Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups

PURPOSE

Prognostic models have been proposed to predict survival for non-small-cell lung cancer (NSCLC). It is important to evaluate whether these models perform better than performance status (PS) alone in stage- and age-specific subgroups.

PATIENTS AND METHODS

The validation cohort included 2060 stage I and 1611 stage IV NSCLC patients from 23CALGB studies. For stage I, Blanchon (B), Chansky (C) and Gail (G) models were evaluated along with the PS only model. For stage IV, Blanchon (B) and Mandrekar (M) models were compared with the PS only model. The c-index was used to assess the concordance between survival and risk scores. The c-index difference (c-difference) and the integrated discrimination improvement (IDI) were used to determine the improvement of these models over the PS only model.

RESULTS

For stage I, B and PS have better survival separation. The c-index for B, PS, C and G are 0.61, 0.58, 0.57 and 0.52, respectively, and B performs significantly better than PS with c-difference=0.034. For stage IV, B, M and PS have c-index 0.61, 0.64 and 0.60, respectively; B and M perform significantly better than PS with c-difference=0.015 and 0.033, respectively.

CONCLUSION

Although some prognostic models have better concordance with survival than the PS only model, the absolute improvement is small. More accurate prognostic models should be developed; the inclusion of tumor genetic variants may improve prognostic models.

Design and inference for cancer biomarker study with an outcome and auxiliary-dependent subsampling

In cancer research, it is important to evaluate the performance of a biomarker (e.g., molecular, genetic, or imaging) that correlates patients' prognosis or predicts patients' response to treatment in a large prospective study. Due to overall budget constraint and high cost associated with bioassays, investigators often have to select a subset from all registered patients for biomarker assessment. To detect a potentially moderate association between the biomarker and the outcome, investigators need to decide how to select the subset of a fixed size such that the study efficiency can be enhanced. We show that, instead of drawing a simple random sample from the study cohort, greater efficiency can be achieved by allowing the selection probability to depend on the outcome and an auxiliary variable; we refer to such a sampling scheme as outcome and auxiliary-dependent subsampling (OADS). This article is motivated by the need to analyze data from a lung cancer biomarker study that adopts the OADS design to assess epidermal growth factor receptor (EGFR) mutations as a predictive biomarker for whether a subject responds to a greater extent to EGFR inhibitor drugs. We propose an estimated maximum-likelihood method that accommodates the OADS design and utilizes all observed information, especially those contained in the likelihood score of EGFR mutations (an auxiliary variable of EGFR mutations) that is available to all patients. We derive the asymptotic properties of the proposed estimator and evaluate its finite sample properties via simulation. We illustrate the proposed method with a data example.

Cancers bronchiques : la radiothérapie prophylactique des aires ganglionnaires a-t-elle encore une place ?

The use of conformal radiotherapy in lung cancer has considerably evolved with the advent of improved staging technologies and methods of radiation delivery. Patients with limited disease, inoperable for medical reasons, may be treated with conformal radiotherapy alone; patients with more advanced disease are treated with combined chemo-radiotherapy. If local control may be improved by radiotherapy dose escalation according to several studies, toxicity and more particularly pulmonary toxicity seems to be related to radiation volume. Thus the use of elective nodal irradiation is being questioned. Data for early stage (stage I) non-small-cell lung cancer treated with conformal radiotherapy or stereotactic hypofractionated radiotherapy strongly supports the use of smaller fields that do not incorporate elective nodal regions; local control and survival rates approach those of surgical series. In locally advanced non-small cell lung cancer, eliminating elective nodal irradiation allows to maximize tumor dose and minimize normal tissue toxicity in combined modality treatments; results are encouraging. The use of staging modalities such as positron emission tomography and eventually oesophageal ultrasonography is increasing, allowing to encompass the tumor volume with more accuracy. Several studies have confirmed that involved-field irradiation results into a regional nodal rate of less than 10%. Further larger-scale studies would be needed to definitely establish "no elective nodal irradiation" as a standard in non-small cell lung cancer. There are very few data concerning small cell lung cancer.