Nomogram For Overall Survival Research Articles

Clinical calculator based on CT and clinicopathologic characteristics predicts short-term prognosis following resection of microsatellite-stabilized diffuse gastric cancer.

Although microsatellite stability/Epithelial-mesenchymal transition (MSS/EMT) subtypes have been reported in multiple cancer prognosis studies, strong confounding factors between MSS/EMT (usually with Lauren's diffuse phenotype) and diffuse gastric cancer (GC) may obscure the independent prognostic value of diffuse GC. Additionally, recent studies suggest a strong correlation between mural stratification based on CT and diffuse GC. This study aims to investigate potential prognostic factors of MSS diffuse GC using mural stratification and to develop a risk assessment model. This retrospective study included 131 patients with MSS diffuse GC who underwent radical surgery. Univariate and multivariate Cox proportional hazards regression analysis was used to identify model predictors and construct a nomogram for overall survival (OS) and recurrence-free survival (RFS) risks. The model's performance was evaluated using ROC, accuracy, and C-index. Internal validation of the model was conducted using the bootstrap resampling method. Among 131 cases, 60 cases (45.8%) exhibited grade 2 mural stratification, which correlated with a poorer tumor prognosis and a more invasive phenotype. Furthermore, a nomogram for predicting OS and RFS prognosis was established based on multivariate results (age, extranodal invasion, mural stratification, and/or P53). The nomogram demonstrated excellent performance, with an AUC of 0.859 (95% CI 0.794-0.924) for OS and 0.859 (95% CI 0.789-0.929) for RFS. Internal validation using 1000 bootstrap samples yielded AUC values of 0.845 and 0.846 for OS and RFS, respectively. Grade 2 mural stratification based on CT imaging revealed a more aggressive invasive phenotype, characterized by increased LN metastasis, higher rates of peritoneal metastasis, and a poorer short-term prognosis. Furthermore, the CT phenotype-based nomogram demonstrates favorable discrimination and calibration, enabling convenient individual short-term prognostic evaluation following resection of MSS diffuse GC.

Construction and validation of an innovative prognostic nomogram for overall survival in cervical cancer patients with lung metastasis: an analysis utilizing the SEER database

PurposeTo facilitate patient consultation and assist in clinical decision-making, we developed a predictive model to analyze the overall survival (OS) rate of cervical cancer patients with concurrent lung metastasis for 6 months, 1 year, or 2 years.MethodsWe extracted data on patients diagnosed with cervical cancer and concurrent lung metastasis between 2010 and 2020 from the Surveillance, Epidemiology, and End Results (SEER) database. Through a random assignment process, these patients were allocated to either a training cohort or a validation cohort, maintaining a 7:3 ratio. Utilizing both univariate and multivariate Cox regression analyses, we determined the independent prognostic factors influencing OS. To enhance predictive accuracy, we developed a nomogram model incorporating these identified independent prognostic variables. Model effectiveness was subsequently assessed using various metrics, including receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).ResultsWe gathered data on 1330 patients diagnosed with cervical cancer with lung metastases. An OS nomogram was developed, accounting for factors such as histological type, presence of metastases in other organs (brain, liver), surgical interventions, radiation therapy, and chemotherapy. The ROC curves, calibration plots, and DCA curves demonstrated the commendable predictive performance of the nomogram in assessing the prognosis of cervical cancer patients with lung metastases in both the training and validation cohorts.ConclusionBy utilizing clinical data from the SEER database, we have effectively devised a nomogram capable of predicting the 6-month, 1-year, and 2-year survival rates of cervical cancer patients with lung metastases. The nomogram boasts high accuracy, offering precise prognostic predictions. Its implementation can guide the formulation of individualized follow-up and treatment plans for enhanced patient care.

A Prognostic Model Based on the Log Odds Ratio of Positive Lymph Nodes Predicts Prognosis of Patients with Rectal Cancer.

