Survival Nomogram for Young Breast Cancer Patients Based on the SEER Database and an External Validation Cohort

Establishment and validation of an immune-related nomogram for the prognosis of pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PDAC) is a highly aggressive neoplasm characterized by limited therapeutic options, particularly in the realm of immunotherapy. This study aims to improve prognosis prediction to guide therapeutic decision-making, and to identify novel targets for immunotherapy of PDAC. We conducted Cox and LASSO regression analyses to develop immune-related gene signature and corresponding nomogram, and the robustness of these signatures was demonstrated using multiple approaches. Additionally, CIBERSORT, ESTIMATE, and xCell algorithms were utilized to assess immune cell infiltration, with experimental validation performed though qPCR. An immune-related gene signature consisting of 18 genes, and the prognostic nomogram was established with superior performance compared to the conventional staging system. Key parameters incorporated into the nomogram included the gene signature, tumor stage, and postoperative treatment. Patients identified as high-risk exhibited an anti-inflammatory tumor microenvironment, characterized by an increase in M2-like tumor-associated macrophages and heightened tumor purity. Notably, the expression of interleukin 6 receptor (IL6R) in PDAC was predominantly derived from macrophages and was significantly associated with patient survival outcomes. Furthermore, attenuated IL-6/IL-6R signaling was found to promote M2-like macrophage differentiation. This study successfully established an immune-related gene signature and a robust nomogram for predicting clinical outcomes in patients with PDAC. Furthermore, we identified IL6R as a promising target for future immunotherapeutic strategies.

Individualized estimation of conditional survival for patients with spinal chordoma

Background

Unlike traditional survival analysis methods, conditional survival (CS) provides enhanced insight by offering a personalized prognosis estimation as time advances for tumor patients. This study aimed to estimate CS and devised a novel CS-nomogram for real-time prediction of 10-year CS for patients with spinal chordoma.

Methods

Patients diagnosed with spinal chordoma from 2000 to 2019, as documented in the Surveillance, Epidemiology, and End Results (SEER) database, were included in this study. CS represents the likelihood of surviving an additional y years given that the patient has already survived x years. It is computed using the equation CS(x|y) = S(x + y)/S(x), where S(x) denotes the patient's survival rate at x years. The univariate Cox hazard regression, least absolute shrinkage and selection operator (LASSO) analysis and best subset regression (BSR) methods were employed for variable selection. Based on these selected factors, the CS-based nomogram and a risk classification system were developed. Finally, several approaches were used to validate the performance of our model.

Results

Between 2000 and 2019, the SEER database identified 730 patients with spinal chordoma, distributed into 510 in the training group and 220 in the validation group. CS analysis showed that patients experienced a gradual augmentation in their 10-year survival rates over the course of each additional year post-diagnosis. We also successfully created a CS-based nomogram model for forecasting 3-, 5-, and 10-year overall survival, along with 10-year CS. The CS-based nomogram incorporating age, tumor size, tumor extension, multiple primary tumors, and surgery demonstrated robust predictive capabilities. Moreover, a novel risk classification system was constructed to aid in tailored management strategies and personalized treatment decisions for spinal chordoma patients.

Conclusions

In contrast to traditional survival assessment methods, our analysis of CS yielded more dynamic and real-time outcomes for spinal chordoma patients. Via our CS-based nomogram model and risk classification system, we have provided more precise prognostic insights for these patients, aiding in treatment planning and follow-up strategy formulation in clinical settings.

A nomogram for predicting overall survival in oral squamous cell carcinoma: a SEER database and external validation study

Purpose

Oral squamous cell carcinoma (OSCC) often presents with unsatisfactory survival outcomes, especially in advanced stages. This study aimed to develop and validate a nomogram incorporating demographic, clinicopathologic, and treatment-related factors to improve the prediction of overall survival (OS) in OSCC patients.

Methods

Data from 15,204 OSCC patients in a US database were retrospectively utilized to construct a prognostic model and generate a nomogram. External validation was performed using an independent cohort of 359 patients from a specialized cancer center in China. Prognostic factors were identified using Cox regression analysis and incorporated into the nomogram. Model performance was evaluated by concordance index (C-index), time-dependent area under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA). A risk stratification system was developed to classify patients into high- and low-risk groups.

Results

Age, sex, primary tumor site, T and N staging, and treatment modalities (including surgery, chemotherapy, and radiotherapy) were found to be independent prognostic factors. The nomogram achieved a C-index of 0.727 in the training set and 0.6845 in the validation set, outperforming the conventional TNM staging system. The nomogram's superior predictive accuracy was confirmed by higher AUC values, better calibration, and improved clinical utility as demonstrated by DCA. Risk stratification, based on the nomogram, distinguished patients into distinct prognostic groups with significant OS differences.

Conclusions

This nomogram provides an effective, personalized tool for predicting OS in OSCC. It offers clinicians a valuable aid for treatment decision-making and improves patient management.

Predicting Overall Survival in Patients with Male Breast Cancer: Nomogram Development and External Validation Study

Background

Male breast cancer (MBC) is an uncommon disease. Few studies have discussed the prognosis of MBC due to its rarity.

Objective

This study aimed to develop a nomogram to predict the overall survival of patients with MBC and externally validate it using cases from China.

Methods

Based on the Surveillance, Epidemiology, and End Results (SEER) database, male patients who were diagnosed with breast cancer between January 2010, and December 2015, were enrolled. These patients were randomly assigned to either a training set (n=1610) or a validation set (n=713) in a 7:3 ratio. Additionally, 22 MBC cases diagnosed at the First Affiliated Hospital of Guangxi Medical University between January 2013 and June 2021 were used for external validation, with the follow-up endpoint being June 10, 2023. Cox regression analysis was performed to identify significant risk variables and construct a nomogram to predict the overall survival of patients with MBC. Information collected from the test set was applied to validate the model. The concordance index (C-index), receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and a Kaplan-Meier survival curve were used to evaluate the accuracy and reliability of the model.

