Tool For Predicting Survival Research Articles

Integrative Models of Histopathological Image Features and Omics Data Predict Survival in Head and Neck Squamous Cell Carcinoma.

BackgroundBoth histopathological image features and genomics data were associated with survival outcome of cancer patients. However, integrating features of histopathological images, genomics and other omics for improving prognosis prediction has not been reported in head and neck squamous cell carcinoma (HNSCC).MethodsA dataset of 216 HNSCC patients was derived from the Cancer Genome Atlas (TCGA) with information of clinical characteristics, genetic mutation, RNA sequencing, protein expression and histopathological images. Patients were randomly assigned into training (n = 108) or validation (n = 108) sets. We extracted 593 quantitative image features, and used random forest algorithm with 10-fold cross-validation to build prognostic models for overall survival (OS) in training set, then compared the area under the time-dependent receiver operating characteristic curve (AUC) in validation set.ResultsIn validation set, histopathological image features had significant predictive value for OS (5-year AUC = 0.784). The histopathology + omics models showed better predictive performance than genomics, transcriptomics or proteomics alone. Moreover, the multi-omics model incorporating image features, genomics, transcriptomics and proteomics reached the maximal 1-, 3-, and 5-year AUC of 0.871, 0.908, and 0.929, with most significant survival difference (HR = 10.66, 95%CI: 5.06–26.8, p < 0.001). Decision curve analysis also revealed a better net benefit of multi-omics model.ConclusionThe histopathological images could provide complementary features to improve prognostic performance for HNSCC patients. The integrative model of histopathological image features and omics data might serve as an effective tool for survival prediction and risk stratification in clinical practice.

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data.

Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.

The GLIM criteria as an effective tool for nutrition assessment and survival prediction in older adult cancer patients

Elderly cancer patients are at particularly high risk for malnutrition because both the disease and the old age threaten their nutritional status. The Global Leadership Initiative on Malnutrition (GLIM) released new universal criteria for diagnosing and grading malnutrition, but the validation of these criteria in elderly cancer population is not well documented. Our objective was to investigate the application of the GLIM criteria in nutrition assessment and survival prediction in elderly cancer patients. This retrospective cohort analysis was conducted on a primary cohort of 1192 cancer patients aged 65 years or older enrolled from a multi-institutional registry, and a validation cohort of 300 elderly cancer patients treated at the First Affiliated Hospital of Sun Yat-sen University. Patients considered at-risk for malnutrition based on the NRS-2002 were assessed using the GLIM criteria. The association between the nutritional status and patients' overall survival (OS) was then analyzed by the Kaplan-Meier method and a Cox model. A nomogram was also established that included additional independent clinical prognostic variables. To determine the predictive accuracy and discriminatory capacity of the nomogram, the C-index, receiver operating characteristic (ROC) curve and calibration curve were evaluated. The percentage of patients considered "at-risk" for malnutrition was 64.8% and 67.3% for the primary and validation cohorts, respectively. GLIM-defined malnutrition was diagnosed in 48.4% of patients in the primary cohort and 46.0% in the validation cohort. In the primary cohort, patients at risk of malnutrition (NRS-2002≥3) showed a worse OS than those with a NRS-2002<3 (HR 1.34, 1.10-1.64; p=0.003). Additionally, patients with GLIM-defined severe malnutrition (HR1.71, 1.37-2.14; p<0.001) or moderate malnutrition (HR1.35, 1.09-1.66; p=0.006) showed a significantly shorter OS compared to those without malnutrition. The nomogram incorporating the domains of the GLIM with other variables was accurate, especially for predicting the 1- and 2-year overall survival rates. The GLIM criteria can be used in elderly cancer patients not only to assess malnutrition, but also to predict survival outcome. The nomogram developed based on the GLIM domains can provide a more accurate prediction of the prognosis than existing systems.

Prognostic value of preoperative systemic inflammation response index in patients with oral squamous cell carcinoma: Propensity score-based analysis.

