ABSTRACT
Despite recent advances in artificial intelligence (AI) software with improved performance in mammography screening for breast cancer, insufficient data are available on its performance in detecting cancers that were initially missed on mammography. In this study, we aimed to determine whether AI software-aided mammography could provide additional value in identifying cancers detected through supplemental screening ultrasound. We searched our database from 2017 to 2018 and included 238 asymptomatic patients (median age, 50 years; interquartile range, 45–57 years) diagnosed with breast cancer using supplemental ultrasound. Two unblinded radiologists retrospectively reviewed the mammograms using commercially available AI software and identified the reasons for missed detection.Clinicopathological characteristics of AI-detected and AI-undetected cancers were compared using univariate and multivariate logistic regression analyses. A total of 253 cancers were detected in 238 patients using ultrasound. In an unblinded review, the AI software failed to detect 187 of the 253 (73.9%) mammography cases with negative findings in retrospective observations. The AI software detected 66 cancers (26.1%), of which 42 (63.6%) exhibited indiscernible findings obscured by overlapping dense breast tissues, even with the knowledge of magnetic resonance imaging and post-wire localization mammography. The remaining 24 cases (36.4%) were considered interpretive errors by the radiologists. Invasive tumor size was associated with AI detection after multivariable analysis (odds ratio, 2.2; 95% confidence intervals, 1.5–3.3; p < 0.001). In the control group of 160 women without cancer, the AI software identified 19 false positives (11.9%, 19/160). Although most ultrasound-detected cancers were not detected on mammography with the use of AI, the software proved valuable in identifying breast cancers with indiscernible abnormalities or those that clinicians may have overlooked.
ABSTRACT
This study aimed to evaluate the imaging and pathological findings in axillary lymph nodes in patients with breast cancer who received concurrent ipsilateral coronavirus disease 2019 (COVID-19) vaccination. Of the 19 women with breast cancer who received concurrent COVID-19 vaccination shot in the arm ipsilateral to breast cancer, axillary lymphadenopathy was observed in 84.2% (16 of 19) of patients on ultrasound (US) and 71.4% (10 of 14) of patients on magnetic resonance imaging (MRI), and 21.0% (4 of 19) of patients were diagnosed with metastasis. Abnormal US and MRI findings of cortical thickening, effacement of the fatty hilum, round shape, and asymmetry in the number or size relative to the contralateral side were noted in more than half of the non-metastatic and metastatic lymph nodes; however, statistical significance was not noted. Axillary lymphadenopathy is commonly observed in patients with breast cancer who receive concurrent ipsilateral COVID-19 vaccination without specific differential imaging features. Thus, understanding the limitations of axillary imaging and cautious interpretation is necessary to avoid overestimation or underestimation of the axillary disease burden.
ABSTRACT
Objective@#This study aimed to investigate the blood-brain barrier (BBB) disruption in mild traumatic brain injury (mTBI) patients with post-concussion syndrome (PCS) using dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging and automatic whole brain segmentation. @*Materials and Methods@#Forty-two consecutive mTBI patients with PCS who had undergone post-traumatic MR imaging, including DCE MR imaging, between October 2016 and April 2018, and 29 controls with DCE MR imaging were included in this retrospective study. After performing three-dimensional T1-based brain segmentation with FreeSurfer software (Laboratory for Computational Neuroimaging), the mean Ktrans and vp from DCE MR imaging (derived using the Patlak model and extended Tofts and Kermode model) were analyzed in the bilateral cerebral/cerebellar cortex, bilateral cerebral/cerebellar white matter (WM), and brainstem. Ktrans values of the mTBI patients and controls were calculated using both models to identify the model that better reflected the increased permeability owing to mTBI (tendency toward higher Ktrans values in mTBI patients than in controls). The Mann-Whitney U test and Spearman rank correlation test were performed to compare the mean Ktrans and vp between the two groups and correlate Ktrans and vp with neuropsychological tests for mTBI patients. @*Results@#Increased permeability owing to mTBI was observed in the Patlak model but not in the extended Tofts and Kermode model. In the Patlak model, the mean Ktrans in the bilateral cerebral cortex was significantly higher in mTBI patients than in controls (p = 0.042). The mean vp values in the bilateral cerebellar WM and brainstem were significantly lower in mTBI patients than in controls (p = 0.009 and p = 0.011, respectively). The mean Ktrans of the bilateral cerebral cortex was significantly higher in patients with atypical performance in the auditory continuous performance test (commission errors) than in average or good performers (p = 0.041). @*Conclusion@#BBB disruption, as reflected by the increased Ktrans and decreased vp values from the Patlak model, was observed throughout the bilateral cerebral cortex, bilateral cerebellar WM, and brainstem in mTBI patients with PCS.
ABSTRACT
Objective@#To determine the prognostic value of MRI-based tumor regression grading (mrTRG) in rectal cancer compared withpathological tumor regression grading (pTRG), and to assess the effect of diffusion-weighted imaging (DWI) on interobserveragreement for evaluating mrTRG. @*Materials and Methods@#Between 2007 and 2016, we retrospectively enrolled 321 patients (male:female = 208:113; meanage, 60.2 years) with rectal cancer who underwent both pre-chemoradiotherapy (CRT) and post-CRT MRI. Two radiologistsindependently determined mrTRG using a 5-point grading system with and without DWI in a one-month interval. Two pathologistsgraded pTRG using a 5-point grading system in consensus. Kaplan-Meier estimation and Cox-proportional hazard models wereused for survival analysis. Cohen’s kappa analysis was used to determine interobserver agreement. @*Results@#According to mrTRG on MRI with DWI, there were 6 mrTRG 1, 48 mrTRG 2, 109 mrTRG 3, 152 mrTRG 4, and 6 mrTRG 5.By pTRG, there were 7 pTRG 1, 59 pTRG 2, 180 pTRG 3, 73 pTRG 4, and 2 pTRG 5. A 5-year overall survival (OS) was significantlydifferent according to the 5-point grading mrTRG (p= 0.024) and pTRG (p= 0.038). The 5-year disease-free survival (DFS)was significantly different among the five mrTRG groups (p= 0.039), but not among the five pTRG groups (p= 0.072). OSand DFS were significantly different according to post-CRT MR variables: extramural venous invasion after CRT (hazard ratio= 2.259 for OS, hazard ratio = 5.011 for DFS) and extramesorectal lymph node (hazard ratio = 2.610 for DFS). For mrTRG, kvalue between the two radiologists was 0.309 (fair agreement) without DWI and slightly improved to 0.376 with DWI. @*Conclusion@#mrTRG may predict OS and DFS comparably or even better compared to pTRG. The addition of DWI on T2-weightedMRI may improve interobserver agreement on mrTRG.