Assessment Of Prognosis In Patients Research Articles

Personalized approach to malignant struma ovarii: Insights from a web-based machine learning tool.

Malignant struma ovarii (MSO) is a rare ovarian tumor characterized by mature thyroid tissue. The diverse symptoms and uncommon nature of MSO can create difficulties in its diagnosis and treatment. This study aimed to analyze data and use machine learning methods to understand the prognostic factors and potential management strategies for MSO. In this retrospective cohort, the Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five machine learning algorithms to predict the 5-year survival. A validation method incorporating the area under the curve of the receiver operating characteristic curve was used to validate the accuracy and reliability of the machine learning models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. The study population comprised 329 patients. Multivariate Cox regression analysis revealed that older age, unmarried status, chemotherapy, and the total number of tumors in patients were poor prognostic factors. Machine learning models revealed that the multilayer perceptron accurately predicted outcomes, followed by the random forest classifier, gradient boosting classifier, K-nearest neighbors, and logistic regression models. The factors that contributed the most were age, marital status, and the total number of tumors in the patients. The present study offers a comprehensive approach for the treatment and prognosis assessment of patients with MSO. The machine learning models we have developed serve as a practical, personalized tool to aid in clinical decision-making processes.

Accuracy of Medical Oncology Prognosis for Metastatic Cancer Patients Evaluated for Enrollment Onto an Ongoing Randomized Clinical Trial.

For patients with metastatic cancer, a key aspect of interdisciplinary care has involved the overall prognosis provided by Medical Oncology. This study represents prospective evaluation of Medical Oncology prognosis accuracy for patients considered for enrollment onto an ongoing randomized controlled trial. The Spine Patient Optimal Radiosurgery Treatment for Symptomatic Metastatic Neoplasms (SPORTSMEN) phase 2 randomized clinical trial examines optimal radiation therapy treatment of symptomatic spinal metastases with a primary end point of pain freedom at 3 months post-treatment. A key eligibility criterion for trial enrollment is overall prognosis exceeding 3 months, typically provided by Medical Oncology. During the first year of trial enrollment, Medical Oncology prognosis for patients considered for SPORTSMEN inclusion was prospectively assessed for accuracy. Twenty-seven patients with documented Medical Oncology prognosis were considered for SPORTSMEN enrollment. The prognosis administered by Medical Oncology exceeded 3 months in 26 patients, and <3 months in 1 patient. The overall accuracy of Medical Oncology prognosis was correct for 15 of 27 patients (56%), significantly worse for inpatients than outpatients (P=0.0381). In patients with metastatic spine disease, the estimated prognosis provided by Medical Oncology is often optimistic, as nearly half of patients assigned a prognosis of >3 months failed to reach this threshold before experiencing death or hospice. These findings indicate that a more heuristic approach to assessing patient prognosis may be necessary to avoid unwarranted prognostic optimism, particularly for inpatients. Such an approach could potentially provide a more compassionate and cost-effective management of these patients' remaining lifespan thereby optimizing quality of life.

Impact of surgical types on overall survival in patients with ductal carcinoma in situ: an analysis based on the SEER database.

Breast cancer, as one of the most common malignancies among women globally, presents a concerning incidence rate, underscoring the importance of addressing the treatment of its precursor lesion, ductal carcinoma in situ (DCIS). Treatment decisions for DCIS, involving the balance between breast-conserving surgery (BCS) and mastectomy, remain an area requiring further investigation. This study aimed to compare influence of different surgical types on overall survival (OS) of patients with DCIS and identify specific subgroups with improved OS to develop an effective survival nomogram for patients. Patient data from the Surveillance, Epidemiology, and End Results (SEER) database for DCIS cohort from 2010 to 2020 were retrieved. Kaplan-Meier (K-M) survival curves were utilized to compare prognostic OS of patients with different surgical methods. Cox regression analysis was employed to determine prognostic factors and establish a nomogram to predict 3-, 5-, and 10-year survival rates. The model was confirmed by Concordance Index (C-index), calibration curves, and receiver operating characteristic (ROC) curves. A total of 71,675 patients with DCIS were included. Patients who underwent subcutaneous mastectomy (SM) demonstrated the best OS compared to other surgical types. Additionally, adjuvant radiotherapy or chemotherapy in combination with surgery significantly improved OS compared to surgery alone. Among DCIS patients aged ≤74 years, those who underwent SM benefited the most in terms of OS, while in the age group of 63-74 years, patients who underwent BCS had significantly higher OS than those who underwent total (simple) mastectomy (TM)/modified radical mastectomy (MRM). Multiple factors were associated with improved OS in DCIS patients, and these factors were integrated into the nomogram to establish OS predictions. The C-index, calibration curves, and ROC curves indicated that the nomogram was suitable for assessing patient prognosis. This study demonstrated that SM treatment yielded the best survival rates for DCIS patients, providing important guidance for future surgical decision-making. Moreover, identifying multiple independent factors related to survival and establishing reliable survival nomograms can assist physicians in developing personalized treatment plans and prolonging patient survival.

