Predictor Of Long-term Survival Research Articles

Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics

The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC). In a retrospective analysis, we included clinical and MRI data from 313 patients diagnosed with primary NPC. Patient classification into progressive and non-progressive categories relied on the occurrence of recurrence or distant metastasis within a 5-year timeframe. Initial screening comprised clinical features and statistically significant image semantic features. Subsequently, MRI radiomics features were extracted from all patients, and optimal features were selected to formulate the Rad-Score.Combining Rad-Score, image semantic features, and clinical features to establish a combined model Evaluation of predictive efficacy was conducted using ROC curves and nomogram specific to NPC progression. Lastly, employing the optimal ROC cutoff value from the combined model, patients were dichotomized into high-risk and low-risk groups, facilitating a comparison of 10-year overall survival (OS) between the groups. The combined model showcased superior predictive performance for NPC progression, reflected by AUC values of 0.84, an accuracy rate of 81.60%, sensitivity at 0.77, and specificity at 0.81 within the training group. In the test set, the AUC value reached 0.81, with an accuracy of 74.6%, sensitivity at 0.82, and specificity at 0.66. The amalgamation of Rad-Score, clinical, and imaging semantic features from multi-parameter MRI exhibited significant promise in prognosticating 5-year PFS for non-distant metastatic NPC patients. The combined model provided quantifiable data for informed and personalized diagnosis and treatment planning.

Identifying and Validating an Angiogenesis-related Signature for the Prognosis of Head and Neck Squamous Cell Carcinoma.

The present study aimed todevelop a prognostic model for HNSCC treatment on the basis of angiogenesis-related signatures. Head and Neck Squamous Cell Carcinoma (HNSCC) is the most frequent malignancy with poor prognostic outcomes in the head and neck. Angiogenesis plays a critical role in tumorigenesis and is expected to be an effective therapeutic target. The RNA-seq dataset TCGA-HNSCC and the hallmark gene set were used for angiogenesis-related RiskScore model construction. The RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA), and the hallmark gene set was used to measure the angiogenesis score using the GSVA R package. Then, the optimal cutoff point for prognostic classification was calculated by the survminer package, and Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify angiogenesis gene modules . Multi/univariable and Lasso Cox analyses were performed to develop the RiskScore model, and the classifier efficiency was evaluated by the Receiver Operating Characteristic curve (ROC). Furthermore, a nomogram was designed for survival probability prediction, and the immune infiltration and immunotherapy differences among different risk patients were assessed. After calculating the angiogenesis score, we found that this indicator and patients' prognosis were closely correlated, especially when patients with a high angiogenesis score had a poor prognosis. Then, WGCNA identified a blue gene module positively correlated with angiogenesis. Multivariate and Lasso Cox analysis further identified 9 risk model genes for developing a RiskScore, which was used to divide low- and high- -risk groups of patients. Those with a high risk tended to show poor prognosis, immune infiltration, and higher immune escape. Finally, a nomogram was developed to optimize the risk model, and it exhibited excellent short- and long-term survival prediction performance. We constructed a reliable RiskScore model for the prognostic prediction of HNSCC patients, contributing to precise therapeutic intervention of the cancer.

Predictors for Long-Term Survival After Resection of Pancreatic Ductal Adenocarcinoma: A Systematic Review and Meta-Analysis.

