Predictor Of Overall Survival In Patients Research Articles

Clinical utility of sarcopenia dynamics assessed by psoas muscle volume in patients with colorectal cancer.

Sarcopenia affects the postoperative prognosis of patients with colorectal cancer (CRC). Recently, it has become possible to measure psoas volume from computed tomography images, and an index called psoas volume index (PVI) has been reported. However, it is unclear whether the dynamics of PVI before and after surgery is associated with clinical outcomes after CRC surgery. This study aimed to evaluate the association between pre- and postoperative PVI dynamics and clinical outcomes after CRC surgery. This study analyzed 1115 patients diagnosed with primary CRC and operated on for treatment between January 2014 and December 2017. Sarcopenia was defined as PVI below the lowest tertile in the preoperative assessment for each sex. The overall population was divided into four groups according to the dynamics of sarcopenia from preoperative to postoperative: group 1 (pre-to postoperative sarcopenia), group 2 (preoperative nonsarcopenia to postoperative sarcopenia), group 3 (pre-to postoperative nonsarcopenia), and group 4 (pre-to postoperative nonsarcopenia). Based on pre- and postoperative sarcopenia dynamics, 343 patients (29.7%) were classified into group 1, 105 patients (9.1%) into group 2, 42 patients (3.6%) into group 3, and 665 patients (57.6%) into group 4. Comparison of overall survival (OS) by the Kaplan-Meier method showed that Group 2 tended to have the worst prognosis (p=0.007). Multivariate analysis showed an increased OS risk in Group 2 in sarcopenia dynamics (Hazard ratio: 2.103, 95% CI: 1.202-3.681, p=0.009). Sarcopenia dynamics using PVI is an independent prognostic predictor of OS in patients with CRC.

Sarcopenia is a prognostic factor in lymphoma patients: a systematic review and meta-analysis

Findings regarding the relationship between sarcopenia and lymphoma have been inconsistent across studies. This study investigated the association between sarcopenia and lymphoma. We systematically searched the Embase, Science Direct, Cochrane Library, and PubMed databases from inception to 31 March 2024 to identify relevant studies. Two researchers independently extracted and evaluated studies that met inclusion and exclusion criteria. Twenty-six studies with 3659 participants were included. Sarcopenic lymphoma patients had poor overall survival (OS) (HR = 1.88; 95% CI: 1.47–2.41; p < 0.001). The heterogeneity was high (I2=80%). However, the result of the Egger test indicated a significant publication bias (p < 0.001). After employing the trim and fill method to adjust for this bias, the HR of OS became non-significant (p > 0.05). The progression-free survival (PFS) was worse in sarcopenic patients (HR = 1.77; 95% CI: 1.37–2.29; p < 0.001; I2=70%). There was no significant publication bias (p > 0.05). In the subgroup analyses, sarcopenia was a negative predictor of OS in lymphoma patients who undergo hematopoietic cell transplantation (HCT) (HR = 1.61;95% CI: 1.19–2.18; I2=30%). Male lymphoma patients with sarcopenia had a significantly worse OS (HR = 2.29; 95% CI:1.24–4.24; p = 0.009). Among patients with primary central nervous system lymphoma (PCNSL), those with sarcopenia defined by temporal muscle thickness (TMT) exhibited significantly worse OS (HR = 2.20; 95% CI:1.04–4.65; p = 0.039; I2=68%). Sarcopenia is associated with worse PFS in lymphoma patients. Subgroup analyses indicate that sarcopenia is a negative predictor of OS after HCT, and male lymphoma patients who suffer from sarcopenia have higher mortality. Sarcopenia defined by TMT is also a negative predictor of OS for patients with PCNSL.

Preoperative metabolic syndrome and prognosis after pancreatectomy for pancreatic cancer: A retrospective study.

We conducted a retrospective study to investigate the impact of metabolic syndrome (MetS), its individual components, and the number of metabolic risk factors on the prognosis of pancreatic cancer (PC) following pancreatectomy. MetS was defined as meeting any three of the following criteria: (1) waist circumference ≥85cm in men or ≥80cm in women; (2) triglycerides ≥150mg/dL or receiving drug treatment for elevated triglycerides; (3) high-density lipoprotein cholesterol <40mg/dL in men or <50mg/dL in women or receiving drug treatment for reduced HDL-C; (4) systolic blood pressure ≥130mmHg and/or diastolic blood pressure ≥85mmHg or receiving drug treatment for hypertension; and (5) fasting glucose, (FG) ≥100mg/dL or receiving drug treatment for elevated glucose. The hazard ratio (HR) and 95% confidence interval (CI) were calculated by the Cox regression model. Six hundred and seven patients who underwent radical resection for PC were enrolled in this study. Among them, 352 patients presented with preoperative MetS. MetS was associated with shorter overall survival (OS) but not with shorter disease-free survival (DFS). The adjusted HR (95% CI) for the poor OS in patients with 3, 4, and 5 metabolic risk components (vs.≤2) were 1.32 (1.03-1.84), 1.64 (1.18-2.29), and 1.96 (1.27-3.04), respectively (p<0.05). Elevated FG emerged as a significant predictor for poor OS and DFS. This study highlights that preoperative MetS serves as a significant predictor for OS in patients with PC, with its predictive value escalating as the number of metabolic risk components increases.

