Nomogram For Overall Survival Research Articles

Nomograms for Predicting Overall and Cancer-Specific Survival Among Second Primary Endometrial Cancer in Primary Colorectal Carcinoma Patients.

Endometrialcancer (EC)isone of the most frequentgynecologic cancers, approximately 20%ofpatients are regarded as high-risk with poor prognosis. However, moredetailsof patients with second primary endometrialcancer (SPEC) after colorectal cancer (CRC) remain poorly understood. We therefore proposed to construct twonomogramsto predict 3- and5-year overall survival (OS) and cancer-specific survival (CSS) rates to facilitateclinicalapplication. A total of 1631 participants wereidentified inthe SEER databasefrom1973to2020. We constructed and validated the nomograms for predicting OS and CSS. Thereceiver operating characteristic curves,calibrationplot, decision curve analysis, C-index, net reclassification improvement, and integrated discrimination improvement were applied to evaluatethe predictive performance. Finally, the Prognosticindex was calculated and usedfor riskstratificationof Kaplan-Meiersurvivalanalysis based on different treatmentoptions. Nomograms of OS and CSS were formulated based on theindependentprognosticfactors utilizingthetrainingset. The3- and 5- years of OS nomogram demonstratedgooddiscrimination (AUC = 0.840 and 0.829, respectively), well-calibrated power,and excellentclinicaleffectiveness. Ournomograms of predicting OS and CSS had a concordance index of 0.801 and 0.866 compared with 0.676 and 0.746 for the AJCC staging system,and more importantly,demonstrated a better forecast accuracy. Chemoradiotherapy displayed a significant survival benefit in the high-risk groups, but proceeding to surgery plus chemotherapy showed a favorable survival for the low groups based on all patients. We developed andinternallyvalidated multivariable models that predict OS and CSS risk of SPEC in patients with a CRC to helpclinicians makeapplicable clinical decisions for patients.

Combined fibrinogen concentration and neutrophil-to-lymphocyte ratio, an integrative model of the inflammatory response and coagulation cascades, for predicting prognosis in patients with upper tract urothelial carcinoma.

Inflammation and coagulation cascades are closely correlated with cancer occurrence and progression. This study investigated the prognostic value of the combination of plasma fibrinogen level and neutrophil-to-lymphocyte ratio (F-NLR) in patients with upper tract urothelial carcinoma (UTUC). The predictive ability of the F-NLR for overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS) was initially established and then further validated in patients who underwent radical nephroureterectomy for UTUC. As a result, patients were divided into three groups following the establishment of cut-off values for the NLR (≥2.53 vs. <2.53) and fibrinogen (≥4.55 vs. <4.55) through receiver operating characteristic (ROC) curve analysis: F-NLR score 0 (low fibrinogen and low NLR), 2 (high fibrinogen and high NLR), or 1 (remaining patients). The F-NLR score was then identified as an independent risk factor for OS, CSS, and PFS (all P-value <0.05) by multivariate regression analysis in both the training and validation cohorts. In addition, F-NLR-based nomograms for OS, CSS, and PFS were developed and evaluated using the concordance index (C-index) and calibration curves. The integration of the F-NLR into existing nomograms improved predictive accuracy compared to the use of nomograms without the F-NLR score. This suggests that the addition of F-NLR is beneficial for enhancing the accuracy of prognosis prediction in patients with UTUC. The F-NLR score may serve as a powerful predictor for patients with UTUC.

Prognostic nomograms for locally advanced cervical cancer based on the SEER database: Integrating Cox regression and competing risk analysis.

Locally advanced cervical carcinoma (LACC) remains a significant global health challenge owing to its high recurrence rates and poor outcomes, despite current treatments. This study aimed to develop a comprehensive risk stratification model for LACC by integrating Cox regression and competing risk analyses. This was done to improve clinical decision making. We analyzed data from 3428 patients with LACC registered in the Surveillance, Epidemiology, and End Results program and diagnosed them between 2010 and 2015. Cox regression and competing risk analyses were used to identify the prognostic factors. We constructed and validated nomograms for overall survival (OS) and disease-specific survival (DSS). Multivariate Cox regression identified key prognostic factors for OS, including advanced International Federation of Gynecology and Obstetrics stage, age, marital status, ethnicity, and tumor size. Notably, International Federation of Gynecology and Obstetrics stages IIIA, IIIB, and IVA had hazard ratios of 2.227, 2.451, and 4.852, respectively, significantly increasing the mortality risk compared to stage IB2. Ethnic disparities were evident, with African Americans facing a 39.8% higher risk than Caucasians did. Competing risk analyses confirmed the significance of these factors in DSS, particularly tumor size. Our nomogram demonstrated high predictive accuracy, with area under the curve values ranging from 0.706 to 0.784 for DSS and 0.717 to 0.781 for OS. Calibration plots and decision curve analyses further validated the clinical utility of this nomogram. We present effective nomograms for LACC risk stratification that incorporate multiple prognostic factors. These models provide a refined approach for individualized patient management and have the potential to significantly enhance therapeutic strategies for LACC.

