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Several studies have suggested a strong relationship between the number of peripherally inserted central catheter (PICC) lumens and the risk of complications. The purpose of this study is to evaluate the impact of the intervention of a clinical pharmacist (CP) on the number of lumens of PICC inserted and assess PICC-related complications.This prospective monocentric study included all consecutive patients from 5 different units who underwent PICC insertions from September 2017 to March 2018. In the intervention group, the CP validated the pertinence of each PICC request according to the patients' records, choice of device (single or double lumen), and incompatibilities between treatments. The control group consisted of patients who underwent PICC insertions without a CP intervention. Complications were prospectively recorded up to PICC removal. Multivariate analyses adjusted for sex, age, and PICC line duration were performed.In the intervention group, 207 PICCs were inserted, 81.2% (n = 168) were single-lumen devices and 18.8% (n = 39) were double-lumen devices. In the control group (n = 77), the use of single-lumen PICCs was significantly lower (n = 48, 62.3%; P = 0.002). After intervention, the overall complication incidence rate decreased from 4.42 to 3.23 per 1000 catheter days ( P = 0.082). Considering the overall population, 216 single-lumen PICCs were inserted with significantly fewer complications than double-lumens (respectively n = 16, 16/216 = 7.4%, versus n = 15, 15/68 = 22.1%, P = 0.002). The adjusted odds ratio of double-lumen PICC was 3.83 (95% confidence interval, 1.46-10.07; P = 0.007).Our study showed the intervention of a CP in the PICC insertion process could increase the use of single-lumen PICCs and tended to reduce associated complications.

Ultra-low dose computed tomography (ULD-CT) was shown to be a good alternative to digital radiographs in various locations. This study aimed to assess the diagnostic sensitivity and specificity of ULD-CT versus digital radiographs in patients consulting for extremity traumas in emergency room.Digital radiography and ULD-CT scan were performed in patients consulting at the emergency department (February-August 2018) for extremity traumas. Fracture detection was evaluated retrospectively by two blinded independent radiologists. Sensitivity and specificity were evaluated using best value comparator (BVC) and a Bayesian latent class model (LCM) approaches and clinical follow-up. Image quality, quality diagnostic and diagnostic confidence level were evaluated (Likert scale). The effective dose received was calculated.Seventy-six consecutive patients (41 men, mean age: 35.2±13.2 years), with 31 wrists/hands and 45 ankles/feet traumas were managed by emergency physicians. According to clinical data, radiography had 3 false positive and 10 false negative examinations, and ULD-CT, 2 of each. Radiography and ULD-CT specificities were similar; sensitivities were lower for radiography, with BVC and Bayesian. With Bayesian, ULD-CT and radiography sensitivities were 90% (95% CI: 87-93%) and 76% (95% CI: 71-81%, P0.0001) and specificities 96% (95% CI: 93-98%) and 93% (95% CI: 87-97%, P=0.84). The inter-observer agreement was higher for ULD-CT for all subjective indexes. The effective dose for ULD-CT and radiography was 0.84±0.14 and 0.58±0.27 µSv (P=0.002) for hand/wrist, and 1.50±0.32 and 1.44±0.78 µSv (P=NS) for foot/ankle.With an effective dose level close to radiography, ULD-CT showed better detection of extremities fractures in the emergency room and may allow treatment adaptation. Further studies need to be performed to assess impact of such examination in everyday practice.ClinicalTrials.gov Identifier: NCT04832490.

