Clinical characteristics and overall survival nomogram of second primary malignancies after prostate cancer, a SEER population-based study

Survival prediction in second primary breast cancer patients with machine learning: An analysis of SEER database

BACKGROUND

Studies have found that first primary cancer (FPC) survivors are at high risk of developing second primary breast cancer (SPBC). However, there is a lack of prognostic studies specifically focusing on patients with SPBC.

METHODS

This retrospective study used data from Surveillance, Epidemiology and End Results Program. We selected female FPC survivors diagnosed with SPBC from 12 registries (from January 1998 to December 2018) to construct prognostic models. Meanwhile, SPBC patients selected from another five registries (from January 2010 to December 2018) were used as the validation set to test the model's generalization ability. Four machine learning models and a Cox proportional hazards regression (CoxPH) were constructed to predict the overall survival of SPBC patients. Univariate and multivariate Cox regression analyses were used for feature selection. Model performance was assessed using time-dependent area under the ROC curve (t-AUC) and integrated Brier score (iBrier).

RESULTS

A total of 10,321 female FPC survivors with SPBC (mean age [SD]: 66.03 [11.17]) were included for model construction. These patients were randomly split into a training set (mean age [SD]: 65.98 [11.15]) and a test set (mean age [SD]: 66.15 [11.23]) with a ratio of 7:3. In validation set, a total of 3,638 SPBC patients (mean age [SD]: 66.28 [10.68]) were finally enrolled. Sixteen features were selected for model construction through univariate and multivariable Cox regression analyses. Among five models, random survival forest model showed excellent performance with a t-AUC of 0.805 (95 %CI: 0.803 - 0.807) and an iBrier of 0.123 (95 %CI: 0.122 - 0.124) on testing set, as well as a t-AUC of 0.803 (95 %CI: 0.801 - 0.807) and an iBrier of 0.098 (95 %CI: 0.096 - 0.103) on validation set. Through feature importance ranking, the top one and other top five key predictive features of the random survival forest model were identified, namely age, stage, regional nodes positive, latency, radiotherapy, and surgery.

CONCLUSIONS

The random survival forest model outperformed CoxPH and other machine learning models in predicting the overall survival of patients with SPBC, which was helpful for the monitoring of high-risk populations.

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

Background

With improving prognosis in upper-tract urothelial carcinoma (UTUC), an increasing number of second primary malignancies (SPMs) are being identified. However, there is limited research on SPMs following UTUC. This study aims to evaluate the risk of SPMs in UTUC patients and create a nomogram to predict their survival rates.

Methods

Utilizing data from the Surveillance, Epidemiology, and End Results (SEER) database, we assessed the risk of SPMs among UTUC patients. Additionally, we developed and validated an overall survival (OS) nomogram for SPM patients post-UTUC diagnosis.

Results

The prevalence of SPMs among UTUC patients was 30.23%, with solid tumors being the most prevalent type of second malignancy, constituting 95.30% of all SPMs. The overall risk of SPMs was significantly elevated across all subgroups. Univariate and multivariate Cox regression analyses identified age, race, gender, UTUC SEER historic stage, surgery, SPM site, histologic type, grade, and SEER historic stage as independent prognostic factors for SPM OS. Subsequently, we developed a nomogram for predicting SPM OS. The C-index for the training and validation sets were 0.72 [95% confidence interval (CI): 0.70-0.74] and 0.71 (95% CI: 0.67-0.75), respectively. The area under the curve (AUC) demonstrated good performance of our model in predicting the 3-year (0.73 and 0.737) and 5-year (0.723 and 0.733) OS of SPMs in both sets.

Conclusions

This study represents the first comprehensive analysis of SPM incidence in UTUC patients and introduces a nomogram for predicting SPM prognosis.

A new perspective on prostate cancer treatment: the interplay between cellular senescence and treatment resistance

The emergence of resistance to prostate cancer (PCa) treatment, particularly to androgen deprivation therapy (ADT), has posed a significant challenge in the field of PCa management. Among the therapeutic options for PCa, radiotherapy, chemotherapy, and hormone therapy are commonly used modalities. However, these therapeutic approaches, while inducing apoptosis in tumor cells, may also trigger stress-induced premature senescence (SIPS). Cellular senescence, an entropy-driven transition from an ordered to a disordered state, ultimately leading to cell growth arrest, exhibits a dual role in PCa treatment. On one hand, senescent tumor cells may withdraw from the cell cycle, thereby reducing tumor growth rate and exerting a positive effect on treatment. On the other hand, senescent tumor cells may secrete a plethora of cytokines, growth factors and proteases that can affect neighboring tumor cells, thereby exerting a negative impact on treatment. This review explores how radiotherapy, chemotherapy, and hormone therapy trigger SIPS and the nuanced impact of senescent tumor cells on PCa treatment. Additionally, we aim to identify novel therapeutic strategies to overcome resistance in PCa treatment, thereby enhancing patient outcomes.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Clinical characteristics and prostate-cancer-specific mortality of competitive risk nomogram in the second primary prostate cancer

Background

With the development of early diagnosis and treatment, the second primary malignancy (SPM) attracts increasing attention. The second primary prostate cancer (spPCa) is an important class of SPM, but remains poorly understood.

