Semi-automatic segmentation of whole-body images in longitudinal studies.

We propose a semi-automatic segmentation pipeline designed for longitudinal studies considering structures with large anatomical variability, where expert interactions are required for relevant segmentations. Our pipeline builds on the regularized Fast Marching (rFM) segmentation approach by Risseret al(2018). It consists in transporting baseline multi-label FM seeds on follow-up images, selecting the relevant ones and finally performing the rFM approach. It showed increased, robust and faster results compared to clinical manual segmentation. Our method was evaluated on 3D synthetic images and patients' whole-body MRI. It allowed a robust and flexible handling of organs longitudinal deformations while considerably reducing manual interventions.

Random forests to predict tumor recurrence following cervical cancer therapy using pre- and per-treatment 18F-FDG PET parameters.

The ability to predict tumor recurrence after chemoradiotherapy of locally advanced cervical cancer is a crucial clinical issue to intensify the treatment of the most high-risk patients. The objective of this study was to investigate tumor metabolism characteristics extracted from pre- and per-treatment 18F-FDG PET images to predict 3-year overall recurrence (OR). A total of 53 locally advanced cervical cancer patients underwent pre- and per-treatment 18F-FDG PET (respectively PET1 and PET2). Tumor metabolism was characterized through several delineations using different thresholds, based on a percentage of the maximum uptake, and applied by region-growing. The SUV distribution in PET1 and PET2 within each segmented region was characterized through 7 intensity and histogram-based parameters, 9 shape descriptors and 16 textural features for a total of 1026 parameters. Predictive capability of the extracted parameters was assessed using the area under the receiver operating curve (AUC) associated to univariate logistic regression models and random forest (RF) classifier. In univariate analyses, 36 parameters were highly significant predictors of 3-year OR (p<;0.01), AUC ranging from 0.72 to 0.83. With RF, the Out-of-Bag (OOB) error rate using the totality of the extracted parameters was 26.42% (AUC=0.72). By recursively eliminating the less important variables, OOB error rate of the RF classifier using the nine most important parameters was 13.21% (AUC=0.90). Results suggest that both pre- and per-treatment 18F-FDG PET exams provide meaningful information to predict the tumor recurrence. RF classifier is able to handle a very large number of extracted features and allows the combination of the most prognostic parameters to improve the prediction.