Overall Survival C-index Research Articles

Prognostic risk score and index including the platelet-to-lymphocyte ratio and lactate dehydrogenase in patients with metastatic or unresectable urothelial carcinoma treated with immune checkpoint inhibitors.

Avelumab and pembrolizumab are administered after platinum-based chemotherapy for the treatment of metastatic urothelial carcinoma. We explored the prognostic factors and risk scores for predicting the outcomes of metastatic or unresectable urothelial carcinoma at the start of treatment with immune checkpoint inhibitors. This retrospective study included patients with metastatic or unresectable urothelial carcinoma treated with avelumab or pembrolizumab after platinum-based chemotherapy between January 2017 and December 2022. Prognostic factors, including patient and tumor characteristics and blood data at the initiation of immune checkpoint inhibitor therapy, were examined. This study included 36 and 207 patients treated with avelumab and pembrolizumab, respectively, for metastatic or unresectable urothelial carcinoma. Eastern Cooperative Oncology Group performance status, presence of visceral metastases, platelet-to-lymphocyte ratio and lactate dehydrogenase levels were independent prognostic factors for predicting overall survival. The median overall survival of patients in the risk-score model was 58.5months (score zero), 27.9months (one), 13.1months (two) and 3.9months (three or higher). The C-index for overall survival was 0.718 for the newly developed risk score compared with 0.679 for the Bellmunt score and 0.703 for the Bellmunt-C-reactive protein score. Additionally, the C-index for overall survival using the immune prognostic index derived from lactate dehydrogenase and the platelet-to-lymphocyte ratio was 0.646 compared with 0.615 for the Lung Immune Prognostic Index. A risk score that includes the platelet-to-lymphocyte ratio and lactate dehydrogenase may serve as a useful model for predicting prognosis following the initiation of immune checkpoint inhibitors in patients with metastatic or unresectable urothelial carcinoma.

Development and validation of a nomogram to predict overall survival in patients with glioma: a population-based study.

The objective is to investigate the prognostic factors associated with gliomas and to develop and assess a predictive nomogram model connected to survival that may serve as an additional resource for the clinical management of glioma patients. From 2010 to 2015, participants included in the study were chosen from the Surveillance Epidemiology and End Results (SEER) database. Gliomas were definitively diagnosed in each of them. They were divided into the training group and the validation cohort at random (7/3 ratio) using a random number table. To identify the independent predictive markers for overall survival (OS), Cox regression analysis was utilized. Subsequently, the training cohort's survival-related nomogram predictive model for OS was created by incorporating the fundamental patient attributes. Following that, the training cohort's model underwent internal validation. The nomogram model's authenticity and reliability were assessed through the computation of receiver operating characteristic (ROC) curves and concordance index (C-index). To evaluate the degree of agreement between the observed and predicted values in the training and validation cohorts, calibration plots were created. Age, primary site, histological type, surgery, chemotherapy, marital status, and grade were the independent predictive factors for OS in the training cohort, according to Cox regression analysis. Moreover, the nomogram model for predicting 1-year, 3-year, and 5-year OS was built using these variables. The C-indexes of OS for glioma patients in the training cohort and internal validation cohort were found to be 0.779 (95% CI=0.769-0.789) and 0.776 (95% CI=0.760-0.792), respectively, according to the results. The ROC curves also demonstrated good discrimination. Additionally, calibration plots demonstrated a fair amount of agreement. In summary, the nomogram prediction model of OS demonstrated a moderate level of reliability in its predictive performance, offering valuable reference data to enable doctors to quickly and easily determine the survival likelihood of patients with gliomas.

A nomogram incorporating treatment data for predicting overall survival in gastroenteropancreatic neuroendocrine tumors: a population-based cohort study.