Multivariable models of outcomes with [177Lu]Lu-PSMA-617: analysis of the phase 3 VISION trial

[177Lu]Lu-PSMA-617 (177Lu-PSMA-617) prolonged life in patients with metastatic castration-resistant prostate cancer (mCRPC) in VISION (NCT03511664). However, distinguishing between patients likely and unlikely to respond remains a clinical challenge. We present the first multivariable models of outcomes with 177Lu-PSMA-617 built using data from VISION, a large prospective phase 3 clinical trial powered for overall survival. Adults with progressive post androgen receptor pathway inhibitor and taxane prostate-specific membrane antigen (PSMA)-positive mCRPC received 177Lu-PSMA-617 plus protocol-permitted standard of care (SoC) or SoC alone. In this post hoc analysis, multivariable Cox proportional hazards models of overall survival (OS) and radiographic progression-free survival (rPFS), and a logistic regression model of prostate-specific antigen response (≥50% decline; PSA50) were constructed and evaluated using C-index or receiver operating characteristic (ROC) analyses with bootstrapping validation. Nomograms were constructed for visualisation. Patients were randomised between June 2018 and October 2019. Data from all 551 patients in the 177Lu-PSMA-617 arm were analysed in multivariable modelling. The OS nomogram (C-index, 0.73; 95% confidence interval [CI], 0.70-0.76) included whole-body maximum standardised uptake value (SUVmax), time since diagnosis, opioid analgesic use, aspartate aminotransferase, haemoglobin, lymphocyte count, presence of PSMA-positive lesions in lymph nodes, lactate dehydrogenase (LDH), alkaline phosphatase (ALP), and neutrophil count. The rPFS nomogram (C-index, 0.68; 0.65-0.72) included SUVmax, time since diagnosis, opioid analgesic use, lymphocyte count, presence of liver metastases by computed tomography, LDH, and ALP. The PSA50 nomogram (area under ROC curve, 0.72; 95% CI, 0.68-0.77) included SUVmax, lymphocyte count and ALP. Performances of the OS and rPFS models were maintained when they were reconstructed excluding SUVmax. These models of outcomes with 177Lu-PSMA-617 are the first built using prospective phase 3 data. They show that a combination of pretreatment laboratory, clinical, and imaging parameters, reflecting both patient and tumour status, influences outcomes. These models are important for aiding treatment selection, patient management, and clinical trial design. Novartis.

Two-Year Outcomes and Biomarker Analysis of Locally Advanced Gastric and Gastroesophageal Junction Adenocarcinoma After Neoadjuvant Chemotherapy and Immunotherapy from the Phase II WuhanUHGI001 Trial.

This study reports the 2-year outcomes and biomarker analysis results of patients with locally advanced gastric and gastroesophageal junction (G/GEJ) adenocarcinoma who received neoadjuvant chemotherapy and immunotherapy in a phase II WuhanUHGI001 trial. Eligible patients with cT3/4aN+M0 locally advanced G/GEJ adenocarcinoma were screened, enrolled, and treated with 3 cycles of neoadjuvant tislelizumab and SOX followed by D2 gastrectomy and another 5 cycles of postoperative adjuvant SOX. The primary endpoint was major pathological response. Of the 49 included patients, 24 (49.0%) achieved major pathological response and 13 (26.5%) achieved pathological complete response. During a median follow-up of 26.8 months, the 2-year progression-free survival (PFS) and overall survival (OS) rates were 69.4% and 81.2%, respectively. Grade 3-4 adverse events occurred in six patients (12.2%) during the neoadjuvant period, eight patients (17.0%) during the postoperative period, and seven patients (15.2%) during the adjuvant period. Biomarker analysis revealed that the pathological complete response showed no association with 2-year PFS and OS. Major pathological response showed a potentially strong association with improved 2-year PFS and OS rates. In addition, preoperative circulating tumor cells combined with pathological responses are helpful in prognosis assessment. In addition, our results showed that T downstaging, lymphocyte-to-monocyte ratio, and CD3+ T cells were independent factors that affect PFS. The signet ring cell component (SRCC), T downstaging, and neutrophil-to-lymphocyte ratio were independent factors affecting OS. Prognostic nomograms of PFS and OS constructed based on the multivariate Cox regression results demonstrated suitable calibration and discrimination ability. Neoadjuvant tislelizumab plus SOX exhibits promising efficacy and acceptable toxicity in patients with locally advanced G/GEJ adenocarcinoma. In addition, our study established a prognostic risk signature and nomograms based on clinicopathological characteristics, which can accurately predict patient outcomes and aid in personalized treatment planning.

