Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer

Based on Cardiopulmonary Exercise Testing to Construct and Validate Nomogram of Long-Term Prognosis Within 12 Months for NSCLC

OBJECTIVE

Construction nomogram was to effectively predict long-term prognosis in patients with non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

The nomogram is developed by a retrospective study of 347 patients with NSCLC who underwent cardiopulmonary exercise testing (CPET) before surgery from May 2019 to February 2022. Cross-validation divided the data into a training cohort and validation cohort. The discrimination and accuracy ability of the nomogram were proofed by concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, the area under the curve (AUC), and time-dependent ROC in validation cohort.

RESULTS

Age, intraoperative blood loss, VO2 peak, and VE/VCO2 slope were included in the model of nomogram. The model demonstrated good discrimination and accuracy with C-index of 0.770 (95% CI: 0.712-0.822). AUC of 6 (AUC: 0.789, 95% CI: 0.726-0.851) and 12 months (AUC: 0.787, 95% CI: 0.724-0.850) were shown in ROC. Time-independent ROC maintains a good effect within 12 months.

CONCLUSION

We developed a nomogram based on CPET. This model has a good ability of discrimination and accuracy. It could help clinicians to make treatment decision in clinical decision.

A prognostic framework for predicting lung signet ring cell carcinoma via a machine learning based cox proportional hazard model

PURPOSE

Signet ring cell carcinoma (SRCC) is a rare type of lung cancer. The conventional survival nomogram used to predict lung cancer performs poorly for SRCC. Therefore, a novel nomogram specifically for studying SRCC is highly required.

METHODS

Baseline characteristics of lung signet ring cell carcinoma were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression and random forest analysis were performed on the training group data, respectively. Subsequently, we compared results from these two types of analyses. A nomogram model was developed to predict 1-year, 3-year, and 5-year overall survival (OS) for patients, and receiver operating characteristic (ROC) curves and calibration curves were used to assess the prediction accuracy. Decision curve analysis (DCA) was used to assess the clinical applicability of the proposed model. For treatment modalities, Kaplan-Meier curves were adopted to analyze condition-specific effects.

RESULTS

We obtained 731 patients diagnosed with lung signet ring cell carcinoma (LSRCC) in the SEER database and randomized the patients into a training group (551) and a validation group (220) with a ratio of 7:3. Eight factors including age, primary site, T, N, and M.Stage, surgery, chemotherapy, and radiation were included in the nomogram analysis. Results suggested that treatment methods (like surgery, chemotherapy, and radiation) and T-Stage factors had significant prognostic effects. The results of ROC curves, calibration curves, and DCA in the training and validation groups demonstrated that the nomogram we constructed could precisely predict survival and prognosis in LSRCC patients. Through deep verification, we found the constructed model had a high C-index, indicating that the model had a strong predictive power. Further, we found that all surgical interventions had good effects on OS and cancer-specific survival (CSS). The survival curves showed a relatively favorable prognosis for T0 patients overall, regardless of the treatment modality.

CONCLUSIONS

Our nomogram is demonstrated to be clinically beneficial for the prognosis of LSRCC patients. The surgical intervention was successful regardless of the tumor stage, and the Cox proportional hazard (CPH) model had better performance than the machine learning model in terms of effectiveness.

Nomogram for predicting invasive lung adenocarcinoma in small solitary pulmonary nodules

Background

This study aimed to construct a clinical prediction model and nomogram to differentiate invasive from non-invasive pulmonary adenocarcinoma in solitary pulmonary nodules (SPNs).

Method

We analyzed computed tomography and clinical features as well as preoperative biomarkers in 1,106 patients with SPN who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University between January 2020 and December 2021. Clinical parameters and imaging characteristics were analyzed using univariate and multivariate logistic regression analyses. Predictive models and nomograms were developed and their recognition abilities were evaluated using receiver operating characteristic (ROC) curves. The clinical utility of the nomogram was evaluated using decision curve analysis (DCA).

Result

The final regression analysis selected age, carcinoembryonic antigen, bronchus sign, lobulation, pleural adhesion, maximum diameter, and the consolidation-to-tumor ratio as associated factors. The areas under the ROC curves were 0.844 (95% confidence interval [CI], 0.817-0.871) and 0.812 (95% CI, 0.766-0.857) for patients in the training and validation cohorts, respectively. The predictive model calibration curve revealed good calibration for both cohorts. The DCA results confirmed that the clinical prediction model was useful in clinical practice. Bias-corrected C-indices for the training and validation cohorts were 0.844 and 0.814, respectively.

Conclusion

Our predictive model and nomogram might be useful for guiding clinical decisions regarding personalized surgical intervention and treatment options.

Integration of Radiomics and Immune-Related Genes Signatures for Predicting Axillary Lymph Node Metastasis in Breast Cancer

BACKGROUND

To develop a radiogenomics nomogram for predicting axillary lymph node (ALN) metastasis in breast cancer and reveal underlying associations between radiomics features and biological pathways.

MATERIALS AND METHODS

This study included 1062 breast cancer patients, 90 patients with both DCE-MRI and gene expression data. The optimal immune-related genes and radiomics features associated with ALN metastasis were firstly calculated, and corresponding feature signatures were constructed to further validate their performances in predicting ALN metastasis. The radiogenomics nomogram for predicting the risk of ALN metastasis was established by integrating radiomics signature, immune-related genes (IRG) signature, and critical clinicopathological factors. Gene modules associated with key radiomics features were identified by weighted gene co-expression network analysis (WGCNA) and submitted to functional enrichment analysis. Gene set variation analysis (GSVA) and correlation analysis were performed to investigate the associations between radiomics features and biological pathways.

RESULTS

The radiogenomics nomogram showed promising predictive power for predicting ALN metastasis, with AUCs of 0.973 and 0.928 in the training and testing groups, respectively. WGCNA and functional enrichment analysis revealed that gene modules associated with key radiomics features were mainly enriched in breast cancer metastasis-related pathways, such as focal adhesion, ECM-receptor interaction, and cell adhesion molecules. GSVA also identified pathway activities associated with radiomics features such as glycogen synthesis, integration of energy metabolism.

CONCLUSION

The radiogenomics nomogram can serve as an effective tool to predict the risk of ALN metastasis. This study provides further evidence that radiomics phenotypes may be driven by biological pathways related to breast cancer metastasis.

Two nomograms constructed for predicting the efficacy and prognosis of advanced non‑small cell lung cancer patients treated with anti‑PD‑1 inhibitors based on the absolute counts of lymphocyte subsets

BACKGROUND

Patients treated with immune checkpoint inhibitors (ICIs) are at risk of considerable adverse events, and the ongoing struggle is to accurately identify the subset of patients who will benefit. Lymphocyte subsets play a pivotal role in the antitumor response, this study attempted to combine the absolute counts of lymphocyte subsets (ACLS) with the clinicopathological parameters to construct nomograms to accurately predict the prognosis of advanced non-small cell lung cancer (aNSCLC) patients treated with anti-PD-1 inhibitors.

METHODS

This retrospective study included a training cohort (n = 200) and validation cohort (n = 100) with aNSCLC patients treated with anti-PD-1 inhibitors. Logistic and Cox regression were conducted to identify factors associated with efficacy and progression-free survival (PFS) respectively. Nomograms were built based on independent influencing factors, and assessed by the concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve.

RESULT

In training cohort, lower baseline absolute counts of CD3+ (P < 0.001) and CD4+ (P < 0.001) were associated with for poorer efficacy. Hepatic metastases (P = 0.019) and lower baseline absolute counts of CD3+ (P < 0.001), CD4+ (P < 0.001), CD8+ (P < 0.001), and B cells (P = 0.042) were associated with shorter PFS. Two nomograms to predict efficacy at 6-week after treatment and PFS at 4-, 8- and 12-months were constructed, and validated in validation cohort. The area under the ROC curve (AUC-ROC) of nomogram to predict response was 0.908 in training cohort and 0.984 in validation cohort. The C-index of nomogram to predict PFS was 0.825 in training cohort and 0.832 in validation cohort. AUC-ROC illustrated the nomograms had excellent discriminative ability. Calibration curves showed a superior consistence between the nomogram predicted probability and actual observation.

CONCLUSION

We constructed two nomogram based on ACLS to help clinicians screen of patients with possible benefit and make individualized treatment decisions by accurately predicting efficacy and PFS for advanced NSCLC patient treated with anti-PD-1 inhibitors.

A Nomogram for Predicting Overall Survival of Patients With Primary Spinal Cord Glioblastoma

OBJECTIVE

Primary spinal cord glioblastoma (PSCGBM) is a rare malignancy with a poor prognosis. To date, no prognostic nomogram for this rare disease was established. Hence, we aimed to develop a nomogram to predict overall survival (OS) of PSCGBM.

METHODS

Clinical data of patients with PSCGBM was retrospectively collected from the neurosurgery department of Soochow University Affiliated Second Hospital and the Surveillance Epidemiology and End Results database. Information including age, sex, race, tumor extension, extent of resection, adjuvant treatment, marital status, income, year of diagnosis and months from diagnosis to treatment were recorded. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors for PSCGBM. A nomogram was constructed to predict 1-year, 1.5-year, and 2-year OS of PSCGBM.

RESULTS

A total of 132 patients were included. The 1-year, 1.5-year, and 2-year OS were 45.5%, 29.5%, and 18.9%, respectively. Four variables: age groups, tumor extension, extent of resection, and adjuvant therapy, were identified as independent prognostic factors. The nomogram showed robust discrimination with a C-index value for the prediction of 1-year OS, 1.5-year OS, and 2-year of 0.71 (95% confidence interval [CI], 0.61-0.70), 0.72 (95% CI, 0.62-0.70), and 0.70 (95% CI, 0.61-0.70), respectively. The calibration curves exhibited high consistencies between the predicted and observed survival probability in this cohort.