The use of peripherally inserted central catheters (PICCs) has increased in cancer patients. This study aimed to compare the incidence of PICC-related bloodstream infections (PICCR-BSIs) in cancer patients treated with chemotherapy and in noncancer patients. We performed a secondary analysis from a retrospective, single-center, observational cohort. The PICCR-BSI incidence rates in cancer and noncancer patients were compared after 1:1 propensity-score matching. Then, the factors associated with PICCR-BSI were assessed in a Cox model. Among the 721 PICCs (627 patients) included in the analysis, 240 were placed in cancer patients for chemotherapy and 481 in noncancer patients. After propensity-score matching, the PICCR-BSI incidence rate was 2.6 per 1000 catheter days in cancer patients and 1.0 per 1000 catheter days in noncancer patients (p < 0.05). However, after adjusting for variables resulting in an imbalance between groups after propensity-score matching, only the number of PICC lumens was independently associated with PICCR-BSI (adjusted hazard ratio 1.81, 95% confidence interval: 1.01–3.22; p = 0.04). In conclusion, the incidence rate of PICCR-BSI is higher in cancer patients treated with chemotherapy than in noncancer patients, but our results also highlight the importance of limiting the number of PICC lumens in such patients.

The spleen is the most commonly injured organ in blunt abdominal trauma. Its management depends on hemodynamic stability. According to the American Association for the Surgery of Trauma-Organ Injury Scale (AAST-OIS ≥ 3), stable patients with high-grade splenic injuries may benefit from preventive proximal splenic artery embolization (PPSAE). This ancillary study, using the SPLASH multicenter randomized prospective cohort, evaluated the feasibility, safety, and efficacy of PPSAE in patients with high-grade blunt splenic trauma without vascular anomaly on the initial CT scan. All patients included were over 18 years old, had high-grade splenic trauma (≥AAST-OIS 3 + hemoperitoneum) without vascular anomaly on the initial CT scan, received PPSAE, and had a CT scan at one month. Technical aspects, efficacy, and one-month splenic salvage were studied. Fifty-seven patients were reviewed. Technical efficacy was 94% with only four proximal embolization failures due to distal coil migration. Six patients (10.5%) underwent combined embolization (distal + proximal) due to active bleeding or focal arterial anomaly discovered during embolization. The mean procedure time was 56.5 min (SD = 38.1 min). Embolization was performed with an Amplatzer™ vascular plug in 28 patients (49.1%), a Penumbra occlusion device in 18 patients (31.6%), and microcoils in 11 patients (19.3%). There were two hematomas (3.5%) at the puncture site without clinical consequences. There were no rescue splenectomies. Two patients were re-embolized, one on Day 6 for an active leak and one on Day 30 for a secondary aneurysm. Primary clinical efficacy was, therefore, 96%. There were no splenic abscesses or pancreatic necroses. The splenic salvage rate on Day 30 was 94%, while only three patients (5.2%) had less than 50% vascularized splenic parenchyma. PPSAE is a rapid, efficient, and safe procedure that can prevent splenectomy in high-grade spleen trauma (AAST-OIS) ≥ 3 with high splenic salvage rates.

To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for different clinical indications.Acquisitions on phantoms were performed at 5 dose levels (CTDIFrom Standard to Smoother levels, noise magnitude and average NPS spatial frequency decreased and the detectability (d') of all simulated lesions increased. For both inserts, TTF values were similar for all three AI-DLR levels from 13 to 6 mGy but decreased from Standard to Smoother levels at 1.8 mGy. Compared to the i4 used in clinical practice, d' values were higher using the Smoother and Smooth levels and close for the Standard level. For all dose levels, except at 1.8 mGy, radiologists considered images satisfactory for clinical use for the 3 levels of AI-DLR, but rated images too smooth using the Smoother level.Use of the Smooth and Smoother levels of AI-DLR reduces the image noise and improves the detectability of lesions and spatial resolution for standard and low-dose levels. Using the Smooth level is apparently the best compromise between the lowest dose level and adequate image quality.• Evaluation of the impact of a new artificial intelligence deep-learning reconstruction (AI-DLR) on image quality and dose compared to a hybrid iterative reconstruction (IR) algorithm. • The Smooth and Smoother levels of AI-DLR reduced the image noise and improved the detectability of lesions and spatial resolution for standard and low-dose levels. • The Smooth level seems the best compromise between the lowest dose level and adequate image quality.

