Persistent lymph nodes after curative chemoradiotherapy for head and neck cancer: imaging predictors of response for decision-making.

PURPOSE: To identify response predictors in patients with head and neck squamous cell carcinoma (N + HNSCC) and persistent lymph nodes after curative chemoradiotherapy treatment (CCRT). MATERIALS AND METHODS: Consecutive patients with N + HNSCC treated with CCRT and persistent lymph nodes at first follow-up between 2015 and 2021 were identified and analyzed. Complete response was defined as the absence of lymph node metastatic involvement in patients with salvage lymphadenectomy or the absence of progression after 1 year of successive follow-ups. Tumour type and location, staging, and human papillomavirus (HPV) status were considered for analysis. The number and size of lymph nodes, type, shape, enhancement and margins on diagnostic and follow-up CT were also analyzed. RESULTS: The cohort included 46 patients with 134 pathological lymph nodes. Logistic regression models showed the following variables to be significant: performance of salvage lymphadenectomy (OR 0.094, [CI 95% 0.004-0.61], p = 0.037); the type of lymphadenopathy on diagnostic CE-CT (solid vs. cystic) (N1: OR = 4.11, [CI 95% 1.11-17.93], p = 0.042 and N3: OR 6.42, [CI 95% 1.2-42.56], p = 0.036); the change of shape (round to oval) on the follow-up CE-CT (OR 9.76, [CI 95% 1.79-8.57], p = 0.016) and the time in days between CCRT and the first follow-up CE-CT (OR 1.06, [CI 95% 1.004-1.13], p = 0.048). CONCLUSIONS: In our experience, the presence of solid lymph nodes on pre-treatment CT and the change in shape from round to oval on post-treatment CT are predictors of response to treatment in patients with N + HNSCC persistent lymph nodes after CCRT. Increasing the temporal interval between treatment and follow-up CT should be considered to avoid unnecessary nodal dissections.

Comparison of chi-squared automatic interaction detection classification trees vs TNM classification for patients with head and neck squamous cell carcinoma.

OBJECTIVES: To compare chi-squared automatic interaction detection (CHAID) classification trees vs the seventh edition of the TNM classification for patients with head and neck squamous cell carcinoma and to assess whether CHAID classification trees might improve results obtained with the TNM classification. DESIGN: Patient disease was classified according to CHAID classification trees and the TNM classification, and the results were compared. SETTING: Academic research. PATIENTS: A total of 3373 patients with carcinoma of the oral cavity, oropharynx, hypopharynx, or larynx. MAIN OUTCOME MEASURES: The 2 classification methods were evaluated objectively, measuring intrastage homogeneity (hazard consistency), interstage heterogeneity (hazard discrimination), and disease stage distribution among patients (balance). In addition, to assess agreement between CHAID classification trees and the TNM classification, we calculated the κ statistic, weighted linearly and quadratically. RESULTS: Objective evaluation of the quality of the classification methods indicated that CHAID classification trees performed better than the TNM classification in terms of hazard consistency (2.51 for CHAID and 3.01 for TNM) and hazard discrimination (70.9% for CHAID and 52.7% for TNM) but not balance (-31.7% for CHAID and -15.5% for TNM). Analysis of concordance between the classification methods showed that the quadratic κ statistic was 0.77 (95% CI, 0.76-0.78) and the linear κ statistic was 0.59 (95% CI, 0.57-0.60) (P < .001 for both). CONCLUSION: CHAID classification trees performed better than the TNM classification and offer potential inclusion of new prognostic factors.