CONCLUSION

We have developed and internally validated a nomogram for predicting the survival outcome of PSCGBM for the first time. The nomogram has the potential to assist clinicians in making individualized predictions of survival outcome of PSCGBM.

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

Purpose

To 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.

Methods

We 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).

Results

We 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.

Conclusion

By 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.

Treatment strategies for stage IA non-small cell lung cancer: A SEER-based population study

BACKGROUND

There are various therapeutic methods for treating stage IA (T1N0M0) non-small cell lung cancer (NSCLC), but no studies have systematically assessed multiple treatments to determine the most effective therapy.

METHODS

Stage IA NSCLC patient data collected between 2004 and 2018 were gathered from the Surveillance, Epidemiology, and End Results (SEER) database. Treatment modalities included observation, chemotherapy alone (CA), radiation alone (RA), radiation+chemotherapy (RC), surgery alone (SA), surgery+chemotherapy (SC), surgery+radiation (SR) and surgery+radiation+chemotherapy (SRC). Comparisons were made of overall survival (OS) and lung cancer-specific survival (LCSS) among patients based on different therapeutic methods by survival analysis.

RESULTS

Ultimately, 89147 patients with stage IA NSCLC between 2004 and 2018 were enrolled in this study. The order of multiple treatment modalities based on the hazard ratio (HR) for OS for the entire cohort revealed the following results: SA (HR: 0.20), SC (HR: 0.25), SR (HR: 0.42), SRC (HR: 0.46), RA (HR: 0.56), RC (HR: 0.72), CA (HR: 0.91) (P<0.001), and observation (HR: Ref). The SA group had the best OS and LCSS, and similar results were found in most subgroup analyses (all P<0.001). The order of surgical modalities based on the HR for OS for the entire cohort revealed the following results: lobectomy (HR: 0.32), segmentectomy (HR: 0.41), wedge resection (HR: 0.52) and local tumor destruction (HR: Ref). Lobectomy had the best effects on OS and LCSS, and similar results were found in all subgroup analyses (all P<0.001).

CONCLUSION

SA appeared to be the optimal treatment modality for patients with stage IA NSCLC, and lobectomy was associated with the best prognosis. There may be some indication and selection bias in our study, and the results of this study should be confirmed in a prospective study.

Does FDG PET-Based Radiomics Have an Added Value for Prediction of Overall Survival in Non-Small Cell Lung Cancer?

Objectives: Radiomics and machine learning are innovative approaches to improve the clinical management of NSCLC. However, there is less information about the additive value of FDG PET-based radiomics compared with clinical and imaging variables. Methods: This retrospective study included 320 NSCLC patients who underwent PET/CT with FDG at initial staging. VOIs were placed on primary tumors only. We included a total of 94 variables, including 87 textural features extracted from PET studies, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. We used the least absolute shrinkage and selection operator (LASSO) regression to select variables with the highest predictive value. Although several radiomics variables are available, the added value of these predictors compared with clinical and imaging variables is still under evaluation. Three hundred and twenty NSCLC patients were included in this retrospective study and underwent 18F-FDG PET/CT at initial staging. In this study, we evaluated 94 variables, including 87 textural features, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. Image-based predictors were extracted from a volume of interest (VOI) positioned on the primary tumor. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to reduce the number of variables and select only those with the highest predictive value. The predictive model implemented with the variables selected using the LASSO analysis was compared with a reference model using only a tumor stage and SUVmax. Results: NGTDM coarseness, SUVmax, and TNM stage survived the LASSO analysis and were used for the radiomic model. The AUCs obtained from the reference and radiomic models were 80.82 (95%CI, 69.01-92.63) and 81.02 (95%CI, 69.07-92.97), respectively (p = 0.98). The median OS in the reference model was 17.0 months in high-risk patients (95%CI, 11-21) and 113 months in low-risk patients (HR 7.47, p < 0.001). In the radiomic model, the median OS was 16.5 months (95%CI, 11-20) and 113 months in high- and low-risk groups, respectively (HR 9.64, p < 0.001). Conclusions: Our results indicate that a radiomic model composed using the tumor stage, SUVmax, and a selected radiomic feature (NGTDM_Coarseness) predicts survival in NSCLC patients similarly to a reference model composed only by the tumor stage and SUVmax. Replication of these preliminary results is necessary.

Low tumor interleukin-1β expression predicts a limited effect of adjuvant platinum-based chemotherapy for patients with completely resected lung adenocarcinoma: An identification and validation study

INTRODUCTION AND OBJECTIVES

Adjuvant platinum-based chemotherapy for completely resected non-small cell lung cancer is associated with modest improvement in survival; nevertheless, no validated biomarker exists for predicting the benefit or harm of adjuvant platinum-based chemotherapy.

MATERIALS AND METHODS

We simultaneously measured 27 cytokines in operative tumor specimens from a discovery cohort (n = 97) by multiplex immunoassay; half of the patients received adjuvant platinum-based chemotherapy, and the other half were observed. We tested possible prognostic and predictive factors in multivariate Cox models for overall survival (OS) and relapse-free survival (RFS), and a tree-based method was applied to detect predictive factors with respect to RFS. The results were validated in an independent validation cohort (n = 93).

RESULTS

Fifty-two of 97 (54 %) patients in the discovery cohort and 50 of 93 (54 %) in the validation cohort received adjuvant chemotherapy; forty-four (85 %) patients in the discovery cohort and 37 (74 %) in the validation cohort received four cycles as planned. In patients with low IL-1β-expressing tumors, RFS and OS were worse after adjuvant chemotherapy than after observation. The limited effect of adjuvant chemotherapy for patients with low IL-1β-expressing tumors was confirmed in the validation cohort. Additionally, RFS and OS were prolonged by adjuvant chemotherapy only in patients with high IL-1β-expressing tumors in the validation cohort.

CONCLUSIONS

This study identified and validated low tumor IL-1β expression as a potential biomarker of a limited response to adjuvant platinum-based chemotherapy after complete resection of pulmonary adenocarcinoma. This finding has the potential to inform adjuvant treatment decisions.

Using MRI radiomics to predict the efficacy of immunotherapy for brain metastasis in patients with small cell lung cancer

BACKGROUND

Brain metastases (BMs) are common in small cell lung cancer (SCLC), and the efficacy of immune checkpoint inhibitors (ICIs) in these patients is uncertain. In this study we aimed to develop and validate a radiomics nomogram based on magnetic resonance imaging (MRI) for intracranial efficacy prediction of ICIs in patients with BMs from SCLC.

METHODS

The training and validation cohorts consisted of 101 patients from two centers. The interclass correlation coefficient (ICC), logistic univariate regression analysis, and random forest were applied to select the radiomic features, generating the radiomics score (Rad-score) through the formula. Using multivariable logistic regression analysis, a nomogram was created by the combined model. The discrimination, calibration, and clinical utility were used to assess the performance of the nomogram. Kaplan-Meier curves were plotted based on the nomogram scores.

RESULTS

Ten radiomic features were selected for calculating the Rad-score as they could differentiate the intracranial efficacy in the training (area under the curve [AUC], 0.759) and the validation cohort (AUC, 0.667). A nomogram was created by combining Rad-score, treatment lines, and neutrophil-to-lymphocyte ratio (NLR). The training cohort obtained an AUC of 0.878 for the combined model, verified in the validation cohort (AUC = 0.875). Kaplan-Meier analyses showed the nomogram was associated with progression-free survival (PFS) (p = 0.0152) and intracranial progression-free survival (iPFS) (p = 0.0052) but not overall survival (OS) (p = 0.4894).

CONCLUSION

A radiomics nomogram model for predicting the intracranial efficacy of ICIs in SCLC patients with BMs can provide suggestions for exploring individual-based treatments for patients.

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

PURPOSE

The purpose of this study was to develop a functional clinical nomogram for predicting 8-year overall survival (OS) of patients with prostate cancer (PCa) primary based on peripheral lymphocyte.

PATIENTS AND METHODS

Using data from a single-institutional registry of 94 patients with PCa in China, this study identified and integrated significant prognostic factors for survival to build a nomogram. The discriminative ability was measured by concordance index (C-index) and ROC curves (Receiver Operating Characteristic Curves). And the predictive accuracy was measured by the calibration curves. Decision curve analyses (DCA) was used to measure the clinical usefulness.

RESULTS

A total of 94 patients were included for analysis. Five independent prognostic factors were identified by LASSO-Cox regression and incorporated into the nomogram: age, the T stage, the absolute counts of peripheral CD3(+)CD4(+) T lymphocytes, CD3(-)CD16(+)CD56(+) NK cells and CD4(+)/CD8(+) ratio. The area under the curve (AUC) values of the predictive model for 5-, 8-, and 10-year overall survival were 0.81, 0.76, and 0.73, respectively. The calibration curves for probability of 5-,8- and 10-year OS showed optimal agreement between nomogram prediction and actual observation. The stratification into different risk groups allowed significant distinction. DCA indicated the good clinical application value of the model.

CONCLUSION

We developed a novel nomogram that enables personalized prediction of OS for patients diagnosed with PCa. This finding revealed a relative in age and survival rate in PCa, and a more favorable prognosis in patients exhibiting higher levels of CD4 + T, CD4+/CD8 + ratio and CD3(-)CD16(+)CD56(+) NK cells specifically. This clinically applicable prognostic model exhibits promising predictive capabilities, offering valuable support to clinicians in informed decision-making process.

Adverse independent prognostic effect of initial lung cancer on female patients with second primary breast cancer: a propensity score-matched study based on the SEER database

OBJECTIVE

To investigate the prognostic impact of initial lung cancer (LC) on second primary breast cancer after LC (LC-BC) and further develop a nomogram for predicting the survival of patients.