Analysis of cancer-specific survival in patients with metastatic colorectal cancer: A evidence-based medicine study.

Metastatic colorectal cancer (mCRC) is a common malignancy whose treatment has been a clinical challenge. Cancer-specific survival (CSS) plays a crucial role in assessing patient prognosis and treatment outcomes. However, there is still limited research on the factors affecting CSS in mCRC patients and their correlation. To predict CSS, we developed a new nomogram model and risk grading system to classify risk levels in patients with mCRC. Data were extracted from the United States Surveillance, Epidemiology, and End Results database from 2018 to 2023. All eligible patients were randomly divided into a training cohort and a validation cohort. The Cox proportional hazards model was used to investigate the independent risk factors for CSS. A new nomogram model was developed to predict CSS and was evaluated through internal and external validation. A multivariate Cox proportional risk model was used to identify independent risk factors for CSS. Then, new CSS columns were developed based on these factors. The consistency index (C-index) of the histogram was 0.718 (95%CI: 0.712-0.725), and that of the validation cohort was 0.722 (95%CI: 0.711-0.732), indicating good discrimination ability and better performance than tumor-node-metastasis staging (C-index: 0.712-0.732). For the training set, 0.533, 95%CI: 0.525-0.540; for the verification set, 0.524, 95%CI: 0.513-0.535. The calibration map and clinical decision curve showed good agreement and good potential clinical validity. The risk grading system divided all patients into three groups, and the Kaplan-Meier curve showed good stratification and differentiation of CSS between different groups. The median CSS times in the low-risk, medium-risk, and high-risk groups were 36 months (95%CI: 34.987-37.013), 18 months (95%CI: 17.273-18.727), and 5 months (95%CI: 4.503-5.497), respectively. Our study developed a new nomogram model to predict CSS in patients with synchronous mCRC. In addition, the risk-grading system helps to accurately assess patient prognosis and guide treatment.

The role of folate receptor-positive circulating tumor cells in the prognosis analysis of esophageal squamous cell carcinomas.

e15026 Background: Patients with ESCC always be discovered at an advanced stage and have a poor prognosis. The Circulating Tumor Cells (CTCs) were investigated for use as a biomarker for diagnosis and assessing the effectiveness of treatment. This study aims to investigate the predictive value of the baseline level of folate receptor-positive circulating tumor cells (FR+CTCs) for disease-free survival (DFS) and overall survival (OS) in patients with esophageal squamous cell carcinomas (ESCC). Methods: A retrospective observational study was conducted from January 2018 to February 2021 and followed up to June 2022 with ESCC patients in Sichuan Cancer Hospital &amp; Institute Esophageal Cancer Case Management Database (SCCH-ECCM Database). Three ml of peripheral blood were obtained from each patient, and FR+CTCs were quantified using ligand-targeted polymerase chain reaction (LT-PCR). Traditional techniques were employed to measure other serum markers. Clinicopathological features were collected from the hospital medical record system, and survival data (DFS and OS) were obtained by outpatient follow-up or phone calls. The correlation between clinicopathological characteristics, survival data, and FR+CTCs was analyzed. Cox regression analysis was used to assess risk factors that might have an impact on DFS and OS. Results: Analysis was conducted on 123 ESCC patients. Of them, 86% were men, and their average age was 62.0. FR+CTCs did not significantly correlate with patient age, sex, albumin, pre-albumin, C-reactive protein (CRP), ferritin, tumor size, grade of differentiation, metastasis of lymph nodes, perineural invasion/vessel invasion, or tumor size (all P&gt;0.05). But for DFS (HR 3.29; 95% CI 1.52-7.15, P=0.003) and OS (HR 3.85; 95% CI 1.53-9.72, P=0.004), preoperative FR+CTCs were an independent prognostic factor. Patients with post-operative FR+CTCs ≥17.42 FU/3ml had a substantially shorter DFS than patients with FR+CTCs &lt; 17.42 FU/3ml (P=0.0012). Patients with FR+CTCs ≥17.42 FU/3ml had a shorter OS than patients with FR+CTCs &lt;17.42 FU/3ml, although the difference was not statistically significant (P=0.51). Conclusions: Patients with ESCC who have elevated FR+CTCs typically have a poorer prognosis. We anticipate that FR+CTCs will be able to guide clinical treatment, assess patient prognosis, and monitor cancer recurrence and metastasis in real-time.