Over the last few decades, the annual global incidence of gastroenteropancreatic neuroendocrine tumours (GEP-NETs) has steadily increased. Because of the complex and inconsistent treatment of GEP-NETs, the prognosis of patients with GEP-NETs is still difficult to assess. The study aimed to construct and validate the nomograms included treatment data for prediction overall survival (OS) in GEP-NETs patients. GEP-NETs patients determined from the Surveillance, Epidemiology, and End Results (SEER)-13 registry database (1992-2018) and with additional treatment data from the SEER-18 registry database (1975-2016). In order to select independent prognostic factors that contribute significantly to patient survival and can be included in the nomogram, multivariate Cox regression analysis was performed using the minimum value of Akaike information criterion (AIC) and we analyzed the relationship of variables with OS by calculating hazard ratios (HRs) and 95% CIs. In addition, we also comprehensively compared the nomogram using to predict OS with the current 7th American Joint Committee on Cancer (AJCC) staging system. From 2004 to 2015, a total of 42662 patients at diagnosis years with GEP-NETs were determined from the SEER database. The results indicated that the increasing incidence of GEP-NETs per year and the highest incidence is in patients aged 50-54. After removing cases lacking adequate clinicopathologic characteristics, the remaining eligible patients ( n =7564) were randomly divided into training (3782 patients) and testing sets (3782 patients). In the univariate analysis, sex, age, race, tumour location, SEER historic stage, pathology type, TNM, stage, surgery, radiation, chemotherapy, and CS tumour size were found to be significantly related to OS. Ultimately, the key factors for predicting OS were determined, involving sex, age, race, tumour location, SEER historic stage, M, N, grade, surgery, radiation, and chemotherapy. For internal validation, the C-index of the nomogram used to estimate OS in the training set was 0.816 (0.804-0.828). For external validation, the concordance index (C-index) of the nomogram used to predict OS was 0.822 (0.812-0.832). In the training and testing sets, our nomogram produced minimum AIC values and C-index of OS compared with AJCC stage. Decision curve analysis (DCA) indicated that the nomogram was better than the AJCC staging system because more clinical net benefits were obtained within a wider threshold probability range. A nomogram combined treatment data may be better discrimination in predicting overall survival than AJCC staging system. The authors highly recommend to use their nomogram to evaluate individual risks based on different clinical features of GEP-NETs, which can improve the diagnosis and treatment outcomes of GEP-NETs patients and improve their quality of life.

Comparing overall survival between pediatric and adult retinoblastoma with the construction of nomogram for adult retinoblastoma: A SEER population-based analysis

BackgroundRetinoblastoma (RB) is a rare primary malignant tumor primarily affecting children. Our study aims to compare the overall survival (OS) between pediatric and adult RB patients and establish a predictive model for adult RB patients' OS to assist clinical decision-making. MethodsThis study retrospectively analyzed data from 1938 RB patients in the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2000 to 2015. Propensity score matching (PSM) ensured balanced characteristics between pediatric and adult groups. A Cox proportional hazards regression model was used to assess prognostic factors, and selected variables were utilized to construct a predictive survival model. The Nomogram model's performance was evaluated through the C-index, time-dependent ROC curves, calibration curves, and decision curve analysis (DCA). ResultsFollowing PSM, adult RB patients had lower OS compared to pediatric RB patients. Independent prognostic factors for adult RB OS included age, gender, disease stage, radiation therapy, income, and diagnosis confirmation. In the training cohort, the Nomogram achieved a C-index for OS of 0.686 and accurately predicted 2-year, 3-year, and 5-year OS with AUC values of 0.672, 0.680, and 0.660, respectively. The C-index, time-dependent ROC curves, calibration curves, and DCA in both training and validation cohorts confirmed the Nomogram's excellent performance. ConclusionIn this study, adult RB patients have worse OS than pediatric RB patients. Consequently, we constructed a Nomogram to predict the risk for adult RB patients. The Nomogram demonstrated good accuracy and reliability, making it suitable for widespread application in clinical practice to assist healthcare professionals in assessing patients’ prognoses.

A Prognostic Model for Survival in Patients with Gastric Signet Ring Cell Carcinoma

Introduction: The objective of our study was to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRCC). Methods: A total of 3,408 GSRCC patients between 1975 and 2017 were screened from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation cohorts. Univariate and multivariate Cox analyses were conducted to identify independent prognostic factors for the construction of a nomogram. The performance of the model was then assessed by the concordance index (C-index), calibration plot, and area under the receiver operating characteristic curve (AUC). Then, the novel nomogram was further assessed by 64 GSRCC patients from our hospital as the external cohort. Results: We identified age, tumor lymph node metastasis (TNM) staging system, surgery, and chemotherapy as significant independent elements of prognosis. On this basis, a nomogram was constructed, with a C-index of OS in the training and validation cohorts of 0.763 (95% CI: 0.751–0.774) and 0.766 (95% CI: 0.748–0.784) and a C-index of CSS of 0.765 (95% CI: 0.753–0.777) and 0.773 (95% CI: 0.755–0.791), respectively. The AUCs of the nomogram for predicting 2- and 5-year OS were 0.848 and 0.885, respectively, and those for predicting CSS were 0.854 and 0.899, respectively, demonstrating the excellent predictive value of the constructed nomogram compared to the traditional AJCC staging system. Similar results were also observed in both the internal and external validation sets. Conclusion: The nomogram provided an accurate tool to predict OS and CSS in patients with GSRCC, which can assist clinicians in making predictions about individual patient survival.

