Aberrant overexpression of the autoantigen protein vimentin promotes Th17 cell differentiation and autoimmune arthritis via activation of STAT3 signaling.

Vimentin contributes to the positioning and function of organelles, cell migration, adhesion, and division. However, secreted vimentin accumulates on the cell surface (Mor-Vaknin et al., 2003; Ramos et al., 2020 [1,2]) where it acts as a coreceptor for viral infection and as an autoantigen in inflammatory and autoimmune diseases. The roles of vimentin in Th17 cells were examined in mice with knockdown of vimentin. We also examined whether STAT3 is required for vimentin expression. Vimentin expression was significantly increased in Th17 cells through STAT3 activation, and vimentin+ IL-17+ T cells were markedly increased in the joint and spleen tissues of CIA mice. The arthritis score and expression levels of proinflammatory cytokines were significantly decreased in CIA mice treated with vimentin shRNA vector. In this study, we demonstrated that vimentin is significantly expressed in Th17 cells through STAT3 activation. Our results provide new insights into the role of vimentin in Th17 cells and the complex pathogenesis of RA.

Chlorophyll a and novel synthetic derivatives alleviate atopic dermatitis by suppressing Th2 cell differentiation via IL-4 receptor modulation.

Atopic dermatitis (AD) treatment has largely relied on non-specific broad immunosuppressants despite their long-term toxicities until the approval of dupilumab, which blocks IL-4 signaling to target Th2 cell responses. Here, we report the discovery of compound 4aa, a novel compound derived from the structure of chlorophyll a, and the efficacy of chlorophyll a to alleviate AD symptoms by oral administration in human AD patients. 4aa downregulated GATA3 and IL-4 in differentiating Th2 cells by potently blocking IL-4 receptor dimerization. In the murine model, oral administration of 4aa reduced the clinical severity of symptoms and scratching behavior by 76% and 72%, respectively. Notably, the elevated serum levels of Th2 cytokines reduced to levels similar to those in the normal group after oral administration of 4aa. Additionally, the toxicological studies showed favorable safety profiles and good tolerance. In conclusion, 4aa may be applied for novel therapeutic developments for patients with AD.

The effect of inhaler prescription on the development of lung cancer in COPD: a nationwide population-based study.

BACKGROUND: COPD is associated with the development of lung cancer. A protective effect of inhaled corticosteroids (ICS) on lung cancer is still controversial. Hence, this study investigated the development of lung cancer according to inhaler prescription and comorbidties in COPD. METHODS: A retrospective cohort study was conducted based on the Korean Health Insurance Review and Assessment Service database. The development of lung cancer was investigated from the index date to December 31, 2020. This cohort included COPD patients (≥ 40 years) with new prescription of inhalers. Patients with a previous history of any cancer during screening period or a switch of inhaler after the index date were excluded. RESULTS: Of the 63,442 eligible patients, 39,588 patients (62.4%) were in the long-acting muscarinic antagonist (LAMA) and long-acting ß2-agonist (LABA) group, 22,718 (35.8%) in the ICS/LABA group, and 1,136 (1.8%) in the LABA group. Multivariate analysis showed no significant difference in the development of lung cancer according to inhaler prescription. Multivariate analysis, adjusted for age, sex, and significant factors in the univariate analysis, demonstrated that diffuse interstitial lung disease (DILD) (HR = 2.68; 95%CI = 1.86-3.85), a higher Charlson Comorbidity Index score (HR = 1.05; 95%CI = 1.01-1.08), and two or more hospitalizations during screening period (HR = 1.19; 95%CI = 1.01-1.39), along with older age and male sex, were independently associated with the development of lung cancer. CONCLUSION: Our data suggest that the development of lung cancer is not independently associated with inhaler prescription, but with coexisting DILD, a higher Charlson Comorbidity Index score, and frequent hospitalization.

Anthocyanin oligomer (grape skin extract) administration improves dry eye disease: A randomised, double-blind, placebo-controlled study.