Results

A total of 2301 patients with MBC in the SEER database and 22 patients with MBC from the study hospital were included. The predictive model included 7 variables: age (hazard ratio [HR] 1.89, 95% CI 1.50-2.38), surgery (HR 0.38, 95% CI 0.29-0.51), marital status (HR 0.75, 95% CI 0.63-0.89), tumor stage (HR 1.17, 95% CI 1.05-1.29), clinical stage (HR 1.41, 95% CI 1.15-1.74), chemotherapy (HR 0.62, 95% CI 0.50-0.75), and HER2 status (HR 2.68, 95% CI 1.20-5.98). The C-index was 0.72, 0.747, and 0.981 in the training set, internal validation set, and external validation set, respectively. The nomogram showed accurate calibration, and the ROC curve confirmed the advantage of the model in clinical validity. The DCA analysis indicated that the model had good clinical applicability. Furthermore, the nomogram classification allowed for more accurate differentiation of risk subgroups, and patients with low-risk MBC demonstrated substantially improved survival outcomes compared with medium- and high-risk patients (P<.001).

Conclusions

A survival prognosis prediction nomogram with 7 variables for patients with MBC was constructed in this study. The model can predict the survival outcome of these patients and provide a scientific basis for clinical diagnosis and treatment.

A nomogram for predicting the risk of liver metastasis in non-functional neuroendocrine neoplasms: A population-based study

BACKGROUND

Non-functional gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are rare tumors, and liver metastasis is the leading cause of death in patients with GEP-NENs. Due to the difficulty in conducting large cohort studies, no reliable tool currently exists to predict the risk of liver metastasis in these patients. This study aimed to develop and validate a nomogram model based on large cohort clinical data to accurately predict the risk of liver metastasis in patients with non-functional GEP-NENs.

METHODS

A retrospective cohort study was conducted, encompassing 838 patients with non-functional GEP-NENs diagnosed between 2009 and 2023. Independent risk factors for liver metastasis were identified through univariate and multivariate logistic regression analyses. A nomogram was constructed based on significant predictors, including T stage, N stage, Ki-67 index, primary tumor site, and BMI. The model's performance was evaluated using the C-index, calibration curves, and decision curve analysis (DCA) for both training and validation cohorts.

RESULTS

The nomogram demonstrated excellent predictive performance, with C-index values of 0.839 and 0.823 for the training and validation sets, respectively. Risk stratification using the nomogram's total score effectively differentiated high-risk from low-risk patients. Kaplan-Meier survival analysis revealed significant survival differences between these groups (P < 0.0001). Moreover, the calibration curves indicated strong agreement between predicted and observed outcomes.

CONCLUSIONS

The developed nomogram is a reliable tool for predicting the risk of liver metastasis in non-functional GEP-NENs. It facilitates early identification of high-risk patients, thereby enabling personalized treatment and timely intervention. Future research should focus on multicenter validation and the integration of molecular markers to enhance the robustness and clinical applicability of the model.

A prognostic nomogram for distant metastasis in thyroid cancer patients without lymph node metastasis

Background

Despite having negative lymph node (N0) status, thyroid cancer (TC) patients can still experience distant metastasis (DM), which significantly impacts their survival. This study aimed to investigate the prognostic factors for DM in TC patients (N0) and develop a predictive nomogram model for analyzing the prognosis of TC N0 patients with DM.

Methods

Data collected from the Surveillance, Epidemiology, and End Results (SEER) database for 18,504 TC patients (N0) between 2004 and 2015 were analyzed. Univariate and multivariate analyses were used to identify independent prognostic factors for DM in TC N0. These independent factors were used to build a nomogram model to predict overall survival (OS) at 1, 3, and 5 years for TC patients (N0) with DM.

Results and conclusion

This study examined the clinicopathological features associated with the risk and prognosis of DM in TC patients (N0), and successfully established and validated a nomogram capable of predicting OS in individual patients with DM. The nomogram is highly useful for the timely identification of TC patients (N0) at high risk of DM by physicians, enabling individualized survival evaluations and treatment for TC patients with DM (N0).

Comparing survival outcomes between neoadjuvant and adjuvant chemotherapy within hormone receptor-positive, human epidermal growth factor receptor 2-negative early breast cancer among young women (≤35): a retrospective cohort study based on SEER database and TJMUCH registry

Breast cancer is a leading cause of cancer morbidity and mortality among young women, who often experience more aggressive disease, which may impact their treatment responses and long-term prognoses. Understanding the effectiveness of neoadjuvant chemotherapy (NAC) versus adjuvant chemotherapy (AC) in this specific population is critical for optimizing treatment strategies and improving prognoses. This research was conducted to compare the prognoses of young women (≤35 years old) with early-stage hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer, who were treated with NAC versus those treated with AC. This study retrospectively analyzed data from young women with HR+/HER2- breast cancer, with complete follow-up information, sourced from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2018) and the Tianjin Medical University Cancer Institute and Hospital (TJMUCH) (2014-2018). Patients from both cohorts were allocated to NAC and AC groups based on their treatment regimens. Categorical variables were compared using chi-square, whereas the Kaplan-Meier method was utilized to generate survival curves; additionally, the log-rank test was employed for survival analysis. Propensity score matching (PSM) was employed to control baseline differences. Analysis of the SEER and TJMUCH cohorts revealed that patients treated with NAC had significantly worse overall survival (OS) compared to those treated with AC, as indicated by Kaplan-Meier curves both before and after PSM. The disease-free survival analysis of the TJMUCH cohort yielded similar results, indicating that patients treated with AC experienced longer periods without disease recurrence compared to their counterparts receiving NAC. Statistically significant differences were observed across both survival metrics, reinforcing the robustness of our findings. Overall, among young women (≤35 years old) with early-stage HR+/HER2- breast cancer, patients treated with AC exhibited a more favorable prognosis and improved survival outcomes compared to those treated with NAC. These findings could potentially influence clinical decision-making and treatment guidelines, advocating for a more tailored approach in managing young women with HR+/HER2- breast cancer.

Nomogram for Predicting Suboptimal Weight Loss at Three Years after Roux-en-Y Gastric Bypass Surgery in Chinese Patients with Obesity and Type 2 Diabetes

INTRODUCTION

Strategies to address suboptimal weight loss after Roux-en-Y gastric bypass (RYGB) can be developed if at-risk patients are identified in advance. This study aimed to build a pre-surgery prediction nomogram for early prediction of insufficient weight loss (IWL) or weight regain (WR) after bariatric surgery in Chinese patients.

METHODS

In this retrospective study, 187 patients with obesity and type 2 diabetes who underwent laparoscopic RYGB were followed yearly for 3 years. Suboptimal weight loss included IWL and WR. IWL was defined as a total weight loss percentage of <25% at 1 year postoperatively, and WR was defined as a maximum weight loss percentage of >20% at 3 years postoperatively. Multivariate logistic regression was performed to identify independent predictors and to establish a nomogram to predict the occurrence of suboptimal weight loss.

