.png){kind=icon}
.png){kind=icon}
Return
Longitudinal MRI-based Delta-Habitat model for predicting pathological complete response to neoadjuvant chemoimmunotherapy in head and neck squamous cell carcinoma
.png){kind=icon}
OA
AI
F
X
M
L
L
A
L
X
X
F
DOI：10.1186/s40644-026-01106-9.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
To develop and validate a machine learning model integrating habitat imaging features before and after neoadjuvant chemoimmunotherapy (NACI) to predict pathological complete response (pCR) to NACI in head and neck squamous cell carcinoma (HNSCC). A multicenter retrospective study was conducted involving 520 HNSCC patients who received NACI across three hospitals. Unlike conventional Delta-Radiomics, we developed Delta-Habitat model that integrates Pre-NACI and Post-NACI MRI spatial habitat radiomics features, and dynamically learns task-specific contributions from each timepoint. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) was used to quantify the contribution of individual features to model predictions. We assessed and compared the diagnostic performance of senior and junior radiologists, both with and without the aid of the Delta-Habitat model. Delta-Habitat model outperformed Pre-NACI feature-based models and traditional radiomics-based Delta-Rad model, with AUC of 0.828, 0.825 and 0.813 in validation and two external test cohorts respectively. Post-treatment neutrophil-to-lymphocyte ratio (Post-NLR) was identified as an independent predictor of pCR. Integrating Delta-Radiomics and clinical features into Delta-Habitat model did not yield a significant improvement in predictive performance, the combined model showed slightly improved AUC over Delta-Habitat alone, reaching 0.877 (train), 0.834 (val), 0.837 (test1), and 0.834 (test2). SHAP analysis revealed that Post-NACI features contributed more substantially to model predictions than Pre-NACI features. The Delta-Habitat model consistently exhibited better performance than radiologists in comparative evaluations. Moreover, senior and junior radiologists both showed increases in AUC values with the assistance of the Delta-Habitat model, by 0.073 and 0.177 respectively on the internal validation cohort and by 0.087 and 0.142 on the external test cohort. The longitudinal MRI-based habitat model can provide valuable information for predicting the response to NACI in HNSCC and enhance radiologists’ diagnostic performance. • The Delta-Habitat model, incorporating Pre-NACI and Post-NACI habitat imaging features, can predict the pCR in HNSCC patients. • The developed model can assist radiologists to improve the interpretation of pCR in HNSCC patients.
Keywords:
Head and neck squamous cell carcinoma
Pathological complete response
Neoadjuvant chemoimmunotherapy
Longitudinal
Habitat imaging
Journal
IF:
3.5
Papers:
1.3K
Citations:
3.5K
Organization
Cited Papers