Genomic Classification and Individualized Prognosis in Multiple Myeloma.

Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years. To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data. Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression-based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited. Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.

Development and validation of prognostic models for small cell lung cancer patients with liver metastasis: a SEER population-based study

BackgroundThis study was to establish and validate prediction models to predict the cancer-specific survival (CSS) and overall survival (OS) of small-cell lung cancer (SCLC) patients with liver metastasis.MethodsIn the retrospective cohort study, SCLC patients with liver metastasis between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training group and testing group (3: 1 ratio). The Cox proportional hazards model was used to determine the predictive factors for CSS and OS in SCLC with liver metastasis. The prediction models were conducted based on the predictive factors. The performances of the prediction models were evaluated by concordance indexes (C-index), and calibration plots. The clinical value of the models was evaluated by decision curve analysis (DCA).ResultsIn total, 8,587 patients were included, with 154 patients experiencing CSS and 154 patients experiencing OS. The median follow-up was 3 months. Age, gender, marital status, N stage, lung metastases, multiple metastases surgery of metastatic site, chemotherapy, and radiotherapy were independent predictive factors for the CSS and OS of SCLC patients with liver metastasis. The prediction models presented good performances of CSS and OS among patients with liver metastasis, with the C-index for CSS being 0.724, whereas the C-index for OS was 0.732, in the training set. The calibration curve showed a high degree of consistency between the actual and predicted CSS and OS. DCA suggested that the prediction models provided greater net clinical benefit to these patients.ConclusionOur prediction models showed good predictive performance for the CSS and OS among SCLC patients with liver metastasis. Our developed nomograms may help clinicians predict CSS and OS in SCLC patients with liver metastasis.

Crafting and Authenticating Prognostic Nomograms for Overall and Cancer-specific Survival in Pediatric Small Renal Masses: A Comprehensive Two-Decade Cohort Study.

The intricate task of diagnosing and managing small renal masses (SRMs) has become progressively convoluted within the realm of clinical practice. Contemporary clinical prediction instruments may succumb to a gradual decay in precision, coupled with an absence of unambiguous guidelines to navigate patient management. This investigation was devised to formulate and authenticate nomograms for the overall survival (OS) and cancer- specific survival (CSS) among patients afflicted with SRMs. The study encompassed a cohort of 2558 pediatric patients diagnosed with SRMs over the period of 2000 to 2019. Independent prognostic indicators for OS and CSS, encompassing historical staging, chemotherapy regimens, surgical interventions, and pathological classifications, were ascertained through the employment of multivariate Cox proportional hazards regression analysis and backward stepwise selection. Through the utilization of multivariate Cox regression models, nomograms for OS and CSS were meticulously crafted, demonstrating commendable discrimination and calibration within the training set (OS C-index: 0.762, CSS C-index: 0.779). The validation set further corroborated the exemplary discrimination and calibration of the nomograms. Moreover, these nomograms adeptly differentiated between patient groups at elevated and diminished risk levels. The nomograms delineated in this research provide propitious predictive accuracy for overall survival and cancer-specific survival in patients suffering from pediatric SRMs, thereby contributing to refined risk stratification and steering the optimal therapeutic course of action. The necessity for supplementary validation prevails before the translation of these findings into clinical practice.

