Liquid Biopsy versus CT: Comparison of Tumor Burden Quantification in 1065 Patients with Metastases.

Background Tumor fraction (TF) at liquid biopsy is a potential noninvasive marker for tumor burden, but validation is needed. Purpose To evaluate TF as a potential surrogate for tumor burden, assessed at contrast-enhanced CT across diverse metastatic cancers. Methods This retrospective monocentric study included patients with cancer and metastatic disease, with TF results and contemporaneous contrast-enhanced CT performed between January 2021 and January 2023.

Performance analysis of a deep-learning algorithm to detect the presence of inflammation in MRI of sacroiliac joints in patients with axial spondyloarthritis.

Objectives: To assess the ability of a previously trained deep-learning algorithm to identify the presence of inflammation on MRI of sacroiliac joints (SIJ) in a large external validation set of patients with axial spondyloarthritis (axSpA).

Methods: Baseline SIJ MRI scans were collected from two prospective randomised controlled trials in patients with non-radiographic (nr-) and radiographic (r-) axSpA (RAPID-axSpA: NCT01087762 and C-OPTIMISE: NCT02505542) and were centrally evaluated by two expert readers (and adjudicator in case of disagreement) for the presence of inflammation by the 2009 Assessment of SpondyloArthritis International Society (ASAS) definition. Scans were processed by the deep-learning algorithm, blinded to clinical information and central expert readings.

Imaging-guided prognostic score-based approach to assess the benefits of combotherapy versus monotherapy with immune checkpoint inhibitors in metastatic MSI-H colorectal cancer patients.

Background: This retrospective study determined survival responses to immune checkpoint inhibitors (ICIs), comparing mono- (mono) and combo-immunotherapy (combo) in patients with microsatellite instability-high (MSI-H) metastatic colorectal cancer (mCRC) by analyzing quantitative imaging data and clinical factors.

Methods: One hundred fifty patients were included from two centers and divided into training (n = 105) and validation (n = 45) cohorts. Radiologists manually annotated chest-abdomen-pelvis computed tomography and calculated tumor burden.

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography.

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. To frequently monitor bone health, low-cost, low-dose, and ubiquitously available diagnostic methods are highly anticipated.

Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer.

Background: Our aim was to explore the prognostic value of anthropometric parameters in a large population of patients treated with immunotherapy.

Methods: We retrospectively included 623 patients with advanced non-small cell lung cancer (NSCLC) (n=318) or melanoma (n=305) treated by an immune-checkpoint-inhibitor having a pretreatment (thorax-)abdomen-pelvis CT scan. An external validation cohort of 55 patients with NSCLC was used.

Trastuzumab deruxtecan in metastatic breast cancer with variable HER2 expression: the phase 2 DAISY trial.

The mechanisms of action of and resistance to trastuzumab deruxtecan (T-DXd), an anti-HER2-drug conjugate for breast cancer treatment, remain unclear. The phase 2 DAISY trial evaluated the efficacy of T-DXd in patients with HER2-overexpressing (n = 72, cohort 1), HER2-low (n = 74, cohort 2) and HER2 non-expressing (n = 40, cohort 3) metastatic breast cancer. In the full analysis set population (n = 177), the confirmed objective response rate (primary endpoint) was 70.

Detection and quantification of pulmonary embolism with artificial intelligence: The SFR 2022 artificial intelligence data challenge.

Purpose: In 2022, the French Society of Radiology together with the French Society of Thoracic Imaging and CentraleSupelec organized their 13th data challenge. The aim was to aid in the diagnosis of pulmonary embolism, by identifying the presence of pulmonary embolism and by estimating the ratio between right and left ventricular (RV/LV) diameters, and an arterial obstruction index (Qanadli's score) using artificial intelligence.

Materials And Methods: The data challenge was composed of three tasks: the detection of pulmonary embolism, the RV/LV diameter ratio, and Qanadli's score.

A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging.

Purpose: The purpose of this study was to develop and evaluate a deep learning model to detect bone marrow edema (BME) in sacroiliac joints and predict the MRI Assessment of SpondyloArthritis International Society (ASAS) definition of active sacroiliitis in patients with chronic inflammatory back pain.

Materials And Methods: MRI examinations of patients from the French prospective multicenter DESIR cohort (DEvenir des Spondyloarthropathies Indifférenciées Récentes) were used for training, validation and testing. Patients with inflammatory back pain lasting three months to three years were recruited.

Better than RECIST and Faster than iRECIST: Defining the Immunotherapy Progression Decision Score to Better Manage Progressive Tumors on Immunotherapy.

Purpose: The objective of the study is to propose the immunotherapy progression decision (iPD) score, a practical tool based on patient features that are available at the first evaluation of immunotherapy treatment, to help oncologists decide whether to continue the treatment or switch rapidly to another therapeutic line when facing a progressive disease patient at the first evaluation.

Experimental Design: This retrospective study included 107 patients with progressive disease at first evaluation according to RECIST 1.1.

Predicting immunotherapy outcomes in patients with MSI tumors using NLR and CT global tumor volume.

Background: Anti-PD-(L)1 treatment is indicated for patients with mismatch repair-deficient (MMRD) tumors, regardless of tumor origin. However, the response rate is highly heterogeneous across MMRD tumors. The objective of the study is to find a score that predicts anti-PD-(L)1 response in patients with MMRD tumors.

