A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria.

OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. METHODS: Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. RESULTS: The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). CONCLUSIONS: We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.

Improving sensitivity through data augmentation with synthetic lymph node metastases for AI-based analysis of PSMA PET-CT images.

BACKGROUND: We developed a fully automated artificial intelligence (AI)AI-based-based method for detecting suspected lymph node metastases in prostate-specific membrane antigen (PSMA)(PSMA) positron emission tomography-computed tomography (PET-CT)(PET-CT) images of prostate cancer patients by using data augmentation that adds synthetic lymph node metastases to the images to expand the training set. METHODS: Synthetic data were derived from original training images to which new synthetic lymph node metastases were added. Thus, the original training set from a previous study (n = 420) was expanded by one synthetic image for every original image (n = 840), which was used to train an AI model. The performance of the AI model was compared to that of nuclear medicine physicians and a previously developed AI model. The human readers were alternately used as a reference and compared to either another reading or AI model. RESULTS: The new AI model had an average sensitivity of 84% for detecting lymph node metastases compared with 78% for human readings. Our previously developed AI method without synthetic data had an average sensitivity of 79%. The number of false positive lesions were slightly higher for the new AI model (average 3.3 instances per patient) compared to human readings and the previous AI model (average 2.8 instances per patient), while the number of false negative lesions was lower. CONCLUSIONS: Creating synthetic lymph node metastases, as a form of data augmentation, on [18F]PSMA-1007F]PSMA-1007 PETPET-CT-CT images improved the sensitivity of an AI model for detecting suspected lymph node metastases. However, the number of false positive lesions increased somewhat.

Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography.

Purpose: Meticulous manual delineations of the prostate and the surrounding organs at risk are necessary for prostate cancer radiation therapy to avoid side effects to the latter. This process is time consuming and hampered by inter- and intraobserver variability, all of which could be alleviated by artificial intelligence (AI). This study aimed to evaluate the performance of AI compared with manual organ delineations on computed tomography (CT) scans for radiation treatment planning. Methods and Materials: Manual delineations of the prostate, urinary bladder, and rectum of 1530 patients with prostate cancer who received curative radiation therapy from 2006 to 2018 were included. Approximately 50% of those CT scans were used as a training set, 25% as a validation set, and 25% as a test set. Patients with hip prostheses were excluded because of metal artifacts. After training and fine-tuning with the validation set, automated delineations of the prostate and organs at risk were obtained for the test set. Sørensen-Dice similarity coefficient, mean surface distance, and Hausdorff distance were used to evaluate the agreement between the manual and automated delineations. Results: The median Sørensen-Dice similarity coefficient between the manual and AI delineations was 0.82, 0.95, and 0.88 for the prostate, urinary bladder, and rectum, respectively. The median mean surface distance and Hausdorff distance were 1.7 and 9.2 mm for the prostate, 0.7 and 6.7 mm for the urinary bladder, and 1.1 and 13.5 mm for the rectum, respectively. Conclusions: Automated CT-based organ delineation for prostate cancer radiation treatment planning is feasible and shows good agreement with manually performed contouring.

MRI morphological characteristics of lymph nodes in anal squamous cell carcinoma.

OBJECTIVES: Pre-treatment staging of anal squamous cell carcinoma (ASCC) includes pelvic MRI and [18F]-fluorodeoxyglucose positron emission tomography with computed tomography (PET-CT). MRI criteria to define lymph node metastases (LNMs) in ASCC are currently lacking. The aim of this study was to describe the morphological characteristics of lymph nodes (LNs) on MRI in ASCC patients with PET-CT-positive LNs. METHODS: ASCC patients treated at Skåne University Hospital between 2009 and 2017 were eligible for inclusion if at least one positive LN according to PET-CT and a pre-treatment MRI were present. All PET-CT-positive LNs and PET-CT-negative LNs were retrospectively identified on baseline MRI. Each LN was independently classified according to pre-determined morphological characteristics by two radiologists blinded to clinical patient information. RESULTS: Sixty-seven ASCC patients were included, with a total of 181 PET-CT-positive LNs identified on baseline MRI with a median short-axis diameter of 9.0 mm (range 7.5-12 mm). MRI morphological characteristics of PET-CT-positive LNs included regular contour (87%), round shape (89%), and homogeneous signal intensity on T2-weighed images (67%). An additional 78 PET-CT-negative LNs were identified on MRI. These 78 LNs had a median size of 6.8 mm (range 5.5-8.0 mm). The majority of PET-CT-negative LNs had a regular contour, round shape, and a homogeneous signal that was congruent to the primary tumor. CONCLUSIONS: There are MRI-specific morphological characteristics for pelvic LNs in ASCC. PET-CT-positive and negative LNs share similar morphological features apart from size, with PET-CT-positive LNs being significantly larger. Further studies are needed to determine discrimination criteria for LNM in ASCC.