Prognostic risk signature in patients with acute myeloid leukemia treated with hypomethylating agents and venetoclax

AbstractHypomethylating agents (HMAs) and venetoclax (Ven) represent the standard of care for patients with acute myeloid leukemia (AML) who are ineligible for intensive chemotherapy. However, the European LeukemiaNet (ELN) risk classifications have been validated for patients treated with intensive therapy. In this study, we validate a recently proposed new molecular prognostic risk signature (mPRS) for patients with AML treated with HMAs and Ven. This classification allocated patients to favorable, intermediate (N/KRAS or FLT3–internal tandem duplication mutations), and lower (TP53 mutations) benefit groups. We retrospectively analyzed 159 patients treated with HMA and Ven. The mPRS classification allocated 74 (47%), 31 (19%), and 54 (34%) patients to the higher, intermediate, and lower-benefit groups, respectively. The overall response rate was 71% (86%, 54%, and 59% in the higher, intermediate, and lower-benefit groups, respectively). The median overall survival (OS) and event-free survival (EFS) times were 30 and 19 months, respectively, in the higher-benefit group; 12 and 8 months in the intermediate-benefit group; and 5 and 4 months in the lower-benefit group (P < .001). The C-index for OS and EFS was higher when stratifying patients according to mPRS classification than with the ELN 2022 classification. The 2-year cumulative incidence of relapse was 35%, 70%, and 60% in the higher, intermediate, and lower-benefit groups, respectively (P = .005). The mPRS classification accurately segregated groups of patients with AML treated with HMA plus Ven. In these patients, N/KRAS and TP53 mutations appear to negatively affect outcomes; therefore, new treatment approaches are warranted.

Clinical characteristics and prognosis of primary small cell carcinoma of the breast: a propensity score-matched, population-based study

ObjectiveThe purpose of this study was to describe the clinicopathological characteristics and prognosis of primary small cell carcinoma of the breast (PSCCB) and compare PSCCB with breast invasive ductal carcinoma...

Evaluation of the Molecular International Prognostic Scoring System (IPSS-M) in Patients (Pts) with Myelodysplastic Syndromes/Neoplasms (MDS) with Missing Molecular Data

TK and JPB Co first; RK and AMZ are Co senior authors. Introduction The IPSSM incorporates molecular features to improve outcome prediction for pts with MDS. However, IPSSM requires assessment of 31 genetic mutations to classify pts into 6 risk groups. Importantly, IPSSM accounts for missing data and provides best, average, and worst IPSSM scores. Here, we used a large cohort of pts with MDS to analyze the performance of IPSSM if molecular data were missing to better understand the performance of the tool in this frequently encountered clinical setting. Methods For this study, we combined data from a publicly available dataset (Kewan et al. 2023 Nat com) with our multicenter VALIDATE database to increase sample size. Only pts with available molecular data were included and IPSSM was calculated twice: a. using TP53 mutation ( TP53 MT) only and assuming that all other molecular mutations are missing (missing molecular data [IPSSM MM]) and b. using the full molecular panel (available molecular data [IPSSM AM]). TP53 MT were included in all IPSSM calculations given frequency and significant impact on survival. Time-to-event analysis from the time of diagnosis was conducted using Kaplan-Meier estimator. The performance of different scores was evaluated by Harrell's c-index. This study was supported by an independent research grant from AbbVie. Results Of 2,789 pts, 2,489 had molecular data and were included. Median age was 72 years (IQR: 65-78). MDS with excess blast 1/2 (42%) was the most common subtype. Overall, 39% of pts treated with HMA and 15% of underwent transplantation. In total, 16% of pts had complex karyotypes. The most prevalent mutations were SF3B1 (21%), ASXL1 (20%), TP53 (15%), SRSF2 (15%), DNMT3A (12%), and RUNX1 (10%), 39% of the pts had &amp;gt;1 gene mutation. Based on IPSSR, pts were classified as very low (13%), low (47%), intermediate (13%), high (13%), and very high (14%) risk. First, we calculated IPSSM score assuming that all molecular data were missing (IPSSM MM) except TP53 MT. Accordingly, pts were classified as very low (4%), low (33%), moderate low (19%), moderate high (14%), high (17%), and very high (14%) risk. IPSSM MM resulted in the re-stratification of 1623 (66%) pts of which 1104 (68%) pts were up-staged, and 519 (32%) pts were down-staged. When applying actual molecular data (IPSS-M AM), pts were classified as very low (14%), low (30%), moderate low (12%), moderate high (10%), high (17%), and very high (17%) risk. When compared with IPSSR, IPSSM AM resulted in re-stratification of 1218 pts (50%) of which 868 pts (71%) were upstaged ( Panel A). When comparing IPSSM MM with IPSSM AM, 1243 pts (51%) were assigned to the same risk group and 1186 pts (49%) were assigned to different risk groups. Amongst reclassified pts, 431 (36%) were upstaged and 755 (64%) were down staged. Only 109 pts (5%) were reclassified with more than one shift. Median follow-up time was 24 (IQR:10-77) months (mo) with a median overall survival (OS) of 50 mo (95% CI: 47-55). The probability of OS based on IPSSM MM and IPSSM AM risk groups was significantly different (p-value: &amp;lt;0.0001 for both), Panel B. Median OS (mo) based on IPSSM MM vs. IPSSM AM were as follows: very low (not reached vs. 125), low (103 vs. 82), moderate low (63 vs. 58), moderate high (46 vs. 43), high (27 vs. 24), and very high (13 vs.15). Median OS based on IPSSR were: very low (not reached), low (78), intermediate (36), high (24), and very high (14). The median LFS (n=1100 pts) based on IPSSM MM vs. IPSSM AM were as follows: very low (41 vs. 78), low (63 vs. 54), moderate low (39 vs. 33), moderate high (26 vs. 29), high (18 vs. 15), and very high (11 vs. 13). IPSSM MM showed comparable performance to IPSSM AM with c-index (95%CI): 0.713 (0.697 -0.728) vs. 0.714 (0.699-0.729) for OS and 0.645 (0.622-0.669) vs. 0.623 (0.599-0.647) for LFS. When used as continuous scores, IPSSM MM continues to be comparable to IPSSM AM for OS (c-index:0.721 vs. 0.730) and LFS (c-index:0.655 vs. 0.641). IPSS-R had a lower c-index for OS (0.688) and LFS (0.622). Conclusions Our study supports prognostic and clinical value of IPSSM even if most molecular data are missing, confirming that IPSSM adjusts well for missing molecular data and can be used in clinical practice if molecular data are largely missing. While molecular testing remains optimal for accurate risk stratification in MDS, our data suggest that clinical, pathological and cytogenetic data continue to be the main determinant of outcome prediction for pts with MDS.