A Novel Prognostic Nomogram Based on TIGIT and NKG2A Can Predict Relapse-Free Survival of Hepatocellular Carcinoma After Hepatectomy.

Hepatocellular carcinoma (HCC) is a major global health concern, and emerging evidence suggests that TIGIT and NKG2A are potential immune checkpoints with implications for HCC progression. This study aimed to evaluate the prognostic significance of TIGIT and NKG2A expression in HCC patients who underwent radical liver resection. We conducted a retrospective analysis of 144 HCC patients who underwent radical liver resection. TIGIT and NKG2A expression levels were assessed using the immunoreactive score. Cox proportional hazards models were utilized to analyze the association between TIGIT/NKG2A expression and clinical characteristics, relapse-free survival (RFS), and overall survival (OS). Prognostic models for OS and RFS was developed and validated using concordance index and calibration curves. Additionally, the random forest algorithm was employed to identify independent risk factors for OS and RFS and their correlation with predicted survival. TIGIT and NKG2A expression were identified as independent risk factors for RFS, while TIGIT expression alone significantly impacted OS. The prognostic models showed good discriminative ability, with concordance indices exceeding 0.7 for predicting 1-, 3-, and 5-year OS or RFS. Calibration curves confirmed the reliability of the nomograms for OS and RFS prediction. The areas under the ROC curve consistently exceeded 0.7 for predicting OS and RFS. Elevated levels of TIGIT and NKG2A expression were associated with diminished RFS, highlighting their importance as prognostic factors. Our study establishes the prognostic significance of TIGIT and NKG2A expression in predicting OS and RFS following radical liver resection for HCC patients. The developed prognostic models incorporating TIGIT and NKG2A expression hold promise for improving risk stratification and clinical management of HCC patients.

Identification and validation of a prognostic model based on immune-related genes in ovarian carcinoma.

A novel valuable prognostic model has been developed on the basis of immune-related genes (IRGs), which could be used to estimate overall survival (OS) in ovarian cancer (OC) patients in The Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset. This prognostic model was engineered by employing LASSO regression in training cohort (TCGA dataset). The corresponding growth predictive values of this model for individualized survival was evaluated using survival analysis, receiver operating characteristic curve (ROC curve), and risk curve analysis. Combined with clinical characteristics, a model risk score nomogram for OS was well built. Thereafter, depended on the model risk score, patients were divided into high and low risk subgroups. The survival difference between these subgroups was measured using Kaplan-Meier survival method. In addition, correlations containing pathway enrichment, treatment, immune cell infiltration and the prognostic model were also analyzed. We established the ovarian cancer cell line W038 for this study and identified the performances of GBP1P1 knockdown on a series of activities including cellular proliferation, apoptosis, migration, and invasion of W038 cells in vitro. We constructed a 25-genes prognostic model (TNFAIP8L3, PI3, TMEM181, GBP1P1 (LOC400759), STX18, KIF26B, MRPS11, CACNA1C, PACSIN3, GMPR, MANF, PYGB, SNRPA1, ST7L, ZBP1, BMPR1B-DT, STAC2, LINC02585, LYPD6, NSG1, ACOT13, FAM120B, LEFTY1, SULT1A2, FZD3). The areas under the curves (AUC) of 1, 2 and 3 years were 0.806, 0.773 and 0.762, in the TCGA cohort, respectively. Besides, the effectiveness of the model was verified using ICGC testing data. Univariate and multivariate Cox regression analysis exposes the risk score as an independent prognosis predictor for OS both in the TCGA and ICGC cohort. In summary, we utilized comprehensive bioinformatics analysis to build an effective prognostic gene model for OC patients. These bioinformatic results suggested that GBP1P1 could act as a novel biomarker for OC. GBP1P1 knockdown substantially inhibited the proliferation, migration, and invasion of W038 cells in vitro, and increased the percentage of apoptotic W038 cells. The analyses of genetic status of patients with 25-genes model might improve the ability to predict the prognosis of patients with OC and help to select patients suit able to therapies. Immune-related gene GBP1P1 might serve as prognostic biomarker for OC.

Prognostic nomograms for young breast cancer: A retrospective study based on the SEER and METABRIC databases.