The purpose of this study was to compare peak skin dose (PSD) and dose map calculated by Dose-Tracking-System® (DTS) software and measured with radiochromic films in patients undergoing abdominopelvic embolization.The PSD measured by radiochromic films (PSDA total of 40 patients were included. There were 32 men and 8 women with a mean age of 73.0 ± 14.6 (SD) years (age range: 30-92 years). Median PSDReal-time DTS software allows computing PSD with high accuracy and generating adequate dose map. PSD

International audience; Purpose: The purpose of this study was to report the initial results of the declaration of care-related adverse events (CRAEs) in radiology in the French National Authority for Health (HAS) database for accreditation of radiological medical teams.Materials and methods: Between October 2018 and December 2020, 48 radiological teams (32 teams in 2019 and 16 teams in 2020; 471 registered radiologists) signed up to the team accreditation process, a system supported by the HAS. Reports of the CRAEs in radiology started in September 2019 after the team registration phase.Results: Among the 89 CRAEs reported, 28 (31%) were targeted as interventional radiology, 27 (30%) as linked to contrast media and 11 (12%) as related to MRI care; the 23 other CRAEs reported included five defaults in the transmission of requests or results, five delays in diagnosis or treatment, four patient-identity monitoring events, four diagnostic radiology complications, four radiation protection events and one patient information problem. The severity was rated as "minor" for 53% of CRAEs and as "serious to critical" or "catastrophic" for 8% and 9% of CRAEs, respectively. They were preventable or probably preventable in 84% of all events.Conclusion: These early results of this nation-wide CRAEs declaration database show the diversity of all CRAEs and their causes in radiological practice in France, and provide a global vision of areas for improvement of the quality of care in radiology. This should convince other radiologists to declare CRAEs and allow, in time, the production of recommendations and patient safety solutions as to limit the risks associated with radiological care.

To compare the spectral performance of three rapid kV switching dual-energy CT (DECT) systems on virtual monoenergetic images (VMIs) at low-energy levels on abdominal imaging.A multi-energy phantom was scanned on three DECT systems equipped with three different gemstone spectral imaging (GSI) platforms: GSI (1st generation, GSI-1st), GSI-Pro (2nd generation, GSI-2nd ), and GSI-Xtream (3rd generation, GSI-3rd). Acquisitions on the phantom were performed with a CTDIFor all GSI platforms, the noise magnitude decreased from 40 to 70 keV, and using AV50 compared to FBP. The average NPS spatial frequency (fDifferences in image quality were found between the GSI platforms for VMIs at low keV. The new DLR algorithm on the GSI-3rd platform reduced noise and improved spatial resolution and detectability without changing the noise texture for VMIs at low keV. The choice of the best energy level in VMIs depends on the platform and the reconstruction algorithm.

Objectives To evaluate the quality of virtual monochromatic images (VMIs) from spectral photon-counting CT (SPCCT) and two energy-integrating detector dual-energy CT (EID-DECT) scanners from the same manufacturer, for the coronary lumen. Methods A 21-cm section of the Mercury v4.0 phantom was scanned using a cardiac CT protocol. VMIs from 40 to 90 keV were reconstructed using high-resolution (HR) parameters for EID-DECT and SPCCT (CB and HRB kernels at 0.67 mm slice thickness, respectively). Ultra-high-resolution (UHR) parameters were used in addition to SPCCT (detailed-2 kernel, 0.43 mm slice thickness). Noise-power-spectrum (NPS), task-based transfer function (TTF), and detectability index (d′) were computed for 2-mm-diameter lumen detection. In consensus, two radiologists analyzed the quality of the images from 8 patients who underwent coronary CTA on both CT systems. Results For all keV images, fpeak, f50, and d′ were higher with SPCCT. The fpeak and f50 were higher with UHR-SPCCT with greater noise and lower d′ compared to those of the HR-SPCCT images. Noise magnitude was constant for all energy levels (keV) with both systems, and lower with HR images, and d′ decreased as keV decreased. Subjective analysis showed greater lumen sharpness and overall quality for HR and UHR-SPCCT images using all keV, with a greater difference at low keV compared to HR-EID-DECT images. Conclusion HR and UHR-SPCCT images gave greater detectability of the coronary lumen for 40 to 90 keV VMIs compared to two EID-DECT systems, with benefits of higher lumen sharpness and overall quality. Key Points • Compared with 2 dual-energy CT systems, spectral photon-counting CT (SPCCT) improved spatial resolution, noise texture, noise magnitude, and detectability of the coronary lumen. • Use of ultra-high-resolution parameters with SPCCT improved spatial resolution and noise texture and provided high detectability of the coronary lumen, despite an increase in noise magnitude. • In eight patients, radiologists found greater overall image quality with SPCCT for all virtual monochromatic images with a greater difference at low keV, compared with dual-energy CT systems.

