Nomogram of conditional survival probability of long-term Survival for Metastatic Colorectal Cancer: A Real-World Data Retrospective Cohort Study from SEER database

Prognostic Factors of Long-Term Survival and Conditional Survival Analysis in MPM Patients Treated with CRS+HIPEC: A Retrospective Study of Two Centers

OBJECTIVES

This study was designed to explore the survival benefit factors of malignant peritoneal mesothelioma (MPM) patients after cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC) and to make dynamic survival prediction by conditional survival (CS).

METHODS

Data of 212 patients with MPM who underwent CRS+HIPEC were retrospectively analyzed. Patients were divided into long-term survival (LTS) group (≥48.0 months) and short-term survival (STS) group (≤16.0 months) according to OS. Conditional survival is the probability of surviving y years after already survived for x years. Univariate and multivariate analyses were performed to explore the favorable factors of LTS. Conditional survival and Kaplan-Meier were applied to assess the postoperative survival probability.

RESULTS

Ninety patients were enrolled: 53 (58.9%) were LTS, and 37 (41.1%) were STS. Univariate analysis revealed 14 meaningful factors (P < 0.05): age, surgery history, Karnofsky performance status, pathological types, tumor vascular emboli, lymphatic metastasis, Ki-67 index, preoperative CA125 level, peritoneal cancer index (PCI), completeness of cytoreduction, bleeding, red blood cell (RBC) transfusion, ascites, and severe adverse events (SAEs). Multivariate analysis identified that PCI ≤ 20, less RBC transfusion and no SAEs were independent prognostic factors for LTS. Five-year CS increased from 27% at 0 years to 84% at 4 years with the increasing number of survival years. The survival curve flattens at postoperative 5 years.

CONCLUSIONS

The key factors in CRS+HIPEC for MPM patients to achieve LTS are lower tumor burden, less intraoperative RBC transfusion, and prevention of SAEs. Malignant peritoneal mesothelioma patients demonstrated a substantial increase in CS over time. Some patients may achieve clinical cure 5 years after surgery.

Prognostic value of combined NP and LHb index with absolute monocyte count in colorectal cancer patients

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high postoperative recurrence and metastasis rates posing significant challenges to patient survival. Identifying reliable and accessible prognostic markers is essential for optimizing treatment strategies. This study investigates the prognostic significance of two preoperative hematological indices, the [neutrophils × platelets]/[lymphocytes × hemoglobin] (NP/LHb) ratio and absolute monocyte count (Mono), in predicting overall survival in CRC patients. A retrospective analysis of 566 patients was conducted, with one cohort serving as an external validation set. Receiver operating characteristic curve analysis identified optimal cut-off values for NP/LHb and Mono, and Kaplan-Meier survival analysis revealed that higher levels of both markers were associated with significantly shorter survival. A novel prognostic model, NPM, integrating NP/LHb and Mono, demonstrated superior predictive accuracy compared to either marker alone. The NPM model was further validated through a nomogram, achieving high predictive performance for 1-, 3-, and 5-year survival. These findings highlight the potential of combining inflammatory and nutritional markers for effective risk stratification in CRC patients. The NPM model offers a simple, cost-effective prognostic tool that may facilitate personalized postoperative management, though further prospective validation is warranted.

Conditional survival of male breast cancer

BACKGROUND

The incidence of male breast cancer has been increasing in recent years; however, the long-term survival outcomes of diagnosed patients remain uncertain. This study was designed to evaluate the conditional survival of male breast cancer patients and to predict the future survival of patients through the conditional nomogram, to provide important suggestions for clinical decision-making.

METHODS

Retrospective data from the SEER database included 3600 male breast cancer patients, divided into training and validation groups (7 : 3 ratio). Overall survival rates were calculated using Kaplan-Meier analysis. Conditional survival analysis described survival at specific years. Time-dependent multivariate Cox analysis identified prognostic factors' impact. The conditional survival nomogram model predicted real-time survival rates.

