5 0 1What ' s known on the subject? and What does the study add? Positron emission tomography/computed tomography (PET/CT) with choline and fl uoride for the detection of metastases in patients with prostate cancer have each been evaluated, with mixed results. Choline PET/CT has been evaluated against pelvic lymphadenectomy, generally with a low sensitivity but a high specifi city; however, the study populations have been heterogenous. Fluoride PET/CT has been evaluated against other imaging methods, such as bone scan, single photon emission CT and MRI, and has been shown to have high specifi city as well as sensitivity for bone metastases, but there are no studies with biopsy verifi cation. This is the fi rst study that evaluates the clinical use of both choline and fl uoride PET/CT on the same patients in a well-defi ned population of patients with high-risk prostate cancer. OBJECTIVE• To investigate how often positron emission tomography/computed tomography (PET/CT) scans, with both 18 F-fl uorocholine and 18 F-fl uoride as markers, add clinically relevant information for patients with prostate cancer who have high-risk tumours and a normal or inconclusive planar bone scan. PATIENTS AND METHODS• Patients with prostate cancer with prostate specifi c antigen (PSA) levels between 20 and 99 ng/mL and/or Gleason score 8 -10 tumours, planned for treatment with curative intent based on routine staging with a negative or inconclusive bone scan, were further investigated with a 18 F-fl uorocholine and a 18 F-fl uoride PET/CT.• None of the patients received hormonal therapy before the staging procedures were completed. RESULTS• For 50 of the 90 included patients (56%) one or both PET/CT scans indicated metastases.• 18 F-fl uorocholine PET/CT indicated lymph node metastases and/or bone metastases in 35 patients (39%).• 18 F-fl uoride PET/CT was suggestive for bone metastases in 37 patients (41%).• In 18 patients (20%) the PET/CT scans indicated widespread metastases, leading to a change in therapy intent from curative to non-curative.• Of the patients with positive scans, 74% had Gleason score 8 -10 tumours. Of the patients with Gleason score 8 -10 tumours, 64% had positive scans. CONCLUSIONS• PET/CT scans with 18 F-fl uorocholine and 18 F-fl uoride commonly detect metastases in patients with high-risk prostate cancer and a negative or inconclusive bone scan.• For 20% of the patients the results of the PET/CT scans changed the treatment plan. KEYWORDS INTRODUCTIONThe most common sites for metastases from prostate cancer are the pelvic and retroperitoneal lymph nodes and the axial skeleton. Radical local therapy is usually not considered for patients with skeletal metastases, widespread or large lymph node metastases. The standard of reference for detection of lymph node metastases is an extended pelvic lymph node dissection (ePLND) [ 1,2 ] . However, this procedure is invasive and associated with complications and morbidity [ 3 ] . Positron emission tomography (PET) with 11 C-or 18 F-labelledAccepted for publication 20...
Background Body composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images. Methods Ethical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3 days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations. Results The accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p < 0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of ± 20%. Conclusions The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.
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