A Statistical Approach to Determine the Optimal Duration of Post-Treatment Follow-Up: Application to Metastatic Nonseminomatous Germ Cell Tumors

BACKGROUND

The objective of this study was to present a statistical method to define an optimal duration of follow-up for patients in remission after treatment for cancer, for detection of recurrences.

PATIENTS AND METHODS

Surveillance duration was estimated using the 2-step approach proposed by Mould et al. Relapse-free interval was modeled using the parametric cure model proposed by Boag. The optimal length of follow-up was then estimated as the minimal elapsed time after which the probability of a patient to relapse and to be cured with success is below a given threshold value. The method is applied to 2 real data sets of patients treated for metastatic non seminomatous germ-cell tumors: T93BP and T93MP.

RESULTS

For the T93BP, cure rate was estimated at 91.3% and proportions of patients who relapsed after 3 and 5 years were estimated at 0.5% and 0.2%. With a probability of success of salvage treatment equal to 80% and 50%, numbers of delayed cases after 5 years were 2 and 1. For T93MP, the proportion of patients who presented relapse after 5 and 10 years were estimated at 5.2% and 2.6%. Considering a probability of salvage treatment equal to 20%, the number of delayed cases after 5 and 10 years were 10 and 5.

CONCLUSION

Using this methodology, duration of post-therapeutic follow-up might be tailored according to an objective criteria: the number of patients who present relapse after the end of follow-up and who could have been treated with success in case of early detection.

Optimal scheduling of post-therapeutic follow-up of patients treated for cancer for early detection of relapses

Post-therapeutic surveillance is one important component of cancer care. However, there still is no evidence-based strategies to schedule patients’ follow-up examinations. Our approach is based on the modeling of the probability of the onset of relapse at an early asymptotic or preclinical stage and its transition to a clinical stage. For that we consider a multistate homogeneous Markov model, which includes the natural history of relapse. The model also handles separately the different types of possible relapses. The optimal schedule is provided by the calendar visit that maximizes a utility function. The methodology has been applied to laryngeal cancer. The different follow-up strategies revealed to be more efficient than those proposed by different scientific societies.

Determining the length of posttherapeutic follow-up for cancer patients using competing risks modeling

BACKGROUND/OBJECTIVE

After a curative treatment for cancer, patients enter into a posttherapeutic surveillance phase. This phase aims to detect relapses as soon as possible to improve the outcome. Mould and others predicted with a simple formula, using a parametric mixture cure model, how long early-stage breast cancer patients should be followed after treatment. However, patients in posttherapeutic surveillance phase are at risk of different events types with different responses according to their prognostic factors and different probabilities to be cured. This paper presents an adaptation of the method proposed by Mould and others, taking into account competing risks. Our loss function estimates, when follow-up is stopped at a given time, the proportion of patients who will fail after this time and who could have been treated successfully.

METHOD

We use the direct approach for cumulative incidence modeling in the presence of competing risks with an improper Gompertz probability distribution as proposed by Jeong and Fine. Prognostic factors can be taken into account, leading to a proportional hazards model. In a second step, the estimates of the Gompertz model are combined with the probability for a patient to be treated successfully in case of relapse for each event type. The method is applied to 2 examples, a numeric fictive example and a real data set on soft tissue sarcoma.

RESULTS

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CONCLUSION

The model presented is a good tool for decision making to determine the total length of posttherapeutic surveillance. It can be applied to all cancers regardless of the localizations.