The aim of the study was to elucidate the relationship between systemic inflammation response index (SIRI) and the prognosis of postoperative oral squamous cell carcinoma (OSCC) patients. The prognostic value of SIRI was evaluated in a prospective cohort consisting of 535 OSCC patients with surgical resection. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) analyses were used to further verify the prognostic value of SIRI. Patients with a higher SIRI had a significantly increased risk of mortality compared with those with a low SIRI (HR [hazard ratio]: 1.60, 95% CI [confidence interval]: 1.04-2.47). The similar association pattern was observed following PSM (HR: 1.97, 95% CI: 1.14-3.40) and IPTW (HR: 1.70, 95% CI: 1.29-2.24) analyses. Of note, receiving postoperative chemotherapy resulted in a 72% of decreased risk of death among patients with a higher SIRI (HR: 0.28, 95% CI: 0.08-0.95). Additionally, a novel prognostic nomogram, based on TNM stage, tumor differentiation, and SIRI, demonstrated superior accuracy for the prediction of overall survival than that of the seventh edition of the AJCC staging system. Preoperative SIRI may be a valuable tool for prediction of survival of OSCC patients.

Gene Expression Profile Prediction in Uveal Melanoma Using Deep Learning: A Pilot Study for the Development of an Alternative Survival Prediction Tool

Gene Expression Profile Prediction in Uveal Melanoma Using Deep Learning: A Pilot Study for the Development of an Alternative Survival Prediction Tool

Fracture risk assessment and clinical decision making for patients with metastatic bone disease.

Metastatic breast, prostate, lung, and other cancers often affect bone, causing pain, increasing fracture risk, and decreasing function. Management of metastatic bone disease (MBD) is clinically challenging when there is potential but uncertain risk of pathological fracture. Management of MBD has become a major focus within orthopedic oncology with respect to fracture and impending fracture care. If impending skeletal-related events (SREs), particularly pathologic fracture, could be predicted, increasing evidence suggests that prophylactic surgical treatment improves patient outcomes. However, current fracture risk assessment and radiographic metrics do not have high accuracy and have not been combined with relevant patient survival tools. This review first explores the prevalence, incidence, and morbidity of MBD and associated SREs for different cancer types. Strengths and limitations of current fracture risk scoring systems for spinal stability and long bone fracture are highlighted. More recent computed tomography (CT)-based structural rigidity analysis (CTRA) and finite element (FE) analysis methods offer advantages of increased specificity (true negative rate), but are limited in availability. Other fracture prediction approaches including parametric response mapping and positron emission tomography/computed tomography measures show early promise. Substantial new information to inform clinical decision-making includes measures of survival, clinical benefits, and economic analysis of prophylactic treatment compared to after-fracture stabilization. Areas of future research include use of big data and machine learning to predict SREs, greater access and refinement of CTRA/FE approaches, combination of clinical survival prediction tools with radiographically based fracture risk assessment, and net benefit analysis for fracture risk assessment and prophylactic treatment.

Immune cell infiltration-based signature for prognosis and immunogenomic analysis in breast cancer.

Breast cancer is one of the most human malignant diseases and the leading cause of cancer-related death in the world. However, the prognostic and therapeutic benefits of breast cancer patients cannot be predicted accurately by the current stratifying system. In this study, an immune-related prognostic score was established in 22 breast cancer cohorts with a total of 6415 samples. An extensive immunogenomic analysis was conducted to explore the relationships between immune score, prognostic significance, infiltrating immune cells, cancer genotypes and potential immune escape mechanisms. Our analysis revealed that this immune score was a promising biomarker for estimating overall survival in breast cancer. This immune score was associated with important immunophenotypic factors, such as immune escape and mutation load. Further analysis revealed that patients with high immune scores exhibited therapeutic benefits from chemotherapy and immunotherapy. Based on these results, we can conclude that this immune score may be a useful tool for overall survival prediction and treatment guidance for patients with breast cancer.

An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning.