Establishment of a prognostic nomogram for elderly patients with limited-stage small cell lung cancer receiving radiotherapy

The present study explored the risk factors associated with radiotherapy in seniors diagnosed with limited-stage small cell lung cancer (LS-SCLC) to construct and validate a prognostic nomogram. The study retrospectively included 137 elderly patients with LS-SCLC who previously received radiotherapy. Univariate and multivariate COX analyses were conducted to identify independent risk factors and determine optimal cut-off values. Kaplan–Meier survival curves and nomograms were constructed to predict survival. Calibration and receiver operating characteristic (ROC) curves were used to evaluate the accuracy and consistency of the nomogram. Illness rating scale-geriatric (CIRS-G) score, treatment strategy, lymphocyte-to-monocyte ratio (LMR), white blood cell-to-monocyte ratio (WMR), and prognostic nutritional index (PNI) were discovered to be independent prognostic factors. Based on the findings of our multivariate analysis, a risk nomogram was developed to assess patient prognosis. Internal bootstrap resampling was utilized to validate the model, and while the accuracy of the AUC curve at 1 year was modest at 0.657 (95% CI 0.458–0.856), good results were achieved in predicting 3- and 5 year survival with AUCs of 0.757 (95% CI 0.670–0.843) and 0.768 (95% CI 0.643–0.893), respectively. Calibration curves for 1-, 3-, and 5 year overall survival probabilities demonstrated good cocsistency between expected and actual outcomes. Patients with concurrent chemoradiotherapy, CIRS-G score > 5 points and low PNI, WMR and LMR correlated with poor prognosis. The nomogram model developed based on these factors demonstrated good predictive performance and provides a simple, accessible, and practical tool for clinicians to guide clinical decision-making and study design.

Global Longitudinal Active Strain Energy Density (GLASED): A Powerful Prognostic Marker in a Community-Based Cohort.

Identifying the imaging method that best predicts all-cause mortality, cardiovascular adverse events and heart failure risk is crucial for tailoring optimal management. Potential prognostic markers include left ventricular myocardial mass, ejection fraction, myocardial strain, stroke work, contraction fraction, pressure-strain product and a new measurement called global longitudinal active strain density (GLASED). This study sought to compare the utility of 23 potential left ventricular prognostic markers of structure and contractile function in a community-based cohort. The impact of cardiovascular magnetic resonance image-derived markers extracted by machine learning algorithms was compared to the future risk of adverse events in a group of 44,957 UK Biobank participants. Most markers, including the left ventricular ejection fraction, have limited prognostic value. GLASED was significantly associated with all-cause mortality and major adverse cardiovascular events, with the largest hazard ratio, highest ranking and differentiated risk in all three tertiles (P ≤ 0.0003). GLASED predicted all-cause mortality and major cardiovascular adverse events better than conventional markers of risk and is recommended for assessing patient prognosis.