METHODS

All patients diagnosed with LC-BC and first primary BC (BC-1) during 2000-2017 were collected from Surveillance, Epidemiology, and End Results database. Pathological features, treatment strategies and survival outcomes were compared between LC-BC and BC-1 before and after propensity score matching (PSM). Cox regression analysis was performed to identify the prognostic factors associated with LC in patients with LC-BC. Additionally, least absolute shrinkage and selection operator regression analysis was used to select clinical characteristics for nomogram construction, which were subsequently evaluated using the concordance index (C-index), calibration curve and decision curve analysis (DCA).

RESULTS

827 429 patients with BC-1 and 1445 patients with LC-BC were included in the analysis. Before and after PSM, patients with BC-1 had a better prognosis than individuals with LC-BC in terms of both overall survival (OS) and breast cancer-specific survival (BCSS). Furthermore, characteristics such as more regional lymph node dissection, earlier stage and the lack of chemotherapy and radiation for LC were found to have a stronger predictive influence on LC-BC. The C-index values (OS, 0.748; BCSS, 0.818), calibration curves and DCA consistently demonstrated excellent predictive accuracy of the nomogram.

CONCLUSION

In conclusion, patients with LC-BC have a poorer prognosis than those with BC-1, and LC traits can assist clinicians estimate survival of patients with LC-BC more accurately.

A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics

Background: Patients diagnosed with early-stage hepatocellular carcinoma (HCC) and diabetes mellitus (DM) are at a higher risk of experiencing complications and facing increased mortality rates. Hence, it is crucial to develop personalized clinical strategies for this particular subgroup upon their admission. The objective of this study is to determine the key prognostic factors in early HCC patients who received liver resection combined with DM and develop a practical personalized model for precise prediction of overall survival in these individuals. Method: A total of 1496 patients diagnosed hepatitis B virus (HBV) - related liver cancer from Beijing You'an Hospital were retrospectively enrolled, spanning from 1 January 2014, to 31 December 2019, and ultimately, 622 eligible patients of hepatocellular carcinoma (HCC) patients with diabetes were included in this present investigation. A multivariate COX regression analysis was conducted to identify prognostic factors that are independent of each other and develop a nomogram. The performance of the nomogram was evaluated using various statistical measures such as the C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both the training and validation groups. Survival rates were estimated using the Kaplan-Meier method. Results: The study included a total of 622 early HCC patients who underwent liver resection combined with DM. Random Forrest model and Multivariate Cox regression analysis revealed that drinking, tumor number, monocyte-to-lymphocyte ratio, white blood cell count and international normalized ratio at admission were identified as independent prognostic factors for early HCC patients who underwent liver resection combined with DM. The nomogram demonstrated good predictive performance in the training and validation cohorts based on the C-index values of 0 .756 and 0 .739 respectively, as well as the area under the curve values for 3-, 5-, and 8-year overall survival (0.797, 0.807, 0.840, and 0.725, 0.791, 0.855). Calibration curves and decision curve analysis indicated high accuracy and net clinical benefit rates. Furthermore, the nomogram successfully stratified enrolled patients into low-risk and high-risk groups based on their risk of overall survival. The difference in overall survival between these two groups was statistically significant in both the training and validation cohorts (p < 0.0001 and p = 0.0064). Conclusion: Our results indicate that the admission characteristics demonstrate a highly effective ability to predict the overall survival of early HCC patients who have undergone liver resection in combination with DM. The developed model has the potential to support healthcare professionals in making more informed initial clinical judgments for this particular subgroup of patients.

Predicting survival in patients with buccal cancer: A study based on SEER database and external validation in China

OBJECTIVE

Buccal mucosa cancer (BMC) is one of the most common oral cancers and has poor prognosis. The study aimed to develop and validate nomograms for predicting the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) of BMC patients.

METHODS

We collected and reviewed information on BMC patients diagnosed between 2004 and 2019 from the Surveillance Epidemiology and End Results database. Two nomograms were developed and validated to predict the OS and CSS based on predictors identified by univariate and multivariate Cox regression. An extra external validation was further performed using data from Sun Yat-sen Memorial Hospital (SYSMH).

RESULTS

A total of 3154 BMC patients included in this study were randomly assigned to training and validation groups in a 2:1 ratio. Independent prognostic predictors were identified, confirmed, and fitted into nomograms for OS and CSS, respectively. The C-indices are 0.767 (Training group OS), 0.801 (Training group CSS), 0.763 (Validation group OS), and 0.781 (Validation group OS), respectively. Moreover, the nomograms exhibited remarkable precision in forecasting and significant clinical significance, as evidenced by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA). The final validation using our data from SYSMH also showed high accuracy and substantial clinical benefits within the nomograms. The C-indices are 0.849 (SYSMH group OS) and 0.916 (SYSMH group CSS). These indexes are better than tumor, node, and metastasis stage based on prediction results.

CONCLUSIONS

The nomograms developed with great performance predicted 1-, 3-, and 5-year OS and CSS of BMC patients. Use of the nomograms in clinical practices shall bring significant benefits to BMC patients.

Toward a Prognostic Model for Mortality Risk in Older People Living With HIV: A Prospective Cohort Study From Southwestern China

OBJECTIVE

The existing prognostic models for mortality risk in people living with HIV (PLWH) may not be applicable for older PLWH because the risk factors were confined to biomarkers and clinical variables. We developed and validated a nomogram for the prognosis of all-cause mortality in older PLWH based on comprehensive predictors.

DESIGN

Prospective cohort study.

SETTING AND PARTICIPANTS

We included 824 participants aged ≥50 years (mean age, 64.0 ± 7.6 years) from 30 study sites in Sichuan, China, and followed up from Nov 2018 to Mar 2021.

METHODS

Data on demographics, biomarkers, and clinical indicators were extracted from the registry; mental and social factors were assessed by a survey. Elastic net was used to select predictors. A nomogram was developed based on Cox proportional hazards regression model to visualize the relative effect size (points) of the selected predictors. The prognostic index (PI) was calculated by summing points of all predictors to quantify mortality risk.

RESULTS

Predictive performance of PI from the nomogram was good, with area under the curve of 0.76 for the training set, and 0.77 for the validation set. Change in CD4 count, virological failure in antiretroviral therapy, and living with comorbidities were robust predictors. Depressive symptoms were an important predictor in men, those aged ≥65 years, and those with time of diagnosis <1 year; low social capital was an additional predictor in people aged <65. Mortality risk increased approximately 10-fold among participants whose PI was in the fourth quartile compared with those in the first quartile (hazard ratio, 9.5; 95% CI, 2.9-31.5).

CONCLUSION AND IMPLICATIONS

Although biological and clinical factors are crucial predictors, mental and social predictors are essential for specific groups. The developed nomogram is useful for identifying risk factors and groups at risk of mortality in older PLWH.

Nomogram for predicting major bleeding after off-pump coronary artery bypass grafting

OBJECTIVE

The purpose of this investigation is to develop a novel nomogram for predicting major bleeding following off-pump coronary artery bypass grafting (CABG).

METHODS

Between January 2012 and December 2022, 541 patients who underwent off-pump isolated primary CABG were included in a retrospective analysis. The primary outcome measure after off-pump CABG was major bleeding. Based on the outcomes of a multivariate analysis, nomograms were constructed. Using receiver operating characteristic analysis and calibration, the predictive accuracy of the nomograms was assessed. Using decision curve analysis (DCA), the clinical benefit of the nomograms was determined.

RESULTS

We categorized 399 and 142 patients in the "no major bleeding group" and "major bleeding group", respectively. Age (odds ratio (OR) 1.038; 95% confidence interval (CI) 1.009-1.068; p = 0.009), body mass index (OR 0.913; 95% CI 0.849-0.982; p = 0.014), hemoglobin (OR 0.958; 95% CI 0.945-0.971; p < 0.001), sodium (OR 0.873; 95% CI 0.807-0.945; p = 0.001), blood urea nitrogen (OR 1.198; 95% CI 1.073-1.338; p = 0.001), and operation time (OR 1.012; 95% CI 1.008-1.017; p < 0.001) were independent predictors for major bleeding after off-pump CABG. The model based on independent predictors exhibited excellent discrimination and calibration, with good agreement between actual and nomogram-estimated probabilities of generalization. DCA demonstrated that nomogram-assisted decisions have a greater positive benefit than treating all patients or none.

CONCLUSIONS

The plotted nomogram accurately predicted major bleeding outcomes following off-pump CABG and may therefore contribute to clinical decision-making, patient treatment, and consultation services.

Monocyte-to-High-Density Lipoprotein-Cholesterol Ratio Predicts Prognosis of Hepatocellular Carcinoma in Patients with Metabolic-Associated Fatty Liver Disease

Purpose

The incidence of non-B and non-C hepatocellular carcinoma (NBNC-HCC) is increasing globally. Metabolically associated fatty liver disease (MAFLD) has been a contributing factor to this rising trend in NBNC-HCC incidence. The monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) is a new prognostic marker that connects systemic inflammation with disorders of lipid metabolism. Therefore, MHR may be a potential prognostic predictor of patients with MAFLD-related HCC (MAFLD-HCC). This study aims to investigate the relationship between the MHR and prognosis of patients with MAFLD-HCC and construct a novel prognostic prediction tool for MAFLD-HCC.

Patients and Methods

This retrospective study of patients with MAFLD-HCC included training (n = 112) and internal validation (n = 37) cohorts. Univariate and multivariate Cox proportional hazard regression analysis was conducted to identify independent risk factors of survival. A visual nomogram was constructed to assess the performance of the two groups. Furthermore, receiver operating characteristic (ROC) curves and calibration curves were used to verify the prognostic discriminative ability of this nomogram, even in the MHR, ALBI grade, and MHR-ALBI model.