BACKGROUND: Dry eye disease is a chronic, progressive ocular disease characterised by ocular discomfort and is one of the most common ophthalmological disorders that affects people's lives. METHODS: This study investigated the clinical efficacy of anthocyanin oligomers (grape skin extract) for the treatment of dry eye. One hundred and eight patients with dry eye were randomly divided into placebo and treatment groups, each with 54 cases. The placebo group received maltodextrin (800 mg/day) and the treatment group received anthocyanin oligomers (800 mg/day). Clinical efficacy, clinical indices, and occurrence of adverse reactions were compared between the two groups. RESULTS: Anthocyanin oligomers were safe and effective in mild-to-moderate dry eye disease, improving the tear break-up time, intraocular pressure, ocular surface disease, and patient symptomatology. CONCLUSIONS: The use of oral anthocyanin oligomers in the treatment of dry eye patients can enhance the therapeutic effect and improve the quality of life of patients while ensuring the safety of treatment, making this therapeutic option suitable for wider application.

Effects of Auricular Acupressure on Hip Flexibility and Pain in Taekwondo Participants: Randomized Controlled Trial.

BACKGROUND: In sports, hip flexibility is essential to reduce injuries and improve performance. AIM: This study aimed to examine the effects of auricular acupressure on hip flexibility and pain in Taekwondo participants. METHOD: This randomized controlled trial was performed in the Republic of Korea from January 2021 to August 2021. The Numeric Rating Scale for Pain and Hip Flexibility was used. Twenty-one participants received auricular pressure once weekly for six weeks, while 17 participants did not receive any intervention. Auricular acupressure was applied to the hip (AH13), Shinmun, and auricular acupressure points associated with the pain areas reported by the participants. RESULTS: Auricular acupressure improved hip flexibility (t = 2.67, p = .011) and back pain (t = 2.11, p = .043). The mean difference in post-pretest hip flexibility in the experimental group was 16.24 degrees (±13.63), whereas that in the control group was 4.77 degrees (±15.07). The mean difference in the experimental group's pre-post-test scores of back pain was 1.24 (±2.64), whereas that in the control group was 0.18 (±1.41). CONCLUSIONS: The results of this study showed that auricular acupressure could be used to treat pain and improve hip flexibility.

Efficient Collaboration Between Radiologists Using the PACS-Integrated Refer Function to Reduce Communication Times.

The purpose of this study was to assess the utility of a picture archiving and communication systems (PACS)-integrated refer function for improving collaboration between radiologists and radiographers during daily reading sessions. Retrospective analysis was conducted on refers sent by radiologists using a PACS-integrated refer system from March 2020 to December 2021. Refers were categorized according to receiver: radiologists in the same division (intra-division), radiologists in a different division (inter-division), and radiographers. The proportions of answered refers, content of refers, and timing of refer posts were evaluated. Additionally, time intervals in minutes from initial refer post to refer response were assessed to assess the efficiency of the refer system and compared according to receivers using the Mann-Whitney U test. Among a total of 691 refers posted by radiologists, 579 (83.8%) were answered directly using the refer function in PACS. Of the answered refers, 346 refers (59.8%) were made between radiologists, and 173 (50%) were intra-division refers. About the content of refers, about 82.6% of radiologists' refers were about imaging interpretation consultation, and about 98.9% of refers from radiologists to radiographers were for image quality control. The median time interval until refer response was 9 min, and this response time did not differ between intra-division and inter-division refers (p = 0.998). Of the refers that got responses, 74.3% (257/346) were sent among radiologists before official reports were made, and the median time until refer response was 9-10 min. The proportion of refers answered by radiographers was 85.7% (233/272). The median time interval until refer response by radiographers was 87 min for all refers, and 63% were made within 6 h. Therefore, the PACS-integrated refer function can facilitate communication between radiologists for image interpretation and quality control.

Consistency of Artificial Intelligence (AI)-based Diagnostic Support Software in Short-term Digital Mammography Reimaging After Core Needle Biopsy.

