Efficacy of combinational treatment versus nicotinamide monotherapy in the prevention of ultraviolet radiation-induced skin cancer.

INTRODUCTION: Ultraviolet radiation (UVR) is the primary risk factor for keratinocyte carcinomas (KC). Oral supplementation with nicotinamide (NAM; NAM-mono) is reported to reduce the formation of new KCs. NAM's photoprotection is mediated by enhanced DNA repair. We wanted to explore whether NAM in combination with anti-proliferative (Metformin; Met) or antioxidant (Phloroglucinol; PG) compounds could potentially enhance its photoprotective effects. METHODS: Hairless mice (C3.Cg-Hrhr/TifBomTac) were treated orally with either a standard dose of NAM monotherapy (600 mg/kg), or NAM (400 mg/kg) combined with Met (200 mg/kg) (NAM-Met) or PG (75 mg/kg) (NAM-PG). Mice were irradiated with 3.5 standard erythema doses of UVR three times per week to induce tumour development. Photoprotective effects were based on i) tumour onset of the first three tumours, ii) skin photodamage, and iii) DNA damage (cyclobutane pyrimidine dimers [CPDs] and pyrimidine-pyrimidone (6-4) photoproducts [6-4PPs]). RESULTS: All mice treated with NAM demonstrated a delay in tumour onset and reduced tumour burden compared to the UV control group (NAM, NAM-Met, NAM-PG vs. UV control: p ≤ 0.015). NAM-mono and NAM-PG increased time until all three tumours with no difference between them, indicating a similar degree of photoprotection. NAM-mono had no effect on DNA damage compared to the UV control group (p > 0.05), whereas NAM-PG reduced 6-4PP lesions (p < 0.01), but not CPDs (p > 0.05) compared to NAM-mono. NAM-Met delayed the onset of the third tumour compared to the UV control but demonstrated a quicker onset compared to NAM-mono, suggesting inferior photoprotection compared to nicotinamide monotherapy. CONCLUSION: NAM-PG was as effective in delaying UVR-induced tumour onset as NAM-mono. The reduction in 6-4PP lesions may indicate that the mechanism of NAM-PG is better suited for photoprotection than NAM-mono. NAM-mono was superior to NAM-Met, indicating a dose-dependency of NAM's photoprotection. These results highlight a potential for combining photoprotective compounds to enhance photoprotection.

Pre-treatment with topical 5-fluorouracil increases the efficacy of daylight photodynamic therapy for actinic keratoses - A randomized controlled trial.

BACKGROUND: Daylight photodynamic therapy (dPDT) and topical 5-fluorouracil (5-FU) are each effective treatments for thin grade I actinic keratosis (AKs), but less so for thicker grade II-III AKs. Prolonged topical treatment regimens can be associated with severe skin reactions and low compliance. This study compares the efficacy of sequential 4 % 5-FU and dPDT with dPDT monotherapy for multiple actinic keratoses. METHODS: Sixty patients with a total of 1547 AKs (grade I: 1278; grade II: 246; grade III: 23) were treated in two symmetrical areas (mean size 75 cm2) of the face or scalp, which were randomized to (i) 4% 5-FU creme twice daily for 7 days before a single dPDT procedure and (ii) dPDT monotherapy. Daylight exposure was either outdoor or indoor daylight. RESULTS: Twelve weeks after treatment 87 % of all AKs cleared after 5-FU+dPDT compared to 74 % after dPDT alone (p<0.0001). For grade II AKs, the lesion response rate increased from 55 % with dPDT monotherapy to 79 % after 5-FU+dPDT (p<0.0056). Moderate/severe erythema was seen in 88 % 5-FU+dPDT areas compared to 41 % of dPDT areas two days after dPDT. Twelve weeks after treatment 75 % of the patients were very satisfied with both treatments. CONCLUSIONS: Sequential 5-FU and dPDT was more effective than dPDT monotherapy in the treatment of AKs, especially for grade II AKs. Local skin reactions were more pronounced after combination treatment, but no patients discontinued the treatment. The combination of 5-FU and dPDT is an effective treatment of large treatment areas with high compliance and satisfaction.

