Purpose: Treatments for locoregionally advanced head and neck cancer (LAHNC) negatively impact swallowing function, but the long-term incidence of severe toxicity requiring esophageal dilation is not well-documented in the population. The aim of this study was to compare the incidence of long-term esophageal dilation across varying treatments for LAHNC. Methods and Materials: We identified 5,223 patients with LAHNC diagnosed from 2000 to 2009 in the SEER-Medicare database. We compared the incidence of esophageal dilation for surgery alone vs. surgery plus adjuvant radiotherapy (RT) and chemoradiotherapy (CRT) vs. definitive RT or CRT. Results: The cumulative incidence of esophageal dilation for all sites at 10 years, according to treatment group were as follows: CRT, 14% (95% confidence interval (CI), 12-17%); definitive RT, 13% (95% CI, 10-16%); surgery alone, 5% (95% CI, 3-7%); surgery and CRT, 15% (95% CI, 11-19%); surgery and adjuvant RT: 10% (95% CI, 8-13%). There was no significant difference in the incidence of esophageal dilation between surgery plus adjuvant RT/CRT or definitive RT/CRT (p = 0.37), but the incidence was significantly increased in both groups compared to surgery alone (p = 0.003). On multivariable analysis, chemotherapy was associated with significantly increased incidence of esophageal dilation (HR 2.9, 95% CI 1.5-5.5, p < 0.001) in oropharyngeal cancers. Conclusions: The incidence of esophageal dilation is similar in LAHNC patients undergoing RT with or without surgery. Chemoradiotherapy increases the long-term risk of esophageal dilation events over surgery alone.

Purpose

Validated models are needed to justify strategies to define planning target volumes (PTVs) for intact cervical cancer used in clinical practice. Our objective was to independently validate a previously published shape model, using data collected prospectively from clinical trials.

Methods and materials

We analyzed 42 patients with intact cervical cancer treated with daily fractionated pelvic intensity modulated radiation therapy and concurrent chemotherapy in one of 2 prospective clinical trials. We collected online cone beam computed tomography (CBCT) scans before each fraction. Clinical target volume (CTV) structures from the planning computed tomography scan were cast onto each CBCT scan after rigid registration and manually redrawn to account for organ motion and deformation. We applied the 95% isodose cloud from the planning computed tomography scan to each CBCT scan and computed any CTV outside the 95% isodose cloud. The primary aim was to determine the proportion of CTVs that were encompassed within the 95% isodose volume. A 1-sample t test was used to test the hypothesis that the probability of complete coverage was different from 95%. We used mixed-effects logistic regression to assess effects of time and patient variability.

Results

The 95% isodose line completely encompassed 92.3% of all CTVs (95% confidence interval, 88.3%-96.4%), not significantly different from the 95% probability anticipated a priori (P=.19). The overall proportion of missed CTVs was small: the grand mean of covered CTVs was 99.9%, and 95.2% of misses were located in the anterior body of the uterus. Time did not affect coverage probability (P=.71).

Conclusions

With the clinical implementation of a previously proposed PTV definition strategy based on a shape model for intact cervical cancer, the probability of CTV coverage was high and the volume of CTV missed was low. This PTV expansion strategy is acceptable for clinical trials and practice; however, we recommend daily image guidance to avoid systematic large misses in select patients.

As genetic information becomes more readily available, there is increasing demand from both patients and providers to develop personalized approaches to cancer care. Investigators are increasingly reporting numbers of studies correlating genomic signatures and other biomarkers to survival endpoints. The extent to which cancer-specific and non-specific effects are reported in contemporary studies is unknown. In this review of 85 high-impact studies associating genetic biomarkers with cancer outcomes, 95% reported significant associations with event-free survival outcomes, yet less than half reported effects on a cancer-specific endpoint. This methodology leaves open the possibility that observed associations are unrelated to cancer.

Purpose

To compare a novel generalized competing event (GCE) model versus the standard Cox proportional hazards regression model for stratifying elderly patients with cancer who are at risk for competing events.

Methods

We identified 84,319 patients with nonmetastatic prostate, head and neck, and breast cancers from the SEER-Medicare database. Using demographic, tumor, and clinical characteristics, we trained risk scores on the basis of GCE versus Cox models for cancer-specific mortality and all-cause mortality. In test sets, we examined the predictive ability of the risk scores on the different causes of death, including second cancer mortality, noncancer mortality, and cause-specific mortality, using Fine-Gray regression and area under the curve. We compared how well models stratified subpopulations according to the ratio of the cumulative cause-specific hazard for cancer mortality to the cumulative hazard for overall mortality (ω) using the Akaike Information Criterion.

Results

In each sample, increasing GCE risk scores were associated with increased cancer-specific mortality and decreased competing mortality, whereas risk scores from Cox models were associated with both increased cancer-specific mortality and competing mortality. GCE models created greater separation in the area under the curve for cancer-specific mortality versus noncancer mortality (P < .001), indicating better discriminatory ability between these events. Comparing the GCE model to Cox models of cause-specific mortality or all-cause mortality, the respective Akaike Information Criterion scores were superior (lower) in each sample: prostate cancer, 28.6 versus 35.5 versus 39.4; head and neck cancer, 21.1 versus 29.4 versus 40.2; and breast cancer, 24.6 versus 32.3 versus 50.8.

Conclusion

Compared with standard modeling approaches, GCE models improve stratification of elderly patients with cancer according to their risk of dying from cancer relative to overall mortality.