Metabolic tumour volume in Hodgkin lymphoma-A comparison between manual and AI-based analysis.

AIM: To compare total metabolic tumour volume (tMTV), calculated using two artificial intelligence (AI)-based tools, with manual segmentation by specialists as the reference. METHODS: Forty-eight consecutive Hodgkin lymphoma (HL) patients staged with [18F] fluorodeoxyglucose positron emission tomography/computed tomography were included. The median age was 35 years (range: 7-75), 46% female. The tMTV was automatically measured using the AI-based tools positron emission tomography assisted reporting system (PARS) (from Siemens) and RECOMIA (recomia.org) without any manual adjustments. A group of eight nuclear medicine specialists manually segmented lesions for tMTV calculations; each patient was independently segmented by two specialists. RESULTS: The median of the manual tMTV was 146 cm3 (interquartile range [IQR]: 79-568 cm3) and the median difference between two tMTV values segmented by different specialists for the same patient was 26 cm3 (IQR: 10-86 cm3). In 22 of the 48 patients, the manual tMTV value was closer to the RECOMIA tMTV value than to the manual tMTV value segmented by the second specialist. In 11 of the remaining 26 patients, the difference between the RECOMIA tMTV and the manual tMTV was small (<26 cm3, which was the median difference between two manual tMTV values from the same patient). The corresponding numbers for PARS were 18 and 10 patients, respectively. CONCLUSION: The results of this study indicate that RECOMIA and Siemens PARS AI tools could be used without any major manual adjustments in 69% (33/48) and 58% (28/48) of HL patients, respectively. This demonstrates the feasibility of using AI tools to support physicians measuring tMTV for assessment of prognosis in clinical practice.

Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging.

Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has emerged as an important imaging technique for prostate cancer. The use of PSMA PET/CT is rapidly increasing, while the number of nuclear medicine physicians and radiologists to interpret these scans is limited. Additionally, there is variability in interpretation among readers. Artificial intelligence techniques, including traditional machine learning and deep learning algorithms, are being used to address these challenges and provide additional insights from the images. The aim of this scoping review was to summarize the available research on the development and applications of AI in PSMA PET/CT for prostate cancer imaging. A systematic literature search was performed in PubMed, Embase and Cinahl according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 publications were included in the synthesis. The included studies focus on different aspects of artificial intelligence in PSMA PET/CT, including detection of primary tumor, local recurrence and metastatic lesions, lesion classification, tumor quantification and prediction/prognostication. Several studies show similar performances of artificial intelligence algorithms compared to human interpretation. Few artificial intelligence tools are approved for use in clinical practice. Major limitations include the lack of external validation and prospective design. Demonstrating the clinical impact and utility of artificial intelligence tools is crucial for their adoption in healthcare settings. To take the next step towards a clinically valuable artificial intelligence tool that provides quantitative data, independent validation studies are needed across institutions and equipment to ensure robustness.

[18F]PSMA-1007 PET is comparable to [99mTc]Tc-DMSA SPECT for renal cortical imaging.