Clinical Evaluation of Deep Learning for Tumor Delineation on 18F-FDG PET/CT of Head and Neck Cancer.

Artificial intelligence (AI) may decrease 18F-FDG PET/CT-based gross tumor volume (GTV) delineation variability and automate tumor-volume-derived image biomarker extraction. Hence, we aimed to identify and evaluate promising state-of-the-art deep learning methods for head and neck cancer (HNC) PET GTV delineation. Methods: We trained and evaluated deep learning methods using retrospectively included scans of HNC patients referred for radiotherapy between January 2014 and December 2019 (ISRCTN16907234). We used 3 test datasets: an internal set to compare methods, another internal set to compare AI-to-expert variability and expert interobserver variability (IOV), and an external set to compare internal and external AI-to-expert variability. Expert PET GTVs were used as the reference standard. Our benchmark IOV was measured using the PET GTV of 6 experts. The primary outcome was the Dice similarity coefficient (DSC). ANOVA was used to compare methods, a paired t test was used to compare AI-to-expert variability and expert IOV, an unpaired t test was used to compare internal and external AI-to-expert variability, and post hoc Bland-Altman analysis was used to evaluate biomarker agreement. Results: In total, 1,220 18F-FDG PET/CT scans of 1,190 patients (mean age ± SD, 63 ± 10 y; 858 men) were included, and 5 deep learning methods were trained using 5-fold cross-validation (n = 805). The nnU-Net method achieved the highest similarity (DSC, 0.80 [95% CI, 0.77-0.86]; n = 196). We found no evidence of a difference between expert IOV and AI-to-expert variability (DSC, 0.78 for AI vs. 0.82 for experts; mean difference of 0.04 [95% CI, -0.01 to 0.09]; P = 0.12; n = 64). We found no evidence of a difference between the internal and external AI-to-expert variability (DSC, 0.80 internally vs. 0.81 externally; mean difference of 0.004 [95% CI, -0.05 to 0.04]; P = 0.87; n = 125). PET GTV-derived biomarkers of AI were in good agreement with experts. Conclusion: Deep learning can be used to automate 18F-FDG PET/CT tumor-volume-derived imaging biomarkers, and the deep-learning-based volumes have the potential to assist clinical tumor volume delineation in radiation oncology.

Oral intake of bucillamine, carvedilol, metformin, or phenformin does not protect against UVR-induced squamous cell carcinomas in hairless mice.

Squamous cell carcinoma represents the second most common type of keratinocyte carcinoma with ultraviolet radiation (UVR) making up the primary risk factor. Oral photoprotection aims to reduce incidence rates through oral intake of photoprotective compounds. Recently, drug repurposing has gained traction as an interesting source of chemoprevention. Because of their reported photoprotective properties, we investigated the potential of bucillamine, carvedilol, metformin, and phenformin as photoprotective compounds following oral intake in UVR-exposed hairless mice. Tumour development was observed in all groups in response to UVR, with only the positive control (Nicotinamide) demonstrating a reduction in tumour incidence (23.8%). No change in tumour development was observed in the four repurposed drug groups compared to the UV control group, whereas nicotinamide significantly reduced carcinogenesis (P = 0.00012). Metformin treatment significantly reduced UVR-induced erythema (P = 0.012), bucillamine and phenformin increased dorsal pigmentation (P = 0.0013, and P = 0.0005), but no other photoprotective effect was observed across the repurposed groups. This study demonstrates that oral supplementation with bucillamine, carvedilol, metformin, or phenformin does not affect UVR-induced carcinogenesis in hairless mice.

Brain perfusion estimation by Tikhonov model-free deconvolution in a long axial field of view PET/CT scanner exploring five different PET tracers.