To evaluate the consistency in the performance of Artificial Intelligence (AI)-based diagnostic support software in short-term digital mammography reimaging after core needle biopsy. Of 276 women who underwent short-term (<3 mo) serial digital mammograms followed by breast cancer surgery from Jan. to Dec. 2017, 550 breasts were included. All core needle biopsies for breast lesions were performed between serial exams. All mammography images were analyzed using a commercially available AI-based software providing an abnormality score (0-100). Demographic data for age, interval between serial exams, biopsy, and final diagnosis were compiled. Mammograms were reviewed for mammographic density and finding. Statistical analysis was performed to evaluate the distribution of variables according to biopsy and to test the interaction effects of variables with the difference in AI-based score according to biopsy. AI-based score of 550 exams (benign or normal in 263 and malignant in 287) showed significant difference between malignant and benign/normal exams (0.48 vs. 91.97 in first exam and 0.62 vs. 87.13 in second exam, P<0.0001). In comparison of serial exams, no significant difference was found in AI-based score. AI-based score difference between serial exams was significantly different according to biopsy performed or not (-0.25 vs. 0.07, P = 0.035). In linear regression analysis, there was no significant interaction effect of all clinical and mammographic characteristics with mammographic examinations performed after biopsy or not. The results from AI-based diagnostic support software for digital mammography was relatively consistent in short-term reimaging even after core needle biopsy.

US, Mammography, and Histopathologic Evaluation to Identify Low Nuclear Grade Ductal Carcinoma in Situ.

Background Low nuclear grade ductal carcinoma in situ (DCIS) identified at biopsy can be upgraded to intermediate to high nuclear grade DCIS at surgery. Methods that confirm low nuclear grade are needed to consider nonsurgical approaches for these patients. Purpose To develop a preoperative model to identify low nuclear grade DCIS and to evaluate factors associated with low nuclear grade DCIS at biopsy that was not upgraded to intermediate to high nuclear grade DCIS at surgery. Materials and Methods In this retrospective study, 470 women (median age, 50 years; interquartile range, 44-58 years) with 477 pure DCIS lesions at surgical histopathologic evaluation were included (January 2010 to December 2015). Patients were divided into the training set (n = 330) or validation set (n = 147) to develop a preoperative model to identify low nuclear grade DCIS. Features at US (mass, nonmass) and at mammography (morphologic characteristics, distribution of microcalcification) were reviewed. The upgrade rate of low nuclear grade DCIS was calculated, and multivariable regression was used to evaluate factors for associations with low nuclear grade DCIS that was not upgraded later. Results A preoperative model that included lesions manifesting as a mass at US without microcalcification and no comedonecrosis at biopsy was used to identify low nuclear grade DCIS, with a high area under the receiver operating characteristic curve of 0.97 (95% CI: 0.94, 1.00) in the validation set. The upgrade rate of low nuclear grade DCIS at biopsy was 38.8% (50 of 129). Ki-67 positivity (odds ratio, 0.04; 95% CI: 0.0003, 0.43; P = .005) was inversely associated with constant low nuclear grade DCIS. Conclusion The upgrade rate of low nuclear grade ductal carcinoma in situ (DCIS) at biopsy to intermediate to high nuclear grade DCIS at surgery occurred in more than a third of patients; low nuclear grade DCIS at final histopathologic evaluation could be identified if the mass was viewed at US without microcalcifications and had no comedonecrosis at histopathologic evaluation of biopsy. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Rahbar in this issue. An earlier incorrect version appeared online. This article was corrected on April 14, 2022.

Multi-omics analysis revealed TEK and AXIN2 are potential biomarkers in multifocal papillary thyroid cancer.

BACKGROUND: Papillary thyroid carcinoma (PTC), the most common endocrine cancer, accounts for 80-85% of all malignant thyroid tumors. This study focused on identifying targets that affect the multifocality of PTC. In a previous study, we determined 158 mRNAs related to multifocality in BRAF-mutated PTC using The Cancer Genome Atlas. METHODS: We used multi-omics data (miRNAs and mRNAs) to identify the regulatory mechanisms of the investigated mRNAs. miRNA inhibitors were used to determine the relationship between mRNAs and miRNAs. We analyzed the target protein levels in patient sera using ELISA and immunohistochemical staining of patients' tissues. RESULTS: We identified 44 miRNAs that showed a negative correlation with mRNA expression. Using in vitro experiments, we identified four miRNAs that inhibit TEK and/or AXIN2 among the target mRNAs. We also showed that the downregulation of TEK and AXIN2 decreased the proliferation and migration of BRAF ( +) PTC cells. To evaluate the diagnostic ability of multifocal PTC, we examined serum TEK or AXIN2 in unifocal and multifocal PTC patients using ELISA, and showed that the serum TEK in multifocal PTC patients was higher than that in the unifocal PTC patients. The immunohistochemical study showed higher TEK and AXIN2 expression in multifocal PTC than unifocal PTC. CONCLUSIONS: Both TEK and AXIN2 play a potential role in the multifocality of PTC, and serum TEK may be a diagnostic marker for multifocal PTC.

