Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases

Background: We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM). Methods: In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated. Results: In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95–0.96) and 0.80 (IQR 0.67–0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29–0.76) for tumor segmentation. Conclusions: Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients. Relevance statement: Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist’s workload and increasing accuracy and consistency. Key points: • Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations. Graphical Abstract: [Figure not available: see fulltext.]

Population differences in brain morphology and microstructure among Chinese, Malay, and Indian neonates

We studied a sample of 75 Chinese, 73 Malay, and 29 Indian healthy neonates taking part in a cohort study to examine potential differences in neonatal brain morphology and white matter microstructure as a function of ethnicity using both structural T2-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We first examined the differences in global size and morphology of the brain among the three groups. We then constructed the T2-weighted MRI and DTI atlases and employed voxel-based analysis to investigate ethnic differences in morphological shape of the brain from the T2-weighted MRI, and white matter microstructure measured by fractional anisotropy derived from DTI. Compared with Malay neonates, the brains of Indian neonates’ tended to be more elongated in anterior and posterior axis relative to the superior-inferior axis of the brain even though the total brain volume was similar among the three groups. Although most anatomical regions of the brain were similar among Chinese, Malay, and Indian neonates, there were anatomical variations in the spinal-cerebellar and cortical-striatal-thalamic neural circuits among the three populations. The population-related brain regions highlighted in our study are key anatomical substrates associated with sensorimotor functions