Adjunctive diagnostic value of targeted electrical impedance imaging to conventional methods in the evaluation of breast lesions

PURPOSE: To determine the diagnostic accuracy of targeted electrical impedance imaging in characterizing breast lesions, and to evaluate whether lesion size, depth and histopathology affect the diagnosis. MATERIAL AND METHODS: A total of 137 women with 145 lesions (79 malignant and 66 benign) found by palpation or mammography were prospectively enrolled in this study. The patients were examined by means of clinical breast examination, mammography, ultrasonography, and electrical impedance imaging with TransScan TS2000. A level of suspicion (LOS) post-processing algorithm (v2.67) was used for TS2000 lesion assessment. Imaging findings were correlated with cytologic (n=54) and histologic diagnoses (n=91). Patients with benign lesions were followed up for a mean of 36 months. RESULTS: TS2000 showed a high sensitivity (86%) which did not differ significantly from that of mammography (87%) and ultrasonography (US) (75%). The specificity of TS2000 (49%) was significantly lower compared to mammography (97%, P<0.0001) and US (100%, P<0.0001). The additive use of TS2000 to mammography and US yielded no significant increase in sensitivity (97%), but the decrease in specificity was significant (46%, P<0.0001). Diagnostic effectiveness of TS2000 (Az=0.68), as measured by the area under the ROC curve, was significantly lower than for mammography (Az=0.93, P<0.0001) and for US (Az=0.91, P<0.0001). When using TS2000 in addition to mammography and US (Az=0.86), a significant impairment was found (P=0.0003). CONCLUSION: The role of targeted electrical impedance imaging as an adjunct to mammography and ultrasonography in the diagnosis of breast lesions is not justified by the result of this study

Can contrast-enhanced MR imaging predict survival in breast cancer?

PURPOSE: To investigate the value of pre-operative contrast-enhanced MR imaging (CE-MRI) in predicting the disease-free and overall survival in breast cancer. MATERIAL AND METHODS: The study population consisted of 50 consecutive patients with histopathologically verified primary breast cancer who pre-operatively underwent CE-MRI examination between 1992 and 1993. A three-time point MR examination was performed where the enhancement rates (C1 and C2), signal enhancement ratio (SER=C1/C2) and washout (W=C1-C2) were calculated. The relation of these MR parameters to disease-free and overall survival was investigated. The median follow-up for surviving patients was 95 months. Univariate and multivariate statistical analyses were performed to evaluate the impact of different factors on prediction of survival. RESULTS: Of the MR parameters examined at univariate analysis, increased C1 (p=0.029), W (p=0.0081) and SER values (p=0.0081) were significantly associated with shorter disease-free survival, and only C1 (p=0.016) was related significantly to overall survival. Multivariate analysis for disease-free survival showed that the SER (p=0.014) and tumor size (p=0.001) were significant and independent predictors. Age (p=0.003), lymph node status (p=0.014), tumor size (p=0.039) and proliferating cell nuclear antigen index (p=0.053) remained independently associated with overall survival at multivariate analysis. C1 was not confirmed as an independent predictor of overall survival. CONCLUSION: Our findings support the presumption that CE-MRI is useful in predicting the disease-free survival in patients with breast cancer

Age-dependent parathormone levels and different CKD-MBD treatment practices of dialysis patients in Hungary - results from a nationwide clinical audit

BACKGROUND: Achieving target levels of laboratory parameters of bone and mineral metabolism in chronic kidney disease (CKD) patients is important but also difficult in those living with end-stage kidney disease. This study aimed to determine if there are age-related differences in chronic kidney disease-mineral and bone disorder (CKD-MBD) characteristics, including treatment practice in Hungarian dialysis patients. METHODS: Data were collected retrospectively from a large cohort of dialysis patients in Hungary. Patients on hemodialysis and peritoneal dialysis were also included. The enrolled patients were allocated into two groups based on their age (=65 years). Characteristics of the age groups and differences in disease-related (epidemiology, laboratory, and treatment practice) parameters between the groups were analyzed. RESULTS: A total of 5008 patients were included in the analysis and the mean age was 63.4+/-14.2 years. A total of 47.2% of patients were women, 32.8% had diabetes, and 11.4% were on peritoneal dialysis. Diabetes (37.9% vs 27.3%), bone disease (42.9% vs 34.1%), and soft tissue calcification (56.3% vs 44.7%) were more prevalent in the older group than the younger group (p<0.001 for all). We found an inverse relationship between age and parathyroid hormone (PTH) levels (p<0.001). Serum PTH levels were lower in patients with diabetes compared with those without diabetes below 80 years (p<0.001). Diabetes and age were independently associated with serum PTH levels (interaction: diabetes x age groups, p=0.138). Older patients were more likely than younger patients to achieve laboratory target ranges for each parameter (Ca: 66.9% vs 62.1%, p<0.001; PO4: 52.6% vs 49.2%, p<0.05; and PTH: 50.6% vs 46.6%, p<0.01), and for combined parameters (19.8% vs 15.8%, p<0.001). Older patients were less likely to receive related medication than younger patients (66.9% vs 79.7%, p<0.001). CONCLUSIONS: The achievement of laboratory target ranges for bone and mineral metabolism and clinical practice in CKD depends on the age of the patients. A greater proportion of older patients met target criteria and received less medication compared with younger patients

