Background: Conditional survival takes into account the time that has elapsed since diagnosis and may have additional informative value. Compared with the static traditional survival evaluation method, conditional survival predictions can be adapted to incorporate the dynamic changes during the disease and provide a more suitable way of identifying time-evolved prognoses.
Methods: Of 3333 patients diagnosed with inflammatory breast cancer between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Results database. The trend of the hazard rate over time was represented by the kernel density smoothing curve. The traditional cancer-specific survival (CSS) rate was estimated by the Kaplan-Meier method. Conditional CSS assessment was defined as the probability that a patient will survive y years given the x years who already survived after diagnosis, and the formula is as follows: CS(y)=CSS(x + y)/CSS(x). 3-year cancer-specific survival (CSS3) and 3-year conditional cancer-specific survival (CS3) were estimated. The Fine-Gray proportional subdistribution hazard model was constructed to screen for time-dependent risk factors associated with cancer-specific death. Subsequently, a nomogram was applied to predict a 5-year survival rate based on the number of years already survived.
Results: Of 3333 patients, the cancer-specific survival (CSS) rate decreased from 57% in the 4th year to 49% in the 6th year, while the comparable 3-year CS (CS3) rate improved from 65% in the first year to 76% in the third year. Overall, the CS3 rate was superior to actuarial cancer-specific survival, which was also found in subgroup analysis, especially in patients with high-risk characteristics. The Fine-Gray's model indicated that remote organ metastasis (M stage), lymph node metastasis (N stage), and surgery all significantly impacted the prognosis for cancer-specific survival. The Fine-Gray's model-based nomogram was constructed to predict 5-year cancer-specific survival immediately after diagnosis and given survival for 1, 2, 3, and 4 years after diagnosis.
Conclusion: High-risk patients had a significantly improved cancer-specific survival prognosis after surviving for 1 or more years after diagnosis with inflammatory breast cancer. The probability of reaching 5-year cancer-specific survival following diagnosis improves with each additional year survived. More effective follow-up is required for patients diagnosed at an advanced N stage, remote organ metastasis, or not received surgery. Additionally, a nomogram and web-based calculator may be helpful for patients with inflammatory breast cancer during follow-up counseling (https://ibccondsurv.shinyapps.io/dynnomapp/).
Keywords: Competing Risk; Conditional Survival; Nomogram; Prediction Model; Survival Analysis.
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