Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms.

Among brain tumors, glioblastoma (GBM) is the most common and the most aggressive type, and brain metastases (BMs) occur in 20%-40% of cancer patients. Even with intensive treatment involving radiotherapy and surgery, which frequently leads to cognitive decline due to doses on healthy brain tissue, the median survival is 15 months for GBM and about 6 to 9 months for BM. Despite these treatments, GBM patients respond heterogeneously as do patients with BM.

Machine learning and deep learning prediction of patient specific quality assurance in breast IMRT radiotherapy plans using Halcyon specific complexity indices.

Introduction: New radiotherapy machines such as Halcyon are capable of delivering dose-rate of 600 monitor-units per minute, allowing large numbers of patients treated per day. However, patient-specific quality assurance (QA) is still required, which dramatically decrease machine availability. Innovative artificial intelligence (AI) algorithms could predict QA result based on complexity metrics.

Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments.

Purpose: MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality.

PET imaging and quantification of small animals using a clinical SiPM-based camera.

Background: Small-animal PET imaging is an important tool in preclinical oncology. This study evaluated the ability of a clinical SiPM-PET camera to image several rats simultaneously and to perform quantification data analysis.

Methods: Intrinsic spatial resolution was measured using 18F line sources, and image quality was assessed using a NEMA NU 4-2018 phantom.

Deep Learning Denoising Improves and Homogenizes Patient [F]FDG PET Image Quality in Digital PET/CT.

Given the constant pressure to increase patient throughput while respecting radiation protection, global body PET image quality (IQ) is not satisfactory in all patients. We first studied the association between IQ and other variables, in particular body habitus, on a digital PET/CT. Second, to improve and homogenize IQ, we evaluated a deep learning PET denoising solution (Subtle PET) using convolutional neural networks.

Artificial intelligence-based Ga-DOTATOC PET denoising for optimizing Ge/Ga generator use throughout its lifetime.

Introduction: The yield per elution of a Ge/Ga generator decreases during its lifespan. This affects the number of patients injected per elution or the injected dose per patient, thereby negatively affecting the cost of examinations and the quality of PET images due to increased image noise. We aimed to investigate whether AI-based PET denoising can offset this decrease in image quality parameters.

Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine.

Background: Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload.

Application of a new spectral deconvolution method for in vitro dosimetry in assessment of targeted alpha therapy.

Background: The improvement of in vitro assessment of targeted alpha therapy (reproducibility, comparability of experiments…) requires precise evaluation of the dose delivered to the cells. To answer this need, a previous study proposed an innovative dosimetry method based on α-spectroscopy and a specific deconvolution process to recover the spatial distribution of Pb isotopes inside in vitro culture wells. Nevertheless, although promising, the deconvolution method was time consuming and only tested for a simple isotope decay chain.

Feasibility of Imaging Small Animals on a 360° Whole-Body Cadmium Zinc Telluride SPECT Camera: a Phantom Study.

Purpose: Single-photon emission computed tomography has found an important place in preclinical cancer research. Nevertheless, the cameras dedicated to small animals are not widely available. The present study aimed to assess the feasibility of imaging small animals by a newly released 360° cadmium zinc telluride camera (VERITON, Spectrum Dynamics, Israel) dedicated to human patients.

Artificial intelligence-based PET denoising could allow a two-fold reduction in [F]FDG PET acquisition time in digital PET/CT.

Purpose: We investigated whether artificial intelligence (AI)-based denoising halves PET acquisition time in digital PET/CT.

Methods: One hundred ninety-five patients referred for [F]FDG PET/CT were prospectively included. Body PET acquisitions were performed in list mode.

The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI.

Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied.

Radioimmunotherapy for Brain Metastases: The Potential for Inflammation as a Target of Choice.

Brain metastases (BM) are frequently detected during the follow-up of patients with malignant tumors, particularly in those with advanced disease. Despite a major progress in systemic anti-cancer treatments, the average overall survival of these patients remains limited (6 months from diagnosis). Also, cognitive decline is regularly reported especially in patients treated with whole brain external beam radiotherapy (WBRT), due to the absorbed radiation dose in healthy brain tissue.

The Impact of Artificial Intelligence CNN Based Denoising on FDG PET Radiomics.

Background: With a constantly increasing number of diagnostic images performed each year, Artificial Intelligence (AI) denoising methods offer an opportunity to respond to the growing demand. However, it may affect information in the image in an unknown manner. This study quantifies the effect of AI-based denoising on FDG PET textural information in comparison to a convolution with a standard gaussian postfilter (EARL1).

Upfront F18-choline PET/CT versus Tc99m-sestaMIBI SPECT/CT guided surgery in primary hyperparathyroidism: the randomized phase III diagnostic trial APACH2.

Background: The common endocrine disorder primary hyperparathyroidism (PHPT) can be cured by surgery. Preoperative localization of parathyroid adenoma (PTA) by imaging is a prerequisite for outpatient minimally invasive parathyroidectomy (MIP). Compared to inpatient bilateral cervical exploration (BCE) which is performed if imaging is inconclusive, MIP is superior in terms of cure and complication rates and less costly.