Improved systemic therapy has made long term (≥ 5 years) overall survival (LTS) after resection of pancreatic ductal adenocarcinoma (PDAC) increasingly common. However, a systematic review on predictors of LTS following resection of PDAC is lacking. The PubMed, Embase, Scopus, and Cochrane CENTRAL databases were systematically searched from inception until March 2023. Studies reporting actual survival data (based on follow-up and not survival analysis estimates) on factors associated with LTS were included. Meta-analyses were conducted by using a random effects model, and study quality was gauged by using the Newcastle-Ottawa Scale (NOS). Twenty-five studies with 27,091 patients(LTS: 2,132, non-LTS: 24,959) who underwent surgical resection for PDAC were meta-analyzed. The median proportion of LTS patients was 18.32% (IQR 12.97-21.18%) based on 20 studies. Predictors for LTS included sex, body mass index (BMI), preoperative levels of CA19-9, CEA, and albumin, neutrophil-lymphocyte ratio, tumor grade, AJCC stage, lymphovascular and perineural invasion, pathologic T-stage, nodal disease, metastatic disease, margin status, adjuvant therapy, vascular resection, operative time, operative blood loss, and perioperative blood transfusion. Most articles received a "good" NOS assessment, indicating an acceptable risk of bias. Our meta-analysis pools all true follow up data in the literature to quantify associations between prognostic factors and LTS after resection of PDAC. While there appears to be evidence of a complex interplay between risk, tumor biology, patient characteristics, and management related factors, no single parameter can predict LTS after the resection of PDAC.

Analysis and prediction of long-term survival using a clinically applicable risk score based on the Electronic Health Record

BackgroundThe long-term survival of a population assigned to a hospital can be essential to anticipate, manage, and provide appropriate hospital healthcare resources or lead preventive actions for high-risk mortality individuals. In this study, we discriminate which electronic health record variables are most relevant to predict the long-term survival of a population, and apply the results to identify high-risk mortality groups. Materials and MethodsA prospective cohort study was conducted on a population of 113,403 individuals alive on July 1st, 2018 from the General Hospital of Castellón (Spain). Considering electronic health record patients’ variables and survival days from the start date of the study, a Kaplan-Meier analysis and a multivariate Cox regression model were performed, and a risk score based on Cox coefficients was applied to predict survival over 3 years. ResultsAll significant covariates from the Cox model (91.5% c-index) were associated with increased mortality risk. Using the proposed risk score, Kaplan-Meier curves show that survival probability in the 3rd year is 99.23% (95% confidence interval (CI) 99.18–99.29) for the low-risk, 91.21% (95% CI 90.67–91.76) for medium-risk, 76.52% (95% CI 75.59–77.46) for the high-risk, and 48.61 % (95% CI 46.85–50.36) for the very high-risk groups. DiscussionThe Cox model obtained is highly predictive, and it has been found that some electronic health record variables little studied to date, such as Clinical Risk Groups, have a strong impact on survival. Regarding clinical application, the proposed risk score is particularly useful for identifying high-risk subpopulations within a large population.

Enhancing long-term survival prediction with two short-term events: Landmarking with a flexible varying coefficient model.

Patients with cardiovascular diseases who experience disease-related short-term events, such as hospitalizations, often exhibit diverse long-term survival outcomes compared to others. In this study, we aim to improve the prediction of long-term survival probability by incorporating two short-term events using a flexible varying coefficient landmark model. Our objective is to predict the long-term survival among patients who survived up to a pre-specified landmark time since the initial admission. Inverse probability weighting estimation equations are formed based on the information of the short-term outcomes before the landmark time. The kernel smoothing method with the use of cross-validation for bandwidth selection is employed to estimate the time-varying coefficients. The predictive performance of the proposed model is evaluated and compared using predictive measures: area under the receiver operating characteristic curve and Brier score. Simulation studies confirm that parameters under the landmark models can be estimated accurately and the predictive performance of the proposed method consistently outperforms existing methods that either do not incorporate or only partially incorporate information from two short-term events. We demonstrate the practical application of our model using a community-based cohort from the Atherosclerosis Risk in Communities (ARIC) study.

