The accuracy of titanium contrast-enhanced mammography: a retrospective multicentric study.

BACKGROUND: Recently, a new mammography system to perform contrast-enhanced mammography has become available in the market. For the high-energy acquisition, it uses a titanium filter instead of a copper one, reducing the tube load while maintaining image quality. PURPOSE: To retrospectively evaluate the accuracy of contrast-enhanced mammography with a titanium filter (TiCEM) in three readers with different grades of experience. MATERIAL AND METHODS: IRB-approved retrospective multicentric lesion by lesion study with 200 lesions, all of them initially classified as BI-RADS categories 0/3/4/5 on mammography and/or ultrasound and with pathological confirmation, in 135 patients. Three readers with different levels of experience (expert, resident, intermediate) blinded to the final diagnosis, retrospectively evaluated the low-energy (LE) images and the combination of LE and recombined (subtracted) images and classified the lesions according to the BI-RADS categories. Reader 1 also categorized the breast density. ROC curves were performed for each reader. RESULTS: Out of the 200 lesions, 82 were benign and 118 malignant (20 DCIS, 10 ILC, 88 IDC). The AUCs of LE versus TiCEM for were: Reader 1: 0.7 vs. 0.88, P < 0.001; Reader 2: 0.63 vs. 0.83, P < 0.001; and Reader 3: 0.63 vs. 0.84, P < 0.001. For the three readers, the AUCs of LE versus TiCEM were significantly superior in both dense and non-dense breasts (P < 0.001). Comparing the AUC of LE for Reader 1 (expert) versus the AUC of TiCEM for Reader 2 (resident) there were significant differences (0.7 vs. 0.83, P < 0.001). CONCLUSION: The accuracy of TiCEM was significantly better for all the readers, in both dense and non-dense breasts. The accuracy of a resident reading a TiCEM study was better than the accuracy of an expert radiologist reading LE images.

The effect of the amount of peritumoral adipose tissue in the detection of additional tumors with digital breast tomosynthesis and ultrasound.

Background Digital breast tomosynthesis (DBT) and ultrasound (US) can detect additional cancers after negative mammography. However, not all cancers are visible by both techniques. Purpose To study the role of the amount of peritumoral fat in the detection of additional cancers with DBT or US. Material and Methods One reader retrospectively reviewed 142 breast cancers in 109 women who underwent mammography, DBT, US, and magnetic resonance imaging (MRI). Two readers in consensus evaluated the additional cancers detected by US, DBT, or MRI, and classified them into four groups according to the amount of peritumoral adipose tissue: group I, >75% of peritumoral fat; group II, 50-74%; group III, 25-49%, and group IV, 0-24%. The detection of additional cancers by US and DBT with respect to the other imaging techniques was evaluated. Results Seventy-eight cancers were detected by mammography and the remaining 64 cancers were detected by DBT, US, or MRI. US and DBT detected 46 (71.8%) and 25 (39.06%) additional tumors, respectively. Statistical significance was only found in group IV ( P < 0.01). Conclusion US detected more tumors than DBT in lesions surrounded by a small amount of fat. No significant differences were found between US and DBT in the detection of additional cancers in the other groups.

Additional US or DBT after digital mammography: which one is the best combination?