RESULTS

Over time, the 5-year real-time survival rate of patients gradually improved, increasing from 70.5 to 74.8, 79.4, 85.8, and 92.9% (respectively, representing 5-year survival rates of 1-4 years after diagnosis). In addition, the improvement in conditional survival rate CS5 showed a nonlinear trend. After 5 years of diagnosis, age, tumor size, and tumor stage had a sustained impact on patient prognosis. Finally, a conditional survival nomogram was constructed to predict the 10-year survival rate in real time.

CONCLUSION

Five years after diagnosis, the conditional survival rate of male patients with breast cancer has improved, but it is not nonlinear. In the first 5 years after diagnosis, patients with older age, larger tumor size, poorer tumor stage, and distant metastasis should be actively followed up and treated to improve their long-term survival.

Revolutionizing prognostic predictions in colorectal cancer: Macrophage‑driven transcriptional insights from single‑cell RNA sequencing and gene co‑expression network analysis

Tumor-associated macrophages have become important biomarkers for cancer diagnosis, prognosis and therapy. The dynamic changes in macrophage subpopulations significantly impact the outcomes of cancer immunotherapy. Hence, identifying additional macrophage-related biomarkers is essential for enhancing prognostic predictions in colorectal cancer (CRC) immunotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene Expression Omnibus (GEO) database. The data were processed, normalized and clustered using the 'Seurat' package. Cell types within each cluster were annotated using the 'SingleR' package. Weighted gene co-expression network analysis identified modules corresponding to specific cell types. A non-negative matrix factorization algorithm was employed to segregate different clusters based on the selected module. Differentially expressed genes (DEGs) were identified across various clusters and a prognostic model was constructed using lasso regression and Cox regression analyses. The robustness of the model was validated using The Cancer Genome Atlas (TCGA) database and GEO microarrays. Additionally, the prognosis, immune characteristics and response to immune checkpoint inhibitor (ICI) therapy were individually analyzed. The scRNA-seq data from GSE200997, consisting of 23 samples, were analyzed. Dimensionality reduction and cluster identification allowed the isolation of the primary myeloid cell subpopulations. The macrophage-related brown module was identified, which was further divided into two clusters. Using the DEGs from these clusters, a prognostic model was developed, comprising five macrophage-related genes. The robustness of the model was confirmed using microarray datasets GSE17536, GSE38832 and GSE39582, as well as TCGA cohort. Patients classified as high-risk by the present model exhibited poorer survival rates, lower tumor mutation burden, reduced microsatellite instability, lower tumor purity, more severe tumor immune dysfunction and exclusion, and less benefit from ICIs therapy compared with low-risk patients. The present prognostic model shows promise as a biomarker for risk stratification and predicting therapeutic efficacy in patients with CRC. However, further well-designed prospective studies are necessary to validate the findings.

Establishment and Validation of a Prognostic Nomogram for Predicting Postoperative Overall Survival in Advanced Stage III-IV Colorectal Cancer Patients

BACKGROUND

Most colorectal cancer (CRC) patients are at an advanced stage when they are first diagnosed. Risk factors for predicting overall survival (OS) in advanced stage CRC patients are crucial, and constructing a prognostic nomogram model is a scientific method for survival analysis.

METHODS

A total of 2956 advanced stage CRC patients were randomised into training and validation groups at a 7:3 ratio. Univariate and multivariate Cox proportional hazards regression analyses were used to screen risk factors for OS and subsequently construct a prognostic nomogram model for predicting 1-, 3-, 5-, 8- and 10-year OS of advanced stage CRC patients. The performance of the model was demonstrated by the area under the curve (AUC) values, calibration curves and decision curve analysis (DCA). Kaplan-Meier curves were used to plot the survival probabilities for different strata of each risk factor.