Generative adversarial networks (GAN)-based data augmentation of rare liver cancers: The SFR 2021 Artificial Intelligence Data Challenge.

Purpose: The 2021 edition of the Artificial Intelligence Data Challenge was organized by the French Society of Radiology together with the Centre National d'Études Spatiales and CentraleSupélec with the aim to implement generative adversarial networks (GANs) techniques to provide 1000 magnetic resonance imaging (MRI) cases of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC), a rare and aggressive subtype of HCC, generated from a limited number of real cases from multiple French centers.

Materials And Methods: A dedicated platform was used by the seven inclusion centers to securely upload their anonymized MRI examinations including all three cross-sectional images (one late arterial and one portal-venous phase T1-weighted images and one fat-saturated T2-weighted image) in compliance with general data protection regulation. The quality of the database was checked by experts and manual delineation of the lesions was performed by the expert radiologists involved in each center.

Toward semi-automatic biologically effective dose treatment plan optimisation for Gamma Knife radiosurgery.

Dose-rate effects in Gamma Knife radiosurgery treatments can lead to varying biologically effective dose (BED) levels for the same physical dose. The non-convex BED model depends on the delivery sequence and creates a non-trivial treatment planning problem. We investigate the feasibility of employing inverse planning methods to generate treatment plans exhibiting desirable BED characteristics using the per iso-centre beam-on times and delivery sequence.

ZeBraInspector, a platform for the automated segmentation and analysis of body and brain volumes in whole 5 days post-fertilization zebrafish following simultaneous visualization with identical orientations.

In recent years, the zebrafish has become a well-established laboratory model. We describe here the ZeBraInspector (ZBI) platform for high-content 3D imaging (HCI) of 5 days post-fertilization zebrafish eleuthero-embryos (EEs). This platform includes a mounting method based on 3D-printed stamps to create a grid of wells in an agarose cast, facilitating batch acquisitions with a fast-confocal laser scanning microscope.

Rare bioparticle detection deep metric learning.

Recent deep neural networks have shown superb performance in analyzing bioimages for disease diagnosis and bioparticle classification. Conventional deep neural networks use simple classifiers such as SoftMax to obtain highly accurate results. However, they have limitations in many practical applications that require both low false alarm rate and high recovery rate, , rare bioparticle detection, in which the representative image data is hard to collect, the training data is imbalanced, and the input images in inference time could be different from the training images.

Prediction of Early Response to Immunotherapy: DCE-US as a New Biomarker.

Purpose: The objective of our study is to propose fast, cost-effective, convenient, and effective biomarkers using the perfusion parameters from dynamic contrast-enhanced ultrasound (DCE-US) for the evaluation of immune checkpoint inhibitors (ICI) early response.

Methods: The retrospective cohort used in this study included 63 patients with metastatic cancer eligible for immunotherapy. DCE-US was performed at baseline, day 8 (D8), and day 21 (D21) after treatment onset.

Influence of Magnetic Field Strength on Magnetic Resonance Imaging Radiomics Features in Brain Imaging, an and Study.

Background: The development and clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for the identification of parameters altering radiomics reproducibility. The aim of this study was to assess the impact of magnetic field strength on magnetic resonance imaging (MRI) radiomics features in neuroradiology clinical practice.

Methods: T1 3D SPGR sequence was acquired on two phantoms and 10 healthy volunteers with two clinical MR devices from the same manufacturer using two different magnetic fields (1.

Deep learning-enabled imaging flow cytometry for high-speed Cryptosporidium and Giardia detection.

Imaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great challenges for data analysis especially when the species have similar morphologies. In this work, we report a deep learning-enabled high-throughput system for predicting Cryptosporidium and Giardia in drinking water.

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity.

Machine Learning-Based Pipeline for High Accuracy Bioparticle Sizing.

High accuracy measurement of size is essential in physical and biomedical sciences. Various sizing techniques have been widely used in sorting colloidal materials, analyzing bioparticles and monitoring the qualities of food and atmosphere. Most imaging-free methods such as light scattering measure the averaged size of particles and have difficulties in determining non-spherical particles.

Myocardial Perfusion Simulation for Coronary Artery Disease: A Coupled Patient-Specific Multiscale Model.

Patient-specific models of blood flow are being used clinically to diagnose and plan treatment for coronary artery disease. A remaining challenge is bridging scales from flow in arteries to the micro-circulation supplying the myocardium. Previously proposed models are descriptive rather than predictive and have not been applied to human data.

Clarification of Definitions of Hyperprogressive Disease During Immunotherapy for Non-Small Cell Lung Cancer.

Importance: Hyperprogressive disease (HPD) is an aggressive pattern of progression reported for patients treated with programmed cell death 1 (PD-1)/programmed cell death 1 ligand (PD-L1) inhibitors as a single agent in several studies. However, the use of different definitions of HPD introduces the risk of describing different tumoral behaviors.

Objective: To assess the accuracy of each HPD definition to identify the frequency of HPD and the association with poorer outcomes of immune-checkpoint inhibitor (ICI) treatment in patients with advanced non-small cell lung cancer (NSCLC) and to provide an optimized and homogenized definition based on all previous criteria for identifying HPD.