This study aimed to compare the prognostic value of rectal cancer by comparing different lymph node staging systems, and a nomogram was constructed based on superior lymph node staging. Overall, 8700 patients with rectal cancer was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The area under the curve (AUC), the C index, and the Akaike informativeness criteria (AIC) were used to examine the predict ability of various lymph node staging methods. Prognostic indicators were assessed using univariate and multivariate COX regression, and further correlation nomograms were created after the data were randomly split into training and validation cohorts. To evaluate the effectiveness of the model, the C index, calibration curves, decision curves (DCA), and receiver operating characteristic curve (ROC) were used. We ran Kaplan-Meier survival analyses to look for variations in risk classification. While compared to the N-stage positive lymph node ratio (LNR), the log odds ratio of positive lymph nodes (LODDS) had the highest predictive effectiveness. Multifactorial COX regression analyses were used to create nomograms for overall survival (OS) and cancer-specific survival (CSS). The C indices of OS and CSS for this model were considerably higher than those for TNM staging in the training cohort. The created nomograms demonstrated good efficacy based on ROC, rectification, and decision curves. Kaplan-Meier survival analysis revealed notable variations in patient survival across various patient strata. Compared to AJCC staging, the LODDS-based nomograms have a more accurate predictive effectiveness in predicting OS and CSS in patients with rectal cancer.

Validating the 2023 FIGO staging system: A nomogram for endometrioid endometrial cancer and adenocarcinoma.

To find the factors impacting overall survival (OS) prognosis in patients with endometrioid endometrial carcinoma (EEC) and adenocarcinoma and to establish a nomogram model to validate the 2023 International Federation of Obstetrics and Gynecology (FIGO) staging system for endometrial cancer. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) training cohort. An independent validation cohort was obtained from the First Affiliated Hospital of Anhui Medical University between 2008 and 2023. Cox regression analysis identified independent prognostic factors for OS in EEC and adenocarcinoma patients. A nomogram predicting OS was developed and validated utilizing the C-index, calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). The relationship between the tumor grade and prognosis of EEC and adenocarcinoma was quantified using net reclassification improvement (NRI), propensity score matching (PSM), and Kaplan-Meier curves. Cox regression analysis identified age, race, marital status, tumor grade, tumor stage, tumor size, and chemotherapy as independent prognostic factors for OS. A nomogram for predicting OS was developed based on these factors. The C-indexes for the OS nomogram was 0.743 and 0.720 for the SEER training set and external validation set, respectively. The area under the ROC (AUC) for the OS nomogram was 0.755, 0.757, and 0.741 for the SEER data subsets and 0.844, 0.719, and 0.743 for the external validation subsets. Calibration plots showed high concordance between the nomogram-predicted and observed OS. DCA also demonstrated the clinical utility of the OS nomogram. NRI, PSM, and survival analyses revealed that tumor grade was the most important histopathological factor for EEC and adenocarcinoma prognosis. Seven independent prognostic variables for the OS of patients with EEC and adenocarcinoma were identified. The established OS nomogram has good predictive ability and clinical utility and validates the 2023 endometrial cancer FIGO staging system.

Nomograms Predict PFS and OS for SCLC Patients After Standardized Treatment: A Real-World Study.

This study aims to investigate the process of small cell lung cancer (SCLC) patients from achieving optimal efficacy to experiencing disease progression until death. It examines the predictive value of the treatment response on progression free survival (PFS) and overall survival (OS) of SCLC patients. We conducted a retrospective analysis on 136 SCLC patients diagnosed from 1992 to 2018. Important prognostic factors were identified to construct nomogram models. The predictive performance of the models was evaluated using the receiver operating characteristic curves and calibration curves. Survival differences between groups were compared using Kaplan-Meier survival curves. Subsequently, an independent cohort consisting of 106 SCLC patients diagnosed from 2014 to 2021 was used for validation. We constructed two nomograms to predict first-line PFS (PFS1) and OS of SCLC. The area under the receiver operating characteristic curves for the PFS1 nomogram predicting PFS at 3-, 6-, and 12-months were 0.919 (95% CI: 0.867-0.970), 0.908 (95% CI: 0.860-0.956) and 0.878 (95% CI: 0.798-0.958), and for the OS nomogram predicting OS at 6-, 12-, and 24-months were 0.814 (95% CI: 0.736-0.892), 0.819 (95% CI: 0.749-0.889) and 0.809 (95% CI: 0.678-0.941), indicating those two models with a high discriminative ability. The calibration curves demonstrated the models had a high degree of consistency between predicted and observed values. According to the risk scores, patients were divided into high-risk and low-risk groups, showing a significant difference in survival rate. And these findings were validated in another independent validation cohort. Based on the patients' treatment response after standardized treatment, we developed and validated two nomogram models to predict PFS1 and OS of SCLC. The models demonstrated good accuracy, reliability and clinical applicability by validating in an independent cohort.