Results

Univariate and multivariate analyses revealed that extrahepatic metastases, Vascular invasion, Barcelona staging B, C, D, elevated ALBI Grade 3, C-reactive protein (CRP), and MHR were independent risk factors for the prognosis of MAFLD-HCC. Moreover, calibration plots showed good discrimination and consistency when the significant factors were entered into the nomogram. Meanwhile, the MHR strongly correlated with the prognosis of cancer under a background of MAFLD-HCC, with a sensitivity of 88.89% and a specificity of 79.61%. Importantly, the performance of the MHR alone (AUC = 86.2) was not only superior to the ALBI grade (AUC = 63.8) but was comparable to the combination of MHR and ALBI (AUC = 88.5).

Conclusion

The novel nomogram demonstrated good value in predicting the overall survival of patients with MAFLD-HCC. The MHR may be a potential predictor of prognosis.

Development of a nomogram for predicting survival in clinical T1N0M1 lung adenocarcinoma: a population-based study

OBJECTIVE

This study aimed to establish a prognostic model for clinical T1N0M1 (cT1N0M1) lung adenocarcinoma patients to evaluate the prognosis of patients in terms of overall survival (OS) rate and cancer-specific survival (CSS) rate.

METHODS

Data of patients with metastatic lung adenocarcinoma from 2010 to 2016 were collected from the Surveillance, Epidemiology and End Results database. Multivariate Cox regression analysis was conducted to identify relevant prognostic factors and used to develop nomograms. The receiver operating characteristic (ROC) curve and calibration curve are used to evaluate the predictive ability of the nomograms.

RESULTS

A total of 45610 patients were finally included in this study. The OS and CSS nomograms were constructed by same clinical indicators such as age (<60 years or ≥60 years), sex (female or male), race (white, black, or others), surgery, radiation, chemotherapy, and the number of metastatic sites, based on the results of statistical Cox analysis. From the perspective of OS and CSS, surgery contributed the most to the prognosis. The ROC curve analysis showed that the survival nomograms could accurately predict OS and CSS. According to the points obtained from the nomograms, survival was estimated by the Kaplan-Meier method, then cT1N0M1 patients were divided into three groups: low-risk group, intermediate-risk group, and high-risk group, and the OS ( P  < 0.001) and CSS ( P  < 0.001) were significantly different among the three groups.

CONCLUSION

The nomograms and risk stratification model provide a convenient and reliable tool for individualized evaluation and clinical decision-making of patients with cT1N0M1 lung adenocarcinoma.

Prediction of Survival Prognosis for Spinal Metastasis From Cancer of Unknown Primary: Derivation and Validation of a Nomogram Model

STUDY DESIGN

Retrospective and prospective cohort study.

OBJECTIVES

Survival estimation is necessary in the decision-making process for treatment in patients with spinal metastasis from cancer of unknown primary (SMCUP). We aimed to develop a novel survival prediction system and compare its accuracy with that of existing survival models.

METHODS

A retrospective derivation cohort of 268 patients and a prospective validation cohort of 105 patients with SMCUP were performed. Univariate and multivariable survival analysis were used to generate independently prognostic variables. A nomogram model for survival prediction was established by integrating these independent predictors based on the size of the significant variables' β regression coefficient. Then, the model was subjected to bootstrap validation with calibration curves and concordance index (C-index). Finally, predictive accuracy was compared with Tomita, revised Tokuhashi and SORG score by the receiver-operating characteristic (ROC) curve.

RESULTS

The survival prediction model included six independent prognostic factors, including pathology (P < .001), visceral metastases (P < .001), Frankel score (P < .001), weight loss (P = .005), hemoglobin (P = .001) and serum tumor markers (P < .001). Calibration curve of the model showed good agreement between predicted and actual mortality risk in 6-, 12-, and 24-month estimation in derivation and validation cohorts. The C-index was .775 in the derivation cohort and .771 in the validation cohort. ROC curve analysis showed that the current model had the best accuracy for SMCUP survival estimation amongst 4 models.

CONCLUSIONS

The novel nomogram system can be applied in survival prediction for SMCUP patients, and furtherly be used to give individualized therapeutic suggestions based on patients' prognosis.

Survival Predictive Nomograms for Non-Surgical Brain Metastases Patients From Non-Small Cell Lung Cancer Receiving Radiotherapy: A Population-Based Study

OBJECTIVE

A high number of Non-Small Cell Lung Cancer (NSCLC) patients with brain metastasis who have not had surgery often have a negative outlook. Radiotherapy remains a most common and effective method. Nomograms were developed to forecast the cancer-specific survival (CSS) and overall survival (OS) in NSCLC individuals with nonoperative brain metastases who underwent radiotherapy.

METHODS

Information was gathered from the Surveillance, Epidemiology, and End Results (SEER) database about patients diagnosed with NSCLC who had brain metastases not suitable for surgery. Nomograms were created and tested using multivariate Cox regression models to forecast CSS and OS at intervals of 1, 2, and 3 years.

RESULTS

The research involved 3413 individuals diagnosed with NSCLC brain metastases who had undergone radiotherapy but had not experienced surgery. These participants were randomly divided into two categories. The analysis revealed that gender, age, ethnicity, marital status, tumor location, tumor laterality, tumor grade, histology, T stage, N stage, chemotherapy, tumor size, lung metastasis, bone metastasis, and liver metastasis were significant independent predictors for OS and CSS. The C-index for the training set for predicting OS was .709 (95% CI, .697-.721), and for the validation set, it was .705 (95% CI, .686-.723), respectively. The C-index for predicting CSS was .710 (95% CI, .697-.722) in the training set and .703 (95% CI, .684-.722) in the validation set, respectively. The nomograms model, as suggested by the impressive C-index, exhibits outstanding differentiation ability. Moreover, the ROC and calibration curves reveal its commendable precision and distinguishing potential.

CONCLUSIONS

For the first time, highly accurate and reliable nomograms were developed to predict OS and CSS in NSCLC patients with non-surgical brain metastases, who have undergone radiotherapy treatment. The nomograms may assist in tailoring counseling strategies and choosing the most effective treatment method.

The predictive value of modified-DeepSurv in overall survivals of patients with lung cancer

The traditional prognostic model may induce the possibility of incorrect assessment of mortality risk under the assumption of linearity. It is urgent to develop a non-linearity precise prognostic model for achieving personalized medicine in lung cancer. In our study, we develop and validate a prognostic model "Modified-DeepSurv" for patients with lung carcinoma based on deep learning and evaluate its value for prognosis, while Cox proportional hazard regression was used to develop another model "CPH." The C-index of the Modified-DeepSurv and CPH was 0.956 (95% confidence interval [CI]: 0.946-0.974) and 0.836 (95% CI: 0.774-0.896), respectively, in the training cohort, while the C-index of the Modified-DeepSurv and CPH was 0.932 (95%CI: 0.908-0.964) and 0.777 (95%CI: 0.633-0.919), respectively, in the test dataset. The Modified-DeepSurv model visualization was realized by a user-friendly graphic interface. Modified-DeepSurv can effectively predict the survival of lung cancer patients and is superior to the conventional CPH model.

Development and validation of a prognostic model of survival for classic heatstroke patients: a multicenter study

Classic heatstroke (CHS) is a life-threatening illness characterized by extreme hyperthermia, dysfunction of the central nervous system and multiorgan failure. Accurate predictive models are useful in the treatment decision-making process and risk stratification. This study was to develop and externally validate a prediction model of survival for hospitalized patients with CHS. In this retrospective study, we enrolled patients with CHS who were hospitalized from June 2022 to September 2022 at 3 hospitals in Southwest Sichuan (training cohort) and 1 hospital in Central Sichuan (external validation cohort). Prognostic factors were identified utilizing least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis in the training cohort. A predictive model was developed based on identified prognostic factors, and a nomogram was built for visualization. The areas under the receiver operator characteristic (ROC) curves (AUCs) and the calibration curve were utilized to assess the prognostic performance of the model in both the training and external validation cohorts. The Kaplan‒Meier method was used to calculate survival rates. A total of 225 patients (median age, 74 [68-80] years) were included. Social isolation, self-care ability, comorbidities, body temperature, heart rate, Glasgow Coma Scale (GCS), procalcitonin (PCT), aspartate aminotransferase (AST) and diarrhea were found to have a significant or near-significant association with worse prognosis among hospitalized CHS patients. The AUCs of the model in the training and validation cohorts were 0.994 (95% [CI], 0.975-0.999) and 0.901 (95% [CI], 0.769-0.968), respectively. The model's prediction and actual observation demonstrated strong concordance on the calibration curve regarding 7-day survival probability. According to K‒M survival plots, there were significant differences in survival between the low-risk and high-risk groups in the training and external validation cohorts. We designed and externally validated a prognostic prediction model for CHS. This model has promising predictive performance and could be applied in clinical practice for managing patients with CHS.

A novel postoperative nomogram and risk classification system for individualized estimation of survival among patients with parotid gland carcinoma after surgery

BACKGROUND

Parotid gland carcinoma (PGC) is a rare but aggressive head and neck cancer, and the prognostic model associated with survival after surgical resection has not yet been established. This study aimed to construct a novel postoperative nomogram and risk classification system for the individualized prediction of overall survival (OS) among patients with resected PGC.

METHODS

Patients with PGC who underwent surgery between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were randomized into training and validation cohorts (7:3). A nomogram developed using independent prognostic factors based on the results of the multivariate Cox regression analysis. Harrell's concordance index (C-index), time-dependent area under the curve (AUC), and calibration plots were used to validate the performance of the nomogram. Moreover, decision curve analysis (DCA) was performed to compare the clinical use of the nomogram with that of traditional TNM staging.