PURPOSE: New total-body PET scanners with a long axial field of view (LAFOV) allow for higher temporal resolution due to higher sensitivity, which facilitates perfusion estimation by model-free deconvolution. Fundamental tracer kinetic theory predicts that perfusion can be estimated for all tracers despite their different fates given sufficiently high temporal resolution of 1 s or better, bypassing the need for compartment modelling. The aim of this study was to investigate whether brain perfusion could be estimated using model-free Tikhonov generalized deconvolution for five different PET tracers, [15O]H2O, [11C]PIB, [18F]FE-PE2I, [18F]FDG and [18F]FET. To our knowledge, this is the first example of a general model-free approach to estimate cerebral blood flow (CBF) from PET data. METHODS: Twenty-five patients underwent dynamic LAFOV PET scanning (Siemens, Quadra). PET images were reconstructed with an isotropic voxel resolution of 1.65 mm3. Time framing was 40 × 1 s during bolus passage followed by increasing framing up to 60 min. AIF was obtained from the descending aorta. Both voxel- and region-based calculations of perfusion in the thalamus were performed using the Tikhonov method. The residue impulse response function was used to estimate the extraction fraction of tracer leakage across the blood-brain barrier. RESULTS: CBF ranged from 37 to 69 mL blood min-1 100 mL of tissue-1 in the thalamus. Voxelwise calculation of CBF resulted in CBF maps in the physiologically normal range. The extraction fractions of [15O]H2O, [18F]FE-PE2I, [11C]PIB, [18F]FDG and [18F]FET in the thalamus were 0.95, 0.78, 0.62, 0.19 and 0.03, respectively. CONCLUSION: The high temporal resolution and sensitivity associated with LAFOV PET scanners allow for noninvasive perfusion estimation of multiple tracers. The method provides an estimation of the residue impulse response function, from which the fate of the tracer can be studied, including the extraction fraction, influx constant, volume of distribution and transit time distribution, providing detailed physiological insight into normal and pathologic tissue.

Attenuation Correction of Long Axial Field-of-View Positron Emission Tomography Using Synthetic Computed Tomography Derived from the Emission Data: Application to Low-Count Studies and Multiple Tracers.

Recent advancements in PET/CT, including the emergence of long axial field-of-view (LAFOV) PET/CT scanners, have increased PET sensitivity substantially. Consequently, there has been a significant reduction in the required tracer activity, shifting the primary source of patient radiation dose exposure to the attenuation correction (AC) CT scan during PET imaging. This study proposes a parameter-transferred conditional generative adversarial network (PT-cGAN) architecture to generate synthetic CT (sCT) images from non-attenuation corrected (NAC) PET images, with separate networks for [18F]FDG and [15O]H2O tracers. The study includes a total of 1018 subjects (n = 972 [18F]FDG, n = 46 [15O]H2O). Testing was performed on the LAFOV scanner for both datasets. Qualitative analysis found no differences in image quality in 30 out of 36 cases in FDG patients, with minor insignificant differences in the remaining 6 cases. Reduced artifacts due to motion between NAC PET and CT were found. For the selected organs, a mean average error of 0.45% was found for the FDG cohort, and that of 3.12% was found for the H2O cohort. Simulated low-count images were included in testing, which demonstrated good performance down to 45 s scans. These findings show that the AC of total-body PET is feasible across tracers and in low-count studies and might reduce the artifacts due to motion and metal implants.

Potential Clinical Impact of LAFOV PET/CT: A Systematic Evaluation of Image Quality and Lesion Detection.

