Individualized conditional survival nomograms for patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy and radical surgery

Conditional survival nomogram for monitoring real-time survival of young non-metastatic nasopharyngeal cancer survivors

BACKGROUND

The aim of this study was to clarify the improvement of the overall survival (OS) over time in young non-metastatic nasopharyngeal carcinoma (NPC) survivors by conditional survival (CS) analysis and to construct a CS-nomogram for updating individualized real-time prognosis.

METHODS

The study included 3409 young non-metastatic NPC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2019). OS was estimated using the Kaplan-Meier method. CS was calculated based on CS(y|x) = OS(y + x)/OS(x), defined as the probability that a patient would survive for another y years after surviving for x years since diagnosis. We identified predictors using the least absolute shrinkage and selection operator (LASSO) regression and developed the CS-nomogram using multivariate Cox regression and the CS formula.

RESULTS

CS analysis showed a continuous increase in 10-year OS for young non-metastatic NPC from the initial 60.4% to 65.0%, 70.2%, 74.2%, 78.2%, 82.6%, 86.9%, 91.1%, 96.2% and 97.0% (surviving 1-9 years after diagnosis, respectively). After screening by LASSO regression, age, race, marital status, histological type, T- and N-status were used as predictors to construct the CS-nomogram. The model accurately estimated the real-time prognosis of survivors during follow-up with a stable time-dependent area under the curve (AUC).

CONCLUSIONS

CS analysis based on SEER database calibrated the real-time prognosis of young non-metastatic NPC survivors, revealing a dynamic improvement during follow-up time. We developed a novel CS-nomogram to update survival data for real-time optimization of monitoring strategies, medical resource allocation, and patient counseling. However, it was important to note that the model still needed external data validation and continuous improvement.

Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer: Analysis of population-based cohort with external validation

Background

Locally advanced breast cancer (LABC) is generally considered to have a relatively poor prognosis. However, with years of follow-up, what is its real-time survival and how to dynamically estimate an individualized prognosis? This study aimed to determine the conditional survival (CS) of LABC and develop a CS-nomogram to estimate overall survival (OS) in real-time.

Methods

LABC patients were recruited from the Surveillance, Epidemiology, and End Results (SEER) database (training and validation groups, n = 32,493) and our institution (testing group, n = 119). The Kaplan-Meier method estimated OS and calculated the CS at year (x+y) after giving x years of survival according to the formula CS(y|x) = OS(y+x)/OS(x). y represented the number of years of continued survival under the condition that the patient was determined to have survived for x years. Cox regression, best subset regression, and the least absolute shrinkage and selection operator (LASSO) regression were used to screen predictors, respectively, to determine the best model to develop the CS-nomogram and its network version. Risk stratification was constructed based on this model.

Results

CS analysis revealed a dynamic improvement in survival occurred with increasing follow-up time (7 year survival was adjusted from 63.0% at the time of initial diagnosis to 66.4, 72.0, 77.7, 83.5, 89.0, and 94.7% year by year [after surviving for 1-6 years, respectively]). In addition, this improvement was non-linear, with a relatively slow increase in the second year after diagnosis. The predictors identified were age, T and N status, grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER 2), surgery, radiotherapy and chemotherapy. A CS-nomogram developed by these predictors and the CS formula was used to predict OS in real-time. The model's concordance indexes (C-indexes) in the training, validation and testing groups were 0.761, 0.768 and 0.810, which were well-calibrated according to the reality. In addition, the web version was easy to use and risk stratification facilitated the identification of high-risk patients.

Conclusions

The real-time prognosis of LABC improves dynamically and non-linearly over time, and the novel CS-nomogram can provide real-time and personalized prognostic information with satisfactory clinical utility.

A prognostic nomogram for stage II/III rectal cancer patients treated with neoadjuvant chemoradiotherapy followed by surgical resection

Background

The purpose of this study was to develop a large population-based nomogram incorporating the log odds of positive nodes (LODDS) for predicting the overall survival (OS) of stage II/III rectal cancer patients treated with neoadjuvant chemoradiotherapy (NCRT) followed by surgical resection.

Methods

The Surveillance, Epidemiology, and End Results database was used to collect information on patients diagnosed with stage II/III rectal cancer between 2010 and 2015 and treated with NCRT followed by surgical resection. The Cox regression analyses were performed to determine the independent prognostic factors. In this study, LODDS was employed instead of American Joint Committee on Cancer (AJCC) 7th N stage to determine lymph node status. Then a nomogram integrating independent prognostic factors was developed to predict the 24-, 36-, and 60-month overall survival. The receiver operating characteristic (ROC) curves and calibration curves were used to validate the nomogram. Furthermore, patients were stratified into three risk groups (high-, middle-, and low-risk) based on the total points obtained from the nomogram. And Kaplan–Meier curves were plotted to compare the OS of the three groups.

Results

A total of 3829 patients were included in the study. Race, sex, age, marital status, T stage, tumor grade, tumor size, LODDS, CEA level, and postoperative chemotherapy were identified as independent prognostic factors, based on which the prognostic nomogram was developed. The area under curve values of the nomogram for the 24-, 36-, and 60-month OS in the training cohort were 0.736, 0.720, and 0.688, respectively; and 0.691, 0.696, and 0.694 in the validation cohort, respectively. In both the validation and training cohorts, the calibration curves showed a high degree of consistency between actual and nomogram-predicted survival rates. The Kaplan–Meier curves showed that the three risk groups had significant differences in overall survival (P < 0.001).

Conclusion

A large population-based nomogram incorporating LODDS was developed to assist in evaluating the prognosis of stage II/III rectal cancer patients treated with NCRT followed by surgical resection. The nomogram showed a satisfactorily discriminative and stable ability to predict the OS for those patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12893-022-01710-z.