Purpose: To evaluate the performance of an artificial intelligence (AI) algorithm in a simulated screening setting and its effectiveness in detecting missed and interval cancers. Methods: Digital mammograms were collected from Bahcesehir Mammographic Screening Program which is the first organized, population-based, 10-year (2009-2019) screening program in Turkey. In total, 211 mammograms were extracted from the archive of the screening program in this retrospective study. One hundred ten of them were diagnosed as breast cancer (74 screen-detected, 27 interval, 9 missed), 101 of them were negative mammograms with a follow-up for at least 24 months. Cancer detection rates of radiologists in the screening program were compared with an AI system. Three different mammography assessment methods were used: (1) 2 radiologists’ assessment at screening center, (2) AI assessment based on the established risk score threshold, (3) a hypothetical radiologist and AI team-up in which AI was considered to be the third reader. Results: Area under curve was 0.853 (95% CI = 0.801-0.905) and the cut-off value for risk score was 34.5% with a sensitivity of 72.8% and a specificity of 88.3% for AI cancer detection in ROC analysis. Cancer detection rates were 67.3% for radiologists, 72.7% for AI, and 83.6% for radiologist and AI team-up. AI detected 72.7% of all cancers on its own, of which 77.5% were screen-detected, 15% were interval cancers, and 7.5% were missed cancers. Conclusion: AI may potentially enhance the capacity of breast cancer screening programs by increasing cancer detection rates and decreasing false-negative evaluations.

The most important prognostic variable for early stage breast cancer is the status of axillary lymph nodes. The aim of this study was to evaluate the diagnostic accuracy of preoperative magnetic resonance imaging (MRI) for metastatic axillary lymph node in breast cancer cases with post-operative sentinel lymph node biopsy (SLNB) results. Women aged between 21 and 73 years who were diagnosed with malignant mass lesion of the breast between 2013 and 2015 were included in this study. The preoperative MR images of patients with diagnosis of breast cancer was evaluated to determine axillary lymph node status. Axillary lymph node size, long axis to short axis ratio, lymph node contours, cortical thickness to anteroposterior diameter ratio, the presence of a fatty hilum and contrast enhancement patterns (homogenous or heterogenous) was noted. Additionally, the presence of comet tail sign which a tail extending from an enhancing breast lesion into the parenchyma and might represent ductal infiltration on post-contrast series was also noted. All data obtained from this evaluation was compared with postoperative SLNB results. Metastatic nodes were found to have a longer short axis when compared to reactive nodes (p = 0.042; p < 0.05). The long axis to short axis ratio was notably lower in metastatic nodes when compared to reactive nodes. Cortical thickness was higher in metastatic nodes when compared to reactive nodes (p = 0.024; p < 0.05). Comet sign was observed in 15 of metastatic nodes (73.3 %) and in one (5 %) reactive node. This difference was statistically significant (p = 0.001; p < 0.01). While fatty hilum was seen in 40 % of metastatic nodes (n = 6), it was seen in all (n = 20) reactive nodes. This difference was statistically significant (p = 0.001; p < 0.01). MRI is a non invasive sensitive and specific imaging modality for evaluating the axilla. We have shown that with the help of comet tail sign and status of fatty hilum contrast enhanced MRI has the highest sensitivity of 84.7 % for detecting axillary lymph node metastases (Singletary et al. in Semin Surg Oncol 21(1):53–59, 2003).