Objectives: To evaluate the image quality (IQ) of a spectral photon-counting CT (SPCCT) using filtered back projection (FBP) and hybrid iterative reconstruction (IR) algorithms (iDose4), in comparison with a dual-layer CT (DLCT) system, and to choose the best image quality according to the IR level for SPCCT. Methods: Two phantoms were scanned using a standard lung protocol (120 kVp, 40 mAs) with SPCCT and DLCT systems. Raw data were reconstructed using FBP and 9 iDose4 levels (i1/i2/i3/i4/i5/i6/i7/i9/i11) for SPCCT and 7 for DLCT (i1/i2/i3/i4/i5/i6/i7). Noise power spectrum and task-based transfer function (TTF) were computed. Detectability index (d′) was computed for detection of 4 mm ground-glass nodule (GGN) and solid nodule. Two chest radiologists performed an IQ evaluation (noise/nodule sharpness/nodule conspicuity/overall IQ) in consensus, and chose the best image for SPCCT. Results: Noise magnitude was −47% ± 2% lower on average with SPCCT than with DLCT for iDose4 range from i1 to i6. Average NPS spatial frequencies increased for SPCCT in comparison with DLCT. TTF also increased, except for the air insert with FBP, and i1/i2/i3. Higher detectability was found for SPCCT for both GGN and solid nodules. IQ for both types of nodule was rated consistently higher with SPCCT than with DLCT for the same iDose4 level. For SPCCT and both nodules, the scores for noise and conspicuity improved with increasing iDose4 level. iDose4 level 6 provided the best subjective IQ for both types of nodule. Conclusions: Higher IQ for GGN and solid nodules was demonstrated with SPCCT compared with DLCT with better detectability using iDose4. Key Points: Using spectral photon-counting CT compared with dual-layer CT, noise magnitude was reduced with improvements in spatial resolution and detectability of ground-glass nodules and solid lung nodules.As the iDose4level increased, noise magnitude was reduced and detectability of ground-glass and solid lung nodules was better for both CT systems.For spectral photon-counting CT imaging, two chest radiologists determined iDose4level 6 as the best image quality for detecting ground-glass nodules and solid lung nodules.

Purpose The purpose of this study was to assess the impact of advanced modeled iterative reconstruction (ADMIRE) algorithm and dose levels on the accuracy of Hounsfield unit (HU) measurement, image noise and contrast-to-noise ratio (CNR) in virtual monochromatic images (VMIs) with low iodine concentrations, and the accuracy of iodine quantification. Materials and methods A CT phantom was scanned with dual-source CT using abdomen-pelvis examination parameters at four dose levels: 5, 8, 11 and 20 mGy. Images were reconstructed using filtered-back projection (FBP) and ADMIRE levels 3 and 5 (A3-A5). HU accuracy was assessed calculating the root-mean-square deviation (RMSDHU). Image noise and CNR were computed on VMIs at 40/50/60/70 keV for 4 iodine inserts with 0.5, 1, 2 and 5 mg/mL concentrations. Accuracy of iodine quantification was assessed by the RMSDiodine and iodine bias (IB). Results The RMSDHU decreased significantly as the dose levels increased compared to 5 mGy, except for 8 mGy with A3 (P = 0.380) and with A5 level (P = 0.945). Noise increased by 63.0 ± 3.0 (standard deviation [SD])% from 20 mGy to 5 mGy. Noise decreased significantly by -53.8 ± 0.9 (SD) % with A5 compared to FBP. The CNR decreased by -43.1 ± 6.5 (SD)% from 20 mGy to 5 mGy. It increased using ADMIRE, and as the ADMIRE levels increased. The RMSDiodine and IB decreased as the dose level increased, and this was similar for all reconstruction types. Conclusion ADMIRE strongly improves image quality in VMIs and slightly improves HU accuracy but does not affect the accuracy of iodine quantification.

