Introduction Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter‐reader variability. Artificial intelligence (AI)‐based methods can provide an objective image analysis. We aimed at developing and validating an AI‐based tool for detection of lymph node lesions. Methods A group of 399 patients with biopsy‐proven PCa who had undergone 18F‐choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI‐based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI‐based lymph node detections were compared to those of two independent readers. The association with PCa‐specific survival was investigated. Results The AI‐based tool detected more lymph node lesions than Reader B (98 vs. 87/117; p = .045) using Reader A as reference. AI‐based tool and Reader A showed similar performance (90 vs. 87/111; p = .63) using Reader B as reference. The number of lymph node lesions detected by the AI‐based tool, PSA, and curative treatment was significantly associated with PCa‐specific survival. Conclusion This study shows the feasibility of using an AI‐based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.
Summary Aim To test the feasibility of a fully automated artificial intelligence‐based method providing PET measures of prostate cancer (PCa). Methods A convolutional neural network (CNN) was trained for automated measurements in 18F‐choline (FCH) PET/CT scans obtained prior to radical prostatectomy (RP) in 45 patients with newly diagnosed PCa. Automated values were obtained for prostate volume, maximal standardized uptake value (SUVmax), mean standardized uptake value of voxels considered abnormal (SUVmean) and volume of abnormal voxels (Volabn). The product SUVmean × Volabn was calculated to reflect total lesion uptake (TLU). Corresponding manual measurements were performed. CNN‐estimated data were compared with the weighted surgically removed tissue specimens and manually derived data and related to clinical parameters assuming that 1 g ≈ 1 ml of tissue. Results The mean (range) weight of the prostate specimens was 44 g (20–109), while CNN‐estimated volume was 62 ml (31–108) with a mean difference of 13·5 g or ml (95% CI: 9·78–17·32). The two measures were significantly correlated (r = 0·77, P<0·001). Mean differences (95% CI) between CNN‐based and manually derived PET measures of SUVmax, SUVmean, Volabn (ml) and TLU were 0·37 (−0·01 to 0·75), −0·08 (−0·30 to 0·14), 1·40 (−2·26 to 5·06) and 9·61 (−3·95 to 23·17), respectively. PET findings Volabn and TLU correlated with PSA (P<0·05), but not with Gleason score or stage. Conclusion Automated CNN segmentation provided in seconds volume and simple PET measures similar to manually derived ones. Further studies on automated CNN segmentation with newer tracers such as radiolabelled prostate‐specific membrane antigen are warranted.
Background The use of molecular imaging in staging of prostate cancer (PC) is debated. In patients with newly diagnosed PC we investigated the diagnostic value of 18 F-flouromethylcholine positron emission tomography/computed tomography ( 18 F-FCH-PET/CT) for the detection of bone and lymph node metastases compared to whole-body bone scintigraphy (WBS) with technetium-99-methylene diphosphonate ( 99m Tc-MDP) and results of extended pelvic lymph node dissection, respectively. Materials and methods Between January 2013 and April 2016, 143 patients, aged 49-83, mean 69, years with newly diagnosed PC and disease characteristics necessitating WBS underwent both WBS and 18 F-FCH-PET/CT using magnetic resonance imaging as standard. Eighty of these patients underwent pelvic lymph node dissection as part of radical prostatectomy or prior to external beam radiation and in these results of 18 F-FCH-PET/CT were compared to histologic findings. Results Bone metastases were detected in 8/143 patients and sensitivity and specificity of WBS were 37.5% and 85.2% versus 100.0% and 96.3% with 18 F-FCH-PET/CT, P=0.63 and 0.002, respectively. Histologically confirmed metastases to regional lymph nodes were found in 25/80 patients. Suspicious choline uptake on PET/CT in pelvic lymph nodes was found in 35 patients. Sensitivity, specificity, PPV, NPV and accuracy of 18 F-FCH-PET/CT in detection of lymph node metastases were 62.5%, 69.6%, 46.9%, 81.3% and 67.5%, respectively. Conclusions Findings in this study suggested that 18 F-FCH-PET/CT is a more sensitive and specific method for detection of bone metastases from PC than WBS and could potentially reduce the need for confirmatory imaging if used instead of WBS. However, 18 F-FCH-PET/CT performs sub-optimally in pre-operative staging of lymph node metastases in patients undergoing extended pelvic lymph node dissection.
ObjectiveThis proof of concept study investigated whether dual time point FDG-PET/CT with image acquisition after 1 and 3 h could be useful in preoperative staging of patients undergoing robot-assisted radical prostatectomy and extended pelvic lymph node dissection for high-risk prostate cancer.ResultsTwenty patients with high-risk prostate cancer underwent dual time point FDG-PET/CT before undergoing surgery. Histologically confirmed lymph node metastases were found in 9/20 (45%). A median of 19 (range 10–41; n = 434) lymph nodes were removed per patient. Pelvic lymph nodes with detectable FDG uptake were seen in two patients only, but the FDG-avid lesion on PET did not correspond with pathological findings in either patient. We found a significant increase in maximal standardized uptake value of the prostate of around 30% between early and late imaging. We found no correlation between clinical findings after radical prostatectomy and PET measurements.
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