tumors. The inflamed phenotype was more prevalent in MMRd (64.7%) and POLE (76.5%) subtypes compared with NSMP (45.3%) and p53abn (21.4%). SCI revealed a strong correlation with disease-free survival in NSMP tumors: inflamed 96.2%, desert 83.2%, and excluded 40.5%. The SCI prognostic impact was also maintained in NSMP cases treated with adjuvant therapy resulting in a significant difference in recurrence between the inflamed and excluded phenotypes. To simplify SCI determination, a subset of immune cell markers was selected as appropriate to define the 3 SCI patterns: high intraepithelial CD8 for the inflamed phenotype; CD68, CD20, and PD-L1 to discriminate between desert and excluded tumors. The integration of SCI into molecular classification could be a promising opportunity to improve the prognostic risk stratification of patients and may guide the therapeutic approach, particularly in the NSMP subtype. Thus, the different patterns of immune response are a new prognostic parameter in the NSMP subtype.

NRI and SIRI are the optimal combinations for prognostic risk stratification in patients with non-small cell lung cancer after EGFR-TKI therapy

NRI and SIRI are the optimal combinations for prognostic risk stratification in patients with non-small cell lung cancer after EGFR-TKI therapy

Glutamine metabolism-related genes predict the prognostic risk of acute myeloid leukemia and stratify patients by subtype analysis

BackgroundAcute myeloid leukemia (AML) is a genetically heterogeneous disease in which glutamine (Gln) contributes to AML progression. Therefore, this study aimed to identify potential prognostic biomarkers for AML based on Gln metabolism-related genes.MethodsGln-related genes that were differentially expressed between Cancer Genome Atlas-based AML and normal samples were analyzed using the limma package. Univariate, least absolute shrinkage, selection operators, and stepwise Cox regression analyses were used to identify prognostic signatures. Risk score-based prognostic and nomogram models were constructed to predict the prognostic risk of AML. Subsequently, consistent cluster analysis was performed to stratify patients into different subtypes, and subtype-related module genes were screened using weighted gene co-expression network analysis.ResultsThrough a series of regression analyses, HGF, ANGPTL3, MB, F2, CALR, EIF4EBP1, EPHX1, and PDHA1 were identified as potential prognostic biomarkers of AML. Prognostic and nomogram models constructed based on these genes could significantly differentiate between high- and low-risk AML with high predictive accuracy. The eight-signature also stratified patients with AML into two subtypes, among which Cluster 2 was prone to a high risk of AML prognosis. These two clusters exhibited different immune profiles. Of the subtype-related module genes, the HOXA and HOXB family genes may be genetic features of AML subtypes.ConclusionEight Gln metabolism-related genes were identified as potential biomarkers of AML to predict prognostic risk. The molecular subtypes clustered by these genes enabled prognostic risk stratification.

Diagnosis and Treatment of Membranous Nephropathy in Integrative Medicine

Membranous nephropathy (MN) is a common glomerular disease. The related pathological changes are primarily characterized by the deposition of immune complexes beneath the epithelial cells of the glomerular basement membrane, accompanied by diffuse thickening of the basement membrane. Its etiology and pathogenesis are not fully understood yet; however, they are associated with genetics, infections, tumors, drugs, heavy metals poisoning, environmental pollution, and the loss of immune tolerance. Over 10 target antigens, including the phospholipase A2 receptor (PLA2R) and thrombospondin type 1 domain containing 7A (THSD7A), associated with MN have been identified, each having different clinical implications. Clinical manifestations of MN patients mainly include proteinuria and nephrotic syndrome, with a propensity to form thrombi. Its diagnostic accuracy can be enhanced by combining clinical manifestations, serum antibody testing, and renal biopsy. Proteinuria, estimated glomerular filtration rate (eGFR), and PLA2R antibody levels are vital for the prognostic risk stratification of MN. Low-risk patients primarily undergo non-immunosuppressive treatment (angiotensin-converting enzyme inhibitor [ACEI]/angiotensin II receptor blocker [ARB], sodium-glucose cotransporter 2 inhibitor [SGLT2i], anticoagulants, and traditional Chinese medicine [TCM] treatments), while high-risk patients need to consider using steroids and immunosuppressants, such as cyclophosphamide (CTX), calcineurin inhibitors (CNIs), and anti-cluster of differentiation 20 (anti-CD20) monoclonal antibodies. TCM holds that MN is the imbalance of Fei (lung), Pi (spleen), and Shen (kidney) function, leading to the generation of Neixie such as Shi, Re, Du, and Yu. This disease is difficult to heal because of Shire and Yu. The treatment is based on the use of Jianpi Bushen, simultaneously emphasizing the use of drugs for Qingre Qushi Huoxue. Dialectical use of TCM can obviously improve the clinical response rate and alleviate symptoms such as edema and fatigue by increasing the serum albumin level more rapidly while introducing fewer adverse reactions. Combined with Western medicine, TCM can also reduce toxicity and increase efficiency. This article systematically reviews the etiology, podocyte antigens, clinical manifestations, diagnostic approaches, risk assessment, and integrative medicine therapeutic strategies for MN, aiming to deepen the understanding of MN and guide clinical practice.

NINJ1: A Novel Sepsis Severity and Mortality Biomarker.