The prognostic role of albumin levels in lung cancer patients receiving third-line or advanced immunotherapy: a retrospective study.

Immunotherapy functions by leveraging immunoregulation drugs to bolster the immune system's capacity to identify and eliminate cancerous cells. In contrast to radiotherapy and chemotherapy, immunotherapy exhibits diminished side effects, heightened efficacy, and prolonged survival rates. Nevertheless, meticulous exploration into the determinants governing the advantageous effects of immunotherapy among patients who have previously undergone multiple prior therapies has yet to be conducted. Albumin (ALB) as a nutritional indicator has not been thoroughly studied for its prognostic effect on efficacy or survival. This study aims to identify factors that influence treatment outcomes among patients undergoing third-line or later immunological therapies. A cohort of 250 lung cancer patients undergoing toripalimab or tislelizumab immunotherapy was the focal point of data collection. The determination of the median value facilitated the establishment of a cut-off point, enabling the categorization of continuous variables. After data collection, a series of statistical analyses of various clinical factors at baseline were performed, including nonparametric tests, logistic regression, and Cox proportional risk modeling. The last follow-up was in May 2022. The primary study endpoint was overall survival (OS). A total of 250 patients were enrolled in the study, of which 129 patients received first- or second-line immunotherapy and 121 patients received third-line or subsequent immunotherapy. According to Cox multifactor regression analysis, in patients receiving either first- or second-line therapy, the ALB level exhibited negligible prognostic relevance (P>0.05). However, in patients subjected to immunotherapy beyond the second line, the ALB level manifested significant prognostic importance (P=0.039). Notably, patients demonstrating elevated ALB levels achieved a higher disease control rate (DCR) (70.0% vs. 52.5%, P=0.05) and displayed a tendency towards a heightened objective response rate (ORR) (20.0% vs. 16.4%, P=0.61) in comparison to those with lower ALB levels. Among patients undergoing immunotherapy in the third line or subsequent treatment phases, elevated ALB levels in baseline correlated with DCR and OS. Thus, the pre-immunotherapy ALB level emerges as an autonomous predictor of OS in patients subjected to third- or later line immunotherapy interventions.

Fibrinogen-to-albumin ratio (FAR) is the best biomarker for the overall survival of patients with non-small-cell lung cancer.

The inflammatory response and the nutritional status are associated with overall survival (OS) in patients with non-small cell lung cancer (NSCLC), but it is unclear which biomarkers are better suited to predict prognosis. This study sought to determine which of the commonly existing inflammatory and nutritional indicators best predicted the OS. This study included 15 compound indicators based on inflammation or nutrition, with cutoff points obtained through the receiver operating characteristic (ROC) curve. Univariate and multivariate Cox proportional risk models were used to evaluate the relationship between these predictors and OS. Kaplan-Meier curves were used for survival analysis, and log-rank tests were used to compare differences between groups. The C-index was calculated to evaluate the predictive ability of the different indicators. The study included 899 patients with NSCLC. In the univariate analysis, all 15 measures were significantly associated with the OS of patients (all p < 0.05). The results of the C-index analysis showed that the fibrinogen-to-albumin ratio (FAR), the systemic immune-inflammation index (SII), and the albumin-to-alkaline phosphatase ratio (AAPR) were the three indices with the best predictive performance. Among them, FAR (C-index = 0.639) had the best predictive power for OS in patients with NSCLC. In the different subgroups, FAR had the highest C-index in male, non-smoking, adenocarcinoma, and stage II patients. The C-index of the platelet-to-lymphocyte ratio (PLR) in female patients was the highest. SII was the highest in smokers, in those aged <65 and ≥65 years, and in stage III patients. The C-index of AAPR was the highest in non-adenocarcinomas. The C-index of the pan-immune-inflammation value (PIV) was the highest in stage I patients. In the multivariate Cox regression analysis, among FAR, SII, and AAPR, only FAR was an independent predictor of OS in patients with NSCLC. A high FAR was associated with a higher risk of death in patients with NSCLC (HR = 1.601, 95% CI = 1.028-2.495). In order to further evaluate the potential prognostic value of FAR, SII, and AAPR in patients with different stages, Cox regression analysis was performed for those with stage I-II and stage III NSCLC. The results showed that FAR was an independent prognostic factor for OS in patients with stage I-II NSCLC. For all patients with NSCLC, the prognostic power of FAR was superior to that of other inflammatory and nutritional indicators.

