Summary Being able to noninvasively modulate brain activity, where and when an experimenter desires, with an immediate path towards human translation is a long-standing goal for neuroscience. To enable robust perturbation of brain activity while leveraging the ability of focused ultrasound to deliver energy to any point of the brain noninvasively, we have developed biocompatible and clinically-translatable nanoparticles that allow ultrasound-induced uncaging of neuromodulatory drugs. Utilizing the anesthetic propofol together with electrophysiological and imaging assays, we show that the neuromodulatory effect of ultrasonic drug uncaging is limited spatially and temporally by the size of the ultrasound focus, the sonication timing, and the pharmacokinetics of the uncaged drug. Moreover, we see secondary effects in brain regions anatomically distinct from and functionally connected to the sonicated region, indicating that ultrasonic drug uncaging could noninvasively map the changes in functional network connectivity associated with pharmacologic action at a particular brain target.

Author Contributions QZ and RDA designed the experiments. QZ performed the chemistry of nanoparticle production and physicochemical characterization, carried out the pharmacokinetics study and in vivo biodistribution of the nanoparticles, and performed the in vitro/in vivo ultrasonic drug uncaging experiments, with guidance from MA and assistance from JBW and AK. Cryo-TEM imaging was performed by MAB, RHC, and NHN. QZ and BCY performed the ultrasound imaging and ultrasonic nicardipine uncaging experiments and their analysis. TI and KFW designed and performed the ultra-high-speed optical imaging and passive cavitation detection of the nanoemulsions. QZ, BCY, and RDA prepared the figures and wrote the manuscript, with input and edits from all the authors.

Purpose While multi‐parametric magnetic resonance imaging (MRI) shows great promise in assisting with prostate cancer diagnosis and localization, subtle differences in appearance between cancer and normal tissue lead to many false positive and false negative interpretations by radiologists. We sought to automatically detect aggressive cancer (Gleason pattern ≥ 4) and indolent cancer (Gleason pattern 3) on a per‐pixel basis on MRI to facilitate the targeting of aggressive cancer during biopsy. Methods We created the Stanford Prostate Cancer Network (SPCNet), a convolutional neural network model, trained to distinguish between aggressive cancer, indolent cancer, and normal tissue on MRI. Ground truth cancer labels were obtained by registering MRI with whole‐mount digital histopathology images from patients who underwent radical prostatectomy. Before registration, these histopathology images were automatically annotated to show Gleason patterns on a per‐pixel basis. The model was trained on data from 78 patients who underwent radical prostatectomy and 24 patients without prostate cancer. The model was evaluated on a pixel and lesion level in 322 patients, including six patients with normal MRI and no cancer, 23 patients who underwent radical prostatectomy, and 293 patients who underwent biopsy. Moreover, we assessed the ability of our model to detect clinically significant cancer (lesions with an aggressive component) and compared it to the performance of radiologists. Results Our model detected clinically significant lesions with an area under the receiver operator characteristics curve of 0.75 for radical prostatectomy patients and 0.80 for biopsy patients. Moreover, the model detected up to 18% of lesions missed by radiologists, and overall had a sensitivity and specificity that approached that of radiologists in detecting clinically significant cancer. Conclusions Our SPCNet model accurately detected aggressive prostate cancer. Its performance approached that of radiologists, and it helped identify lesions otherwise missed by radiologists. Our model has the potential to assist physicians in specifically targeting the aggressive component of prostate cancers during biopsy or focal treatment.

Purpose: Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis; however, subtle differences between cancer and confounding conditions render prostate MRI interpretation challenging. The tissue collected from patients who undergo radical prostatectomy provides a unique opportunity to correlate histopathology images of the prostate with preoperative MRI to accurately map the extent of cancer from histopathology images onto MRI. We seek to develop an open-source, easy-to-use platform to align presurgical MRI and histopathology images of resected prostates in patients who underwent radical prostatectomy to create accurate cancer labels on MRI. Methods: Here, we introduce RAdiology Pathology Spatial Open-Source multi-Dimensional Integration (RAPSODI), the first open-source framework for the registration of radiology and pathology images. RAPSODI relies on three steps. First, it creates a three-dimensional (3D) reconstruction of the histopathology specimen as a digital representation of the tissue before gross sectioning. Second, RAPSODI registers corresponding histopathology and MRI slices. Third, the optimized transforms are applied to the cancer regions outlined on the histopathology images to project those labels onto the preoperative MRI. Results: We tested RAPSODI in a phantom study where we simulated various conditions, for example, tissue shrinkage during fixation. Our experiments showed that RAPSODI can reliably correct multiple artifacts. We also evaluated RAPSODI in 157 patients from three institutions that underwent radical prostatectomy and have very different pathology processing and scanning. RAPSODI was evaluated in 907 corresponding histpathology-MRI slices and achieved a Dice coefficient of 0.97 AE 0.01 for the prostate, a Hausdorff distance of 1.99 AE 0.70 mm for the prostate boundary, a urethra deviation of 3.09 AE 1.45 mm, and a landmark deviation of 2.80 AE 0.59 mm between registered histopathology images and MRI. Conclusion: Our robust framework successfully mapped the extent of cancer from histopathology slices onto MRI providing labels from training machine learning methods to detect cancer on MRI.

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