We performed a systematic evaluation of the diagnostic performance of LAFOV PET/CT with increasing acquisition time. The first 100 oncologic adult patients referred for 3 MBq/kg 2-[18F]fluoro-2-deoxy-D-glucose PET/CT on the Siemens Biograph Vision Quadra were included. A standard imaging protocol of 10 min was used and scans were reconstructed at 30 s, 60 s, 90 s, 180 s, 300 s, and 600 s. Paired comparisons of quantitative image noise, qualitative image quality, lesion detection, and lesion classification were performed. Image noise (n = 50, 34 women) was acceptable according to the current standard of care (coefficient-of-varianceref < 0.15) after 90 s and improved significantly with increasing acquisition time (PB < 0.001). The same was seen in observer rankings (PB < 0.001). Lesion detection (n = 100, 74 women) improved significantly from 30 s to 90 s (PB < 0.001), 90 s to 180 s (PB = 0.001), and 90 s to 300 s (PB = 0.002), while lesion classification improved from 90 s to 180 s (PB < 0.001), 180 s to 300 s (PB = 0.021), and 90 s to 300 s (PB < 0.001). We observed improved image quality, lesion detection, and lesion classification with increasing acquisition time while maintaining a total scan time of less than 5 min, which demonstrates a potential clinical benefit. Based on these results we recommend a standard imaging acquisition protocol for LAFOV PET/CT of minimum 180 s to maximum 300 s after injection of 3 MBq/kg 2-[18F]fluoro-2-deoxy-D-glucose.

Oral phytochemicals as photoprotectants in UVR exposed hairless mice: A study of hesperidin methyl chalcone, phloroglucinol, and syringic acid.

Ultraviolet radiation is the primary risk factor for keratinocyte carcinoma. Because of increasing incidence rates, new methods of photoprotection must be explored. Oral supplementation with photoprotective compounds presents a promising alternative. Phytochemical compounds like hesperidin methyl chalcone, phloroglucinol, and syringic acid are particularly of interest because of their antioxidant properties. Our primary outcome was to evaluate the effects of oral phytochemicals on photocarcinogenesis with time until tumour onset as the primary endpoint. A total of 125 hairless C3.Cg-Hrhr/TifBom Tac mice were randomised to receive tap water supplemented with either 100 mg/kg hesperidin methyl chalcone, phloroglucinol, or syringic acid, 600 mg/kg nicotinamide as a positive control, or no supplementation. The mice were irradiated with 3.5 standard erythema doses thrice weekly to induce photocarcinogenesis. Supplementation with the phytochemicals phloroglucinol and syringic acid and nicotinamide delayed tumour onset from a median of 140 days to 151 (p = 0.036), 157 days (p = 0.02), and 178 (p = 2.7·10-5), respectively. Phloroglucinol and nicotinamide supplementation reduced tumour number. Nicotinamide increased UV-induced pigmentation and reduced oedema formation, while phloroglucinol supplementation reduced epidermal thickness. These results indicate that oral supplementation with phloroglucinol and syringic acid protects against photocarcinogenesis in hairless mice, but not to the same extent as nicotinamide.

Improving the efficacy of photodynamic therapy for actinic keratosis: A comprehensive review of pharmacological pretreatment strategies.

BACKGROUND: Photodynamic therapy (PDT) is approved for treatment of actinic keratoses (AKs) and field-cancerisation. Pretreatment with pharmacological compounds holds potential to improve PDT efficacy, through direct interaction with PpIX formation or through an independent response, both of which may improve PDT treatment. OBJECTIVE: To present the currently available clinical evidence of pharmacological pretreatments prior to PDT and to associate potential clinical benefits with the pharmacological mechanisms of action of the individual compounds. METHODS: A comprehensive search on the Embase, MEDLINE, and Web of Science databases was performed. RESULTS: In total, 16 studies investigated 6 pretreatment compounds: 5-fluorouracil (5-FU), diclofenac, retinoids, salicylic acid, urea, and vitamin D. Two of these, 5-FU and vitamin D, robustly increased the efficacy of PDT across multiple studies, illustrated by mean increases in clearance rates of 21.88% and 12.4%, respectively. Regarding their mechanisms, 5-FU and vitamin D both increased PpIX accumulation, while 5-FU also induced a separate anticarcinogenic response. Pretreatment with diclofenac for four weeks improved the clearance rate in one study (24.9%), administration of retinoids had a significant effect in one of two studies (16.25%), while salicylic acid and urea did not lead to improved PDT efficacy. Diclofenac and retinoids demonstrated independent cytotoxic responses, whereas salicylic acid and urea acted as penetration enhancers to increase PpIX formation. CONCLUSION: 5-FU and vitamin D are well-tested, promising candidates for pharmacological pretreatment prior to PDT. Both compounds affect the haem biosynthesis, providing a target for potential pretreatment candidates. KEY WORDS: Photodynamic Therapy, Actinic Keratosis,Pre-tretment,Review,enhancement.

