Diffusion tensor imaging in detecting gliomas sub-regions of infiltration, local and remote recurrences: a systematic review.

Given that glioma cells tend to infiltrate and migrate along WM tracts, leading to demyelination and axonal injuries, Diffusion Tensor Imaging (DTI) emerged as a promising tool for identifying major "high-risk areas" of recurrence within the peritumoral brain zone (PBZ) or at a distance throughout the adjacents white matter tracts. Of our systematic review is to answer the following research question: In patients with brain tumor, is DTI able to recognizes within the peri-tumoral brain zone (PBZ) areas more prone to local (near the surgical cavity) or remote recurrence compared to the conventional imaging techniques?. We conducted a comprehensive literature search to identify relevant studies in line with the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. 15 papers were deemed compatible with our research question and included. To enhance the paper's readability, we have categorized our findings into two distinct groups: the first delves into the role of DTI in detecting PBZ sub-regions of infiltration and local recurrences (n = 8), while the second group explores the feasibility of DTI in detecting white matter tract infiltration and remote recurrences (n = 7). DTI values and, within a broader framework, radiomics investigations can provide precise, voxel-by-voxel insights into the state of PBZ and recurrences. Better defining the regions at risk for potential recurrence within the PBZ and along WM bundles will allow targeted therapy.

Aggressive PitNETs and Potential Target Therapies: A Systematic Review of Molecular and Genetic Pathways.

Recently, advances in molecular biology and bioinformatics have allowed a more thorough understanding of tumorigenesis in aggressive PitNETs (pituitary neuroendocrine tumors) through the identification of specific essential genes, crucial molecular pathways, regulators, and effects of the tumoral microenvironment. Target therapies have been developed to cure oncology patients refractory to traditional treatments, introducing the concept of precision medicine. Preliminary data on PitNETs are derived from preclinical studies conducted on cell cultures, animal models, and a few case reports or small case series. This study comprehensively reviews the principal pathways involved in aggressive PitNETs, describing the potential target therapies. A search was conducted on Pubmed, Scopus, and Web of Science for English papers published between 1 January 2004, and 15 June 2023. 254 were selected, and the topics related to aggressive PitNETs were recorded and discussed in detail: epigenetic aspects, membrane proteins and receptors, metalloprotease, molecular pathways, PPRK, and the immune microenvironment. A comprehensive comprehension of the molecular mechanisms linked to PitNETs' aggressiveness and invasiveness is crucial. Despite promising preliminary findings, additional research and clinical trials are necessary to confirm the indications and effectiveness of target therapies for PitNETs.

Discretized superimposed optical fiber long-period gratings.

A novel technique, to the best of our knowledge, for the inscription of superimposed long-period gratings with arbitrary grating pitches is proposed and experimentally validated. The technique is based on the discretization of an ideal continuous sinusoidal refractive index (RI) pattern with a step function. The RI variation is induced by means of the irradiation of a photosensitive fiber with a 248 nm UV laser beam. The nonlinear relation between the induced RI change and the UV fluence was experimentally derived. Two superimposed long-period grating (LPGs) with different grating pitches have been realized with the discretization technique; the transmission spectrum was compared with that of two superimposed LPGs obtained with the traditional square wave RI modulation. The validity of the proposed technique was demonstrated by the better spectral characteristics of the discretized superimposed LPGs.

UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images.

Purpose: The aim of this work is the development and characterization of a model observer (MO) based on convolutional neural networks (CNNs), trained to mimic human observers in image evaluation in terms of detection and localization of low-contrast objects in CT scans acquired on a reference phantom. The final goal is automatic image quality evaluation and CT protocol optimization to fulfill the ALARA principle. Approach: Preliminary work was carried out to collect localization confidence ratings of human observers for signal presence/absence from a dataset of 30,000 CT images acquired on a PolyMethyl MethAcrylate phantom containing inserts filled with iodinated contrast media at different concentrations. The collected data were used to generate the labels for the training of the artificial neural networks. We developed and compared two CNN architectures based respectively on Unet and MobileNetV2, specifically adapted to achieve the double tasks of classification and localization. The CNN evaluation was performed by computing the area under localization-ROC curve (LAUC) and accuracy metrics on the test dataset. Results: The mean of absolute percentage error between the LAUC of the human observer and MO was found to be below 5% for the most significative test data subsets. An elevated inter-rater agreement was achieved in terms of S-statistics and other common statistical indices. Conclusions: Very good agreement was measured between the human observer and MO, as well as between the performance of the two algorithms. Therefore, this work is highly supportive of the feasibility of employing CNN-MO combined with a specifically designed phantom for CT protocol optimization programs.

Addressing signal alterations induced in CT images by deep learning processing: A preliminary phantom study.

PURPOSE: We investigate, by an extensive quality evaluation approach, performances and potential side effects introduced in Computed Tomography (CT) images by Deep Learning (DL) processing. METHOD: We selected two relevant processing steps, denoise and segmentation, implemented by two Convolutional Neural Networks (CNNs) models based on autoencoder architecture (encoder-decoder and UNet) and trained for the two tasks. In order to limit the number of uncontrolled variables, we designed a phantom containing cylindrical inserts of different sizes, filled with iodinated contrast media. A large CT image dataset was collected at different acquisition settings and two reconstruction algorithms. We characterized the CNNs behavior using metrics from the signal detection theory, radiological and conventional image quality parameters, and finally unconventional radiomic features analysis. RESULTS: The UNet, due to the deeper architecture complexity, outperformed the shallower encoder-decoder in terms of conventional quality parameters and preserved spatial resolution. We also studied how the CNNs modify the noise texture by using radiomic analysis, identifying sensitive and insensitive features to the denoise processing. CONCLUSIONS: The proposed evaluation approach proved effective to accurately analyze and quantify the differences in CNNs behavior, in particular with regard to the alterations introduced in the processed images. Our results suggest that even a deeper and more complex network, which achieves good performances, is not necessarily a better network because it can modify texture features in an unwanted way.