Eun‐Kyung Kim

PURPOSE: To develop a practical thyroid imaging reporting and data system (TIRADS) with which to categorize thyroid nodules and stratify their malignant risk. MATERIALS AND METHODS: The institutional review board approved this retrospective study, and the requirement to obtain informed consent for the review of images and records was waived. From May to December 2008, ultrasonographically (US)-guided fine-needle aspiration biopsy (FNAB) was performed in 3674 focal thyroid nodules in 3414 consecutive patients. The study included the 1658 thyroid nodules (≥1 cm in maximum diameter at US) in 1638 patients (1373 women, 265 men) for which pathologic diagnosis or follow-up findings were available. Univariate and multivariate analyses with generalized estimating equations were performed to investigate the relationship between suspicious US features and thyroid cancer. A score for each significant factor was assigned and multiplied by the β coefficient obtained for each significant factor from multivariate logistic regression analysis. Scores for each significant factor were then added, resulting in an equation that fitted the probability of malignancy in thyroid nodules. The authors evaluated the fitted probability by using a regression equation; the risk of malignancy was determined according to the number of suspicious US features. RESULTS: The following US features showed a significant association with malignancy: solid component, hypoechogenicity, marked hypoechogenicity, microlobulated or irregular margins, microcalcifications, and taller-than-wide shape. As the number of suspicious US features increased, the fitted probability and risk of malignancy also increased. Positive predictive values according to the number of suspicious US features were significantly different (P < .001). CONCLUSION: Risk stratification of thyroid malignancy by using the number of suspicious US features allows for a practical and convenient TIRADS.

OBJECTIVE: The purpose of our study was to provide new sonographic criteria for fine-needle aspiration biopsy of nonpalpable solid thyroid nodules. MATERIALS AND METHODS: Sonographic scans of 155 nonpalpable thyroid nodules in 132 patients were prospectively classified as having positive or negative findings. Sonographic findings that suggested malignancy included microcalcifications, an irregular or microlobulated margin, marked hypoechogenicity, and a shape that was more tall than it was wide. If even one of these sonographic features was present, the nodule was classified as positive (malignant). If a nodule had none of the features described, it was classified as negative (benign). The final diagnosis of a lesion as benign (n = 106) or malignant (n = 49) was confirmed by fine-needle aspiration biopsy and follow-up (>6 months) in 83 benign nodules, by fine-needle aspiration biopsy and surgery in 44 malignant and 15 benign lesions, and by surgery alone in five malignant and eight benign lesions. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated on the basis of our proposed classification method. RESULTS: Of 82 lesions classified as positive, 46 were malignant. Of 73 lesions classified as negative, three were malignant. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy based on our sonographic classification method were 93.8%, 66%, 56.1%, 95.9%, and 74.8%, respectively. CONCLUSION: Considering the high level of sensitivity of our proposed sonographic classification, fine-needle aspiration biopsy should be performed on thyroid nodules classified as positive, regardless of palpability.

The rate of detection of thyroid nodules and carcinomas has increased with the widespread use of ultrasonography (US), which is the mainstay for the detection and risk stratification of thyroid nodules as well as for providing guidance for their biopsy and nonsurgical treatment. The Korean Society of Thyroid Radiology (KSThR) published their first recommendations for the US-based diagnosis and management of thyroid nodules in 2011. These recommendations have been used as the standard guidelines for the past several years in Korea. Lately, the application of US has been further emphasized for the personalized management of patients with thyroid nodules. The Task Force on Thyroid Nodules of the KSThR has revised the recommendations for the ultrasound diagnosis and imaging-based management of thyroid nodules. The review and recommendations in this report have been based on a comprehensive analysis of the current literature and the consensus of experts.

Despite the recent considerable progress, the reversibility and cycle life of silicon anodes in lithium-ion batteries are yet to be improved further to meet the commercial standards. The current major industry, instead, adopts silicon monoxide (SiOx, x ≈ 1), as this phase can accommodate the volume change of embedded Si nanodomains via the silicon oxide matrix. However, the poor Coulombic efficiencies (CEs) in the early period of cycling limit the content of SiOx, usually below 10 wt % in a composite electrode with graphite. Here, we introduce a scalable but delicate prelithiation scheme based on electrical shorting with lithium metal foil. The accurate shorting time and voltage monitoring allow a fine-tuning on the degree of prelithiation without lithium plating, to a level that the CEs in the first three cycles reach 94.9%, 95.7%, and 97.2%. The excellent reversibility enables robust full-cell operations in pairing with an emerging nickel-rich layered cathode, Li[Ni0.8Co0.15Al0.05]O2, even at a commercial level of initial areal capacity of 2.4 mAh cm(-2), leading to a full cell energy density 1.5-times as high as that of graphite-LiCoO2 counterpart in terms of the active material weight.

BACKGROUND: Mammography is the current standard for breast cancer screening. This study aimed to develop an artificial intelligence (AI) algorithm for diagnosis of breast cancer in mammography, and explore whether it could benefit radiologists by improving accuracy of diagnosis. METHODS: In this retrospective study, an AI algorithm was developed and validated with 170 230 mammography examinations collected from five institutions in South Korea, the USA, and the UK, including 36 468 cancer positive confirmed by biopsy, 59 544 benign confirmed by biopsy (8827 mammograms) or follow-up imaging (50 717 mammograms), and 74 218 normal. For the multicentre, observer-blinded, reader study, 320 mammograms (160 cancer positive, 64 benign, 96 normal) were independently obtained from two institutions. 14 radiologists participated as readers and assessed each mammogram in terms of likelihood of malignancy (LOM), location of malignancy, and necessity to recall the patient, first without and then with assistance of the AI algorithm. The performance of AI and radiologists was evaluated in terms of LOM-based area under the receiver operating characteristic curve (AUROC) and recall-based sensitivity and specificity. FINDINGS: The AI standalone performance was AUROC 0·959 (95% CI 0·952-0·966) overall, and 0·970 (0·963-0·978) in the South Korea dataset, 0·953 (0·938-0·968) in the USA dataset, and 0·938 (0·918-0·958) in the UK dataset. In the reader study, the performance level of AI was 0·940 (0·915-0·965), significantly higher than that of the radiologists without AI assistance (0·810, 95% CI 0·770-0·850; p<0·0001). With the assistance of AI, radiologists' performance was improved to 0·881 (0·850-0·911; p<0·0001). AI was more sensitive to detect cancers with mass (53 [90%] vs 46 [78%] of 59 cancers detected; p=0·044) or distortion or asymmetry (18 [90%] vs ten [50%] of 20 cancers detected; p=0·023) than radiologists. AI was better in detection of T1 cancers (73 [91%] vs 59 [74%] of 80; p=0·0039) or node-negative cancers (104 [87%] vs 88 [74%] of 119; p=0·0025) than radiologists. INTERPRETATION: The AI algorithm developed with large-scale mammography data showed better diagnostic performance in breast cancer detection compared with radiologists. The significant improvement in radiologists' performance when aided by AI supports application of AI to mammograms as a diagnostic support tool. FUNDING: Lunit.