Multiple cell death modalities are implicated in sepsis pathobiology. However, the clinical relevance of NINJ1, a key mediator of plasma membrane rupture during lytic cell death, in sepsis progression and outcomes has remained poorly explored. Circulating NINJ1 levels were measured in 116 septic ICU patients, 16 non-septic ICU controls, and 16 healthy controls. Comparative analysis of serum NINJ1 across these groups was performed. Correlations between NINJ1 and clinical disease severity scores (SOFA, APACHE II) as well as laboratory parameters were examined in the sepsis cohort. Furthermore, we assessed the prognostic performance of NINJ1 for predicting 28-day mortality in septic patients using receiver operating characteristic (ROC) analyses. Circulating NINJ1 levels were elevated in septic patients and positively correlated with sepsis severity scores. NINJ1 also showed positive correlations with liver injury markers (AST/ALT) and coagulation parameters (D-dimer, APTT, PT, TT) in sepsis. Further analysis using the ISTH overt DIC scoring system revealed an association between NINJ1 and sepsis-induced coagulopathy.ROC analysis demonstrated NINJ1 outperformed traditional inflammatory biomarkers PCT and CRP in predicting 28-day sepsis mortality, although its prognostic accuracy was lower than SOFA and APACHE II scores. Combining NINJ1 with SOFA improved mortality prediction from an AUC of 0.6843 to 0.773. Circulating NINJ1 serves as a novel sepsis biomarker indicative of disease severity, coagulopathy and mortality risk, and its integration with SOFA and APACHE II scores substantially enhances prognostic risk stratification. These findings highlight the prospective clinical utility of NINJ1 for sepsis prognostication and monitoring, warranting further validation studies to facilitate implementation.

Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images

BackgroundStratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts.MethodsThis retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint.ResultsIn the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63–4.66, p-value < 0.001) after adjusting for established risk factors. In the ER+/HER2- Nottingham histological grade (NHG) 2 subgroup, the HR was 2.20 (95% CI: 1.22–3.98, p-value = 0.009) between low- and high-risk groups.ConclusionThe results indicate an independent prognostic value of Stratipath Breast among all breast cancer patients, as well as in the clinically relevant ER+/HER2- subgroup and the NHG2/ER+/HER2- subgroup. Improved risk stratification of intermediate-risk ER+/HER2- breast cancers provides information relevant for treatment decisions of adjuvant chemotherapy and has the potential to reduce both under- and overtreatment. Image-based risk stratification provides the added benefit of short lead times and substantially lower cost compared to molecular diagnostics and therefore has the potential to reach broader patient groups.

Expression of lncRNA UCA1 in Acute Myeloid Leukemia Patients and Its Clinical Significance

To investigate the expression level of urothelial carcinoembryonic antigen 1 (lncRNA UCA1) in the bone marrow of acute myeloid leukemia (AML) patients, and to explore the clinical significance of lncRNA UCA1 expression level in AML patients. Bone marrow samples of 50 AML patients were collected as experimental group, and bone marrow samples of 20 iron deficiency anemia (IDA) patients were collected as control group. The relevant clinicopathological characteristics of AML patients were collected. Real-time quantitative PCR (qRT-PCR) was used to detect the expression level of lncRNA UCA1 in the experimental and control groups, and the relationships between lncRNA UCA1 expression and clinical pathological characteristics and prognosis in AML patients were analyzed. Kaplan-Meier curves were used to analyze the effect of lncRNA UCA1 on the overall survival (OS) of AML patients; And Cox regression model was used to analyze the factors affecting the prognosis of AML patients. Compared with the control group, the expression level of lncRNA UCA1 was significantly elevated in patients with AML (P < 0.001); The proportion of patients with hemoglobin lower than 90 g/L in lncRNA UCA1 high expression group was significantly higher than that in lncRNA UCA1 low expression group (P =0.004); The expression level of lncRNA UCA1 was higher in M1, M2, and M4 subtypes, while it was lower in M0 and M5 subtypes, and the difference was statistically significant (P =0.009). There were no significant difference in sex, age, white blood cell (WBC) count, platelet (PLT) count, bone marrow blasts , chemotherapy regimen and efficacy, karyotype, gene mutation, and prognostic risk stratification between patients in UCA1 high expression group and those in UCA1 low expression group (all P > 0.05). The OS of patients with high expression of lncRNA UCA1 was significantly shorter than that of patients with low expression of lncRNA UCA1 (P =0.0229). The expression level of lncRNA UCA1 is significantly upregulated in AML patients. High expression of lncRNA UCA1 is associated with poor clinicopathological features and poor prognosis. Therefore, lncRNA UCA1 can be used as a prognostic indicator and a potential therapeutic target for AML patients.

A prognostic model and novel risk classification system for radical gallbladder cancer surgery: A population-based study and external validation

BackgroundThis research aimed to create a predictive model and an innovative risk classification system for patients with gallbladder cancer who undergo radical surgery. MethodsA cohort of 1387 patients diagnosed with gallbladder cancer was selected from the SEER database. The researchers devised a prognostic tool known as a nomogram, which was subjected to assessment and fine-tuning using various statistical measures such as the concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve, decision curve analysis (DCA), and risk stratification were included in the catalog of comparisons. An external validation set comprising 93 patients from Nanchong Central Hospital was gathered for evaluation purposes. ResultsThe nomogram effectively incorporated seven variables and demonstrated satisfactory discriminatory ability, as evidenced by the C-index (training cohort: 0.737, validation cohort: 0.730) and time-dependent AUC (>0.7). Additionally, calibration plots confirmed the excellent alignment between the nomogram and actual observations. Our investigation unveiled NRI scores of 0.79, 0.81, and 0.81 in the training group, while the validation group exhibited NRI values of 0.82, 0.77, and 0.78. Additionally, when evaluating CSS at three-, six-, and nine-year intervals using DCA curves, our established nomograms demonstrated significantly improved performance compared to the old model (P < 0.05), showcasing enhanced discriminatory ability. The results of the external validation set proved the above results. ConclusionsThe current investigation has devised a practical prognostic nomogram and risk stratification framework to aid healthcare practitioners in evaluating the postoperative outlook of individuals who have received extensive surgical treatment for gallbladder carcinoma.