An alternative approach to histopathological validation of PET imaging for radiation therapy image-guidance: a proof of concept.
OBJECTIVE
In radiotherapy, PET images can be used to guide the delivery of selectively escalated doses to biologically relevant tumour subvolumes. Validation of PET for such applications requires demonstration of spatial coincidence between PET tracer uptake pattern and the histopathologically confirmed target. This study introduces a novel approach to histopathological validation of PET image segmentation for radiotherapy guidance.


METHODS
Sequential tissue sections from surgically excised whole-tumour specimens were used to acquire full 3D-sets of both histopathological images (microscopy) and PET tracer distribution images (autoradiography). After these datasets were accurately registered, a full 3D autoradiographic distribution of PET tracer was reconstructed and used to obtain synthetic PET images (sPET) by simulating the image deterioration induced by processes involved in PET image formation. To illustrate the method, sPET images were used in this study to investigate spatial coincidence between high FDG uptake areas and the distribution of viable tissue in two small animal tumour models.


RESULTS
The reconstructed 3D autoradiographic distribution of the PET tracer was spatially coherent, as indicated by the high average value of the normalised pixel-by-pixel correlation of intensities between successive slices (0.84 ± 0.05 and 0.94 ± 0.02). The loss of detail in the sPET images versus the 3D autoradiography was significant as indicated by Dice coefficient values corresponding to the two tumours (0 and 0.1 at 70% threshold). The maximum overlap between the FDG segmented volumes and the extent of the viable tissue as indicated by Dice coefficient values, was 0.8 for one tumour (for the image thresholded at 22% of max intensity) and 0.88 for the other (threshold of 14% of max intensity).


CONCLUSIONS
It was demonstrated that the use of synthetic PET images for histopathological validation allows for bypassing a technically challenging and error-prone step of registering non-invasive PET images with histopathology.
Department of Radiation Oncology, Virginia Commonwealth University Medical Center, United States; Department of Radiation Oncology, Stanford University School of Medicine, United States. Electronic address: maxente@stanford.edu.
Marian
M
Axente
Department of Radiation Oncology, Virginia Commonwealth University Medical Center, United States.
Jun
J
He
Department of Radiation Oncology, Virginia Commonwealth University Medical Center, United States.
Christopher P
CP
Bass
Department of Radiology, Center for Molecular Imaging, Virginia Commonwealth University Medical Centre, United States.
Gobalakrishnan
G
Sundaresan
Department of Radiology, Center for Molecular Imaging, Virginia Commonwealth University Medical Centre, United States.
Jamal
J
Zweit
Department of Radiation Oncology, Virginia Commonwealth University Medical Center, United States.
Jeffrey F
JF
Williamson
Department of Radiation Oncology, Virginia Commonwealth University Medical Center, United States.
Andrei
A
Pugachev
D000818:Animals; D001345:Autoradiography / Q000379:methods; D005260:Female; D019788:Fluorodeoxyglucose F18; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology / Q000532:radiotherapy; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D051379:Mice; D008819:Mice, Nude; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000532:radiotherapy*; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D061089:Radiotherapy, Image-Guided
D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18
Histopathological validation; Image-guidance; PET; Small-animal imaging