RESULTS

In this study, 5077 patients who underwent surgery for PGC were included. Age, sex, marital status, tumor grade, histology, TNM stage, surgery type, radiotherapy, and chemotherapy were independent prognostic factors. Based on these independent factors, a postoperative nomogram was developed. The C-index of the proposed nomogram was 0.807 (95% confidence interval 0.797-0.817). Meanwhile, the time-dependent AUC (> 0.8) indicated that the nomogram had a satisfactory discriminative ability. The calibration curves showed good concordance between the predicted and actual probabilities of OS, and DCA curves indicated that the nomogram had a better clinical application value than the traditional TNM staging. Moreover, a risk classification system was built that could perfectly classify patients with PGC into three risk groups.

CONCLUSIONS

This study constructed a novel postoperative nomogram and corresponding risk classification system to predict the OS of patients with PGC after surgery. These tools can be used to stratify patients with high or low risk of mortality and provide high-risk patients with more directed therapies and closer follow-up.

Establishment and validation of a competitive risk model for predicting cancer-specific survival in patients with osteosarcoma: a population-based study

BACKGROUND

Osteosarcoma is the most common primary bone tumor with a poor prognosis. The aim of this study was to establish a competitive risk model nomogram to predict cancer-specific survival in patients with osteosarcoma.

METHODS

Patient data was obtained from the Surveillance, Epidemiology, and End Results database in the United States. A sub-distribution proportional hazards model was used to analyze independent risk factors affecting cancer-specific mortality (CSM) in osteosarcoma patients. Based on these risk factors, a competitive risk model was constructed to predict 1-year, 3-year, and 5-year cancer-specific survival (CSS) in osteosarcoma patients. The reliability and accuracy of the nomogram were evaluated using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves.

RESULTS

A total of 2900 osteosarcoma patients were included. The analysis showed that age, primary tumor site, M stage, surgery, chemotherapy, and median household income were independent risk factors influencing CSM in patients. The competitive risk model was constructed to predict CSS in osteosarcoma patients. In the training and validation sets, the C-index of the model was 0.756 (95% CI 0.725-0.787) and 0.737 (95% CI 0.717-0.757), respectively, and the AUC was greater than 0.7 for both. The calibration curves also demonstrated a high consistency between the predicted survival rates and the actual survival rates, confirming the accuracy and reliability of the model.

CONCLUSION

We established a competitive risk model to predict 1-year, 3-year, and 5-year CSS in osteosarcoma patients. The model demonstrated good predictive performance and can assist clinicians and patients in making clinical decisions and formulating follow-up strategies.

Chromosome 1p36 candidate gene ZNF436 predicts the prognosis of neuroblastoma: a bioinformatic analysis

BACKGROUND

Genetic 1p deletion is reported in 30% of all neuroblastoma and is associated with the unfavorable prognosis of neuroblastoma. The expressions and prognosis of 1p candidate genes in neuroblastoma are unclear.

METHODS

Public neuroblastoma cohorts were obtained for secondary analysis. The prognosis of 1p candidate genes in neuroblastoma was determined using Kaplan-Meier and cox regression analysis. The prediction of the nomogram model was determined using timeROC.

RESULTS

First, we confirmed the bad prognosis of 1p deletion in neuroblastoma. Moreover, zinc finger protein 436 (ZNF436) located at 1p36 region was down-regulated in 1p deleted neuroblastoma and higher ZNF436 expression was associated with the longer event free survival and overall survival of neuroblastoma. The expression levels of ZNF436 were lower in neuroblastoma patients with MYCN amplification or age at diagnosis ≥ 18months, or with stage 4 neuroblastoma. ZNF436 had robust predictive values of MYCN amplification and overall survival of neuroblastoma. Furthermore, the prognostic significance of ZNF436 in neuroblastoma was independent of MYCN amplification and age of diagnosis. Combinations of ZNF436 with MYCN amplification or age of diagnosis achieved better prognosis. At last, we constructed a nomogram risk model based on age, MYCN amplification and ZNF436. The nomogram model could predict the overall survival of neuroblastoma with high specificity and sensitivity.

CONCLUSIONS

Chromosome 1p36 candidate gene ZNF436 was a prognostic maker of neuroblastoma.

Development of a well-defined tool to predict the overall survival in lung cancer patients: an African based cohort

BACKGROUND

Nomogram is a graphic representation containing the expressed factor of the mathematical formula used to define a particular phenomenon. We aim to build and internally validate a nomogram to predict overall survival (OS) in patients diagnosed with lung cancer (LC).

METHODS

We included 1200 LC patients from a single institution registry diagnosed from 2013 to 2021. The independent prognostic factors of LC patients were identified via cox proportional hazard regression analysis. Based on the results of multivariate cox analysis, we constructed the nomogram to predict the OS of LC patients.

RESULTS

We finally included a total of 1104 LC patients. Age, medical urgency at diagnosis, performance status, radiotherapy, and surgery were identified as prognostic factors, and integrated to build the nomogram. The model performance in predicting prognosis was measured by receiver operating characteristic curve. Calibration plots of 6-, 12-, and 24- months OS showed optimal agreement between observations and model predictions.

CONCLUSION

We have developed and validated a unique predictive tool that can offer patients with LC an individual OS prognosis. This useful prognostic model could aid doctors in making decisions and planning therapeutic trials.

Risk prediction of second primary malignancies in patients after rectal cancer: analysis based on SEER Program

BACKGROUND

This study will focus on exploring the clinical characteristics of rectal cancer (RC) patients with Second Primary Malignancies (SPMs) and constructing a prognostic nomogram to provide clinical treatment decisions.

METHODS

We determined the association between risk factors and overall survival (OS) while establishing a nomogram to forecast the further OS status of these patients via Cox regression analysis. Finally, we evaluated the performance of the prognostic nomogram to predict further OS status.

RESULTS

Nine parameters were identified to establish the prognostic nomogram in this study, and, the C-index of the training set and validation set was 0.691 (95%CI, 0.662-0.720) and 0.731 (95%CI, 0.676-0.786), respectively. The calibration curve showed a high agreement between the predicted and actual results, and the receiver operating characteristic (ROC) curves verified the superiority of our model for clinical usefulness. In addition, the nomogram classification could more precisely differentiate risk subgroups and improved the discrimination of SPMs' prognosis.

CONCLUSIONS

We systematically explored the clinical characteristics of SPMs after RC and constructed a satisfactory nomogram.

Unraveling molecular networks in thymic epithelial tumors: deciphering the unique signatures

Thymic epithelial tumors (TETs) are a rare and diverse group of neoplasms characterized by distinct molecular signatures. This review delves into the complex molecular networks of TETs, highlighting key aspects such as chromosomal abnormalities, molecular subtypes, aberrant gene mutations and expressions, structural gene rearrangements, and epigenetic changes. Additionally, the influence of the dynamic tumor microenvironment on TET behavior and therapeutic responses is examined. A thorough understanding of these facets elucidates TET pathogenesis, offering avenues for enhancing diagnostic accuracy, refining prognostic assessments, and tailoring targeted therapeutic strategies. Our review underscores the importance of deciphering TETs' unique molecular signatures to advance personalized treatment paradigms and improve patient outcomes. We also discuss future research directions and anticipated challenges in this intriguing field.

A practical nomogram and risk stratification system for predicting survival outcomes in neuroblastoma patients: a SEER population-based study

BACKGROUND

Neuroblastoma (NB) is a childhood malignancy with marked heterogeneity, resulting in highly variable outcomes among patients. This study aims to establish a novel nomogram and risk stratification system to predict the overall survival (OS) for patients with NB.

METHODS

We analyzed neuroblastoma patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The nomogram was constructed using independent risk factors for OS, identified through univariate and multivariate Cox regression analyses. The accuracy of this nomogram was evaluated with the concordance index, receiver operating characteristic curve, calibration curve, and decision curve analysis. In addition, we developed a risk stratification system based on the total score of each patient in the nomogram.

RESULTS

A total of 2185 patients were randomly assigned to the training group and the testing group. Six risk factors, including age, chemotherapy, brain metastases, primary site, tumor stage, and tumor size, were identified in the training group. Using these factors, a nomogram was constructed to predict 1-, 3-, and 5-year OS of NB patients. This model exhibited superior accuracy in the training and testing groups, exceeding traditional tumor stage prediction. Subgroup analysis suggested worse prognosis for retroperitoneal origin in the intermediate-risk group and adrenal gland origin in the high-risk group compared to other sites. Additionally, the prognosis for high-risk patients significantly improved after surgery. We also developed a web application to make the nomogram more user-friendly in clinical practices.

CONCLUSION

This nomogram demonstrates excellent accuracy and reliability, offering more precise personalized prognostic predictions to clinical patients.

Development and validation of a nomogram for predicting overall survival of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy

INTRODUCTION

Currently, the prognosis of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy is poor. The goal of this research was to develop and validate a novel nomogram for exactly predicting the overall survival (OS) of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy.

METHODS

The data applied in our research were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. We divided selected data into a training cohort and a validation cohort using R software, with a ratio of 7:3. Univariate Cox regression and multivariate Cox regression were utilized to select significant variables to build the nomogram. To validate our nomogram, calibration curves, receiver operating characteristic curves (ROC), decision curve analysis (DCA), and Kaplan-Meier survival curves were employed. The nomogram model was also compared with the tumor-node-metastasis (TNM) staging system by utilizing net reclassification index (NRI) and integrated discrimination improvement (IDI).

RESULTS

Eight variables-age, sex, operative type, LN removed number, chemotherapy, AJCC stage, M stage, histology-were statistically significant in the multivariate Cox regression analysis and were selected to develop our nomogram. Based on ROC curves, calibration curves, and DCA analysis, our novel nomogram demonstrated good predictive accuracy and clinical utility. Using Kaplan-Meier (KM) survival curves and log-rank tests, the risk stratification system was able to stratify patients based on their estimated mortality risk. The nomogram performed better than the TNM staging system based on the NRI and IDI indexes.

