Quantitative differences in volumetric calculations for radiation dosimetry in segmental Y90 treatment planning using hybrid angiography-CT compared with anatomic segmentation.

OBJECTIVE: To compare treatment volumes reconstructed from hybrid Angio-CT catheter-directed infusion imaging and Couinaud anatomic model as well as the implied differences in Y-90 radiation dosimetry. METHODS: Patients who underwent transarterial radioembolization (TARE) using Y-90 glass microspheres with pretreatment CT or MRI imaging as well as intraprocedural angiography-CT (Angio-CT) were analysed. Treatment volumes were delineated using both tumoural angiosomes (derived from Angio-CT) and Couinaud anatomic landmarks. Segmental and lobar treatment volumes were calculated via semi-automated contouring software. Volume and dose differences were compared by the two-tailed Student t test or Wilcoxon signed-rank test. Factors affecting volume and dose differences were assessed via simple and/or multiple variable linear regression analysis. RESULTS: From September 2018 to March 2021, 44 patients underwent 45 lobar treatments and 38 patients received 56 segmental treatments. All target liver lobes and all tumours were completely included within the field-of-view by Angio-CT. Tumour sizes ranged between 1.1 and 19.5 cm in diameter. Segmental volumes and treatment doses were significantly different between the Couinaud and Angio-CT volumetry methods (316 vs 404 mL, P < .0001 and 253 vs 212 Gy, P < .01, respectively). Watershed tumours were significantly correlated with underestimated volumes by the Couinaud anatomic model (P < .001). There was a significant linear relationship between tumour diameter and percent volume difference (R2 = 0.44, P < .0001). The Couinaud model overestimated volumes for large tumours that exhibited central hypovascularity/necrosis and for superselected peripheral tumours. CONCLUSIONS: Angio-CT may confer advantages over the Couinaud anatomic model and enable more accurate, personalized dosimetry for TARE. ADVANCES IN KNOWLEDGE: Angio-CT may confer advantages over traditional cross-sectional and cone-beam CT imaging for selective internal radiation therapy planning.

An Interactive App with Multi-parametric MRI - Whole-Mount Histology Correlation for Enhanced Prostate MRI Training of Radiology Residents.

RATIONALE AND OBJECTIVES: To validate the educational value of a newly created learning application in enhancing prostate MRI training of radiologists for detecting prostate cancer using an observer study. MATERIALS AND METHODS: An interactive learning app, LearnRadiology, was developed using a web-based framework to display multi-parametric prostate MRI images with whole-mount histology for 20 cases curated for unique pathology and teaching points. Twenty new prostate MRI cases, different from the ones used in the web app, were uploaded on 3D Slicer. Three radiologists (R1: radiologist; R2, R3: residents) blinded to pathology results were asked to mark areas suspected of cancer and provide a confidence score (1-5, with 5 being high confidence level). Then after a minimum memory washout period of 1 month, the same radiologists used the learning app and then repeated the same observer study. The diagnostic performance for detecting cancers before and after accessing the learning app was measured by correlating MRI with whole-mount pathology by an independent reviewer. RESULTS: The 20 subjects included in the observer study had 39 cancer lesions (13 Gleason 3 + 3, 17 Gleason 3 + 4, 7 Gleason 4 + 3, and 2 Gleason 4 + 5 lesions). The sensitivity (R1: 54% â†’ 64%, P = 0.08; R2: 44% â†’ 59%, P = 0.03; R3: 62% â†’ 72%, P = 0.04) and positive predictive value (R1: 68% â†’ 76%, P = 0.23; R2: 52% â†’ 79%, P = 0.01; R3: 48% â†’ 65%, P = 0.04) for all 3 radiologists improved after using the teaching app. The confidence score for true positive cancer lesion also improved significantly (R1: 4.0 ± 1.0 â†’ 4.3 ± 0.8; R2: 3.1 ± 0.8 â†’ 4.0 ± 1.1; R3: 2.8 ± 1.2 â†’ 4.1 ± 1.1; P < 0.05). CONCLUSION: The web-based and interactive LearnRadiology app learning resource can support medical student and postgraduate education by improving diagnostic performance of trainees for detecting prostate cancer.