WCLC 2025 - Posters & ePosters-P2.08 .31 Clinical and Imaging Characteristics Associated With Pathologic Response After Neoadjuvant Chemoimmunotherapy in Stage II-III NSCLC

P2.08 .31 Clinical and Imaging Characteristics Associated With Pathologic Response After Neoadjuvant Chemoimmunotherapy in Stage II-III NSCLC

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This study investigated clinical and imaging biomarkers predictive of pathologic complete response (pCR) in stage II–III non–small cell lung cancer (NSCLC) patients undergoing neoadjuvant chemoimmunotherapy (NCIT). Accurately predicting pCR non-invasively before surgery is critical for personalized treatment strategies, as pCR correlates strongly with long-term survival.<br /><br />In a retrospective analysis of 82 patients treated with NCIT followed by surgery, sequential CT and PET/CT scans were examined to assess clinical and imaging characteristics. Using multivariable logistic regression with backward stepwise selection, three factors independently predicted pCR: the change in maximum standardized uptake value (ΔSUVmax; odds ratio [OR] 616.73, p=0.002), emergence of new tumor cavitation (OR 8.00, p=0.014), and the presence of residual radiographic extranodal extension (rENE) in N2 lymph nodes on post-treatment CT (OR 0.13, p=0.015).<br /><br />These variables were incorporated into a predictive model for pCR, which demonstrated excellent performance with an area under the curve (AUC) of 0.90. Survival analysis showed that patients with a high predicted probability of pCR had significantly longer event-free survival (EFS) compared to those with low predicted pCR (p<0.001). In multivariable Cox regression to control for other factors, high predicted pCR status remained a strong independent predictor of improved EFS (hazard ratio 0.04, p=0.003).<br /><br />This study highlights that combining specific imaging features—post-treatment residual rENE in N2 nodes, ΔSUVmax, and new tumor cavitation—enables accurate prediction of pathologic response and stratification of recurrence risk in NSCLC patients receiving NCIT. Such a predictive tool can facilitate response-adapted treatment decisions and improve clinical outcomes by identifying patients more likely to achieve complete tumor eradication before surgery.

Asset Subtitle

Dong Young Jeong

Meta Tag

Speaker Dong Young Jeong

Topic Local-Regional Non-small Cell Lung Cancer

Keywords

non-small cell lung cancer

pathologic complete response

neoadjuvant chemoimmunotherapy

maximum standardized uptake value

tumor cavitation

radiographic extranodal extension

N2 lymph nodes

predictive model

event-free survival

logistic regression