CONCLUSIONS

We developed and validated a nomogram to predict prognosis of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy. Using this nomogram, clinicians may find this nomogram useful in predicting OS of targeted patients and making more appropriate treatment decisions.

Survival nomogram for patients with de novo metastatic breast cancer based on the SEER database and an external validation cohort

Background

On average, 5-10% of patients are diagnosed with metastatic breast cancer (MBC) at the initial diagnosis. This study aimed to develop a nomogram to predict the overall survival (OS) in these patients.

Methods

The nomogram was based on a retrospective study of 9435 patients with de novo MBC from the Surveillance, Epidemiology, and End Results (SEER) database. The predictive accuracy and discriminative ability of the nomogram were determined using the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), and calibration curve. Decision curve analysis (DCA) was employed to evaluate the benefits and advantages of our new predicting model over the 8th edition of the American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) staging system. The results were validated in a retrospective study of 103 patients with de novo MBC from January 2013 to June 2022 at an institution in northwest China.

Results

Multivariate analysis of the primary cohort revealed that independent factors for survival were age at diagnosis, pathological type, histological grade, T stage, N stage, molecular subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, surgery, chemotherapy, and radiotherapy. The nomogram achieved a C-index of 0.688 (95% confidence interval [CI], 0.682-0.694) in the training cohort and 0.875 (95% CI, 0.816-0.934) in the validation cohort. The AUC of the nomograms indicated good specificity and sensitivity in the training and validation cohorts, respectively. Calibration curves showed favorable consistency between the predicted and actual survival probabilities. Additionally, the DCA curve produced higher net gains than by the AJCC-TNM staging system. Finally, risk stratification can accurately identify groups of patients with de novo MBC at different risk levels.

Conclusions

The nomogram showed favorable predictive and discriminative abilities for OS in patients with de novo MBC. Other populations from different countries or prospective studies are needed to further validate the nomogram.

Two novel clinical tools to predict the risk of bone metastasis and overall survival in esophageal cancer patients: a large population-based retrospective cohort study

BACKGROUND

The aim of this study was to construct two web-based nomograms to predict the probability of bone metastasis (BM) in esophageal cancer (EC) patients and the prognostic of EC patients with BM (ECBM).

METHODS

We collected the data of EC and ECBM patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015. Independent risk variables for the development of BM in EC patients were identified using univariate and multivariate logistic regression analyses. Univariate and multivariate Cox regression analyses were used to assess independent prognostic variables in ECBM patients. And then, constructed two nomograms to predict the risk of bone metastases and overall survival (OS) of ECBM patients. Survival differences were studied by Kaplan-Meier (K-M) survival analysis. The predictive efficacy and clinical applicability of these two nomograms were assessed by using receiver operating characteristic (ROC) curve, the area under curve (AUC), calibration curve and decision curve analysis (DCA).

RESULTS

We selected a total of 6839 patients with EC, of which 326 (4.77%) had BM at the time of initial diagnosis. The results of K-M survival and Cox regression analysis showed significant effects of BM on the OS in EC patients. Age, N stage, tumor size and brain/liver/lung organ metastasis were identified as BM-related risk variables. Chemotherapy and brain/liver organ metastasis were identified as ECBM-related prognostic variables. The ROC, AUC, calibration curves and DCA of two nomograms all showed excellent predictive efficacy and clinical applicability.

CONCLUSIONS

These two nomograms were constructed and validated, which could objectively predict the risk of BM in EC patients and the prognostic in ECBM patients. These tools are expected to make valuable contributions in clinical work, informing surgeons in making decisions about patient care.

A prognostic nomogram that includes MPV in esophageal squamous cell carcinoma

BACKGROUND

Mean platelet volume (MPV), as a marker of platelet activity, has been shown to be an efficient prognostic biomarker in several types of cancer. Using MPV, this study aimed to create and validate a prognostic nomogram to the overall survival in esophageal squamous cell carcinoma (ESCC) patients.

METHODS

The nomogram was constructed and tested using data from a retrospective study of 1893 patients who were randomly assigned to the training and testing cohorts with a 7:3 randomization. In order to screen out the optimal predictors for overall survival (OS), we conducted the LASSO-cox regression, univariate, and multivariate cox regression analyses. Subsequently, the predictive accuracy of the nomogram was validated in both the training and the testing cohorts. Finally, decision curve analysis (DCA) was used to confirm clinical validity.

RESULTS

Age, MPV, nerve invasion, T stage, and N stage were found as independent prognostic variables for OS and were further developed into a nomogram. The nomogram's prediction accuracy for 1-, 3-, and 5-year OS was 0.736, 0.749, 0.774, and 0.724, 0.719, 0.704 in the training and testing cohorts, respectively. Furthermore, DCA results indicated that nomograms outperformed the AJCC 8th and conventional T, N staging systems in both the training and testing cohorts.

CONCLUSIONS

The nomogram, in conjunction with MPV and standard clinicopathological markers, could improve the accuracy of prediction of OS in ESCC patients.

Retrospective study: risk assessment model for osteoporosis-a detailed exploration involving 4,552 Shanghai dwellers

Background

Osteoporosis, a prevalent orthopedic issue, significantly influences patients' quality of life and results in considerable financial burden. The objective of this study was to develop and validate a clinical prediction model for osteoporosis risk, utilizing computer algorithms and demographic data.

Method

In this research, a total of 4,552 residents from Shanghai were retrospectively included. LASSO regression analysis was executed on the sample's basic characteristics, and logistic regression was employed for analyzing clinical characteristics and building a predictive model. The model's diagnostic capacity for predicting osteoporosis risk was assessed using R software and computer algorithms.

Results

The predictive nomogram model for bone loss risk, derived from the LASSO analysis, comprised factors including BMI, TC, TG, HDL, Gender, Age, Education, Income, Sleep, Alcohol Consumption, and Diabetes. The nomogram prediction model demonstrated impressive discriminative capability, with a C-index of 0.908 (training set), 0.908 (validation set), and 0.910 (entire cohort). The area under the ROC curve (AUC) of the model was 0.909 (training set), 0.903 (validation set), and applicable to the entire cohort. The decision curve analysis further corroborated that the model could efficiently predict the risk of bone loss in patients.

Conclusion

The nomogram, based on essential demographic and health factors (Body Mass Index, Total Cholesterol, Triglycerides, High-Density Lipoprotein, Gender, Age, Education, Income, Sleep, Alcohol Consumption, and Diabetes), offered accurate predictions for the risk of bone loss within the studied population.

A Nomogram for Predicting Survival in Advanced Non-Small-Cell Lung Carcinoma Patients: A Population-Based Study

Non-small-cell lung cancer (NSCLC) remains the most common malignant cancer. We identified 43140 advanced NSCLC patients from the SEER database to develop and validate a new prognostic model. The prognostic performance was evaluated by P value, concordance index, net reclassification index, integrated discrimination improvement, and decision curve analysis. The following variables were contained in the final prognostic model: age, sex, race, TNM stage, and grade and treatment options. Compared to the AJCC staging system, this prognostic model is conducive to the implementation of individualized clinical treatment schemes and can be an important part of the precise medical care of NSCLC tumors.

Clinical-mediated discovery of pyroptosis in CD8+ T cell and NK cell reveals melanoma heterogeneity by single-cell and bulk sequence

Histologically, melanoma tissues had fewer positive cells percentage of pyroptosis-related genes (PRGs), GZMA, GSDMB, NLRP1, IL18, and CHMP4A in epidermal than in normal skin. Pyroptosis, a new frontier in cancer, affects the tumor microenvironment and tumor immunotherapy. Nevertheless, the role of pyroptosis remains controversial, which reason is partly due to the heterogeneity of the cellular composition in melanoma. In this study, we present a comprehensive analysis of the single-cell transcriptome landscape of pyroptosis in melanoma specimens. Our findings reveal dysregulation in the expression of PRGs, particularly in immune cells, such as CD8+ cells (representing CD8+ T cells) and CD57+ cells (representing NK cells). Additionally, the immunohistochemical and multiplex immunofluorescence staining experiments results further confirmed GZMA+ cells and GSDMB+ cells were predominantly expressed in immune cells, especially in CD8 + T cells and NK cells. Melanoma specimens secreted a minimal presence of GZMA+ merged CD8+ T cells (0.11%) and GSDMB+ merged CD57+ cells (0.08%), compared to the control groups exhibiting proportions of 4.02% and 0.62%, respectively. The aforementioned findings indicate that a reduced presence of immune cells within tumors may play a role in diminishing the ability of pyroptosis, consequently posing a potential risk to the anti-melanoma properties. To quantify clinical relevance, we constructed a prognostic risk model and an individualized nomogram (C-index=0.58, P = 0.002), suggesting a potential role of PRGs in malignant melanoma prevention. In conclusion, our integrated single-cell and bulk RNA-seq analysis identified immune cell clusters and immune gene modules with experiment validation, contributing to our better understanding of pyroptosis in melanoma.

A multidimensional nomogram combining clinical factors and imaging features to predict 1-year recurrence of low back pain with or without radicular pain after spinal manipulation/mobilization

BACKGROUND

In this retrospective study, we aimed to develop a nomogram to predict recurrence during a 1-year period of spinal manipulation/mobilization (SM/M) in patients with low back pain (LBP) with greater pain intensity, more severe comorbid conditions, or a neuropathic component.

METHODS

A total of 786 consecutive patients with LBP treated with SM/M as primary therapy were divided into training (n = 545) and validation (n = 241) sets. Cox regression analyses were used to assess the relative value of clinical factors and lumbar magnetic resonance imaging features associated with recurrence during the 1-year period. Predictors of recurrence with significant differences were used to construct a nomogram in the training set. We evaluated the performance of the model on the training and validation sets to determine its discriminative ability, calibration, and clinical utility. The prognostic value of the nomogram for predicting recurrence was assessed using Kaplan-Meier analysis and time-dependent receiver operating characteristic analyses.