Screening and diagnostic breast MRI:how do they impact surgical treatment? Insights from the MIPA study

Objectives: To report mastectomy and reoperation rates in women who had breast MRI for screening (S-MRI subgroup) or diagnostic (D-MRI subgroup) purposes, using multivariable analysis for investigating the role of MRI referral/nonreferral and other covariates in driving surgical outcomes. Methods: The MIPA observational study enrolled women aged 18-80 years with newly diagnosed breast cancer destined to have surgery as the primary treatment, in 27 centres worldwide. Mastectomy and reoperation rates were compared using non-parametric tests and multivariable analysis. Results: A total of 5828 patients entered analysis, 2763 (47.4%) did not undergo MRI (noMRI subgroup) and 3065 underwent MRI (52.6%); of the latter, 2441/3065 (79.7%) underwent MRI with preoperative intent (P-MRI subgroup), 510/3065 (16.6%) D-MRI, and 114/3065 S-MRI (3.7%). The reoperation rate was 10.5% for S-MRI, 8.2% for D-MRI, and 8.5% for P-MRI, while it was 11.7% for noMRI (p&nbsp;≤&nbsp;0.023 for comparisons with D-MRI and P-MRI). The overall mastectomy rate (first-line mastectomy plus conversions from conserving surgery to mastectomy) was 39.5% for S-MRI, 36.2% for P-MRI, 24.1% for D-MRI, and 18.0% for noMRI. At multivariable analysis, using noMRI as reference, the odds ratios for overall mastectomy were 2.4 (p&nbsp;&lt;&nbsp;0.001) for S-MRI, 1.0 (p&nbsp;=&nbsp;0.957) for D-MRI, and 1.9 (p&nbsp;&lt;&nbsp;0.001) for P-MRI. Conclusions: Patients from the D-MRI subgroup had the lowest overall mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). This analysis offers an insight into how the initial indication for MRI affects the subsequent surgical treatment of breast cancer. Key points: • Of 3065 breast MRI examinations, 79.7% were performed with preoperative intent (P-MRI), 16.6% were diagnostic (D-MRI), and 3.7% were screening (S-MRI) examinations. • The D-MRI subgroup had the lowest mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). • The S-MRI subgroup had the highest mastectomy rate (39.5%) which aligns with higher-than-average risk in this subgroup, with a reoperation rate (10.5%) not significantly different to that of all other subgroups

Magnetic resonance imaging before breast cancer surgery: results of an observational multicenter international prospective analysis (MIPA).

Funder: Bayer AGFunder: Università degli Studi di MilanoOBJECTIVES: Preoperative breast magnetic resonance imaging (MRI) can inform surgical planning but might cause overtreatment by increasing the mastectomy rate. The Multicenter International Prospective Analysis (MIPA) study investigated this controversial issue. METHODS: This observational study enrolled women aged 18-80 years with biopsy-proven breast cancer, who underwent MRI in addition to conventional imaging (mammography and/or breast ultrasonography) or conventional imaging alone before surgery as routine practice at 27 centers. Exclusion criteria included planned neoadjuvant therapy, pregnancy, personal history of any cancer, and distant metastases. RESULTS: Of 5896 analyzed patients, 2763 (46.9%) had conventional imaging only (noMRI group), and 3133 (53.1%) underwent MRI that was performed for diagnosis, screening, or unknown purposes in 692/3133 women (22.1%), with preoperative intent in 2441/3133 women (77.9%, MRI group). Patients in the MRI group were younger, had denser breasts, more cancers ≥ 20 mm, and a higher rate of invasive lobular histology than patients who underwent conventional imaging alone (p < 0.001 for all comparisons). Mastectomy was planned based on conventional imaging in 22.4% (MRI group) versus 14.4% (noMRI group) (p < 0.001). The additional planned mastectomy rate in the MRI group was 11.3%. The overall performed first- plus second-line mastectomy rate was 36.3% (MRI group) versus 18.0% (noMRI group) (p < 0.001). In women receiving conserving surgery, MRI group had a significantly lower reoperation rate (8.5% versus 11.7%, p < 0.001). CONCLUSIONS: Clinicians requested breast MRI for women with a higher a priori probability of receiving mastectomy. MRI was associated with 11.3% more mastectomies, and with 3.2% fewer reoperations in the breast conservation subgroup. KEY POINTS: • In 19% of patients of the MIPA study, breast MRI was performed for screening or diagnostic purposes. • The current patient selection to preoperative breast MRI implies an 11% increase in mastectomies, counterbalanced by a 3% reduction of the reoperation rate. • Data from the MIPA study can support discussion in tumor boards when preoperative MRI is under consideration and should be shared with patients to achieve informed decision-making

