The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study.

Purpose: Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC).

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.

Objective: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.

Methods: Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015-2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment.

Automatic Segmentation with Deep Learning in Radiotherapy.

This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed.

Blood- and Imaging-Derived Biomarkers for Oncological Outcome Modelling in Oropharyngeal Cancer: Exploring the Low-Hanging Fruit.

Aims: To assess whether CT-based radiomics and blood-derived biomarkers could improve the prediction of overall survival (OS) and locoregional progression-free survival (LRPFS) in patients with oropharyngeal cancer (OPC) treated with curative-intent RT.

Methods: Consecutive OPC patients with primary tumors treated between 2005 and 2021 were included. Analyzed clinical variables included gender, age, smoking history, staging, subsite, HPV status, and blood parameters (baseline hemoglobin levels, neutrophils, monocytes, and platelets, and derived measurements).

Comparison of automated segmentation techniques for magnetic resonance images of the prostate.

Background: Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs.

Methods: This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy.

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer.

Background: No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed.

Brain metastases from NSCLC treated with stereotactic radiotherapy: prediction mismatch between two different radiomic platforms.

Background And Purpose: Radiomics enables the mining of quantitative features from medical images. The influence of the radiomic feature extraction software on the final performance of models is still a poorly understood topic. This study aimed to investigate the ability of radiomic features extracted by two different radiomic platforms to predict clinical outcomes in patients treated with radiosurgery for brain metastases from non-small cell lung cancer.

Quality assurance for automatically generated contours with additional deep learning.

Objective: Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model's use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours.

The dosimetric impact of axillary nodes contouring variability in breast cancer radiotherapy: An AIRO multi-institutional study.

Aim: To quantify the dosimetric impact of contouring variability of axillary lymph nodes (L2, L3, L4) in breast cancer (BC) locoregional radiotherapy (RT).

Materials And Methods: 18 RT centres were asked to plan a locoregional treatment on their own planning target volume (single centre, SC-PTV) which was created by applying their institutional margins to the clinical target volume of the axillary nodes of three BC patients (P1, P2, P3) previously delineated (SC-CTV). The gold standard CTVs (GS-CTVs) of P1, P2 and P3 were developed by BC experts' consensus and validated with STAPLE algorithm.

Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist.

Background And Purpose: Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT).

The FEL in the SXL project at MAX IV.

In this paper the design of the free-electron laser (FEL) in the SXL (Soft X-ray Laser) project at the MAX IV Laboratory is presented. The target performance parameters originate in a science case put forward by Swedish users and the SXL FEL is foreseen to be driven by the existing MAX IV 3 GeV linac. The SXL project is planned to be realized in different stages and in this paper the focus is on Phase 1, where the basic operation mode for the FEL will be SASE (self-amplified spontaneous emission), with an emphasis on short pulses.

Resolution enhancement in NMR spectra by deconvolution with compressed sensing reconstruction.

NMR spectroscopy is one of the basic tools for molecular structure elucidation. Unfortunately, the resolution of the spectra is often limited by inter-nuclear couplings. The existing workarounds often alleviate the problem by trading it for another deficiency, such as spectral artefacts or difficult sample preparation and, thus, are rarely used.

Real-time tracking of protein unfolding with time-resolved x-ray solution scattering.

The correct folding of proteins is of paramount importance for their function, and protein misfolding is believed to be the primary cause of a wide range of diseases. Protein folding has been investigated with time-averaged methods and time-resolved spectroscopy, but observing the structural dynamics of the unfolding process in real-time is challenging. Here, we demonstrate an approach to directly reveal the structural changes in the unfolding reaction.

Signaling Mechanism of Phytochromes in Solution.

Phytochrome proteins guide the red/far-red photoresponse of plants, fungi, and bacteria. Crystal structures suggest that the mechanism of signal transduction from the chromophore to the output domains involves refolding of the so-called PHY tongue. It is currently not clear how the two other notable structural features of the phytochrome superfamily, the so-called helical spine and a knot in the peptide chain, are involved in photoconversion.

MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218).

Objectives: Radiomic involves testing the associations of a large number of quantitative imaging features with clinical characteristics. Our aim was to extract a radiomic signature from axial T2-weighted (T2-W) magnetic resonance imaging (MRI) of the whole prostate able to predict oncological and radiological scores in prostate cancer (PCa).

Methods: This study included 65 patients with localized PCa treated with radiotherapy (RT) between 2014 and 2018.

Cell-Free Protein Synthesis of Small Intrinsically Disordered Proteins for NMR Spectroscopy.

Cell-free protein synthesis (CFPS) is an established method to produce recombinant proteins and has been used in a wide variety of applications. The use of CFPS has almost from the onset been favorably linked to the production of isotopically labelled proteins for NMR spectroscopy as the resulting labelling of the produced protein is defined by the chosen amino acids during reaction setup. Here we describe how to set up production and isotopic labelling of small intrinsically disordered proteins (IDPs) for NMR spectroscopy applications using an E.

Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy.

In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability.

The primary structural photoresponse of phytochrome proteins captured by a femtosecond X-ray laser.

Phytochrome proteins control the growth, reproduction, and photosynthesis of plants, fungi, and bacteria. Light is detected by a bilin cofactor, but it remains elusive how this leads to activation of the protein through structural changes. We present serial femtosecond X-ray crystallographic data of the chromophore-binding domains of a bacterial phytochrome at delay times of 1 ps and 10 ps after photoexcitation.

Transient IR spectroscopy identifies key interactions and unravels new intermediates in the photocycle of a bacterial phytochrome.

Phytochromes are photosensory proteins in plants, fungi, and bacteria, which detect red- and far-red light. They undergo a transition between the resting (Pr) and photoactivated (Pfr) states. In bacterial phytochromes, the Pr-to-Pfr transition is facilitated by two intermediate states, called Lumi-R and Meta-R.

Effects of MRI image normalization techniques in prostate cancer radiomics.

The variance in intensities of MRI scans is a fundamental impediment for quantitative MRI analysis. Intensity values are not only highly dependent on acquisition parameters, but also on the subject and body region being scanned. This warrants the need for image normalization techniques to ensure that intensity values are consistent within tissues across different subjects and visits.

Modulation of Structural Heterogeneity Controls Phytochrome Photoswitching.

Phytochromes sense red/far-red light and control many biological processes in plants, fungi, and bacteria. Although the crystal structures of dark- and light-adapted states have been determined, the molecular mechanisms underlying photoactivation remain elusive. Here, we demonstrate that the conserved tongue region of the PHY domain of a 57-kDa photosensory module of Deinococcus radiodurans phytochrome changes from a structurally heterogeneous dark state to an ordered, light-activated state.

Chromophore-Protein Interplay during the Phytochrome Photocycle Revealed by Step-Scan FTIR Spectroscopy.

Phytochrome proteins regulate many photoresponses of plants and microorganisms. Light absorption causes isomerization of the biliverdin chromophore, which triggers a series of structural changes to activate the signaling domains of the protein. However, the structural changes are elusive, and therefore the molecular mechanism of signal transduction remains poorly understood.

FemtoMAX - an X-ray beamline for structural dynamics at the short-pulse facility of MAX IV.

The FemtoMAX beamline facilitates studies of the structural dynamics of materials. Such studies are of fundamental importance for key scientific problems related to programming materials using light, enabling new storage media and new manufacturing techniques, obtaining sustainable energy by mimicking photosynthesis, and gleaning insights into chemical and biological functional dynamics. The FemtoMAX beamline utilizes the MAX IV linear accelerator as an electron source.

Metabolic engineering of Escherichia coli for enhanced arginine biosynthesis.

Background: Arginine is a high-value product, especially for the pharmaceutical industry. Growing demand for environmental-friendly and traceable products have stressed the need for microbial production of this amino acid. Therefore, the aim of this study was to improve arginine production in Escherichia coli by metabolic engineering and to establish a fermentation process in 1-L bioreactor scale to evaluate the different mutants.