RESULTS

There was no statistically significant difference (p > 0.05) in the 32 clinical variables between patients in the training and validation groups. Univariate and multivariate Cox proportional hazards regression analyses demonstrated that age, location, TNM, chemotherapy, liver metastasis, lung metastasis, MSH6, CEA, CA199, CA125 and CA724 were risk factors for OS. We estimated the AUC values for the nomogram model to predict 1-, 3-, 5-, 8- and 10-year OS, which in the training group were 0.826 (95% CI: 0.807-0.845), 0.836 (0.819-0.853), 0.839 (0.820-0.859), 0.835 (0.809-0.862) and 0.825 (0.779-0.870) respectively; in the validation group, the corresponding AUC values were 0.819 (0.786-0.852), 0.831 (0.804-0.858), 0.830 (0.799-0.861), 0.815 (0.774-0.857) and 0.802 (0.723-0.882) respectively. Finally, the 1-, 3-, 5-, 8- and 10-year OS rates for advanced stage CRC patients were 73.4 (71.8-75.0), 49.5 (47.8-51.4), 43.3 (41.5-45.2), 40.1 (38.1-41.9) and 38.6 (36.6-40.8) respectively.

CONCLUSION

We constructed and validated an original nomogram for predicting the postoperative OS of advanced stage CRC patients, which can help facilitates physicians to accurately assess the individual survival of postoperative patients and identify high-risk patients.

Long-term prognostic characteristics of patients with clinical stage IA part-solid lung adenocarcinoma: a conditional survival analysis

OBJECTIVES

Despite excellent 5-year survival, there are limited data on the long-term prognostic characteristics of clinical stage IA part-solid lung adenocarcinoma. The objective was to elucidate the dynamics of prognostic characteristics through conditional survival analysis.

METHODS

Consecutive patients who underwent complete resection for clinical stage IA part-solid lung adenocarcinoma between 2011 and 2015 were retrospectively reviewed. Conditional survival is defined as the probability of surviving further y years, conditional on the patient has already survived x years. The conditional recurrence-free survival (CRFS) and conditional overall survival (COS) were analysed to evaluate prognosis over time, with conditional Cox regression analysis performed to identify time-dependent prognostic factors.

RESULTS

A total of 1539 patients were included with a median follow-up duration of 98.4 months, and 80 (5.2%) patients experienced recurrence. Among them, 20 (1.3%) recurrence cases occurred after 5 years of follow-up with 100% intrathoracic recurrence. The 5-year CRFS increased from 95.8% to 97.4%, while the 5-year COS maintained stable. Multivariable Cox analysis revealed that histologic subtype was always an independent prognostic factor for CRFS even after 5 years of follow-up, while the independent prognostic value of consolidation-to-tumour ratio, visceral pleural invasion and lymph node metastasis was observed only within 5 years. Besides, age, pathologic size and lymph node metastasis maintained independent predictive value for COS during long-term follow-up, while consolidation-to-tumour ratio was predictive for COS only within 5 years of follow-up.

CONCLUSIONS

The independent prognostic factors for clinical stage IA part-solid lung adenocarcinoma changed over time, along with gradually increasing 5-year CRFS and stable 5-year COS.

Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database

BACKGROUND AND AIM

In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression.

METHODS

In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model.

RESULTS

In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival.

CONCLUSIONS

Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

The conditional recurrence-free survival after R0 hepatectomy for locally advanced intrahepatic cholangiocarcinoma: A competing risk analysis based on inflammation-nutritional status

Background

Conditional survival analysis can serve as a dynamic prognostic metric, which helps to estimate the real-time survival probability over time. The present study conducted a conditional recurrence-free survival (CRFS) analysis for locally advanced intrahepatic cholangiocarcinoma (ICC) after R0 hepatectomy from an inflammatory-nutritional perspective using the competing risk method.

Methods

We extracted the medical data of 164 locally advanced ICC patients after R0 resection from Sun Yat-sen University Cancer Center. The calculation formula of the CRFS rate is CRFS(y/x) = RFS(y + x)/RFS(x). Univariable and multivariable COX regression analysis and competing risk analysis were conducted to identify RFS indicators.