RESULTS

A nomogram comprising hospitalization time, previous history of LBP, disease duration, lumbar range of motion, lower extremity tendon reflex, muscle strength, ratio of herniation to uncompressed dural sac area, and Pfirrmann classification was established for recurrence during a 1-year period after SM/M in patients with LBP. Favorable calibration and discrimination were observed in the nomogram training and validation sets (C-index 0.753 and 0.779, respectively). Decision curve analysis confirmed the clinical utility of the nomogram. Over a 1-year period, the nomogram showed satisfactory performance in predicting recurrence in LBP after SM/M.

CONCLUSION

We established and validated a novel nomogram that can accurately predict a patient's risk of LBP recurrence following SM/M. This realistic prognostic model may aid doctors and therapists in their decision-making process and strategy optimization for non-surgical treatment of LBP using SM/M.

Development and validation of a prognostic nomogram for gastrointestinal stromal tumors in the postimatinib era: A study based on the SEER database and a Chinese cohort

BACKGROUND

After the standardization, recording and follow-up of imatinib use that significantly prolongs survival of gastrointestinal stromal tumors (GISTs), a comprehensive reassessment of the prognosis of GISTs is necessary and more conductive to treatment options.

METHODS

A total of 2185 GISTs between 2013 and 2016 were obtained from the Surveillance, Epidemiology, and End Results database and comprised our training (n = 1456) and internal validation cohorts (n = 729). The risk factors extracted from univariate and multivariate analyses were used to establish a predictive nomogram. The model was evaluated and tested in the validation cohort internally and in 159 patients with GIST diagnosed between January 2015 and June 2017 in Xijing Hospital externally.

RESULTS

The median OS was 49 months (range, 0-83 months) in the training cohort and 51 months (0-83 months) in the validation cohort. The concordance index (C-index) of the nomogram was 0.777 (95% CI, 0.752-0.802) and 0.7787 (0.7785, bootstrap corrected) in training and internal validation cohorts, respectively, and 0.7613 (0.7579, bootstrap corrected) in the external validation cohort. Receiver operating characteristic curves and calibration curves for 1-, 3-, and 5-year overall survival (OS) showed a high degree of discrimination and calibration. The area under the curve showed that the new model performed better than the TNM staging system. In addition, the model could be dynamically visualized on a webpage.

CONCLUSION

We developed a comprehensive survival prediction model for assessing the 1-, 3- and 5-year OS of patients with GIST in the postimatinib era. This predictive model outperforms the traditional TNM staging system and sheds light on the improvement of the prognostic prediction and the selection of treatment strategies for GISTs.

Development and validation of prognostic nomogram for T1-3N0M0 non-small cell lung cancer after curative resection

BACKGROUND

Radical resection plus lymph node dissection is a common treatment for patients with T_1-3N₀M₀ non-small cell lung cancer (NSCLC). Few models predicted the survival outcomes of these patients. This study aimed to developed a nomogram for predicting their overall survival (OS).

MATERIALS AND METHODS

This study involved 3002 patients with T_1-3N₀M₀ NSCLC after curative resection between January 1999 and October 2013. 1525 Patients from Sun Yat-sen University Cancer Center were randomly allocated to training cohort and internal validation cohort in a ratio of 7:3. 1477 patients from ten institutions were recruited as external validation cohort. A nomogram was constructed based on the training cohort and validated by internal and external validation cohort to predict the OS of these patients. The accuracy and practicability were tested by Harrell's C-indexes, calibration plots and decision curve analyses (DCA).

RESULTS

Age, sex, histological classification, pathological T stage, and HI standard were independent factors for OS and were included in our nomogram. The C-index of the nomogram for OS estimates were 0.671 (95% CI, 0.637-0.705),0.632 (95% CI, 0.581-0.683), and 0.645 (95% CI, 0.617-0.673) in the training cohorts, internal validation cohorts, and external validation cohort, respectively. The calibration plots and DCA for predictions of OS were in excellent agreement. An online version of the nomogram was built for convenient clinical practice.

CONCLUSIONS

Our nomogram can predict the OS of patients with T_1-3N₀M₀ NSCLC after curative resection. The online version of our nomogram offer opportunities for fast personalized risk stratification and prognosis prediction in clinical practice.

Nomogram Based on Liver Function Test Indicators for Survival Prediction in Nasopharyngeal Carcinoma Patients Receiving PD-1 Inhibitor Therapy

PURPOSE

The aim of this study was to investigate the prognostic significance of PD-1 inhibitor therapy in nasopharyngeal carcinoma (NPC) and to develop a nomogram to estimate individual risks.

METHODS

We retrospectively analyzed 162 NPC patients who were administered the PD-1 inhibitor combined with radiotherapy and chemotherapy at the Sun Yat-Sen University Cancer Center. In total, 108 NPC patients were included in the training cohort and 54 NPC patients were included in the validation cohort. Univariate and multivariate Cox survival analyses were performed to determine the prognostic factors for 1-year and 2-year progression-free survival (PFS). In addition, a nomogram model was constructed to predict the survival probability of PFS. A consistency index (C-index), a decision curve, a clinical impact curve, and a standard curve were used to measure predictive accuracy, the clinical net benefit, and the consistency of prognostic factors.

RESULTS

Univariate and multivariate analyses indicated that the metastasis stage, the levels of ALT, the AST/ALT ratio, and the LDH were independent risk factors associated with the prognosis of PD-1 inhibitor therapy. A nomogram based on these four indicators was constructed and the Kaplan-Meier survival analysis showed that patients with a higher total score have a shorter PFS. The C-index of this model was 0.732 in the training cohort and 0.847 in the validation cohort, which are higher than those for the TNM stages (training cohort: 0.617; validation cohort: 0.727; p <0.05). Decision Curve Analysis (DCA), Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI) showed that our model has better prediction accuracy than TNM staging.

CONCLUSIONS

Predicting PFS in NPC patients based on liver function-related indicators before PD-1 treatment may help clinicians predict the efficacy of PD-1 treatment in these patients.

Nomogram predicting overall survival of stage IIIB non-small-cell lung cancer patients based on the SEER database

PURPOSE

We aimed to evaluate the prognostic value of stage IIIB non-small-cell (NSCLC) lung cancer patients and to construct a nomogram to effectively predict their overall survival (OS).

METHODS

In total, 4323 patients with stage IIIB NSCLC diagnosed between 1975 and 2018 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Multiple prognostic factors were combined to construct a nomogram for predicting OS of patients with stage IIIB NSCLC. The discrimination and calibration of the nomogram were evaluated by C-indexes and calibration curves. The nomogram was evaluated for predictive ability using receiver operating characteristic (ROC) curves, decision curve analysis curve (DCA), and clinical impact curve (CIC).

RESULTS

The nomogram was built on data of 4323 patients with stage IIIB NSCLC and consisted of the following prognostic factors: age, race, sex, primary labeled, pathology, T stage, whether to receive surgery, whether to receive radiotherapy, and whether to receive chemotherapy. The C-index in the training and validation sets for the nomogram was 0.672 (95% CI: 0.661-0.683) and 0.675 (95% CI: 0.656-0.694), respectively. According to scores of the nomogram, patients in the complete set, validation set, and training set were classified into two risk groups, low risk and high risk.

CONCLUSIONS

We developed the first validated nomogram to estimate the OS of patients with stage IIIB NSCLC. The nomogram was based on nine prognostic factors and provided an individualized risk estimate of 3-year and 5-year OS survival in patients with stage IIIB NSCLC.

Development and validation of a nomogram for predicting the overall survival of patients with testicular cancer

BACKGROUND

The purpose of this study was to develop and validate a nomogram to predict survival in testicular cancer patients.

METHODS

Testicular cancer patients diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were selected for this study. A random sampling method was used to divide patients into training and validation cohorts, which accounted for 30% and 70% of the total sample, respectively. The nomogram was developed using the training cohort and evaluated using the C index, calibration chart, and area under the receiver operating characteristic curve (AUC).

RESULTS

Seven risk factors that affect the survival of testicular cancer patients (AJCC stage, marital status, age at diagnosis, race, SEER historic stage A, surgery status, and origin) were identified using Cox proportional hazard regression analysis. The nomogram has a higher C index (0.897) and AUC when compared with the AJCC staging system. The results of the calibration chart of the nomogram show that the predicted survival of testicular cancer patients at 3, 5, and 10 years after diagnosis is very close to their actual survival.

CONCLUSIONS

We developed and validated a nomogram for predicting the survival rate of testicular cancer patients at 3, 5, and 10 years after diagnosis. This nomogram has better discrimination, calibration, and clinical validity than the AJCC staging system. This indicates that the nomogram can be used to predict the survival of testicular cancer patients effectively, and provide a reference for patient treatment strategies.

Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study

BACKGROUND

Improved markers for predicting recurrence are needed to stratify patients with localised (stage I-III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities-clinical, genomic, and histopathological-to improve the predictive accuracy for localised renal cell carcinoma recurrence.

METHODS

In this retrospective analysis and validation study, we developed a histopathological whole-slide image (WSI)-based score using deep learning allied to digital scanning of conventional haematoxylin and eosin-stained tumour tissue sections, to predict tumour recurrence in a development dataset of 651 patients with distinctly good or poor disease outcome. The six single nucleotide polymorphism-based score, which was detected in paraffin-embedded tumour tissue samples, and the Leibovich score, which was established using clinicopathological risk factors, were combined with the WSI-based score to construct a multimodal recurrence score in the training dataset of 1125 patients. The multimodal recurrence score was validated in 1625 patients from the independent validation dataset and 418 patients from The Cancer Genome Atlas set. The primary outcome measured was the recurrence-free interval (RFI).