IMG-02. USING MRI RADIOMICS AND MACHINE LEARNING TO PREDICT OVERALL SURVIVAL FOR PEDIATRIC DIFFUSE MIDLINE GLIOMAS

Abstract BACKGROUND Pediatric diffuse midline gliomas (DMG) are aggressive central nervous system tumors with a median overall survival (OS) of less than one year from diagnosis. There is no curative therapy for DMG, and radiation therapy (RT) is the standard treatment. Magnetic resonance imaging (MRI) is the common noninvasive tool for DMG diagnosis and monitoring of tumor response to therapy. We developed an automatic pipeline to segment subregions of DMG and select MRI features that predict patient OS. METHODS We acquired diagnostic and post-RT multisequence MRI (T1, T1ce, T2, T2 FLAIR) and manual segmentations of pediatric DMG patients from two centers: 53 from Children’s National Hospital (internal) and 16 from Children’s Hospital of Philadelphia (external). We pretrained a deep learning model on a public adult brain tumor dataset, and finetuned the model to segment tumor core (TC) and whole tumor (WT) of DMG. We used PyRadiomics and sequential feature selection to select discriminative features based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 1-year survival from diagnosis. One model used only diagnostic tumor features and the other used both diagnostic and post-RT features. RESULTS For segmentation, Dice score (mean [median]±SD) was 0.91 (0.94)±0.12 and 0.74 (0.83)±0.32 for TC, and 0.88 (0.91)±0.07 and 0.86 (0.89)±0.06 for WT for internal and external cohorts, respectively. For OS prediction, accuracy was 77% and 81% at time of diagnosis, and 85% and 78% post-RT for internal and external cohorts, respectively. Heterogeneous WT intensity in baseline T2 FLAIR and smaller post-RT TC/WT volume ratio indicate longer OS. CONCLUSIONS Our method accurately predict OS for DMG patients and can be extended to other rare pediatric brain tumors. With automated and standardized analysis, early prognostication of OS can guide patient risk stratification and clinical decisions.

Predicting overall survival from tumor dynamics metrics using parametric statistical and machine learning models: application to patients with RET-altered solid tumors.

In oncology drug development, tumor dynamics modeling is widely applied to predict patients' overall survival (OS) via parametric models. However, the current modeling paradigm, which assumes a disease-specific link between tumor dynamics and survival, has its limitations. This is particularly evident in drug development scenarios where the clinical trial under consideration contains patients with tumor types for which there is little to no prior institutional data. In this work, we propose the use of a pan-indication solid tumor machine learning (ML) approach whereby all three tumor metrics (tumor shrinkage rate, tumor regrowth rate and time to tumor growth) are simultaneously used to predict patients' OS in a tumor type independent manner. We demonstrate the utility of this approach in a clinical trial of cancer patients treated with the tyrosine kinase inhibitor, pralsetinib. We compared the parametric and ML models and the results showed that the proposed ML approach is able to adequately predict patient OS across RET-altered solid tumors, including non-small cell lung cancer, medullary thyroid cancer as well as other solid tumors. While the findings of this study are promising, further research is needed for evaluating the generalizability of the ML model to other solid tumor types.

Construction and Validation of a MRI‑Based Radiomic Nomogram to Predict Overall Survival in Patients with Local Advanced Cervical Cancer: A Multicenter Study

BackgroundCervical cancer is the fourth most common cancer among women. Radiomics has emerged as a new approach providing valuable information for cancer management. The aim of this study was to construct a radiomics nomogram to accurately predict survival outcomes in patients with locally advanced cervical cancer. MethodsThis retrospective study enrolled a total of 582 locally advanced cervical cancer patients from three center (training cohort: n=228; internal validation cohort: n=98; external validation cohort: n=256). Radiomic features were extracted from pretreatment MRI images. Least absolute shrinkage and selection operator logistic regression (LASSO) was applied to select radiomic features and calculated the radiomic scores. Univariate and multivariate Cox proportional hazards regression analyses were used to identify the independent prognostic clinic-radiological factors for cervical cancer, which were incorporated into the nomogram. ResultsA total of 6 radiomic features were found to be associated with overall survival of locally advanced cervical cancer patients. The AUC of radiomic scores in the training cohort was 0.634-0.708 for the training cohort, 0.725-0.762 for internal validation cohort and 0.788-0.881 for the external validation cohort. Age, parametrial invasion, and radiomic score were the independent prognostic indicators for cervical cancer patients (Age: HR=1.041, 95% CI=1.012-1.071, p=0.006; Parametrial invasion: HR=4.755, 95% CI=1.493-15.144, p=0.008; HR=2.324, 95% CI=1.050-5.143, p=0.037). The nomogram model incorporating these factors showed favorable discrimination in predicting the overall survival rates of cervical cancer patients, with the AUC values of 0.809, 0.808, and 0.862 for 1-, 2-, and 3-year predictions. The decision curve analysis (DCA) indicated that the nomogram model achieved the highest clinical net benefit across the entire range of reasonable threshold probabilities. ConclusionThe nomogram, incorporating clinicopathological factors and radiomic features derived from MRI images, showed satisfactory discrimination in predicting the overall survival rates of locally advanced cervical cancer patients.

