Abstract
Rationale: Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction of patient prognosis. Many studies focus on promoting newly developed imaging biomarkers without a rigorous comparison to other established parameters. To assess the true value and leverage the potential of all efforts in this field, a multi-parametric evaluation of the available biomarkers for PDAC survival prediction is warranted. Here we present a multiparametric analysis to assess the predictive value of established parameters and the added contribution of newly developed imaging features such as biomarkers for overall PDAC patient survival. Methods: 103 patients with resectable PDAC were retrospectively enrolled. Clinical and histopathological data (age, sex, chemotherapy regimens, tumor size, lymph node status, grading and resection status), morpho-molecular and genetic data (tumor morphology, molecular subtype, tp53, kras, smad4 and p16 genetics), image-derived features and the combination of all parameters were tested for their prognostic strength based on the concordance index (CI) of multivariate Cox proportional hazards survival modelling after unsupervised machine learning preprocessing. Results: The average CIs of the out-of-sample data were: 0.63 for the clinical and histopathological features, 0.53 for the morpho-molecular and genetic features, 0.65 for the imaging features and 0.65 for the combined model including all parameters. Conclusions: Imaging-derived features represent an independent survival predictor in PDAC and enable the multiparametric, machine learning-assisted modelling of postoperative overall survival with a high performance compared to clinical and morpho-molecular/genetic parameters. We propose that future studies systematically include imaging-derived features to benchmark their additive value when evaluating biomarker-based model performance.
Highlights
Pancreatic ductal adenocarcinoma (PDAC), despite its relative rarity, remains among the deadliest tumor entities in the developed world
The key driver of such therapy escape phenomena is likely to be tumor heterogeneity, both on the genetic level, with the four driver genes kras, smad4, tp53 and p16 [4,5] having been shown to lead to distinct survival outcomes [6,7]
Evidence has emerged that the machine learning-based analysis of pre-therapeutic imaging can provide a decision guidance based on the non-invasive derivation of quantitative, whole-tumor characteristics [10,11]
Summary
Pancreatic ductal adenocarcinoma (PDAC), despite its relative rarity, remains among the deadliest tumor entities in the developed world. Evidence has emerged that the machine learning-based analysis of pre-therapeutic imaging can provide a decision guidance based on the non-invasive derivation of quantitative, whole-tumor characteristics [10,11]. Image pattern analysis and machine learning approaches (often termed radiomics) have yielded encouraging results in the prediction of molecular PDAC subtypes and of patient survival from magnetic resonance or computed tomography imaging [12,13]. In a disease as complex as PDAC, the integration of clinical information, invasive biomarkers and imaging-derived data may provide a more comprehensive prognostic assessment of the tumor than any single modality [17]. We present a multimodal, data-driven workflow including clinical information, histo-morphologic/genetic parameters and computed tomography-derived imaging biomarkers for modelling overall patient survival in the post-operative setting
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