Young breast cancer (YBC) is a subset of breast cancer that is often more aggressive, but less is known about its prognosis. In this study, we aimed to generate nomograms to predict the overall survival (OS) and breast cancer-specific survival (BCSS) of YBC patients. Data of women diagnosed with YBC between 2010 and 2020 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated into a training cohort (n = 15,227) and internal validation cohort (n = 6,526) at a 7:3 ratio. With the Cox regression models, significant prognostic factors were identified and used to construct 3-, 5-, and 10-year nomograms of OS and BCSS. Data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database were used as an external validation cohort (n = 90). We constructed nomograms incorporating 10 prognostic factors for OS and BCSS. These nomograms demonstrated strong predictive accuracy for OS and BCSS in the training cohort, with C-indexes of 0.806 and 0.813, respectively. The calibration curves verified that the nomograms have good prediction accuracy. Decision curve analysis demonstrated their practical clinical value for predicting YBC patient survival rates. Additionally, we provided dynamic nomograms to improve the operability of the results. The risk stratification ability assessment also showed that the OS and BCSS rates of the low-risk group were significantly better than those of the high-risk group. Here, we generated and validated more comprehensive and accurate OS and BCSS nomograms than models previously developed for YBC. These nomograms can help clinicians evaluate patient prognosis and make clinical decisions.

Nomograms predicting benefit after immunotherapy in oral bifidobacteria supplementation ICC patients: a retrospective study

PurposeThe objective of this study was to develop nomograms for predicting outcomes following immunotherapy in patients diagnosed with intrahepatic cholangiocarcinoma (ICC).Patients and methodsA retrospective analysis was conducted on data from 75 ICC patients who received immunotherapy at Jinling Hospital and Drum Hospital. The discriminative power, accuracy, and clinical applicability of the nomograms were assessed using the concordance index (C-index), calibration curve, and decision curve analysis (DCA). The predictive performance of the nomograms for overall survival (OS) and progression-free survival (PFS) was evaluated using the area under the receiver operating characteristic (ROC) curve. Kaplan-Meier curves were also generated for validation purposes.ResultsMultivariable analysis identified independent prognostic factors for OS, including CA19-9 levels, portal vein tumor thrombus (PVTT) grade, bifidobacteria administration, and surgery. The C-index of the nomogram for OS prediction was 0.722 (95% confidence interval [CI]: 0.661–0.783). Independent prognostic factors for PFS included CA19-9 levels, albumin, and bilirubin, with a C-index of 0.678 (95% CI: 0.612–0.743) for the nomogram predicting PFS. Calibration curves demonstrated strong concordance between predicted and observed outcomes, while DCA and Kaplan-Meier curves further supported the clinical utility of the nomogram.ConclusionThe nomogram developed in this study demonstrated favorable performance in predicting the prognosis of ICC patients undergoing immunotherapy. Additionally, our findings, for the first time, identified probiotics as a potential prognostic marker for immunotherapy. This prognostic model has the potential to enhance patient selection for immunotherapy and improve clinical decision-making.

Overall survival and disease-free survival prediction in Chinese women with breast cancer aged 70 years or older by using nomograms.

By analyzing the existing data of this study, a prediction tool for the overall breast cancer survival and disease-free survival (DFS) of elderly women was established. Clinicopathologic data were collected from elderly women with BC who were admitted to the Tianjin Medical University Cancer Institute and Hospital from August 2014 to December 2017. Independent prognostic factors for BC in elderly patients were confirmed using the Cox proportional hazards model. Nomograms were developed with these factors for predicting the 3- and 5-year overall survival (OS) as well as DFS. The nomograms' discrimination ability and calibration were assessed through the area under the curve (AUC), concordance index (C-index), decision curve analysis (DCA), and calibration plots. We enroled 889 elderly patients with BC, and the results showed that the 3-year OS rate was 93.4% (95%CI = 91.8%-95.1%), the 3-year DFS rate was 87.8% (95%CI = 85.7%-90.0%), the 5-year OS rate was 85.6% (95%CI = 83.3%-87.9%), and the 5-year DFS rate was 80.1%(95%CI = 77.5%-82.8%). The corrected C-indices of the OS and DFS nomograms were 0.799 and 0.667, respectively (95%CI = 0.767-0.830 and 0.632-0.702, respectively). Relatively high AUC values were shown by the nomograms for estimating OS and DFS. The DCA revealed that the constructed nomograms had net benefits for clinical application. The calibration curves demonstrated an excellent correspondence between the data predicted by the nomograms and the actual survival data. Survival curves indicated that risk stratification could differentiate OS and DFS. This study developed novel and practical nomograms for individual prediction of DFS and OS in elderly BC patients. These nomograms can predict 3- and 5-year OS as well as DFS in the elderly BC patient population, thereby enabling personalized risk assessment and risk-based therapy.

