Purpose: This study aims to determine whether longitudinal changes in CT radiomic features (RFs) and systemic inflammatory indices outperform single-time-point assessment in predicting survival in advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs). Materials and methods: We retrospectively acquired pretreatment (T0) and first disease assessment (T1) RFs and systemic inflammatory indices from a single-center cohort of stage IV NSCLC patients and computed their delta (Δ) variation as [(T1-T0)/T0]. RFs from the primary tumor were selected for building baseline-radiomic (RAD) and Δ-RAD scores using the linear combination of standardized predictors detected by LASSO Cox regression models. Cox models were generated using clinical features alone or combined with baseline and Δ blood parameters and integrated with baseline-RAD and Δ-RAD. All models were 3-fold cross-validated. A prognostic index (PI) of each model was tested to stratify overall survival (OS) through Kaplan-Meier analysis. Results: We included 90 ICI-treated NSCLC patients (median age 70 y [IQR=42 to 85], 63 males). Δ-RAD outperformed baseline-RAD for predicting OS [c-index: 0.632 (95%CI: 0.628 to 0.636) vs. 0.605 (95%CI: 0.601 to 0.608) in the test splits]. Integrating longitudinal changes of systemic inflammatory indices and Δ-RAD with clinical data led to the best model performance [Integrated-Δ model, c-index: 0.750 (95% CI: 0.749 to 0.751) in training and 0.718 (95% CI: 0.715 to 0.721) in testing splits]. PI enabled significant OS stratification within all the models (P-value <0.01), reaching the greatest discriminative ability in Δ models (high-risk group HR up to 7.37, 95% CI: 3.9 to 13.94, P<0.01). Conclusion: Δ-RAD improved OS prediction compared with single-time-point radiomic in advanced ICI-treated NSCLC. Integrating Δ-RAD with a longitudinal assessment of clinical and laboratory data further improved the prognostic performance.

Longitudinal Changes of CT-radiomic and Systemic Inflammatory Features Predict Survival in Advanced Non–Small Cell Lung Cancer Patients Treated With Immune Checkpoint Inhibitors / Balbi, Maurizio; Mazzaschi, Giulia; Leo, Ludovica; Moron Dalla Tor, Lucas; Milanese, Gianluca; Marrocchio, Cristina; Silva, Mario; Mura, Rebecca; Favia, Pasquale; Bocchialini, Giovanni; Trentini, Francesca; Minari, Roberta; Ampollini, Luca; Quaini, Federico; Roti, Giovanni; Tiseo, Marcello; Sverzellati, Nicola. - In: JOURNAL OF THORACIC IMAGING. - ISSN 0883-5993. - (2024). [10.1097/rti.0000000000000801]

Longitudinal Changes of CT-radiomic and Systemic Inflammatory Features Predict Survival in Advanced Non–Small Cell Lung Cancer Patients Treated With Immune Checkpoint Inhibitors

Balbi, Maurizio;Mazzaschi, Giulia;Leo, Ludovica;Moron Dalla Tor, Lucas;Milanese, Gianluca;Marrocchio, Cristina;Silva, Mario;Mura, Rebecca;Favia, Pasquale;Bocchialini, Giovanni;Trentini, Francesca;Minari, Roberta;Ampollini, Luca;Quaini, Federico;Roti, Giovanni;Tiseo, Marcello;Sverzellati, Nicola
2024-01-01

Abstract

Purpose: This study aims to determine whether longitudinal changes in CT radiomic features (RFs) and systemic inflammatory indices outperform single-time-point assessment in predicting survival in advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs). Materials and methods: We retrospectively acquired pretreatment (T0) and first disease assessment (T1) RFs and systemic inflammatory indices from a single-center cohort of stage IV NSCLC patients and computed their delta (Δ) variation as [(T1-T0)/T0]. RFs from the primary tumor were selected for building baseline-radiomic (RAD) and Δ-RAD scores using the linear combination of standardized predictors detected by LASSO Cox regression models. Cox models were generated using clinical features alone or combined with baseline and Δ blood parameters and integrated with baseline-RAD and Δ-RAD. All models were 3-fold cross-validated. A prognostic index (PI) of each model was tested to stratify overall survival (OS) through Kaplan-Meier analysis. Results: We included 90 ICI-treated NSCLC patients (median age 70 y [IQR=42 to 85], 63 males). Δ-RAD outperformed baseline-RAD for predicting OS [c-index: 0.632 (95%CI: 0.628 to 0.636) vs. 0.605 (95%CI: 0.601 to 0.608) in the test splits]. Integrating longitudinal changes of systemic inflammatory indices and Δ-RAD with clinical data led to the best model performance [Integrated-Δ model, c-index: 0.750 (95% CI: 0.749 to 0.751) in training and 0.718 (95% CI: 0.715 to 0.721) in testing splits]. PI enabled significant OS stratification within all the models (P-value <0.01), reaching the greatest discriminative ability in Δ models (high-risk group HR up to 7.37, 95% CI: 3.9 to 13.94, P<0.01). Conclusion: Δ-RAD improved OS prediction compared with single-time-point radiomic in advanced ICI-treated NSCLC. Integrating Δ-RAD with a longitudinal assessment of clinical and laboratory data further improved the prognostic performance.
2024
Longitudinal Changes of CT-radiomic and Systemic Inflammatory Features Predict Survival in Advanced Non–Small Cell Lung Cancer Patients Treated With Immune Checkpoint Inhibitors / Balbi, Maurizio; Mazzaschi, Giulia; Leo, Ludovica; Moron Dalla Tor, Lucas; Milanese, Gianluca; Marrocchio, Cristina; Silva, Mario; Mura, Rebecca; Favia, Pasquale; Bocchialini, Giovanni; Trentini, Francesca; Minari, Roberta; Ampollini, Luca; Quaini, Federico; Roti, Giovanni; Tiseo, Marcello; Sverzellati, Nicola. - In: JOURNAL OF THORACIC IMAGING. - ISSN 0883-5993. - (2024). [10.1097/rti.0000000000000801]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2995054
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