BACKGROUND: Scintigraphy using technetium-99m labelled dimercaptosuccinic acid ([99mTc]Tc-DMSA), taken up in the proximal tubules, is the standard in functional imaging of the renal cortex. Recent guidelines recommend performing [99mTc]Tc-DMSA scintigraphy with single photon emission computed tomography (SPECT). Prostate-specific membrane antigen (PSMA) targeted positron emission tomography (PET) is used for staging and localization of recurrence in prostate cancer. A high renal uptake is often seen on PSMA PET, concordant with known PSMA expression in proximal tubules. This suggests PSMA PET could be used analogous to [99mTc]Tc-DMSA scintigraphy for renal cortical imaging. [18F]PSMA-1007 is a promising radiopharmaceutical for this purpose due to low urinary clearance. In this study, we aimed to compare [18F]PSMA-1007 PET to [99mTc]Tc-DMSA SPECT regarding split renal uptake and presence of renal uptake defects, in patients with prostate cancer. Three readers interpreted PET and SPECT images regarding presence of renal uptake defects, with each kidney split into cranial, mid and caudal segments. Kidneys were segmented in PET and SPECT images, and left renal uptake as a percentage of total renal uptake was measured. RESULTS: Twenty patients with prostate cancer were included. 2 participants had single kidneys; thus 38 kidneys were evaluated. A total of 29 defects were found on both [99mTc]Tc-DMSA SPECT and [18F]PSMA-1007 PET. Cohen's kappa for concordance regarding presence of any defect was 0.76 on a per-segment basis and 0.67 on a per-kidney basis. Spearman's r for left renal uptake percentage between [99mTc]Tc-DMSA SPECT and [18F]PSMA-1007 PET was 0.95. CONCLUSIONS: [18F]PSMA-1007 PET is comparable to [99mTc]Tc-DMSA SPECT for detection of uptake defects in this setting. Measurements of split renal function made using [18F]PSMA-1007 PET are valid and strongly correlated to measurements made with [99mTc]Tc-DMSA SPECT.

AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT.

BACKGROUND: Segmenting the whole-body somatostatin receptor-expressing tumour volume (SRETVwb) on positron emission tomography/computed tomography (PET/CT) images is highly time-consuming but has shown value as an independent prognostic factor for survival. An automatic method to measure SRETVwb could improve disease status assessment and provide a tool for prognostication. This study aimed to develop an artificial intelligence (AI)-based method to detect and quantify SRETVwb and total lesion somatostatin receptor expression (TLSREwb) from [68Ga]Ga-DOTA-TOC/TATE PET/CT images. METHODS: A UNet3D convolutional neural network (CNN) was used to train an AI model with [68Ga]Ga-DOTA-TOC/TATE PET/CT images, where all tumours were manually segmented with a semi-automatic method. The training set consisted of 148 patients, of which 108 had PET-positive tumours. The test group consisted of 30 patients, of which 25 had PET-positive tumours. Two physicians segmented tumours in the test group for comparison with the AI model. RESULTS: There were good correlations between the segmented SRETVwb and TLSREwb by the AI model and the physicians, with Spearman rank correlation coefficients of r = 0.78 and r = 0.73, respectively, for SRETVwb and r = 0.83 and r = 0.81, respectively, for TLSREwb. The sensitivity on a lesion detection level was 80% and 79%, and the positive predictive value was 83% and 84% when comparing the AI model with the two physicians. CONCLUSION: It was possible to develop an AI model to segment SRETVwb and TLSREwb with high performance. A fully automated method makes quantification of tumour burden achievable and has the potential to be more widely used when assessing PET/CT images.

FDG-PET/CT for lymph node staging prior to radical cystectomy.

BACKGROUND: 18F-Fluorodeoxyglucose positron emission combined with computed tomography (FDG-PET/CT) has been proposed to improve preoperative staging in patients with bladder cancer subjected to radical cystectomy (RC). OBJECTIVE: Our aim was to assess the accuracy of FDG-PET/CT for lymph node staging ascertained at the multidisciplinary tumour board compared to lymph node status in the surgical lymphadenectomy specimen obtained at RC, and to explore potential factors associated with false-positive FDG-PET/CT results. DESIGN, SETTING AND PARTICIPANTS: Consecutive patients with bladder cancer undergoing RC with extended lymph node dissection between 2011 and 2019 without preoperative chemotherapy in a tertial referral cystectomy unit were included in the study. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES: Sensitivity, specificity, positive and negative predictive values and likelihood ratios were calculated. Potential factors investigated for association with false-positive FDG-PET/CT were; bacteriuria within four weeks prior to FDG-PET/CT, Bacillus Calmette-Guerin (BCG) treatment within 12 months prior to FDG-PET/CT and transurethral resection of bladder tumour (TURB) within four weeks prior to FDG-PET/CT. RESULTS: Among 157 patients included for analysis, 44 (28%) were clinically node positive according to FDG-PET/CT. The sensitivity and specificity for detection of lymph node metastasis were 50% and 84%, respectively, and the corresponding positive predictive and negative predictive values were 61% and 76%. Positive and negative likelihood ratios were 3.0 and 0.6, respectively. No association was found between bacteriuria, previous BCG treatment or TURB within 28 days and false-positive FDG-PET/CT results. CONCLUSIONS: Preoperative FDG-PET/CT prior to RC had a clinically meaningful high specificity (84%) but lower sensitivity (50%) for detection of lymph node metastases compared to lymph node status in an extended pelvic lymphadenectomy template. We could not identify any factors associated with false-positive FDG-PET/CT outcomes.