RESULTS

Multivariate logistic regression revealed that male sex (OR 4.268, 95% CI: 1.413-12.890), body mass index (OR 0.816, 95% CI: 0.705-0.946), and glycated hemoglobin (OR 1.493, 95% CI: 1.049-2.126) were independent predictors of IWL/WR. The AUC value of the nomogram constructed from the above three factors was 0.781. The Hosmer-Lemeshow test showed that the model had a good fit (p = 0.143). The calibration curve of the nomogram is close to an ideal diagonal line. Furthermore, the decision curve analysis demonstrated the good net benefits of the model.

CONCLUSIONS

A nomogram based on pre-surgery factors was developed to predict postoperative IWL/WR. This provides a convenient and useful tool for predicting suboptimal weight loss before surgery.

Marginal semiparametric accelerated failure time cure model for clustered survival data

The semiparametric accelerated failure time mixture cure model is an appealing alternative to the proportional hazards mixture cure model in analyzing failure time data with long-term survivors. However, this model was only proposed for independent survival data and it has not been extended to clustered or correlated survival data, partly due to the complexity of the estimation method for the model. In this paper, we consider a marginal semiparametric accelerated failure time mixture cure model for clustered right-censored failure time data with a potential cure fraction. We overcome the complexity of the existing semiparametric method by proposing a generalized estimating equations approach based on the expectation-maximization algorithm to estimate the regression parameters in the model. The correlation structures within clusters are modeled by working correlation matrices in the proposed generalized estimating equations. The large sample properties of the regression estimators are established. Numerical studies demonstrate that the proposed estimation method is easy to use and robust to the misspecification of working matrices and that higher efficiency is achieved when the working correlation structure is closer to the true correlation structure. We apply the proposed model and estimation method to a contralateral breast cancer study and reveal new insights when the potential correlation between patients is taken into account.

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

Background

Young breast cancer (YBC) is a subset of breast cancer that is often more aggressive, but less is known about its prognosis. In this study, we aimed to generate nomograms to predict the overall survival (OS) and breast cancer-specific survival (BCSS) of YBC patients.

Methods

Data of women diagnosed with YBC between 2010 and 2020 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated into a training cohort (n = 15,227) and internal validation cohort (n = 6,526) at a 7:3 ratio. With the Cox regression models, significant prognostic factors were identified and used to construct 3-, 5-, and 10-year nomograms of OS and BCSS. Data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database were used as an external validation cohort (n = 90).

Results

We constructed nomograms incorporating 10 prognostic factors for OS and BCSS. These nomograms demonstrated strong predictive accuracy for OS and BCSS in the training cohort, with C-indexes of 0.806 and 0.813, respectively. The calibration curves verified that the nomograms have good prediction accuracy. Decision curve analysis demonstrated their practical clinical value for predicting YBC patient survival rates. Additionally, we provided dynamic nomograms to improve the operability of the results. The risk stratification ability assessment also showed that the OS and BCSS rates of the low-risk group were significantly better than those of the high-risk group.

Conclusions

Here, we generated and validated more comprehensive and accurate OS and BCSS nomograms than models previously developed for YBC. These nomograms can help clinicians evaluate patient prognosis and make clinical decisions.

Analysis of factors influencing bronchiectasis patients with active pulmonary tuberculosis and development of a nomogram prediction model

Background

To identify the risk factors for bronchiectasis patients with active pulmonary tuberculosis (APTB) and to develop a predictive nomogram model for estimating the risk of APTB in bronchiectasis patients.

Methods

A retrospective cohort study was conducted on 16,750 bronchiectasis patients hospitalized at the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between January 2019 and December 2023. The 390 patients with APTB were classified as the case group, while 818 patients were randomly sampled by computer at a 1:20 ratio from the 16,360 patients with other infections to serve as the control group. Relevant indicators potentially leading to APTB in bronchiectasis patients were collected. Patients were categorized into APTB and inactive pulmonary tuberculosis (IPTB) groups based on the presence of tuberculosis. The general characteristics of both groups were compared. Variables were screened using the least absolute shrinkage and selection operator (LASSO) analysis, followed by multivariate logistic regression analysis. A nomogram model was established based on the analysis results. The model's predictive performance was evaluated using calibration curves, C-index, and ROC curves, and internal validation was performed using the bootstrap method.

Results

LASSO analysis identified 28 potential risk factors. Multivariate analysis showed that age, gender, TC, ALB, MCV, FIB, PDW, LYM, hemoptysis, and hypertension are independent risk factors for bronchiectasis patients with APTB (p < 0.05). The nomogram demonstrated strong calibration and discrimination, with a C-index of 0.745 (95% CI: 0.715-0.775) and an AUC of 0.744 for the ROC curve. Internal validation using the bootstrap method produced a C-index of 0.738, further confirming the model's robustness.

Conclusion

The nomogram model, developed using common clinical serological characteristics, holds significant clinical value for assessing the risk of APTB in bronchiectasis patients.

Development and validation of a nomogram for predicting overall survival of head and neck adenoid cystic carcinoma

This study aimed to develop and validate a nomogram using clinical variables to guide personalized treatment strategies for adenoid cystic carcinoma of the head and neck (ACCHN). Data from 1069 patients with ACCHN diagnosed between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were used to construct the nomogram. External validation was performed using an independent cohort of 70 patients from Fujian Cancer Hospital. Multivariate Cox regression analysis was conducted using IBM SPSS version 26.0 and R Software version 4.2.3. The concordance index (C-index) and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy of the nomogram. Age, tumor site, surgery, N stage, M stage, and TNM stage were identified as independent prognostic factors through univariate and multivariate Cox analyses. The nomogram demonstrated superior predictive performance compared to the TNM staging system, effectively stratifying patients into high-risk and low-risk groups. This nomogram offers a valuable tool for predicting overall survival in patients with ACCHN and tailoring individualized treatment approaches.

Development and validation of a predictive model for tumor lysis syndrome in childhood acute lymphoblastic leukemia

BACKGROUND

Tumor lysis syndrome (TLS) frequently manifests shortly after induction chemotherapy for acute lymphoblastic leukemia (ALL), with the potential for swift progression. This study endeavored to develop a nomogram to predict the risk of TLS, utilizing clinical indicators present at the time of ALL diagnosis.