CT-based radiomics combined with hematologic parameters for survival prediction in locally advanced esophageal cancer patients receiving definitive chemoradiotherapy

ObjectivesThe purpose of this study was to investigate the prognostic significance of radiomics in conjunction with hematological parameters in relation to the overall survival (OS) of individuals diagnosed with esophageal squamous cell carcinoma (ESCC) following definitive chemoradiotherapy (dCRT).MethodsIn this retrospective analysis, a total of 122 patients with locally advanced ESCC were included. These patients were randomly assigned to either the training cohort (n = 85) or the validation cohort (n = 37). In the training group, the least absolute shrinkage and selection operator (LASSO) regression was utilized to choose the best radiomic features for calculating the Rad-score. To develop a nomogram model, both univariate and multivariate analyses were conducted to identify the clinical factors and hematologic parameters that could predict the OS. The performance of the predictive model was evaluated using the C-index, while the accuracy was assessed through the calibration curve.ResultsThe Rad-score was calculated by selecting 10 radiomic features through LASSO regression. OS was predicted independently by neutrophil-to-monocyte ratio (NMR) and Rad-score according to the results of multivariate analysis. Patients who had a Rad-score > 0.47 and an NMR > 9.76 were at a significant risk of mortality. A nomogram was constructed using the findings from the multivariate analysis. In the training cohort, the nomogram had a C-index of 0.619, while in the validation cohort, it was 0.573. The model’s accuracy was demonstrated by the calibration curve, which was excellent.ConclusionA prognostic model utilizing radiomics and hematologic parameters was developed, enabling the prediction of OS in patients with ESCC following dCRT.Critical relevance statementPatients with esophageal cancer who underwent definitive chemoradiotherapy may benefit from including CT radiomics in the nomogram model.Key points• Predicting the prognosis of ESCC patients before treatment is particularly important.• Patients with a Rad-score > 0.47 and neutrophil-to-monocyte ratio > 9.76 had a high risk of mortality.• CT-based radiomics nomogram model could be used to predict the survival of patients.Graphical

A novel ferroptosis-related gene signature for overall survival prediction in patients with gastric cancer.

The global diagnosis rate and mortality of gastric cancer (GC) are among the highest. Ferroptosis and iron-metabolism have a profound impact on tumor development and are closely linked to cancer treatment and patient's prognosis. In this study, we identified six PRDEGs (prognostic ferroptosis- and iron metabolism-related differentially expressed genes) using LASSO-penalized Cox regression analysis. The TCGA cohort was used to establish a prognostic risk model, which allowed us to categorize GC patients into the high- and the low-risk groups based on the median value of the risk scores. Our study demonstrated that patients in the low-risk group had a higher probability of survival compared to those in the high-risk group. Furthermore, the low-risk group exhibited a higher tumor mutation burden (TMB) and a longer 5-year survival period when compared to the high-risk group. In summary, the prognostic risk model, based on the six genes associated with ferroptosis and iron-metabolism, performs well in predicting the prognosis of GC patients.

A nomogram based on peripheral lymphocyte for predicting 8-year survival in patients with prostate cancer: a single-center study using LASSO-cox regression

PurposeThe purpose of this study was to develop a functional clinical nomogram for predicting 8-year overall survival (OS) of patients with prostate cancer (PCa) primary based on peripheral lymphocyte.Patients and methodsUsing data from a single-institutional registry of 94 patients with PCa in China, this study identified and integrated significant prognostic factors for survival to build a nomogram. The discriminative ability was measured by concordance index (C-index) and ROC curves (Receiver Operating Characteristic Curves). And the predictive accuracy was measured by the calibration curves. Decision curve analyses (DCA) was used to measure the clinical usefulness.ResultsA total of 94 patients were included for analysis. Five independent prognostic factors were identified by LASSO-Cox regression and incorporated into the nomogram: age, the T stage, the absolute counts of peripheral CD3(+)CD4(+) T lymphocytes, CD3(-)CD16(+)CD56(+) NK cells and CD4(+)/CD8(+) ratio. The area under the curve (AUC) values of the predictive model for 5-, 8-, and 10-year overall survival were 0.81, 0.76, and 0.73, respectively. The calibration curves for probability of 5-,8- and 10-year OS showed optimal agreement between nomogram prediction and actual observation. The stratification into different risk groups allowed significant distinction. DCA indicated the good clinical application value of the model.ConclusionWe developed a novel nomogram that enables personalized prediction of OS for patients diagnosed with PCa. This finding revealed a relative in age and survival rate in PCa, and a more favorable prognosis in patients exhibiting higher levels of CD4 + T, CD4+/CD8 + ratio and CD3(-)CD16(+)CD56(+) NK cells specifically. This clinically applicable prognostic model exhibits promising predictive capabilities, offering valuable support to clinicians in informed decision-making process.