AI-CAD for differentiating lesions presenting as calcifications only on mammography: outcome analysis incorporating the ACR BI-RADS descriptors for calcifications.

OBJECTIVES: To evaluate how AI-CAD triages calcifications and to compare its performance to an experienced breast radiologist. METHODS: Among routine mammography performed between June 2016 and May 2018, 535 lesions detected as calcifications only on mammography in 500 women (mean age, 48.8 years) that were additionally interpreted with additional magnification views were included in this study. One dedicated breast radiologist retrospectively reviewed the magnification mammograms to assess morphology, distribution, and final assessment category according to ACR BI-RADS. AI-CAD analyzed routine mammograms providing AI-CAD marks and corresponding AI-CAD scores (ranging from 0 to 100%), for which values ≥ 10% were considered positive. Ground truth in terms of malignancy or benignity was confirmed with a histopathologic diagnosis or at least 1 year of imaging follow - up. RESULTS: Of the 535 calcifications, 215 (40.2%) were malignant. Calcifications with positive AI-CAD scores showed significantly higher PPVs compared to calcifications with negative scores for all morphology (all p < 0.05). PPVs were significantly higher in calcifications with positive AI-CAD scores compared to those with negative scores for BI-RADS 3, 4a, or 4b assessments (all p < 0.05). AI-CAD and the experienced radiologist did not show significant difference in diagnostic performance; sensitivity 92.1% vs 95.4% (p = 0.125), specificity 71.9% vs 72.5% (p = 0.842), and accuracy 80.0% vs 81.7% (p = 0.413). CONCLUSION: Among calcifications with same morphology or BI-RADS assessment, those with positive AI-CAD scores had significantly higher PPVs. AI-CAD showed similar diagnostic performances to the experienced radiologist for calcifications detected on mammography. KEY POINTS: • Among calcifications with same morphology or BI-RADS assessment, those with positive AI-CAD scores had significantly higher PPVs. • AI-CAD showed similar diagnostic performance to an experienced radiologist in assessing lesions detected as calcifications only on mammography. • Among malignant calcifications, calcifications with positive AI-CAD scores showed higher rates of invasive cancers than calcifications with negative scores (all p > 0.05).

Depiction of breast cancers on digital mammograms by artificial intelligence-based computer-assisted diagnosis according to cancer characteristics.

OBJECTIVE: To evaluate how breast cancers are depicted by artificial intelligence-based computer-assisted diagnosis (AI-CAD) according to clinical, radiological, and pathological factors. MATERIALS AND METHODS: From January 2017 to December 2017, 896 patients diagnosed with 930 breast cancers were enrolled in this retrospective study. Commercial AI-CAD was applied to digital mammograms and abnormality scores were obtained. We evaluated the abnormality score according to clinical, radiological, and pathological characteristics. False-negative results were defined by abnormality scores less than 10. RESULTS: The median abnormality score of 930 breasts was 87.4 (range 0-99). The false-negative rate of AI-CAD was 19.4% (180/930). Cancers with an abnormality score of more than 90 showed a high proportion of palpable lesions, BI-RADS 4c and 5 lesions, cancers presenting as mass with or without microcalcifications and invasive cancers compared with low-scored cancers (all p < 0.001). False-negative cancers were more likely to develop in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers and DCIS compared to detected cancers. CONCLUSION: Breast cancers depicted with high abnormality scores by AI-CAD are associated with higher BI-RADS category, invasive pathology, and higher cancer stage. KEY POINTS: • High-scored cancers by AI-CAD included a high proportion of BI-RADS 4c and 5 lesions, masses with or without microcalcifications, and cancers with invasive pathology. • Among invasive cancers, cancers with higher T and N stage and HER2-enriched subtype were depicted with higher abnormality scores by AI-CAD. • Cancers missed by AI-CAD tended to be in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers by radiologists.

Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI.

