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短时长动态[F]DCFPyL正电子发射断层显像(PET)与CT灌注成像用于定位前列腺癌中主要的前列腺内病变:与数字组织病理学对照验证及与120分钟时的[F]DCFPyL PET/MR比较

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

作者信息

Yang Dae-Myoung, Alfano Ryan, Bauman Glenn, Thiessen Jonathan D, Chin Joseph, Pautler Stephen, Moussa Madeleine, Gomez Jose A, Rachinsky Irina, Gaed Mena, Chung Kevin J, Ward Aaron, Lee Ting-Yim

机构信息

Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada.

Robarts Research Institute, The University of Western Ontario, London, ON, Canada.

出版信息

EJNMMI Res. 2021 Oct 15;11(1):107. doi: 10.1186/s13550-021-00844-0.

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 [F]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 [F]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

[F]DCFPyL PET and CTP parametric maps of 15 patients were analyzed. SUV and a model combining K and k of [F]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 K and k model and 0.7 for SUV.

CONCLUSIONS

We have validated using co-registered digital histopathological images that parameters from kinetic analysis of 22-min dynamic [F]DCFPyL PET can accurately localize DILs in PCa for targeting of biopsy, radiotherapy, and focal ablative therapies. Short-duration dynamic [F]DCFPyL PET was not inferior to SUV in this diagnostic task.

CLINICAL TRIAL REGISTRATION NUMBER

NCT04009174 (ClinicalTrials.gov).

摘要

目的

患者的局限性前列腺癌(PCa)的特征是腺体中有一个主要病灶(主要前列腺内病变,DIL)。准确识别DIL可通过更精确地靶向活检、放疗和局部消融治疗实现更准确的诊断和治疗。本研究的目的是对照数字组织病理学图像验证[F]DCFPyL PET和CT灌注(CTP)检测和定位DIL的性能。

方法

多模态图像集:在根治性前列腺切除术之前,为患者采集了体内T2加权(T2w)-MRI、22分钟动态[F]DCFPyL PET/CT、CTP以及注射后2小时PET/MR。用T2w-MRI对植入基准标记的离体腺体进行成像。使用基准标记和解剖标志将所有图像与病理学家标注的全层腺体中部组织学切片的数字图像进行配准。在数字组织病理学图像上绘制包含DIL和非DIL组织的感兴趣区域,并叠加在PET和CTP参数图上。使用参数向后消除的逻辑回归来选择区分DIL和非DIL体素的最敏感参数集。进行留一患者交叉验证以确定诊断性能。

结果

分析了15例患者的[F]DCFPyL PET和CTP参数图。SUV以及结合[F]DCFPyL的K和k的模型在区分DIL和非DIL体素方面表现出最准确的性能。两种检测模型的AUC均为0.90,错误率<10%。与数字组织病理学相比,检测到的DIL对于K和k模型的平均骰子相似系数为0.8,对于SUV为0.7。

结论

我们使用配准的数字组织病理学图像验证了,22分钟动态[F]DCFPyL PET动力学分析的参数可准确在PCa中定位DIL,用于活检、放疗和局部消融治疗的靶向。在这项诊断任务中,短时间动态[F]DCFPyL PET并不逊色于SUV。

临床试验注册号

NCT⁃04009⁃174(ClinicalTrials.gov)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a425/8519985/8a5051312642/13550_2021_844_Fig1_HTML.jpg

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