Neutrophil-to-lymphocyte ratio and monocyte-to-eosinophil ratio as prognostic indicators for advanced nasopharyngeal carcinoma.

To determine the predictive value of the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-eosinophil ratio (NER), lymphocyte-to-eosinophil ratio (LER), monocyte-to-eosinophil ratio (MER), systemic inflammatory response index (SIRI), and ratio of inflammatory cells before and after treatment for predicting survival in advanced nasopharyngeal carcinoma (NPC) and to provide a reference for treatment. A retrospective review of 70 patients was performed. Serological indexes were obtained by drawing blood before and after systemic therapy. The cutoff values of these indexes were determined by receiver operating characteristic (ROC) curves. The prognostic value of the indexes for overall survival (OS) and distant metastasis free survival (DMFS) was evaluated. Survival analysis showed that a smaller pretreatment LMR value was associated with poor OS; larger pretreatment NER, LER, MER, and SIRI values were associated with poor OS; a smaller posttreatment LMR value was associated with poor OS; larger posttreatment NLR, NER, MER, and SIRI values were associated with poor OS; a smaller pretreatment LMR value was associated with poor DMFS; larger pretreatment NLR, NER, LER, and MER values were associated with poor DMFS; and larger posttreatment NLR, NER, LER, and MER values were associated with poor DMFS. Furthermore, a larger neutrophil after treatment-to-neutrophil before treatment ratio was associated with poor OS and DMFS. Logistic regression analysis showed that pretreatment MER and posttreatment NLR were independent predictors of OS in patients with advanced NPC; moreover, pretreatment and posttreatment MER and NLR were independent prognostic factors for DMFS in patients with advanced NPC. The NLR, NER and MER can be used to predict survival in advanced NPC patients. Eosinophils might be one of the factors for the good prognosis of NPC patients. In addition, an increased number of neutrophils after treatment may indicate a favorable prognosis.

The prognostic role of lymphocyte-to-monocyte ratio in patients with resectable pancreatic cancer: a systematic review and meta-analysis.

This systematic review and meta-analysis examined whether the lymphocyte-to-monocyte ratio (LMR) can serve as an indicator for predicting the prognosis of patients with resectable pancreatic cancer. This meta-analysis was registered with PROSPERO: CRD42023461260. A systematic literature search was conducted in the PubMed, Embase, Cochrane, and Web of Science databases up to September 2023 to assess whether LMR can predict the prognosis of patients with resectable pancreatic cancer. The outcomes measured included subgroup analyses of overall survival (OS) with hazard ratios (HR) and confidence intervals of geographical region, patient population, and LMR threshold. A sensitivity analysis was also performed for OS and HR and confidence intervals were calculated for recurrence-free survival (RFS). A total of 14 eligible articles, comprising 4,019 patients, were included in the comprehensive analysis. The results of this comprehensive analysis indicate that LMR is a robust predictor of OS, demonstrating strong prognostic significance (HR = 0.55, 95% CI [0.44-0.69], I2 = 79%, P < 0.00001). This predictive significance extended to various types of pancreatic cancer, such as pancreatic ductal adenocarcinoma (HR = 0.73, 95% CI [0.57-0.93], I2 = 46%, P = 0.01), pancreatic neuroendocrine neoplasms (HR = 0.81, 95% CI [0.66-0.99], P = 0.04) and other subtypes (HR = 0.40, 95% CI [0.22-0.72], I2 = 89%, P < 0.00001), but not to pancreatic head cancer (HR = 0.46, 95% CI [0.16-1.13], I2 = 59%, P = 0.12). LMR retained its predictive value across different regions, including Asia (HR = 0.62, 95% CI [0.47-0.76], I2 = 68%, P < 0.0001), Europe (HR = 0.78, 95% CI [0.67-0.91], I2 = 0%, P = 0.002), and the Americas (HR = 0.14, 95% CI [0.08-0.24], I2 = 0%, P < 0.00001). Notably, both LMR cut-off values greater than or equal to three (HR = 0.62, 95% CI [0.47-0.82], I2 = 67%, P = 0.0009) and less than three (HR = 0.47, 95% CI [0.32-0.69], I2 = 85%, P = 0.0001) exhibited prognostic significance. The sensitivity analysis for OS confirmed the strong predictive value of LMR, whereas LMR did not exhibit predictive significance for RFS (HR = 0.35, 95% CI [0.09-1.32], I2 = 95%, P = 0.12). In both subgroups categorized by Newcastle-Ottawa Scale (NOS) scores of ≥7 (HR = 0.66, 95% CI [0.54-0.80], I2 = 53%, P = 0.04) and <7 (HR = 0.41, CI [0.23-0.72], I2 = 89%, P < 0.00001), LMR was demonstrated to have predictive value. Despite the observed heterogeneity and potential biases in the included studies, the findings of this study suggest that LMR may serve as a valuable predictor of OS in patients with resectable pancreatic cancer.

