Background: Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance by comparing it to the manual segmentations performed by radiologists. Methods: An atlas-based segmentation was developed to remove the high-signal intensity normal tissues on WB-DWI and to restrict the lesion area to the skeleton. Then, an outlier threshold-based segmentation was applied to WB-DWI images, and the segmented area's signal intensity was compared to the average signal intensity of a lowfat muscle on T1-weighted images. This method was validated in 22 whole-body DWI images of patients diagnosed with MM. Dice similarity coefficient (DSC), sensitivity and positive predictive value (PPV) were computed to evaluate the SA performance against the gold standard (GS) and to compare with the radiologists. A nonparametric Wilcoxon test was also performed. Apparent diffusion coefficient (ADC) histogram metrics and lesion volume were extracted for the GS segmentation and for the correctly identified lesions by SA and their correlation was assessed. Results: The mean inter-radiologists DSC was 0.323 ± 0.268. The SA vs GS achieved a DSC of 0.274 ± 0.227, sensitivity of 0.764 ± 0.276 and PPV 0.217 ± 0.207. Its distribution was not significantly different from the mean DSC of inter-radiologist segmentation (p = 0.108, Wilcoxon test). ADC and lesion volume intraclass correlation coefficient (ICC) of the GS and of the correctly identified lesions by the SA was 0.996 for the median and 0.894 for the lesion volume (p < 0.001). The duration of the lesion volume segmentation by the SA was, on average, 10.22 ± 0.86 min, per patient. Conclusions: The SA provides equally reproducible segmentation results when compared to the manual segmentation of radiologists. Thus, the proposed method offers robust and efficient segmentation of MM lesions on WB-DWI. This method may aid accurate assessment of tumor burden and therefore provide insights to treatment response assessment.
Background
Cancer and its treatment represent major stressors requiring that patients make multiple adaptations. Despite evidence that poor adaptation to stressors is associated with more distress and negative affect (NA), neuroimmune dysregulation and poorer health outcomes, current understanding is very limited of how NA covaries with central nervous system changes to account for these associations.
Methods
NA was correlated with brain metabolic activity using 18F‐fluorodeoxyglucose positron emission tomography/computed tomography (18F‐FDG PET/CT) in several regions of interest in 61 women with metastatic breast cancer. Patients underwent 18F‐FDG PET/CT and completed an assessment of NA using the Brief Symptom Inventory.
Results
Regression analyses revealed that NA was significantly negatively correlated with the standardized uptake value ratio of the insula, thalamus, hypothalamus, ventromedial prefrontal cortex, and lateral prefrontal cortex. Voxel‐wise correlation analyses within these 5 regions of interest demonstrated high left‐right symmetry and the highest NA correlations with the anterior insula, thalamus (medial and ventral portion), lateral prefrontal cortex (right Brodmann area 9 [BA9], left BA45, and right and left BA10 and BA8), and ventromedial prefrontal cortex (bilateral BA11).
Conclusions
The regions of interest most strongly negatively associated with NA represent key areas for successful adaptation to stressors and may be particularly relevant in patients with metastatic breast cancer who are dealing with multiple challenges of cancer and its treatment.
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