Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-based diagnosis in patients with uterine sarcomas. Fifteen sequences of MRI for patients (uterine sarcoma group: n = 63; uterine leiomyoma: n = 200) were used to train the models. Six radiologists (three specialists, three practitioners) interpreted the same images for validation. The most important individual sequences for diagnosis were axial T2-weighted imaging (T2WI), sagittal T2WI, and diffusion-weighted imaging. These sequences also represented the most accurate combination (accuracy: 91.3%), achieving diagnostic ability comparable to that of specialists (accuracy: 88.3%) and superior to that of practitioners (accuracy: 80.1%). Moreover, radiologists’ diagnostic accuracy improved when provided with DNN results (specialists: 89.6%; practitioners: 92.3%). Our DNN models are valuable to improve diagnostic accuracy, especially in filling the gap of clinical skills between interpreters. This method can be a universal model for the use of deep learning in the diagnostic imaging of rare tumors.
In this single-center retrospective study, we intended to evaluate the frequencies and characteristics of computed tomography findings of pancreatobiliary inflammation (PBI) in patients treated with lenvatinib and the relationship of these findings with treatment-planning changes. We included 78 patients (mean ± standard deviation, 69.8 ± 9.4 years, range: 39–84 years, 62 men) with hepatocellular carcinoma (n = 62) or thyroid carcinoma (n = 16) who received lenvatinib (June 2016–September 2020). Two radiologists interpreted the posttreatment computed tomography images and assessed the radiological findings of PBI (symptomatic pancreatitis, cholecystitis, or cholangitis). The PBI effect on treatment was statistically evaluated. PBI (pancreatitis, n = 1; cholecystitis, n = 7; and cholangitis, n = 2) was diagnosed in 11.5% (9/78) of the patients at a median of 35 days after treatment initiation; 6 of 9 patients discontinued treatment because of PBI. Three cases of cholecystitis and 1 of cholangitis were accompanied by gallstones, while the other 5 were acalculous. The treatment duration was significantly shorter in patients with PBI than in those without (median: 44 days vs. 201 days, P = .02). Overall, 9 of 69 patients without PBI showed asymptomatic gallbladder subserosal edema. Lenvatinib-induced PBI developed in 11.5% of patients, leading to a significantly shorter treatment duration. Approximately 55.6% of the PBI cases were acalculous. The recognition of this phenomenon would aid physicians during treatment planning in the future.
Objective Identify appropriate reconstruction modes of Forward-projected model-based Iterative Reconstruction SoluTion (FIRST) in temporal bone computed tomography (CT) and investigate the contribution of spatial resolution and noise to the visual assessment. Methods Sixteen temporal bone CT images were reconstructed. Two blinded radiologists assessed 8 anatomical structures and classified the visual assessment. These visual scores were compared across reconstruction modes among each anatomical structure. Visual scores and contrast-to-noise ratio, noise power spectrum (NPS) at low (NPSLow) and high (NPSHigh) spatial frequencies, and 50% modulation transfer function relationships were evaluated. Results Visual scores differed significantly for the stapedius muscle and osseous spiral lamina, with CARDIAC SHARP, BONE, and LUNG modes for the osseous spiral lamina scoring highest. Visual scores significantly positively correlated with NPSLow, NPSHigh, and 50% modulation transfer function but negatively with contrast-to-noise ratio. Conclusions Modes providing higher spatial resolution and lower noise reduction showed an improved visual assessment of CT images reconstructed with FIRST.
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