Pretreatment MR-based radiomics in patients with glioblastoma: A systematic review and meta-analysis of prognostic endpoints

PurposeRecent studies have shown promise of MR-based radiomics in predicting the survival of patients with untreated glioblastoma. This study aimed to comprehensively collate evidence to assess the prognostic value of radiomics in glioblastoma. MethodsPubMed-MEDLINE, Embase, and Web of Science were searched to find original articles investigating the prognostic value of MR-based radiomics in glioblastoma published up to July 14, 2023. Concordance indexes (C-indexes) and Cox proportional hazards ratios (HRs) of overall survival (OS) and progression-free survival (PFS) were pooled via random-effects modeling. For studies aimed at classifying long-term and short-term PFS, a hierarchical regression model was used to calculate pooled sensitivity and specificity. Between-study heterogeneity was assessed using the Higgin inconsistency index (I2). Subgroup regression analysis was performed to find potential factors contributing to heterogeneity. Publication bias was assessed via funnel plots and the Egger test. ResultsAmong 1371 abstracts, 18 and 17 studies were included for qualitative and quantitative data synthesis, respectively. Respective pooled C-indexes and HRs for OS were 0.65 (95 % confidence interval [CI], 0.58–0.72) and 2.88 (95 % CI, 2.28–3.64), whereas those for PFS were 0.61 (95 % CI, 0.55–0.66) and 2.78 (95 % CI, 1.91–4.03). Among 4 studies that predicted short-term PFS, the pooled sensitivity and specificity were 0.77 (95 % CI, 0.58–0.89) and 0.60 (95 % CI, 0.45–0.73), respectively. There was a substantial between-study heterogeneity among studies with the survival endpoint of OS C-index (n = 9, I2 = 83.8 %). Publication bias was not observed overall. ConclusionPretreatment MR-based radiomics provided modest prognostic value in both OS and PFS in patients with glioblastoma.

A nomogram for individually predicting overall survival for elderly patients with early breast cancer: a consecutive cohort study.