Short- and Long-term survival prediction in patients with acute type A aortic dissection undergoing open surgery

BackgroundAcute Type A aortic dissection (ATAAD) is a life-threatening cardiovascular disease associated with high mortality rates, where surgical intervention remains the primary life-saving treatment. However, the mortality rate for ATAAD operations continues to be alarmingly high. To address this critical issue, our study aimed to assess the correlation between preoperative laboratory examination, clinical imaging data, and postoperative mortality in ATAAD patients. Additionally, we sought to establish a reliable prediction model for evaluating the risk of postoperative death.MethodsIn this study, a total of 384 patients with acute type A aortic dissection (ATAAD) who were admitted to the emergency department for surgical treatment were included. Based on preoperative laboratory examination and clinical imaging data of ATAAD patients, logistic analysis was used to obtain independent risk factors for postoperative in-hospital death. The survival prediction model was based on cox regression analysis and displayed as a nomogram.ResultsLogistic analysis identified several independent risk factors for postoperative in-hospital death, including Marfan syndrome, previous cardiac surgery history, previous renal dialysis history, direct bilirubin, serum phosphorus, D-dimer, white blood cell, multiple aortic ruptures and age. A survival prediction model based on cox regression analysis was established and presented as a nomogram. The model exhibited good discrimination and significantly improved the prediction of death risk in ATAAD patients.ConclusionsIn this study, we developed a novel survival prediction model for acute type A aortic dissection based on preoperative clinical features. The model demonstrated good discriminatory power and improved accuracy in predicting the risk of death in ATAAD patients undergoing open surgery.

(214) - Prediction of Long-Term Survival by Three Risk Assessment Models in Patients with Pulmonary Arterial Hypertension: A SPAHR Analysis

(214) - Prediction of Long-Term Survival by Three Risk Assessment Models in Patients with Pulmonary Arterial Hypertension: A SPAHR Analysis

SurvConvMixer: robust and interpretable cancer survival prediction based on ConvMixer using pathway-level gene expression images

Cancer is one of the leading causes of deaths worldwide. Survival analysis and prediction of cancer patients is of great significance for their precision medicine. The robustness and interpretability of the survival prediction models are important, where robustness tells whether a model has learned the knowledge, and interpretability means if a model can show human what it has learned. In this paper, we propose a robust and interpretable model SurvConvMixer, which uses pathways customized gene expression images and ConvMixer for cancer short-term, mid-term and long-term overall survival prediction. With ConvMixer, the representation of each pathway can be learned respectively. We show the robustness of our model by testing the trained model on absolutely untrained external datasets. The interpretability of SurvConvMixer depends on gradient-weighted class activation mapping (Grad-Cam), by which we can obtain the pathway-level activation heat map. Then wilcoxon rank-sum tests are conducted to obtain the statistically significant pathways, thereby revealing which pathways the model focuses on more. SurvConvMixer achieves remarkable performance on the short-term, mid-term and long-term overall survival of lung adenocarcinoma, lung squamous cell carcinoma and skin cutaneous melanoma, and the external validation tests show that SurvConvMixer can generalize to external datasets so that it is robust. Finally, we investigate the activation maps generated by Grad-Cam, after wilcoxon rank-sum test and Kaplan–Meier estimation, we find that some survival-related pathways play important role in SurvConvMixer.

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients.

Merkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual's prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model. This study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient's survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors' effect on overall survival and establish a novel CS-based nomogram. A total of 3,843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival. The rates progressed from an initial 41-50%, 61, 70, 78, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance. CS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a dynamic estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.

Prognostic value of neutrophil-to-lymphocyte ratio at diagnosis in colorectal cancer: propensity score analysis.

Baseline neutrophil-to-lymphocyte ratio (NLR) at the time of colorectal cancer (CRC) diagnosis has been proposed as a predictor of long-term survival. The aim of the study was to analyse its usefulness in a homogeneous population with control of the main confounding factors. Observational study of 836 patients operated on for CRC who were divided into two groups: NLR ≤ 3.3 vs NLR > 3.3. To control for confounders, they were matched one-to-one by propensity analysis. A final sample of 526 patients remained for study. The two groups were mismatched in terms of age, comorbidity, tumour stage, rectal location, and neoadjuvant therapy. Once matching was performed, baseline NLR was statistically significantly associated with long-term survival (p < 0.001) and behaved as an independent prognostic factor for survival (p = 0.001; HR: 1,996; 95% CI: 1.32-3.00) when adjusted in a Cox regression model using age (p < 0,001; HR: 1,04; IC95%: 1,02-1,06) and the Charlson Comorbidity Index (p < 0,001; HR: 1,40; IC95%: 1,27-1,55). Neoadjuvant therapy lost its statistical significance (p = 0,137; HR: 1,59; IC95%: 0,86-2,93). A high baseline NLR (> 3.3) in patients with colorectal cancer at diagnosis represents a poor prognostic factor in terms of survival. Its use in routine practice could intensify therapeutic strategies and follow-up in these patients.