Methods

We retrospectively analyzed 3,322 patients with spPCa diagnosed between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Chi-square test was applied to compare demographic and clinical variables and analyze causes of death. Multivariate competitive risk regression model was used to identify risk factors associated with prostate-cancer-specific mortality (PCSM), and these factors were enrolled to build a nomogram of competitive risk. The C-index, calibration curve, and decision curve analysis (DCA) were employed to evaluate the discrimination ability of our nomogram.

Results

The median follow-up (interquartile range, IQR) time was 47 (24-75) months, and the median (IQR) diagnosis interval between the first primary cancer (FPC) and spPCa was 32 (16-57) months. We found that the three most common sites of SPM were the urinary system, digestive system, and skin. Through multivariate competitive risk analysis, we enrolled race (p < 0.05), tumor-node-metastasis (TNM) stage (p < 0.001), Gleason score (p < 0.05), surgery (p = 0.002), and radiotherapy (p = 0.032) to construct the model to predict the outcomes of spPCa. The C-index was 0.856 (95% CI, 0.813-0.899) and 0.905 (95% CI, 0.941-0.868) in the training and validation set, respectively. Moreover, both the calibration curve and DCA illustrated that our nomogram performed well in predicting PCSM.

Conclusion

In conclusion, we identified four risk factors associated with the prognosis of spPCa and construct a competing risk nomogram, which performed well in predicting the 3-, 5-, and 10-year PCSM.

Long-term survival and second malignant tumor prediction in pediatric, adolescent, and young adult cancer survivors using Random Survival Forests: a SEER analysis

Survival and second malignancy prediction models can aid clinical decision making. Most commonly, survival analysis studies are performed using traditional proportional hazards models, which require strong assumptions and can lead to biased estimates if violated. Therefore, this study aims to implement an alternative, machine learning (ML) model for survival analysis: Random Survival Forest (RSF). In this study, RSFs were built using the U.S. Surveillance Epidemiology and End Results to (1) predict 30-year survival in pediatric, adolescent, and young adult cancer survivors; and (2) predict risk and site of a second tumor within 30 years of the first tumor diagnosis in these age groups. The final RSF model for pediatric, adolescent, and young adult survival has an average Concordance index (C-index) of 92.9%, 94.2%, and 94.4% and average time-dependent area under the receiver operating characteristic curve (AUC) at 30-years since first diagnosis of 90.8%, 93.6%, 96.1% respectively. The final RSF model for pediatric, adolescent, and young adult second malignancy has an average C-index of 86.8%, 85.2%, and 88.6% and average time-dependent AUC at 30-years since first diagnosis of 76.5%, 88.1%, and 99.0% respectively. This study suggests the robustness and potential clinical value of ML models to alleviate physician burden by quickly identifying highest risk individuals.

An introduction to male breast cancer for urologists: epidemiology, diagnosis, principles of treatment, and special situations

Breast cancer (BC) is mainly considered a disease in women, but male BC (MaBC) accounts for approximately 1.0% of BC diagnoses and 0.5% of malignant neoplasms in the western population. The stigmatization of MaBC, the fact that men are less likely to undergo regular health screenings, and the limited knowledge of health professionals about MaBC contribute to men being diagnosed at more advanced stages. The aim of this article is to increase the visibility of MaBC among urologists, who have more contact with male patients. This review highlights key points about the disease, the risk factors associated with MaBC, and the options for treatment. Obesity and increased population longevity are among the important risk factors for MaBC, but published studies have identified family history as extremely relevant in these patients and associated with a high penetrance at any age. There is currently no screening for MaBC in the general population, but the possibility of screening in men at high risk for developing BC can be considered. The treatment of MaBC is multidisciplinary, and, because of its rarity, there are no robust clinical studies evaluating the role of systemic therapies in the management of both localized and metastatic disease. Therefore, in current clinical practice, treatment strategies for men with breast cancer are extrapolated from information arising from studies in female patients.

Risk and prognosis of second primary cancers among ovarian cancer patients, based on SEER database

The purposes of the present study were to elucidate the risk and prognostic effect of second primary cancers (SPCs) development, as well as the factors influencing the prognosis of OC patients with SPCs. A statistically significant increase in SPCs risk was observed among OC patients during 2004-2015. The independent factors were used to construct the SPCs-prediction nomogram and the OS-prediction nomogram. Both nomogram were subjected to internal validation and performed well. OC patients with SPCs have a better prognosis than patients without SPCs. Propensity score matching (PSM) was applied to reduce confounding.