Estimation of brain amyloid accumulation using deep learning in clinical [11C]PiB PET imaging.

INTRODUCTION: Estimation of brain amyloid accumulation is valuable for evaluation of patients with cognitive impairment in both research and clinical routine. The development of high throughput and accurate strategies for the determination of amyloid status could be an important tool in patient selection for clinical trials and amyloid directed treatment. Here, we propose the use of deep learning to quantify amyloid accumulation using standardized uptake value ratio (SUVR) and classify amyloid status based on their PET images. METHODS: A total of 1309 patients with cognitive impairment scanned with [11C]PIB PET/CT or PET/MRI were included. Two convolutional neural networks (CNNs) for reading-based amyloid status and SUVR prediction were trained using 75% of the PET/CT data. The remaining PET/CT (n = 300) and all PET/MRI (n = 100) data was used for evaluation. RESULTS: The prevalence of amyloid positive patients was 61%. The amyloid status classification model reproduced the expert reader's classification with 99% accuracy. There was a high correlation between reference and predicted SUVR (R2 = 0.96). Both reference and predicted SUVR had an accuracy of 97% compared to expert classification when applying a predetermined SUVR threshold of 1.35 for binary classification of amyloid status. CONCLUSION: The proposed CNN models reproduced both the expert classification and quantitative measure of amyloid accumulation in a large local dataset. This method has the potential to replace or simplify existing clinical routines and can facilitate fast and accurate classification well-suited for a high throughput pipeline.

Scanner agnostic large-scale evaluation of MS lesion delineation tool for clinical MRI.

Introduction: Patients with MS are MRI scanned continuously throughout their disease course resulting in a large manual workload for radiologists which includes lesion detection and size estimation. Though many models for automatic lesion segmentation have been published, few are used broadly in clinic today, as there is a lack of testing on clinical datasets. By collecting a large, heterogeneous training dataset directly from our MS clinic we aim to present a model which is robust to different scanner protocols and artefacts and which only uses MRI modalities present in routine clinical examinations. Methods: We retrospectively included 746 patients from routine examinations at our MS clinic. The inclusion criteria included acquisition at one of seven different scanners and an MRI protocol including 2D or 3D T2-w FLAIR, T2-w and T1-w images. Reference lesion masks on the training (n = 571) and validation (n = 70) datasets were generated using a preliminary segmentation model and subsequent manual correction. The test dataset (n = 100) was manually delineated. Our segmentation model https://github.com/CAAI/AIMS/ was based on the popular nnU-Net, which has won several biomedical segmentation challenges. We tested our model against the published segmentation models HD-MS-Lesions, which is also based on nnU-Net, trained with a more homogenous patient cohort. We furthermore tested model robustness to data from unseen scanners by performing a leave-one-scanner-out experiment. Results: We found that our model was able to segment MS white matter lesions with a performance comparable to literature: DSC = 0.68, precision = 0.90, recall = 0.70, f1 = 0.78. Furthermore, the model outperformed HD-MS-Lesions in all metrics except precision = 0.96. In the leave-one-scanner-out experiment there was no significant change in performance (p < 0.05) between any of the models which were only trained on part of the dataset and the full segmentation model. Conclusion: In conclusion we have seen, that by including a large, heterogeneous dataset emulating clinical reality, we have trained a segmentation model which maintains a high segmentation performance while being robust to data from unseen scanners. This broadens the applicability of the model in clinic and paves the way for clinical implementation.

