Mark Schuiveling, Hong Liu, Daniel Eek, Martina Hanusová, Isabella A J van Duin, Laurens S Ter Maat, Janneke C van der Weerd, Franchette van den Berkmortel, Christian U Blank, Gerben E Breimer, Femke H Burgers, Marye Boers-Sonderen, Alfons J M van den Eertwegh, Jan Willem B de Groot, John B A G Haanen, Geke A P Hospers, Ellen Kapiteijn, Djura Piersma, Lieke Simkens, Hans M Westgeest, Anne M R Schrader, Paul J van Diest, Jiaqi Lv, Yijie Zhu, Carmen Guadalupe Colin Tenorio, Brinder Singh Chohan, Mark Eastwood, Shan E Ahmed Raza, Nima Torbati, Anastasia Meshcheryakova, Diana Mechtcheriakova, Amirreza Mahbod, Daniel Adams, Adrian Galdran, Josien P W Pluim, Willeke A M Blokx, Karijn P M Suijkerbuijk, Mitko Veta
Organized the Panoptic segmentation of nUclei and tissue in advanced MelanomA (PUMA) challenge to evaluate whether the spatial localization of TILs and other immune cell subsets on melanoma H&E slides was associated with treatment outcomes. Top-performing algorithms improved detection of immune cell subsets through pathology foundation model features, multi-stage tissue-context learning, and class-specific architectures. Across challenge participants, TIL density showed the most consistent association with treatment response and survival.
Patients with advanced melanoma are treated with immune checkpoint inhibitors (ICIs), yet <50% of patients achieve a durable response while all patients are exposed to the risk of severe side effects. Tumor-infiltrating lymphocytes (TILs) in pathology images are associated with ICI outcomes, but manual assessment is subjective. In addition, the predictive value of other immune cell subsets, including plasma cells, neutrophils, histiocytes, and melanophages, remains unclear. We organized the Panoptic segmentation of nUclei and tissue in advanced MelanomA (PUMA) challenge to evaluate whether the spatial localization of TILs and other immune cell subsets on melanoma H&E slides collected before start of treatment was associated with treatment outcomes. Algorithm performance was evaluated on a hidden test set, after which top-ranked algorithms were applied to pre-treatment metastatic whole-slide images from a large, multicenter cohort of patients with advanced melanoma treated with first-line ICIs (n = 1102). Automatically quantified tissue features and immune cell subsets were then associated with clinical outcomes. Top-performing algorithms improved detection of immune cell subsets through pathology foundation model features, multi-stage tissue-context learning, and class-specific architectures, although accuracy for rare classes remained limited. Across challenge participants, TIL density showed the most consistent association with treatment response and survival. Associations for stromal TILs were weaker, while plasma cells, histiocytes, melanophages, neutrophils, necrosis and blood vessels did not show independent associations with outcomes. Overall, the PUMA challenge advanced immune cell detection in melanoma histopathology and showed that intratumoral lymphocytes are most consistently associated with treatment response and survival.