Theranostic agents present a promising clinical approach for cancer detection and treatment. We herein introduce a microbubble and liposome complex (MB-Lipo) developed for ultrasound (US) imaging and activation. The MB-Lipo particles have a hybrid structure consisting of a MB complexed with multiple Lipos. The MB components are used to generate high echo signals in US imaging, while the Lipos serve as a versatile carrier of therapeutic materials. MB-Lipo allows high contrast US imaging of tumor sites. More importantly, the application of high acoustic pressure bursts MBs, which releases therapeutic Lipos and further enhances their intracellular delivery through sonoporation effect. Both imaging and drug release could thus be achieved by a single US modality, enabling in situ treatment guided by real-time imaging. The MB-Lipo system was applied to specifically deliver anti-cancer drug and genes to tumor cells, which showed enhanced therapeutic effect. We also demonstrate the clinical potential of MB-Lipo by imaging and treating tumor in vivo.
Renal papillary necrosis is not a pathologic entity but rather a descriptive term for a condition--necrosis of the renal papillae--that has various possible causes. The renal medulla and papillae are vulnerable to ischemic necrosis because of the peculiar arrangement of their blood supply and the hypertonic environment. The etiology of renal papillary necrosis includes diabetes, analgesic abuse or overuse, sickle cell disease, pyelonephritis, renal vein thrombosis, tuberculosis, and obstructive uropathy. Renal papillary necrosis has been diagnosed with the use of intravenous urography and ultrasonography, but contrast material-enhanced computed tomography (CT) may better depict a full range of typical features, including contrast material-filled clefts in the renal medulla, nonenhanced lesions surrounded by rings of excreted contrast material, and hyperattenuated medullary calcifications. In the presence of papillary sloughing, CT may depict hydronephrosis and filling defects in the renal pelvis or ureter, which also may contain calcifications. During healing, the epithelialized papillary tip appears blunted. Shrinkage of the kidney, a common sequela, also may be detected at CT. Multi-detector row CT depicts these and other features more clearly and directly than single-detector row CT, given the advantages of thinner sections and multiplanar reformation, and it may help identify the condition at an earlier stage, when effective treatment can reverse the ischemic process. Familiarity with the CT features of the condition therefore is useful for its successful diagnosis and management.
In this research, we exploit an image-based deep learning framework to distinguish three major subtypes of renal cell carcinoma (clear cell, papillary, and chromophobe) using images acquired with computed tomography (CT). A biopsy-proven benchmarking dataset was built from 169 renal cancer cases. In each case, images were acquired at three phases(phase 1, before injection of the contrast agent; phase 2, 1 min after the injection; phase 3, 5 min after the injection). After image acquisition, rectangular ROI (region of interest) in each phase image was marked by radiologists. After cropping the ROIs, a combination weight was multiplied to the three-phase ROI images and the linearly combined images were fed into a deep learning neural network after concatenation. A deep learning neural network was trained to classify the subtypes of renal cell carcinoma, using the drawn ROIs as inputs and the biopsy results as labels. The network showed about 0.85 accuracy, 0.64–0.98 sensitivity, 0.83–0.93 specificity, and 0.9 AUC. The proposed framework which is based on deep learning method and ROIs provided by radiologists showed promising results in renal cell subtype classification. We hope it will help future research on this subject and it can cooperate with radiologists in classifying the subtype of lesion in real clinical situation.
Awareness of the different lesion detectability on DWI according to the time lapse after the symptom onset can help in diagnosing the patients with suspected TGA. High field strength is another important factor to increase the lesion detectability on DWI.
Background:Three-dimensional (3D) printing has been introduced as a novel technique to produce 3D objects. We tried to evaluate the clinical usefulness of 3D-printed renal model in performing partial nephrectomy (PN) and also in the education of medical students.Materials and Methods:We prospectively produced personalized renal models using 3D-printing methods from preoperative computed tomography (CT) images in a total of 10 patients. Two different groups (urologist and student group) appraised the clinical usefulness of 3D-renal models by answering questionnaires.Results:After application of 3D renal models, the urologist group gave highly positive responses in asking clinical usefulness of 3D-model among PN (understanding personal anatomy: 8.9 / 10, preoperative surgical planning: 8.2 / 10, intraoperative tumor localization: 8.4 / 10, plan for further utilization in future: 8.3 / 10, clinical usefulness in complete endophytic mass: 9.5 / 10). The student group located each renal tumor correctly in 47.3% when they solely interpreted the CT images. After the introduction of 3D-models, the rate of correct answers was significantly elevated to 70.0% (p < 0.001). The subjective difficulty level in localizing renal tumor was also significantly low (52% versus 27%, p < 0.001) when they utilized 3D-models.Conclusion:The personalized 3D renal model was revealed to significantly enhance the understanding of correct renal anatomy in patients with renal tumors in both urologist and student groups. These models can be useful for establishing the perioperative planning and also education program for medical students.
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