Survival outcomes in prostate cancer patients with a prior cancer

Clinical characteristics analysis and prognostic nomogram for predicting survival in patients with second primary prostate cancer: a population study based on SEER database

BACKGROUND AND AIMS

Second primary prostate cancer (SPPCa) is a common type of secondary malignancy that negatively impacts patient prognosis. This study aimed to identify prognostic factors for SPPCa patients and develop nomograms to assess their prognosis.

METHODS

Patients diagnosed with SPPCa between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The study cohort was randomly divided into a training set and a validation set. Cox regression analysis, Kaplan‒Meier survival analysis, and least absolute shrinkage and selection operator regression analysis were used to identify independent prognostic factors and develop the nomogram. The nomograms were evaluated using the concordance index (C-index), calibration curve, area under the curve (AUC), and Kaplan-Meier analysis.

RESULTS

A total of 5342 SPPCa patients were included in the study. Independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) were identified as age, interval between diagnoses, first primary tumor site, and AJCC stage, N stage, M stage, PSA, Gleason score, and SPPCa surgery. Nomograms were constructed based on these prognostic factors, and the performance was evaluated using the C-index (OS: 0.733, CSS: 0.838), AUC, calibration curve, and Kaplan-Meier analysis, which demonstrated excellent predictive accuracy.

CONCLUSION

We successfully established and validated nomograms to predict OS and CSS in SPPCa patients using the SEER database. These nomograms provide an effective tool for risk stratification and prognosis assessment in SPPCa patients, which will aid clinicians in optimizing treatment strategies for this patient population.

Cancer Prevention for Survivors: Incidence of Second Primary Cancers and Sex Differences-A Population-Based Study from an Italian Cancer Registry

BACKGROUND

The number of cancer survivors continues to increase, thanks to advances in cancer diagnosis and treatment. Unfortunately, the incidence of a second primary cancer (SPC) is also increasing, but limited studies reporting incidence data are available regarding multiple cancers. This study presents our observations on multiple primary malignant cancers, the associations between sites, and the inherent sex differences.

PATIENTS AND METHODS

We report the data, disaggregated by sex, concerning the SPCs that were recorded in the "Registro Tumori Integrato" (RTI) a population-based cancer registry in Sicily, Italy, as observed in the period from 2003 to 2017, in a total population of approximately 2,300,000. SPCs were divided into synchronous and metachronous cancers. The International Classification of Diseases for Oncology, third edition (ICD-O-3), was used for topographical and morphological classifications. Multiple primary cancers with multi-organ primitiveness were selected from the database of the RTI by extracting patients with more than one diagnosis. SPCs had different histology or morphology from the particular cancer that was considered to be the index cancer case. Multicenter or multifocal cancers, or metastases, were excluded. The percentages of cancer by sex and topography, the average age of incidence, and a breakdown by age were computed.

RESULTS

Differences were observed between sexes in terms of incidence and site for SPCs. The most frequent SPC was skin cancer (20% of the SPCs observed). The associations among sites of multiple cancers are reported.

CONCLUSION

There are many gaps in our knowledge of sex differences in cancer. The study of multiple primary cancers could bring more likely opportunities for evaluation of the cancer burden and trends that can be used to identify new research areas by population health programs, as well as for clinical researchers.

Development and validation of a competing risk model for second primary pancreatic ductal adenocarcinoma: A population-based study

Background

With advances in early diagnosis and treatment, the number of cancer survivors continues to grow, and more and more cancer survivors face the threat of second primary cancer (SPM). Second primary pancreatic ductal adenocarcinoma (spPDAC) is an important subclass of SPM, but its prognostic characteristics are poorly understood.

Methods

A total of 5,439 spPDAC samples and 67,262 primary pancreatic ductal adenocarcinoma (pPDAC) samples were extracted from the SEER database for this study. Survival differences between spPDAC and pPDAC samples were compared using Kaplan–Meier curves and log-rank tests. The Fine and Gray proportional subdistributed hazard method was used to analyze potential associations between clinical variables and pancreatic ductal adenocarcinoma-specific death (PDACSD) and death from other causes. After that, the clinical variables significantly related to PDACSD were screened out to construct a competing risk nomogram, which was used to evaluate the probability of the occurrence of PDACSD. The C-index was used to evaluate the discriminative ability of the model. The area under the curve (AUC) was used to verify the discrimination of the model. The calibration curve was used to verify the calibration of the model. Decision curve analysis (DCA) was used to validate the clinical utility of the model.

Results

Compared with patients with spPDAC, the pPDAC sample had a better prognosis (p = 0.0017). Across all spPDAC samples, the three most common sites of first-present cancer were the prostate, breast, and digestive system. Age (p < 0.001), race (p = 0.006), interval (p = 0.016), location (p < 0.001), T stage (p = 0.003), M stage (p < 0.001), chemotherapy (p < 0.001), and radiotherapy (p = 0.006) were the clinical variables associated with PDACSD screened by multivariate competing risks analysis. The concordance index values for the training and validation sets were 0.665 (95% CI, 0.655, 0.675) and 0.666 (95% CI, 0.650, 0.682), respectively. AUC, calibration curve, and DCA indicated that the model we constructed had good discrimination, calibration, and clinical utility.

Conclusions

In conclusion, we first analyzed the impact of previous cancer history on prognosis. We then constructed a competing risk model that can predict the probability of developing PDACSD in spPDAC. This model has good discriminative ability, calibration, and clinical practicability and has certain guiding value for clinical decision-making.