Examinando por Autor "LeBlanc, Marissa"

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  • Miniatura
    Ítem
    Joint probability framework for the development and validation of a prognostic model for the conditional outcome of quality of life: a retrospective study in historical European cohorts of survivors of head and neck cancer
    (Elsevier Ltd, 2026-05-01) Moreira-Soares, Mauricio ; Fossen, Erlend I. F. ; Bilbao Jayo, Aritz; Almeida, Aitor ; López López, Laura; Alonso, Itziar; Cabrera Umpiérrez, Maria Fernanda; Fico, Giuseppe; Singer, Susanne; Taylor, Katherine J. ; Thomas, Steve; Pring, Miranda; Licitra, Lisa; Cavalieri, Stefano; Frigessi, Arnoldo; LeBlanc, Marissa
    Background: Conditional outcomes are outcomes defined only under specific circumstances. For example, future quality of life (QoL) can only be ascertained when patients are alive. In prognostic models involving conditional outcomes, a choice must be made on the precise target of prediction: one could target future QoL, given that the individual is still alive (conditional) or target future QoL jointly with the event of being alive (unconditional). We aimed to (1) introduce a probabilistic framework for prognostic models for conditional outcomes, and (2) apply this framework to develop a prognostic model for QoL 3 years after diagnosis in patients with head and neck cancer. Methods: A joint probability framework was proposed for prognostic model development for a conditional outcome dependent on a post-baseline variable. The framework involved two submodels: one for QoL and one for survival. Joint probability was estimated with conformal estimators using MAPIE with least absolute shrinkage and selection operator (LASSO) regression and XGBoost models. We included patients with head and neck cancer who were alive with no evidence of disease 12 months after diagnosis from the UK-based Head and Neck 5000 cohort (N=3572) and made QoL predictions 3 years after diagnosis. Predictors included clinical and demographic characteristics and longitudinal measurements of QoL. External validation was performed in longitudinal studies led by the University of Mainz (Mainz, Germany) and the Istituto Nazionale dei Tumori (Milan, Italy). A total of 497 patients were used in external validation of the QoL submodel and 281 were used in external validation of the survival submodel. Model performance was evaluated with C-statistics for discrimination, calibration plots, and R2 or mean absolute error (MAE), or both, for overall performance as appropriate. Findings: Of 3572 patients, 400 (11·2%) were dead by the time of prediction (3 years after diagnosis), whereas 73 (26·0%) of 281 patients were dead in the validation set for the survival submodel. Model performance was assessed for prediction of QoL, both conditionally and jointly with survival. C-statistics ranged from 0·66 to 0·74 in internal validation and 0·60 to 0·80 in external validation. In internal validation, R2 and MAEs ranged from 0·37 to 0·5 and 12·2 to 13·5, respectively, whereas R2 and MAEs ranged from 0.35 to 0.41 and 13.0 to 12.3, respectively, in external validation. The calibration plots showed reasonable calibration in external validation. External performance was weaker for the survival submodel than for the QoL submodel. An application programming interface and dashboard were developed. Interpretation: Our probabilistic framework for conditional outcomes provides both joint and conditional predictions and thus the flexibility needed to answer different clinical questions. Our model had reasonable performance in external validation and has potential as a tool in long-term follow-up of QoL in patients with head and neck cancer. Funding: EU’s Horizon 2020 research and innovation programme.
  • Miniatura
    Ítem
    A multicenter randomized trial for quality of life evaluation by non-invasive intelligent tools during post-curative treatment follow-up for head and neck cancer: clinical study protocol
    (Frontiers Media S.A., 2023-01-31) Cavalieri, Stefano; Vener, Claudia; LeBlanc, Marissa; López Pérez, Laura; Fico, Giuseppe; Resteghini, Carlo; Monzani, Dario; Marton, Giulia; Pravettoni, Gabriella; Moreira-Soares, Mauricio; Filippidou, Despina Elizabeth; Almeida, Aitor; Bilbao Jayo, Aritz ; Mehanna, Hisham; Singer, Susanne; Thomas, Steve; Lacerenza, Luca; Manfuso, Alfonso; Copelli, Chiara; Mercalli, Franco; Frigessi, Arnoldo; Martinelli, Elena; Licitra, Lisa; Estevez-Priego, Estefania
    Patients surviving head and neck cancer (HNC) suffer from high physical, psychological, and socioeconomic burdens. Achieving cancer-free survival with an optimal quality of life (QoL) is the primary goal for HNC patient management. So, maintaining lifelong surveillance is critical. An ambitious goal would be to carry this out through the advanced analysis of environmental, emotional, and behavioral data unobtrusively collected from mobile devices. The aim of this clinical trial is to reduce, with non-invasive tools (i.e., patients’ mobile devices), the proportion of HNC survivors (i.e., having completed their curative treatment from 3 months to 10 years) experiencing a clinically relevant reduction in QoL during follow-up. The Big Data for Quality of Life (BD4QoL) study is an international, multicenter, randomized (2:1), open-label trial. The primary endpoint is a clinically relevant global health-related EORTC QLQ-C30 QoL deterioration (decrease ≥10 points) at any point during 24 months post-treatment follow-up. The target sample size is 420 patients. Patients will be randomized to be followed up using the BD4QoL platform or per standard clinical practice. The BD4QoL platform includes a set of services to allow patients monitoring and empowerment through two main tools: a mobile application installed on participants’ smartphones, that includes a chatbot for e-coaching, and the Point of Care dashboard, to let the investigators manage patients data. In both arms, participants will be asked to complete QoL questionnaires at study entry and once every 6 months, and will undergo post-treatment follow up as per clinical practice. Patients randomized to the intervention arm (n=280) will receive access to the BD4QoL platform, those in the control arm (n=140) will not. Eligibility criteria include completing curative treatments for non-metastatic HNC and the use of an Android-based smartphone. Patients undergoing active treatments or with synchronous cancers are excluded. Clinical Trial Registration: ClinicalTrials.gov, identifier (NCT05315570).