Scintillation camera images contain a large amount of Poisson noise. We have investigated whether noise can be removed in whole-body bone scans using convolutional neural networks (CNNs) trained with ...sets of noisy and noiseless images obtained by Monte Carlo simulation.
: Three CNNs were generated using 3 different sets of training images: simulated bone scan images, images of a cylindric phantom with hot and cold spots, and a mix of the first two. Each training set consisted of 40,000 noiseless and noisy image pairs. The CNNs were evaluated with simulated images of a cylindric phantom and simulated bone scan images. The mean squared error between filtered and true images was used as difference metric, and the coefficient of variation was used to estimate noise reduction. The CNNs were compared with gaussian and median filters. A clinical evaluation was performed in which the ability to detect metastases for CNN- and gaussian-filtered bone scans with half the number of counts was compared with standard bone scans.
: The best CNN reduced the coefficient of variation by, on average, 92%, and the best standard filter reduced the coefficient of variation by 88%. The best CNN gave a mean squared error that was on average 68% and 20% better than the best standard filters, for the cylindric and bone scan images, respectively. The best CNNs for the cylindric phantom and bone scans were the dedicated CNNs. No significant differences in the ability to detect metastases were found between standard, CNN-, and gaussian-filtered bone scans.
Noise can be removed efficiently regardless of noise level with little or no resolution loss. The CNN filter enables reducing the scanning time by half and still obtaining good accuracy for bone metastasis assessment.
Cervical cancer is the fourth most common female malignancy.
18
F-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is routinely performed in patients with ...locally advanced cervical cancer for staging and treatment response evaluation. With this retrospective, observational cohort study, we wanted to investigate the prognostic value of the maximum standardised uptake value (SUVmax) and the volumetric parameters of metabolic tumour volume (MTV) and total lesion glycolysis (TLG) before and after treatment in women with cervical cancer, with overall survival (OS) and recurrence as outcome measures.
Women with cervical cancer referred for curative radiotherapy and who underwent two PET-CT scans (before treatment and approximately 7 months post-treatment) were included. SUVmax, MTV and TLG were measured at baseline and post-treatment on the primary tumour, pelvic and distant lymph node metastases, distant organ metastases, and on total tumour burden. The PET parameters were associated with OS by Cox regression and recurrence by multivariable logistic regression. Kaplan-Meier curves and C-index were used to visualise the prognostic potential of the different measures.
A total of 133 patients were included. At the primary tumour level and on total tumour burden, age- and clinical-stage adjusted analyses showed a significant association between PET parameters and OS and recurrence when measured post-treatment. At baseline (pre-treatment), MTV and TLG were associated with OS and recurrence, whereas SUVmax was not. C-index from adjusted Cox models on total tumour burden showed higher values for the post-treatment PET compared to baseline. Kaplan-Meier curves demonstrated a greater prognostic potential for MTV and TLG compared to SUVmax, both at baseline and post-treatment.
The FDG PET-CT-derived parameters SUVmax, MTV, and TLG measured post-treatment can predict OS and recurrence in cervical cancer. Parameters measured before treatment had overall lower prognostic potential, and only MTV and TLG showed significant association to OS and recurrence.
The aim of this study was to develop a deep learning-based method for segmentation of bones in CT scans and test its accuracy compared to manual delineation, as a first step in the creation of an ...automated PET/CT-based method for quantifying skeletal tumour burden.
Convolutional neural networks (CNNs) were trained to segment 49 bones using manual segmentations from 100 CT scans. After training, the CNN-based segmentation method was tested on 46 patients with prostate cancer, who had undergone 18F-choline-PET/CT and 18F-NaF PET/CT less than three weeks apart. Bone volumes were calculated from the segmentations. The network’s performance was compared with manual segmentations of five bones made by an experienced physician. Accuracy of the spatial overlap between automated CNN-based and manual segmentations of these five bones was assessed using the Sørensen-Dice index (SDI). Reproducibility was evaluated applying the Bland-Altman method.
The median (SD) volumes of the five selected bones were by CNN and manual segmentation: Th7 41 (3.8) and 36 (5.1), L3 76 (13) and 75 (9.2), sacrum 284 (40) and 283 (26), 7th rib 33 (3.9) and 31 (4.8), sternum 80 (11) and 72 (9.2), respectively. Median SDIs were 0.86 (Th7), 0.85 (L3), 0.88 (sacrum), 0.84 (7th rib) and 0.83 (sternum). The intraobserver volume difference was less with CNN-based than manual approach: Th7 2% and 14%, L3 7% and 8%, sacrum 1% and 3%, 7th rib 1% and 6%, sternum 3% and 5%, respectively. The average volume difference measured as ratio volume difference/mean volume between the two CNN-based segmentations was 5–6% for the vertebral column and ribs and ≤3% for other bones.