PET/CT imaging 2 h after injection of [18F]PSMA-1007 can lead to higher staging of prostate cancer than imaging after 1 h.

BACKGROUND: [18F]PSMA-1007 is a prostate specific membrane antigen (PSMA) ligand for positron emission tomography (PET) imaging of prostate cancer. Current guidelines recommend imaging 90-120 min after injection but strong data about optimal timing is lacking. Our aim was to study whether imaging after 1 h and 2 h leads to a different number of detected lesions, with a specific focus on lesions that might lead to a change in treatment. METHODS: 195 patients underwent PET with computed tomography imaging 1 and 2 h after injection of [18F]PSMA-1007. Three readers assessed the status of the prostate or prostate bed and suspected metastases. We analyzed the location and number of found metastases to determine N- and M-stage of patients. We also analyzed standardized uptake values (SUV) in lesions and in normal tissue. RESULTS: Significantly more pelvic lymph nodes and bone metastases were found and higher N- and M-stages were seen after 2 h. In twelve patients (6.1%) two or three readers agreed on a higher N- or M-stage after 2 h. Conversely, in two patients (1.0%), two readers agreed on a higher stage at 1 h. SUVs in suspected malignant lesions and in normal tissues were higher at 2 h, but lower in the blood pool and urinary bladder. CONCLUSIONS: Imaging at 2 h after injection of [18F]PSMA-1007 leads to more suspected metastases found than after 1 h, with higher staging in some patients and possible effect on patient treatment.

Artificial Intelligence Increases the Agreement among Physicians Classifying Focal Skeleton/Bone Marrow Uptake in Hodgkin's Lymphoma Patients Staged with [18F]FDG PET/CT-a Retrospective Study.

Purpose: Classification of focal skeleton/bone marrow uptake (BMU) can be challenging. The aim is to investigate whether an artificial intelligence-based method (AI), which highlights suspicious focal BMU, increases interobserver agreement among a group of physicians from different hospitals classifying Hodgkin's lymphoma (HL) patients staged with [18F]FDG PET/CT. Methods: Forty-eight patients staged with [18F]FDG PET/CT at Sahlgenska University Hospital between 2017 and 2018 were reviewed twice, 6 months apart, regarding focal BMU. During the second time review, the 10 physicians also had access to AI-based advice regarding focal BMU. Results: Each physician's classifications were pairwise compared with the classifications made by all the other physicians, resulting in 45 unique pairs of comparisons both without and with AI advice. The agreement between the physicians increased significantly when AI advice was available, which was measured as an increase in mean Kappa values from 0.51 (range 0.25-0.80) without AI advice to 0.61 (range 0.19-0.94) with AI advice (p = 0.005). The majority of the physicians agreed with the AI-based method in 40 (83%) of the 48 cases. Conclusion: An AI-based method significantly increases interobserver agreement among physicians working at different hospitals by highlighting suspicious focal BMU in HL patients staged with [18F]FDG PET/CT.

Conservative Management First Strategy in Aortic Vascular Graft and Endograft Infections.

OBJECTIVE: The aim of this study was to describe and present the outcomes of a specific treatment protocol for aortic vascular graft and endograft infections (VGEIs) without explantation of the infected graft. METHODS: This was a retrospective, observational single centre cohort study carried out between 2012 and 2022 at a tertiary hospital. An aortic VGEI was defined according to the Management of Aortic Graft Infection Collaboration (MAGIC) criteria. Fitness for graft excision was assessed by a multidisciplinary team and included an evaluation of the patient's general condition, septic status, and anatomical complexity. Antimicrobial treatments were individualised. The primary outcome was survival at the last available follow up; secondary outcomes were antimicrobial treatment duration, infection eradication, treatment failure despite antimicrobial treatment, and the development of aortic fistulation. RESULTS: Fifty patients were included in the study, of whom 42 (84%) had had previous endovascular repair. The median patient age was 72 years (range 51 - 82 years) and median duration of treatment with antimicrobials was 18 months (range 1 - 164 months). Kaplan-Meier analysis estimated the 30 day survival to be 98% (95% confidence interval [CI] 96 - 100), the one year survival rate to be 88% (95% CI 83.4 - 92.6), and the three year survival rate to be 79% (95% CI 72.7 - 84.7). Twenty-four (48%) patients were able to discontinue antibiotic treatment after a median of 16 months (range 4 - 81 months). When categorised according to infected graft location, deaths occurred in four (40%) patients with thoracic, two (40%) with paravisceral, seven (30%) with infrarenal VGEIs, and in one (25%) patient with an aorto-iliac VGEI; no (0%) patient with a thoraco-abdominal VGEI died. CONCLUSION: Identifying the microbiological aetiology in patients with aortic VGEI enables individualised, specific antibiotic treatment, which may be useful in patients with a VGEI excluded from surgery. This single centre retrospective analysis of patients with VGEIs without fistula selected for conservative treatment suggests that conservative management of aortic VGEIs with targeted antibiotic therapy without graft excision is potentially effective, and that antimicrobial treatment will not necessarily be needed indefinitely.