Results

Considering death before recurrence as a competing risk factor, the conditional RFS rates every 6 months gradually increased over time. The 24-month RFS rate increased from 29.2 % to 49.9 %, 68.5 %, and 85.1 % given 6, 12, and 18-month already recurrence-free survival, respectively. Both in multivariate COX regression analysis and competing risk analysis, tumor diameter and number, lymph node metastasis, aggregate systemic inflammation index score (AISI), and albumin-bilirubin score (ALBI) all remained significant. For both AISI and ALBI variables, the CRFS rates in the low-value set were higher than those of the high-value set.

Conclusions

Conditional RFS rates of locally advanced ICC after R0 hepatectomy dynamically increased over time, which contributed to reducing survivors' psychological distress and facilitating personalized follow-up schedules. In addition, a person's inflammatory and nutritional status significantly impact the recurrence risk. Oncologists should consider the role of inflammation-nutritional status when making decisions for patients with locally advanced ICC.

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.

A novel conditional survival nomogram for monitoring real-time prognosis of non-metastatic colorectal cancer

AIMS

The aim of this study is to enhance the accuracy of monitoring and treatment information for patients diagnosed with colorectal cancer (CRC).

METHODS

Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, a cohort of 335,948 eligible CRC patients was included in this investigation. Conditional survival probability and actuarial overall survival were employed as methodologies to investigate the association between clinicopathological characteristics and cancer prognosis.

RESULTS

Among CRC patients, the 5-year survival rate was 59%, while the 10-year survival rate was 42%. Over time, conditional survival showed a consistent increase, with rates reaching 45% and 48% for individuals surviving 1 and 2 years, respectively. Notably, patients with unfavorable tumor stages exhibited substantial improvements in conditional survival, thereby narrowing the disparity with actuarial overall survival over time.

CONCLUSION

This study underscores the significance of time-dependent conditional survival probability, particularly for patients with a poorer prognosis. The findings suggest that long-term CRC survivors may experience improved cancer prognosis over time.

Identification and validation of a novel six-gene signature based on mucinous adenocarcinoma-related gene molecular typing in colorectal cancer

BACKGROUND AND OBJECTIVES

Colorectal mucinous adenocarcinoma (MAC) is a particular pathological type that has yet to be thoroughly studied. This study aims to investigate the characteristics of colorectal MAC-related genes in colorectal cancer (CRC), explore the role of MAC-related genes in accurately classifying CRC, and further construct a prognostic signature.

METHODS

CRC samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). MAC-related differentially expressed genes (DEGs) were analyzed in TCGA samples. Based on colorectal MAC-related genes, TCGA CRC samples were molecularly typed by the non-negative matrix factorization (NMF). According to the molecular subtype characteristics, the RiskScore signature was constructed through univariate Cox, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Clinical significance in CRC of the RiskScore signature was analyzed. A nomogram was further built based on the RiskScore signature.

RESULTS

From the colorectal MAC-related genes, three distinct molecular subtypes were identified. A RiskScore signature composed of six CRC subtype-related genes (CALB1, MMP1, HOXC6, ZIC2, SFTA2, and HYAL1) was constructed. Patients with high-RiskScores had the worse prognoses. RiskScores led to differences in gene mutation characteristics, antitumor drug sensitivity, and tumor microenvironment of CRC. A nomogram based on the signature was developed to predict the one-, three-, and five-year survival of CRC patients.

CONCLUSION

MAC-related genes were able to classify CRC. A RiskScore signature based on the colorectal MAC-related molecular subtype was constructed, which had important clinical significance for guiding the accurate stratification of CRC patients.

Development and validation of comprehensive nomograms from the SEER database for predicting early mortality in metastatic rectal cancer patients

BACKGROUND

Metastatic rectal cancer is an incurable malignancy, which is prone to early mortality. We aimed to establish nomograms for predicting the risk of early mortality in patients with metastatic rectal cancer.