METHODS

We retrospectively gathered data from 2243 patients with ALL, spanning December 2008 to December 2021, utilizing the clinical research big data platform of the National Center for Clinical Research on Children's Health and Diseases. The Least Absolute Shrinkage and Selection Operator (LASSO) method was employed to filter variables and identify predictors, followed by the application of multivariate logistic regression to construct the nomogram.

RESULTS

The LASSO regression identified six critical variables among ALL patients, upon which a nomogram was subsequently constructed. Multifactorial logistic regression revealed that an elevated white blood cell count (WBC), serum phosphorus <2.1 mmol/L, potassium <3.5 mmol/L, aspartate transaminase (AST) ≥50 U/L, uric acid (UA) ≥476μmol/L, and the presence of acute kidney injury (AKI) at the time of initial diagnosis were significant risk factors for the development of TLS in ALL patients (P<0.05). The predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.824 [95 % CI (0.783, 0.865)], with an internal validation AUC of 0.859 [95 % CI (0.806, 0.912)]. The Hosmer-Lemeshow goodness-of-fit test confirmed the model's robustness (P=0.687 for the training cohort; P=0.888 for the validation cohort). Decision curve analysis (DCA) indicated that the predictive model provided substantial clinical benefit across threshold probabilities ranging from 10 % to 70 %.

CONCLUSIONS

A nomogram incorporating six predictive variables holds significant potential for accurately forecasting TLS in pediatric patients with ALL.

Development and Validation of a Prognostic Nomogram for Breast Cancer Patients With Multi-Organ Metastases: An Analysis of the Surveillance, Epidemiology, and End Results Program Database

BACKGROUND

Multi-organ metastases represent a substantial life-threatening risk for breast cancer (BC) patients. Nonetheless, the current dearth of assessment tools for patients with multi-organ metastatic BC adversely impacts their evaluation.

METHODS

We conducted a retrospective analysis of BC patients with multi-organ metastases using data from the SEER database from 2010 to 2019. The patients were randomly allocated into a training cohort and a validation cohort in a 7:3 ratio. Univariate COX regression analysis, the LASSO, and multivariate Cox regression analyses were performed to identify independent prognostic factors in the training set. Based on these factors, a nomogram was constructed to estimate overall survival (OS) probability for BC patients with multi-organ metastases. The performance of the nomogram was evaluated using C-indexes, ROC curves, calibration curves, decision curve analysis (DCA) curves, and the risk classification system for validation.

RESULTS

A total of 3626 BC patients with multi-organ metastases were included in the study, with 2538 patients in the training cohort and 1088 patients in the validation cohort. Age, grade, metastasis location, surgery, chemotherapy, and subtype were identified as significant independent prognostic factors for OS in BC patients with multi-organ metastases. A nomogram for predicting 1-year, 3-year, and 5-year OS was constructed. The evaluation metrics, including C-indexes, ROC curves, calibration curves, and DCA curves, demonstrated the excellent predictive performance of the nomogram. Additionally, the risk grouping system effectively stratified BC patients with multi-organ metastases into distinct prognostic categories.

CONCLUSION

The developed nomogram showed high accuracy in predicting the survival probability of BC patients with multi-organ metastases, providing valuable information for patient counseling and treatment decision making.

Machine learning predictive models and risk factors for lymph node metastasis in non-small cell lung cancer

BACKGROUND

The prognosis of non-small cell lung cancer (NSCLC) is substantially affected by lymph node metastasis (LNM), but there are no noninvasive, inexpensive methods of relatively high accuracy available to predict LNM in NSCLC patients.

METHODS

Clinical data on NSCLC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for LNM were recognized LASSO and multivariate logistic regression. Six predictive models were constructed with machine learning based on risk factors. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Subgroup analysis with different T-stages was performed on an optimal model. A webpage LNM risk calculator for optimal model was built using the Shinyapps.io platform.

RESULTS

We enrolled 64,012 NSCLC patients, of whom 26,611 (41.57%) had LNM. Using multivariate logistic regression, we finally identified 10 independent risk factors for LNM: age, sex, race, histology, primary site, grade, T stage, M stage, tumor size, and bone metastases. GLM is the optimal model among all six machine learning models in both the training and validation cohorts. Subgroup analyses revealed that GLM has good predictability for populations with different T staging. A webpage LNM risk calculator based on GLM was posted on the shinyapps.io platform ( https://wubopredict.shinyapps.io/dynnomapp/ ).

CONCLUSION

The predictive model based on GLM can be used to precisely predict the probability of LNM in NSCLC patients, which was proven effective in all subgroup analyses according to T staging.

Exploring the role of mitophagy-related genes in breast cancer: subtype classification and prognosis prediction

Background: Breast cancer (BC) is the most common cancer among women globally and poses the leading health threat to women worldwide, with persistently high incidence rates. Mitophagy is a selective autophagy process that specifically targets mitochondria within the cell, maintaining cellular energy balance and metabolic health by identifying and degrading damaged mitochondria. Although there is an understanding of the relationship between mitophagy and cancer, the specific mechanisms remain unclear due to the complexity and diversity of mitophagy, suggesting that it could be an effective and more targeted therapeutic approach for BC. Methods: In this study, we meticulously examined the BC expression and clinical pathology data from The Cancer Genome Atlas (TCGA) to assess the expression profiles, copy number variations (CNV), and to investigate the correlation, function, and prognostic impact of 34 mitophagy-related genes (MRGs). Differentially expressed genes (DEGs) were identified based on group classification. Lasso and Cox regression were used to determine prognostic genes for constructing a nomogram. Single-cell analysis mapped the distribution of these genes in BC cells. Additionally, the association between gene-derived risk scores and factors such as immune infiltration, tumor mutational burden (TMB), cancer stem cell (CSC) index, and drug responses was studied. In vitro experiments were conducted to confirm the analyses. Results: We included 34 MRGs and subsequently generated a risk score for 7 genes, including RPLP2, PCDHGA2, PRKAA2, CLIC6, FLT3, CHI3L1, and IYD. It was found that the low-risk group had better overall survival (OS) in BC, higher immune scores, but lower tumor mutational burden (TMB) and cancer stem cell (CSC) index, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and staging were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and showed good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS of 0.895, 0.765, and 0.728, respectively. Conclusion: Our findings underscore the profound impact of prognostic genes on the immune response and prognostic outcomes in BC, indicating that they can provide new avenues for personalized BC treatment and potentially improve clinical outcomes.