Gated F-FDG PET/CT of the Lung Using a Respiratory Spirometric Gating Device: A Feasibility Study.

Spirometric gating devices (SGDs) can measure the respiratory signal with high temporal resolution and accuracy. The primary objective of this study was to assess the feasibility and tolerance of a gated lung PET/CT acquisition using an SGD. The secondary objective was to compare the technical quality, accuracy, and interoperability of the SGD with that of a standard respiratory gating device, Real-Time Position Management (RPM), based on measurement of vertical thoracoabdominal displacement.

The long- and short-term variability of breathing induced tumor motion in lung and liver over the course of a radiotherapy treatment.

Purpose: To evaluate the short and long-term variability of breathing induced tumor motion.

Materials And Methods: 3D tumor motion of 19 lung and 18 liver lesions captured over the course of an SBRT treatment were evaluated and compared to the motion on 4D-CT. An implanted fiducial could be used for unambiguous motion information.

Limits of radiomic-based entropy as a surrogate of tumor heterogeneity: ROI-area, acquisition protocol and tissue site exert substantial influence.

Entropy is a promising quantitative imaging biomarker for characterizing cancer imaging phenotype. Entropy has been associated with tumor gene expression, tumor metabolism, tumor stage, patient prognosis, and treatment response. Our hypothesis states that tumor-specific biomarkers such as entropy should be correlated between synchronous metastases.

Treating patients with Dynamic Wave Arc: First clinical experience.

Background And Purpose: Dynamic Wave Arc (DWA) is a system-specific noncoplanar arc technique that combines synchronized gantry-ring rotation with D-MLC optimization. This paper presents the clinical workflow, quality assurance program, and reports the geometric and dosimetric results of the first patient cohort treated with DWA.

Methods And Materials: The RayStation TPS was clinically integrated on the Vero SBRT platform for DWA treatments.

Population Pharmacokinetic Approach Applied to Positron Emission Tomography: Computed Tomography for Tumor Tissue Identification in Patients with Glioma.

Background And Aims: 18F-fluoro-ethyl-tyrosine (FET) is a radiopharmaceutical used in positron emission tomography (PET)-computed tomography in patients with glioma. We propose an original approach combining a radiotracer-pharmacokinetic exploration performed at the voxel level (three-dimensional pixel) and voxel classification to identify tumor tissue. Our methodology was validated using the standard FET-PET approach and magnetic resonance imaging (MRI) data acquired according to the current clinical practices.

Population Pharmacokinetics of Tracers: A New Tool for Medical Imaging?

Positron emission tomography-computed tomography is a medical imaging method measuring the activity of a radiotracer chosen to accumulate in cancer cells. A recent trend of medical imaging analysis is to account for the radiotracer's pharmacokinetic properties at a voxel (three-dimensional-pixel) level to separate the different tissues. These analyses are closely linked to population pharmacokinetic-pharmacodynamic modelling.

Dynamic Scintigraphy With SPECT-CT of Postoperative Salivary Leak.

This 65 year-old woman, 1 month postoperative after maxillary sinus carcinoma (pT4N0cM0) excision and reconstruction, presented with significant left facial clear fluid wound leakage. A salivary or cerebrospinal leakage was suspected. Fluid analysis, CT, and MRI were noncontributory.

Comparison of an alternative and existing binning methods to reduce the acquisition duration of 4D PET/CT.

Purpose: Respiratory motion is a source of artifacts that reduce image quality in PET. Four dimensional (4D) PET/CT is one approach to overcome this problem. Existing techniques to limiting the effects of respiratory motions are based on prospective phase binning which requires a long acquisition duration (15-25 min).

Nonsurgical giant cell tumour of the tendon sheath or of the diffuse type: are MRI or 18F-FDG PET/CT able to provide an accurate prediction of long-term outcome?

Purpose: To investigate whether MRI (RECIST 1.1, WHO criteria and the volumetric approach) or (18)F-FDG PET/CT (PERCIST 1.0) are able to predict long-term outcome in nonsurgical patients with giant cell tumour of the tendon sheath or of the diffuse type (GCT-TS/DT).

A new respiratory gating device to improve 4D PET/CT.

Purpose: Respiratory motion creates artifacts in positon emission tomography with computed tomography (PET/CT) images especially for lung tumors, and can alter diagnosis. To account for motion effects, respiratory gating techniques have been developed. However, the lack of measures strongly correlated with tumor motion limits their accuracy.

The retrospective binning method improves the consistency of phase binning in respiratory-gated PET/CT.

This study assesses the accuracy of prospective phase-gated PET/CT data binning and presents a retrospective data binning method that improves image quality and consistency. Respiratory signals from 17 patients who underwent 4D PET/CT were analysed to evaluate the reproducibility of temporal triggers used for the standard phase-based gating method. Breathing signals were reprocessed to implement retrospective PET data binning.