Diffusion-relaxation correlation spectrum imaging (DR CSI) parameters correlate with histopathologic microscopic prostate tissue compartments—epithelium, stroma, and lumen—in men with prostate cancer. Background Microstructural MRI has the potential to improve diagnosis and characterization of prostate cancer (PCa), but validation with histopathology is lacking. Purpose To validate ex vivo diffu...

Magnetic resonance imaging (MRI) underestimation of prostate cancer extent complicates the definition of focal treatment margins. To validate focal treatment margins produced by an artificial intelligence (AI) model. Testing was conducted retrospectively in an independent dataset of 50 consecutive patients who had radical prostatectomy for intermediate-risk cancer. An AI deep learning model incorporated multimodal imaging and biopsy data to produce three-dimensional cancer estimation maps and margins. AI margins were compared with conventional MRI regions of interest (ROIs), 10-mm margins around ROIs, and hemigland margins. The AI model also furnished predictions of negative surgical margin probability, which were assessed for accuracy. Comparing AI with conventional margins, sensitivity was evaluated using Wilcoxon signed-rank tests and negative margin rates using chi-square tests. Predicted versus observed negative margin probability was assessed using linear regression. Clinically significant prostate cancer (International Society of Urological Pathology grade ≥2) delineated on whole-mount histopathology served as ground truth. The mean sensitivity for cancer-bearing voxels was higher for AI margins (97%) than for conventional ROIs (37%, p < 0.001), 10-mm ROI margins (93%, p = 0.24), and hemigland margins (94%, p < 0.001). For index lesions, AI margins were more often negative (90%) than conventional ROIs (0%, p < 0.001), 10-mm ROI margins (82%, p = 0.24), and hemigland margins (66%, p = 0.004). Predicted and observed negative margin probabilities were strongly correlated (R² = 0.98, median error = 4%). Limitations include a validation dataset derived from a single institution's prostatectomy population. The AI model was accurate and effective in an independent test set. This approach could improve and standardize treatment margin definition, potentially reducing cancer recurrence rates. Furthermore, an accurate assessment of negative margin probability could facilitate informed decision-making for patients and physicians. Artificial intelligence was used to predict the extent of tumors in surgically removed prostate specimens. It predicted tumor margins more accurately than conventional methods.

Objective: This investigation was performed to evaluate the registration accuracy between magnetic resonance imaging (MRI) and pathology using three-dimensional (3D) printed molds. Methods: Tissue-mimicking prostate phantoms were manufactured with embedded fiducials. The fiducials were used to measure and compare target registration error (TRE) between phantoms that were sliced by hand versus phantoms sliced within 3D-printed molds. Subsequently, 10 radical prostatectomy specimens were placed inside molds, scanned with MRI, and then sliced. The ex vivo scan was used to assess the true location of whole mount (WM) slides relative to in vivo MRI. The TRE between WM and in vivo MRI was measured using anatomic landmarks. Results: Manually sliced phantoms had a 4.1 mm mean TRE, whereas mold-sliced phantoms had a 1.9 mm mean TRE. Similarly, mold-assisted slicing reduced mean angular misalignment around the left-right (LR) anatomic axis from 10.7 to 4.5 degrees. However, ex vivo MRI revealed that excised prostates were misaligned within molds, including a mean 14-degree rotation about the LR axis. The mean in-plane TRE was 3.3 mm using molds alone, and 2.2 mm after registration was corrected with ex vivo MRI. Conclusion: Patient-specific molds improved accuracy relative to manual slicing techniques in a phantom model. However, the registration accuracy of surgically resected specimens was limited by their imperfect fit within molds. This limitation can be overcome with the addition of ex vivo imaging. Significance: The accuracy of 3D printed molds was characterized, quantifying their utility for facilitating MRI-pathology registration.

Focal laser ablation is a minimally invasive method of treating cancerous lesions in organs such as prostate, liver and brain. Oncologic control is achieved by inducing hyperthermia throughout the target while minimizing damage to surrounding tissue. Consequently, successful clinical outcomes are contingent upon achieving desired ablation volumes. Magnetic resonance thermometry is frequently used to monitor the formation of the induced thermal damage zone and inform the decision to terminate energy delivery. However, due to the associated cost and complexity there is growing interest in the development of alternative approaches. Here we investigate the utility of real-time interstitial interrogation of laser-tissue interaction as an inexpensive alternative monitoring modality that provides direct assessment of tissue coagulation without the need for organ specific calibration. The optical contrast mechanism was determined using a Monte Carlo model. Subsequently, four interstitial probe designs were manufactured and assessed in a tissue mimicking phantom under simultaneous magnetic resonance imaging. Finally, the optimal probe design was evaluated in ex vivo bovine muscle. It was found to be capable of providing sufficient feedback to achieve pre-defined ablation radii in the range 4-7mm with a mean absolute error of 0.3mm. This approach provides an inexpensive monitoring modality that may facilitate widespread adoption of focal laser ablation.