Clinical Value of Diffusion-Weighted Whole-Body Imaging with Background Body Signal Suppression (DWIBS) for Staging of Patients with Suspected Head and Neck Cancer

(1) Background: To determine the importance of diffusion-weighted whole-body MRI with background body signal suppression (DWIBS) in the staging process of patients with suspected head and neck carcinomas. (2) Methods: A total of 30 patients (24 male, 6 female) with a median age of 67 years with clinically suspected head and neck carcinoma with pathologic cervical nodal swelling in ultrasound underwent the staging procedure with computed tomography (CT) and whole-body MRI including DWIBS. (3) Results: In a total of 9 patients, abnormalities in the routine work-up of pretherapeutic staging were found. Five cases of either secondary cancer or distant metastases were only visible in DWIBS, while being missed on CT. One diagnosis was only detectable in CT and not in DWIBS, whereas three diagnoses were recognizable in both modalities. (4) Conclusions: DWIBS in addition to a standard neck MRI in cervical lymphadenopathy suspicious for head and neck cancer yielded additional clinically relevant diagnoses in 17% of cases that would have been missed by current staging routine procedures. DWIBS offered a negative predictive value of 98.78% for ruling out distant metastases or secondary malignancies

Diagnostic Value of Diffusion-Weighted Imaging with Background Body Signal Suppression (DWIBS) for the Pre-Therapeutic Loco-Regional Staging of Cervical Cancer: A Feasibility and Interobserver Reliability Study

Background: Cervical cancer is one of the leading causes of cancer-related deaths and the fourth most common cancer among women worldwide. Magnetic resonance imaging (MRI) is the modality of choice for loco-regional staging of cervical cancer in the primary diagnostic workup beginning with at least stage IB. Methods: We retrospectively analyzed 16 patients with histopathological proven cervical cancer (FIGO IB1–IVA) for the diagnostic accuracy of standard MRI and standard MRI with diffusion-weighted imaging with background body signal suppression (DWIBS) for the correct pre-therapeutic assessment of the definite FIGO category. Results: In 7 out of 32 readings (22%), DWIBS improved diagnostic accuracy. With DWIBS, four (13%) additional readings were assigned the correct major (I–IV) FIGO stages pre-therapeutically. Interobserver reliability of DWIBS was weakest for parametrial infiltration (k = 0.43; CI-95% 0.00–1.00) and perfect for tumor size <2 cm, infiltration of the vaginal lower third, infiltration of adjacent organs and loco-regional nodal metastases (k = 1.000; CI-95% 1.00–1.00). Conclusions: The pre-therapeutic staging of cervical cancer has a high diagnostic accuracy and interobserver reliability when using standard MRI but can be further optimized with the addition of DWIBS sequences when reporting is performed by an experienced radiologist

Application of A U-Net for Map-like Segmentation and Classification of Discontinuous Fibrosis Distribution in Gd-EOB-DTPA-Enhanced Liver MRI

We aimed to evaluate whether U-shaped convolutional neuronal networks can be used to segment liver parenchyma and indicate the degree of liver fibrosis/cirrhosis at the voxel level using contrast-enhanced magnetic resonance imaging. This retrospective study included 112 examinations with histologically determined liver fibrosis/cirrhosis grade (Ishak score) as the ground truth. The T1-weighted volume-interpolated breath-hold examination sequences of native, arterial, late arterial, portal venous, and hepatobiliary phases were semi-automatically segmented and co-registered. The segmentations were assigned the corresponding Ishak score. In a nested cross-validation procedure, five models of a convolutional neural network with U-Net architecture (nnU-Net) were trained, with the dataset being divided into stratified training/validation (n = 89/90) and holdout test datasets (n = 23/22). The trained models precisely segmented the test data (mean dice similarity coefficient = 0.938) and assigned separate fibrosis scores to each voxel, allowing localization-dependent determination of the degree of fibrosis. The per voxel results were evaluated by the histologically determined fibrosis score. The micro-average area under the receiver operating characteristic curve of this seven-class classification problem (Ishak score 0 to 6) was 0.752 for the test data. The top-three-accuracy-score was 0.750. We conclude that determining fibrosis grade or cirrhosis based on multiphase Gd-EOB-DTPA-enhanced liver MRI seems feasible using a 2D U-Net. Prospective studies with localized biopsies are needed to evaluate the reliability of this model in a clinical setting