Renewable risk assessment of heterogeneous streaming time-to-event cohorts

The analysis of streaming time-to-event cohorts has garnered significant research attention. Most existing methods require observed cohorts from a study sequence to be independent and identically sampled from a common model. This assumption may be easily violated in practice. Our methodology operates within the framework of online data updating, where risk estimates for each cohort of interest are continuously refreshed using the latest observations and historical summary statistics. At each streaming stage, we introduce parameters to quantify the potential discrepancy between batch-specific effects from adjacent cohorts. We then employ penalized estimation techniques to identify nonzero discrepancy parameters, allowing us to adaptively adjust risk estimates based on current data and historical trends. We illustrate our proposed method through extensive empirical simulations and a lung cancer data analysis.

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

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

Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer

Breast cancer (BC) is the most prevalent cancer in women globally. The tumor microenvironment (TME), comprising epithelial tumor cells and stromal elements, is vital for breast tumor development. N6-methyladenosine (m6A) modification plays a key role in RNA metabolism, influencing its various aspects such as stability and translation. There is a notable link between m6A methylation and immune cells in the TME, although this relationship is complex and not fully deciphered. In this research, BC expression and clinicopathological data from TCGA were scrutinized to assess expression profiles, mutations, and CNVs of 31 m6A genes and immune microenvironment-related genes, examining their correlations, functions, and prognostic impacts. Lasso and Cox regression identified prognostic genes for constructing a nomogram. Single-cell analyses mapped the distribution and patterns of these genes in BC cell development. We investigated associations between gene-derived risk scores and factors like immune infiltration, TME, checkpoints, TMB, CSC indices, and drug response. As a complement to computational analyses, in vitro experiments were conducted to confirm these expression patterns. We included 31 m6A regulatory genes and discovered a correlation between these genes and the extent of immune cell infiltration. Subsequently, a 7-gene risk score was generated, encompassing HSPA2, TAP1, ULBP2, CXCL1, RBP1, STC2, and FLT3. It was observed that the low-risk group exhibited better overall survival (OS) in BC, with higher immune scores but lower tumor mutational burden (TMB) and cancer stem cell (CSC) indices, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and stage were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and demonstrated good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS being 0.848, 0.807, and 0.759, respectively. Our findings highlight the profound impact of prognostic-related genes on BC immune response and prognostic outcomes, suggesting that modulation of the m6A-immune pathway could offer new avenues for personalized BC treatment and potentially improve clinical outcomes.

Development and validation of a nomogram for breast cancer-related lymphedema

To establish and validate a predictive model for breast cancer-related lymphedema (BCRL) among Chinese patients to facilitate individualized risk assessment. We retrospectively analyzed data from breast cancer patients treated at a major single-center breast hospital in China. From 2020 to 2022, we identified risk factors for BCRL through logistic regression and developed and validated a nomogram using R software (version 4.1.2). Model validation was achieved through the application of receiver operating characteristic curve (ROC), a calibration plot, and decision curve analysis (DCA), with further evaluated by internal validation. Among 1485 patients analyzed, 360 developed lymphedema (24.2%). The nomogram incorporated body mass index, operative time, lymph node count, axillary dissection level, surgical site infection, and radiotherapy as predictors. The AUCs for training (N = 1038) and validation (N = 447) cohorts were 0.779 and 0.724, respectively, indicating good discriminative ability. Calibration and decision curve analysis confirmed the model's clinical utility. Our nomogram provides an accurate tool for predicting BCRL risk, with potential to enhance personalized management in breast cancer survivors. Further prospective validation across multiple centers is warranted.

Development and validation of nomograms for predicting survival in small cell lung cancer patients with brain metastases: a SEER population-based analysis

OBJECTIVE

To develop prognostic nomograms for overall survival (OS) and cancer-specific survival (CSS) probabilities in small cell lung cancer (SCLC) patients with brain metastasis (BM).

METHODS

SCLC patients with BM from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2015) were randomly allocated to training (n=1771) and validation (n=757) cohorts. Independent prognostic factors for OS and CSS were determined using univariate and multivariate Cox regression analyses in the training cohort, and prognostic nomograms for OS and CSS were constructed based on these factors. The efficacy of the nomograms was assessed using area under the receiver operating characteristic (ROC) curves (AUCs), calibration curves, decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI), with the TNM staging model as a comparator.

RESULTS

Multivariate Cox analysis identified age, sex, race, tumor size, N staging, and presence of liver/bone/lung metastases, chemotherapy, and radiotherapy as independent prognostic factors for both OS and CSS. Prognostic nomograms were developed based on these factors. In both the training and validation cohorts, the AUC values of the nomograms for OS and CSS were significantly above 0.7, surpassing those for TNM staging. Calibration curves demonstrated a high degree of concordance between predicted and actual survival. The constructed nomograms showed superior clinical utility compared to the TNM staging system, as evidenced by NRI, IDI, and DCA.

CONCLUSIONS

This retrospective study successfully developed and validated prognostic nomograms for SCLC patients with BM, providing valuable tools for oncologists to enhance prognosis evaluation and guide clinical decision-making.

Construction of a novel nomogram for predicting overall survival in patients with Siewert type II AEG based on LODDS: a study based on the seer database and external validation

Background

In recent years, the incidence of adenocarcinoma of the esophagogastric junction (AEG) has been rapidly increasing globally. Despite advances in the diagnosis and treatment of AEG, the overall prognosis for AEG patients remains concerning. Therefore, analyzing prognostic factors for AEG patients of Siewert type II and constructing a prognostic model for AEG patients is important.

Methods

Data of primary Siewert type II AEG patients from the SEER database from 2004 to 2015 were obtained and randomly divided into training and internal validation cohort. Additionally, data of primary Siewert type II AEG patients from the China Medical University Dandong Central Hospital from 2012 to 2018 were collected for external validation. Each variable in the training set underwent univariate Cox analysis, and variables with statistical significance (p < 0.05) were added to the LASSO equation for feature selection. Multivariate Cox analysis was then conducted to determine the independent predictive factors. A nomogram for predicting overall survival (OS) was developed, and its performance was evaluated using ROC curves, calibration curves, and decision curves. NRI and IDI were calculated to assess the improvement of the new prediction model relative to TNM staging. Patients were stratified into high-risk and low-risk groups based on the risk scores from the nomogram.