OBJECTIVES: To investigate machine learning approaches for radiomics-based prediction of prognostic biomarkers and molecular subtypes of breast cancer using quantification of tumor heterogeneity and angiogenesis properties on magnetic resonance imaging (MRI). METHODS: This prospective study examined 291 invasive cancers in 288 patients who underwent breast MRI at 3 T before treatment between May 2017 and July 2019. Texture and perfusion analyses were performed and a total of 160 parameters for each cancer were extracted. Relationships between MRI parameters and prognostic biomarkers were analyzed using five machine learning algorithms. Each model was built using only texture features, only perfusion features, or both. Model performance was compared using the area under the receiver-operating characteristic curve (AUC) and the DeLong method, and the importance of MRI parameters in prediction was derived. RESULTS: Texture parameters were associated with the status of hormone receptors, human epidermal growth factor receptor 2, and Ki67, tumor size, grade, and molecular subtypes (p < 0.002). Perfusion parameters were associated with the status of hormone receptors and Ki67, grade, and molecular subtypes (p < 0.003). The random forest model integrating texture and perfusion parameters showed the highest performance (AUC = 0.75). The performance of the random forest model was the best with a special scale filter of 0 (AUC = 0.80). The important parameters for prediction were texture irregularity (entropy) and relative extracellular extravascular space (Ve). CONCLUSIONS: Radiomic machine learning that integrates tumor heterogeneity and angiogenesis properties on MRI has the potential to noninvasively predict prognostic factors of breast cancer. KEY POINTS: • Machine learning, integrating tumor heterogeneity and angiogenesis properties on MRI, can be applied to predict prognostic biomarkers and molecular subtypes in breast cancer. • The random forest model showed the best predictive performance among the five machine learning models (logistic regression, decision tree, naïve Bayes, random forest, and artificial neural network). • The most important MRI parameters for predicting prognostic factors in breast cancer were texture irregularity (entropy) among texture parameters and relative extracellular extravascular space (Ve) among perfusion parameters.

Mammographic Surveillance After Breast-Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection.

BACKGROUND. Postoperative mammograms present interpretive challenges due to postoperative distortion and hematomas. The application of digital breast tomosyn-thesis (DBT) and artificial intelligence-based computer-aided detection (AI-CAD) after breast-conserving therapy (BCT) has not been widely investigated. OBJECTIVE. The purpose of our study was to assess the impact of additional DBT or AI-CAD on recall rate and diagnostic performance in women undergoing mammographic surveillance after BCT. METHODS. This retrospective study included 314 women (mean age, 53.3 ± 10.6 [SD] years; four with bilateral breast cancer) who underwent BCT followed by DBT (mean interval from surgery to DBT, 15.2 ± 15.4 months). Three breast radiologists independently reviewed images in three sessions: digital mammography (DM), DM with DBT (DM plus DBT), and DM with AI-CAD (DM plus AI-CAD). Recall rates and diagnostic performance were compared between DM, DM plus DBT, and DM plus AI-CAD using the readers' mean results. RESULTS. Of the 314 women, six breast recurrences (three ipsilateral and three contralateral) had developed at the time of surveillance mammography. The ipsilateral breast recall rate was lower for DM plus AI-CAD (1.9%) than for DM (11.2%) or DM plus DBT (4.1%) (p < .001). The contralateral breast recall rate was significantly lower for DM plus AI-CAD (1.5%, p < .001) than for DM (6.6%) but for not DM plus DBT (2.7%, p = .08). In the ipsilateral breast, accuracy was higher for DM plus AI-CAD (97.0%) than for DM (88.5%) or DM plus DBT (94.8%) (p < .05); specificity was higher for DM plus AI-CAD (98.3%) than for DM (89.3%) or DM plus DBT (96.1%) (p < .05); sensitivity was significantly lower for DM plus AI-CAD (22.2%) than for DM (66.7%, p = .03) but not DM plus DBT (22.2%, p > .99). In the contralateral breast, accuracy was significantly higher for DM plus AI-CAD (97.1%) than for DM (92.5%, p < .001) but not DM plus DBT (96.1%, p = .25); specificity was significantly higher for DM plus AI-CAD (98.6%) than for DM (93.7%, p < .001) but not DM plus DBT (97.5%) (p = .09); sensitivity was not different between DM (33.3%), DM plus DBT (22.2%), and DM plus AI-CAD (11.1%) (p > .05). CONCLUSION. After BCT, adjunct DBT or AI-CAD reduced recall rates and improved accuracy in the ipsilateral and contralateral breasts compared with DM. In the ipsilateral breast, the addition of AI-CAD resulted in a lower recall rate and higher accuracy than the addition of DBT. CLINICAL IMPACT. AI-CAD may help address the challenges of interpreting post-BCT surveillance mammograms.