Structural Connectivity Patterns of Side Effects Induced by Subthalamic Deep Brain Stimulation for Parkinson's Disease

Background: Tractography based on diffusion-weighted magnetic resonance imaging (DWI) models the structural connectivity of the human brain. Deep brain stimulation (DBS) targeting the subthalamic nucleus is an effective treatment for advanced Parkinson's disease, but may induce adverse effects. This study investigated the relationship between structural connectivity patterns of DBS electrodes and stimulation-induced side effects.Materials and Methods: Twenty-one patients with Parkinson's disease treated with bilateral subthalamic DBS were examined. Overall, 168 electrode contacts were categorized as inducing or noninducing depending on their capability for inducing side effects such as motor effects, paresthesia, dysarthria, oculomotor effects, hyperkinesia, and other complications as assessed during the initial programming session. Furthermore, the connectivity of each contact with target regions was evaluated by probabilistic tractography based on DWI. Finally, stimulation sites and structural connectivity patterns of inducing and noninducing contacts were compared.Results: Inducing contacts differed across the various side effects and from those mitigating Parkinson's symptoms. Although contacts showed a largely overlapping spatial distribution within the subthalamic region, they could be distinguished by their connectivity patterns. In particular, inducing contacts were more likely connected with supplementary motor areas (hyperkinesia, dysarthria), frontal cortex (oculomotor), fibers of the internal capsule (paresthesia), and the basal ganglia-thalamo-cortical circuitry (dysarthria).Discussion: Side effects induced by DBS seem to be associated with distinct connectivity patterns. Cerebellar connections are hardly associated with side effects, although they seem relevant for mitigating motor symptoms in Parkinson's disease. A symptom-specific, connectivity-based approach for target planning in DBS may enhance treatment outcomes and reduce adverse effects. Impact statementTractography based on diffusion-weighted magnetic resonance imaging has become a prominent technique for investigating the connectivity of human brain networks in vivo. However, the relationship between structural connections and brain function is still hardly known. The present study examined the relationship between adverse behavioral effects induced by deep brain stimulation (DBS) and tractography patterns in individual brains. The results suggest that DBS-based side effects depend on the structural connections of electrode contacts rather than their location. Network-based target planning in DBS may improve treatment by avoiding side effects. Moreover, the adopted approach may serve as a paragon for investigating structure/function relationships

Application of A U-Net for Map-Like Segmentation and Classification of Discontinuous Fibrosis Distribution in Gd-EOB-DTPA-Enhanced Liver MRI

We aimed to evaluate whether U-shaped convolutional neuronal networks can be used to segment liver parenchyma and indicate the degree of liver fibrosis/cirrhosis at the voxel level using contrast-enhanced magnetic resonance imaging. This retrospective study included 112 examinations with histologically determined liver fibrosis/cirrhosis grade (Ishak score) as the ground truth. The T1-weighted volume-interpolated breath-hold examination sequences of native, arterial, late arterial, portal venous, and hepatobiliary phases were semi-automatically segmented and co-registered. The segmentations were assigned the corresponding Ishak score. In a nested cross-validation procedure, five models of a convolutional neural network with U-Net architecture (nnU-Net) were trained, with the dataset being divided into stratified training/validation (n = 89/90) and holdout test datasets (n = 23/22). The trained models precisely segmented the test data (mean dice similarity coefficient = 0.938) and assigned separate fibrosis scores to each voxel, allowing localization-dependent de�termination of the degree of fibrosis. The per voxel results were evaluated by the histologically determined fibrosis score. The micro-average area under the receiver operating characteristic curve of this seven-class classification problem (Ishak score 0 to 6) was 0.752 for the test data. The top�three-accuracy-score was 0.750. We conclude that determining fibrosis grade or cirrhosis based on multiphase Gd-EOB-DTPA-enhanced liver MRI seems feasible using a 2D U-Net. Prospective studies with localized biopsies are needed to evaluate the reliability of this model in a clinical setting