BACKGROUNDAlthough survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata.OBJECTIVETo compare statistical and machine learning algorithms in their ability to predict survival in GBM patients and deploy the best performing model as an online survival calculator.METHODSPatients undergoing an operation for a histopathologically confirmed GBM were extracted from the Surveillance Epidemiology and End Results (SEER) database (2005-2015) and split into a training and hold-out test set in an 80/20 ratio. Fifteen statistical and machine learning algorithms were trained based on 13 demographic, socioeconomic, clinical, and radiographic features to predict overall survival, 1-yr survival status, and compute personalized survival curves.RESULTSIn total, 20 821 patients met our inclusion criteria. The accelerated failure time model demonstrated superior performance in terms of discrimination (concordance index = 0.70), calibration, interpretability, predictive applicability, and computational efficiency compared to Cox proportional hazards regression and other machine learning algorithms. This model was deployed through a free, publicly available software interface (https://cnoc-bwh.shinyapps.io/gbmsurvivalpredictor/).CONCLUSIONThe development and deployment of survival prediction tools require a multimodal assessment rather than a single metric comparison. This study provides a framework for the development of prediction tools in cancer patients, as well as an online survival calculator for patients with GBM. Future efforts should improve the interpretability, predictive applicability, and computational efficiency of existing machine learning algorithms, increase the granularity of population-based registries, and externally validate the proposed prediction tool.

Machine Learning to Predict 5-Year Survival among Pediatric Acute Myeloid Leukemia Patients and Development of OSPAM-C Online Survival Prediction Tool

Machine Learning to Predict 5-Year Survival among Pediatric Acute Myeloid Leukemia Patients and Development of OSPAM-C Online Survival Prediction Tool

Regional spreading of symptoms at diagnosis as a prognostic marker in amyotrophic lateral sclerosis: a population-based study

ObjectiveThe lack of prognostic biomarkers in patients with amyotrophic lateral sclerosis (ALS) induced researchers to develop clinical evaluation tools for stratification and survival prediction. We assessed the correlation between patterns...

Tumour Microenvironment (TME) Characterization Identified Prognosis and Immunotherapy Response in Muscle-Invasive Bladder Cancer (MIBC)

Background: Tumour microenvironment (TME), consisting of a great diversity of immune and stromal cells together with the factors that they secrete, are collaborating to build a chronic inflammatory, immunosuppressive, and pro-angiogenic intratumoural atmosphere, which have been reported to correlated with patients’ outcomes and treatment efficacy. In this study, we aimed at establishing a signature for prediction of prognosis and immunotherapeutic response in muscle-invasive bladder cancer (MIBC) through tumour microenvironment (TME) characterization. Methods: We depicted the TME pattern in MIBC through “Estimation of STromal and Immune cells in MAlignant Tumours using Expression data” (ESTIMATE) algorithm and differential expression genes (DEGs) were submitted to build a TME-related signature (TMERS). Then, the influence of the TMERS on MIBC was comprehensively analysed. Results: TMERS was strongly capable to predict the overall survival (OS) and disease free survival (DFS), serving as an independent prognostic factor through univariate and multivariate cox regression analyses in MIBC. Compared with other clinicopathological features, the TMERS showed much higher predictive accuracy and contributed more in the nomogram. Moreover, we found that TMERS scores are highly positive correlated with immune infiltration as well as expression of immune checkpoints. The value of TMERS in assessing immunotherapy response was conducted using TIDE algorithm/subclass mapping and confirmed in several cohorts receiving immune-checkpoint inhibitors (ICIs). Furthermore, TMERS has a negative correlation with tumour mutation burden (TMB), which is a potential predictive biomarker for immunotherapy. Remarkably, combination of TMERS and TMB was a more effective tool for survival and ICIs therapy prediction. Conclusion: We established a novel TMERS, which have depicted the TME pattern, acting as a robust negative independent prognostic factor and predictive biomarker for response to ICIs when compiling with TMB. Funding Statement: This work was supported by grants from National Natural Science Foundation of China (Grant No. 81802550), Beijing Postdoctoral Research Foundation (Grant No. ZZ2019‐ 04), Hubei Provincial Natural Science Foundation of China (Grant No. 2018CFB221), Fundamental Research Funds for the Central Universities (Grant No. 2042018kf0055). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: The authors stated: Not applicable.