Elderly patients with breast cancer are highly heterogeneous, and tumor load and comorbidities affect patient prognosis. Prediction models can help clinicians to implement tailored treatment plans for elderly patients with breast cancer. This study aimed to establish a prediction model for breast cancer, including comorbidities and tumor characteristics, in elderly patients with breast cancer. All patients were ≥65 years old and admitted to the Peking Union Medical College Hospital. The clinical and pathological characteristics, recurrence, and death were observed. Overall survival (OS) was analyzed using the Kaplan-Meier curve and a prediction model was constructed using Cox proportional hazards model regression. The discriminative ability and calibration of the nomograms for predicting OS were tested using concordance (C)-statistics and calibration plots. Clinical utility was demonstrated using decision curve analysis (DCA). Based on 2,231 patients, the 5- and 10-year OS was 91.3% and 78.4%, respectively. We constructed an OS prediction nomogram for elderly patients with early breast cancer (PEEBC). The C-index for OS in PEEBC in the training and validation cohorts was 0.798 and 0.793, respectively. Calibration of the nomogram revealed a good predictive capability, as indicated by the calibration plot. DCA demonstrated that our model is clinically useful. The nomogram accurately predicted the 3-year, 5-year, and 10-year OS in elderly patients with early breast cancer.

A Prognostic Model Based on Residual Cancer Burden and Tumor-Infiltrating Lymphocytes on Residual Disease after Neoadjuvant Therapy in HER2+ Breast Cancer.

We aim to evaluate the prognostic significance of tumor-infiltrating lymphocyte on residual disease (RD-TIL) in HER2+ patients with breast cancer who failed to achieve pathologic complete response (pCR) after anti-HER2+ chemotherapy (CT)-based neoadjuvant treatment (NAT). We assessed the feasibility of combining the prognostic information provided by residual cancer burden (RCB) and RD-TILs into a composite score (RCB+TIL). HER2+ patients with breast cancer treated with CT+anti-HER2-based NAT at three institutions were retrospectively included. RCB and TIL levels were evaluated on hematoxylin and eosin-stained slides from surgical samples according to available recommendations. Overall survival (OS) was used as an outcome measure. A total of 295 patients were included, of whom 195 had RD. RCB was significantly associated with OS. Higher RD-TILs were significantly associated with poorer OS as compared with lower RD-TILs (15% cutoff). In multivariate analysis, both RCB and RD-TIL maintained their independent prognostic value. A combined score, RCB+TIL, was calculated from the estimated coefficient of RD-TILs and the RCB index in a bivariate logistic model for OS. The RCB+TIL score was significantly associated with OS. The C-index for OS of the RCB+TIL score was numerically higher than that of RCB and significantly higher than that of RD-TILs. We have reported an independent prognostic impact of RD-TILs after anti-HER2+CT NAT, which might underlie an imbalance of the RD microenvironment towards immunosuppressive features. We provided a new composite prognostic score based on RCB+TIL, which was significantly associated with OS and proved to be more informative than the isolated evaluation of RCB and RD-TILs.

18 F]-FDG PET radiomic model as prognostic biomarker in diffuse large B-cell lymphoma.

To evaluate the association between radiomic features (RFs) extracted from 18 F-FDG PET/CT (18 F-FDG-PET) with progression-free survival (PFS) and overall survival (OS) in diffuse large-B-cell lymphoma (DLBCL) patients eligible to first-line chemotherapy. DLBCL patients who underwent 18 F-FDG-PET prior to first-line chemotherapy were retrospectively analyzed. RFs were extracted from the lesion showing the highest uptake. A radiomic score to predict PFS and OS was obtained by multivariable Elastic Net Cox model. Radiomic univariate model, clinical and combined clinical-radiomic multivariable models to predict PFS and OS were obtained. 112 patients were analyzed. Median follow-up was 34.7months (Inter-Quartile Range (IQR) 11.3-66.3months) for PFS and 41.1 (IQR 18.4-68.9) for OS. Radiomic score resulted associated with PFS and OS (p<0.001), outperforming conventional PET parameters. C-index (95% CI) for PFS prediction were 0.67 (0.58-0.76), 0.81 (0.75-0.88) and 0.84 (0.77-0.91) for clinical, radiomic and combined clinical-radiomic model, respectively. C-index for OS were 0.77 (0.66-0.89), 0.84 (0.76-0.91) and 0.90 (0.81-0.98). In the Kaplan-Meier analysis (low-IPI vs. high-IPI), the radiomic score was significant predictor of PFS (p<0.001). The radiomic score was an independent prognostic biomarker of survival in DLBCL patients. The extraction of RFs from baseline 18 F-FDG-PET might be proposed in DLBCL to stratify high-risk versus low-risk patients of relapse after first-line therapy, especially in low-IPI patients.

Proposal for a Two-Tier Re-classification of Stage IV/M1 domain of Renal Cell Carcinoma into M1 ("Oligometastatic") and M2 ("Polymetastatic") subdomains: Analysis of the Registry for Metastatic Renal Cell Carcinoma (REMARCC).