Glycometabolism and lipid metabolism related genes predict the prognosis of endometrial carcinoma and their effects on tumor cells

BackgroundGlycometabolism and lipid metabolism are critical in cancer metabolic reprogramming. The primary aim of this study was to develop a prognostic model incorporating glycometabolism and lipid metabolism-related genes (GLRGs) for accurate prognosis assessment in patients with endometrial carcinoma (EC).MethodsData on gene expression and clinical details were obtained from publicly accessible databases. GLRGs were obtained from the Genecards database. Through nonnegative matrix factorization (NMF) clustering, molecular groupings with various GLRG expression patterns were identified. LASSO Cox regression analysis was employed to create a prognostic model. Use rich algorithms such as GSEA, GSVA, xCELL ssGSEA, EPIC,CIBERSORT, MCPcounter, ESTIMATE, TIMER, TIDE, and Oncoppredict to analyze functional pathway characteristics of the forecast signal, immune status, anti-tumor therapy, etc. The expression was assessed using Western blot and quantitative real-time PCR techniques. A total of 113 algorithm combinations were combined to screen out the most significant GLRGs in the signature for in vitro experimental verification, such as colony formation, EdU cell proliferation, wound healing, apoptosis, and Transwell assays.ResultsA total of 714 GLRGs were found, and 227 of them were identified as prognostic-related genes. And ten GLRGs (AUP1, ESR1, ERLIN2, ASS1, OGDH, BCKDHB, SLC16A1, HK2, LPCAT1 and PGR-AS1) were identified to construct the prognostic model of patients with EC. Based on GLRGs, the risk model’s prognosis and independent prognostic value were established. The signature of GLRGs exhibited a robust correlation with the infiltration of immune cells and the sensitivity to drugs. In cytological experiments, we selected HK2 as candidate gene to verify its value in the occurrence and development of EC. Western blot and qRT-PCR revealed that HK2 was substantially expressed in EC cells. According to in vitro experiments, HK2 knockdown can increase EC cell apoptosis while suppressing EC cell migration, invasion, and proliferation.ConclusionThe GLRGs signature constructed in this study demonstrated significant prognostic value for patients with endometrial carcinoma, thereby providing valuable guidance for treatment decisions.

Fibroblast growth factor 21 (FGF21): diagnostic, prognostic and therapeutic aspects in heart failure. A review

Chronic heart failure (CHF) is a global medical, social and economic problem. Currently, the search and study of new biomarkers that can provide early diagnosis of CHF, serve as a laboratory tool for assessing the effectiveness of treatment, or be used as prognostic markers and risk stratification criteria are ongoing. Scientists' interest is focused, in particular, on studying the role of fibroblast growth factor 21 (FGF21) in CHF. There is increasing evidence highlighting the value of FGF21 as a new marker for the diagnosis and assessment of prognosis in patients with CHF. The role of FGF21 in CHF is very interesting due to its cardioprotective aspects. Final confirmation of the diagnostic, prognostic and therapeutic roles of FGF21 will come from future studies.

Impact of Changes in Rectus Femoris Cross-Sectional Area Measured by Ultrasound on the Prognosis of Patients With Acute Heart Failure.

Low muscle mass in patients with acute heart failure (AHF) is associated with poor prognosis; however, this is based on a single baseline measurement, with little information on changes in muscle mass during hospitalization and their clinical implications. This study investigated the relationship between changes in rectus femoris cross-sectional area (RFCSA) on ultrasound and the prognosis of patients with AHF.Methods and Results: This is a retrospective evaluation of 284 AHF patients (mean [±SD] age 79.1±11.9 years; 116 female). RFCSA assessments at admission (pre-RFCSA), ∆RFCSA (i.e., the percentage change in RFCSA from admission to 2 weeks), and composite prognosis (all-cause death and heart failure-related readmission) within 1 year were determined. Patients were divided into 4 groups according to their median pre-RFCSA and ∆RFCSA after sex stratification: Group A, higher pre-RFCSA/better ∆RFCSA; Group B, higher pre-RFCSA/worse ∆RFCSA; Group C, lower pre-RFCSA/better ∆RFCSA; Group D, lower pre-RFCSA/worse ∆RFCSA. In the Cox regression analysis, with Group A as the reference, the cumulative event rate of Group C (hazard ratio [HR] 3.39; 95% confidence interval [CI] 0.71-16.09; P=0.124) did not differ significantly; however, the cumulative event rates of Group B (HR 7.93; 95% CI 1.99-31.60; P=0.003) and Group D (HR 9.24; 95% CI 2.57-33.26; P<0.001) were significantly higher. ∆RFCSA during hospitalization is useful for risk assessment of prognosis in patients with AHF.