Patterns and Prognosis of Local Recurrence of Nasopharyngeal Carcinoma after Intensity-modulated Radiotherapy.

Objective: To investigate the patterns of local failure and prognosis in patients with locally recurrent nasopharyngeal carcinoma (rNPC) after primary intensity-modulated radiotherapy (IMRT). Methods: The data of 298 patients with locally rNPC after IMRT were retrospectively analyzed. Magnetic resonance images of the initial and recurrent tumors were reviewed and, for patients with extra-nasopharyngeal local recurrence, the gross tumor volume of local recurrence was transferred to the original IMRT plan for dosimetry analysis. Significant prognostic factors for overall survival (OS) were selected by multivariate Cox regression analysis. Results: The commonest recurrence sites were the nasopharynx (93%, 277/298) and skull base (53.7%, 160/298). Of the 21 patients with extra-nasopharyngeal recurrence (19 cases valid), 12 had in-field failures, 4 had marginal failures, and 3 had out-field failures. The ethmoid sinus (57.1%, 4/7) and nasal cavity (28.6%, 2/7) were the most frequent sites of marginal and out-field failures. After median follow-up of 37 months, the 3-year and estimated 5-year OS rates were 57.3% and 41.7%, respectively. Multivariate analysis showed that age, recurrence interval, plasma Epstein-Barr virus (EBV) DNA level, and recurrent T stage were independent prognostic factors for OS. Conclusions: Local failure after IMRT occurs most commonly in the nasopharynx and skull base. In patients with extra-nasopharyngeal recurrence, in-field failure remains the main failure pattern, and marginal and out-field failures mainly occur in the ethmoid sinus and nasal cavity. Elder age, shorter recurrence interval, detectable plasma EBV DNA, and advanced recurrent T stage are negative predictors of OS in patients with rNPC.

Evaluation of Five Prognostic Scores in Patients Receiving Chemoradiation for Primary Glioblastoma Multiforme.

Prognostic factors can facilitate treatment personalization in patients with glioblastoma multiforme (GBM). This study investigated different Glasgow prognostic scores (GPS) and the LabBM score in patients with GBM receiving chemoradiation following resection or biopsy. Four GPS versions, LabBM score, and 10 other factors were retrospectively investigated for progression-free survival (PFS) and overall survival (OS) in 86 patients. GPS versions included original GPS (oGPS), modified GPS (mGPS), high-sensitivity mGPS (HS-mGPS), and high-sensitivity oGPS (HS-oGPS). On multivariate analysis, higher oGPS was significantly associated with worse OS (p=0.006). On univariate analyses, trends were found for associations between higher mGPS and worse OS (p=0.098) and between higher LabBM scores and worse PFS (p=0.059). The oGPS was an independent predictor of OS in patients receiving chemoradiation for GBM and can help personalizing the treatment for these patients. The LabBM score may be useful for predicting PFS.

MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping

Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon’s algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.

CT-based radiomics nomogram for overall survival prediction in patients with cervical cancer treated with concurrent chemoradiotherapy.

To establish and validate a hybrid radiomics model to predict overall survival in cervical cancer patients receiving concurrent chemoradiotherapy (CCRT). We retrospectively collected 367 cervical cancer patients receiving chemoradiotherapy from the First Affiliated Hospital of Soochow University in China and divided them into a training set and a test set in a ratio of 7:3. Handcrafted and deep learning (DL)-based radiomics features were extracted from the contrast-enhanced computed tomography (CT), and the two types of radiomics signatures were calculated based on the features selected using the least absolute shrinkage and selection operator (LASSO) Cox regression. A hybrid radiomics nomogram was constructed by integrating independent clinical risk factors, handcrafted radiomics signature, and DL-based radiomics signature in the training set and was validated in the test set. The hybrid radiomics nomogram exhibited favorable performance in predicting overall survival, with areas under the receiver operating characteristic curve (AUCs) for 1, 3, and 5 years in the training set of 0.833, 0.777, and 0.871, respectively, and in the test set of 0.811, 0.713, and 0.730, respectively. Furthermore, the hybrid radiomics nomogram outperformed the single clinical model, handcrafted radiomics signature, and DL-based radiomics signature in both the training (C-index: 0.793) and test sets (C-index: 0.721). The calibration curves and decision curve analysis (DCA) indicated that our hybrid nomogram had good calibration and clinical benefits. Finally, our hybrid nomogram demonstrated value in stratifying patients into high- and low-risk groups (cutoff value: 5.6). A high-performance hybrid radiomics model based on pre-radiotherapy CT was established, presenting strengths in risk stratification.