Clinical characteristics, survival and prognostic nomogram for patients with esophageal mucinous adenocarcinoma: a SEER population-based analysis.

Esophageal mucinous adenocarcinoma (MAC) is a rare adenocarcinoma (AC) subtype. Limited research exists on its incidence, survival rates, and treatment responses. This study utilized the Surveillance, Epidemiology, and End Results (SEER) database to compare the clinical characteristics and prognoses of patients with esophageal MAC, AC, and signet-ring cell carcinoma (SRC), and developed nomograms to predict outcomes. Patient information was retrieved from the SEER database from 2004 to 2015. The baseline characteristics were balanced using propensity score matching (PSM). Prognostic factors for esophageal MAC patients were identified by univariate and multivariate Cox analyses. A total of 497 esophageal MAC, 21,109 esophageal AC and 1,144 esophageal SRC patients were selected. MAC patients were more likely to have a higher pathological grade (P<0.001), and later T stage (P<0.001) and American Joint Committee on Cancer (AJCC) stage (P=0.003) than AC patients. The proportion of grade I-II MAC patients was higher than that of SRC patients. The overall survival (OS) and cancer-specific survival (CSS) of MAC patients were similar to those of AC patients. However, MAC patients had significantly better OS and CSS than SRC patients. After PSM analysis, the OS and CSS of MAC patients were similar to those of AC and SRC patients (all P>0.05). In MAC patients, N stage, M stage, and surgery were independent predictive factors for both OS and CSS. The area under the curve (AUC) and calibration curves demonstrated high precision and discrimination. Decision curve analysis (DCA) demonstrated that the CSS and OS nomograms have high potential clinical value. Esophageal MAC patients had similar survival compared with esophageal AC and esophageal SRC patients. The nomograms provide OS and CSS predictions for MAC patients, to aid clinicians in predicting patients' prognoses.

Clinical significance of the modified Naples prognostic score in patients with stage II-III colon cancer undergoing curative resection: a retrospective study from the real world.

The Naples prognostic score (NPS) determined by the nutritional and inflammatory condition of an individual is attracting growing attention for predicting postoperative outcomes in a variety of malignancies. The study aimed to assess the clinical significance of a modified NPS (M-NPS) and establish and validate nomograms incorporating M-NPS in curative stage II-III colon cancer patients. We retrospectively analyzed 328 stage II-III colon cancer patients receiving radical surgical resection at our hospital from January 2011 to December 2016. Kaplan-Meier (KM) survival analysis and Cox regression analysis were executed for overall survival (OS) and cancer-specific survival (CSS). Independent predictive indicators were applied to develop nomograms. The model's performance was evaluated using many different methods. Of a total of 328 cases, 153 cases were in group 0, 145 in group 1, and 30 in group 2. In terms of OS or CSS, there were obvious differences between groups 0 and 1, and between groups 0 and 2. Age, obstruction, N stage, gross tumor type, and M-NPS group were independent prognostic indicators for OS, while obstruction, gross tumor type, M-NPS group, and N stage were independent predictive parameters for CSS. Furthermore, the training and validation sets were randomly allocated among a cohort of 328 patients. OS and CSS prediction nomograms were developed. In the training and validation cohort, the C-index and ROC analysis showed good discrimination, calibration curves exhibited an excellent level of consistency between model-predicted survival and actual survival outcomes, and DCA curves demonstrated good clinical performance. M-NPS is a reliable survival predictor in patients with curative stage II-III colon cancer. Nomograms incorporating M-NPS for OS and CSS have good predictive performance and clinical utility.

The significance of risk stratification through nomogram-based assessment in determining postmastectomy radiotherapy for patients diagnosed with pT1 − 2N1M0 breast cancer

