Corral-Jaime, J. (Jesús)
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- Effects of dose modifications on the safety and efficacy of dacomitinib for EGFR mutation-positive non-small-cell lung cancer(Future Medicine Ltd, 2019) Linke, R. (Rolf); Pluzanski, A. (Adam); Wu, Y.L. (Yi-Long); Mok, T.S. (Tony S.); Nakagawa, K. (Kazuhiko); Corral-Jaime, J. (Jesús); Migliorino, M.R. (Maria Rita); Tan, W. (Weiwei); Devgan, G. (Geeta); Quinn, S. (Susan); Wang, T. (Tao); Rosell, R. (Rafael); Lee, K.H. (Ki Hyeong)Aim: We evaluated reasons for dacomitinib dose reduction (DR) and examined adverse event (AE) incidence, key efficacy end points (progression-free survival [PFS]/overall survival [OS]), and pharmacokinetics in dose-reducing patients in the ARCHER 1050 trial. Patients & methods: Newly diagnosed patients with EGFR mutation-positive, advanced non-small-cell lung cancer received oral dacomitinib (45 mg once-daily [QD]), with stepwise toxicity-managing DR (30 and 15 mg QD) permitted. Results: Skin toxicities (62.7%) were the most common DR-leading AEs. The AE incidence and severity decreased following DRs. Initial plasma dacomitinib exposure (45 mg QD) was generally lower in patients remaining at 45 mg QD compared with dose-reducing patients. Median PFS and OS were similar in all dacomitinib-treated patients and dose-reducing patients. Conclusion: Tolerability-guided dose modifications enabled patients to continue with dacomitinib and benefit from PFS/OS improvement.
- COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images(2020) Duran-Lopez, L. (Lourdes); Corral-Jaime, J. (Jesús); Dominguez-Morales, J.P. (Juan Pedro); Vicente-Diaz, S. (Saturnino); Linares-Barranco, A. (Alejandro)The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19.
- Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients(2023) Seijo, L. (Luis); Bermejo-Peláez, D. (David); Gil-Bazo, I. (Ignacio); Farina, B. (Benito); Domine, M. (Manuel); Ledesma-Carbayo, M.J. (María J.); Ramos-Guerra, A.D. (Ana Delia); Corral-Jaime, J. (Jesús); Palacios-Miras, C. (Carmelo); Gallardo-Madueño, G. (Guillermo); Rubio-Pérez, J. (Jaime); Vilalta, A. (Anna); Muñoz-Barrutia, A. (Arrate); Alcázar, A. (Andrés); Peces-Barba, G. (German)Background Identifying predictive non-invasive biomarkers of immunotherapy response is crucial to avoid premature treatment interruptions or ineffective prolongation. Our aim was to develop a non-invasive biomarker for predicting immunotherapy clinical durable benefit, based on the integration of radiomics and clinical data monitored through early anti-PD-1/PD-L1 monoclonal antibodies treatment in patients with advanced non-small cell lung cancer (NSCLC).MethodsIn this study, 264 patients with pathologically confirmed stage IV NSCLC treated with immunotherapy were retrospectively collected from two institutions. The cohort was randomly divided into a training (n = 221) and an independent test set (n = 43), ensuring the balanced availability of baseline and follow-up data for each patient. Clinical data corresponding to the start of treatment was retrieved from electronic patient records, and blood test variables after the first and third cycles of immunotherapy were also collected. Additionally, traditional radiomics and deep-radiomics features were extracted from the primary tumors of the computed tomography (CT) scans before treatment and during patient follow-up. Random Forest was used to implementing baseline and longitudinal models using clinical and radiomics data separately, and then an ensemble model was built integrating both sources of information.ResultsThe integration of longitudinal clinical and deep-radiomics data significantly improved clinical durable benefit prediction at 6 and 9 months after treatment in the independent test set, achieving an area under the receiver operating characteristic curve of 0.824 (95% CI: [0.658,0.953]) and 0.753 (95% CI: [0.549,0.931]). The Kaplan-Meier survival analysis showed that, for both endpoints, the signatures significantly stratified high- and low-risk patients (p-value< 0.05) and were significantly correlated with progression-free survival (PFS6 model: C-index 0.723, p-value = 0.004; PFS9 model: C-index 0.685, p-value = 0.030) and overall survival (PFS6 models: C-index 0.768, p-value = 0.002; PFS9 model: C-index 0.736, p-value = 0.023).ConclusionsIntegrating multidimensional and longitudinal data improved clinical durable benefit prediction to immunotherapy treatment of advanced non-small cell lung cancer patients. The selection of effective treatment and the appropriate evaluation of clinical benefit are important for better managing cancer patients with prolonged survival and preserving quality of life.
- SEOM clinical guidelines for anaemia treatment in cancer patients (2020)(Springer Nature, 2021) Escobar-Álvarez, Y. (Yolanda); Peñas, R. (Ramón) de las; Perez-Altozano, J. (Javier); Ros-Martínez, S. (Silverio); Sabino-Álvarez, A. (Araceli); Blasco-Cordellat, A. (Ana); Brozos-Vázquez, E. (Elena); Corral-Jaime, J. (Jesús); García-Escobar, I. (Ignacio); Beato-Zambrano, C. (Carmen)Anaemia is defned by the presence of haemoglobin (Hb) levels<13 g/dL in men and 12 g/dL in women. Up to 39% of cancer patients present it at the time of diagnosis and up to 40% have iron defciency. Anaemia causes fatigue, functional deterioration and a reduction in the quality of life; it has also been associated with a poorer response to anti-tumour treatment and lower survival. Basic diagnostic tests for anaemia are simple and should be a routine part of clinical practice. These guidelines review the available evidence on the use of diferent therapies for treating anaemia: erythropoiesis-stimulating agents, iron supplements, and transfusion of blood products.