Fabrication and evaluation of bilateral Helmholtz radiofrequency coil for thermo-stable breast image with reduced artifacts.

PURPOSE: The positron emission tomography (PET)-magnetic resonance (MR) system is a newly emerging technique that yields hybrid images with high-resolution anatomical and metabolic information. With PET-MR imaging, a definitive diagnosis of breast abnormalities will be possible with high spatial accuracy and images will be acquired for the optimal fusion of anatomic locations. Therefore, we propose a PET-compatible two-channel breast MR coil with minimal disturbance to image acquisition which can be used for simultaneous PET-MR imaging in patients with breast cancer. MATERIALS AND METHODS: For coil design and construction, the conductor loops of the Helmholtz coil were tuned, matched, and subdivided with nonmagnetic components. Element values were optimized with an electromagnetic field simulation. Images were acquired on a GE 600 PET-computed tomography (CT) and GE 3.0 T MR system. For this study, we used the T1-weighted image (volunteer; repetition time (TR), 694 ms; echo time (TE), 9.6 ms) and T2-weighted image (phantom; TR, 8742 ms; TE, 104 ms) with the fast spin-echo sequence. RESULTS: The results of measuring image factors with the proposed radiofrequency (RF) coil and standard conventional RF coil were as follows: signal-to-noise ratio (breast; 207.7 vs. 175.2), percent image uniformity (phantom; 89.22%-91.27% vs. 94.63%-94.77%), and Hounsfield units (phantom; -4.51 vs. 2.38). CONCLUSIONS: Our study focused on the feasibility of proposed two-channel Helmholtz loops (by minimizing metallic components and soldering) for PET-MR imaging and found the comparable image quality to the standard conventional coil. We believe our work will help significantly to improve image quality with the development of a less metallic breast MR coil.

Risk factors for postoperative delirium in patients with colorectal cancer.

AIM: The purpose of this study was to identify the incidence of postoperative delirium in PCC (patients with colorectal cancer) and the related factors of postoperative delirium by analysing the differences in the general, disease-related and operation-related characteristics. BACKGROUND: Previous studies had some limitations in generalising the risk factors for postoperative delirium in PCC due to the lack of relevant factors, such as disease- and operation-related characteristics. There is a need to investigate the factors of postoperative delirium by including cancer and surgical characteristics. DESIGN: The current study is a cross-sectional study to investigate the correlated factors of postoperative delirium in PCC. METHODS: A total of 196 patients who underwent colorectal cancer surgery at the Keimyung University Dongsan Hospital in Korea participated in the study. Data collection was performed from 15 August 2018 to 10 July 2019. Patients' general, disease-related and operation-related characteristics were collected from questionnaires and electronic medical records. Data analysis was performed using descriptive statistics, t test, Chi-square test and logistic regression using SPSS/WIN 22.0. The STROBE checklist has been used to report this study. RESULTS: The results of this study showed that 26 (13.2%) PCC exhibited postoperative delirium and the risk factors for postoperative delirium were physical activity (OR = 2.94, p = .001), infection (OR = 2.17, p = .001) and nutritional status (OR = 1.10, p = .028). CONCLUSION: To reduce and prevent the occurrence of postoperative delirium in PCC, encouraging participation in physical activity before and after surgery are required, and regular monitoring of the infection symptoms and nutritional status. RELEVANCE TO CLINICAL PRACTICE: Based on the results of this study, postoperative delirium in PCC could be decreased by encouraging physical activity immediately following operation, monitoring the signs and symptoms of infection using diverse objective laboratory findings and maintaining the nutritional status within the normal range.

Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment.