Establishment and validation of a nomogram for predicting overall survival of upper-tract urothelial carcinoma with bone metastasis: a population-based study

BackgroundBone metastasis (BM) carries a poor prognosis for patients with upper-tract urothelial carcinoma (UTUC). This study aims to identify survival predictors and develop a prognostic nomogram for overall survival (OS) in UTUC patients with BM.MethodsThe Surveillance, Epidemiology, and End Results database was used to select patients with UTUC between 2010 and 2019. The chi-square test was used to assess the baseline differences between the groups. Kaplan–Meier analysis was employed to assess OS. Univariate and multivariate analyses were conducted to identify prognostic factors for nomogram establishment. An independent cohort was used for external validation of the nomogram. The discrimination and calibration of the nomogram were evaluated using concordance index (C-index), area under receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). All statistical analyses were performed using SPSS 23.0 and R software 4.2.2.ResultsThe mean OS for UTUC patients with BM was 10 months (95% CI: 8.17 to 11.84), with 6-month OS, 1-year OS, and 3-year OS rates of 41%, 21%, and 3%, respectively. Multi-organ metastases (HR = 2.21, 95% CI: 1.66 to 2.95, P < 0.001), surgery (HR = 0.72, 95% CI: 0.56 to 0.91, P = 0.007), and chemotherapy (HR = 0.37, 95% CI: 0.3 to 0.46, P < 0.001) were identified as independent prognostic factors. The C-index was 0.725 for the training cohort and 0.854 for the validation cohort, and all AUC values were > 0.679. The calibration curve and DCA curve showed the accuracy and practicality of the nomogram.ConclusionsThe OS of UTUC patients with BM was poor. Multi-organ metastases was a risk factor for OS, while surgery and chemotherapy were protective factors. Our nomogram was developed and validated to assist clinicians in evaluating the OS of UTUC patients with BM.

Prognostic value of preoperative combined neutrophil, monocyte, and lymphocyte scores in patients with renal cell carcinoma undergoing laparoscopic nephrectomy: A retrospective study.

In a multi-institutional clinical study, we assessed the prognostic significance of a novel indicator preoperative peripheral blood immune (PBIS) scores that combined ratios of preoperative lymphocyte, monocyte, and neutrophil of renal cell carcinoma (RCC) patients undergoing laparoscopic nephrectomy. Between January 2014 and December 2019, 438 patients with RCC were retrospectively analyzed in three centers. We used X-tile software to obtain the optimum cut-off values for neutrophils, monocytes, and lymphocytes to classify the patients. To assess the relationship between PBIS score and overall survival (OS), and cancer-specific survival (CSS) in patients with RCC by Kaplan-Meier survival curves and Cox regression analyses. In addition, predictive OS and CSS nomograms were constructed. The discriminative ability of nomogram and predictive performance accuracy were verified with consistency index (C-index), calibration curves, receiver operating curve (ROC) curves, decision curve analysis (DCA) curves, and time-dependent ROC curves. The optimum cutoff values for monocytes, lymphocytes, and neutrophils were 0.46, 1.01, and 4.50, respectively. We divided patients into four subgroups according to PBIS scores, which were significantly associated with M-stage (p = 0.008), T-stage (p < 0.001), N-stage (p = 0.006), and AJCC stage (p < 0.001). Multivariate Cox regression analysis revealed that RCC patients with lower PBIS scores showed a worse postoperative prognosis and served as an independent predictor of OS (p = 0.002) and CSS (p < 0.001). Ultimately, the nomograms based on PBIS scores demonstrated excellent predictive performance for OS (C-index: 0.770) and CSS (C-index: 0.828) through the analysis of calibration curves, ROC curves, DCA curves, and time-dependent ROC curves. PBIS score served as novel and effective predictor to accurately predict OS and CSS in patients with RCC receiving laparoscopic nephrectomy.

Impact of Preoperative Neutrophil to Prealbumin Ratio Index (NPRI) on Short-Term Complications and Long-Term Prognosis in Patients Undergoing Laparoscopic Radical Surgery for Colorectal Cancer.

