Updating and Optimizing Anatomic Atlases for Elective Radiation of Para-Aortic Lymph Nodes in Cervical Cancer.

PURPOSE

Previous studies have proposed 2 different contouring guidelines for the prophylactic radiation of para-aortic lymph nodes (PANs) for locally advanced cervical cancer. Because PAN-mapping atlases in current literature are limited to small patient samples and nodal populations, we updated the PAN atlas with a large data set of positron emission tomography (PET)-positive PANs on PET/computed tomography (CT) from patients with cervical cancer.

METHODS AND MATERIALS

We identified 176 PET-positive PANs on pretreatment PET/CT of 47 patients with diagnosed International Federation of Gynecology and Obstetrics stage IB to IVA cervical cancer. PANs were classified as left-lateral para-aortic (LPA), aortocaval (AC), or right paracaval (RPC). PAN clinical target volume (CTV) contours were drawn for all patients based on previously published guidelines by Takiar (CTV-T) and Keenan (CTV-K) and nodal volumetric coverage was assessed.

RESULTS

We identified 94 LPA nodes (54%), 71 AC nodes (40%), and 11 (6%) RPC nodes. CTV-T had improved nodal center coverage of 97.6% compared with 85.0% for CTV-K (P < .001). Nodal center coverage for CTV-K and CTV-T (with corresponding PAN) were 79 (84.0%) and 93 (99.0%) LPA nodes (P = .001), 64 (90.1%) and 68 (95.8%) AC nodes (P = .221), and 5 (45.5%) and 9 (81.8%) RPC nodes (P = .134), respectively. Additionally, our updated PAN atlas identified nodal centers anterior to the aorta and inferior vena cava that are not covered by CTV-T but covered by CTV-K due to the 10 mm anterior aortic expansion of CTV-K.

CONCLUSIONS

We have updated the PAN anatomic map of 176 PET-positive nodes from 47 patients and demonstrated that CTV-T has significantly better PAN coverage over CTV-K for posterior LPA and retrocaval regions for our data set. Additionally, we suggest a modification that includes a blend of CTV-T and CTV-K to provide optimal coverage for the mapped nodes anterior to the great vessels in our data set.