scholarly journals Short-duration dynamic [18F]DCFPyL PET and CT perfusion imaging to localize dominant intraprostatic lesions in prostate cancer: validation against digital histopathology and comparison to [18F]DCFPyL PET/MR at 120 minutes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dae-Myoung Yang ◽  
Ryan Alfano ◽  
Glenn Bauman ◽  
Jonathan D. Thiessen ◽  
Joseph Chin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Short Duration ◽  
Ct Perfusion ◽  
Dice Similarity Coefficient ◽  
Anatomical Landmarks ◽  
Fiducial Markers ◽  
Clinical Trial Registration ◽  
Ablative Therapies ◽  
Histopathological Images ◽  
Parametric Maps

Abstract Purpose Localized prostate cancer (PCa) in patients is characterized by a dominant focus in the gland (dominant intraprostatic lesion, DIL). Accurate DIL identification may enable more accurate diagnosis and therapy through more precise targeting of biopsy, radiotherapy and focal ablative therapies. The goal of this study is to validate the performance of [18F]DCFPyL PET and CT perfusion (CTP) for detecting and localizing DIL against digital histopathological images. Methods Multi-modality image sets: in vivo T2-weighted (T2w)-MRI, 22-min dynamic [18F]DCFPyL PET/CT, CTP, and 2-h post-injection PET/MR were acquired in patients prior to radical prostatectomy. The explanted gland with implanted fiducial markers was imaged with T2w-MRI. All images were co-registered to the pathologist-annotated digital images of whole-mount mid-gland histology sections using fiducial markers and anatomical landmarks. Regions of interest encompassing DIL and non-DIL tissue were drawn on the digital histopathological images and superimposed on PET and CTP parametric maps. Logistic regression with backward elimination of parameters was used to select the most sensitive parameter set to distinguish DIL from non-DIL voxels. Leave-one-patient-out cross-validation was performed to determine diagnostic performance. Results [18F]DCFPyL PET and CTP parametric maps of 15 patients were analyzed. SUVLate and a model combining Ki and k4 of [18F]DCFPyL achieved the most accurate performance distinguishing DIL from non-DIL voxels. Both detection models achieved an AUC of 0.90 and an error rate of < 10%. Compared to digital histopathology, the detected DILs had a mean dice similarity coefficient of 0.8 for the Ki and k4 model and 0.7 for SUVLate. Conclusions We have validated using co-registered digital histopathological images that parameters from kinetic analysis of 22-min dynamic [18F]DCFPyL PET can accurately localize DILs in PCa for targeting of biopsy, radiotherapy, and focal ablative therapies. Short-duration dynamic [18F]DCFPyL PET was not inferior to SUVLate in this diagnostic task. Clinical trial registration number: NCT04009174 (ClinicalTrials.gov).

scholarly journals Short-Duration Dynamic [18F]DCFPyL PET and CT Perfusion Imaging to Localize Dominant Intraprostatic Lesions in Prostate Cancer: Validation Against Digital Histopathology and Comparison to [18F]DCFPyL PET/MR at 120 Minutes

2021 ◽  
Author(s):  
Dae-Myoung Yang ◽  
Ryan Alfano ◽  
Glenn Bauman ◽  
Jonathan D Thiessen ◽  
Joseph Chin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Short Duration ◽  
Ct Perfusion ◽  
Dice Similarity Coefficient ◽  
Anatomical Landmarks ◽  
Fiducial Markers ◽  
Ablative Therapies ◽  
Histopathology Images ◽  
Parametric Maps

Abstract Purpose: Localized prostate cancer (PCa) in patients is characterized by a dominant focus in the gland (dominant intraprostatic lesion, DIL). Accurate DIL identification may enable more accurate diagnosis and therapy through more precise targeting of biopsy, radiotherapy and focal ablative therapies. The goal of this study is to validate the performance of [18F]DCFPyL PET and CT Perfusion (CTP) for detecting and localizing DIL against digital histopathology images. Methods: Multi-modality image sets: in-vivo T2-weighted (T2w) MRI, 22-min dynamic [18F]DCFPyL PET/CT, CTP, and 2-hr post-injection PET/MR were acquired in patients prior to radical prostatectomy. The explanted gland with implanted fiducial markers was imaged with T2w MRI. All images were co-registered to the pathologist-annotated digital images of whole-mount mid-gland histology sections using fiducial markers and anatomical landmarks. Regions of interest encompassing DIL and non-DIL tissue were drawn on the digital histopathology images and superimposed on PET and CTP parametric maps. Logistic regression with backward elimination of parameters was used to select the most sensitive parameter set to distinguish DIL from non-DIL voxels. Leave-one-patient-out cross-validation was performed to determine diagnostic performance. Results: [18F]DCFPyL PET and CTP parametric maps of 15 patients were analyzed. SUVLate and a model combining Ki and k4 of [18F]DCFPyL achieved the most accurate performance distinguishing DIL from non-DIL voxels. Both detection models achieved an AUC of 0.90 and an error rate of <10%. Compared to digital histopathology, the detected DILs had a mean dice similarity coefficient of 0.76 for the Ki and k4 model and 0.73 for SUVLate. Conclusions: We have validated using co-registered digital histopathology images that parameters from kinetic analysis of 22-min dynamic [18F]DCFPyL PET can accurately localize DILs in PCa for targeting of biopsy, radiotherapy, and focal ablative therapies. Short-duration dynamic [18F]DCFPyL PET was not inferior to SUVLate in this diagnostic task.Trial Registration: ClinicalTrials.gov, NCT04009174. Registered 12 February, 2012 – Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04009174?term=NCT04009174&draw=2&rank=1.