We hypothesized that two-tier re-classification of the "M" (metastasis) domain of the Tumor-Node-Metastasis (TNM) staging of Renal Cell Carcinoma (RCC) may improve staging accuracy than the current monolithic classification, as advancements in the understanding of tumor biology have led to increased recognition of the heterogeneous potential of metastatic RCC (mRCC). Multicenter retrospective analysis of patients from the REMARCC (REgistry of MetAstatic RCC) database. Patients were stratified by number of metastases into two groups, M1 (≤3, "Oligometastatic") and M2 (>3, "Polymetastatic"). Primary outcome was overall survival (OS). Secondary outcomes were cancer-specific survival (CSS). Cox-regression and Kaplan-Meier (KMA) analysis were utilized for outcomes, and receiver operating characteristic analysis (ROC) was utilized to assess diagnostic accuracy compared to current "M" staging. 429 patients were stratified into proposed M1 and M2 groups (M1 = 286/M2 = 143; median follow-up 19.2 months). Cox-regression revealed M2 classification as an independent risk factor for worsened all-cause mortality (HR=1.67, p=0.001) and cancer-specific mortality (HR=1.74, p<0.001). Comparing M1-oligometastatic vs. M2-polymetastatic groups, KMA revealed significantly higher 5-year OS (36% vs. 21%, p<0.001) and 5-year CSS (39% vs. 17%, p<0.001). ROC analyses comparing OS and CSS, for M1/M2 reclassification versus unitary M designation currently in use demonstrated improved c-index for OS (M1/M2 0.635 vs. unitary M 0.500) and CSS (M1/M2 0.627 vs. unitary M 0.500). Subclassification of Stage "M" domain of mRCC into two clinical substage categories based on metastatic burden corresponds to distinctive tumor groups whose oncological potential varies significantly and result in improved predictive capability compared to current staging.

Nomograms for Predicting Survival Outcomes in Patients with Neuroendocrine Neoplasms of the Gallbladder Undergoing Primary Tumor Resection: A Population-Based Study.

Neuroendocrine neoplasms of the gallbladder (GB-NENs) are a rare group of histologically heterogeneous tumors, and surgical resection of the primary tumor is the mainstream treatment at the moment. The current study aimed to establish and validate novel nomograms for patients with GB-NENs undergoing primary tumor resection to predict the 6-, 12-, and 18-month overall survival (OS) and cancer-specific survival (CSS). Clinicopathological information of patients with GB-NENs undergoing primary tumor resection between 2004 and 2018 was derived from the Surveillance, Epidemiology, and End Results (SEER) database. Candidate prognostic factors were selected by Cox regression analyses, and the nomograms were constructed. Finally, concordance index (C-index), calibration plot, area under the curve from the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were utilized to assess the effective performance of the nomograms. A total of 221 patients with GB-NENs undergoing resection were enrolled in this retrospective study. Using the Cox regression analyses, age, pathological classification, tumor size, and SEER stage were identified as the independent prognostic factors of patients with GB-NENs undergoing resection, and nomograms were constructed. The C-indexes of OS and CSS in training dataset were 0.802 (95% CI: 0.757-0.848) and 0.846 (95% CI: 0.798-0.895), while those of internal validation dataset were 0.862 (95% CI: 0.802-0.922) and 0.879 (95% CI: 0.824-0.934), respectively. Taken together, the nomograms are accurate enough to predict the prognostic factors of GB-NEN patients undergoing resection, allowing for treatment decision-making and clinical monitoring for future clinical work.

Prognostic risk factors and nomogram construction for sebaceous carcinoma: A population-based analysis.

