Lung Cancer

PV QA 4 - Poster Viewing Q&A 4

TU_26_3573 - Predicting per-lesion local recurrence in locally advanced lung cancer using metabolic tumor volume on pre- and mid-radiation FDG-PET

Tuesday, October 23
2:45 PM - 4:15 PM
Location: Innovation Hub, Exhibit Hall 3

Predicting per-lesion local recurrence in locally advanced lung cancer using metabolic tumor volume on pre- and mid-radiation FDG-PET
M. S. Binkley1, J. L. Koenig2, Q. Sodji3, P. G. Maxim4, M. Diehn4, B. W. Loo Jr4, and M. F. Gensheimer5; 1Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, 2Stanford University School of Medicine, Stanford, CA, 3Georgia Regents University, Augusta, GA, 4Stanford Cancer Institute, Stanford, CA, 5University of Washington, Seattle, WA

Purpose/Objective(s): PET metrics, including metabolic tumor volume (MTV), have been proposed as prognostic imaging biomarkers in patients treated with definitive radiation therapy (RT) for locally advanced non-small cell lung cancer (NSCLC). We evaluated whether the per-lesion change in MTV (ΔMTV) between pre- and mid-treatment PET can be used to predict individual sites of disease progression in this setting.

Materials/Methods: We retrospectively reviewed the records of patients with stage III NSCLC treated with definitive RT (dose≥60 Gy) from 2006 to 2017. We included patients with pre- and mid-RT PET/CT and follow up >3 months. PET Edge, a gradient-based method included in a commercially available deformable registration algorithm, was used to delineate treated lesions (lung tumors and regional lymph nodes) and measure MTVs. The distributions of pre-RT (MTVpre) and mid-RT (MTVmid) MTVs were right-skewed and thus log transformed for analysis. ΔMTV was defined as (MTVmid-MTVpre)/MTVpre. Local recurrence (LR) was defined per lesion (lung tumor or lymph node) as recurrence within the planning target volume based on review of follow-up imaging. Cumulative incidence rates and uni- and multivariate (MVA) Cox regression were used to evaluate the association between ΔMTV and LR for each lesion. Models were adjusted for the competing risk of death. Receiver operating characteristic (ROC) curves were developed to assess the utility of MTV parameters as predictors of LR (R, version 3.3).

Results: We identified 80 patients with 272 treated lesions (84 lung tumors and 188 lymph nodes; median 3 lesions per patient). Patients had median age 68 years (IQR 64-76 years). 65% (n=52) of patients were male, 48.8% (n=39) had adenocarcinoma, and 36.3% (n=29) had squamous cell carcinoma. 73 (91.3%) patients received concurrent chemotherapy. Median follow-up was 14 months. Overall survival was 75.0% at 1 year and 50.6% at 2 years. Cumulative incidence of LR was 20.3% at 1 year and 21.3% at 2 years per lesion. When adjusting for lesion location (lung primary versus lymph node) and age in MVA, MTVpre (HR=1.47, 95% CI=1.09-1.99, p=0.02) and ΔMTV (HR=1.06, 95% CI=1.04-1.10, p<0.001) were significantly associated with LR. We did not observe a higher rate of LR for nodal vs primary lesions. We developed a point-based model (0-2) where target lesions accumulate 1 point each for MTVpre>25cc and decrease in ΔMTV<65%. At 1 year, lesions with 0, 1, and 2 points had predicted LR rates of 14%, 21%, and 36%, respectively (p=0.02).

Conclusion: We found that high MTVpre and low ΔMTV were independently associated with per-lesion LR in locally advanced lung cancer, suggesting that both pre- and mid-RT PET provide complementary predictive information. If validated in larger cohorts, this may be the basis for designing adaptive dose painting strategies to maximize therapeutic index.

Author Disclosure: M.S. Binkley: None. J.L. Koenig: None. Q. Sodji: None. P.G. Maxim: None. M. Diehn: Employee; Kaiser Permanente. Consultant; Roche. Stock; CiberMed. B.W. Loo: Research Grant; RaySearch, Varian Medical Systems Inc. Stock; TibaRay, Inc. Vice-chair; National Comprehensive Cancer Network. Chair; American College of Radiology. Board Member; TibaRay, Inc. M.F. Gensheimer: None.

Send Email for Michael Binkley


Assets

TU_26_3573 - Predicting per-lesion local recurrence in locally advanced lung cancer using metabolic tumor volume on pre- and mid-radiation FDG-PET



Attendees who have favorited this

Please enter your access key

The asset you are trying to access is locked. Please enter your access key to unlock.

Send Email for Predicting per-lesion local recurrence in locally advanced lung cancer using metabolic tumor volume on pre- and mid-radiation FDG-PET