Machine Learning Facilitates Hotspot Classification in PSMA-PET/CT with Nuclear Medicine Specialist Accuracy.
Machine Learning Facilitates Hotspot Classification in PSMA-PET/CT with Nuclear Medicine Specialist Accuracy.
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DOI：
10.3390/diagnostics10090622
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发表时间：
2020-08-22
期刊：
影响因子：
--
通讯作者：
Bundschuh RA
中科院分区：
文献类型：
--
作者：
Moazemi S;Khurshid Z;Erle A;Lütje S;Essler M;Schultz T;Bundschuh RA
Gallium-68 prostate-specific membrane antigen positron emission tomography (68Ga-PSMA-PET) is a highly sensitive method to detect prostate cancer (PC) metastases. Visual discrimination between malignant and physiologic/unspecific tracer accumulation by a nuclear medicine (NM) specialist is essential for image interpretation. In the future, automated machine learning (ML)-based tools will assist physicians in image analysis. The aim of this work was to develop a tool for analysis of 68Ga-PSMA-PET images and to compare its efficacy to that of human readers. Five different ML methods were compared and tested on multiple positron emission tomography/computed tomography (PET/CT) data-sets. Forty textural features extracted from both PET- and low-dose CT data were analyzed. In total, 2419 hotspots from 72 patients were included. Comparing results from human readers to those of ML-based analyses, up to 98% area under the curve (AUC), 94% sensitivity (SE), and 89% specificity (SP) were achieved. Interestingly, textural features assessed in native low-dose CT increased the accuracy significantly. Thus, ML based on 68Ga-PSMA-PET/CT radiomics features can classify hotspots with high precision, comparable to that of experienced NM physicians. Additionally, the superiority of multimodal ML-based analysis considering all PET and low-dose CT features was shown. Morphological features seemed to be of special additional importance even though they were extracted from native low-dose CTs.
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影响因子：
--
作者：
Khurshid, Zain;Ahmadzadehfar, Hojjat;Bundschuh, Ralph A
通讯作者：
Bundschuh, Ralph A
影响因子：
9.3
作者：
Bundschuh, Ralph A.;Dinges, Julia;Essler, Markus
通讯作者：
Essler, Markus
影响因子：
9.3
作者：
Calais, Jeremie;Fendler, Wolfgang P.;Herrmann, Ken
通讯作者：
Herrmann, Ken
影响因子：
5.9
作者：
Bates, Anthony;Miles, Kenneth
通讯作者：
Miles, Kenneth
影响因子：
4
作者：
Yordanova A;Eppard E;Kürpig S;Bundschuh RA;Schönberger S;Gonzalez-Carmona M;Feldmann G;Ahmadzadehfar H;Essler M
通讯作者：
Essler M