We evaluated and compared the mammographic density assessment of an artificial intelligence-based computer-assisted diagnosis (AI-CAD) program using inter-rater agreements between radiologists and an automated density assessment program. Between March and May 2020, 488 consecutive mammograms of 488 patients (56.2 ± 10.9 years) were collected from a single institution. We assigned four classes of mammographic density based on BI-RADS (Breast Imaging Reporting and Data System) using commercial AI-CAD (Lunit INSIGHT MMG), and compared inter-rater agreements between radiologists, AI-CAD, and another commercial automated density assessment program (Volpara®). The inter-rater agreement between AI-CAD and the reader consensus was 0.52 with a matched rate of 68.2% (333/488). The inter-rater agreement between Volpara® and the reader consensus was similar to AI-CAD at 0.50 with a matched rate of 62.7% (306/488). The inter-rater agreement between AI-CAD and Volpara® was 0.54 with a matched rate of 61.5% (300/488). In conclusion, density assessments by AI-CAD showed fair agreement with those of radiologists, similar to the agreement between the commercial automated density assessment program and radiologists.

Application of Artificial Intelligence Computer-Assisted Diagnosis Originally Developed for Thyroid Nodules to Breast Lesions on Ultrasound.

As thyroid and breast cancer have several US findings in common, we applied an artificial intelligence computer-assisted diagnosis (AI-CAD) software originally developed for thyroid nodules to breast lesions on ultrasound (US) and evaluated its diagnostic performance. From January 2017 to December 2017, 1042 breast lesions (mean size 20.2 ± 11.8 mm) of 1001 patients (mean age 45.9 ± 12.9 years) who underwent US-guided core-needle biopsy were included. An AI-CAD software that was previously trained and validated with thyroid nodules using the convolutional neural network was applied to breast nodules. There were 665 benign breast lesions (63.0%) and 391 breast cancers (37.0%). The area under the receiver operating characteristic curve (AUROC) of AI-CAD to differentiate breast lesions was 0.678 (95% confidence interval: 0.649, 0.707). After fine-tuning AI-CAD with 1084 separate breast lesions, the diagnostic performance of AI-CAD markedly improved (AUC 0.841). This was significantly higher than that of radiologists when the cutoff category was BI-RADS 4a (AUC 0.621, P < 0.001), but lower when the cutoff category was BI-RADS 4b (AUC 0.908, P < 0.001). When applied to breast lesions, the diagnostic performance of an AI-CAD software that had been developed for differentiating malignant and benign thyroid nodules was not bad. However, an organ-specific approach guarantees better diagnostic performance despite the similar US features of thyroid and breast malignancies.

Added Value of MRI for Invasive Breast Cancer including the Entire Axilla for Evaluation of High-Level or Advanced Axillary Lymph Node Metastasis in the Post-ACOSOG Z0011 Trial Era.

Background In the post-American College of Surgeons Oncology Group Z0011 trial era, radiologists have increasingly focused on excluding high-level or advanced axillary lymph node metastasis (ALNM) by using an additional MRI scan positioned higher than lower axillae; however, the value of these additional scans remains undetermined. Purpose To evaluate whether a standard MRI protocol is sufficient to exclude high-level or advanced ALNM in breast cancer or additional MRI of entire axilla is needed. Materials and Methods This retrospective study evaluated women with invasive breast cancer who underwent breast MRI from April 2015 to December 2016. Some underwent neoadjuvant chemotherapy (NAC) and others underwent upfront surgery. Standard (routine axial scans including the lower axillae) and combined (routine axial scans plus additional scans including the entire axilla) MRI protocols were compared for high-level or advanced ALNM detection. Clinical-pathologic characteristics were analyzed. Uni- and multivariable logistic regression was performed to identify predictors of high-level or advanced ALNM. Results A total of 435 women (mean age ± standard deviation, 52 years ± 11) were evaluated (65 in the NAC group, 370 in the non-NAC group). With the standard MRI protocol, predictors of high-level ALNM were peritumoral edema (odds ratio [OR], 12.3; 95% CI: 3.9, 39.4; P < .001) and positive axilla (OR, 5.9; 95% CI: 2.0, 15.2; P < .001). Only three of 289 women with negative axillae without peritumoral edema had high-level ALNM. Predictors of advanced ALNM were positive axillae (OR, 8.9; 95% CI: 3.7, 21.5; P < .001) and peritumoral edema (OR, 2.8; 95% CI: 1.1, 6.9; P = .03). Only six of 310 women who had negative axillae without peritumoral edema had advanced ALNM. Conclusion The performance of standard MRI was satisfactory in excluding high-level and advanced axillary lymph node metastasis in most patients with breast cancer. However, the presence of peritumoral edema or positive axillae in the MRI findings emphasizes the benefits of a combined MRI protocol. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Abe in this issue.