Integrating bulk and single-cell sequencing data to construct a Scissor+ dendritic cells prognostic model for predicting prognosis and immune responses in ESCC

Esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity with various immune cell infiltration patterns, which have been associated with therapeutic sensitivity and resistance. In particular, dendritic cells (DCs) are recently discovered to be associated with prognosis and survival in cancer. However, how DCs differ among ESCC patients has not been fully comprehended. Recently, the advance of single-cell RNA sequencing (scRNA-seq) enables us to profile the cell types, states, and lineages in the heterogeneous ESCC tissues. Here, we dissect the ESCC tumor microenvironment at high resolution by integrating 192,078 single cells from 60 patients, including 4379 DCs. We then used Scissor, a method that identifies cell subpopulations from single-cell data that are associated bulk samples with genomic and clinical information, to stratify DCs into Scissorhi and Scissorlow subtypes. We applied the Scissorhi gene signature to stratify ESCC scRNAseq patient, and we found that PD-L1, TIGIT, PVR and IL6 ligand-receptor-mediated cell interactions existed mainly in Scissorhi patients. Finally, based on the Scissor results, we successfully developed a validated prognostic risk model for ESCC and further validated the reliability of the risk prediction model by recruiting 40 ESCC clinical patients. This information highlights the importance of these genes in assessing patient prognosis and may help in the development of targeted or personalized therapies for ESCC.

Integrating iron metabolism-related gene signature to evaluate prognosis and immune infiltration in nasopharyngeal carcinoma

BackgroundDysregulation of iron metabolism has been shown to have significant implications for cancer development. We aimed to investigate the prognostic and immunological significance of iron metabolism-related genes (IMRGs) in nasopharyngeal carcinoma (NPC).MethodsMultiple Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets were analyzed to identify key IMRGs associated with prognosis. Additionally, the immunological significance of IMRGs was explored.ResultsA novel risk model was established using the LASSO regression algorithm, incorporating three genes (TFRC, SLC39A14, and ATP6V0D1).This model categorized patients into low and high-risk groups, and Kaplan–Meier analysis revealed significantly shorter progression-free survival for the high-risk group (P < 0.0001). The prognostic model’s accuracy was additionally confirmed by employing time-dependent Receiver Operating Characteristic (ROC) curves and conducting Decision Curve Analysis (DCA). High-risk patients were found to correlate with advanced clinical stages, specific tumor microenvironment subtypes, and distinct morphologies. ESTIMATE analysis demonstrated a significant inverse relationship between increased immune, stromal, and ESTIMATE scores and lowered risk score. Immune analysis indicated a negative correlation between high-risk score and the abundance of most tumor-infiltrating immune cells, including dendritic cells, CD8+ T cells, CD4+ T cells, and B cells. This correlation extended to immune checkpoint genes such as PDCD1, CTLA4, TIGIT, LAG3, and BTLA. The protein expression patterns of selected genes in clinical NPC samples were validated through immunohistochemistry.ConclusionThis study presents a prognostic model utilizing IMRGs in NPC, which could assist in assessing patient prognosis and provide insights into new therapeutic targets for NPC.

От неалкогольной к метаболически ассоциированной жировой болезни печени. Стеатометрия и кардиометаболические факторы как предикторы развития стеатогепатита