Results

Age, Differentiation grade, T stage, M stage, and LODDS (Log Odds of Positive Lymph Nodes)were independent prognostic factors for OS. The AUC values of the ROC curves for the nomogram in the training set, internal validation set, and external validation set were all greater than 0.7 and higher than those of TNM staging alone. Calibration curves indicated consistency between the predicted and actual outcomes. Decision curve analysis showed moderate net benefit. The NRI and IDI values of the nomogram were greater than 0 in the training, internal validation, and external validation sets. Risk stratification based on the nomogram's risk score demonstrated significant differences in survival rates between the high-risk and low-risk groups.

Conclusion

We developed and validated a nomogram for predicting overall survival (OS) in patients with Siewert type II AEG, which assists clinicians in accurately predicting mortality risk and recommending personalized treatment strategies.

Development and validation of a random survival forest model for predicting long-term survival of early-stage young breast cancer patients based on the SEER database and an external validation cohort

Young breast cancer (YBC) patients often face a poor prognosis, hence it's necessary to construct a model that can accurately predict their long-term survival in early stage. To realize this goal, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) databases between January 2010 and December 2020, and meanwhile, enrolled an independent external cohort from Tianjin Medical University Cancer Institute and Hospital. The study aimed to develop and validate a prediction model constructed using the Random Survival Forest (RSF) machine learning algorithm. By applying the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, we pinpointed key prognostic factors for YBC patients, which were used to create a prediction model capable of forecasting the 3-year, 5-year, 7-year, and 10-year survival rates of YBC patients. The RSF model constructed in the study demonstrated exceptional performance, achieving C-index values of 0.920 in the training set, 0.789 in the internal validation set, and 0.701 in the external validation set, outperforming the Cox regression model. The model's calibration was confirmed by Brier scores at various time points, showcasing its excellent accuracy in prediction. Decision curve analysis (DCA) underscored the model's importance in clinical application, and the Shapley Additive Explanations (SHAP) plots highlighted the importance of key variables. The RSF model also proved valuable in risk stratification, which has effectively categorized patients based on their survival risks. In summary, this study has constructed a well-performed prediction model for the evaluation of prognostic factors influencing the long-term survival of early-stage YBC patients, which is significant in risk stratification when physicians handle YBC patients in clinical settings.

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

OBJECTIVE

We developed a simple, rapid predictive model to evaluate the prognosis of older patients with lung adenocarcinoma.

METHODS

Demographic characteristics and clinical information of patients with lung adenocarcinoma aged ≥60 years were retrospectively analyzed using Surveillance, Epidemiology, and End Results (SEER) data. We built nomograms of overall survival and cancer-specific survival using Cox single-factor and multi-factor regression. We used the C-index, calibration curve, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) to evaluate performance of the nomograms.

RESULTS

We included 14,117 patients, divided into a training set and validation set. We used the chi-square test to compare baseline data between groups and found no significant differences. We used Cox regression analysis to screen out independent prognostic factors affecting survival time and used these factors to construct the nomogram. The ROC curve, calibration curve, C-index, and DCA curve were used to verify the model. The final results showed that our predictive model had good predictive ability, and showed better predictive ability compared with tumor-node-metastasis (TNM) staging. We also achieved good results using data of our center for external verification.

CONCLUSION

The present nomogram could accurately predict prognosis in older patients with lung adenocarcinoma.

A scoping review of web-based, interactive, personalized decision-making tools available to support breast cancer treatment and survivorship care

PURPOSE

We reviewed existing personalized, web-based, interactive decision-making tools available to guide breast cancer treatment and survivorship care decisions in clinical settings.

METHODS

The study was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). We searched PubMed and related databases for interactive web-based decision-making tools developed to support breast cancer treatment and survivorship care from 2013 to 2023. Information on each tool's purpose, target population, data sources, individual and contextual characteristics, outcomes, validation, and usability testing were extracted. We completed a quality assessment for each tool using the International Patient Decision Aid Standard (IPDAS) instrument.

RESULTS

We found 54 tools providing personalized breast cancer outcomes (e.g., recurrence) and treatment recommendations (e.g., chemotherapy) based on individual clinical (e.g., stage), genomic (e.g., 21-gene-recurrence score), behavioral (e.g., smoking), and contextual (e.g., insurance) characteristics. Forty-five tools were validated, and nine had undergone usability testing. However, validation and usability testing included mostly White, educated, and/or insured individuals. The average quality assessment score of the tools was 16 (range: 6-46; potential maximum: 63).

CONCLUSIONS

There was wide variation in the characteristics, quality, validity, and usability of the tools. Future studies should consider diverse populations for tool development and testing.

IMPLICATIONS FOR CANCER SURVIVORS

There are tools available to support personalized breast cancer treatment and survivorship care decisions in clinical settings. It is important for both cancer survivors and physicians to carefully consider the quality, validity, and usability of these tools before using them to guide care decisions.

Predictive value of HTS grade in patients with intrahepatic cholangiocarcinoma undergoing radical resection: a multicenter study from China

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is a highly malignant tumor with a poor prognosis. This study aimed to investigate whether Hemoglobin, Albumin, Lymphocytes, and Platelets (HALP) score and Tumor Burden Score (TBS) serves as independent influencing factors following radical resection in patients with ICC. Furthermore, we sought to evaluate the predictive capacity of the combined HALP and TBS grade, referred to as HTS grade, and to develop a prognostic prediction model.

METHODS

Clinical data for ICC patients who underwent radical resection were retrospectively analyzed. Univariate and multivariate Cox regression analyses were first used to find influencing factors of prognosis for ICC. Receiver operating characteristic (ROC) curves were then used to find the optimal cut-off values for HALP score and TBS and to compare the predictive ability of HALP, TBS, and HTS grade using the area under these curves (AUC). Nomogram prediction models were constructed and validated based on the results of the multivariate analysis.