FINDINGS

The multimodal recurrence score had significantly higher predictive accuracy than the three single-modal scores and clinicopathological risk factors, and it precisely predicted the RFI of patients in the training and two validation datasets (areas under the curve at 5 years: 0·825-0·876 vs 0·608-0·793; p<0·05). The RFI of patients with low stage or grade is usually better than that of patients with high stage or grade; however, the RFI in the multimodal recurrence score-defined high-risk stage I and II group was shorter than in the low-risk stage III group (hazard ratio [HR] 4·57, 95% CI 2·49-8·40; p<0·0001), and the RFI of the high-risk grade 1 and 2 group was shorter than in the low-risk grade 3 and 4 group (HR 4·58, 3·19-6·59; p<0·0001).

INTERPRETATION

Our multimodal recurrence score is a practical and reliable predictor that can add value to the current staging system for predicting localised renal cell carcinoma recurrence after surgery, and this combined approach more precisely informs treatment decisions about adjuvant therapy.

FUNDING

National Natural Science Foundation of China, and National Key Research and Development Program of China.

Lung Large Cell Neuroendocrine Carcinoma: A Population-Based Retrospective Cohort Study

BACKGROUNDS

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rarely high-grade neuroendocrine carcinoma of the lung with features of both small cell and non-small cell lung cancer. In this study, we aim to construct a prognostic nomogram that integrates the clinical features and treatment options to predict disease-specific survival (DSS).

METHODS

A total of 713 patients diagnosed with LCNEC were from the US National Cancer Institute's Surveillance Epidemiology and End Results (SEER) registry between 2010-2016. Cox proportional hazards analysis was conducted to choose the significant predictors of DSS. External validation was performed using 77 patients with LCNEC in the West China Hospital Sichuan University between 2010-2018. The predictive accuracy and discriminative capability were estimated by the concordance index (C-index), calibration curve, and receiver operating characteristic (ROC) curve. The clinical applicability of the nomogram was verified through the decision curve analysis (DCA). Additionally, we conducted a subgroup analysis of data available in the external cohort that may impact prognosis but was not recorded in the SEER database.

RESULTS

Six independent risk factors for DSS were identified and integrated into the nomogram. The nomogram achieved good C- indexes of 0.803 and 0.767 in the training and validation group, respectively. Moreover, the calibration curves for the probability of survival showed good agreement between prediction by nomogram and actual observation in 1-, 3- and 5-year DSS. The ROC curves demonstrated the prediction accuracy of the established nomogram (all Area Under Curve (AUC) > 0.8). DCA exhibited the favorable clinical applicability of the nomogram in the prediction of LCNEC survival. A risk classification system was built which could perfectly classify LCNEC patients into high-, medium- and low-risk groups (p < 0.001). The survival analysis conducted on the West China Hospital cohort indicated that whole brain radiation therapy (WBRT), prophylactic cranial irradiation (PCI), surgical procedures, tumor grade, Ki-67, and PD-L1 expression were not significantly associated with DSS.

CONCLUSION

This study has effectively developed a prognostic nomogram and a corresponding risk stratification system, which demonstrate promising potential for predicting the DSS of patients with LCNEC.

Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study

Purpose

This study aimed to screen out computed tomography (CT) morphological features and clinical characteristics of patients with lung cancer to identify chronic obstructive pulmonary disease (COPD). Further, we aimed to develop and validate different diagnostic nomograms for predicting whether lung cancer is comorbid with COPD.

Patients and Methods

This retrospective study examined data from 498 patients with lung cancer (280 with COPD, 218 without COPD; 349 in training cohort, 149 in validation cohort) from two centers. Five clinical characteristics and 20 CT morphological features were evaluated. Differences in all variables were assessed between COPD and non-COPD groups. Models were developed using multivariable logistic regression to identify COPD, including clinical, imaging, and combined nomograms. Receiver operating characteristic curves were used to evaluate and compare the performance of nomograms.

Results

Age, sex, interface, bronchus cutoff sign, spine-like process, and spiculation sign were independent predictors of COPD in patients with lung cancer. In the training and validation cohorts, the clinical nomogram showed good performance to predict COPD in lung cancer patients (areas under the curves [AUCs] of 0.807 [95% CI, 0.761-0.854] and 0.753 [95% CI, 0.674-0.832]); while the imaging nomogram showed slightly better performance (AUCs of 0.814 [95% CI, 0.770-0.858] and 0.780 [95% CI, 0.705-0.856]). For the combined nomogram generated with clinical and imaging features, the performance was further improved (AUC=0.863 [95% CI, 0.824-0.903], 0.811 [95% CI, 0.742-0.880] in the training and validation cohort). At 60% risk threshold, there were more true negative predictions (48 vs 44) and higher accuracy (73.15% vs 71.14%) for the combined nomogram compared with the clinical nomogram in the validation cohort.

Conclusion

The combined nomogram developed with clinical and imaging features outperformed clinical and imaging nomograms; this provides a convenient method to detect COPD in patients with lung cancer using one-stop CT scanning.

Nomogram Predicting the Prognosis of Patients with Surgically Resected Stage IA Non-small Cell Lung Cancer

The American Joint Committee on Cancer (AJCC) 8th stage system was limited in accuracy for predicting prognosis of stage IA non-small cell lung cancer (NSCLC) patients. This study aimed to establish and validate two nomograms that predict overall survival (OS) and lung cancer-specific survival (LCSS) in surgically resected stage IA NSCLC patients. Postoperative patients with stage IA NSCLC in SEER database between 2004 and 2015 were examined. Survival and clinical information according to the inclusion and exclusion criteria were collected. All patients were randomly divided into the training cohort and validation cohort with a ratio of 7:3. Independent prognosis factors were evaluated using univariate and multivariate Cox regression analyses, and predictive nomogram was established based on these factors. Nomogram performance was measured using the C-index, calibration plots, and DCA. Patients were grouped by quartiles of nomogram scores and survival curves were plotted by Kaplan-Meier analysis. In total, 33,533 patients were included in the study. The nomogram contained 12 prognostic factors in OS and 10 prognostic factors in LCSS. In the validation set, the C-index was 0.652 for predicting OS and 0.651 for predicting LCSS. The calibration curves for the nomogram-predicted probability of OS and LCSS showed good agreement between the actual observation and nomogram prediction. DCA indicated that the clinical value of the nomograms were higher than AJCC 8th stage for predicting OS and LCSS. Nomogram scores related risk stratification revealed statistically significant difference which have better discrimination than AJCC 8th stage. The nomogram can accurately predict OS and LCSS in surgically resected patients with stage IA NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13193-022-01700-w.

The application of clinical variable-based nomogram in predicting overall survival in malignant phyllodes tumors of the breast

Background: We aimed to explore prognostic risk factors in patients with malignant phyllodes tumors (PTs) of the breast and construct a survival prediction model. Methods: The Surveillance, Epidemiology, and End Results database was used to collect information on patients with malignant breast PTs from 2004 to 2015. The patients were randomly divided into training and validation groups using R software. Univariate and multivariate Cox regression analyses were used to screen out independent risk factors. Then, a nomogram model was developed in the training group and validated in the validation group, and the prediction performance and concordance were evaluated. Results: The study included 508 patients with malignant PTs of the breast, including 356 in the training group and 152 in the validation group. Univariate and multivariate Cox proportional hazard regression analyses showed that age, tumor size, tumor stage, regional lymph node metastasis (N), distant metastasis (M) and tumor grade were independent risk factors for the 5-year survival rate of patients with breast PTs in the training group (p < 0.05). These factors were used to construct the nomogram prediction model. The results showed that the C-indices of the training and validation groups were 0.845 (95% confidence interval [CI] 0.802-0.888) and 0.784 (95% CI 0.688-0.880), respectively. The calibration curves of the two groups were close to the ideal 45° reference line and showed good performance and concordance. Receiver operating characteristic and decision curve analysis curves showed that the nomogram has better predictive accuracy than other clinical factors. Conclusion: The nomogram prediction model constructed in this study has good predictive value. It can effectively assess the survival rates of patients with malignant breast PTs, which will aid in the personalized management and treatment of clinical patients.

Overall survival comparison between pediatric and adult Ewing sarcoma of bone and adult nomogram construction: a large population-based analysis

Background

Ewing sarcoma (ES) is a common primary bone tumor in children. Our study aimed to compare overall survival (OS) between pediatric and adult bone ES patients, identify independent prognostic factors and develop a nomogram for predicting OS in adult patients with ES of bone.

Methods

We retrospectively analyzed data for the 2004-2015 period from the Surveillance, Epidemiology, and End Results (SEER) database. To guarantee well-balanced characteristics between the comparison groups, propensity score matching (PSM) was used. Kaplan-Meier (KM) curves were used to compare OS between pediatric and adult patients with ES of bone. Univariate and multivariate Cox regression analyses were used to screen independent prognostic factors for ES of bone, and a prognostic nomogram was constructed by using the factors identified. The prediction accuracy and clinical benefit were evaluated using receiver operating characteristic (ROC) curves, areas under the curves (AUCs), calibration curves, and decision curve analysis (DCA).

Results

Our results showed that adult ES patients had lower OS than younger ES patients. Age, surgery, chemotherapy, and TNM stage were independent risk factors for bone ES in adults and were used to develop a nomogram. AUCs for 3-, 5-, and 10-year OS were 76.4 (67.5, 85.3), 77.3 (68.6, 85.9) and 76.6 (68.6, 84.5), respectively. Calibration curves and DCA results indicated excellent performance for our nomogram.

Conclusion

We found that ES pediatric patients have better OS than adult ES patients, and we constructed a practical nomogram to predict the 3-, 5- and 10-year OS of adult patients with ES of bone based on independent prognostic factors (age, surgery, chemotherapy, T stage, N stage and M stage).