ObjectiveTo explore the high-risk factors affecting the prognosis of pT1 − 2N1M0 patients after mastectomy, establish a nomogram prediction model, and screen the radiotherapy benefit population.MethodThe clinical data of 936 patients with pT1 − 2N1M0 who underwent mastectomy in the fourth hospital of Hebei Medical University from 2010 to 2016 were retrospectively analyzed. There were 583 patients received postmastectomy radiotherapy(PMRT), and 325 patients without PMRT. Group imbalances were mitigated using the propensity score matching (PSM) method, and the log-rank test was employed to compare overall survival (OS) and disease-free survival (DFS) between the cohorts. The efficacy of PMRT across various risk groups was evaluated using a nomogram model.ResultThe median follow-up period was 98 months, Patients who received PMRT demonstrated significantly improved 5-year and 8-year OS and DFS compared to those who did not (P < 0.001). Multivariate analysis revealed that age, primary tumor site, positive lymph node, stage, and Ki-67 level independently influenced OS, while age, primary tumor site, and stage independently affected DFS. PMRT drastically enhanced OS in the high-risk group (P = 0.001), but did not confer benefits in the low-risk and intermediate risk groups (P = 0.057, P = 0.099). PMRT led to a significant improvement in disease-free survival (DFS) among patients in the intermediate and high-risk groups (P = 0.036, P = 0.001), whereas the low-risk group did not experience a significant benefit (P = 0.475).ConclusionAge ≤ 40 years, tumor located in the inner quadrant or central area, T2 stage, 2–3 lymph nodes metastasis, and Ki67 > 30% were the high-risk factors affecting the prognosis of this cohort of patients. In OS nomogram, patients with a risk score of 149 or higher who received PMRT exhibited improved OS. Similarly, in DFS nomogram, patients with a risk score of 123 or higher who received PMRT demonstrated enhanced DFS.

A novel nomogram based on the number of positive lymph nodes can predict the overall survival of patients with pancreatic head cancer after radical surgery

BackgroundThis study aimed to construct a novel nomogram based on the number of positive lymph nodes to predict the overall survival of patients with pancreatic head cancer after radical surgery.Materials and methods2271 and 973 patients in the SEER Database were included in the development set and validation set, respectively. The primary clinical endpoint was OS (overall survival). Univariate and multivariate Cox regression analyses were used to screen independent risk factors of OS, and then independent risk factors were used to construct a novel nomogram. The C-index, calibration curves, and decision analysis curves were used to evaluate the predictive power of the nomogram in the development and validation sets.ResultsAfter multivariate Cox regression analysis, the independent risk factors for OS included age, tumor extent, chemotherapy, tumor size, LN (lymph nodes) examined, and LN positive. A nomogram was constructed by using independent risk factors for OS. The C-index of the nomogram for OS was 0.652 [(95% confidence interval (CI): 0.639–0.666)] and 0.661 (95%CI: 0.641–0.680) in the development and validation sets, respectively. The calibration curves and decision analysis curves proved that the nomogram had good predictive ability.ConclusionsThe nomogram based on the number of positive LN can effectively predict the overall survival of patients with pancreatic head cancer after surgery.

Prognostic nomogram of overall survival for radiation therapy in hepatocellular carcinoma: a population study based on the SEER database and an external cohort.

Radiotherapy (RT) plays an important role in the treatment of hepatocellular carcinoma (HCC). To screen patients who benefit most from RT, a nomogram for survival prediction of RT based on a large sample of patients with HCC was created and validated. A total of 2,252 cases collected from the Surveillance, Epidemiology, and End Results (SEER) database were separated into a training or an internal validation cohort in a 7:3 ratio (n = 1,565:650). An external validation cohort of cases from our institute was obtained (n = 403). LASSO regression and Cox analyses were adopted to develop a nomogram for survival prediction. The decision curve analysis (DCA), calibration curve, and time-dependent receiver operating characteristic curves (TROCs) demonstrated the reliability of the predictive model. For patients with HCC who received RT, the analyses revealed that the independent survival prediction factors were T stage {T2 vs. T1, hazard ratio (HR) =1.452 [95% CI, 1.195-1.765], p < 0.001; T3 vs. T1, HR = 1.469 [95% CI, 1.168-1.846], p < 0.001; T4 vs. T1, HR = 1.291 [95% CI, 0.951-1.754], p = 0.101}, N stage (HR = 1.555 [95% CI, 1.338-1.805], p < 0.001), M stage (HR = 3.007 [95% CI, 2.645-3.418], p < 0.001), max tumor size (>2 and ≤5 vs. ≤2 cm, HR = 1.273 [95% CI, 0.992-1.633], p = 0.057; >5 and ≤10 vs. ≤2 cm, HR = 1.625 [95% CI, 1.246-2.118], p < 0.001; >10 vs. ≤2 cm, HR = 1.784 [95% CI, 1.335-2.385], p < 0.001), major vascular invasion (MVI) (HR = 1.454 [95% CI, 1.028-2.057], p = 0.034), alpha fetoprotein (AFP) (HR = 1.573 [95% CI, 1.315-1.882], p < 0.001), and chemotherapy (HR = 0.511 [95% CI, 0.454-0.576], p < 0.001). A nomogram constructed with these prognostic factors demonstrated outstanding predictive accuracy. The area under the curve (AUC) in the training cohort for predicting overall survival (OS) at 6, 12, 18, and 24 months was 0.824 (95% CI, 0.803-0.846), 0.824 (95% CI, 0.802-0.845), 0.816 (95% CI, 0.792-0.840), and 0.820 (95% CI, 0.794-0.846), respectively. The AUCs were similar in the other two cohorts. The DCA and calibration curve demonstrated the reliability of the predictive model. For patients who have been treated with RT, a nomogram constructed with T stage, N stage, M stage, tumor size, MVI, AFP, and chemotherapy has good survival prediction ability.