The new deep learning-based method for automated segmentation of bones in CT scans provided highly accurate bone volumes in a fast and automated way and, thus, appears to be a valuable first step in the development of a clinical useful processing procedure providing reliable skeletal segmentation as a key part of quantification of skeletal metastases.
Background
Artificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online ...platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image.
Results
The RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones).
Conclusion
The paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request at
www.recomia.org
for research purposes.
To develop an artificial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin's lymphoma (HL) undergoing staging with FDG-PET/CT. The ...results of the AI in a separate test group were compared to the interpretations of independent physicians. The skeleton and bone marrow were segmented using a convolutional neural network. The training of AI was based on 153 un-treated patients. Bone uptake significantly higher than the mean BMU was marked as abnormal, and an index, based on the total squared abnormal uptake, was computed to identify the focal uptake. Patients with an index above a predefined threshold were interpreted as having focal uptake. As the test group, 48 un-treated patients who had undergone a staging FDG-PET/CT between 2017-2018 with biopsy-proven HL were retrospectively included. Ten physicians classified the 48 cases regarding focal skeleton/BMU. The majority of the physicians agreed with the AI in 39/48 cases (81%) regarding focal skeleton/bone marrow involvement. Inter-observer agreement between the physicians was moderate, Kappa 0.51 (range 0.25-0.80). An AI-based method can be developed to highlight suspicious focal skeleton/BMU in HL patients staged with FDG-PET/CT. Inter-observer agreement regarding focal BMU is moderate among nuclear medicine physicians.
Background
Scintigraphy with technetium‐99m‐labelled dimercaptosuccinic acid (99mTcTc‐DMSA) is widely used for renal cortical imaging. Uptake of 99mTcTc‐DMSA has been shown to correlate with ...glomerular filtration rate (GFR). Prostate‐specific membrane antigen (PSMA) radiopharmaceuticals used for positron emission tomography (PET) show high renal uptake and are being investigated for renal imaging. 68GaGa‐PSMA‐11 PET parameters have been shown to correlate with estimated GFR (eGFR). The aim of this study was to investigate the relationship between renal 18FPSMA‐1007 uptake and eGFR.
Methods
Hundred and eighty‐five patients underwent PET imaging at 1 and 2 h after injection of 4.0 ± 0.2 MBq 18FPSMA‐1007. Serum creatinine levels were measured and GFR estimated using the Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) and Modification of Diet in Renal Disease (MDRD) equations. Fifteen patients were excluded due to missing or incorrect data. Thus, data from 170 patients were analyzed. Kidneys were segmented in the PET images using a convolutional neural network with manual correction. For each kidney, mean standardized uptake value (SUVmean) and segmentation volume in millilitres were measured. Linear regression analyses were performed with eGFR as the outcome variable.
Results
Variation in the eGFR values was explained to a significant degree by SUVmean and renal segmentation volume in both the 1 and 2 h images. This correlation was stronger for CKD‐EPI eGFR (1 h R2 = 0.64; 2 h R2 = 0.64) than for MDRD eGFR (1 h R2 = 0.47; 2 h R2 = 0.45).
Conclusion
Renal 18FPSMA‐1007 uptake parameters correlate with eGFR and are indicative of renal cortical function.
This study investigates the patterns of PET-positive lymph nodes (LNs) in anal cancer. The aim was to provide information that could inform future anal cancer radiotherapy contouring guidelines.
The ...baseline 18F-FDG PET-CTs of 190 consecutive anal cancer patients were retrospectively assessed. LNs with a Deauville score (DS) of ≥3 were defined as PET-positive. Each PET-positive LN was allocated to a LN region and a LN sub-region; they were then mapped on a standard anatomy reference CT. The association between primary tumor localization and PET-positive LNs in different regions were analyzed.
PET-positive LNs (n = 412) were identified in 103 of 190 patients (54%). Compared to anal canal tumors with extension into the rectum, anal canal tumors with perianal extension more often had inguinal (P < 0.001) and less often perirectal (P < 0.001) and internal iliac (P < 0.001) PET-positive LNs. Forty-two patients had PET-positive LNs confined to a solitary region, corresponding to first echelon nodes. The most common solitary LN region was inguinal (25 of 42; 60%) followed by perirectal (26%), internal iliac (10%), and external iliac (2%). No PET-positive LNs were identified in the ischiorectal fossa or in the inguinal area located posterolateral to deep vessels. Skip metastases above the bottom of the sacroiliac joint were quite rare. Most external iliac PET-positive LNs were located posterior to the external iliac vein; only one was located in the lateral external iliac sub-region.