BACKGROUND: New reconstruction algorithms based on deep learning have been developed to correct the image texture changes related to the use of iterative reconstruction algorithms. The purpose of this study was to evaluate the impact of a new deep learning image reconstruction [Advanced intelligent Clear-IQ Engine (AiCE)] algorithm on image-quality and dose reduction compared to a hybrid iterative reconstruction (AIDR 3D) algorithm and a model-based iterative reconstruction (FIRST) algorithm. METHODS: Acquisitions were carried out using the ACR 464 phantom (and its body ring) at six dose levels (volume computed tomography dose index 15/10/7.5/5/2.5/1 mGy). Raw data were reconstructed using three levels (Mild/Standard/Strong) of AIDR 3D, of FIRST and AiCE. Noise-power-spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index was computed to model the detection of a small calcification (1.5-mm diameter and 500 HU) and a large mass in the liver (25-mm diameter and 120 HU). RESULTS: NPS peaks were lower with AiCE than with AIDR 3D (−41%±6% for all levels) or FIRST (−15%±6% for Strong level and −41%±11% for both other levels). The average NPS spatial frequency was lower with AICE than AIDR 3D (−9%±2% using Mild and −3%±2% using Strong) but higher than FIRST for Standard (6%±3%) and Strong (25%±3%) levels. For acrylic insert, values of TTF at 50 percent were higher with AICE than AIDR 3D and FIRST, except for Mild level (−6%±6% and −13%±3%, respectively). For bone insert, values of TTF at 50 percent were higher with AICE than AIDR 3D but lower than FIRST (−19%±14%). For both simulated lesions, detectability index values were higher with AICE than AIDR 3D and FIRST (except for Strong level and for the small feature; −21%±14%). Using the Standard level, dose could be reduced by −79% for the small calcification and −57% for the large mass using AICE compared to AIDR 3D. CONCLUSIONS: The new deep learning image reconstruction algorithm AiCE generates an image-quality with less noise and/or less smudged/smooth images and a higher detectability than the AIDR 3D or FIRST algorithms. The outcomes of our phantom study suggest a good potential of dose reduction using AiCE but it should be confirmed clinically in patients.

BACKGROUND: Many computed tomography (CT) navigation systems have been developed to help radiologists improve the accuracy and safety of the procedure. We evaluated the accuracy of one CT computer-assisted guided procedure with different reduction dose protocols. METHODS: A total of 128 punctures were randomly made by two operators on two different anthropomorphic phantoms. The tube voltage was fixed to 100 kVp. Tube currents (mAs) were defined to obtain 4 dose levels: 180 mAs (D1.00), 90 mAs (D0.50), 45 mAs (D0.25) and 15 mAs (D0.10) with respective volume CT dose index (CTDIvol) of 7.02, 3.52, 1.75 and 0.59 mGy. The raw data were reconstructed using level 2 of advanced model-based iterative reconstruction (ADMIRE) (A2) for D1.00, A3 for D0.50, A4 for D0.25 and A5 for D0.10. Two 12-mm targets per phantom were selected. The mean Euclidean distance (EuD) between the tip of the needle and the isocenter of the target was measured for each puncture. The different measures were compared by paired Student’s t-tests. RESULTS: The mean EuD was 7.0±3.1 mm for the 128 punctures performed. Regardless of which phantom was considered, no significant difference in accuracy occurred between the 4 dose levels, which were 7.1±3.5 mm for D1.00; 7.1±3.1 mm for D0.50; 7.2±3.0 mm for D0.25 and 6.6±2.6 mm for D0.10. CONCLUSIONS: Abdominal CT-guided procedures, using computer-assisted navigation and iterative reconstruction algorithms, allow precise punctures on anthropomorphic phantoms with a dose reduction of −92% compared to a standard protocol.

