Transformer-based AI approach to unravel long-term, time-dependent prognostic complexity in patients with advanced NSCLC and PD-L1 ≥50%: insights from the pembrolizumab 5-year global registry.
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Cortellini Alessio, Santo Valentina, Brunetti Leonardo, Garbo Edoardo, Pinato David J, La Cava Giulia, Naidoo Jarushka, Katz Artur, Loza Monica, Neal Joel W, Genova Carlo, Gettinger Scott, Kim So Yeon, Jayakrishnan Ritujith, El Zarif Talal, Russano Marco, Pecci Federica, Di Federico Alessandro, Alessi Joao V, Montrone Michele, Owen Dwight H, Ramella Sara, Signorelli Diego, Fidler Mary Jo, Li Mingjia, Camerini Andrea, Halmos Balazs, Vincenzi Bruno, Metro Giulio, Passiglia Francesco, Yendamuri Sai, Guida Annalisa, Ghidini Michele, D'Alessio Antonio, Banna Giuseppe L, Fulgenzi Claudia A M, Grisanti Salvatore, Grossi Francesco, D'Incecco Armida, Josephides Eleni, Van Hemelrijck Mieke, Russo Alessandro, Gelibter Alain, Spinelli Gianpaolo, Verrico Monica, Tomasik Bartłomiej, Giusti Raffaele, Balachandran Kirsty, Bria Emilio, Sebastian Martin, Rost Maximilian, Forster Martin, Mukherjee Uma, Landi Lorenza, Mazzoni Francesca, Aujayeb Avinash, Dupont Manuel, Curioni-Fontecedro Alessandra, Chiari Rita, Sforza Vincenzo, Tiseo Marcello, Friedlaender Alex, Addeo Alfredo, Zoratto Federica, De Tursi Michele, Cantini Luca, Roca Elisa, Mountzios Giannis, Rocco Danilo, Della Gravara Luigi, Kalvapudi Sukumar, Inno Alessandro, Bironzo Paolo, Di Marco Barros Rafael, O'Reilly David, Fitzpatrick Orla, Karapanagiotou Eleni, Monnet Isabelle, Baena Javier, Macerelli Marianna, Piedra Aida, Agustoni Francesco, Cortinovis Diego Luigi, Tonini Giuseppe, Minuti Gabriele, Bennati Chiara, Mezquita Laura, Gorría Teresa, Servetto Alberto, Beninato Teresa, Lo Russo Giuseppe, Prelaj Arsela, De Giglio Andrea, Rogado Jacobo, Moliner Laura, Nadal Ernest, Biello Federica, Nana Frank Aboubakar, Dingemans Anne-Marie, Aerts Joachim G J V, Ferrara Roberto, Abu Hejleh Taher, Takada Kazuki, Naqash Abdul Rafeh, Garassino Marina Chiara, Peters Solange, Wakelee Heather A, Nassar Amin H, Ricciuti Biagio, Soda Paolo, Caruso Camillo Maria, Guarrasi Valerio
Аннотация
METHODS: We analyzed the Pembro-real 5Y registry, a global real-world dataset of 1050 patients treated across 61 institutions in 14 countries with a long-term follow-up and a large panel of baseline variables. Two complementary approaches were employed: ridge regression, chosen for its ability to address multicollinearity while retaining interpretability, and not another imputation method (NAIM), a transformer-based artificial intelligence model designed to handle missing data without imputation. Endpoints included risk of death at 6, 12, 24, 60 months and 5-year survival.
RESULTS: The ridge regression model achieved a c-statistic of 0.66 (95% CI: 0.59 to 0.72) for the risk of death and an area under the curve (AUC) of 0.72 (95% CI: 0.65 to 0.78) for 5-year survival, identifying Eastern Cooperative Oncology Group Performance Status (ECOG-PS)≥2, increasing age, and metastatic burden as primary risk factors. However, wide CIs for some predictors highlighted statistical instability. NAIM demonstrated robust handling of missing data, with a c-index of 62.98±2.11 for risk of death and an AUC of 60.52±3.71 for 5-year survival. The comprehensive SHapley Additive exPlanations analysis revealed dynamic, time-dependent patterns, with early mortality dominated by acute factors (eg, ECOG-PS, steroids) and long-term outcomes increasingly influenced by systemic health markers (eg, absence of hypertension, increasing body mass index). Unexpected insights included the protective role of dyslipidemia (but not statins) and the nuanced impact of smoking status, reflecting evolving disease dynamics and host-tumor interplay.
CONCLUSIONS: Our integrative framework illuminates the complexity of long-term outcomes in patients with NSCLC treated with pembrolizumab, uncovering dynamic, non-linear prognostication trends. This analysis provides insights into patient trajectories, emphasizing the need for holistic, long-term management strategies.