BACKGROUND: Digital mammography (DM) is widespread used for the detection of breast cancer, but its sensitivity drops in dense breasts. It is well known that additional breast ultrasound (US) and digital breast tomosynthesis (DBT) increase the sensitivity of DM. However, to our knowledge, there are no articles comparing the role of both additional techniques. PURPOSE: To assess the diagnostic performance of DM and the different combinations of DM + additional DBT and DM + additional US in an enriched sample of patients. MATERIAL AND METHODS: Retrospective study in an enriched sample of 1042 patients. Out of them, 84 patients had histologically proven malignant lesions and 258 patients had benign lesions. Finally 700 patients with normal explorations or benign lesions without biopsy confirmation (but stable for at least 12 months) were included. All of them underwent DM, US, and DBT examinations that were retrospectively reviewed by one expert radiologist, blinded to the final diagnoses. The DBT examinations were performed using one single view with wide angle (50°). The reader categorized the cases as benign (BI-RADS 1 or 2) or malignant (BI-RADS 3-5) for DM and the different combination of techniques. The sensitivity (SE) and specificity (SP) were calculated with the PEPI software and the ROC curves of the different techniques and combinations were calculated by using the SPSS 15.0 software. RESULTS: The SE and SP of DM were 69.05% and 88.20%, respectively. Additional DBT significantly increased the AUC of DM as well as additional US or the combination DM + DBT + US (P < 0.05). However there were no significant differences between the AUC of DM + US and DM + DBT (P = 0.7). CONCLUSION: Additional US, DBT, or both, in combination with DM, significantly increased the AUC of DM. However, there were no significant differences between DM + DBT and DM + US.

Use of remote response devices: an effective interactive method in the long- term learning.

OBJECTIVES: The aim of this study was to evaluate whether the use of personal response systems (PRS) or clickers improved learning and retention of radiology concepts within a group of medical students. MATERIALS AND METHODS: A total of 175 medical students attended 17 thoracic radiology lectures. Half of the information was taught with traditional teaching methods. The other half was performed using multiple-choice Power Point slides with PRS. Three months later, the students were tested using questions about the topics explained with and without PRS. We compared the average numbers of correct answers, wrong answers and unanswered questions between the topics explained with PRS and those without. RESULTS: The average number of correct answers was significantly higher in the interactive teaching (PRS) questions than in the passive education questions (63.6 vs. 53.2 %, p < 0.05). The percentages of wrong and unanswered interactive teaching questions were significantly lower than those in the passive education questions (23.4 vs. 27.4 % p < 0.005 and 13 vs. 19.5 % p < 0.005 respectively). CONCLUSIONS: Interactive learning with the use of remote response devices (PRS) is an effective method in teaching radiology because it improves learning and retention of knowledge.

Analysis of the changes induced by bevacizumab using a high temporal resolution DCE-MRI as prognostic factors for response to further neoadjuvant chemotherapy.

BACKGROUND: Antiangiogenic drugs are being used in the treatment of locally advanced breast cancer. The effect of these drugs can be monitorized using high temporal resolution dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). PURPOSE: To evaluate changes in tumor microvasculature induced by bevacizumab and the usefulness of these changes predicting response to further neoadjuvant therapy. MATERIAL AND METHODS: Seventy patients with locally advanced breast cancers were treated with one cycle of bevacizumab followed by neoadjuvant therapy, combining bevacizumab and cytotoxic chemotherapy. Two DCE-MRI were performed before and after bevacizumab. Changes in tumoral volume, pharmacodynamic curves, and pharmacokinetic variables (K(trans), Kep, Ve, AUC90) in a ROI (ROI 1) encompassing the entire tumor and in another ROI (ROI 2) in the area of higher values of K(trans) were analyzed. Correlations with pathological response were made: parametrical and non-parametrical statistical analysis and ROC curves were used; a P < 0.05 was considered significant. RESULTS: Significant changes in tumoral volume (-4%), pharmacodynamic curves, and pharmacokinetic variables in ROI 1 K(trans) (-45%), Kep (-38%), Ve (-11%), and AUC90 (-44%) and ROI 2 K(trans) (-43%), Kep (-39%), Ve (-5%), and AUC90 (-45%) were observed after bevacizumab (P < 0.05). The effect of bevacizumab was not different between responders and non-responders (P > 0.05), and these changes could not predict response to further neoadjuvant therapy. CONCLUSION: Bevacizumab induces remarkable tumoral volume, pharmacodynamics, and pharmacokinetic changes. However, these changes could not be used as early predictors for response to further neoadjuvant therapy.