Non-alcoholic metabolic dysfunction-associated fatty liver disease is the most common liver sickness linked with the high risk of hepatic and extrahepatic complications. The article provides an overview of changes in the nomenclature of this disease, analysis of risk factors for its development and the results of own research. The aim of the study was to estimate the clinical significance of steatometry (CAP function) by Fibroscan and cardiometabolic factors in assessing the risk of steatohepatitis development. Material and methods. Patients with hepatic steatosis accompanied by metabolic disorders were examined. Steatometry as well as routine physical and laboratory examination, including biochemical blood tests and abdominal ultrasound were provided. Statistical analysis included χ2 test, Spearman's rank correlation test, Student's t-test, Mann–Whitney test, multiple linear regression analysis. Results. 74 patients were included. Main characteristics of the cohort: 62.2% male, mean age – 48.8 years, cases of steatohepatitis – 41.9%. It was revealed that the number of metabolic factors in an individual patient correlated with the presence of steatohepatitis (χ2 = 3.29; p &lt; 0.05) and the severity of steatosis by steatometry (rs = 0.37; p &lt; 0.05). Individual clinical and laboratory parameters influenced the severity of steatosis with varying degrees. The most significant parameters were: abdominal circumference, concentration of HDL, insulin, activity of ALT, GGTP, and the presence of arterial hypertension. Using these indicators, a formula for assessing the risk of severe liver steatosis was created. Conclusion. Steatometry is to be considered as an important diagnostic method for the prognosis assessment in patients who have risk factors for the development and progression of non-alcoholic metabolic dysfunction-associated fatty liver disease. Severe steatosis is a predictor of steatohepatitis. The number of metabolic factors present in an individual patient is important in assessing the risk of severe liver steatosis and steatohepatitis.

Nomogram for personalized prognostic assessment of children with favorable histology Wilms tumor: A retrospective analysis

ObjectiveThis retrospective study aimed to construct and validate a nomogram for personalized prognostic assessment of favorable histology Wilms tumor (FHWT) based on clinical and pathological variables. Methods and MaterialsThis was a retrospective study collected data from patients who underwent surgery for FHWT between March 2007 and November 2022 at Beijing Children's Hospital. Univariate and multivariate Cox proportional hazards regression analyses were conducted to determine the significance variables and constructed the nomogram in predicting event-free survival (EFS) in FHWT patients. ResultsA total of 401 FHWT patients were included in the study, with the median age of the patients was 3.4 years. The overall 1-, 3-, and 5-year OS rates were 98.2%, 96.3%, and 93.9%. The 1-, 3-, and 5-year EFS rates were 91.2%, 88.2%, and 86.6%. Subgroup analysis revealed age greater than 2 years was associated with a worse prognosis than age less than or equal to 2 years (P < 0.001), and patients with high-risk Wilms tumors were associated with a higher rate of recurrence and death (P < 0.001). Multivariate analysis showed that age (HR: 2.449, 95%CI: 1.004–5.973), stage (HR: 1.970, 95% CI:1.408–2.756), and histological risk (HR:9.414, 95% CI: 4.318–20.525) were identified as independent predictors of EFS (P < 0.05) and used to construct the nomogram. The prognostic nomogram demonstrated good calibration, great clinical utility, and the time-dependent receiver operating curve analysis showed that the nomogram had precise predictability, with area under the curve values of 0.85(95CI:0.796–0.913), 0.85(95CI:0.80–0.91), and 0.88(95CI:0.839–0.937) for 1-,3-year and 5-year EFS. ConclusionThis study provides valuable insights into the clinical characteristics and outcomes of FHWT patients. Accurate staging and histological risk assessment are important in predicting outcomes, and the prognostic nomogram we developed can be a useful tool for clinicians to assess patient prognosis and make informed treatment decisions.

Impact of Alcian blue and periodic acid Schiff expression on the prognosis of gastric signet ring cell carcinoma.