Nomogram for predicting the overall survival of patients with early-onset prostate cancer: A population-based retrospective study

Background

The incidence of early‐onset prostate cancer (PCa) has increased significantly over the past few decades. It is necessary to develop a prognostic nomogram for the prediction of overall survival (OS) in early‐onset PCa patients.

Methods

A total of 23,730 early‐onset PCa patients (younger than 55 years old) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled for the current study, and randomly separated into the training cohort and the validation cohort. 361 eligible early‐onset PCa patients from The Cancer Genome Atlas‐Prostate Adenocarcinoma (TCGA‐PRAD) cohort were obtained as the external validation cohort. Independent predictors were selected by univariate and multivariate Cox regression analysis, and a prognostic nomogram was constructed for 1‐, 3‐, and 5‐year OS. The accurate and discriminative abilities of the nomogram were evaluated by the concordance index (C‐index), receiver operating characteristic curve (ROC), calibration plot, net reclassification index (NRI), and integrated discrimination improvement (IDI).

Results

Multivariate Cox analysis showed that race, marital status, TNM stage, prostate‐specific antigen, Gleason score, and surgery were significantly associated with poor prognosis of PCa. A nomogram consisting of these variables was established, which had higher C‐indexes than the TNM system (training cohort: 0.831 vs. 0.746, validation cohort: 0.817 vs. 0.752). Better AUCs of the nomogram than the TNM system at 1, 3, and 5 years were found in both the training cohort and the validation cohort. The 3‐year and 5‐year AUCs of the nomogram in the TCGA‐PRAD cohort were 0.723 and 0.679, respectively. The calibration diagram, NRI, and IDI also showed promising prognostic value in OS.

Conclusions

We developed an effective prognostic nomogram for OS prediction in early‐onset PCa patients, which will further assist both the precise clinical treatment and the assessment of long‐term outcomes.

Clinicopathological Features, Prognostic Factors and Survival in Patients With Pancreatic Cancer Bone Metastasis

Purpose

The purpose of this study is to reveal the clinicopathological features and identify risk factors of prognosis among patients with pancreatic cancer bone metastasis (PCBM).

Patients and Methods

Patients with PCBM were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016. Independent predictors for survival of those patients were determined by the univariate and multivariate Cox regression analysis. Forest plots were drawn by GraphPad 8.0.1 and used to visually display the results of multivariate analysis.

Results

We identified 2072 eligible PCBM patients, of which 839 patients (40.5%) were female. Patients with age >60 years accounted for 70.6%. Multivariable Cox regression analysis indicated that age, pathological type, chemotherapy, liver metastasis, lung metastasis, and marital status were independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS). Kaplan–Meier survival curves showed that for patients with PCBM, age ≤60 years, non-ductal adenocarcinoma type, chemotherapy, no liver metastasis, no lung metastasis, and married status were correlated with increased survival. This population-based study showed that 1-year OS and CSS were 13.6% and 13.7%, respectively.

Conclusion

The present study identified six independent predictors of prognosis in PCBM, including age, pathological type, chemotherapy, liver metastasis, lung metastasis, and marital status. Knowledge of these survival predictors is helpful for clinicians to accelerate clinical decision process and design personalized treatment for patients with PCBM.

Second primary malignancy among malignant solid tumor survivors aged 85 years and older

The cancer burden in the oldest old has increased rapidly. This study aimed to investigate the epidemiology of second primary malignancy (SPM) in malignant solid tumor survivors aged 85 years and older utilizing the Surveillance, Epidemiology, and End Results (SEER) database. A total of 128,466 malignant solid tumor patients had been identified between 2000 and 2011, including 6774 patients who developed a SPM. The overall crude incidence of developing a SPM was 5.3%. Considering death as a competing event, the 3, 5, and 10-year cumulative incidence was 1.9%, 3.2%, and 5.4%, respectively. Relative younger age, male gender, surgery history, local stage and first primary malignancy (FPM) site located in the urinary system were related to higher cumulative incidence. A median time interval of 24.0 months was found between diagnosis of FPM and SPM. The most common SPM site was digestive system, whereas the least common was oral cavity and pharynx. The median overall survival (OS) was 49.0 months, and the median survival after SPM was 13.0 months. Relative older age, male gender and black race were associated with worse OS and survival after SPM, as well as higher hazard ratios of death. In conclusions, this study performed a comprehensive analysis of SPM among malignant solid tumor survivors aged 85 years and older. Additional studies are needed to characterize the specific cancer type of interest.