Chronological Trends of Breast Ductal Carcinoma In Situ: Clinical, Radiologic, and Pathologic Perspectives.

BACKGROUND: Because no prior studies have evaluated the chronological trends of ductal carcinoma in situ (DCIS) despite the increasing number of surgeries performed for DCIS, this study analyzed how the clinical, radiologic, and pathologic characteristics of DCIS changed during a 10-year period. METHODS: Of 7123 patients who underwent primary breast cancer surgery at a single institution from 2006 to 2015, 792 patients with pure DCIS were included in this study. The chronological trends of age, symptoms, method for detecting either mammography or ultrasonography, tumor size, nuclear grade, comedonecrosis, and molecular markers were calculated using Poisson regression for all patients and asymptomatic patients. RESULTS: During 10 years, DCIS surgery rates significantly increased (p < 0.001). Despite the high percentage of DCIS detected on mammography, the detection rate for DCIS by mammography significantly decreased (97.3% in 2006 to 67.6% in 2015; p = 0.025), whereas the detection rate by ultrasound significantly increased (2.7% to 31.0%; p < 0.001). Conservation surgery rates (odds ratio [OR], 1.058), low-to-intermediate nuclear grade rates (OR, 1.069), and the absence of comedonecrosis (OR, 1.104) significantly increased over time (all p < 0.05). Estrogen receptor (ER) negativity (OR, 0.935) and human epidermal growth factor receptor 2 (HER2) positivity rates (OR, 0.953) significantly decreased (all p < 0.05). The same trends were observed for the 613 asymptomatic patients. CONCLUSION: The rate of DCIS detected on ultrasound only significantly increased during 10 years. Low-to-intermediate nuclear grade rates significantly increased, whereas ER negativity and HER2 positivity rates significantly decreased during the same period. These findings suggest that DCIS detected on screening ultrasound is less aggressive than DCIS detected on mammography.

Application of artificial intelligence-based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms.

OBJECTIVE: To compare the diagnostic agreement and performances of synthetic and conventional mammograms when artificial intelligence-based computer-assisted diagnosis (AI-CAD) is applied. MATERIAL AND METHOD: From January 2017 to April 2017, 192 patients (mean age 53.7 ± 11.7 years) diagnosed with 203 breast cancers were enrolled in this retrospective study. All patients underwent digital breast tomosynthesis (DBT) with digital mammograms (DM) simultaneously. Commercial AI-CAD was applied to the reconstructed synthetic mammograms (SM) from DBT and DM respectively and abnormality scores were calculated. We compared the median abnormality scores between DM and SM with the Wilcoxon signed-rank test and used the Bland-Altman analysis to evaluate agreements between the two mammograms and to investigate clinicopathological factors which might affect agreement. Diagnostic performances were compared using an area under the receiver operating characteristic curve (AUC). RESULT: The abnormality scores showed a mean difference (bias) of - 3.26 (95% limits of agreement: - 32.69, 26.18) between the two mammograms by the Bland-Altman analysis. The concordance correlation coefficient was 0.934 (95% CI: 0.92, 0.946), suggesting high reproducibility. SM showed higher abnormality scores in cancer with distortion and occult findings, T1 and N0 cancer, and luminal type cancer than DM (all p ≤ 0.001). Diagnostic performance did not differ between the mammograms (AUC 0.945 for conventional mammograms, 0.938 for synthetic mammograms, p = 0.499). CONCLUSION: AI-CAD can also work well on synthetic mammograms, showing good agreement and comparable diagnostic performance compared to its application to DM. KEY POINTS: • AI-CAD which was developed based on imaging findings of digital mammograms can also be applied to synthetic mammograms. • AI-CAD showed good agreement and similar diagnostic performance when applied to both synthetic and digital mammograms. • With AI-CAD, synthetic mammograms showed relatively higher abnormality scores in cancer with distortion and occult findings, T1 and N0 cancer, and luminal type cancer than digital mammograms.