Ledesma-Carbayo, M.J. (María J.)
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- A new workflow for image-guided intraoperative electron radiotherapy using projection-based pose tracking(IEEE, 2020) Ortuño, J.E. (Juan E.); Ledesma-Carbayo, M.J. (María J.); Santos, A. (Andrés); Gowami, S.S. (Subhra. S); Calvo, F.A. (Felipe A.); Pascau, J. (Javier)A new workflow is proposed to update the intraoperative electron radiotherapy (IOERT) planning refreshing the position and orientation (pose) of a virtual applicator with respect to the preoperative computed tomography (CT) with the actual pose during surgery. The workflow proposed relies on a robust registration of the preoperative CT and intraoperative projection radiographs acquired with a C-arm system. The workflow initially performs a geometric calibration of the C-arm using fiducials placed on the applicator. In the next step, a point-based 2D–3D registration based on fiducials positioned on the patient’s skin is performed, followed by an intensity-based registration that refines the point-based registration result. The performance of the workflow has been evaluated using a realistic physical phantom consisting of a pig lower limb and its corresponding CT and 7 C-arm projections at different poses. The accuracy has been measured with respect to the applicator origin and axis before and after the registration refinement process. A feasibility study with human data is also included. Error analysis revealed angular accuracy of 0.9 ± 0.7 degrees and translational accuracy of 1.9 ± 1 mm. Our experiments demonstrated that the proposed workflow can achieve subdegree angular accuracy in locating the applicator with respect to the preoperative CT to update and supervise the IOERT planning right before radiation delivery. The proposed workflow could be easily implementable in a routine, corresponding to a significant improvement in quality assurance during IOERT procedures.
- Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT(2022) Seijo, L. (Luis); Sellarés, J. (Jacobo); Bermejo-Peláez, D. (David); Sánchez, M. (Marcelo); Bastarrika, G. (Gorka); Ledesma-Carbayo, M.J. (María J.); Gotera-Rivera, C. (Carolina); Benegas, M. (Mariana); Palacios-Miras, C. (Carmelo); Cuerpo, S. (Sara); Luengo-Oroz, M. (Miguel); San-José-Estépar, R. (Raúl); Gallardo-Madueño, G. (Guillermo); Fernández-Velilla, M. (María); Peces-Barba, G. (German)The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score.
- Intraoperative computed tomography imaging for dose calculation in intraoperative electron radiation therapy: Initial clinical observations(2020) Calvo-Haro, J. (José); Ledesma-Carbayo, M.J. (María J.); Desco, M. (Manuel); García-Vázquez, V. (Verónica); Calvo, F.A. (Felipe A.); Pascau, J. (Javier); Solé, C. (Claudio)In intraoperative electron radiation therapy (IOERT) the energy of the electron beam is selected under the conventional assumption of water-equivalent tissues at the applicator end. However, the treatment field can deviate from the theoretic flat irradiation surface, thus altering dose profiles. This patient-based study explored the feasibility of acquiring intraoperative computed tomography (CT) studies for calculating three-dimensional dose distributions with two factors not included in the conventional assumption, namely the air gap from the applicator end to the irradiation surface and tissue heterogeneity. In addition, dose distributions under the conventional assumption and from preoperative CT studies (both also updated with intraoperative data) were calculated to explore whether there are other alternatives to intraoperative CT studies that can provide similar dose distributions. The IOERT protocol was modified to incorporate the acquisition of intraoperative CT studies before radiation delivery in six patients. Three studies were not valid to calculate dose distributions due to the presence of metal artefacts. For the remaining three cases, the average gamma pass rates between the doses calculated from intraoperative CT studies and those obtained assuming water-equivalent tissues or from preoperative CT studies were 73.4% and 74.0% respectively. The agreement increased when the air gap was included in the conventional assumption (98.1%) or in the preoperative CT images (98.4%). Therefore, this factor was the one mostly influencing the dose distributions of this study. Our experience has shown that intraoperative CT studies are not recommended when the procedure includes the use of shielding discs or surgical retractors unless metal artefacts are removed. IOERT dose distributions calculated under the conventional assumption or from preoperative CT studies may be inaccurate unless the air gap (which depends on the surface irregularities of the irradiated volume and on the applicator pose) is included in the calculations.
- 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.