Neural network approach to the segmentation and classification of dynamic magnetic resonance images of the breast: comparison with empiric and quantitative kinetic parameters

RATIONALE AND OBJECTIVE: An artificial neural network (ANN)-based segmentation method was developed for dynamic contrast-enhanced magnetic resonance (MR) imaging of the breast and compared with quantitative and empiric parameter mapping techniques. MATERIALS AND METHODS: The study population was composed of 10 patients with seven malignant and three benign lesions undergoing dynamic MR imaging of the breast. All lesions were biopsied or surgically excised, and examined by means of histopathology. A T1-weighted 3D FLASH (fast low angle shot sequence) was acquired before and seven times after the intravenous administration of gadopentetate dimeglumine at a dose of 0.1 mmol/kg body weight. Motion artifacts on MR images were eliminated by voxel-based affine and nonrigid registration techniques. A two-layered feed-forward back-propagation network was created for pixel-by-pixel classification of signal intensity-time curves into benign/malignant tissue types. ANN output was statistically compared with percent-enhancement (E), signal enhancement ratio (SER), time-to-peak, subtracted signal intensity (SUB), pharmacokinetic parameter rate constant (k(ep)), and correlation coefficient to a predefined reference washout curve. RESULTS: ANN was successfully applied to the classification of breast MR images identifying structures with benign or malignant enhancement kinetics. Correlation coefficient (logistic regression, odds ratio [OR] = 12.9; 95% CI: 7.7-21.8), k(ep) (OR = 1.8; 95% CI: 1.2-2.6), and time-to-peak (OR = 0.45; 95% CI: 0.3-0.7) were independently associated to ANN output classes. SER, E, and SUB were nonsignificant covariates. CONCLUSION: ANN is capable of classifying breast lesions on MR images. Mapping correlation coefficient, k(ep) and time-to-peak showed the highest association with the ANN result

Application of artificial neural networks to the analysis of dynamic MR imaging features of the breast

The discriminative ability of established diagnostic criteria for MRI of the breast is assessed, and their relative relevance using artificial neural networks (ANNs) is determined. A total of 89 women with 105 histopathologically verified breast lesions (73 invasive cancers, 2 in situ cancers, and 30 benign lesions) were included in this study. A T1-weighted 3D FLASH sequence was acquired before and seven times after the intravenous administration of gadopentetate dimeglumine at a dose of 0.2 mmol/kg body weight. ANN models were built to test the discriminative ability of kinetic, morphologic, and combined MR features. The subjects were randomly divided into two parts: a training set of 59 lesions and a verification set of 46 lesions. The training set was used for learning, and the performance of each model was evaluated on the verification set by measuring the area under the ROC curve (Az). An optimally minimized model was constructed using the most relevant input variables that were determined by the automatic relevance determination (ARD) method. ANN models were compared with the performance of a human reader. Margin type, time-to-peak enhancement, and washout ratio showed the highest discriminative ability among diagnostic criteria and comprised the minimized model. Compared with the expert radiologist (Az = 0.799), using the same prediction scale, the minimized ANN model performed best (Az = 0.771), followed by the best kinetic (Az = 0.743), the maximized (Az = 0.727), and the morphologic model (Az = 0.678). The performance of a neural network prediction model is comparable to that of an expert radiologist. A neurostatistical approach is preferred for the analysis of diagnostic criteria when many parameters are involved and complex nonlinear relationships exist in the data set

Dynamic MR imaging of the breast. Analysis of kinetic and morphologic diagnostic criteria

PURPOSE: To assess the value of kinetic and architectural diagnostic criteria on dynamic MR breast imaging, and to construct a scoring system for lesion characterization. MATERIAL AND METHODS: A total of 92 women with 109 histopathologically verified breast lesions were included in this retrospective study. The patients were examined by a 1.5 T system using a dedicated double breast coil. A dynamic examination with one precontrast and seven postcontrast series was performed, using a T1-weighted 3D FLASH sequence. Thirty lesions (15 malignant and 15 benign) were randomly chosen for the validation set, and the remaining 79 lesions (62 malignant and 17 benign) formed the estimation set, in which multivariate analysis was performed in order to select the most important features. These parameters were then used for constructing the scoring system, which was tested on the validation set. The scoring system was compared with the routine standard evaluation that used all established diagnostic criteria. ROC curves were generated to assess the diagnostic accuracy of different approaches. RESULTS: In the multivariate analysis of the 79 lesions, time-to-peak enhancement and the descriptor of margins were found to be the most important independent factors for distinguishing benign from malignant lesions, and formed the basis of the scoring system. The areas under the ROC curves for the standard evaluation, and the scoring system were 0.813 and 0.880 in the 30 lesions. CONCLUSION: Time-to-peak enhancement and the descriptor of margins appear to be the most important diagnostic criteria for mass lesions in dynamic breast MR imaging