Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge.

By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables. This is especially true in Head and Neck Cancer (HNC). The goal of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge was to develop and compare modern image analysis methods to best extract and leverage this information automatically. We present here the post-analysis of HECKTOR 2nd edition, at the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021. The scope of the challenge was substantially expanded compared to the first edition, by providing a larger population (adding patients from a new clinical center) and proposing an additional task to the challengers, namely the prediction of Progression-Free Survival (PFS). To this end, the participants were given access to a training set of 224 cases from 5 different centers, each with a pre-treatment FDG-PET/CT scan and clinical variables. Their methods were subsequently evaluated on a held-out test set of 101 cases from two centers. For the segmentation task (Task 1), the ranking was based on a Borda counting of their ranks according to two metrics: mean Dice Similarity Coefficient (DSC) and median Hausdorff Distance at 95th percentile (HD95). For the PFS prediction task, challengers could use the tumor contours provided by experts (Task 3) or rely on their own (Task 2). The ranking was obtained according to the Concordance index (C-index) calculated on the predicted risk scores. A total of 103 teams registered for the challenge, for a total of 448 submissions and 29 papers. The best method in the segmentation task obtained an average DSC of 0.759, and the best predictions of PFS obtained a C-index of 0.717 (without relying on the provided contours) and 0.698 (using the expert contours). An interesting finding was that best PFS predictions were reached by relying on DL approaches (with or without explicit tumor segmentation, 4 out of the 5 best ranked) compared to standard radiomics methods using handcrafted features extracted from delineated tumors, and by exploiting alternative tumor contours (automated and/or larger volumes encompassing surrounding tissues) rather than relying on the expert contours. This second edition of the challenge confirmed the promising performance of fully automated primary tumor delineation in PET/CT images of HNC patients, although there is still a margin for improvement in some difficult cases. For the first time, the prediction of outcome was also addressed and the best methods reached relatively good performance (C-index above 0.7). Both results constitute another step forward toward large-scale outcome prediction studies in HNC

The impact of optimal respiratory gating and image noise on evaluation of intra-tumor heterogeneity in 18F-FDG positron emission tomography imaging of lung cancer

Contains fulltext : 165808.pdf (Publisher’s version ) (Closed access)Assessment of measurement accuracy of intra-tumor heterogeneity using texture features in positron emission tomography (PET) images is essential to characterize cancer lesions with high precision. In this study, we investigated the influence of respiratory motion and varying noise levels on quantification of texture features in patients with lung cancer.Respiratory gating was performed on list-mode data of 60 lung cancer patients, who underwent (18)F-fluorodeoxyglucose (FDG) PET, using an optimal respiratory gating algorithm (ORG). The ORG images were reconstructed using a duty cycle (percentage of the total acquired PET data) of 35\%. In addition to ORG images, non-gated images with varying statistical quality (using 35\% and 100\% of PET data) were reconstructed to investigate the effects of image noise. Several global image-derived indices and texture parameters (entropy, high intensity emphasis (HIE), zone percentage (ZP), and dissimilarity) that have been associated with patient outcome, were calculated. Clinical impact of ORG and image noise on assessment of intra-tumor heterogeneity was evaluated using Cox regression models with overall survival (OS) as outcome measure for non-small cell lung cancer patients. Threshold for statistical significance was adjusted for multiple comparisons using Bonferroni.Respiratory motion significantly affected intra-tumor heterogeneity quantification for lesions in the lower lung lobes (p0.007). The mean increase of entropy, dissimilarity, ZP, and HIE, for lower lobe lesions was 1.3�1.5\% (P = 0.02), 11.6�11.8\% (P = 0.006) 2.3�2.2\% (P = 0.002), and 16.8\%�17.2\% (P = 0.006), respectively. No significant differences were observed for lesions located in the upper lung lobes (p>0.007). Differences in the statistical quality of the PET images affected the texture parameters to a lesser extent than respiratory motion, with no statistically significant differences between the images. The median follow-up time of this patient cohort was 35 months (range 7 - 39 months). In multivariate analysis for OS, total lesion glycolysis (TLG) and HIE were the two most relevant image-derived indices considered to be independent significant co-variates for the model, regardless of the image type considered.The results of this study suggest that the tested textural features are robust in the presence of respiratory motion artefacts and varying levels of image noise