Publications

PURPOSE

Despite the rapidly increasing use of [F]fluorodeoxyglucose (FDG) -positron emission tomography (PET), the comparison of anatomic and functional imaging in the assessment of clinical outcomes has been lacking. In addition, there has not been a rigorous evaluation of how common radiologic criteria or the location of the radiology reader (local v central) compare in the ability to predict benefit. In this study, we aimed to compare the effectiveness of various radiologic response assessments for the prediction of overall survival (OS) within the same data set of patients with sarcoma.

METHODS

We analyzed assessments made during a clinical trial of a novel IGF1R antibody in Ewing sarcoma: PET Response Criteria in Solid Tumors (PERCIST) for functional imaging and WHO criteria (performed locally and centrally), RECIST, and volumetric analysis for anatomic imaging. We compared the effectiveness of the various criteria for the prediction of progression and survival.

RESULTS

For volume analysis, progression-defined as cumulative lesion volume increase of 100% at 6 weeks-was the optimal cutoff for decreased OS (P < .001). Assessment of the day-9 FDG-PET scan was associated with reduced OS in progressors compared with nonprogressors (P = .001) and with improved OS in responders compared with nonresponders. Significant variations in response (18% to 44%) and progression (9% to 50%) were observed between the different criteria. The comparison of central and local interpretation of anatomic imaging produced similar outcomes. PET was superior to anatomic imaging in identification of a response. Volume analysis identified the most responders among the anatomic imaging criteria.

CONCLUSION

An early signal with FDG-PET on day 9 and volume analysis were the best predictors of benefit. Validation of the volumetric analysis is required.

Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome-wide association studies. We conducted a nested case-control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95 % CI 1.69-3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95 % CI 0.57-0.64), and an Asian-specific PRS had AUROC 0.64 (95 % CI 0.53-0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p = 0.01). The BCSC-PRS model classified 18 % of cases as high-risk (5-year risk ≥3 %), compared with 7 % using the BCSC model. The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Further consideration of the PRS's role in decision-making around screening and prevention strategies is merited.