This study aims to evaluate the impact and predictive value of the preoperative NPRI on short-term complications and long-term prognosis in patients undergoing laparoscopic radical surgery for colorectal cCancer (CRC). A total of 302 eligible CRC patients were included, assessing five inflammation-and nutrition-related markers and various clinical features for their predictive impact on postoperative outcomes. Emphasis was on the novel indicator NPRI to elucidate its prognostic and predictive value for perioperative risks. Multivariate logistic regression analysis identified a history of abdominal surgery, prolonged surgical duration, CEA levels ≥5 ng/mL, and NPRI ≥ 3.94 × 10-2 as independent risk factors for postoperative complications in CRC patients. The Clavien--Dindo complication grading system highlighted the close association between preoperative NPRI and both common and severe complications. Multivariate analysis also identified a history of abdominal surgery, tumor diameter ≥5 cm, poorly differentiated or undifferentiated tumors, and NPRI ≥ 2.87 × 10-2 as independent risk factors for shortened overall survival (OS). Additionally, a history of abdominal surgery, tumor maximum diameter ≥5 cm, tumor differentiation as poor/undifferentiated, NPRI ≥ 2.87 × 10-2, and TNM Stage III were determined as independent risk factors for shortened disease-free survival (DFS). Survival curve results showed significantly higher 5-year OS and DFS in the low NPRI group compared to the high NPRI group. The incorporation of NPRI into nomograms for OS and DFS, validated through calibration and decision curve analyses, attested to the excellent accuracy and practicality of these models. Preoperative NPRI independently predicts short-term complications and long-term prognosis in patients undergoing laparoscopic colorectal cancer surgery, enhancing predictive accuracy when incorporated into nomograms for patient survival.

The predictive value of next generation sequencing for matching advanced hepatocellular carcinoma patients to targeted and immunotherapy.

Targeted and Immunotherapy has emerged as a new first-line treatment for advanced hepatocellular carcinoma (aHCC). To identify the appropriate targeted and immunotherapy, we implemented next generation sequencing (NGS) to provide predictive and prognostic values for aHCC patients. Pretreatment samples from 127 HCC patients were examined for genomic changes using 680-gene NGS, and PD-L1 expression was detected by immunohistochemistry. Demographic and treatment data were included for analyses of links among treatment outcomes, drug responses, and genetic profiles. A prognostic index model for predicting benefit from treatment was constructed, taking into account of biomarkers, including TP53, TERT, PD-L1, and tumor mutation burden (TMB) as possible independent prognostic factors. The multivariate Cox regression analyses showed that PD-L1≥1% (HR 25.07, 95%CI 1.56 - 403.29, p=0.023), TMB≥5Mb (HR 86.67, 95% CI 4.00 - 1876.48, p=0.004), TERT MU (HR 84.09, 95% CI 5.23 - 1352.70, p=0.002) and TP53 WT (HR 0.01, 95%CI 0.00 - 0.47, p=0.022) were independent risk factors for overall survival (OS), even after adjusting for various confounders. A prognostic nomogram for OS was developed, with an area under the ROC curve of 0.91, 0.85, and 0.98 at 1-, 2-, and 3- year, respectively, and a prognostic index cutoff of 1.2. According to the cutoff value, the patients were divided into the high-risk group (n=29) and low-risk group (n=98). The benefit of targeted and immunotherapy in the low-risk group was not distinguishable according to types of agents. However, treatment of Atezolizumab and Bevacizumab appeared to provide longer OS in the high-risk group (12 months vs 9.2, 9, or 5 months for other treatments, p<0.001). The prognostic model constructed by PD-L1, TMB, TERT, and TP53 can identify aHCC patients who would benefit from targeted and immunotherapy, providing insights for the personalized treatment of HCC.

Predictors of cancer-specific survival and overall survival among patients aged ≥60 years with lung adenocarcinoma using the SEER database.

We developed a simple, rapid predictive model to evaluate the prognosis of older patients with lung adenocarcinoma. Demographic characteristics and clinical information of patients with lung adenocarcinoma aged ≥60 years were retrospectively analyzed using Surveillance, Epidemiology, and End Results (SEER) data. We built nomograms of overall survival and cancer-specific survival using Cox single-factor and multi-factor regression. We used the C-index, calibration curve, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) to evaluate performance of the nomograms. We included 14,117 patients, divided into a training set and validation set. We used the chi-square test to compare baseline data between groups and found no significant differences. We used Cox regression analysis to screen out independent prognostic factors affecting survival time and used these factors to construct the nomogram. The ROC curve, calibration curve, C-index, and DCA curve were used to verify the model. The final results showed that our predictive model had good predictive ability, and showed better predictive ability compared with tumor-node-metastasis (TNM) staging. We also achieved good results using data of our center for external verification. The present nomogram could accurately predict prognosis in older patients with lung adenocarcinoma.

Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients.

