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BACKGROUND: In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy. OBJECTIVE: This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status. METHODS: A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in DWIBS and STIR sequences and were divided into training (n = 75) and test (n = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models. RESULTS: For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548–0.982), 0.801 (0.597–1.000), and 0.779 (0.567–0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548–0.982), 0.757 (0.538–0.977), and 0.779 (0.567–0.992), respectively. CONCLUSIONS: Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status.

[BACKGROUND] We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR). [METHODS] We used 2, 928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2, 295 non-COVID-19 cases were included in the study. We randomly divided cases into training and tuning sets at a ratio of 8:2. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR. [RESULTS] In external validation, the sensitivity and specificity of the model were 0.869 and 0.432, at the low-level cutoff, 0.724 and 0.721, at the high-level cutoff. Area under the receiver operating characteristic was 0.76. [CONCLUSIONS] Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner at emergency departments. Further studies are warranted to improve model specificity.

This study was designed to evaluate the efficacy and safety of nonselective bilateral embolization of the internal iliac arteries (IIAs) with n-butyl-2-cyanoacrylate (NBCA) in hemodynamically unstable patients with pelvic fractures.Twelve patients underwent nonselective bilateral embolization of the IIAs using NBCA diluted with lipiodol at our institution between January 2004 and March 2014. We analyzed the time of bilateral occlusion of the IIAs, the time from admission to entrance into the interventional radiology room, the need for repeat embolization, outcomes, cause of death, follow-up period, and complications.The mean duration of bilateral occlusion of the IIAs was 17 min (range, 4-34 min), and the mean time from admission to entrance into the interventional radiology room was 89 min (range, 28-168 min). All patients underwent technically successful embolization. Repeat embolization was required after treatment in three patients. The mortality rate was 33.3%. Complications after embolization were suspected in one patient.Nonselective bilateral embolization of IIAs with NBCA could be a choice of treatment for hemodynamically unstable patients with severe pelvic fracture hemorrhage.

(1) Chl (a+a')及びChl (b+b')は碾茶乾燥工程,特に3段目乾燥以降に大きく減少した.Phy (a+a')及びPhy (b+b')は蒸熱後急増したが,その後工程とともに減少した.乾燥工程での高温処理による分解が原因と考えられる.(2)碾茶荒茶ではChl a'及びChl b'のエピマーの存在率が30及び48%と,煎茶荒茶と比較して高いことが示された.碾茶乾燥工程におけるエピマーの熱安定性に起因するものと推定される.(3) 各色素類の減少に起因して,総含有量が乾燥工程とともに減少することが,また,Phy (a+a')の減少に起因して,Phy変化率も乾燥工程とともに減少することが明らかとなった.(4) 碾茶荒茶でのクロロフィラーゼ活性は生葉の約18分の1で,50秒蒸し煎茶荒茶のそれとほぼ同じであった.このことにより,碾茶乾燥工程におけるクロロフィラーゼ活性の低下は比較的大きいことが明らかとなった.(5) 碾茶荒茶にはPBaは全く含まれておらず,碾茶製造工程においてはクロロフィラーゼは作用できる条件にないことが示唆された.