METHODS

In this study, clinical data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database.We utilized X-tile software to determine the optimal cut-off points of age and tumor size in diagnosis. Significant independent risk factors for all-cause and cancer-specific early mortality were determined by the univariate and multivariate logistic regression analyses, then we construct two practical nomograms. In order to assess the predictive performance of nomograms, we performed calibration plots, time-dependent receiver-operating characteristic curve (ROC), decision curve analysis (DCA) and clinical impact curve (CIC).

RESULTS

A total of 2570 metastatic rectal cancer patients were included in the study. Multivariate logistic regression analyses revealed that age at diagnosis, CEA level, tumor size, surgical intervention, chemotherapy, radiotherapy, and metastases to bone, brain, liver, and lung were independently associated with early mortality of metastatic rectal cancer patients in the training cohort. The area under the curve (AUC) values of nomograms for all-cause and cancer-specific early mortality were all higher than 0.700. Calibration curves indicated that the nomograms accurately predicted early mortality and exhibited excellent discrimination. DCA and CIC showed moderately positive net benefits.

CONCLUSIONS

This study successfully generated applicable nomograms that predicted the high-risk early mortality of metastatic rectal cancer patients, which can assist clinicians in tailoring more effective treatment regimens.

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Background

Merkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual's prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model.

Methods

This study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient's survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors' effect on overall survival and establish a novel CS-based nomogram.

Results

A total of 3,843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival. The rates progressed from an initial 41-50%, 61, 70, 78, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance.

Conclusion

CS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a dynamic estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.

The impact of the HER2-low status on conditional survival in patients with breast cancer

Introduction

With recent advances in breast cancer (BC) treatment, the disease-free survival (DFS) of patients is increasing and the risk factors for recurrence and metastasis are changing. However, a dynamic approach to assessing the risk of recurrent metastasis in BC is currently lacking. This study aimed to develop a dynamically changing prediction model for recurrent metastases based on conditional survival (CS) analysis.

Methods

Clinical and pathological data from patients with BC who underwent surgery at the Affiliated Hospital of Qingdao University between August 2011 and August 2022 were retrospectively analysed. The risk of recurrence and metastasis in patients with varying survival rates was calculated using CS analysis, and a risk prediction model was constructed.

Results

A total of 4244 patients were included in this study, with a median follow-up of 83.16 ± 31.59 months. Our findings suggested that the real-time DFS of patients increased over time, and the likelihood of DFS after surgery correlated with the number of years of prior survival. We explored different risk factors for recurrent metastasis in baseline patients, 3-year, and 5-year disease-free survivors, and found that low HER2 was a risk factor for subsequent recurrence in patients with 5-year DFS. Based on this, conditional nomograms were developed. The nomograms showed good predictive ability for recurrence and metastasis in patients with BC.

Conclusion

Our study showed that the longer patients with BC remained disease-free, the greater their chances of remaining disease-free again. Predictive models for recurrence and metastasis risk based on CS analysis can help improve the confidence of patients fighting cancer and help doctors personalise treatment and follow-up plans.

Conditional survival probability of distant-metastatic hepatocellular carcinoma: A population-based study

BACKGROUND

The prognosis of many patients with distant metastatic hepatocellular carcinoma (HCC) improved after they survived for several months. Compared with traditional survival analysis, conditional survival (CS) which takes into account changes in survival risk could be used to describe dynamic survival probabilities.

AIM

To evaluate CS of distant metastatic HCC patients.

METHODS

Patients diagnosed with distant metastatic HCC between 2010 and 2015 were extracted from the Surveillance, Epidemiology and End Results database. Univariate and multivariate Cox regression analysis were used to identify risk factors for overall survival (OS), while competing risk model was used to identify risk factors for cancer-specific survival (CSS). Six-month CS was used to calculate the probability of survival for an additional 6 mo at a specific time after initial diagnosis, and standardized difference (d) was used to evaluate the survival differences between subgroups. Nomograms were constructed to predict CS.