RESULTS

Among 423 patients, 234 (55.3%) were male and 202 (47.8) were aged ≥ 60 years. The cut-off value of HALP was found to be 37.1 and for TBS to be 6.3. Our univariate results showed that HALP, TBS, and HTS grade were prognostic factors of ICC patients (all P < 0.05), and ROC results showed that HTS had the best predictive value. The Kaplan-Meier curve showed that the prognosis of ICC patients was worse with increasing HTS grade. Additionally, multivariate regression analysis showed that HTS grade, carbohydrate antigen 19-9 (CA19-9), tumor differentiation, and vascular invasion were independent influencing factors for Overall survival (OS) and that HTS grade, CA19-9, CEA, vascular invasion and lymph node invasion were independent influencing factors for recurrence-free survival (RFS) (all P < 0.05). In the first, second, and third years of the training group, the AUCs for OS were 0.867, 0.902, and 0.881, and the AUCs for RFS were 0.849, 0.841, and 0.899, respectively. In the first, second, and third years of the validation group, the AUCs for OS were 0.727, 0.771, and 0.763, and the AUCs for RFS were 0.733, 0.746, and 0.801, respectively. Through the examination of calibration curves and using decision curve analysis (DCA), nomograms based on HTS grade showed excellent predictive performance.

CONCLUSIONS

Our nomograms based on HTS grade had excellent predictive effects and may thus be able to help clinicians provide individualized clinical decision for ICC patients.

Prediction model of adnexal masses with complex ultrasound morphology

Background

Based on the ovarian-adnexal reporting and data system (O-RADS), we constructed a nomogram model to predict the malignancy potential of adnexal masses with sophisticated ultrasound morphology.

Methods

In a multicenter retrospective study, a total of 430 subjects with masses were collected in the adnexal region through an electronic medical record system at the Fourth Hospital of Harbin Medical University during the period of January 2019-April 2023. A total of 157 subjects were included in the exception validation cohort from Harbin Medical University Tumor Hospital. The pathological tumor findings were invoked as the gold standard to classify the subjects into benign and malignant groups. All patients were randomly allocated to the validation set and training set in a ratio of 7:3. A stepwise regression analysis was utilized for filtering variables. Logistic regression was conducted to construct a nomogram prediction model, which was further validated in the training set. The forest plot, C-index, calibration curve, and clinical decision curve were utilized to verify the model and assess its accuracy and validity, which were further compared with existing adnexal lesion models (O-RADS US) and assessments of different types of neoplasia in the adnexa (ADNEX).

Results

Four predictors as independent risk factors for malignancy were followed in the preparation of the diagnostic model: O-RADS classification, HE4 level, acoustic shadow, and protrusion blood flow score (all p < 0.05). The model showed moderate predictive power in the training set with a C-index of 0.959 (95%CI: 0.940-0.977), 0.929 (95%CI: 0.884-0.974) in the validation set, and 0.892 (95%CI: 0.843-0.940) in the external validation set. It showed that the predicted consequences of the nomogram agreed well with the actual results of the calibration curve, and the novel nomogram was clinically beneficial in decision curve analysis.

Conclusion

The risk of the nomogram of adnexal masses with complex ultrasound morphology contained four characteristics that showed a suitable predictive ability and provided better risk stratification. Its diagnostic performance significantly exceeded that of the ADNEX model and O-RADS US, and its screening performance was essentially equivalent to that of the ADNEX model and O-RADS US classification.

Exploring the most appropriate lymph node staging system for node-positive breast cancer patients and constructing corresponding survival nomograms

BACKGROUND

The lymph node (LN) status is a crucial prognostic factor for breast cancer (BC) patients. Our study aimed to compare the predictive capabilities of three different LN staging systems in node-positive BC patients and develop nomograms to predict overall survival (OS).

METHODS

We enrolled 71,213 eligible patients from the SEER database, and 667 cases from our hospital were used for external validation. All patients were divided into two groups based on the number of removed lymph nodes (RLNs). The prognostic abilities of pN stage, positive LN ratio (LNR), and log odds of positive LN (LODDS) were compared using the C-indexes and AUC values. LASSO regression was performed to identify significant factors associated with prognosis and develop corresponding nomogram models.

RESULTS

Our study found that LNR had superior predictive performance compared to pN and LODDS among patients with RLNs < 10, while pN performed better in patients with RLNs ≥ 10. In the training set, the nomogram models exhibited excellent clinical applicability, as evidenced by the C-indexes, ROC curves, calibration plots, and decision curve analysis curves. Moreover, the nomogram classification accurately differentiated risk subgroups and improved discrimination. These results were externally validated in the validation cohort.

CONCLUSION

Physicians should select different LN staging systems based on the number of RLNs. Our novel nomograms demonstrated excellent performance in both internal and external validations, which may assist in patient counseling and guide treatment decision-making.

Construction and validation of a SEER-based prognostic nomogram for young and middle-aged males patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common digestive tumor, and we aimed to develop and validate nomogram models, predicting the overall survival (OS) of young and middle-aged male patients with HCC.

METHODS

We extracted eligible data from relevant patients between 2000 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database. In addition, randomly divided all patients into two groups (training and validation = 7:3). The nomogram was established using effective risk factors based on univariate and multivariate analysis. The area under the time-dependent curve, calibration plots, and decision curve analysis (DCA) were used to evaluate the effective performance of the nomogram. The risk stratifications of the nomogram and the AJCC criteria-based tumor stage were compared.

RESULTS

11 variables were selected by univariate and multivariate analysis to establish the nomogram of HCC. The AUC values of 3, 4, and 5 years of the time-ROC curve are 0.858, 0.862 and 0.859 for the training cohort, and 0.858, 0.877 and 0.869 for the validation cohort, respectively, indicating that the nomogram has a good ability of discrimination. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. In addition, the decision curve DCA showed that the nomogram was clinically useful and had better discriminative ability to recognize patients at high risk than the AJCC criteria-based tumor stage.

CONCLUSION

Prognostic nomogram of young and middle-aged male patients with HCC was developed and validated to help clinicians evaluate the prognosis of patients.

Risk factors and survival prediction of young breast cancer patients with liver metastases: a population-based study

Background

The risk and prognosis of young breast cancer (YBC) with liver metastases (YBCLM) remain unclear. Thus, this study aimed to determine the risk and prognostic factors in these patients and construct predictive nomogram models.

Methods

This population-based retrospective study was conducted using data of YBCLM patients from the Surveillance, Epidemiology, and End Results database between 2010 and 2019. Multivariate logistic and Cox regression analyses were used to identify independent risk and prognostic factors, which were used to construct the diagnostic and prognostic nomograms. The concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the performances of the established nomogram models. Propensity score matching (PSM) analysis was used to balance the baseline characteristics between the YBCLM patients and non-young patients with BCLM when comparing overall survival (OS) and cancer-specific survival (CSS).

