Machine learning-based analysis of [¹⁸F]DCFPyL PET radiomics for risk stratification in primary prostate cancer

PURPOSE: Quantitative prostate-specific membrane antigen (PSMA) PET analysis may provide for non-invasive and objective risk stratification of primary prostate cancer (PCa) patients. We determined the ability of machine learning-based analysis of quantitative [18F]DCFPyL PET metrics to predict metastatic disease or high-risk pathological tumor features. METHODS: In a prospective cohort study, 76 patients with intermediate- to high-risk PCa scheduled for robot-assisted radical prostatectomy with extended pelvic lymph node dissection underwent pre-operative [18F]DCFPyL PET-CT. Primary tumors were delineated using 50-70% peak isocontour thresholds on images with and without partial-volume correction (PVC). Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). For comparison, models were also trained using standard PET features (SUVs, volume, total PSMA uptake). Model performance was validated using 50 times repeated 5-fold cross-validation yielding the mean receiver-operator characteristic curve AUC. RESULTS: The radiomics-based machine learning models predicted LNI (AUC 0.86 ± 0.15, p < 0.01), nodal or distant metastasis (AUC 0.86 ± 0.14, p < 0.01), Gleason score (0.81 ± 0.16, p < 0.01), and ECE (0.76 ± 0.12, p < 0.01). The highest AUCs reached using standard PET metrics were lower than those of radiomics-based models. For LNI and metastasis prediction, PVC and a higher delineation threshold improved model stability. Machine learning pre-processing methods had a minor impact on model performance. CONCLUSION: Machine learning-based analysis of quantitative [18F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. These findings indicate that PSMA expression detected on PET is related to both primary tumor histopathology and metastatic tendency. Multicenter external validation is needed to determine the benefits of using radiomics versus standard PET metrics in clinical practice

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function

Repeatability of Quantitative 18F-DCFPyL PET/CT Measurements in Metastatic Prostate Cancer

Quantitative evaluation of radiolabeled prostate-specific membrane antigen (PSMA) PET scans may be used to monitor treatment response in patients with prostate cancer (PCa). To interpret longitudinal differences in PSMA uptake, the intrinsic variability of tracer uptake in PCa lesions needs to be defined. The aim of this study was to investigate the repeatability of quantitative PET/CT measurements using 18F-DCFPyL ([2-(3-(1-carboxy-5-[(6-18F-fluoro-pyridine-3-carbonyl)-amino]-pentyl)-ureido)-pentanedioic acid], a second-generation 18F-PSMA-ligand) in patients with PCa. Methods: Twelve patients with metastatic PCa were prospectively included, of whom 2 were excluded from final analyses. Patients received 2 whole-body 18F-DCFPyL PET/CT scans (median dose, 317 MBq; uptake time, 120 min) within a median of 4 d (range, 1-11 d). After semiautomatic (isocontour-based) tumor delineation, the following lesion-based metrics were derived: mean, peak, and maximum tumor-to-blood ratio; SUVmean, SUVpeak, and SUVmax normalized to body weight; tumor volume; and total lesion uptake (TLU). Additionally, patient-based total tumor volume (TTV) (sum of PSMA-positive tumor volumes) and total tumor burden (TTB) (sum of all lesion TLUs) were derived. Repeatability was analyzed using repeatability coefficients (RC) and intraclass correlation coefficients. Additionally, the effect of point-spread function (PSF) image reconstruction on the repeatability of uptake metrics was evaluated. Results: In total, 36 18F-DCFPyL PET-positive lesions were analyzed (≤5 lesions per patient). The RCs for mean, peak, and maximum tumor-to-blood ratio were 31.8%, 31.7%, and 37.3%, respectively. For SUVmean, SUVpeak, and SUVmax, the RCs were 24.4%, 25.3%, and 31.0%, respectively. All intraclass correlation coefficients were at least 0.97. Tumor volume delineations were quite repeatable, with an RC of 28.1% for individual lesion volumes and 17.0% for TTV. TTB had an RC of 23.2% and 33.4% when based on SUVmean and mean tumor-to-blood ratio, respectively. Small lesions (<4.2 cm3) had worse repeatability for volume measurements. The repeatability of SUVpeak, TLU, and all patient-level metrics was not affected by PSF reconstruction. Conclusion:18F-DCFPyL uptake measurements are quite repeatable and can be used for clinical validation in future treatment response assessment studies. Patient-based TTV may be preferred for multicenter studies because its repeatability was both high and robust to different image reconstructions