RESULTS

Positive α-fetoprotein expression, higher T stage (T3 and T4), N1 stage, non-primary site surgery, non-chemotherapy, non-radiotherapy, and lung metastasis were independent risk factors for actual OS and CSS through univariate and multivariate analysis. Actual survival rates decreased over time, while CS rates gradually increased. As for the 6-month CS, the survival difference caused by chemotherapy and radiotherapy gradually disappeared over time, and the survival difference caused by lung metastasis reversed. Moreover, the influence of age and gender on survival gradually appeared. Nomograms were fitted for patients who have lived for 2, 4 and 6 mo to predict 6-month conditional OS and CSS, respectively. The area under the curve (AUC) of nomograms for conditional OS decreased as time passed, and the AUC for conditional CSS gradually increased.

CONCLUSION

CS for distant metastatic HCC patients substantially increased over time. With dynamic risk factors, nomograms constructed at a specific time could predict more accurate survival rates.

Prognostic value of extrahepatic metastasis on colon cancer with liver metastasis: a retrospective cohort study

INTRODUCTION

The occurrence of metastasis is a threat to patients with colon cancer (CC), and the liver is the most common metastasis organ. However, the role of the extrahepatic organs in patients with liver metastasis (LM) has not been distinctly demonstrated. Therefore, this research aimed to explore the prognostic value of extrahepatic metastases (EHMs).

METHODS

In this retrospective study, a total of 13,662 colon patients with LM between 2010 and 2015 were selected from the Surveillance, Epidemiology, and End Results database (SEER). Fine and Gray's analysis and K-M survival analysis were utilized to explore the impacts of the number of sites of EHMs and different sites of EHMs on prognosis. Finally, a prognostic nomogram model based on the number of sites of EHMs was constructed, and a string of validation methods was conducted, including concordance index (C-index), receiver operating characteristic curves (ROC), and decision curve analysis (DCA).

RESULTS

Patients without EHMs had better prognoses in cancer-specific survival (CSS) and overall survival (OS) than patients with EHMs (p < 0.001). Varied EHM sites of patients had different characteristics of primary location site, grade, and histology. Cumulative incidence rates for CSS surpassed that for other causes in patients with 0, 1, 2, ≥ 3 EHMs, and the patients with more numbers of sites of EHMs revealed worse prognosis in CSS (p < 0.001). However, patients with different EHM sites had a minor difference in cumulative incidence rates for CSS (p = 0.106). Finally, a nomogram was constructed to predict the survival probability of patients with EHMs, which is based on the number of sites of EHMs and has been proven an excellent predictive ability.

CONCLUSION

The number of sites of EHMs was a significant prognostic factor of CC patients with LM. However, the sites of EHMs showed limited impact on survival. Furthermore, a nomogram based on the number of sites of EHMs was constructed to predict the OS of patients with EHMs accurately.

Prognosis and conditional nomogram of cervical spine fracture in patients with severe spinal cord injury: a multicenter retrospective study

INTRODUCTION

Cervical spine fractures with severe spinal cord injury (SCI) are common following cervical spine trauma and are associated with a high mortality rate. Understanding the mortality patterns of patients with cervical spine fractures and severe SCI can offer valuable evidence to surgeons and family members who are required to make critical healthcare decisions. The authors aimed to evaluate the instantaneous death risk and conditional survival (CS) of such patients and developed conditional nomograms to account for different periods of survivors and predict the survival rates.

METHODS

Their instantaneous death risks were calculated using the hazard function, and the Kaplan-Meier method was used to evaluate the survival rates. Cox regression was used to choose the variables for the construction of the nomograms. The area under the receiver operating characteristic curve and calibration plots were used to validate the performance of the nomograms.