Prognostic nomogram for bladder cancer with brain metastases: a National Cancer Database analysis

BackgroundThis study aimed to establish and validate a nomogram for predicting brain metastasis in patients with bladder cancer (BCa) and assess various treatment modalities using a primary cohort comprising 234 patients with clinicopathologically-confirmed BCa from 2004 to 2015 in the National Cancer Database.MethodsMachine learning method and Cox model were used for nomogram construction. For BCa patients with brain metastasis, surgery of the primary site, chemotherapy, radiation therapy, palliative care, brain confinement of metastatic sites, and the Charlson/Deyo Score were predictive features identified for building the nomogram.ResultsFor the original 169 patients considered in the model, the areas under the receiver operating characteristic curve (AUC) were 0.823 (95% CI 0.758–0.889, P < 0.001) and 0.854 (95% CI 0.785–0.924, P < 0.001) for 0.5- and 1-year overall survival respectively. In the validation cohort, the nomogram displayed similar AUCs of 0.838 (95% CI 0.738–0.937, P < 0.001) and 0.809 (95% CI 0.680–0.939, P < 0.001), respectively. The high and low risk groups had median survivals of 1.91 and 5.09 months for the training cohort and 1.68 and 8.05 months for the validation set, respectively (both P < 0.0001).ConclusionsOur prognostic nomogram provides a useful tool for overall survival prediction as well as assessing the risk and optimal treatment for BCa patients with brain metastasis.

New trial of convection enhanced drug delivery (CED) in DIPG- applying the SIOPe DIPG survival prediction model for power calculation.

Abstract Background In diffuse intrinsic pontine glioma (DIPG) drug resistance is in part due to inadequate penetration of blood-brain barrier (BBB) by systemically administered drugs. Convection-enhanced drug delivery (CED) techniques have been established to bypass BBB. Trial design to measure efficacy requires evidence to justify power calculation. Aims To apply SIOPe DIPG registry survival prediction tool to pilot cohort of children with DIPG treated with CED of carboplatin and sodium valproate. Methods Case note and imaging review of 9 children with typical DIPG on imaging who were treated on compassionate basis with CED intra-tumoural infusions of carboplatin (0.12 mg/ml) and sodium valproate (14.4 mg/ml), after radiotherapy (n=9) and chemotherapy (n=4). Each had Renishaw device placed with 4 micro-catheters located within tumour mass. Up to 8 treatment cycles of CED infusions delivered through 2 pairs of catheters on 2 days to encompass pontine tumour volume. Survival prediction was performed using clinical criteria: age, sex, duration of symptoms, prior chemotherapy; and radiological criteria: absence of distant metastases; disease involving more than 50% of, and confined to, pons, ring enhancement at diagnosis. Results Cases were categorized as intermediate or high-risk using SIOPe risk scoring with predicted median overall survival (OS) of 9.7, and 7.0 months, respectively. Four patients, categorized as high-risk, had median overall survival (OS) of 14.2 months. Five children, categorized as standard-risk, had median OS of 16.0 months. Conclusions Results justify a phase 2 trial of CED carboplatin and sodium valproate powered to detect at least 4-month prolongation of survival.

Validation of the REVEAL Prognostic Equation and Risk Score Calculator in Incident Systemic Sclerosis-Associated Pulmonary Arterial Hypertension.