Predictors of long-term survival after resection of adenocarcinoma arising from intraductal papillary mucinous neoplasm: an international multicenter study

Predictors of long-term survival after resection of adenocarcinoma arising from intraductal papillary mucinous neoplasm: an international multicenter study

Intermediate and long-term survival prediction using prognostic scores in patients undergoing salvage TIPS for uncontrolled variceal bleeding

Intermediate and long-term survival prediction using prognostic scores in patients undergoing salvage TIPS for uncontrolled variceal bleeding

Improving prediction of treatment response and prognosis in colorectal cancer with AI-based medical image analysis

&lt;p&gt;The heterogeneous response and prognosis of patients with colorectal cancer (CRC) to standard treatment regimens remains a challenge for clinical management. Individually weak prognostic markers, defined by gene mutations and protein expression, are difficult to apply in routine clinical practice because of their high acquisition cost and mediocre prediction accuracy. Visual evaluation of medical images, including radiology and digital pathology images, is an important part of CRC management. With the rapid development of artificial intelligence (AI), high-dimensional imaging features other than visual information are increasingly being used to develop imaging markers. At different stages of treatment, accurate predictions of treatment response and prognosis may help in selecting patients and tailoring their treatment. Here, we review the current state of AI applied to the medical imaging of CRC and describe its recent progress in short-term response and long-term survival prediction. In addition, we illustrate how these AI-based approaches may affect clinical decision-making. Although few approaches have been applied in routine clinical practice, their results are promising. Finally, we discuss the challenges in applying AI in clinical practice and possible future solutions from three perspectives: model interpretability, model generalizability, and patient privacy protection. This comprehensive assessment underscores the transformative potential of AI in CRC management and emphasizes the need for further exploration and integration into routine clinical workflows.&lt;/p&gt;

A new chemotherapy strategy for advanced hepatocellular carcinoma with exrahepatic metastasis: predictors of long-term survival

The prognosis of hepatocellular carcinoma (HCC) with extrahepatic metastasis (EHM) is extremely poor. This study aimed to identify prognostic factors for systemic chemotherapy of HCC with EHM. Eighty-five patients who received systemic chemotherapy for HCC with EHM between May 2014 and October 2021 were retrospectively evaluated. Patient demographic data and characteristics of hepatic tumors and EHM were assessed to identify factors that were significantly associated with prognosis. Of the 85 patients, 68 (80.0%) had pulmonary metastasis, 11 (12.9%) had abdominal lymph node metastasis, 7 (8.2%) had abdominal metastasis, and 4 (4.7%) had bone metastasis. The median overall survival (OS) was 17.0 months, and the median progression-free survival (PFS) was 5.1 months. Univariate analysis of OS showed that synchronous EHM-HCC, serum albumin level<35 g/l and number of hepatic tumors>1 were significantly associated with poorer OS. The results of the multivariate analysis indicated that the serum albumin level and number of hepatic tumors were independent prognostic factors. Subgroup analysis of patients with 0, 1, or 2 of these independent prognostic factors showed that the median OS was 24.0 months, 16.2 months and 7.7 months and that the ORR was 38.3%, 22.6% and 0, respectively. Systemic chemotherapy is beneficial for well-selected HCC patients with EHM. The number of hepatic tumors and serum albumin level were independent risk factors for prognosis, and the number of risk factors significantly influenced OS. Therefore, these factors need to be considered before administering systemic chemotherapy for HCC patients with EHM.