RESULTS

The authors finally included 450 patients with cervical spine fractures and severe SCI using propensity score matching. The instantaneous death risk was the highest during the first 12 months after injury. Surgical treatment can help decrease the instantaneous death risk quickly, especially in early-term surgery. The 5-year CS increased constantly from 73.3% at baseline to 88.0% after 2 years of survival. Conditional nomograms were constructed at baseline and in those who survived for 6 and 12 months. The area under the receiver operating characteristic curve and calibration curves indicated that the nomograms had a good performance.

CONCLUSION

Their results improve our understanding of the instantaneous death risk of patients in different periods following injury. CS demonstrated the exact survival rate among medium-term and long-term survivors. Conditional nomograms are suitable for different survival periods in predicting the probability of survival. Conditional nomograms help in understanding the prognosis and improve the shared decision-making approaches.

Conditional survival analysis and real-time prognosis prediction for cervical cancer patients below the age of 65 years

Background

Survival prediction for cervical cancer is usually based on its stage at diagnosis or a multivariate nomogram. However, few studies cared whether long-term survival improved after they survived for several years. Meanwhile, traditional survival analysis could not calculate this dynamic outcome. We aimed to assess the improvement of survival over time using conditional survival (CS) analysis and developed a novel conditional survival nomogram (CS-nomogram) to provide individualized and real-time prognostic information.

Methods

Cervical cancer patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method estimated cancer-specific survival (CSS) and calculated the conditional CSS (C-CSS) at year y+x after giving x years of survival based on the formula C-CSS(y|x) =CSS(y+x)/CSS(x). y indicated the number of years of further survival under the condition that the patient was determined to have survived for x years. The study identified predictors by the least absolute shrinkage and selection operator (LASSO) regression and used multivariate Cox regression to demonstrate these predictors' effect on CSS and to develop a nomogram. Finally, the CSS possibilities predicted by the nomogram were brought into the C-CSS formula to create the CS-nomogram.

Results

A total of 18,511 patients aged <65 years with cervical cancer from 2004 to 2019 were included in this study. CS analysis revealed that the 15-year CSS increased year by year from the initial 72.6% to 77.8%, 84.5%, 88.8%, 91.5%, 93.5%, 94.8%, 95.7%, 96.4%, 97.3%, 98.0%, 98.5%, 99.1%, and 99.4% (after surviving for 1-13 years, respectively), and found that when survival exceeded 5-6 years, the risk of death from cervical cancer would be less than 5% in 10-15 years. The CS-nomogram constructed using tumor size, lymph node status, distant metastasis status, and histological grade showed strong predictive performance with a concordance index (C-index) of 0.805 and a stable area under the curve (AUC) between 0.795 and 0.816 over 15 years.

Conclusions

CS analysis in this study revealed the gradual improvement of CSS over time in long-term survived cervical cancer patients. We applied CS to the nomogram and developed a CS-nomogram successfully predicting individualized and real-time prognosis.

A novel conditional survival nomogram for monitoring real-time prognosis of non-metastatic triple-negative breast cancer

BACKGROUND

Conditional survival (CS) is defined as the possibility of further survival after patients have survived for several years since diagnosis. This may be highly valuable for real-time prognostic monitoring, especially when considering individualized factors. Such prediction tools were lacking for non-metastatic triple-negative breast cancer (TNBC). Therefore, this study estimated CS and developed a novel CS-nomogram for real-time prediction of 10-year survival.

METHODS

We recruited 32,836 non-metastatic TNBC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2019), who were divided into training and validation groups according to a 7:3 ratio. The Kaplan-Meier method estimated overall survival (OS), and the CS was calculated using the formula CS(y|x) =OS(y+x)/OS(x), where OS(x) and OS(y+x) were the survival of x- and (x+y)-years, respectively. The least absolute shrinkage and selection operator (LASSO) regression identified predictors to develop the CS-nomogram.

RESULTS

CS analysis reported gradual improvement in real-time survival over time since diagnosis, with 10-year OS updated annually from an initial 69.9% to 72.8%, 78.1%, 83.0%, 87.0%, 90.3%, 93.0%, 95.0%, 97.0%, and 98.9% (after 1-9 years of survival, respectively). The LASSO regression identified age, marriage, race, T status, N status, chemotherapy, surgery, and radiotherapy as predictors of CS-nomogram development. This model had a satisfactory predictive performance with a stable 10-year time-dependent area under the curves (AUCs) between 0.75 and 0.86.