A prognostic equation and risk score calculator derived from the Registry to Evaluate Early and Long-term Pulmonary Arterial Hypertension Disease Management (REVEAL) are being used to predict 1-year survival in patients with pulmonary arterial hypertension (PAH), but little is known about the performance of these REVEAL survival prediction tools in systemic sclerosis (SSc)-associated PAH (SSc-PAH). Prospectively gathered data from the Johns Hopkins Pulmonary Hypertension Program and Pulmonary Hypertension Assessment and Recognition of Outcome in Scleroderma Registries were used to evaluate the predictive accuracy of the REVEAL models for predicting the probability of 1-year survival in patients with SSc-PAH. Model discrimination was assessed by comparison of the Harrell's C-index, model fit was assessed using multivariable regression techniques, and model calibration was assessed by comparing predicted to observed survival estimates. The validation cohort consisted of 292 SSc-PAH patients with a 1-year survival rate of 87.4%. The C-index for predictive accuracy of the REVEAL prognostic equation (0.734, 95% confidence interval [95% CI] 0.652-0.816) and for the risk score (0.743, 95% CI 0.663-0.823) demonstrated good discrimination, comparable to that in the model development cohort. The calibration slope was 0.707 (95% CI 0.400-1.014), indicating that the overall model fit was marginal (P = 0.06). The magnitude of risk assigned to low distance on the 6-minute walk test (6MWD) and elevated serum levels of brain natriuretic peptide (BNP) was lower in the validation cohort than was originally seen in the REVEAL derivation cohort. Model calibration was poor, particularly for the highest risk groups. In predicting 1-year survival in patients newly diagnosed as having SSc-PAH, the REVEAL prognostic equation and risk score provide very good discrimination but poor calibration. REVEAL prediction scores should be interpreted with caution in newly diagnosed SSc-PAH patients, particularly those with higher predicted risk of poor 1-year survival resulting from a low 6MWD or a high BNP serum level.

18F-FDG PET/CT and Urothelial Carcinoma: Impact on Management and Prognosis-A Multicenter Retrospective Study.

Objectives: To evaluate the ability of 18F-labeled fluoro-2-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) to predict survivorship of patients with bladder cancer (BC) and/or upper urinary tract carcinoma (UUTC). Materials: Data from patients who underwent FDG PET/CT for suspicion of recurrent urothelial carcinoma (UC) between 2007 and 2015 were retrospectively collected in a multicenter study. Disease management after the introduction of FDG PET/CT in the diagnostic algorithm was assessed in all patients. Kaplan-Meier and log-rank analysis were computed for survival assessment. A Cox regression analysis was used to identify predictors of recurrence and death, for BC, UUTC, and concomitant BC and UUTC. Results: Data from 286 patients were collected. Of these, 212 had a history of BC, 38 of UUTC and 36 of concomitant BC and UUTC. Patient management was changed in 114/286 (40%) UC patients with the inclusion of FDG PET/CT, particularly in those with BC, reaching 74% (n = 90/122). After a mean follow-up period of 21 months (Interquartile range: 4–28 mo.), 136 patients (47.4%) had recurrence/progression of disease. Moreover, 131 subjects (45.6%) died. At Kaplan-Meier analyses, patients with BC and positive PET/CT had a worse overall survival than those with a negative scan (log-rank < 0.001). Furthermore, a negative PET/CT scan was associated with a lower recurrence rate than a positive examination, independently from the primary tumor site. At multivariate analysis, in patients with BC and UUTC, a positive FDG PET/CT resulted an independent predictor of disease-free and overall survival (p < 0,01). Conclusions: FDG PET/CT has the potential to change patient management, particularly for patients with BC. Furthermore, it can be considered a valid survival prediction tool after primary treatment in patients with recurrent UC. However, a firm recommendation cannot be made yet. Further prospective studies are necessary to confirm our findings.

PROgnostic Model for Advanced Cancer (PRO-MAC)

ObjectiveTo develop and validate a simple prognostic tool for early prediction of survival of patients with advanced cancer in a tertiary care setting.DesignProspective cohort study with 2 years’ follow-up.SettingSingle tertiary...

A robust panel based on tumour microenvironment genes for prognostic prediction and tailoring therapies in stage I-III colon cancer.