Through Diffusion Weighted Imaging (DWI), information related to early molecular changes, changes in the permeability of cell membranes, and early morphologic and physiologic changes such as cell swelling can be obtained. We investigated the correlation between the prognostic factors of breast cancer and apparent diffusion coefficient (ADC) in DWI sequences of malignant lesions. Retrospective cross-sectional study. Patients who were referred to our clinic between September 2012 and September 2013, who underwent dynamic breast MRI before or after biopsy and whose biopsy results were determined as malignant, were included in our study. Before the dynamic analysis, DWI sequences were taken. ADC relationship with all prognostic factors was investigated. Pearson correlation test was used to compare the numerical data, while Spearman correlation and Fisher exact tests were used to compare the categorical data. The advanced relationships were evaluated with linear regression analysis and univariate analysis. The efficiency of the parameters was evaluated using ROC analysis. The significance level (P) was accepted as 0.05. In total, 41 female patients with an average age of 49.4 years (age interval 21-77) and 44 lesions were included into the study. In the Pearson correlation test, no statistically significant difference was determined between ADC and the patient's age and tumor size. In the Spearman correlation test, a statistically significant difference was determined between nuclear grade (NG) and ADC (r=-0.424, p=0.04); no statistically significant correlation was observed between the other prognostic factors with each other and ADC values. In the linear regression analysis, the relationship of NG with ADC was found to be more significant alone than when comparing all parameters (corrected r2=0.196, p=0.005). Further evaluations between the NG and ADC correlation were carried out with ROC analysis. A statistically significant difference was determined when NG 1 separately was compared with NG 2 and 3 (p=0.03). A statistically significant difference was also determined (p=0.05) in the comparison of NG 1 with only NG 3. No statistically significant difference was determined when NG 2 separately was compared with NG 1 and NG 3 and when NG 3 separately was compared with NG 1 and 2 (p=0.431, p=0.097). We found that ADC values obtained by breast DWI showed a higher correlation with the NG of breast cancer, which is an important factor in the patient's treatment. Predictions can be made about NG by analyzing the ADC values. Additional studies are needed, however, and the ADC value of the lesion can be used as a prognostic factor proving the aggressiveness.

To compare the adipose and muscle tissue areas in patients who responded differently to neoadjuvant chemotherapy. One hundred and eighty six patients diagnosed with breast cancer who underwent neoadjuvant chemotherapy between January 2015- October 2019 and were operated after the treatment were retrospectively included in the study. Pathological results were divided into five groups using the Miller-Payne grading systems. Grade 1 indicating no significant reduction in malignant cells; Grade 2: a minor loss of malignant cells (≤ 30 %); Grade 3: reduction in malignant cells between 30 % and 90 %; Grade 4: disappearance of malignant cells >90 %; Grade 5: no malignant cells identifiable. Pre-treatment PET CT scans were evaluated, and calculation of body composition parameters were performed on a single axial section passing through the L3 vertebrae. Spearman’s correlation test was used to analyze the correlation between SAT, VAT, MT parameters and pathological responses. There was no strong correlation between the 5 groups separated according to neoadjuvant chemotherapy treatment response and tissue distributions. However, that there was a very low correlation found between superficial adipose tissue and pathological response (r=, 156). In conclusion, our results have provided a very low correlation between SAT and more than 30 % response. More research is required to evaluate the role of the body fat and muscle parameters in response to neoadjuvant chemotherapy in larger patient populations.

Objectives: This study aims to investigate the efficacy of abbreviated breast magnetic resonance imaging (MRI) in neoadjuvant chemotherapy (NAC) response evaluation. Methods: MR images of 50 locally advanced breast cancer patients who underwent standard protocol (SP) breast MRI before and after NAC were re-evaluated retrospectively. Abbreviated protocol (AP) was obtained by extracting images from SP and then evaluating them in a separate session. Protocols were compared with the histological findings after surgery as the reference standard. Results: A statistically significant difference was found between the two protocols in response evaluation by the McNemar test (p=0.018). However, the Kappa value was 0.62 (p<0.001), which indicates substantial agreement. No statistically significant differences were found between the two protocols (AP and SP) and pathological results in the McNemar test (p=0.12, p=0.60, respectively). Kappa values were 0.48 (p<0.001) and 0.60 (p<0.001), respectively, which indicates moderate agreement for both protocols with higher values by SP evaluation. The residual maximum median diameters were smaller than the pathology, with both protocols (p<0.001). Conclusion: Despite the statistical differences, there was a significant correlation in response evaluation between the two protocols. The pathological results were moderately correlated with both protocols, with SP slightly higher. However, the residual maximum median diameters were smaller than the pathology with both protocols. These results may limit the use of AP in evaluating the local extent of the tumor, especially in patients who will undergo breast-conserving surgery.