To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS). Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, and change rates of these were calculated. Multivariate Cox regression analysis showed ΔBMD, ΔSFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); ΔPMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms' predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms. CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably.

Prognostic Nomograms for Patients with Primary Sarcomatoid Carcinoma of The Urinary Bladder: Based on The SEER Database.

The present study aimed to develop nomograms based on the SEER database to predict the prognosis for patients with primary sarcomatoid carcinoma of the urinary bladder (SCUB). Patients with primary SCUB were identified in the Surveillance, Epidemiology, and End Results (SEER) database, between 1975 and 2017. Univariate and multivariable Cox analysis were conducted to identify the independent prognostic factors for developing the overall survival (OS) and cancer-specific survival (CSS) nomograms. Then, concordance index (C-index), receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the accuracy of the nomogram model. In addition, the model was further compared with TNM staging system. A total of 238 eligible patients with primary SCUB were selected from the SEER database. As suggested by Cox-analysis, age, sex, T stage, M stage, tumor size, and surgery type of primary site were identified as the independent factors for predicting both OS and CSS. We developed OS and CSS nomograms with a favorable C-index by using these prognostic factors. The C-indexes of the OS and CSS nomogram in the present study were 0.738 (0.701-0.775) and 0.763 (0.724-0.802), which were superior to those of the AJCC TNM staging with 0.621 (0.576-0.666) and 0.637 (0.588-0.686) respectively, showing better discriminatory ability. Subsequently, the ROC curves showed that the 1-, 3- and 5-year AUCs (area under the curve) of OS nomogram (i.e., 0.793, 0.807 and 0.793) were higher than those of the TNM stage((i.e., 0.659, 0.676, 0.659). Similarly, as for CSS model, them ((i.e., 0.823, 0.804 and 0.804) were aslo exceed those of TNM stage((i.e., 0.683, 0.682, 0.682). Furthermore, the calibration curves indicated a good consistency between the predictive survival and the actual survival. Finally, patients were stratified by risk, and Kaplan-Meier survival curve suggested that the prognosis of the low-risk group was significantly better than that of the high-risk group. We developed nomograms with the SEER database, which could help predict the prognosis of SCUB individuals more accurately.

Abstract 1108: The impact of adjuvant radiotherapy (ART) and survival prediction by machine learning in intracranial hemangiopericytoma (HPC)

Abstract Background: Intracranial HPC is a rare brain tumor that accounts for less than 1% of all primary central nervous system malignancies. Surgery is the primary treatment, and while radiation may reduce the risk of local recurrence, its overall effectiveness remains uncertain. Therefore, it is crucial to develop reliable predictions for personalized care and improved management. In this study, we used machine learning (ML) techniques to predict the survival of patients with intracranial HPC. Methods: Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database (2000-2019). Patients with other malignancies or lack of pathological diagnosis were excluded. T-tests and chi-square tests compared variables, while Kaplan-Meier, log-rank tests, and Cox regression identified prognostic factors for overall survival (OS) and cancer-specific survival (CSS). Patient records were randomly divided into training (80 %) and validation (20 %) sets. Univariate Cox regression and least absolute shrinkage and selection operator were used to identify the significant factors linked to survival, which were used to establish a nomogram for predicting the 3- and 5-year OS and CSS. Results: Of the 367 included patients, 121 underwent surgery and 246 underwent surgery + ART. The median survival time was 54 months. Patients who underwent surgery had 5-year OS rates of 61% and 86% for CSS, while those treated with surgery + ART had 81% and 96% survival rates, respectively. The two treatment groups showed significant differences in the OS and CSS (p&amp;lt;0.022 and p&amp;lt;0.017, respectively). Age ≥ 50 years was a poor prognostic factor for OS, whereas ART, localized stage and regional stage were good prognostic factors. No significant factors were associated with CSS. To construct the OS Nomogram, three predictors, age, stage, and treatment method, were chosen. In the training and validation sets, the C-indices are 0.799 and 0.736, respectively. The receiver operating characteristic curves (AUCs) for the 3- and 5-year OS in the training set were 0.594 and 0.617, respectively, whereas those in the validation set were 0.56 and 0.59. For the CSS nomogram, two factors, age and stage, were utilized. The C-indices for the training and validation sets are 0.814 and 0.774, respectively. In the training set, the AUCs for 3- and 5-year CSS were 0.585 and 0.605, respectively, and 0.563 in the validation set. The most important risk factor was the distant stage, while the most important protective factor was age less than 50 years. Conclusion: This study shows that ART has a significant impact on the treatment of Intracranial HPC and provides better survival outcomes. ML has been used to create prediction models to support personalized patient care. These findings provide valuable insights into the management of this rare brain tumor and the potential of data-driven approaches to improve treatment and outcomes. Citation Format: Sakhr Alshwayyat, Anas Alaa Al-Khalili, Ayah Erjan, Haya Kamal, Tala Abdulsalam, Mustafa Alshwayyat. The impact of adjuvant radiotherapy (ART) and survival prediction by machine learning in intracranial hemangiopericytoma (HPC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1108.