Long-term Survival Among Patients With De Novo Human Epidermal Growth Receptor 2-Positive Metastatic Breast Cancer in Manitoba.

Although metastatic breast cancer (MBC) is considered incurable, human epidermal growth receptor 2 (HER2)-directed therapy has improved outcomes significantly, with some patients experiencing durable responses to treatment. The aim of this study was to identify potential predictors of long-term survival (LTS) among patients with de novo HER2-positive MBC who received HER2-directed treatment. Eligible patients from 2008 to 2018 were identified using the Manitoba Cancer Registry. LTS was defined as survival ≥5 years from the time of diagnosis. Univariate logistic regression models were performed to assess variables of clinical interest and the odds of LTS. Overall survival (OS) was defined as the time from diagnosis of MBC to death of any cause. OS was estimated using the Kaplan-Meier method with log-rank comparative analyses as a univariate analysis. A Cox proportional hazards model was used for OS estimates in a univariate analysis. A total of 62 patients were diagnosed with de novo HER2-positive MBC and received HER2-directed therapy. Eighteen (29%) achieved LTS. The median OS of the whole cohort was 50.2 months (95% CI: 28.6-not reached). Radiographic response to first-line treatment was associated with LTS; complete and partial responses were both associated with higher odds of LTS (odds ratio: 28.33 [95% CI: 2.47-4006.71, P = 0.0043] and odds ratio: 7.80 [95% CI: 0.7317-1072.00, P = 0.0972], respectively). The best radiographic response was associated with improved OS. Radiographic response to first-line HER2-directed therapy is a predictor for LTS in patients with de novo HER2-positive MBC. Larger studies are needed to identify patients who can safely discontinue HER2-targeted therapy.

Cardiovascular and inflammatory biomarkers for non-invasive detection of coronary artery obstruction and prediction of long-term survival in patients with suspected chronic coronary syndrome

Abstract Introduction and methods: The knowledge about diagnostic and prognostic value of cardiac and inflammatory biomarkers in patients with chronic coronary syndrome (CCS) is limited. To address this, we analyzed serum levels of selected biomarkers in 2536 patients with suspected CCS (35% female) from the Leipzig LIFE Heart Study who were admitted for coronary angiography. The median follow-up was 10 years. The following biomarkers were considered: high sensitive troponin T (hsTNT), N-terminal pro B-type natriuretic peptide (NT-proBNP), copeptin, high sensitive C-reactive protein (hsCRP) and interleukin-6 (IL-6). Patients were stratified according to the angiographic severity of coronary artery disease (CAD): CAD0 (no sclerosis), CAD1 (non-obstructive, i.e., stenosis &amp;lt; 50%), CAD2 (≥ one stenosis ≥ 50%). Group comparison (GC) included GC1: CAD0 + 1 vs. CAD2, GC2: CAD0 vs. CAD1 + 2. Using age-, sex and symptom-based pre-test probability (PTP) table for obstructive CAD (i.e. CAD 2) which was published in the current CCS guidelines, patients were further classified into the following three categories: PTP &amp;lt; 5% (n=559), PTP 5-15% (n=545) and PTP &amp;gt; 15% (n=1432). Results CAD0, CAD1, CAD2 were apparent in 999, 529, and 1008 patients, respectively. Upon adjustment for traditional risk factors (TRF) the levels of hsTNT, NT-proBNP and IL-6 showed significant difference in GC1 (hsTNT: p=2.0x10-10, NT-proBNP: p=0.049, IL6: p=0.030). In GC2 only elevated hsTNT remained significant (p=1.9x10-7). In the ROC analysis only hsTNT slightly improved the AUC for the GC1-comparison in addition to TRF (0.729 with vs 0.712 witt hsTNT, p=0.013). Within the PTP subcategories the following results were obtained: PTP &amp;lt; 5%: AUC 0.747 with vs 0.730 without hsTNT, p=0.017; PTP 5-15 %: AUC 0.636 with vs 0.670 without hsTNT, p&amp;lt;0.001; PTP &amp;gt; 15%: AUC 0.699 with vs 0.677 without hsTNT, p= 0.024). In PTP 5-15% subgroup TropT cut-off value &amp;lt; 3 pg/mL (N= 119 / 23.4%) showed sensitivity of 89.7% and specificity of 73.1% for CAD2 detection. Ten years survival in groups CAD0, CAD1, CAD2 were 88.3%, 77.3%, 72.4%, respectively. In the multivariate analysis elevated hsTNT, NT-proBNP, copeptin and IL-6 remained significant mortality predictors in CAD2 patients with hazard ratios of similar magnitude (i.e. 1.5, 1.3, 1.4 and 1.3 per unit on the log-scale of the parameters, respectively). hsCRP did not reach significance. In the model stratified into tertiles according to the effects of classical risk factors and the joint biomarker levels for except hsCRP, hazard rates were 3.13 (tertile 2 vs 1) and 11.2 (tertile 3 vs 1). Conclusions In the present study hsTNT substantially improved the detection of obstructive CAD particularly in patients with intermediate PTP 5-15%. Furthermore, the studied biomarkers enable fast and precise non-invasive prediction of mortality risk in patients with suspected CCS, allowing tailored primary and secondary CAD prevention in this high-risk group.