Healthy Tissue Uptake of Ga-68-Prostate-Specific Membrane Antigen, F-18-DCFPyL, F-18-Fluoromethylcholine, and F-18-Dihydrotestosterone

PET is increasingly used for prostate cancer (PCa) diagnostics. Important PCa radiotracers include 68Ga-prostate-specific membrane antigen HBED-CC ( 68Ga-PSMA), 18F-DCFPyL, 18F-fluoromethylcholine ( 18F-FCH), and 18F-dihydrotestosterone ( 18F-FDHT). Knowledge on the variability of tracer uptake in healthy tissues is important for accurate PET interpretation, because malignancy is suspected only if the uptake of a lesion contrasts with its background. Therefore, the aim of this study was to quantify uptake variability of PCa tracers in healthy tissues and identify stable reference regions for PET interpretation. Methods: A total of 232 PCa PET/CT scans from multiple hospitals was analyzed, including 87 68Ga-PSMA scans, 50 18F-DCFPyL scans, 68 18F-FCH scans, and 27 18F-FDHT scans. Tracer uptake was assessed in the blood pool, lung, liver, bone marrow, and muscle using several SUVs (SUV max, SUV mean, SUV peak). Variability in uptake between patients was analyzed using the coefficient of variation (COV%). For all tracers, SUV reference ranges (95th percentiles) were calculated, which could be applicable as image-based quality control for future PET acquisitions. Results: For 68Ga-PSMA, the lowest uptake variability was observed in the blood pool (COV, 19.9%), which was significantly more stable than all other tissues (COV, 29.8%-35.2%; P = 0.001-0.024). For 18F-DCFPyL, the lowest variability was observed in the blood pool and liver (COV, 14.4% and 21.7%, respectively; P = 0.001-0.003). The least variable 18F-FCH uptake was observed in the liver, blood pool, and bone marrow (COV, 16.8%-24.2%; P = 0.001-0.012). For 18F-FDHT, low uptake variability was observed in all tissues, except the lung (COV, 14.6%-23.6%; P = 0.001-0.040). The different SUV types had limited effect on variability (COVs within 3 percentage points). Conclusion: In this multicenter analysis, healthy tissues with limited uptake variability were identified, which may serve as reference regions for PCa PET interpretation. These reference regions include the blood pool for 68Ga-PSMA and 18F-DCFPyL and the liver for 18F-FCH and 18F-FDHT. Healthy tissue SUV reference ranges are presented and applicable as image-based quality control

Prostate Specific Membrane Antigen Positron Emission Tomography/Computerized Tomography in the Evaluation of Initial Response in Candidates Who Underwent Salvage Radiation Therapy after Radical Prostatectomy for Prostate Cancer

PURPOSE: We assessed predictors of short-term oncologic outcomes of patients who underwent salvage radiation therapy for biochemical recurrence after robot-assisted laparoscopic radical prostatectomy without evidence of metastases on prostate specific membrane antigen positron emission tomography/computerized tomography. MATERIALS AND METHODS: We retrospectively analyzed 194 patients with biochemical recurrence after robot-assisted laparoscopic radical prostatectomy who underwent prostate specific membrane antigen positron emission tomography/computerized tomography prior to salvage radiation therapy. Patients with lymph node or distant metastases on restaging imaging or at the time of extended pelvic lymph node dissection during robot-assisted laparoscopic radical prostatectomy were excluded, as were patients who received androgen deprivation therapy during or prior to salvage radiation therapy. A multivariable logistic regression analysis was performed to assess predictors of treatment response, defined as prostate specific antigen value ≤0.1 ng/ml after salvage radiation therapy. RESULTS: Overall treatment response after salvage radiation therapy was 75% (146/194 patients). On multivariable analysis, prostate specific antigen value at initiation of salvage radiation therapy (OR 0.42, 95% CI 0.27-0.62, p <0.001), pathological T stage (pT3a vs pT2 OR 0.28, 95% CI 0.11-0.69, p=0.006; pT3b vs pT2 OR 0.26, 95% CI 0.09-0.71, p=0.009) and local recurrent disease on imaging (OR 5.53, 95% CI 1.96-18.52, p=0.003) were predictors of treatment response. CONCLUSIONS: Salvage radiation therapy in patients without evidence of metastases on prostate specific membrane antigen positron emission tomography/computerized tomography showed a good overall treatment response of 75%. Higher treatment response rates were observed in patients with lower prostate specific antigen values at initiation of salvage radiation therapy, those with local recurrent disease on imaging and those with lower pathological T stage (pT2 vs pT3a/b)