The Alcian blue (AB) and periodic acid Schiff (PAS) stains are representative mucus markers in gastric signet ring cell carcinoma (SRCC). They are low-cost special staining methods used to detect acidic mucus and neutral mucus, respectively. However, the clinical importance of the special combined AB and PAS stain is unclear. To investigate AB expression, PAS expression and the AB-to-PAS (A/P) ratio in gastric SRCC patients and to assess patient prognosis. Paraffin-embedded sections from 83 patients with gastric SRCC were stained with AB and PAS, and signet ring cell positivity was assessed quantitatively. Immunohistochemical staining for Ki67, protein 53 (P53) and human epidermal growth factor receptor 2 (HER2) was performed simultaneously. The cancer-specific survival (CSS) rate was estimated via Kaplan-Meier analysis. Cox proportional hazards models were used for univariate and multivariate survival analyses. Kaplan-Meier survival analysis revealed that the 3-year CSS rate was significantly greater in the high-PAS-expression subgroup than in the low-PAS-expression subgroup (P < 0.001). The 3-year CSS rate in the A/P ≤ 0.5 group was significantly greater than that in the A/P > 0.5 group (P = 0.042). Univariate Cox regression analysis revealed that the factors affecting prognosis included tumor diameter, lymph node metastasis, vessel carcinoma embolus, tumor stage, the A/P ratio and the expression of Ki67, P53 and the PAS. Cox multivariate regression analysis confirmed that low PAS expression [hazard ratio (HR) = 3.809, 95% confidence interval (CI): 1.563-9.283, P = 0.003] and large tumor diameter (HR = 2.761, 95%CI: 1.086-7.020, P = 0.033) were independent risk factors for poor prognosis. A/P > 0.5 is potentially a risk factor for prognosis, and low PAS expression is an independent risk factor in the prognosis of gastric SRCC. PAS expression and the A/P ratio could help in predicting the clinical prognosis of patients with SRCC.

The prognostic value of preoperative D-dimer to albumin ratio for overall survival and progression-free survival in colorectal cancer.

Introduction: This study aimed to explore the predictive value of the D-dimer-to-albumin ratio (DAR) for progression-free survival (PFS) and overall survival (OS) in patients with colorectal cancer (CRC). Methods: The Kaplan-Meier method was used to plot survival curves for PFS and OS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive efficacy of the DAR for PFS and OS in patients with CRC. Cox proportional hazards regression analysis was used to analyze prognostic factors influencing outcomes. A nomogram based on the DAR was constructed to predict 1-, 3-, and 5-year prognoses of patients with CRC; its predictive ability was evaluated using the concordance index (C-index) and calibration curves. Additionally, the clinical utility of the DAR-based nomogram was validated using an internal randomized validation cohort. Results: A total of 1,339 patients with CRC who underwent surgery were enrolled. The optimal cut-off value for DAR was determined to be 3.320, dividing patients into low (<3.320 [n = 470]) and high (≥3.320 [n = 869]) DAR groups. Compared with other composite immune inflammatory markers, DAR exhibited superior prognostic predictive efficacy. Patients with a high DAR had a significantly worse prognosis than those with a low DAR (PFS, 50.9% versus [vs.] 69.4%, p < 0.001; OS, 52.9% vs. 73.8%, p < 0.001). DAR also demonstrated significant prognostic stratification for most tumor subgroups, particularly in the stage III-IV subgroup and normal carcinoembryonic antigen subgroup. DAR has been identified as an independent predictive indicator of PFS/OS in patients with CRC. For every standard deviation increase in DAR, the risk for PFS/OS in patients with CRC was reduced by 9.5% (hazard ratio [HR] 1.095 [95% confidence interval (CI) 1.013-1.185]; p = 0.022) and 9.3% (HR 1.093 [95% CI 1.012-1.180]; p = 0.024), respectively. The DAR-based nomogram was confirmed to demonstrate good prognostic prediction accuracy and achieved high evaluation in the internal validation cohort. Conclusion: Preoperative DAR is a promising biomarker for predicting PFS and OS among patients with CRC. The DAR-based prognostic prediction nomogram may serve as an effective tool for the comprehensive assessment of prognosis in patients with CRC.

Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model

BackgroundIdentification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid...

A prognostic nomogram integrating carcinoembryonic antigen levels for predicting overall survival in elderly patients with stage II-III colorectal cancer.

With the aging of the population, colorectal surgeons will have to face more elderly colorectal cancer (CRC) patients in the future. We aim to analyze independent risk factors affecting overall survival in elderly (age ≥65 years) patients with stage II-III CRC and construct a nomogram to predict patient survival. A total of 3,016 elderly CRC patients with stage II-III were obtained from the SEER database. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analyses were used to screen independent prognostic factors, and a survival prediction nomogram was constructed based on the results. The consistency index (C-index), decision curve analysis (DCA), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to compare the predictive ability between the nomogram and tumor-node-metastasis (TNM) stage system. All patients were classified into high-risk and low-risk groups based on risk scores calculated by nomogram. The Kaplan-Meier method was used to compare the survival differences between two groups. The 3- and 5-year area under the curve (AUC) values of the prediction nomogram model were 76.6% and 74.8%, respectively. The AIC, BIC, and C-index values of the nomogram model were 6,032.502, 15,728.72, and 0.707, respectively, which were better than the TNM staging system. Kaplan-Meier survival analysis showed a significant survival difference between high-risk and low-risk groups (P<0.0001). We constructed a prediction nomogram for stage II-III elderly CRC patients by combining pre-treatment carcinoembryonic antigen (CEA) levels, which can accurately predict patient survival. This facilitates clinicians to accurately assess patient prognosis and identify high-risk patients to adopt more aggressive and effective treatment strategies.