Metabolic parameters of [18F]FDG PET-CT before and after radiotherapy may predict survival and recurrence in cervical cancer.

BACKGROUND: Cervical cancer is the fourth most common female malignancy. [18F]-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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Automated quantification of PET/CT skeletal tumor burden in prostate cancer using artificial intelligence: The PET index.

PURPOSE: Consistent assessment of bone metastases is crucial for patient management and clinical trials in prostate cancer (PCa). We aimed to develop a fully automated convolutional neural network (CNN)-based model for calculating PET/CT skeletal tumor burden in patients with PCa. METHODS: A total of 168 patients from three centers were divided into training, validation, and test groups. Manual annotations of skeletal lesions in [18F]fluoride PET/CT scans were used to train a CNN. The AI model was evaluated in 26 patients and compared to segmentations by physicians and to a SUV 15 threshold. PET index representing the percentage of skeletal volume taken up by lesions was estimated. RESULTS: There was no case in which all readers agreed on prevalence of lesions that the AI model failed to detect. PET index by the AI model correlated moderately strong to physician PET index (mean r = 0.69). Threshold PET index correlated fairly with physician PET index (mean r = 0.49). The sensitivity for lesion detection was 65-76% for AI, 68-91% for physicians, and 44-51% for threshold depending on which physician was considered reference. CONCLUSION: It was possible to develop an AI-based model for automated assessment of PET/CT skeletal tumor burden. The model's performance was superior to using a threshold and provides fully automated calculation of whole-body skeletal tumor burden. It could be further developed to apply to different radiotracers. Objective scan evaluation is a first step toward developing a PET/CT imaging biomarker for PCa skeletal metastases.

[18 F]PSMA-1007 renal uptake parameters: Reproducibility and relationship to estimated glomerular filtration rate.

BACKGROUND: Scintigraphy with technetium-99m-labelled dimercaptosuccinic acid ([99m Tc]Tc-DMSA) is widely used for renal cortical imaging. Uptake of [99m Tc]Tc-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. [68 Ga]Ga-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 [18 F]PSMA-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 [18 F]PSMA-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 [18 F]PSMA-1007 uptake parameters correlate with eGFR and are indicative of renal cortical function.

Sentinel Node Identification with Hybrid Tracer-guided and Conventional Dynamic Sentinel Node Biopsy in Penile Cancer: A Prospective Study in 130 Patients from the Two National Referral Centres in Sweden.