Evaluating Prognosis of Gastrointestinal Metastatic Neuroendocrine Tumors: Constructing a Novel Prognostic Nomogram Based on NETPET Score and Metabolic Parameters from PET/CT Imaging.

The goal of this study is to compare the prognostic performance of NETPET scores, based on gallium-68 DOTANOC (68Ga-DOTANOC) and fluorine-18 fluorodeoxyglucose (18F-FDG) Positron Emission Tomography-Computed Tomography (PET-CT), and PET-CT metabolic parameters in metastatic gastrointestinal neuroendocrine tumors (GI-NET), while constructing and validating a nomogram derived from dual-scan PET-CT. In this retrospective study, G1-G3 GI-NET patients who underwent 68Ga-DOTANOC and 18F-FDG PET scans were enrolled and divided into training and internal validation cohorts. Three grading systems were constructed based on NETPET scores and standardized uptake value maximum (SUVmax). LASSO regression selected variables for a multivariable Cox model, and nomograms predicting progression-free survival (PFS) and overall survival (OS) were created. The prognostic performance of these systems was assessed using time-dependent receiver-operating characteristic (ROC) curves, concordance index (C-index), and other methods. Nomogram evaluation involved calibration curves, decision curve analysis (DCA), and the aforementioned methods in both cohorts. In this study, 223 patients (130 males; mean age ± SD: 52.6 ± 12 years) were divided into training (148) and internal validation (75) cohorts. Dual scans were classified based on NETPET scores (D1-D3). Single 68Ga-DOTANOC and 18F-FDG PET-CT scans were stratified into S1-S3 and F1-F3 based on SUVmax. The NETPET score-based grading system demonstrated the best OS and PFS prediction (C-index, 0.763 vs. 0.727 vs. 0.566). Nomograms for OS and PFS exhibited superior prognostic performance in both cohorts (all AUCs > 0.8). New classification based on NETPET score predicts patient OS/PFS best. PET-CT-based nomograms show accurate OS/PFS forecasts.

The long-term outcomes of local tumor destruction versus partial nephrectomy for cT1a non-clear cell renal cell carcinoma and development of prognostic nomograms

PurposeThere is a lack of authoritative opinions on local tumor destruction (LTD) for clinical T1a (cT1a) non-clear cell renal cell carcinoma (nccRCC). We aim to compare the outcomes of cT1a nccRCC after partial nephrectomy (PN) or LTD and explore prognostic factors.MethodsPatients diagnosed with cT1a nccRCC receiving LTD or PN between 2000 and 2020 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. A 1:1 propensity score matching (PSM) was performed for patients receiving LTD and PN. Kaplan–Meier survival analysis, Cox regression analysis, competing risk regression models, and subgroup analysis were used to compare outcomes and identify prognostic factors. Prognostic nomograms were established and evaluated based on the multivariate models.ResultsA total of 3664 cT1a nccRCC patients were included. The LTD group had poorer overall survival (OS) and similar cancer-specific survival (CSS) compared with the PN group before and after PSM (p < 0.05), while the other-cause mortality rate of the LTD group was higher than that of the PN group. Age, marital status, household income, prior tumor history, interval between diagnosis and treatment, treatments, and tumor size were identified as independent predictive factors for OS. Age, tumor size, prior tumor history, and histological type were identified as independent predictive factors for CSS. Then the nomograms predicting OS and CSS were constructed based on these prognostic factors, which showed excellent performance in risk stratification and accuracy.ConclusionLTD could achieve comparable cancer-control effects as PN among cT1a nccRCC patients. The OS and CSS nomograms worked effectively for prognosis assessment.

