Utility of 18F-FDG PET/CT uptake values in predicting response to neoadjuvant chemoimmunotherapy in resectable non-small cell lung cancer.

INTRODUCTION: Reliable predictive markers are lacking for resectable non-small cell lung cancer (NSCLC) patients treated with neoadjuvant chemoimmunotherapy. The present study investigated the utility of SUVmax values acquired from PET/CT to predict the response to neoadjuvant chemoimmunotherapy for resectable NSCLC. MATERAL AND METHODS: SUVmax, clinical and pathological outcomes, were collected from patients in 5 hospitals. Patients who received dynamic PET/CT surveillance were divided into cohorts A (chemoimmunotherapy) and B (chemotherapy), respectively, while cohort C (chemoimmunotherapy) comprised patients undergoing post-therapy PET/CT. Associations between SUVmax and major pathologic response (MPR) were evaluated through receiver operating characteristic (ROC) curves. RESULTS: A total of 129 cases with an MPR rate of 46.5 % was identified. In neoadjuvant chemoimmunotherapy, ΔSUVmax% (AUC: 0.890, 95 % CI: 0.761-0.949) and post-therapy SUVmax (AUC: 0.933, 95 % CI: 0.802-0.959) could accurately predict MPR. On the contrary, the baseline SUVmax was not associated with MPR (p = 0.184). Furthermore, an independent cohort C proved that post-therapy SUVmax could serve as an independent predictor (AUC: 0.928, 95 % CI: 0.823-0.958). In addition, robust predictive performance could be observed when we use the optimal cut-off point of both ΔSUVmax% (54.4 %, AUC: 0.912, 95 % CI: 0.824-0.994) and post-therapy SUVmax (3.565, AUC: 0.912, 95 % CI: 0.824-0.994) in neoadjuvant chemoimmunotherapy. The RNA data revealed that the expression of PFKFB4, a key enzyme in glycolysis, was positively correlated with SUVmax value and tumor cell proliferation after neoadjuvant chemoimmunotherapy. CONCLUSION: These findings highlighted that the ΔSUVmax% and remained SUVmax were accurate and non-invasive tests for the prediction of MPR after neoadjuvant chemoimmunotherapy.

Proposal for Rib invasion as an independent T descriptor for non-small cell lung cancer: A propensity-score matching analysis.

INTRODUCTION: To evaluate the prognosis between patients with non-small cell lung cancer (NSCLC) invading difference depth of chest wall and estimate the impact of rib invasion on the pathological T classifications (pT). METHODS: We retrospectively evaluated 521 patients with resected pT3-4 NSCLC. Propensity-score matching (PSM) balanced the known confounders of the prognosis, resulting in two sets (rib invasion vs the pT3 and pT4 group). Recurrence-free survival (RFS) and Overall survival (OS) was assessed by Cox regression and Kaplan-Meier methods. Time-dependent receiver operating characteristic (ROC) curves were used to assess the additional benefit for survival prediction after reclassifying rib invasion cases. RESULTS: Chest wall invasion occurred in 171 patients (62 rib invasion, 51 parietal pleural invasion [PL3] and 58 soft tissue invasion). Rib invasion was found to be an independent prognostic factor for both RFS (p = 0.006) and OS (p < 0.001) of pT3-4 NSCLC. The survival of rib invasion group was the worst (RFS: 13.1%; OS: 19.8%), followed by PL3 (RFS: 34.2%, P = 0.001; OS: 48.8%; p < 0.001) and the soft tissue invasion group (RFS: 40.6%, p = 0.001; OS: 57.7%, p < 0.001). Besides, the prognosis of rib invasion group was also found to be worse than those of pT3 (RFS: p < 0.001; OS: p < 0.001) and pT4 group (RFS: p = 0.002; OS: p < 0.001). After PSM, the 5-year RFS rate of rib invasion group were still lower than that of pT3 and pT4 group (p < 0.001); the 5-year OS rate of rib invasion was similar with that of pT4 group (p = 0.066) but lower than that of pT3 group (p = 0.014). The time-dependent ROC curves demonstrated that reclassifying rib invasion as pT4 disease provided an additional benefit for survival prediction (p < 0.001). CONCLUSION: The rib invasion group had a worse prognosis than the PL3 and pT3 groups. The prognostic impact of rib invasion should be further validated as a pT4 disease in the TNM classification.