Results

A total of 18,275 YBC were identified, of whom 400 had LM. T stage, N stage, molecular subtypes, and bone, lung, and brain metastases were independent risk factors for LM developing in YBC. The established diagnostic nomogram showed that bone metastases contributed the most risk of LM developing, with a C-index of 0.895 (95% confidence interval 0.877-0.913) for this nomogram model. YBCLM had better survival than non-young patients with BCLM in unmatched and matched cohorts after propensity score matching analysis. The multivariate Cox analysis demonstrated that molecular subtypes, surgery and bone, lung, and brain metastases were independently associated with OS and CSS, chemotherapy was an independent prognostic factor for OS, and marital status and T stage were independent prognostic factors for CSS. The C-indices for the OS- and CSS-specific nomograms were 0.728 (0.69-0.766) and 0.74 (0.696-0.778), respectively. The ROC analysis indicated that these models had excellent discriminatory power. The calibration curve also showed that the observed results were consistent with the predicted results. DCA showed that the developed nomogram models would be effective in clinical practice.

Conclusion

The present study determined the risk and prognostic factors of YBCLM and further developed nomograms that can be used to effectively identify high-risk patients and predict survival outcomes.

Risk factors and nomogram-based prediction of the risk of limb weakness in herpes zoster

Background

Limb weakness is a less common complication of herpes zoster (HZ). There has been comparatively little study of limb weakness. The aim of this study is to develop a risk nomogram for limb weakness in HZ patients.

Methods

Limb weakness was diagnosed using the Medical Research Council (MRC) muscle power scale. The entire cohort was assigned to a training set (from January 1, 2018 to December 30, 2019, n = 169) and a validation set (from October 1, 2020 to December 30, 2021, n = 145). The least absolute shrinkage and selection operator (LASSO) regression analysis method and multivariable logistic regression analysis were used to identify the risk factors of limb weakness. A nomogram was established based on the training set. The discriminative ability and calibration of the nomogram to predict limb weakness were tested using the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). A validation set was used to further assess the model by external validation.

Results

Three hundred and fourteen patients with HZ of the extremities were included in the study. Three significant risk factors: age (OR = 1.058, 95% CI: 1.021–1.100, P = 0.003), VAS (OR = 2.013, 95% CI: 1.101–3.790, P = 0.024), involving C6 or C7 nerve roots (OR = 3.218, 95% CI: 1.180–9.450, P = 0.027) were selected by the LASSO regression analysis and the multivariable logistic regression analysis. The nomogram to predict limb weakness was constructed based on the three predictors. The area under the ROC was 0.751 (95% CI: 0.673–0.829) in the training set and 0.705 (95% CI: 0.619–0.791) in the validation set. The DCA indicated that using the nomogram to predict the risk of limb weakness would be more accurate when the risk threshold probability was 10–68% in the training set and 15–57% in the validation set.

Conclusion

Age, VAS, and involving C6 or C7 nerve roots are potential risk factors for limb weakness in patients with HZ. Based on these three indicators, our model predicted the probability of limb weakness in patients with HZ with good accuracy.

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.

Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy

PURPOSE

While a pathologic complete response (pCR) is regarded as a surrogate endpoint for pos-itive outcomes in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC), fore-casting the prognosis of non-pCR patients is still an open issue. This study aimed to create and evaluate nomogram models for estimating the likelihood of disease-free survival (DFS) for non-pCR patients.

METHODS

A retrospective analysis of 607 non-pCR BC patients was conducted (2012-2018). After converting continuous variables to categorical variables, variables entering the model were progressively identified by univariate and multivariate Cox regression analyses, and then pre-NAC and post-NAC nomogram models were developed. Regarding their discrimination, ac-curacy, and clinical value, the performance of the models was evaluated by internal and external validation. Two risk assessments were performed for each patient based on two models; patients were separated into different risk groups based on the calculated cut-off values for each model, including low-risk (assessed by the pre-NAC model) to low-risk (assessed by the post-NAC model), high-risk to low-risk, low-risk to high-risk, and high-risk to high-risk groups. The DFS of different groups was assessed using the Kaplan-Meier method.

RESULTS

Both pre-NAC and post-NAC nomogram models were built with clinical nodal (cN) status and estrogen receptor (ER), Ki67, and p53 status (all p < 0.05), showing good discrimination and calibration in both internal and external validation. We also assessed the performance of the two models in four subtypes, with the tri-ple-negative subtype showing the best prediction. Patients in the high-risk to high-risk subgroup have significantly poorer survival rates (p < 0.0001).

CONCLUSION

Two robust and effective nomo-grams were developed to personalize the prediction of DFS in non-pCR BC patients treated with NAC.

Lymph node ratio is a superior predictor in surgically treated early-onset pancreatic cancer

Background

The prognostic performance of four lymph node classifications, the 8th American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) N stage, lymph node ratio (LNR), log odds of positive lymph nodes (LODDS), and examined lymph nodes (ELN) in early-onset pancreatic cancer (EOPC) remains unclear.

Methods

The Surveillance, Epidemiology, and End Results (SEER) database was searched for patients with EOPC from 2004 to 2016. 1048 patients were randomly divided into training (n = 733) and validation sets (n = 315). The predictive abilities of the four lymph node staging systems were compared using the Akaike information criteria (AIC), receiver operating characteristic area under the curve (AUC), and C-index. Multivariate Cox analysis was performed to identify independent risk factors. A nomogram based on lymph node classification with the strongest predictive ability was established. The nomogram’s precision was verified by the C-index, calibration curves, and AUC. Kaplan–Meier analysis and log-rank tests were used to compare differences in survival at each stage of the nomogram.

Results

Compared with the 8th N stage, LODDS, and ELN, LNR had the highest C-index and AUC and the lowest AIC. Multivariate analysis showed that N stage, LODDS, LNR were independent risk factors associated with cancer specific survival (CSS), but not ELN. In the training set, the AUC values for the 1-, 3-, and 5-year CSS of the nomogram were 0.663, 0.728, and 0.760, respectively and similar results were observed in the validation set. In addition, Kaplan–Meier survival analysis showed that the nomogram was also an important factor in the risk stratification of EOPC.

Conclusion

We analyzed the predictive power of the four lymph node staging systems and found that LNR had the strongest predictive ability. Furthermore, the novel nomogram prognostic staging mode based on LNR was also an important factor in the risk stratification of EOPC.