An Investigation of Lesion Detection Accuracy for Artificial Intelligence-Based Denoising of Low-Dose 64Cu-DOTATATE PET Imaging in Patients with Neuroendocrine Neoplasms.

Frequent somatostatin receptor PET, for example, 64Cu-DOTATATE PET, is part of the diagnostic work-up of patients with neuroendocrine neoplasms (NENs), resulting in high accumulated radiation doses. Scan-related radiation exposure should be minimized in accordance with the as-low-as-reasonably achievable principle, for example, by reducing injected radiotracer activity. Previous investigations found that reducing 64Cu-DOTATATE activity to below 50 MBq results in inadequate image quality and lesion detection. We therefore investigated whether image quality and lesion detection of less than 50 MBq of 64Cu-DOTATATE PET could be restored using artificial intelligence (AI). Methods: We implemented a parameter-transferred Wasserstein generative adversarial network for patients with NENs on simulated low-dose 64Cu-DOTATATE PET images corresponding to 25% (PET25%), or about 48 MBq, of the injected activity of the reference full dose (PET100%), or about 191 MBq, to generate denoised PET images (PETAI). We included 38 patients in the training sets for network optimization. We analyzed PET intensity correlation, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean-square error (MSE) of PETAI/PET100% versus PET25%/PET100% Two readers assessed Likert scale-defined image quality (1, very poor; 2, poor; 3, moderate; 4, good; 5, excellent) and identified lesion-suspicious foci on PETAI and PET100% in a subset of the patients with no more than 20 lesions per organ (n = 33) to allow comparison of all foci on a 1:1 basis. Detected foci were scored (C1, definite lesion; C0, lesion-suspicious focus) and matched with PET100% as the reference. True-positive (TP), false-positive (FP), and false-negative (FN) lesions were assessed. Results: For PETAI/PET100% versus PET25%/PET100%, PET intensity correlation had a goodness-of-fit value of 0.94 versus 0.81, PSNR was 58.1 versus 53.0, SSIM was 0.908 versus 0.899, and MSE was 2.6 versus 4.7. Likert scale-defined image quality was rated good or excellent in 33 of 33 and 32 of 33 patients on PET100% and PETAI, respectively. Total number of detected lesions was 118 on PET100% and 115 on PETAI Only 78 PETAI lesions were TP, 40 were FN, and 37 were FP, yielding detection sensitivity (TP/(TP+FN)) and a false discovery rate (FP/(TP+FP)) of 66% (78/118) and 32% (37/115), respectively. In 62% (23/37) of cases, the FP lesion was scored C1, suggesting a definite lesion. Conclusion: PETAI improved visual similarity with PET100% compared with PET25%, and PETAI and PET100% had similar Likert scale-defined image quality. However, lesion detection analysis performed by physicians showed high proportions of FP and FN lesions on PETAI, highlighting the need for clinical validation of AI algorithms.

DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants.

