Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases

ABSTRACT: Background Finding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase that cata- lyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associ- ated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance. Methods We trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73High vs. CD73Low ) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the perfor- mance on a hold-out test set. Results TabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman’s ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73High vs rad-CD73 Low patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020)

The VLT-FLAMES Tarantula Survey: XXX. Red stragglers in the clusters Hodge 301 and SL 639

Aims: We estimate physical parameters for the late-type massive stars observed as part of the VLT-FLAMES Tarantula Survey (VFTS) in the 30 Doradus region of the Large Magellanic Cloud (LMC). Methods: The observational sample comprises 20 candidate red supergiants (RSGs) which are the reddest ((B − V) > 1 mag) and brightest (V < 16 mag) objects in the VFTS. We use optical and near-infrared (near-IR) photometry to estimate their temperatures and luminosities, and introduce the luminosity–age diagram to estimate their ages. Results: We derive physical parameters for our targets, including temperatures from a new calibration of (J − Ks)0 colour for luminous cool stars in the LMC, luminosities from their J-band magnitudes (thence radii), and ages from comparisons with current evolutionary models. We show that interstellar extinction is a significant factor for our targets, highlighting the need to take it into account in the analysis of the physical parameters of RSGs. We find that some of the candidate RSGs could be massive AGB stars. The apparent ages of the RSGs in the Hodge 301 and SL 639 clusters show a significant spread (12–24 Myr). We also apply our approach to the RSG population of the relatively nearby NGC 2100 cluster, finding a similarly large spread. Conclusions We argue that the effects of mass transfer in binaries may lead to more massive and luminous RSGs (which we call “red stragglers”) than expected from single-star evolution, and that the true cluster ages correspond to the upper limit of the estimated RSG ages. In this way, the RSGs can serve as a new and potentially reliable age tracer in young star clusters. The corresponding analysis yields ages of 24-3+5 Myr for Hodge 301, 22-5+6 Myr for SL 639, and 23-2+4 Myr for NGC 2100

Wideband Analysis of Mutual Coupling Compensation Methods

The performance of various mutual coupling estimation and compensation methods is quantified and compared for wideband operation. Their main limitations and the required procedures to estimate the coupling matrix are outlined. It is determined that only the full-wave method is accurate at all frequencies. The performance tradeoffs associated with the other methods are described. General guidelines based on the electrical size and separation of the array elements are formulated for each method. The results can be valuable for both wideband and narrowband receiving antenna arrays

La surveillance en exploitation des enceintes de confinement et des aéroréfrigérants à tirage naturel du parc nucléaire d’EDF

EDF surveille l’ensemble des ouvrages de son Parc de production électronucléaire pendant toute leur durée de vie. Dans cet article, on s’intéresse à la surveillance des enceintes de confinement des réacteurs à eau sous pression (REP) et des coques d’aéroréfrigérant. On présente les objectifs de la surveillance de ces ouvrages, quelques technologies ainsi que les traitements associés à certaines mesures. On évoque également l’organisation d’EDF pour préparer l’avenir et développer de nouveaux outils et méthodes, notamment au travers de partenariats

Prediction of CD3 T-cell infiltration status in colorectal liver metastases: a radiomics-based imaging biomarker

Colorectal cancer (CRC) continues to be a leading cause of cancer-related death in the developed world due to metastatic progression of the disease. In an effort to improve the understanding of tumor biology and developing prognostic tools, it was found that CD3+ tumor infiltrating lymphocytes (TIL) had a very strong prognostic value in primary CRC as well as in colorectal liver metastases (CLM). Quantification of TILs remains labor intensive and requires tissue samples, hence being of limited use in the pre-operative period or in the context of non-operable disease. Computed tomography (CT) images however are widely available for patients with CLM. In this study, we propose a pipeline to predict CD3 T-cell infiltration in CLM from pre-operative CT images. Radiomic features were extracted from 58 automatically segmented CLM lesions. Subsequently, dimensionality reduction was performed by training an autoencoder (AE) on the full feature set. We then used AE bottleneck embeddings to predict CD3 T-cell density, stratified into two categories: CD3hi and CD3low. For this, we implemented a 1D convolutional neural network (1D-CNN) and compared its performance against five machine learning models using 5-fold cross-validation. Results showed that the proposed 1D-CNN outperformed the other trained models achieving a mean accuracy of 0.69 (standard deviation [SD], 0.01) and a mean area under the receiver operating curve (AUROC) of 0.75 (SD, 0.02) on the validation set. Our findings demonstrate a relationship between CT radiomic features and CD3 tumor infiltration status with the potential of noninvasively determining CD3 status from preoperative CT images