CONCLUSIONS

Survival of non-metastatic TNBC survivors improved dynamically and non-linearly with survival time. The study developed a CS-nomogram that provided more accurate prognostic data than traditional nomograms, aiding clinical decision-making and reducing patient anxiety.

Tumor Differentiation is the Dominant Prognostic Factor for Patients with Colorectal Neuroendocrine Neoplasms with Distant Metastasis

PURPOSE

Colorectal neuroendocrine neoplasms (NENs) are rare tumors. The prognosis and prognostic factors of metastatic colorectal NENs have not been fully elucidated.

METHODS

We retrospectively enrolled 77 consecutive patients diagnosed with colorectal NENs with synchronous distant metastases between 2000 and 2021. All patients were assigned to the neuroendocrine tumor (NET) group or the neuroendocrine carcinoma (NEC) group based on histological differentiation. Propensity score matching (PSM) was performed to minimize confounding bias. The Kaplan-Meier method was used to calculate the survival rates. Univariate and multivariate logistic regression analyses were performed to identify prognostic factors.

RESULTS

In total, 35 (45.5%) and 42 (54.5%) patients had well-differentiated NETs and poorly differentiated NECs, respectively. The median overall survival (OS) was 26 months for the entire cohort, and the 1-year, 3-year, and 5-year OS rates were 69.4%, 41.4%, and 27.8%, respectively. In the subgroup analysis, the median OS was 62 and 10 months for NETs and NECs, respectively. Univariate analysis demonstrated that patients with a primary tumor located in the colon, ulcerative tumors and poorly differentiated tumors were at higher risk for poorer progression-free survival (PFS) and OS. However, only histological differentiation was identified as an independent factor affecting OS (hazard ratio (HR) = 8.28, 95% confidence interval (CI): 2.98-23.01, P < 0.001) in multivariate analysis. After PSM, histological differentiation was further confirmed as the dominant factor affecting OS (HR = 6.09, 95% CI: 1.96-18.95, P=0.002)).

CONCLUSION

Histological differentiation was the most dominant prognostic factor in patients with metastatic colorectal NENs. Patients with well-differentiated NETs had a good chance of long-term survival.

Lymph node status and its impact on the prognosis of left-sided and right-sided colon cancer: A SEER population-based study

Background

Some significant differences exist between the outcomes of left‐ and right‐sided colon cancer patients. The presence of nodal metastases is a critical prognostic factor, especially in the absence of distant metastasis. Our research studied the lymph nodes status of left‐ and right‐sided colon cancer patients to determine the influence of this factor on prognosis.

Methods

Our data were obtained from the Surveillance, Epidemiology and End Results (SEER) database. We used the chi‐square test to analyze the clinicopathological characteristics. The X‐tile program was adopted to acquire optimal cutoff points of lymph node index. Kaplan–Meier curves were used to analyze prognosis and multivariate Cox regression models were performed to identify the independent factors associated with survival. Nomograms were built to predict the overall survival of patients, Harrell's C‐index and calibration plots were used to validate the nomograms.

Results

The study included 189,941 patients with colon cancer without metastasis (left 69,885, right 120,056) between 2004 and 2015. There are more patients with adequate examined lymph nodes in right‐sided. Lymph node status in patients with right colon cancer has a more significant impact on the risk of death. LODDS (C‐index: 0.583; AIC: 6875.4) was used to assess lymph node status. The nomograms showed that lymph node status was the main factor to predict the outcome in right‐sided colon patients.

Conclusions

The influence of lymph node status on predicting prognosis is significantly different between patients with left and right colon cancer without metastasis. The tumor site needs to be considered when lymph node status is used to assess the outcome of patients.