Development and Validation of a Prognostic Nomogram Model for HER2-Positive Male Breast Cancer Patients.

HER2-positive male breast cancer (MBC) is a rare condition that has a poor prognosis. The purpose of this study was to establish a nomogram model for predicting the prognosis of HER2-positive MBC patients. 240 HER2-positive MBC patients from 2004 to 2015 were retrieved from the surveillance, epidemiology, and end results (SEER) database. All HER2-positive MBC patients were divided randomly into training (n = 144) and validation cohorts (n = 96) according to a ratio of 6:4. Univariate and multivariate Cox regression analyses were used to determine the prognostic factors associated with HER2-positive MBC patients. A clinical prediction model was constructed to predict the overall survival of these patients. The nomogram model was assessed by using receiver operating characteristics (ROC) curves, calibration plots and decision curve analysis (DCA). The Cox regression analysis showed that T-stage, M-stage, surgery and chemotherapy were independent risk factors for the prognosis of HER2-positive MBC patients. The model could also accurately predict the Overall survival (OS) of the patients. In the training and validation cohorts, the C indexes of the OS nomograms were 0.746 (0.677-0.815) and 0.754 (0.679-0.829), respectively. Calibration curves and DCA verified the reliability and accuracy of the clinical prediction model. In conclusion, the predictive model constructed had good clinical utility and can help the clinician to select appropriate treatment strategies for HER2-positive MBC patients.

A nomogram for overall survival of second primary cancers following upper-tract urothelial carcinoma: a SEER population-based study.

With improving prognosis in upper-tract urothelial carcinoma (UTUC), an increasing number of second primary malignancies (SPMs) are being identified. However, there is limited research on SPMs following UTUC. This study aims to evaluate the risk of SPMs in UTUC patients and create a nomogram to predict their survival rates. Utilizing data from the Surveillance, Epidemiology, and End Results (SEER) database, we assessed the risk of SPMs among UTUC patients. Additionally, we developed and validated an overall survival (OS) nomogram for SPM patients post-UTUC diagnosis. The prevalence of SPMs among UTUC patients was 30.23%, with solid tumors being the most prevalent type of second malignancy, constituting 95.30% of all SPMs. The overall risk of SPMs was significantly elevated across all subgroups. Univariate and multivariate Cox regression analyses identified age, race, gender, UTUC SEER historic stage, surgery, SPM site, histologic type, grade, and SEER historic stage as independent prognostic factors for SPM OS. Subsequently, we developed a nomogram for predicting SPM OS. The C-index for the training and validation sets were 0.72 [95% confidence interval (CI): 0.70-0.74] and 0.71 (95% CI: 0.67-0.75), respectively. The area under the curve (AUC) demonstrated good performance of our model in predicting the 3-year (0.73 and 0.737) and 5-year (0.723 and 0.733) OS of SPMs in both sets. This study represents the first comprehensive analysis of SPM incidence in UTUC patients and introduces a nomogram for predicting SPM prognosis.

Nomograms to predict the long-term prognosis for non-metastatic invasive lobular breast carcinoma: a population-based study

Invasive lobular breast carcinoma (ILC) is one potential subset that “clinicopathologic features” can conflict with “long-term outcome” and the optimal management strategy is unknown in such discordant situations. The present study aims to predict the long-term, overall survival (OS) and cancer-specific survival (CSS) of ILC. The clinical information of patients with non-metastatic ILC was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2020. A total of 31451 patients were enrolled and divided into the training cohort (n=22,017) and validation cohort (n=9434). The last follow-up was December, 31, 2020 and the median follow-up period was 99 months (1–203). Age, marriage, estrogen (ER) status, progesterone (PR) status, grade, tumor size, lymph node ratio (LNR) and combined summary (CS) stage were prognostic factors for both OS and CSS of ILC, whereas chemotherapy and radiation were independent protect factors for OS. The nomograms exhibited satisfactory discriminative ability. For the training and validation cohorts, the C-index of the OS nomogram was 0.765 (95% CI 0.762–0.768) and 0.757 (95% CI 0.747–0.767), and the C-index of the CSS nomogram were 0.812 (95% CI 0.804–0.820) and 0.813 (95% CI 0.799–0.827), respectively. Additionally, decision curve analysis (DCA) demonstrated that the nomograms had superior predictive performance than traditional American Joint Committee on Cancer (AJCC) TNM stage. The novel nomograms to predict long-term prognosis based on LNR are reliable tools to predict survival, which may assist clinicians in identifying high-risk patients and devising individual treatments for patients with ILC. Our findings should aid public health prevention strategies to reduce cancer burden. We provide two R/Shiny apps (https://ilc-survival2024.shinyapps.io/osnomogram/; https://ilc-survival2024.shinyapps.io/cssnomogram/) to visualize findings.