Predictive nomograms of repeat intrahepatic recurrence and overall survival after radiofrequency ablation of recurrent colorectal liver metastases

Objectives This study was conducted to develop nomograms for predicting repeat intrahepatic recurrence (rIHR) and overall survival (OS), after radiofrequency ablation (RFA), treatment in patients with recurrent colorectal liver metastases (CLMs) after hepatectomy based on clinicopathologic features. Methods A total of 160 consecutive patients with recurrent CLMs after hepatectomy who were treated with ultrasound-guided percutaneous RFA from 2012 to 2022 were retrospectively included. Patients were randomly divided into a training cohort and a validation cohort, with a ratio of 8:2. Potential prognostic factors associated with rIHR and OS, after RFA, were identified by using the competing-risks and Cox proportional hazard models, respectively, and were used to construct the nomogram. The nomogram was evaluated by Harrell’s C-index and a calibration curve. Results The 1-, 2-, and 3-year rIHR rates after RFA were 58.8%, 70.2%, and 74.2%, respectively. The 1-, 3- and 5-year OS rates were 96.3%, 60.4%, and 38.5%, respectively. In the multivariate analysis, mutant RAS, interval from hepatectomy to intrahepatic recurrence ≤ 12 months, CEA level >5 ng/ml, and ablation margin <5 mm were the independent predictive factors for rIHR. Mutant RAS, largest CLM at hepatectomy >3 cm, CEA level >5 ng/ml, and extrahepatic disease were independent predictors of poor OS. Two nomograms for rIHR and OS were constructed using the respective significant variables. In both cohorts, the nomogram demonstrated good discrimination and calibration. Conclusions The established nomograms can predict individual risk of rIHR and OS after RFA for recurrent CLMs and contribute to improving individualized management.

Nomograms based on the lymphocyte–albumin–neutrophil ratio (LANR) for predicting the prognosis of nasopharyngeal carcinoma patients after definitive radiotherapy

Much evidence has accumulated to show that inflammation and nutritional status are associated with the prognosis of patients with various cancers. The present study was designed to explore the prognostic role of the LANR in NPC patients receiving definitive radiotherapy and to construct a nomogram for predicting patient survival. This study retrospectively reviewed 805 NPC patients (604 in the training cohort and 201 in the validation cohort) who received definitive radiotherapy between January 2013 and December 2019. The clinical data and pretreatment laboratory test data, including lymphocyte count, neutrophil count, and serum ALB concentration, were collected for all patients. The LANR was calculated as the albumin × lymphocyte/neutrophil ratio. Patients in the training cohort and validation cohort were categorized into high-LANR and low-LANR groups according to the corresponding cutoff values. The independent prognostic factors for overall survival (OS), progression-free survival (PFS), relapse-free survival (RFS), and metastasis-free survival (MFS) were evaluated by univariate and multivariate Cox regression analyses, and a nomogram was subsequently constructed. The performance of the nomogram was evaluated by the concordance index (C-index) and calibration curve. A low LANR (< 14.3) was independently associated with worse OS, PFS and MFS in NPC patients. A prognostic prediction nomogram was established based on T stage, N stage, Eastern Cooperative Oncology Group (ECOG) score, treatment modality, and LANR and was validated. The C-indices of the nomograms for OS and PFS in the training cohort were 0.729 and 0.72, respectively. The C-indices of the nomograms for OS and PFS in the validation cohort were 0.694 and 0.695, respectively. The calibration curve revealed good consistency between the actual survival and the nomogram prediction. Patients with NPC with low pretreatment LANR had a poor prognosis. The nomogram established on the basis of the LANR was efficient and clinically useful for predicting survival in NPC patients who underwent definitive radiotherapy.

Prediction of all-cause death and specific causes of death in patients with gastric cancer with liver metastasis: a Surveillance, Epidemiology, and End Results–based study