Polypharmacy Independently Predicts Overall Survival in Patients with Lymphoid Cancer

Introduction Comorbidity is common among patients with lymphoid cancer (LC) and may limit treatment options. Often, comorbidity is accompanied by medication leading to polypharmacy in multimorbid patients. Whereas comorbidity is difficult to eliminate, discontinuing inappropriate medication may offer an attractive modifiable risk. Aim To investigate the effect of polypharmacy and comorbidity on overall survival (OS) in patients with LC. Methods We retrieved data on all Danish patients registered with chronic lymphocytic leukemia (CLL) from the Danish CLL Register since 2008 and with mantle cell lymphoma (MCL), diffuse large B-cell lymphoma (DLBCL), and follicular lymphoma (FL) from the Danish Lymphoma Register since 2005. All patients were diagnosed before June 2019; those with more than one LC diagnosis were excluded. Baseline data were used to calculate international prognostic indices (IPI). International Classification of Diseases (ICD10) diagnosis codes were retrieved from the Danish Patient Register, and Charlson comorbidity index (CCI) scores were calculated (Quan et al., Am J Epidemiol, 2011) at time of LC diagnosis. All patients were given a hematological CCI score of 2. Information on medication prescribed within the year up until LC diagnosis was retrieved from the Danish Prescription Register, and the number of drug classes (using the third ATC level, e.g. C03) was used to define polypharmacy as at least 5 drug classes. OS was calculated from time of diagnosis until death or end of follow-up. Multivariable analyses were equally adjusted for CCI, polypharmacy, and appropriate IPI. Results We identified 15,314 patients with LC including 5155 with CLL, 953 with MCL, 6182 with DLBCL, and 3024 with FL. The median age was 69 years (IQR 60;76). CLL-IPI and R-IPI are provided in the Figure. MIPI was low, intermediate, and high in 137 (15%), 297 (32%), and 493 (53%) patients, respectively, and FLIPI2 was low, intermediate, and high in 391 (16%), 1469 (62%), and 518 (22%), respectively. The median CCI score was 2 (including the hematological CCI score of 2, IQR 2;2). Most common comorbidities included chronic pulmonary disease (5%), congestive heart failure (CHF; 3%), rheumatic disease (RD; 3%), and metastatic cancer (MC; 2%). MC (3% vs 1-2%), RD (4% vs 2%), and AIDS (0.3% vs 0.0%) were significantly more prevalent in DLBCL as compared with other LCs ( P≤0.00054). Patients had most frequently been prescribed antibacterial (J01; 50%), analgesic (N02; 40%), renin-angiotensin system (C09; 33%), antithrombotic (B01; 32%), lipid modifying (C10; 30%), anti-inflammatory/rheumatic (M01; 29%), dyspepsia related (A02; 27%), and diuretic (C03; 27%) drugs. Antibacterials (54% vs 46-51%), analgesics (46% vs 33-36%), dyspepsia related (32% vs 22-28%), anti-inflammatory/antirheumatic (33% vs 24-28%), and systemic corticosteroid (H02; 13% vs 6-8%) drugs were being prescribed significantly more often in patients with DLBCL as compared with those with other LCs ( P&amp;lt;0.0001). In multivariable analyses adjusted for disease specific IPIs and CCI (2 vs &amp;gt;2), polypharmacy (&amp;lt;5 vs ≥5) was an independent prognostic marker of OS in CLL (hazard ratio [HR] 1.7, 95% confidence interval [CI]; 1.5;2.0), MCL (HR 1.4, 95% CI 1.2;1.7), DLBCL (HR 1.6, 95% CI 1.5;1.8), and FL (HR 1.8, 95% CI 1.6;2.1; P&amp;lt;0.0001). Using polypharmacy as a continuous variable (0-2 vs 3-4 vs 5-6 vs 7-8 vs 9-10 vs &amp;gt;10), we further demonstrated a dose-response effect on OS in CLL, DLBCL (Figure), MCL, and FL (data not shown). Similar to CLL and DLBCL, FL with &amp;gt;2 drug classes were significantly associated with shorter OS, whereas in MCL, polypharmacy groups were small ( n≤262) and only significantly associated with shorter OS for groups with ≥5 drug classes. Conclusion Polypharmacy is an important prognostic marker of OS independent of the CCI and IPIs in CLL, MCL, DLBCL, and FL. Polypharmacy is most likely a result of an underlying comorbidity, still informing the prognosis further than register based CCI scores. Further studies will seek to clarify the extent of medication that may be considered for discontinuation in newly diagnosed patients. Moreover, these results indicate that consistent collection of data on both comorbidity and polypharmacy at baseline in clinical trials may improve subgroup analyses and clinical translation of results. This is particularly important when extrapolating clinical trial results to ineligible populations.

