Prostate MRI and quality: lessons learned from breast imaging rad-path correlation

Radiologic-pathologic correlation of Prostate Imaging Reporting & Data System (PI-RADS) scores ensures local quality and offers opportunities to improve future iterations of the reporting system. Tracking positive predictive values of lesion-targeted biopsies helps provide generalizable population-level risks associated with each PI-RADS category and can highlight the sources of variation. While this process of formalized pathologic correlation is somewhat new to abdominal radiology, we are fortunate to have a model to follow which was developed by our colleagues in breast imaging. If the success and multi-national adoption of BI-RADS is an indicator, building a scoring system anchored on a histologic reference is an important step to ensuring diagnostic quality and reliability.

Automated Radiology-Pathology Module Correlation Using a Novel Report Matching Algorithm by Organ System

OBJECTIVES AND RATIONALE

Radiology-pathology correlation is time-consuming and is not feasible in most clinical settings, with the notable exception of breast imaging. The purpose of this study was to determine if an automated radiology-pathology report pairing system could accurately match radiology and pathology reports, thus creating a feedback loop allowing for more frequent and timely radiology-pathology correlation.

METHODS

An experienced radiologist created a matching matrix of radiology and pathology reports. These matching rules were then exported to a novel comprehensive radiology-pathology module. All distinct radiology-pathology pairings at our institution from January 1, 2016 to July 1, 2016 were included (n = 8999). The appropriateness of each radiology-pathology report pairing was scored as either "correlative" or "non-correlative." Pathology reports relating to anatomy imaged in the specific imaging study were deemed correlative, whereas pathology reports describing anatomy not imaged with the particular study were denoted non-correlative.

RESULTS

Overall, there was 88.3% correlation (accuracy) of the radiology and pathology reports (n = 8999). Subset analysis demonstrated that computed tomography (CT) abdomen/pelvis, CT head/neck/face, CT chest, musculoskeletal CT (excluding spine), mammography, magnetic resonance imaging (MRI) abdomen/pelvis, MRI brain, musculoskeletal MRI (excluding spine), breast MRI, positron emission tomography (PET), breast ultrasound, and head/neck ultrasound all demonstrated greater than 91% correlation. When further stratified by imaging modality, CT, MRI, mammography, and PET demonstrated excellent correlation (greater than 96.3%). Ultrasound and non-PET nuclear medicine studies demonstrated poorer correlation (80%).

CONCLUSION

There is excellent correlation of radiology imaging reports and appropriate pathology reports when matched by organ system. Rapid, appropriate radiology-pathology report pairings provide an excellent opportunity to close feedback loop to the interpreting radiologist.

Developing a methodology for three-dimensional correlation of PET-CT images and whole-mount histopathology in non-small-cell lung cancer

BACKGROUND

Understanding the three-dimensional (3D) volumetric relationship between imaging and functional or histopathologic heterogeneity of tumours is a key concept in the development of image-guided radiotherapy. Our aim was to develop a methodologic framework to enable the reconstruction of resected lung specimens containing non-small-cell lung cancer (NSCLC), to register the result in 3D with diagnostic imaging, and to import the reconstruction into a radiation treatment planning system.

METHODS AND RESULTS

We recruited 12 patients for an investigation of radiology-pathology correlation (RPC) in nsclc. Before resection, imaging by positron emission tomography (PET) or computed tomography (CT) was obtained. Resected specimens were formalin-fixed for 1-24 hours before sectioning at 3-mm to 10-mm intervals. To try to retain the original shape, we embedded the specimens in agar before sectioning. Consecutive sections were laid out for photography and manually adjusted to maintain shape. Following embedding, the tissue blocks underwent whole-mount sectioning (4-mum sections) and staining with hematoxylin and eosin. Large histopathology slides were used to whole-mount entire sections for digitization. The correct sequence was maintained to assist in subsequent reconstruction. Using Photoshop (Adobe Systems Incorporated, San Jose, CA, U.S.A.), contours were placed on the photographic images to represent the external borders of the section and the extent of macroscopic disease. Sections were stacked in sequence and manually oriented in Photoshop. The macroscopic tumour contours were then transferred to MATLAB (The Mathworks, Natick, MA, U.S.A.) and stacked, producing 3D surface renderings of the resected specimen and embedded gross tumour. To evaluate the microscopic extent of disease, customized "tile-based" and commercial confocal panoramic laser scanning (TISSUEscope: Biomedical Photometrics, Waterloo, ON) systems were used to generate digital images of whole-mount histopathology sections. Using the digital whole-mount images and imaging software, we contoured the gross and microscopic extent of disease. Two methods of registering pathology and imaging were used. First, selected pet and ct images were transferred into Photoshop, where they were contoured, stacked, and reconstructed. After importing the pathology and the imaging contours to MATLAB, the contours were reconstructed, manually rotated, and rigidly registered. In the second method, MATLAB tumour renderings were exported to a software platform for manual registration with the original pet and ct images in multiple planes. Data from this software platform were then exported to the Pinnacle radiation treatment planning system in DICOM (Digital Imaging and Communications in Medicine) format.

CONCLUSIONS

There is no one definitive method for 3D volumetric RPC in nsclc. An innovative approach to the 3D reconstruction of resected nsclc specimens incorporates agar embedding of the specimen and whole-mount digital histopathology. The reconstructions can be rigidly and manually registered to imaging modalities such as ct and pet and exported to a radiation treatment planning system.