The Leading Role of Brain and Abdominal Radiological Features in the Work-Up of Anti-NMDAR Encephalitis in Children: An Up-To-Date Review.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis (NMDARe) is the most common cause of nonviral encephalitis, mostly affecting young women and adolescents with a strong female predominance (F/M ratio of around 4:1). NMDARe is characterized by the presence of cerebrospinal fluid (CSF) antibodies against NMDARs, even though its pathophysiological mechanisms have not totally been clarified. The clinical phenotype of NMDARe is composed of both severe neurological and neuropsychiatric symptoms, including generalized seizures with desaturations, behavioral abnormalities, and movement disorders. NMDARe is often a paraneoplastic illness, mainly due to the common presence of concomitant ovarian teratomas in young women. Abdominal ultrasonography (US) is a key imaging technique that should always be performed in suspected patients. The timely use of abdominal US and the peculiar radiological features observed in NMDARe may allow for a quick diagnosis and a good prognosis, with rapid improvement after the resection of the tumor and the correct drug therapy.

MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer.

Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the "tumor core" (TC) and the "tumor border" (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based "clinical-radiomic" machine learning model properly predicted the treatment response (AUC = 0.793, p = 5.6 × 10-5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

Tumor detectability and conspicuity comparison of standard b1000 and ultrahigh b2000 diffusion-weighted imaging in rectal cancer.

PURPOSE: To compare tumor detectability and conspicuity of standard b = 1000 s/mm2 (b1000) versus ultrahigh b = 2000 s/mm2 (b2000) diffusion-weighted imaging (DWI) in rectal cancer. METHODS: Fifty-five patients for a total of 81 3T DWI-MR scans were retrospectively evaluated by two differently experienced readers. A comparison between b1000 and b2000 for tumor detectability and conspicuity was performed. The conspicuity was qualitatively and quantitatively assessed by using three-point scale and whole tumor volume manual delineation, respectively. Receiver-operating characteristic curve (ROC) with area under the curve (AUC) analysis provided diagnostic accuracy in tumor detectability of restaging MR scans. Qualitative scores and quantitative features including mean signal intensity, variance, 10th percentile and 90th percentile, were compared using the Wilcoxon test. Interobserver agreement (IOA) for qualitative and quantitative data was calculated using Cohen's Kappa and intraclass correlation coefficient (ICC) respectively. RESULTS: Diagnostic accuracy was comparable between b1000 and b2000 for both readers (p > 0.05). Overall quality scores were significantly better for b2000 than b1000 (2.29 vs 1.65 Reader 1, p = 0.01; 2.18 vs 1.69 Reader 2, p = 0.04). IOA was equally good for both b values (k = 0.86 b1000, k = 0.86 b2000). Quantitative analysis revealed more uniform signal (measured in variance) of b2000 in both healthy surrounding tissue (p < 0.05) and tumor (p < 0.05), with less outliers (measured using 10th and 90th percentile). Additionally, b2000 offered lower mean signal intensity in tissue sorrounding the tumor (p < 0.05). Finally, ICC improved from 0.92 (b1000) to 0.97 (b2000). CONCLUSION: Ultrahigh b value (b2000) may improve rectal cancer conspicuity and introbserver agreement maintaining comparable diagnostic accuracy to standard b1000.

Rectal cancer MRI: protocols, signs and future perspectives radiologists should consider in everyday clinical practice.

Magnetic resonance imaging (MRI) allows to non-invasively evaluate rectal cancer staging and to assess the presence of "prognostic signs" such as the distance from the anorectal junction, the mesorectal fascia infiltration and the extramural vascular invasion. Moreover, MRI plays a crucial role in the assessment of treatment response after chemo-radiation therapy, especially considering the growing interest in the new conservative policy (wait and see, minimally invasive surgery). We present a practical overview regarding the state of the art of the MRI protocol, the main signs that radiologists should consider for their reports during their clinical activity and future perspectives. TEACHING POINTS: • MRI protocol for rectal cancer staging and re-staging. • MRI findings that radiologists should consider for reports during everyday clinical activity. • Perspectives regarding the development of latest technologies.