The results support some specific modifications to the elective clinical target volume (CTV) in anal cancer. These changes would lead to reduced volumes of normal tissue being irradiated, which could contribute to a reduction in radiation side-effects.
Background
The aim of the study was to develop and test an artificial intelligence (AI)-based method to improve the quality of
18
Ffluorodeoxyglucose (FDG) positron emission tomography (PET) images.
...Methods
A convolutional neural network (CNN) was trained by using pairs of excellent (acquisition time of 6 min/bed position) and standard (acquisition time of 1.5 min/bed position) or sub-standard (acquisition time of 1 min/bed position) images from 72 patients. A test group of 25 patients was used to validate the CNN qualitatively and quantitatively with 5 different image sets per patient: 4 min/bed position, 1.5 min/bed position with and without CNN, and 1 min/bed position with and without CNN.
Results
Difference in hotspot maximum or peak standardized uptake value between the standard 1.5 min and 1.5 min CNN images fell short of significance. Coefficient of variation, the noise level, was lower in the CNN-enhanced images compared with standard 1 min and 1.5 min images. Physicians ranked the 1.5 min CNN and the 4 min images highest regarding image quality (noise and contrast) and the standard 1 min images lowest.
Conclusions
AI can enhance
18
FFDG-PET images to reduce noise and increase contrast compared with standard images whilst keeping SUV
max/peak
stability. There were significant differences in scoring between the 1.5 min and 1.5 min CNN image sets in all comparisons, the latter had higher scores in noise and contrast. Furthermore, difference in SUV
max
and SUV
peak
fell short of significance for that pair. The improved image quality can potentially be used either to provide better images to the nuclear medicine physicians or to reduce acquisition time/administered activity.
Background
18
FPSMA-1007 is a promising tracer for integrated positron emission tomography and computed tomography (PET/CT).
Objective
Our aim was to assess the diagnostic accuracy of
18
FPSMA-1007 ...PET/CT for primary staging of lymph node metastasis before robotic-assisted laparoscopy (RALP) with extended lymph node dissection (ePLND).
Design, Setting and Participants
The study was a retrospective cohort in a tertiary referral center. Men with prostate cancer that underwent surgical treatment for intermediate- or high-risk prostate cancer between May 2019 and August 2021 were included.
Interventions
18
FPSMA-1007 PET/CT for initial staging followed by RALP and ePLND.
Outcome measurements and statistical analyses
Sensitivity and specificity were calculated both for the entire cohort and for patients with lymph node metastasis ≥ 3 mm. Positive (PPV) and negative (NPV) predictive values were calculated.
Results and limitations
Among 104 patients included in the analyses, 26 patients had lymph node metastasis based on pathology reporting and metastases were ≥ 3 mm in size in 13 of the cases (50%). In the entire cohort, the sensitivity and specificity of
18
FPSMA-1007 were 26.9% (95% confidence interval (CI); 11.6–47.8) and 96.2% (95% CI; 89.2–99.2), respectively. The sensitivity and specificity of
18
FPSMA-1007 to detect a lymph node metastasis ≥ 3 mm on PET/CT were 53.8% (95% CI; 25.1–80.8) and 96.7% (95% CI; 90.7–99.3), respectively. PPV was 70% and NPV 93.6%.
Conclusions
In primary staging of intermediate- and high-risk prostate cancer,
18
FPSMA-1007 PET/CT is highly specific for prediction of lymph node metastases, but the sensitivity for detection of metastases smaller than 3 mm is limited. Based on our results,
18
FPSMA-1007 PET/CT cannot completely replace ePLND.
Patient summary
This study investigated the use of an imaging method based on a prostate antigen-specific radiopharmaceutical tracer to detect lymph node prostate cancer metastasis. We found that it is unreliable to discover small metastasis.
PDF
![[18F]PSMA‐1007 renal uptake...](http://api.summon.serialssolutions.com/2.0.0/image/issn/XR4PS5YY4K/1475-0961_1475-097X/small "[18F]PSMA‐1007 renal uptake parameters: Reproducibility and relationship to estimated glomerular filtration rate")