International audience; Purpose: the purpose of this multicenter study was to evaluate the clinical success at three months of prostate artery embolization (PAE), assess PAE safety in centers with various experiences and identify factors associated with PAE success.Patients and methods: this multicenter, retrospective study included patients who underwent PAE for lower urinary tract symptoms (LUTS) including those with indwelling urinary catheter. PAE clinical success was defined as either 25% improvement of the International Prostate Symptom Score (IPSS) or 1-point improvement of quality of life (QoL) score, or catheter removal at three months. Multivariable analyses were performed using a logistic regression adjusted on patient variables, technical parameters and center experience in PAE.Results: a total of 383 men (mean age, 68.4±9.7 [standard deviation] years; range: 46-94) with LUTS, including 99 (25.8%) patients with indwelling urinary catheter, were included in seven centers from January 2017 to March 2019. Five patients reported major complications (1.3%), three (0.8%) penile ulceration, three (0.8%) acute urinary retention, one (0.3%) prostatic abscess, and 56 (14.6%) minor complications. Follow up data were available for 271 patients (center 1: n= 159; other centers: n= 112). Clinical success was reported in 232 patients (85.6%). In multivariable analyses, presence of cardiovascular comorbidities (diabetes, stroke history, myocardial infarction and lower limb artery disease) was the single independent variable inversely associated with PAE clinical success (odds ratio= 0.396; 95% confidence interval: 0.17-0.91; P= 0.029). There was no center effect.Conclusion: our results show that PAE is safe and effective in centers with various PAE experiences. Cardiovascular comorbidity is the single independent variable associated with PAE failure.

Background: This study aimed to retrospectively analyze dosimetric indicators recorded since 2012 for thoracic, abdominal or pelvic embolizations to evaluate the contribution of new tools and technologies in dose reduction. Methods: Dosimetric indicators (dose area product (DAP) and air kerma (AK)) from 1449 embolizations were retrospectively reviewed from August 2012 to March 2022. A total of 1089 embolizations were performed in an older fixed C-Arm system (A1), 222 in a newer fixed C-Arm system (A2) and 138 in a 4DCT system (A3). The embolization procedures were gathered to compare A1, A2 and A3. Results: DAP were significantly lower with A2 compared to A1 for all procedures (median −50% ± 5%, p < 0.05), except for uterine elective embolizations and gonadal vein embolization. The DAP values were significantly lower with A3 than with A1 (p < 0.001). CT scan was used for guidance in 90% of embolization procedures. Conclusions: The last C-Arm technology allowed a median reduction of 50% of the X-ray dose. The implementation of a CT scan inside the IR room allowed for more precise 3D-guidance with no increase of the dose delivered.

Targeting neovessels in chronic tendinopathies has emerged as a new therapeutic approach and several embolization agents have been reported. The aim of this study was to investigate the feasibility of embolization with different agents in a porcine model of patellar tendinopathy and evaluate their safety and efficacy. Eight 3-month-old male piglets underwent percutaneous injection of collagenase type I to induce patellar tendinopathies (n = 16 tendons). They were divided into four groups (2 piglets, 4 tendons/group): the control group, 50–100 µm microspheres group, 100–300 µm microspheres group, and the Imipenem/Cilastatin (IMP/CS) group. Angiography and embolization were performed for each patellar tendon on day 7 (D7). The neovessels were evaluated visually with an angiography on day 14. The pathological analysis assessed the efficacy (Bonar score, number of neovessels/mm2) and safety (off-target persistent cutaneous ischemic modifications and presence of off-target embolization agents). The technical success was 92%, with a failed embolization for one tendon due to an arterial dissection. Neoangiogenesis was significantly less important in the embolized groups compared to the control group angiographies (p = 0.04) but not with respect to histology (Bonar score p = 0.15, neovessels p = 0.07). Off-target cutaneous embolization was more frequently depicted in the histology of the 50–100 µm microspheres group (p = 0.02). Embolization of this animal model with induced patellar tendinopathy was technically feasible with different agents and allowed assessing the safety and efficacy of neovessel destruction. Particles smaller than 100 µm seemed to be associated with more complications.