Purpose: Conventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET), which provides additional information on metabolic properties. In neuro-oncology, patients often undergo brain and skull altering treatment, which is known to challenge MRI-based attenuation correction (MR-AC) methods and thereby impact the simplified semi-quantitative measures such as tumor-to-brain ratio (TBR) used in clinical routine. The aim of the present study was to examine the applicability of our deep learning method, DeepDixon, for MR-AC in [18F]FET PET/MRI scans of a post-surgery glioma cohort with metal implants. Methods: The MR-AC maps were assessed for all 194 included post-surgery glioma patients (318 studies). The subgroup of 147 patients (222 studies, 200 MBq [18F]FET PET/MRI) with tracer uptake above 1 ml were subsequently reconstructed with DeepDixon, vendor-default atlas-based method, and a low-dose computed tomography (CT) used as reference. The biological tumor volume (BTV) was delineated on each patient by isocontouring tracer uptake above a TBR threshold of 1.6. We evaluated the MR-AC methods using the recommended clinical metrics BTV and mean and maximum TBR on a patient-by-patient basis against the reference with CT-AC. Results: Ninety-seven percent of the studies (310/318) did not have any major artifacts using DeepDixon, which resulted in a Dice coefficient of 0.89/0.83 for tissue/bone, respectively, compared to 0.84/0.57 when using atlas. The average difference between DeepDixon and CT-AC was within 0.2% across all clinical metrics, and no statistically significant difference was found. When using DeepDixon, only 3 out of 222 studies (1%) exceeded our acceptance criteria compared to 72 of the 222 studies (32%) with the atlas method. Conclusion: We evaluated the performance of a state-of-the-art MR-AC method on the largest post-surgical glioma patient cohort to date. We found that DeepDixon could overcome most of the issues arising from irregular anatomy and metal artifacts present in the cohort resulting in clinical metrics within acceptable limits of the reference CT-AC in almost all cases. This is a significant improvement over the vendor-provided atlas method and of particular importance in response assessment.

Impact of Reduced Image Noise on Deauville Scores in Patients with Lymphoma Scanned on a Long-Axial Field-of-View PET/CT-Scanner.

BACKGROUND: Total body and long-axial field-of-view (LAFOV) PET/CT represent visionary innovations in imaging enabling either improved image quality, reduction in injected activity-dose or decreased acquisition time. An improved image quality may affect visual scoring systems, including the Deauville score (DS), which is used for clinical assessment of patients with lymphoma. The DS compares SUVmax in residual lymphomas with liver parenchyma, and here we investigate the impact of reduced image noise on the DS in patients with lymphomas scanned on a LAFOV PET/CT. METHODS: Sixty-eight patients with lymphoma underwent a whole-body scan on a Biograph Vision Quadra PET/CT-scanner, and images were evaluated visually with regard to DS for three different timeframes of 90, 300, and 600 s. SUVmax and SUVmean were calculated from liver and mediastinal blood pool, in addition to SUVmax from residual lymphomas and measures of noise. RESULTS: SUVmax in liver and in mediastinal blood pool decreased significantly with increasing acquisition time, whereas SUVmean remained stable. In residual tumor, SUVmax was stable during different acquisition times. As a result, the DS was subject to change in three patients. CONCLUSIONS: Attention should be drawn towards the eventual impact of improvements in image quality on visual scoring systems such as the DS.

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI.

In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland-Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm3 (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms.

A zero-dose synthetic baseline for the personalized analysis of [18F]FDG-PET: Application in Alzheimer's disease.

Purpose: Brain 2-Deoxy-2-[18F]fluoroglucose ([18F]FDG-PET) is widely used in the diagnostic workup of Alzheimer's disease (AD). Current tools for uptake analysis rely on non-personalized templates, which poses a challenge as decreased glucose uptake could reflect neuronal dysfunction, or heterogeneous brain morphology associated with normal aging. Overcoming this, we propose a deep learning method for synthesizing a personalized [18F]FDG-PET baseline from the patient's own MRI, and showcase its applicability in detecting AD pathology. Methods: We included [18F]FDG-PET/MRI data from 123 patients of a local cohort and 600 patients from ADNI. A supervised, adversarial model with two connected Generative Adversarial Networks (GANs) was trained on cognitive normal (CN) patients with transfer-learning to generate full synthetic baseline volumes (sbPET) (192 × 192 × 192) which reflect healthy uptake conditioned on brain anatomy. Synthetic accuracy was measured by absolute relative %-difference (Abs%), relative %-difference (RD%), and peak signal-to-noise ratio (PSNR). Lastly, we deployed the sbPET images in a fully personalized method for localizing metabolic abnormalities. Results: The model achieved a spatially uniform Abs% of 9.4%, RD% of 0.5%, and a PSNR of 26.3 for CN subjects. The sbPET images conformed to the anatomical information dictated by the MRI and proved robust in presence of atrophy. The personalized abnormality method correctly mapped the pathology of AD subjects while showing little to no anomalies for CN subjects. Conclusion: This work demonstrated the feasibility of synthesizing fully personalized, healthy-appearing [18F]FDG-PET images. Using these, we showcased a promising application in diagnosing AD, and theorized the potential value of sbPET images in other neuroimaging routines.

