Session Item

To investigate the value of radiomic features derived from 18F-FDG PET/CT images of the primary tumour assessed before treatment to predict early progression in patients with locally advanced pancreatic cancer (LAPC).

Among one-hundred four patients with pathologically proven LAPC treated at our institution with initial chemotherapy followed by curative chemoradiation (CRT) from July 2013 to May 2021, a secondary analysis with baseline 18F-FDG PET/CT was conducted in fifty-seven patients. All pre-treatment PET/CT were performed at a single PET/CT Centre. Clinical factors such as semiquantitative PET parameters, including standardized uptake value (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), were also reported.

Early progression (EP) was defined temporally as a progression at the first evaluation, at 3 months from the start of treatment. EP was evaluated by CT scan, resulting in a dichotomous label of progression.

A 3D Volume of Interest (VOI) was placed over the primary tumour. The lesions were manually delineated. Three families of hand-crafted features were extracted from the VOIs of each patient's images, from both CT and PET acquisitions, thus quantifying grey intensity and tissue texture. The final dataset was then constructed by adding clinical data from each patient.

The predictive pipeline consisted of a feature selection phase followed by a sequence of two cascading decision trees in which the second uses the predictions of the first as additional features for sample prediction and optimising the binarization threshold for classification in the training phase to be applied later in the testing phase. The whole system follows a ten fold cross-validation approach.

The quality of the proposed model was appraised by means of receiver operating characteristics (ROC) and areas under the ROC curve (AUC).

Figure 1 shows the final performance. To the best of our knowledge, this is the first study for feasibility and hypothesis generation of a radiomic strategy to predict early progression in LAPC and our data suggests that a specific signature can be identified (AUC 0.83; prediction accuracy 80.7%).

Figure 1

This model based on clinical and PET/CT radiomic features assessed before treatment can predict the early progression in LAPC patients. It could be a promising pre-treatment, non-invasive, approach that can assist physicians in evaluating the risk of early progression in patients individually, and thus achieving a personalized treatment and a better clinical outcome. The identification of the external validation dataset is actually ongoing.