BACKGROUND: Studies suggest that a hybrid indocyanine green (ICG)-99mTc-nanocolloid tracer improves sentinel node (SN) identification compared to conventional dynamic sentinel node biopsy (DSNB). OBJECTIVE: To investigate hybrid tracer-guided SN identification in a multicentre setting and determine false-negative (FN) and complication rates. DESIGN, SETTING, AND PARTICIPANTS: A total of 130 patients with penile cancer scheduled for DSNB were prospectively included between February 2016 and December 2017 at two national Swedish referral centres. ICG-99mTc-nanocolloid hybrid tracer was used in the standard DSNB protocol. INTERVENTION: SNs were identified intraoperatively using radioguidance, fluorescence imaging, and blue dye. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The number of SNs identified by each tracer and the rates of complications and nodal recurrence during median follow-up of 34 mo were recorded. Differences in proportions between groups were compared using χ2 and McNemar's tests. RESULTS AND LIMITATIONS: Overall, 453 SNs were identified preoperatively via single-photon emission computed tomography/computed tomography. Among the 425 SNs excised, radioguidance, fluorescence, and blue dye identified 414 (97%), 363 (85%), and 349 (82%), respectively. Fluorescence imaging helped to detect six SNs that were negative using the other tracers, two of which were from the same patient and contained metastases. Histopathological examination detected 33 metastatic SNs in 20/130 patients (15%). The FN rate was 12% per groin (95% confidence interval 8-16%). CONCLUSIONS: Identification of SNs in patients with penile cancer relies mainly on radioguidance, while fluorescence (ICG) and blue dye methods for optical SN identification are comparable. However, the value of fluorescence imaging should be further evaluated in studies with long-term follow-up. PATIENT SUMMARY: In this study, we investigated addition of a dye called indocyanine green (ICG) for assessment of lymph nodes in patients with cancer of the penis. ICG did not improve the rate of detection of nodes most likely to harbour cancer because of their location in the drainage pathway for lymphatic fluid, but did help in identifying additional metastases.

Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [18F]-PSMA-1007 PET-CT.

Here, we aimed to develop and validate a fully automated artificial intelligence (AI)-based method for the detection and quantification of suspected prostate tumour/local recurrence, lymph node metastases, and bone metastases from [18F]PSMA-1007 positron emission tomography-computed tomography (PET-CT) images. Images from 660 patients were included. Segmentations by one expert reader were ground truth. A convolutional neural network (CNN) was developed and trained on a training set, and the performance was tested on a separate test set of 120 patients. The AI method was compared with manual segmentations performed by several nuclear medicine physicians. Assessment of tumour burden (total lesion volume (TLV) and total lesion uptake (TLU)) was performed. The sensitivity of the AI method was, on average, 79% for detecting prostate tumour/recurrence, 79% for lymph node metastases, and 62% for bone metastases. On average, nuclear medicine physicians' corresponding sensitivities were 78%, 78%, and 59%, respectively. The correlations of TLV and TLU between AI and nuclear medicine physicians were all statistically significant and ranged from R = 0.53 to R = 0.83. In conclusion, the development of an AI-based method for prostate cancer detection with sensitivity on par with nuclear medicine physicians was possible. The developed AI tool is freely available for researchers.

Biokinetics and dosimetry of 18 F-PSMA-1007 in patients with prostate cancer.

PURPOSE: Positron emission tomography-computed tomography (PET-CT) using prostate-specific membrane antigen (PSMA) ligands is a method for imaging prostate cancer. A recent tracer, 18 F-PSMA-1007, offers advantages concerning production and biokinetics compared to the standard tracer (68 Ga-PSMA-11). Until now, radiation dosimetry data for this ligand was limited to the material of three healthy volunteers. The purpose of this study is to study the biokinetics and dosimetry of 18 F-PSMA-1007. METHODS: Twelve patients with prostate cancer were injected with 4 MBq/kg 18 F-PSMA-1007. Eight PET-CT scans with concomitant blood sampling were performed up to 330 min after injection. Urine was collected until the following morning. Volumes of interest for radiation-sensitive organs and organs with high uptake of 18 F-PSMA-1007 were drawn in the PET images. A biokinetic compartment model was developed using activity data from PET images and blood and urine samples. Time-activity curves and time-integrated activity coefficients for all delineated organs were calculated. The software IDAC-dose 2.1 was used to calculate the absorbed and effective doses. RESULTS: High concentrations of activity were noted in the liver, kidneys, parts of the small intestine, spleen, salivary glands, and lacrimal glands. The elimination through urine was 8% of injected activity in 20 h. The highest absorbed doses coefficients were in the lacrimal glands, kidneys, salivary glands, liver, and spleen (98-66 µGy/MBq). The effective dose coefficient was 25 µSv/MBq. CONCLUSION: The effective dose of 18 F-PSMA-1007 is 6.0-8.0 mSv for a typical patient weighing 80 kg injected with 3-4 MBq/kg.

Assessing the accuracy of [18F]PSMA-1007 PET/CT for primary staging of lymph node metastases in intermediate- and high-risk prostate cancer patients.

BACKGROUND: [18F]PSMA-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 [18F]PSMA-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: [18F]PSMA-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 [18F]PSMA-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 [18F]PSMA-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, [18F]PSMA-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, [18F]PSMA-1007 PET/CT cannot completely replace ePLND. 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.