BackgroundTumour microenvironment (TME) is critical for the regulation of cancer development as well as therapy. The objective of the current study was the development of a robust prognostic model based on TME-relevant genes.MethodsFive public microarray datasets providing clinical information were obtained. The least absolute shrinkage and selection operator regression method was used to reduce the dimensionality of robust prognostic genes identified via the bootstrap method.FindingsWe established a prognostic panel, designated as tumour microenvironment risk score (TMRS), consisting of 100 genes. With specific risk score formulae, the TMRS panel possesses a strong ability to predict relapse-free survival and overall survival through both univariate and multivariate analyses. Compared with the TNM stage, the TMRS panel showed much higher predictive accuracy. Further analysis revealed that patients with higher TMRS scores exhibited no therapeutic benefits from adjuvant chemotherapy, probably due to the activation of stromal relevant pathways and infiltration of stromal cells. Besides colon cancer, the TMRS panel was also revealed to be a reliable tool for prognostic prediction and chemotherapeutic decision-making in gastric cancer. Its value in predicting immunotherapy outcomes was also confirmed in two other cohorts consisting of metastatic urothelial carcinoma patients and melanoma patients.InterpretationOur TMRS panel may be an effective tool for survival prediction and treatment guidance in patients with stage I–III colon cancer.FundThis work was supported by the National Natural Science Foundation of China (No. 81772580) and Guangzhou Planed Project of Science and Technology (No. 201803010070).

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.

Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available.

A novel six-microRNA-based model to improve prognosis prediction of breast cancer.

Current tumor-node-metastasis (TNM) stage is unable to accurately predict the overall survival (OS) in breast cancer (BC) patients. This study aimed to construct a microRNA (miRNA)-based model to improve survival prediction of BC. We confirmed 99 differentially expressed miRNAs (DEMs) in 1044 BC samples compared to 102 adjacent normal breast tissues from The Cancer Genome Atlas (TCGA) database. Prognostic DEMs were used to establish a miRNA-based nomogram via Cox regression model. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses (KEGG) were executed to analyze target genes of miRNAs. A six-miRNA signature was screened to effectively distinguish high-risk patients in the primary and validation cohort (all P<0.001). Furthermore, we established a novel prognostic model incorporating the six-miRNA signature and clinical risk factors to predict 5-year OS of BC. Time-dependent receiver operating characteristic analysis suggested that the predictive accuracy of the six-miRNA-based nomogram was distinctly higher than that of TNM stage (0.758 vs 0.650, P<0.001). GO and KEGG pathway analyses showed that the 39 target genes mainly enrichment in protein binding, cytoplasm and MAPK signaling pathway. Our six-miRNA-based model is a reliable prognostic tool for survival prediction and provides information for individualized treatment decisions in BC patients.

Clinical Significance of 18F-Fluorodeoxyglucose-Positron Emission Tomography-Positive Lymph Nodes to Outcomes of Trimodal Therapy for Esophageal Squamous Cell Carcinoma.

The clinical significance of lymph node (LN) status determined by preoperative 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) has not been investigated in patients with locally advanced esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (NCRT) followed by surgery (trimodal therapy). This study reviewed 132 consecutive patients with ESCC who had been preoperatively evaluated using FDG-PET before and after NCRT to analyze associations among LN status according to PET findings, pathologic LN metastasis, and prognosis of ESCC after trimodal therapy. Lymph nodes that were PET-positive both before and after NCRT comprised significant predictive markers of pathologic LN metastasis in station-by-station analyses (sensitivity, specificity, and accuracy respectively 41.7%, 95.0%, and 92.7% before, and 12.0%, 99.4%, and 95.6% after NCRT; both p < 0.0001). The numbers of LNs evaluated using PET before and after NCRT were significantly associated with those of pathologic metastatic LNs. Uni- and multivariable analyses selected LN status determined by PET before NCRT as a significant independent predictor of both recurrence-free [LN-negative vs LN-positive: hazard ratio (HR) 1.90; 95% confidence interval (CI) 1.02-3.23; p = 0.045] and overall survival (HR 2.62; 95% CI 1.29-5.30; p = 0.01). The status of LN determined by preoperative FDG-PET is significantly associated with pathologic LN status and the prognosis of ESCC with trimodal therapy. Thus, FDG-PET is a useful diagnostic tool for preoperative prediction of pathologic LN metastasis and survival among patients with ESCC.