The purpose of this study was to assess the impact of the new artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and radiation dose compared with iterative reconstruction algorithm in lumbar spine computed tomography (CT) examination.Acquisitions on phantoms were performed using a tube current modulation system for four DoseRight Indexes (DRI) (i.e., 26/23/20/15). Raw data were reconstructed using the Level 4 of iDoseThe Noise magnitude was lower with AI-DLR than i4 and decreased from Standard to Smooth (-31 ± 0.1 [SD]%) and Smooth to Smoother (-48 ± 0.1 [SD]%). The average NPS spatial frequency was similar with i4 (0.43 ± 0.01 [SD] mmUsing Smooth and Smoother levels with CT allows a significant dose reduction (up to 72%) with a high detectability of lytic and sclerotic bone lesions and a clinical overall image quality.

Recently, computed tomography (CT) manufacturers have developed deep-learning-based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels.To assess the impact of an artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications.Acquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU-41 phantoms were performed at five dose levels (CTDIFrom Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: -66.3% ± 0.5% for mediastinal images and -63.1% ± 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: -35.3% ± 2.2% for mediastinal images and -13.3% ± 2.2% for parenchymal images), and the detectability (d') of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low-contrast polyethylene insert and the opposite for the high-contrast air insert. Compared to the i4 used in clinical practice, d' values were higher using Smoother (mean for all dose levels: 338.7% ± 29.4%), Smooth (103.4% ± 11.2%), and Standard (34.1% ± 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% ± 53.2% for GGO and 136.9% ± 1.6% for HCP) and Smooth (36.4% ± 22.1% and 24.1% ± 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required.With AI-DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.

Ischaemic conditioning in rectal cancer surgery by preoperative embolization of the inferior mesenteric artery is feasible and safe. It seems to protect from ischaemic stress during the surgical procedure.

The purpose of this study was to analyze the dose to the uterus (UD) calculated for pregnant women per computed tomography (CT) acquisition and per CT examination in our Institution.Consecutive pregnant women who underwent CT examination from June 2014 to February 2022 and for whom UD calculation was performed by a medical physicist were retrospectively included. UDs were computed per CT acquisition using the CT Expo 2.4 software and were summed up to obtain the total UD per CT examination. The CTDIA total of 256 pregnant women with a mean age of 29.4 ± 5.5 (SD) years (range: 18-48) at 24.5 ± 10.4 (SD) weeks of amenorrhea (range: 1-40) were included. UDs were computed for 339 CT acquisitions. The CTDIGreatest UDs during CT examinations are observed when the pelvis is directly exposed to X-rays. With current dose levels and in optimized practices, UDs per CT acquisition and CT examination are always below 100 mGy. UD calculations cannot be performed for CT examinations that do not directly expose the pelvis (i.e., those1 mGy).

