[ 68 Ga]Ga-PSMA and [ 68 Ga]Ga-RM2 PET/MRI vs. Histopathological Images in Prostate Cancer: A New Workflow for Spatial Co-Registration.
Samuele GhezzoIlaria NeriPaola MapelliAnnarita SaviAna Maria Samanes GajateGiorgio BrembillaCarolina BezziBeatrice MaghiniTommaso VillaAlberto BrigantiFrancesco MontorsiFrancesco De CobelliMassimo FreschiArturo ChitiMaria PicchioPaola ScifoPublished in: Bioengineering (Basel, Switzerland) (2023)
This study proposed a new workflow for co-registering prostate PET images from a dual-tracer PET/MRI study with histopathological images of resected prostate specimens. The method aims to establish an accurate correspondence between PET/MRI findings and histology, facilitating a deeper understanding of PET tracer distribution and enabling advanced analyses like radiomics. To achieve this, images derived by three patients who underwent both [ 68 Ga]Ga-PSMA and [ 68 Ga]Ga-RM2 PET/MRI before radical prostatectomy were selected. After surgery, in the resected fresh specimens, fiducial markers visible on both histology and MR images were inserted. An ex vivo MRI of the prostate served as an intermediate step for co-registration between histological specimens and in vivo MRI examinations. The co-registration workflow involved five steps, ensuring alignment between histopathological images and PET/MRI data. The target registration error (TRE) was calculated to assess the precision of the co-registration. Furthermore, the DICE score was computed between the dominant intraprostatic tumor lesions delineated by the pathologist and the nuclear medicine physician. The TRE for the co-registration of histopathology and in vivo images was 1.59 mm, while the DICE score related to the site of increased intraprostatic uptake on [ 68 Ga]Ga-PSMA and [ 68 Ga]Ga-RM2 PET images was 0.54 and 0.75, respectively. This work shows an accurate co-registration method for histopathological and in vivo PET/MRI prostate examinations that allows the quantitative assessment of dual-tracer PET/MRI diagnostic accuracy at a millimetric scale. This approach may unveil radiotracer uptake mechanisms and identify new PET/MRI biomarkers, thus establishing the basis for precision medicine and future analyses, such as radiomics.
Keyphrases
- pet ct
- contrast enhanced
- prostate cancer
- positron emission tomography
- magnetic resonance imaging
- deep learning
- radical prostatectomy
- diffusion weighted imaging
- convolutional neural network
- magnetic resonance
- optical coherence tomography
- computed tomography
- primary care
- benign prostatic hyperplasia
- chronic kidney disease
- prognostic factors
- lymph node
- machine learning
- newly diagnosed
- high resolution
- end stage renal disease
- ejection fraction
- patient reported outcomes
- fine needle aspiration