A deep learning-based whole-body solution for PET/MRI attenuation correction.

BACKGROUND: Deep convolutional neural networks have demonstrated robust and reliable PET attenuation correction (AC) as an alternative to conventional AC methods in integrated PET/MRI systems. However, its whole-body implementation is still challenging due to anatomical variations and the limited MRI field of view. The aim of this study is to investigate a deep learning (DL) method to generate voxel-based synthetic CT (sCT) from Dixon MRI and use it as a whole-body solution for PET AC in a PET/MRI system. MATERIALS AND METHODS: Fifteen patients underwent PET/CT followed by PET/MRI with whole-body coverage from skull to feet. We performed MRI truncation correction and employed co-registered MRI and CT images for training and leave-one-out cross-validation. The network was pretrained with region-specific images. The accuracy of the AC maps and reconstructed PET images were assessed by performing a voxel-wise analysis and calculating the quantification error in SUV obtained using DL-based sCT (PETsCT) and a vendor-provided atlas-based method (PETAtlas), with the CT-based reconstruction (PETCT) serving as the reference. In addition, region-specific analysis was performed to compare the performances of the methods in brain, lung, liver, spine, pelvic bone, and aorta. RESULTS: Our DL-based method resulted in better estimates of AC maps with a mean absolute error of 62 HU, compared to 109 HU for the atlas-based method. We found an excellent voxel-by-voxel correlation between PETCT and PETsCT (R2 = 0.98). The absolute percentage difference in PET quantification for the entire image was 6.1% for PETsCT and 11.2% for PETAtlas. The regional analysis showed that the average errors and the variability for PETsCT were lower than PETAtlas in all regions. The largest errors were observed in the lung, while the smallest biases were observed in the brain and liver. CONCLUSIONS: Experimental results demonstrated that a DL approach for whole-body PET AC in PET/MRI is feasible and allows for more accurate results compared with conventional methods. Further evaluation using a larger training cohort is required for more accurate and robust performance.

Deep learning based low-activity PET reconstruction of [11C]PiB and [18F]FE-PE2I in neurodegenerative disorders.

PURPOSE: Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim was to investigate the feasibility of using activity reduction in clinical [18F]FE-PE2I and [11C]PiB PET/CT scans, simulating low injected activity or scanning time reduction, in combination with AI-assisted denoising. METHODS: A total of 162 patients with clinically uncertain Alzheimer's disease underwent amyloid [11C]PiB PET/CT and 509 patients referred for clinically uncertain Parkinson's disease underwent dopamine transporter (DAT) [18F]FE-PE2I PET/CT. Simulated low-activity data were obtained by random sampling of 5% of the events from the list-mode file and a 5% time window extraction in the middle of the scan. A three-dimensional convolutional neural network (CNN) was trained to denoise the resulting PET images for each disease cohort. RESULTS: Noise reduction of low-activity PET images was successful for both cohorts using 5% of the original activity with improvement in visual quality and all similarity metrics with respect to the ground-truth images. Clinically relevant metrics extracted from the low-activity images deviated < 2% compared to ground-truth values, which were not significantly changed when extracting the metrics from the denoised images. CONCLUSION: The presented models were based on the same network architecture and proved to be a robust tool for denoising brain PET images with two widely different tracer distributions (delocalized, ([11C]PiB, and highly localized, [18F]FE-PE2I). This broad and robust application makes the presented network a good choice for improving the quality of brain images to the level of the standard-activity images without degrading clinical metric extraction. This will allow for reduced dose or scan time in PET/CT to be implemented clinically.