Prospective Evaluation of Quantitative F-18-FDG-PET/CT for Pre-Operative Thoracic Lymph Node Staging in Patients with Lung Cancer as a Target for Computer-Aided Diagnosis.

Purpose: Pre-operative assessment of thoracic lymphonodal (LN) involvement in patients with lung cancer (LC) is crucial when choosing the treatment modality. Visual assessment of F-18-FDG-PET/CT (PET/CT) is well established, however, there is still a need for prospective quantitative data to differentiate benign from malignant lesions which would simplify staging and guide the further implementation of computer-aided diagnosis (CAD).

Methods: In this prospective study, 37 patients with confirmed lung cancer (m/f = 24/13; age: 70 [52-83] years) were analyzed.

Neurofilament Levels Are Reflecting the Loss of Presynaptic Dopamine Receptors in Movement Disorders.

Neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH) are biomarkers for neuroaxonal damage. We assessed whether NfL and other biomarker levels in the CSF are correlated to the loss of presynaptic dopamine transporters in neurons as detected with dopamine transporter SPECT (DaTscan). We retrospectively identified 47 patients (17 Alzheimer's dementia, 10 idiopathic Parkinson's disease, 7 Lewy body dementia, 13 progressive supranuclear palsy or corticobasal degeneration) who received a DaTscan and a lumbar puncture.

Asphericity of Somatostatin Receptor Expression in Neuroendocrine Tumors: An Innovative Predictor of Outcome in Everolimus Treatment?

Background: in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NET), the mTOR inhibitor everolimus is associated with significant improvement in progression-free survival (PFS). This study evaluated the lesional asphericity (ASP) in pretherapeutic somatostatin receptor (SSR) imaging as the first imaging-based prognostic marker for PFS.

Methods: this retrospective bicentric cohort study included 30 patients (f = 13, median age, 66.

Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm-Evaluation of Diagnostic Performance.

The bone scan index (BSI), initially introduced for metastatic prostate cancer, quantifies the osseous tumor load from planar bone scans. Following the basic idea of radiomics, this method incorporates specific deep-learning techniques (artificial neural network) in its development to provide automatic calculation, feature extraction, and diagnostic support. As its performance in tumor entities, not including prostate cancer, remains unclear, our aim was to obtain more data about this aspect.

[Operability and Pathological Response of Non-Small Cell Lung Cancer (NSCLC) after Neoadjuvant Therapy with Immune Checkpoint Inhibition].

Background:  The blockade of immune escape mechanisms (e. g. PD1 /PD-L1) using immune checkpoint inhibition (ICI) can significantly prolong survival and induce remission in patients with advanced non-small cell lung cancer (NSCLC).