Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [F-18]-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 [F-18]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\u27 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

Triple dosing with high doses of buprenorphine: Withdrawal and plasma concentrations.

In maintenance treatment for opiate addiction, buprenorphine can be administered less frequently than daily due to its long half-life

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. 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

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

Abstract 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

Biokinetics and dosimetry of 18F-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, 18F-PSMA-1007, offers advantages concerning production and biokinetics compared to the standard tracer (68Ga-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 18F-PSMA-1007. Methods: Twelve patients with prostate cancer were injected with 4 MBq/kg 18F-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 18F-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 18F-PSMA-1007 is 6.0–8.0 mSv for a typical patient weighing 80 kg injected with 3–4 MBq/kg

Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians

Purpose: The aim of this study was to develop and validate an artificial intelligence (AI)-based method using convolutional neural networks (CNNs) for the detection of pelvic lymph node metastases in scans obtained using [18F]PSMA-1007 positron emission tomography-computed tomography (PET-CT) from patients with high-risk prostate cancer. The second goal was to make the AI-based method available to other researchers. Methods: [18F]PSMA PET-CT scans were collected from 211 patients. Suspected pelvic lymph node metastases were marked by three independent readers. A CNN was developed and trained on a training and validation group of 161 of the patients. The performance of the AI method and the inter-observer agreement between the three readers were assessed in a separate test group of 50 patients. Results: The sensitivity of the AI method for detecting pelvic lymph node metastases was 82%, and the corresponding sensitivity for the human readers was 77% on average. The average number of false positives was 1.8 per patient. A total of 5–17 false negative lesions in the whole cohort were found, depending on which reader was used as a reference. The method is available for researchers at www.recomia.org. Conclusion: This study shows that AI can obtain a sensitivity on par with that of physicians with a reasonable number of false positives. The difficulty in achieving high inter-observer sensitivity emphasizes the need for automated methods. On the road to qualifying AI tools for clinical use, independent validation is critical and allows performance to be assessed in studies from different hospitals. Therefore, we have made our AI tool freely available to other researchers