Sebaceous gland carcinoma (SGC) is a rare tumor for which there are currently no effective tools to predict patient outcomes. We analyzed the clinical and pathological prognostic risk factors of sebaceous carcinoma based on population data and created a nomogram of related risk factors, which can more accurately predict the 3-, 5-, and 10-year overall survival (OS) rates of patients. SGC patients between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to training and validation cohorts. Relevant risk factors were identified by univariate and multivariate COX hazards regression methods and combined to produce a correlation nomogram. The concordance index (C-index), the area under the receiver operating characteristic (AUC) curve, and calibration plots have demonstrated the predictive power of the nomogram. Decision curve analysis (DCA) was used to measure nomograms in clinical practice. A total of 2844 eligible patients were randomly assigned to 70% of the training group (n=1990) and 30% of the validation group (n=854) in this study. The derived meaningful prognostic factors were applied to the establishment of the nomogram. The C-index for OS was 0.725 (95% CI: 0.706-0.741) in the training cohort and 0.710 (95% CI: 0.683-0.737) in the validation cohort. The AUC and calibration plots of 3-, 5-, and 10-year OS rates showed that the nomogram had good predictive power. DCA demonstrated that the nomogram constructed in this study could provide a clinical net benefit. We created a novel nomogram of prognostic factors for SGC, which more accurately and comprehensively predicted 3-, 5-, and 10-year OS in SGC patients. This can help clinicians identify high-risk patients as early as possible, carry out personalized treatment, follow-up, and monitoring, and improve the survival rate of SGC patients.

Inversed albumin-to-globulin ratio and underlying liver disease severity as a prognostic factor for survival in hepatocellular carcinoma patients undergoing transarterial chemoembolization.

Previous studies have shown that an inversed albumin-to-globulin ratio (IAGR) is a predictor of the prognosis of many cancers. However, the prognostic value of an IAGR for patients with hepatocellular carcinoma (HCC) who undergo transarterial chemoembolization (TACE) is still unclear. This study aims to evaluate the predictive value of an IAGR for the prognosis of those patients. This study retrospectively analyzed 396 patients with HCC who received TACE. Using a cut-off value of 1.0 for the albumin-to-globulin ratio, patients were divided into a normal albumin-to-globulin ratio (NAGR) (≥1) and an IAGR (<1) group. Univariate and multivariate analyses and time-dependent receiver operating characteristic analyses were performed to identify risk factors of overall survival (OS) and cancer-specific survival (CSS). Survival nomograms were constructed based on the multivariable analysis results and further evaluated using the consistency index (C-index) and calibration curve. A total of 396 patients were included in the final analysis and were divided into the NAGR group (n = 298, 75.3%) and the IAGR (n = 98, 24.7%) group. The median OS and CSS were significantly worse in the IAGR group than in the NAGR group (OS: 8 vs. 26 months, CSS: 10 vs. 41 months, both P < 0.001). Multivariate analyses demonstrated that an IAGR was an independent risk factor for predicting worse OS [hazard ratio (HR), 2.024; 95% confidence interval (CI): 1.460-2.806] and CSS (HR: 2.439; 95% CI: 1.651-3.601). The nomogram-based model-related C-indexes for OS and CSS prediction were 0.715 (95% CI: 0.697-0.733) and 0.750 (95% CI: 0.729-0.771), and the calibration of the nomogram showed good consistency. The IAGR along with underlying liver disease severity were the useful prognostic predictors of OS and CSS among patients with HCC undergoing TACE and might be useful to identify high-risk patients.

Epidemiologic characteristics and a prognostic nomogram for patients with vulvar cancer: results from the Surveillance, Epidemiology, and End Results (SEER) program in the United States, 1975 to 2016.

To elucidate clinical characteristics and build a prognostic nomogram for patients with vulvar cancer. The study population was drawn from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly assigned to training and validation sets. Cox proportional hazards model and competing risk model were used to identify the prognostic parameters of overall survival (OS) and cancer-specific survival (CSS) to construct a nomogram. The nomogram was assessed by concordance index (C-index), area under the curve (AUC), calibration plot, and decision curve analysis (DCA). A total of 20,716 patients were included in epidemiological analysis, of whom 7,025 patients were selected in survival analysis, including 4,215 and 2,810 in training and validation sets, respectively. The multivariate Cox model showed that the predictors for OS were age, marital status, histopathology, differentiation and tumor node metastasis (TNM) stages, whether to undergo surgery and chemotherapy. However, the predictors for CSS were age, race, differentiation and TNM stages, whether to undergo surgery and radiation. The C-index for OS and CSS in the training set were 0.76 and 0.80. The AUC in the training set for 1-, 3- and 5-year OS and CSS were 0.84, 0.81, 0.80 and 0.88, 0.85, 0.83, respectively, which was similar in the validation set. The calibration curves showed good agreement between prediction and actual observations. DCA revealed that the nomogram had a better discrimination than TNM stages. The nomogram showed accurate prognostic prediction in OS and CSS for vulvar cancer, which could provide guidance to clinical practice.