The mitotic rate as a prognostic biomarker after preoperative radiotherapy for high-grade limb/trunk soft tissue sarcoma

PurposeCurrently there is no generally accepted standardized approach for the pathological evaluation of soft tissue sarcoma (STS) histology appearance after preoperative radiotherapy (PORT). This study aimed to investigate the prognostic value of pathological appearance after PORT for patients with high-grade limb/trunk STS. MethodsA cohort of 116 patients with high-grade STS of the limb/trunk treated with PORT followed by resection were evaluated. Patient characteristics, imaging tumor morphology (size, volume), and histopathology (mitotic and necrosis rate, viable cell, hyalinization/fibrosis cytopathic effect) were reviewed and reassessed. Disease free survival (DFS) and overall survival (OS) were calculated using the Kaplan-Meier method, and the hazard ratio was derived from Cox proportional hazard models. Two predictive nomograms were calculated based on significant predictors identified. ResultsThe 5-year DFS and OS were 52.9% and 70.3%, respectively. Tumor size before (HR:1.07, 95%CI: 1.01–1.14) and after PORT (HR:1.08, 95%CI: 1.01–1.14), tumor volume (HR:1.06, 95%CI: 1.01–1.12), mitotic rate after PORT (HR: 1.06, 95%CI: 1.02–1.11), mitotic rate change after PORT (HR:1.04, 95%CI:1.00–1.09) were independent risk factors for DFS. Tumor size before (HR:1.08, 95%CI: 1.03–1.14) and after PORT (HR:1.09, 95%CI: 1.04–1.15), tumor volume (HR:1.05, 95%CI: 1.01–1.09), mitotic rate after PORT (HR: 1.09, 95%CI: 1.04–1.13), mitotic rate change after PORT (HR:1.05, 95%CI:1.01–1.09) were independent risk factors for OS. The C-index of pathologic predictive nomogram based on mitotic rate for DFS and OS were 0.67 and 0.73, respectively. The C-index of morphology-pathology predictive nomogram for OS was 0.79. ConclusionTumor size before and after PORT, tumor volume, mitotic rate after PORT, mitotic rate change after PORT were independent risk factors for DFS and OS in high-grade STS patients treated with PORT. The mitotic rate, independent of tumor morphology, showed its potential as a prognostic biomarker for pathologic evaluation in patients treated with PORT.

Neoadjuvant chemotherapy for resectable gastric cancer is associated with better outcomes than neoadjuvant chemoradiation.

National Comprehensive Cancer NetworkGuidelines recommend neoadjuvant chemotherapy (CTx) or chemoradiation (CRTx) for advanced resectable gastric cancer, irrespective of the tumor location. The aim of this study is to compare survival benefits between neoadjuvant CTx and CRTx using the National Cancer Database (NCDB). Using the NCDB, we retrospectively reviewed patients who underwent gastrectomy after neoadjuvant CRTx or CTx between 2004 and 2018. The cohort included 14 266 patients, with 6458 (45.3%) receiving neoadjuvant CTx and 7808 (54.7%) receiving neoadjuvant CRTx. Both treatment groups exhibited significant differences in various demographic and clinical factors, including sex, age, race, tumor locations, stages, and adjuvant treatment (all p < 0.001). While the complete pathological response was more prevalent in the CRTx group (p < 0.001), overall survival (OS) was significantly extended in the CTx group (p < 0.001). Subgroup survival analyses, accounting for tumor location and clinical/pathological stage, consistently revealed longer OS in the CTx group (p < 0.001). The direct comparison showed an approximately 20%-30% improved 5-year OS in the CTx group across the majority of American Joint Committee on Cancer (AJCC) T/N category tables. Multivariate analysis confirmed neoadjuvant CTx was an independent protective factor (hazard ratio = 0.811; p < 0.001). A nomogram for OS based on multivariate analysis was also proposed, revealing a significant improvement in the c-index compared to the current AJCC staging (0.654 vs. 0.596). Patients undergoing neoadjuvant CRTx demonstrated significantly shorter survival compared to patients undergoing CTx at the same stage. The current AJCC staging may lead to an overestimation of survival in patients with neoadjuvant CRTx.