Machine Learning Classification of Body Part, Imaging Axis, and Intravenous Contrast Enhancement on CT Imaging.

Purpose: The development and evaluation of machine learning models that automatically identify the body part(s) imaged, axis of imaging, and the presence of intravenous contrast material of a CT series of images. Methods: This retrospective study included 6955 series from 1198 studies (501 female, 697 males, mean age 56.5 years) obtained between January 2010 and September 2021. Each series was annotated by a trained board-certified radiologist with labels consisting of 16 body parts, 3 imaging axes, and whether an intravenous contrast agent was used. The studies were randomly assigned to the training, validation and testing sets with a proportion of 70%, 20% and 10%, respectively, to develop a 3D deep neural network for each classification task. External validation was conducted with a total of 35,272 series from 7 publicly available datasets. The classification accuracy for each series was independently assessed for each task to evaluate model performance. Results: The accuracies for identifying the body parts, imaging axes, and the presence of intravenous contrast were 96.0% (95% CI: 94.6%, 97.2%), 99.2% (95% CI: 98.5%, 99.7%), and 97.5% (95% CI: 96.4%, 98.5%) respectively. The generalizability of the models was demonstrated through external validation with accuracies of 89.7 - 97.8%, 98.6 - 100%, and 87.8 - 98.6% for the same tasks. Conclusions: The developed models demonstrated high performance on both internal and external testing in identifying key aspects of a CT series.

Targeted delivery of irinotecan to colon cancer cells using epidermal growth factor receptor-conjugated liposomes.

BACKGROUND: CPT-11 (irinotecan) is one of the most efficient agents used for colorectal cancer chemotherapy. However, as for many other chemotherapeutic drugs, how to minimize the side effects of CPT-11 still needs to be thoroughly described. OBJECTIVES: This study aimed to develop the CPT-11-loaded DSPE-PEG 2000 targeting EGFR liposomal delivery system and characterize its targeting specificity and therapeutic effect on colorectal cancer (CRC) cells in vitro and in vivo. RESULTS: The synthesized liposome exhibited spherical shapes (84.6 ± 1.2 nm to 150.4 nm ± 0.8 nm of estimated average sizes), good stability, sustained release, and enough drug loading (55.19%). For in vitro experiments, SW620 cells treated with CPT-11-loaded DSPE-PEG2000 targeting EGFR liposome showed lower survival extended level of intracellular ROS production. In addition, it generated an enhanced apoptotic cell rate by upregulating the protein expression of both cleaved-caspase-3 and cleaved-caspase-9 compared with those of SW620 cells treated with free CPT-11. Importantly, the xenograft model showed that both the non-target and EGFR-targeted liposomes significantly inhibited tumor growth compared to free CPT-11. CONCLUSIONS: Compared with the non-target CPT-11-loaded DSPE-PEG2000 liposome, CPT-11-loaded DSPE-PEG2000 targeting EGFR liposome treatment showed much better antitumor activity in vitro in vivo. Thus, our findings provide new assets and expectations for CRC targeting therapy.

Automatic Tracking of Hyoid Bone Displacement and Rotation Relative to Cervical Vertebrae in Videofluoroscopic Swallow Studies Using Deep Learning.

The hyoid bone displacement and rotation are critical kinematic events of the swallowing process in the assessment of videofluoroscopic swallow studies (VFSS). However, the quantitative analysis of such events requires frame-by-frame manual annotation, which is labor-intensive and time-consuming. Our work aims to develop a method of automatically tracking hyoid bone displacement and rotation in VFSS. We proposed a full high-resolution network, a deep learning architecture, to detect the anterior and posterior of the hyoid bone to identify its location and rotation. Meanwhile, the anterior-inferior corners of the C2 and C4 vertebrae were detected simultaneously to automatically establish a new coordinate system and eliminate the effect of posture change. The proposed model was developed by 59,468 VFSS frames collected from 1488 swallowing samples, and it achieved an average landmark localization error of 2.38 pixels (around 0.5% of the image with 448 × 448 pixels) and an average angle prediction error of 0.065 radians in predicting C2-C4 and hyoid bone angles. In addition, the displacement of the hyoid bone center was automatically tracked on a frame-by-frame analysis, achieving an average mean absolute error of 2.22 pixels and 2.78 pixels in the x-axis and y-axis, respectively. The results of this study support the effectiveness and accuracy of the proposed method in detecting hyoid bone displacement and rotation. Our study provided an automatic method of analyzing hyoid bone kinematics during VFSS, which could contribute to early diagnosis and effective disease management.

Assessing the Performance of Models from the 2022 RSNA Cervical Spine Fracture Detection Competition at a Level I Trauma Center.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the performance of the top models from the RSNA 2022 Cervical Spine Fracture Detection challenge on a clinical test dataset of both noncontrast and contrast-enhanced CT scans acquired at a level I trauma center. Materials and Methods Seven top-performing models in the RSNA 2022 Cervical Spine Fracture Detection challenge were retrospectively evaluated on a clinical test set of 1,828 CT scans (1,829 series: 130 positive for fracture, 1,699 negative for fracture; 1,308 noncontrast, 521 contrast-enhanced) from 1,779 patients (mean age, 55.8 ± 22.1 years; 1,154 male). Scans were acquired without exclusion criteria over one year (January to December 2022) from the emergency department of a neurosurgical and level I trauma center. Model performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. False positive and false negative cases were further analyzed by a neuroradiologist. Results Although all 7 models showed decreased performance on the clinical test set compared with the challenge dataset, the models maintained high performances. On noncontrast CT scans, the models achieved a mean AUC of 0.89 (range: 0.81-0.92), sensitivity of 67.0% (range: 30.9%-80.0%), and specificity of 92.9% (range: 82.1%-99.0%). On contrast-enhanced CT scans, the models had a mean AUC of 0.88 (range: 0.76-0.94), sensitivity of 81.9% (range: 42.7%-100.0%), and specificity of 72.1% (range: 16.4%-92.8%). The models identified 10 fractures missed by radiologists. False-positives were more common in contrast-enhanced scans and observed in patients with degenerative changes on noncontrast scans, while false-negatives were often associated with degenerative changes and osteopenia. Conclusion The winning models from the 2022 RSNA AI Challenge demonstrated a high performance for cervical spine fracture detection on a clinical test dataset, warranting further evaluation for their use as clinical support tools. ©RSNA, 2024.

Moisture-Stable FAPbI3 Perovskite Achieved by Atomic Structure Negotiation.

Broad impact in the research community may be anticipated when a material's properties are capable of being manipulated artificially. Such a possibility has been explored here in the FAPbI3 perovskite structure of perovskite solar cells, which involves undesirable phase transition at working temperature, despite many attempts to resolve the issue. Essential steps have been taken here toward solving this problem by adopting an opposite strategy to incorporate the water molecules into the perovskite structure under the current materials framework by new structural physics maneuvering. The secondary bonding of the perovskite structure has been relocated, which altered the microstructure to remove the internal strain that caused the phase transition, resulting in not only a 10-fold enhancement in the moisture/structure stability but also a bandgap comparable to that of the favored α-FAPbI3. All this opens an unprecedented avenue in perovskite research, which will hopefully be of intrinsic interest to the broad materials research community as well.