A novel PANoptosis-related long non-coding RNA index to predict prognosis, immune microenvironment and personalised treatment in hepatocellular carcinoma.

PANoptosis is involved in the interaction of apoptosis, necroptosis and pyroptosis, playing a role in programmed cell death. Moreover, long non-coding RNAs (lncRNAs) regulate the PCD. This work aims to explore the role of PANoptosis-associated lncRNAs in hepatocellular carcinoma (HCC). Co-expression analysis identified PANoptosis-associated lncRNAs in HCC. Cox and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were utilised to filter lncRNAs and establish a PANoptosis-related lncRNA index (PANRI). Additionally, Cox, Kaplan-Meier and receiver operating characteristic (ROC) curves were utilised to systematically evaluate the PANRI. Furthermore, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), single sample gene set enrichment analysis (ssGSEA) and immune checkpoints were performed to analyse the potential of the PANRI in differentiating different tumour immune microenvironment (TIME) populations. The consensus clustering algorithm was used to distinguish individuals with HCC having different TIME subtypes. Finally, HCC cell lines HepG2 were utilised for further validation in in vitro experiments. The PANRI differentiates patients according to risk. Notably, ESTIMATE and ssGSEA algorithms revealed a high immune infiltration status in high-risk patients. Additionally, consensus clustering divided the patients into three clusters to identify different subtypes of TIME. Moreover, in vitro results showed that siRNA-mediated silencing of AL049840.4 inhibited the viability and migration of HepG2 cells and promoted apoptosis. This is the first PANoptosis-related, lncRNA-based risk index in HCC to assess patient prognosis, TIME and response to immunotherapy. This study offers novel perspectives on the role of PANoptosis-associated lncRNAs in HCC.

TBACkp: HER2 expression status classification network focusing on intrinsic subenvironmental characteristics of breast cancer liver metastases

The HER2 expression status in breast cancer liver metastases is a crucial indicator for the diagnosis, treatment, and prognosis assessment of patients. And typical diagnosis involves assessing the HER2 expression status through invasive procedures like biopsy. However, this method has certain drawbacks, such as being difficult in obtaining tissue samples and requiring long examination periods. To address these limitations, we propose an AI-aided diagnostic model. This model enables rapid diagnosis. It diagnoses a patient's HER2 expression status on the basis of preprocessed images, which is the region of the lesion extracted from a CT image rather than from an actual tissue sample. The algorithm of the model adopts a parallel structure, including a Branch Block and a Trunk Block. The Branch Block is responsible for extracting the gradient characteristics between the tumor sub-environments, and the Trunk Block is for fusing the characteristics extracted by the Branch Block. The Branch Block contains CNN with self-attention, which combines the advantages of CNN and self-attention to extract more meticulous and comprehensive image features. And the Trunk Block is so designed that it fuses the extracted image feature information without affecting the transmission of the original image features. The Conv-Attention is used to calculate the attention in the Trunk Block, which uses kernel dot product and is responsible for providing the weight for the self-attention in the process of using convolution induced deviation calculation. Combined with the structure of the model and the method used, we refer to this model as TBACkp. The dataset comprises the enhanced abdominal CT images of 151 patients with liver metastases from breast cancer, together with the corresponding HER2 expression levels for each patient. The experimental results are as follows: (AUC: 0.915, ACC: 0.854, specificity: 0.809, precision: 0.863, recall: 0.881, F1-score: 0.872). The results demonstrate that this method can accurately assess the HER2 expression status in patients when compared with other advanced deep learning model.