Microcalcifications on mammograms obtained before neoadjuvant chemotherapy (NAC), peritumoral edema on MRI scans obtained after NAC, and clinical-pathologic variables were associated with survival ... Background Little is known regarding findings at imaging associated with survival in patients with luminal breast cancer treated with neoadjuvant chemotherapy (NAC). Purpose To determine the relati...

Suh JW, et al. J Cardiovasc Imaging. 2018 Sep;26(3):125-134. https://doi.org/10.4250/jcvi.2018.26.e20

Early identification of infants at higher risk of developing bronchopulmonary dysplasia (BPD) may enable a targeted approach to reduce BPD. We aimed to evaluate the hypothesis that the interstitial pneumonia pattern on the day 7 chest radiograph predicts BPD or death before 36 weeks postmenstrual age (PMA). A retrospective cohort study was performed on 336 preterm infants (birth weight < 1500 g and gestational age < 32 postmenstrual weeks) who were admitted to a single tertiary academic center between January 2008 and December 2014. Day 7 chest radiographs were independently reviewed by two pediatric radiologists who were unaware of the clinical information regarding each individual infant. Data from 304 infants who survived more than 7 days after birth were collected. The interstitial pneumonia pattern on the day 7 chest radiograph was independently associated with BPD or death before 36 weeks PMA (odds ratio [OR] 4.0, 95% confidence interval [CI] 1.1–14.4). The interstitial pneumonia pattern on the day 7 chest radiograph predicted BPD or death with a specificity of 98%. Histologic chorioamnionitis was a preceding factor that was independently associated with the interstitial pneumonia pattern on the day 7 chest radiograph (OR 3.7, 95% CI 1.3–10.3). The interstitial pneumonia pattern on the day 7 chest radiograph has a high specificity for predicting BPD or death and can be utilized to select high-risk preterm infants who will benefit from potentially preventive interventions against BPD.

The present study evaluated the diagnostic performance of artificial intelligence-based computer-aided diagnosis (AI-CAD) compared to that of dedicated breast radiologists in characterizing suspicious microcalcification on mammography. We retrospectively analyzed 435 unilateral mammographies from 420 patients (286 benign; 149 malignant) undergoing biopsy for suspicious microcalcification from June 2003 to November 2019. Commercial AI-CAD was applied to the mammography images, and malignancy scores were calculated. Diagnostic performance was compared between radiologists and AI-CAD using the area under the receiving operator characteristics curve (AUC). The AUCs of radiologists and AI-CAD were not significantly different (0.722 vs. 0.745, p = 0.393). The AUCs of the adjusted category were 0.726, 0.744, and 0.756 with cutoffs of 2%, 10%, and 38.03% for AI-CAD, respectively, which were all significantly higher than those for radiologists alone (all p < 0.05). None of the 27 cases downgraded to category 3 with a cutoff of 2% were confirmed as malignant on pathological analysis, suggesting that unnecessary biopsies could be avoided. Our findings suggest that the diagnostic performance of AI-CAD in characterizing suspicious microcalcification on mammography was similar to that of the radiologists, indicating that it may aid in making clinical decisions regarding the treatment of breast microcalcification.