BackgroundGastric cancer (GC), considered the fifth most prevalent malignancy, is the fourth leading cause of cancer death worldwide. This cancer is heterogeneous and invasive and often metastasizes to the liver. The survival of patients with GC, especially cancer-specific survival (CSS), is a matter of concern to their families and medical workers in clinical practice. However, efficient tools for early risk prediction are lacking. Thus, this study aimed to develop a nomogram for forecasting the overall survival (OS) and CSS of patients with GC with liver metastasis (GCLM) based on the Surveillance, Epidemiology, and End Results (SEER) database. MethodsInformation on individuals with GCLM was acquired from the SEER database from January 2000 to December 2015. Patients’ data were randomized into the train cohort and the test cohort. The independent factors for CSS and OS were determined by univariate and multivariate competing risk analyses and Cox proportional hazards analysis, and the nomograms for predicting CSS and OS were constructed. The receiver operating characteristic curve and calibration curve were used to measure the accuracy and calibration of nomograms. ResultsOur study included 4372 patients with GCLM, with 3060 patients in the train set and 1312 in the test set. The mean follow-up period was 12.31 months. The independent factors influencing the OS of patients with GCLM were age, bone metastasis, chemotherapy, grade, lung metastasis, stage, primary site, radiotherapy, surgical primary site, T stage, and tumor size. The concordance Index (C-index) of the constructed nomogram for OS were 0.718 (SE, 0.004) in the train set and 0.0.680 (SE, 0.006) in the test set. The independent factors affecting the CSS of patients with GCLM were age, chemotherapy, grade, lung metastasis, stage, radiotherapy, regional lymph node positive, surgical primary site, and total number of tumors. The C-index for the constructed nomogram for CSS were 0.696 (SE, 0.005) in the train set and 0.696 (SE, 0.008) in the test set. ConclusionThe constructed nomograms showed satisfactory performance in predicting the OS and CSS of patients with GCLM, which can help clinicians formulate follow-up and rehabilitation strategies conducive to survival. At the same time, it can provide more family and social support for high-risk groups.

Risk stratification in gastric cancer lung metastasis: Utilizing an overall survival nomogram and comparing it with previous staging.

Gastric cancer (GC) is prevalent and aggressive, especially when patients have distant lung metastases, which often places patients into advanced stages. By identifying prognostic variables for lung metastasis in GC patients, it may be possible to construct a good prediction model for both overall survival (OS) and the cumulative incidence prediction (CIP) plot of the tumour. To investigate the predictors of GC with lung metastasis (GCLM) to produce nomograms for OS and generate CIP by using cancer-specific survival (CSS) data. Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance, epidemiology, and end results program database. The major observational endpoint was OS; hence, patients were separated into training and validation groups. Correlation analysis determined various connections. Univariate and multivariate Cox analyses validated the independent predictive factors. Nomogram distinction and calibration were performed with the time-dependent area under the curve (AUC) and calibration curves. To evaluate the accuracy and clinical usefulness of the nomograms, decision curve analysis (DCA) was performed. The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer (AJCC) staging system by utilizing Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). Finally, the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared. For the purpose of creating the OS nomogram, a CIP plot based on CSS was generated. Cox multivariate regression analysis identified eleven significant prognostic factors (P < 0.05) related to liver metastasis, bone metastasis, primary site, surgery, regional surgery, treatment sequence, chemotherapy, radiotherapy, positive lymph node count, N staging, and time from diagnosis to treatment. It was clear from the DCA (net benefit > 0), time-dependent ROC curve (training/validation set AUC > 0.7), and calibration curve (reliability slope closer to 45 degrees) results that the OS nomogram demonstrated a high level of predictive efficiency. The OS prediction model (New Model AUC = 0.83) also performed much better than the old Cox-AJCC model (AUC difference between the new model and the old model greater than 0) in terms of risk stratification (P < 0.0001) and verification using the IDI and NRI. The OS nomogram for GCLM successfully predicts 1- and 3-year OS. Moreover, this approach can help to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment.

Novel nomograms based on microvascular invasion grade for early-stage hepatocellular carcinoma after curative hepatectomy

Microvascular invasion (MVI) is a critical risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). This study aimed to firstly develop and validate nomograms based on MVI grade for predicting recurrence, especially early recurrence, and overall survival in patients with early-stage HCC after curative resection. We retrospectively reviewed the data of patients with early-stage HCC who underwent curative hepatectomy in the First Affiliated Hospital of Fujian Medical University (FHFU) and Mengchao Hepatobiliary Hospital of Fujian Medical University (MHH). Kaplan–Meier curves and Cox proportional hazards regression models were used to analyse disease-free survival (DFS) and overall survival (OS). Nomogram models were constructed on the datasets from the 70% samples of and FHFU, which were validated using bootstrap resampling with 30% samples as internal validation and data of patients from MHH as external validation. A total of 703 patients with early-stage HCC were included to create a nomogram for predicting recurrence or metastasis (DFS nomogram) and a nomogram for predicting survival (OS nomogram). The concordance indexes and calibration curves in the training and validation cohorts showed optimal agreement between the predicted and observed DFS and OS rates. The predictive accuracy was significantly better than that of the classic HCC staging systems.