There is an urgent clinical need to identify patients likely to benefit from immune checkpoint inhibitor ICI treatment. Approaches available in the clinic today, such as PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB), are insufficient for this task, in part as differences in microenvironments expressed by individual tumors may lead to heterogeneous response patterns. Recent efforts exploring the utility of quantitative imaging (radiomic) biomarkers to predict response to ICIs have shown promise to provide a more accurate and scalable method. In contrast to previously published models, our work focuses on generalizable models for predicting individual lesion-level as well as patient-level response at 3-month follow-up per RECIST criteria, using a large multi-institutional “real-world” dataset. The models combine radiomics features with demographic, molecular, and laboratory values routinely available in patients’ electronic medical records. We analyzed radiomic characteristics of 6,295 primary and metastatic lesions from 1,206 metastatic NSCLC patients treated with anti-PD-1/anti-PD-L1 ICIs from 8 institutions across the US and Europe. Patients with unavailable PD-L1 IHC, imaging follow-up, or with oncogenic driver mutations were excluded from analysis, resulting in a total dataset of 766 subjects randomly assigned to training (N=514) and validation sets (N=252). Using gradient-boosted decision tree algorithms, we developed a multi-modal predictive model to identify patients responding to ICI therapy at 3-months and evaluated its performance against an imaging-only CT radiomics model and the clinical standard of care, biopsy-based PD-L1 IHC. The multi-modal model contains CT radiomic features capturing lesion heterogeneity and spicularity, patient demographics, PD-L1 TPS, and tumor burden volume in the lung, lymph nodes, and the liver. Under the two-tailed DeLong test, the multi-modal model demonstrated statistically significant benefit over the current standard of care (PD-L1 IHC) in predicting multi-lesion 3-month response: 0.81 (P=.005) area under the receiver operating characteristic curve (ROC-AUC) in first-line ICI monotherapy patients, 0.72 (P=.044) in all-lines ICI monotherapy, and 0.71 (P=.025) in all-lines ICI-chemotherapy combination. The imaging-only model demonstrated predictive performance comparable to PD-L1 IHC: 0.71 (P=.226), 0.61 (P=.905), 0.62 (P=.674) on the same cohorts respectively. A multi-modal CT radiomics-based approach demonstrated predictive accuracy benefit over the current clinical standard and may provide an opportunity for more personalized patient management, such as risk-based escalation/de-escalation of concurrent chemotherapy in NSCLC patients. We will evaluate this methodology in prospective studies. Citation Format: Ravi B. Parikh, Petr Jordan, Rita J. Ciaravino, Ryan A. Beasley, Arpan A. Patel, Dwight H. Owen, Arya Amini, Brendan D. Curti, Ray Page, Aurelie Swalduz, Jean-Paul Beregi, Jan Chrusciel, Eric Snyder, Pritam Mukherjee, Heather M. Selby, Soohee Lee, Roshanthi Weerasinghe, Shwetha Pindikuri, Jakob B. Weiss, Andrew L. Wentland, Anish Kirpalani, An Liu, Olivier Gevaert, George Simon, Hugo JWL Aerts. Multi-institutional validation of a radiomics-based artificial intelligence method for predicting response to PD-1/PD-L1 immune checkpoint inhibitor (ICI) therapy in stage IV NSCLC. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5618.

[EN] The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R & D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology., CHAIMELEON has been funded by as a Horizon 2020 project (RIA, topic DT-TDS-05-2020-AI for Health Imaging; call SC1-FA-DTS-2019-1, under Grant Agreement No. 952172)

Background: Percutaneous cryoablation with liquid nitrogen is a new technique being used in the treatment of some malignant tumors. Our objective was to assess its feasibility in the ablation of tumor lesions of various sizes and locations. Methods: This retrospective, monocentric study included all consecutive patients who underwent percutaneous cryoablation with liquid nitrogen between December 2019 and March 2021. Cryoablation was performed using 10G or 13G cryoprobes. The ablation volume was measured on post-treatment CT or MRI. Results: 22 patients (24 lesions) were included, 16 of whom were men (73%), while median age was 66 years. The lesions were located in the bone (42%), kidney (29%), soft tissue (17%), lung (8%), or liver (4%). It was feasible in all tumor locations and produced median ablation zones 25 mm in width and 35 mm in length, with a 23 min median freezing time. Freezing duration was correlated with the ice volume (p Spearman = 0.02), but not with the ablation volume (p = 0.11). The average difference between the ablation zone and ice ball sizes were −6.4 mm in width and −7.7 mm in length. Both ice and ablation volumes were larger when using the 10G probe as compared to when the 13G was used. No complications were reported. Discussion: We showed that this technique was safe and feasible in all organs tested. The freezing duration was correlated with the ice ball size, but not with the ablation zone.