Clinicopathological Features and Survival Trends of Non-Epithelial Ovarian Cancer: Analysis of the Surveillance, Epidemiology, and End Results (SEER) Database

Introduction: Owing to their low incidence, no reliable statistics about prognostication derived from large sample sizes have been reported of malignant ovarian germ cell tumors (MOGCTs) and sex cord-stromal tumors (SCSTs). The present study aimed to investigate the clinicopathological prognostic factors and the survival trends of MOGCTs and SCSTs. Materials and Methods: Patients with MOGCTs and SCSTs were recorded in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed between 2000 and 2019. Clinical, demographic, and treatment characteristics were compared between groups of MOGCTs and SCSTs. Cox risk regression analysis and Kaplan-Meier survival curves were used to compare overall survival (OS) and cancer-specific survival (CSS) and to assess the prognostic factors. Results: Information about 2,506 patients with MOGCTs and 1,556 patients with SCSTs was extracted from the SEER database, respectively. Aged <40 years and single were more common in patients with MOGCTs than in those with SCSTs. The vast majority of patients with MOGCTs and SCSTs underwent surgery (98.1% vs. 94.5%; p < 0.001), and women with MOGCTs were more likely to receive chemotherapy than women with SCSTs (56.1% vs. 32.2%; p < 0.001). For both patients before and after propensity-score matching, the 5-year OS rates of patients with SCSTs were lower than those of patients with MOGCTs (p < 0.05). In multivariate Cox regression analysis, both age and surgery were independent predictors of OS in patients with MOGCTs and SCSTs. FIGO staging was an independent predictor of CSS in MOGCT patients. Tumor size and chemotherapy were also independent predictors of CSS in patients with SCSTs. Conclusion: Compared to patients with SCSTs, those with MOGCTs tended to be younger and had a higher OS and CSS. Adjuvant chemotherapy after surgery did not prolong OS and CSS in patients with SCSTs.

PSMA PET/CT for Response Assessment and Overall Survival Prediction in Patients with Metastatic Castration-Resistant Prostate Cancer Treated with Androgen Receptor Pathway Inhibitors.

We aimed to evaluate the role of prostate-specific membrane antigen (PSMA) PET/CT for response assessment and outcome prediction in patients with metastatic castration-resistant prostate cancer (mCRPC) treated with androgen receptor pathway inhibitors (ARPIs), including abiraterone acetate or enzalutamide. Methods: We retrospectively analyzed 30 ARPI-treated mCRPC patients who underwent 68Ga-PSMA-11 PET/CT within 8 wk before (baseline) and 12 ± 4 wk after treatment initiation. Total PSMA tumor volume was calculated using the fixed threshold method (SUV ≥ 3). Patients were categorized as PSMA responders (PSMA-Rs) or PSMA nonresponders (PSMA-NRs) on the basis of both European Association of Urology/European Association of Nuclear Medicine (EAU/EANM) criteria and Response Evaluation Criteria in PSMA PET/CT (RECIP) 1.0. PSMA-R included patients with a complete response, a partial response, or stable disease, and PSMA-NR included those with progressive disease. On the basis of prostate-specific antigen (PSA), patients were classified as biochemical responders if PSA decreased by at least 50% and as nonresponders if it did not. The Φ-coefficient was used to evaluate the correlation of PSMA- and PSA-based responses. Survival analysis was performed using the Cox regression hazard model and the Kaplan-Meier method. Predictive accuracy was tested for both response criteria. Results: On the basis of PSMA PET/CT, 13 (43%) patients were PSMA-NR according to the EAU/EANM criteria and 11 (37%) patients were PSMA-NR according to RECIP 1.0. Significant correlations were observed between PSMA- and PSA-based responses for both criteria (Φ = 0.79 and 0.66, respectively). After a median follow-up of 25 mo (interquartile range, 21-43 mo), the median overall survival was significantly longer for PSMA-R than PSMA-NR (54 vs. 22 mo) for both the EAU/EANM criteria and RECIP 1.0, with hazard ratios of 6.9 (95% CI, 1.9-26; P = 0.004) and 5.6 (95% CI, 1.69-18.26, P = 0.005), respectively. No significant difference in predictive accuracy was found between the 2 criteria (C-index, 0.79 vs. 0.76, respectively, P = 0.54). Flare phenomena at the second PSMA PET study were not observed in our cohort. Conclusion: Our results demonstrate that PSMA PET/CT is a valuable imaging biomarker for response assessment and overall survival prediction when performed at 3 mo after ARPI treatment initiation in mCRPC patients. Both proposed PSMA response criteria (EAU/EANM and RECIP 1.0) seem to perform equally well. No PSMA flare was observed. Prospective validation of these findings is strongly needed.