Artificial intelligence (AI) enhanced ECG predicts obstructive cardiovascular disease and death using secondary data; a benchmarked comparison of different methods

Abstract Introduction Assessment of pretest-probability of chronic coronary artery disease (CAD) is based on the modified Diamond and Forrester criteria, including age, gender, and symptoms. However these criteria suffer from low accuracy, requiring further diagnostics prior to coronary angiography (CA), e.g. via coronary computed tomography angiography. Artificial intelligence (AI)-enhanced evaluations of broadly available resting ECGs have shown excellent results in the detection of multiple cardiovascular diseases. In this analysis, we aimed to evaluate and compare the ability of neural networks, random forest and support vector machines as different AI-based approaches regarding obstructive CAD and long-term mortality using structured ECG data. Methods The cohort of the ECAD registry includes patients undergoing CA at our heart and vascular center between 2004 and 2019. Patients with a digitally available resting ECG within 90 days prior to CA, containing structured data on 648 characteristics for each ECG, were included. The overall cohort was divided in a learning (60%), validation (20%) and a test cohort (20%). Obstructive CAD was defined by percutaneous intervention as by discretion of the interventional cardiologist. The incidence of death due to any cause was evaluated during follow-up. Logistic regression (LR) and linear discriminate analysis (LDA) were used as benchmarks. Calculations were run 100 times and compared by their mean area under the receiver operating curve (AUC) in the validation cohort. An initial neural net (NN1) was improved by sophisticated hyperparameter tuning, dropout layers and weight regularization (NN2). Feature reduction lead to NN3. The initial random forests (RF1) was adapted for the risk factors (RF2). Results Data from 7076 CAs were included. An obstructive CAD requiring revascularization was present in 2075 cases (29.3 %). Neural networks with feature selection based on ECG characteristics outperformed traditional risk factors and provided highest area under the ROC-curve (table 1). During a median follow-up of 2.4 years (Q1: 0.8; Q3: 6.3), 1137 patients (16.1 %) died. For prediction of all-cause mortality, neural networks based on structured ECG data provided better prediction as compared to risk factors. All NNTs and the RF models exceeded the LR and LDA as benchmark, while SVM did not hold up to conventional multivariate analyses (table 1). Conclusion Based on structured data from resting ECGs, NNTs and RF analyses improve the prediction of obstructive CAD and long-term survival in patients undergoing CA.

