@article {aHernandeza,
	title = {Comparative Analysis and Interpretability of Survival Models for Melanoma Prognosis},
	journal = {Computers in Biology and Medicine},
	volume = {190},
	year = {2025},
	month = {05/2025},
	abstract = {<p>The increasing incidence of cutaneous melanoma highlights the critical need for precision medicine to optimize treatment strategies and improve patient outcomes. This study evaluated and compared the performance and interpretability of various survival models for melanoma patients. We analyzed survival outcomes, including overall survival (OS), specific survival (SS), and disease-free survival (DFS), using the Xarxa Melanoma database, which comprises data from over 9,000 patients across 19 hospitals collected over a decade. The performance of Cox Proportional Hazards, Random Survival Forest, XGBoost, DeepSurv, and DeepHit models was assessed using the Concordance Index (C-Index), time-dependent Area Under the Curve (tdAUC), ROC-AUC, and Inverse Brier score. Our analysis showed that the Random Survival Forest outperformed the other methods, achieving C-Index scores of 0.846 for OS, 0.869 for SS, and 0.846 for DFS. Furthermore, by applying an interpretability method specifically designed for survival analysis, we identified histological subtypes associated with poor prognoses. Additionally, this study reaffirms the established importance of the Breslow index as a key prognostic factor in melanoma survival prediction.</p>},
	issn = {0010-4825},
	doi = {https://doi.org/10.1016/j.compbiomed.2025.110027},
	url = {https://www.sciencedirect.com/science/article/pii/S0010482525003786?via\%3Dihub},
	author = {Carlos Hernandez and Sebastian Podlipnik and Joan Ficapal and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana}
}
@conference {cHernandez22a,
	title = {Contrastive and attention-based multiple instance learning for the prediction of sentinel lymph node status from histopathologies of primary melanoma tumours.},
	booktitle = {Cancer Prevention through early detecTion (Caption) Workshop at 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022)},
	year = {2022},
	month = {09/2022},
	abstract = {<p>Sentinel lymph node status is a crucial prognosis factor for melanomas; nonetheless, the invasive surgery required to obtain it always puts the patient at risk. In this study, we develop a Deep Learning-based approach to predict lymph node metastasis from Whole Slide Images of primary tumours. Albeit very informative, these images come with complexities that hamper their use in machine learning applications, namely their large size and limited datasets. We propose a pre-training strategy based on self-supervised contrastive learning to extract better image feature representations and an attention-based Multiple Instance Learning approach to enhance the model{\textquoteright}s performance. With this work, we quantitatively demonstrate that combining both methods improves various classification metrics and qualitatively show that contrastive learning encourages the network to output higher attention scores to tumour tissue and lower scores to image artifacts.</p>},
	author = {Carlos Hernandez and Marc Combalia and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana}
}