scholarly journals Cerebral CT Perfusion in Acute Stroke: The Effect of Lowering the Tube Load and Sampling Rate on the Reproducibility of Parametric Maps

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1121
Author(s):  
Georgios S. Ioannidis ◽  
Søren Christensen ◽  
Katerina Nikiforaki ◽  
Eleftherios Trivizakis ◽  
Kostas Perisinakis ◽  
...  
Keyword(s):  
Cerebral Blood Volume ◽  
Ct Perfusion ◽  
Sampling Rate ◽  
Volumetric Analysis ◽  
Sampling Interval ◽  
Diagnostic Efficiency ◽  
Temporal Sampling ◽  
Sampling Intervals ◽  
The Mean ◽  
Parametric Maps

The aim of this study was to define lower dose parameters (tube load and temporal sampling) for CT perfusion that still preserve the diagnostic efficiency of the derived parametric maps. Ninety stroke CT examinations from four clinical sites with 1 s temporal sampling and a range of tube loads (mAs) (100–180) were studied. Realistic CT noise was retrospectively added to simulate a CT perfusion protocol, with a maximum reduction of 40% tube load (mAs) combined with increased sampling intervals (up to 3 s). Perfusion maps from the original and simulated protocols were compared by: (a) similarity using a voxel-wise Pearson’s correlation coefficient r with in-house software; (b) volumetric analysis of the infarcted and hypoperfused volumes using commercial software. Pearson’s r values varied for the different perfusion metrics from 0.1 to 0.85. The mean slope of increase and cerebral blood volume present the highest r values, remaining consistently above 0.7 for all protocol versions with 2 s sampling interval. Reduction of the sampling rate from 2 s to 1 s had only modest impacts on a TMAX volume of 0.4 mL (IQR −1–3) (p = 0.04) and core volume of −1.1 mL (IQR −4–0) (p < 0.001), indicating dose savings of 50%, with no practical loss of diagnostic accuracy. The lowest possible dose protocol was 2 s temporal sampling and a tube load of 100 mAs.

scholarly journals Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiang Liu ◽  
Chao Han ◽  
Yingpu Cui ◽  
Tingting Xie ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Bone Metastases ◽  
Model Development ◽  
Whole Body ◽  
Pelvic Bone ◽  
Skeletal Metastases ◽  
Dice Similarity Coefficient ◽  
Patient Level ◽  
Lesion Level

ObjectiveTo establish and evaluate the 3D U-Net model for automated segmentation and detection of pelvic bone metastases in patients with prostate cancer (PCa) using diffusion-weighted imaging (DWI) and T1 weighted imaging (T1WI) images.MethodsThe model consisted of two 3D U-Net algorithms. A total of 859 patients with clinically suspected or confirmed PCa between January 2017 and December 2020 were enrolled for the first 3D U-Net development of pelvic bony structure segmentation. Then, 334 PCa patients were selected for the model development of bone metastases segmentation. Additionally, 63 patients from January to May 2021 were recruited for the external evaluation of the network. The network was developed using DWI and T1WI images as input. Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD) were used to evaluate the segmentation performance. Sensitivity, specificity, and area under the curve (AUC) were used to evaluate the detection performance at the patient level; recall, precision, and F1-score were assessed at the lesion level.ResultsThe pelvic bony structures segmentation on DWI and T1WI images had mean DSC and VS values above 0.85, and the HD values were &lt;15 mm. In the testing set, the AUC of the metastases detection at the patient level were 0.85 and 0.80 on DWI and T1WI images. At the lesion level, the F1-score achieved 87.6% and 87.8% concerning metastases detection on DWI and T1WI images, respectively. In the external dataset, the AUC of the model for M-staging was 0.94 and 0.89 on DWI and T1WI images.ConclusionThe deep learning-based 3D U-Net network yields accurate detection and segmentation of pelvic bone metastases for PCa patients on DWI and T1WI images, which lays a foundation for the whole-body skeletal metastases assessment.

The Use of Fiducial Markers for IGRT in Prostate Cancer

2008 ◽  
pp. 85-94
Author(s):  
BRIAN DAVIS ◽  
JON KRUSE ◽  
CHRISTOPHER GOULET ◽  
MICHAEL HERMAN
Keyword(s):  
Prostate Cancer ◽  
Fiducial Markers
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