416 Prediction of Long-Term Survival after Out-of-Hospital Cardiac Arrest using Disease, Social, and Resuscitation Characteristics in Denmark and the Netherlands

416 Prediction of Long-Term Survival after Out-of-Hospital Cardiac Arrest using Disease, Social, and Resuscitation Characteristics in Denmark and the Netherlands

SNP of Aromatase Predict Long-term Survival and Aromatase Inhibitor Toxicity in Patients with Early Breast Cancer: A Biomarker Analysis of the GIM4 and GIM5 Trials.

In estrogen receptor-positive (ER+) breast cancer, single-nucleotide polymorphisms (SNP) in the aromatase gene might affect aromatase inhibitors (AI) metabolism and efficacy. Here, we assessed the impact of SNP on prognosis and toxicity of patients receiving adjuvant letrozole. We enrolled 886 postmenopausal patients in the study. They were treated with letrozole for 2 to 5 years after taking tamoxifen for 2 to 6 years, continuing until they completed 5 to 10 years of therapy. Germline DNA was genotyped for SNP rs4646, rs10046, rs749292, and rs727479. Log-rank test and Cox model were used for disease-free survival (DFS) and overall survival (OS). Cumulative incidence (CI) of breast cancer metastasis was assessed through competing risk analysis, with contralateral breast cancer, second malignancies and non-breast cancer death as competing events. CI of skeletal and cardiovascular events were assessed using DFS events as competing events. Subdistribution HR (sHR) with 95% confidence intervals were calculated through Fine-Gray method. No SNP was associated with DFS. Variants rs10046 [sHR 2.03, (1.04-2.94)], rs749292 [sHR 2.11, (1.12-3.94)], and rs727479 [sHR 2.62, (1.17-5.83)] were associated with breast cancer metastasis. Three groups were identified on the basis of the number of these variants (0, 1, >1). Variant-based groups were associated with breast cancer metastasis (10-year CI 2.5%, 7.6%, 10.7%, P = 0.035) and OS (10-year estimates 96.5%, 93.0%, 89.6%, P = 0.030). Co-occurrence of rs10046 and rs749292 was negatively associated with 10-year CI of skeletal events (3.2% vs. 10%, P = 0.033). A similar association emerged between rs727479 and cardiovascular events (0.3% vs. 2.1%, P = 0.026). SNP of aromatase gene predict risk of metastasis and AI-related toxicity in ER+ early breast cancer, opening an opportunity for better treatment individualization.

A Modified NAR Scoring Model Incorporating Immune Infiltration Characteristics to Better Predict Long-Term Survival Following Neoadjuvant Radiotherapy in Rectal Cancer.

(1) Background: The neoadjuvant rectal (NAR) score has been developed as a prognostic tool for survival in locally advanced rectal cancer (LARC). However, the NAR score only incorporates weighted cT, ypT, and ypN categories. This long-term follow-up study aims to modify a novel prognostic scoring model and identify a short-term endpoint for survival. (2) Methods: The prognostic factors for overall survival (OS) were explored through univariate and multivariate analyses. Based on Cox regression modeling, nomogram plots were constructed. Area under the curve (AUC) and concordance indices were used to evaluate the performance of the nomogram. Receiver operating characteristic (ROC) analysis was conducted to compare the efficiency of the nomogram with other prognostic factors. (3) Results: After a long-term follow-up, the 5-year OS was 67.1%. The mean NAR score was 20.4 ± 16.3. Multivariate analysis indicated that CD8+ T-cell, lymphovascular invasion, and the NAR score were independent predictors of OS. The modified NAR scoring model, incorporating immune infiltration characteristics, exhibited a high C-index of 0.739 for 5-year OS, significantly outperforming any individual factor. Moreover, the predictive value of the nomogram was superior to the AJCC stage and pathological complete regression at 3-year, 5-year, and 10-year time points, respectively. Over time, the model's predictions of long-term survival remained consistent and improved in accuracy. (4) Conclusions: The modified NAR scoring model, incorporating immune infiltration characteristics, demonstrates high accuracy and consistency in predicting OS.