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Breast cancer, which is the most common cancer in women, is a major problem both in Poland and worldwide. Mammography remains the primary screening method. However, the sensitivity of mammographic screening is lower in women with dense glandular breasts due to tissue overlap and the effect of the glandular tissue obscuring the tumor and the fact that tumors and glandular tissue show similar X-ray absorption. Consequently, other methods are being sought to increase breast cancer detection rates. Currently, the most common and used methods are ultrasonography, magnetic resonance imaging and advanced mammographic methods (digital breast tomosynthesis and contrast-enhanced spectral mammography). Despite many advantages and superiority over mammography in dense breasts, they also have many disadvantages. Ultrasound is operator-dependent and the other techniques are expensive or not widely available. The Automated Breast Ultrasound Service (ABUS) technique appears to be a good option in terms of both effectiveness and lower cost.

The climate models are extremely complex pieces of software. They reflect best knowledge on physical components of the climate, nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a crash of simulation. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to crash of simulation, and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the dataset used in this research using different methodology. We confirm the main conclusion of the original study concerning suitability of machine learning for prediction of crashes. We show, that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three other are relevant but redundant, and two are not relevant at all. We also show that the variance due to split of data between training and validation sets has large influence both on accuracy of predictions and relative importance of variables, hence only cross-validated approach can deliver robust prediction of performance and relevance of variables. [ABSTRACT FROM AUTHOR]

Metaplastic breast cancer (BC-Mp) presents diagnostic and therapeutic complexities, with scant literature available. Correct assessment of tumor size by ultrasound (US) and full-field digital mammography (FFDM) is crucial for treatment planning., Methods: A retrospective cohort study was conducted on databases encompassing records of BC patients (2012-2022) at the National Research Institutes of Oncology (Warsaw, Gliwice and Krakow Branches). Inclusion criteria comprised confirmed diagnosis in postsurgical pathology reports with tumor size details (pT) and availability of tumor size from preoperative US and/or FFDM. Patients subjected to neoadjuvant systemic treatment were excluded. Demographics and clinicopathological data were gathered., Results: Forty-five females were included. A total of 86.7% were triple-negative. The median age was 66 years (range: 33-89). The median pT was 41.63 mm (6-130), and eight patients were N-positive. Median tumor size assessed by US and FFDM was 31.81 mm (9-100) and 34.14 mm (0-120), respectively. Neither technique demonstrated superiority ( p > 0.05), but they both underestimated the tumor size ( p = 0.002 for US and p = 0.018 for FFDM). Smaller tumors (pT1-2) were statistically more accurately assessed by any technique ( p < 0.001). Only pT correlated with overall survival., Conclusion: The risk of underestimation in tumor size assessment with US and FFDM has to be taken into consideration while planning surgical procedures for BC-Mp.

This single center study includes a comparative analysis of the diagnostic performance of full-field digital mammography (FFDM), contrast-enhanced mammography (CEM) and automatic breast ultrasound (ABUS) in the group of patients with breast American College of Radiology (ACR) categories C and D as well as A and B with FFDM. The study involved 297 patients who underwent ABUS and FFDM. Breast types C and D were determined in 40% of patients with FFDM and low- energy CEM. CEM was performed on 76 patients. Focal lesions were found in 131 patients, of which 115 were histopathologically verified. The number of lesions detected in patients with multiple lesions were 40 from 48 with ABUS, 13 with FFDM and 21 with CEM. Compliance in determining the number of foci was 82% for FFDM and 91% for both CEM and ABUS. In breast types C and D, 72% of all lesions were found with ABUS, 56% with CEM and 29% with FFDM ( p = 0.008, p = 0.000); all invasive cancers were diagnosed with ABUS, 83% with CEM and 59% with FFDM ( p = 0.000, p = 0.023); 100% DCIS were diagnosed with ABUS, 93% with CEM and 59% with FFDM. The size of lesions from histopathology in breast ACR categories A and B was 14-26 mm, while in breast categories C and D was 11-37 mm. In breast categories C and D, sensitivity of ABUS, FFDM and CEM was, respectively, 78.05, 85.37, 92.68; specificity: 40, 13.33, 8.33; PPV (positive predictive value): 78.05, 72.92, 77.55; NPV (negative predictive value): 40, 25, 25, accuracy: 67.86, 66.07, 73.58. In breast categories A and B, sensitivity of ABUS, FFDM and CEM was, respectively, 81.25, 93.75, 93.48; specificity: 18.18, 18.18, 16.67; PPV: 81.25, 83.33, 89.58; NPV: 18.18, 40, 25; accuracy: 69.49, 79.66, 84.62. The sensitivity of the combination of FFDM and ABUS was 100 for all types of breast categories; the accuracy was 75 in breast types C and D and 81.36 in breast types A and B. The study confirms the predominance of C and D breast anatomy types and the low diagnostic performance of FFDM within that group and indicates ABUS and CEM as potential additive methods in breast cancer diagnostics.

BACKGROUND This retrospective study from a single center aimed to compare the performance of full-field digital mammography (FFDM) vs automated breast ultrasound (ABUS) in the identification and characterization of suspicious breast lesions in 117 patients who underwent core-needle biopsy (CNB) of the breast. MATERIAL AND METHODS The study involved a group of 301 women. Every patient underwent FFDM followed by ABUS, which were assessed in concordance with BI-RADS (Breast Imaging Reporting and Data System) classification. RESULTS No focal lesions were found in 168 patients. In 133 patients, 117 histopathologically verified focal lesions were found. Among them, 78% appeared to be malignant and 22% benign. ABUS detected 246 focal lesions, including 115 classified as BI-RADS 4 or 5 and submitted to verification, while FFDM revealed 122 lesions, including 75 submitted to verification. The analysis revealed that combined application of both methods caused sensitivity to increase to 100, and improved accuracy improvement. Margin assessments in these examinations are consistent (P<0.00), the lesion's margin type with both methods depends on its malignant or benign character (P<0.03), lesion margins distribution on ABUS depends on estrogen receptor presence (P=0.033), and there was significant correlation between malignant character of the lesion and retraction phenomenon sign (P=0.033). ABUS obtained higher compliance between the size of the lesion in histopathology compared to FFDM (P>0.05). CONCLUSIONS The results shows that ABUS is comparable to FFDM, and even outperforms it in a few of the analyzed categories, suggesting that the combination of these 2 methods may have an important role in breast cancer detection.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a high sensitivity in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We used a deep learning (DL) system to improve the overall accuracy of breast cancer diagnosis and personalize management of patients undergoing DCE-MRI. On the internal test set ( n = 3936 exams), our system achieved an area under the receiver operating characteristic curve (AUROC) of 0.92 (95% CI: 0.92 to 0.93). In a retrospective reader study, there was no statistically significant difference ( P = 0.19) between five board-certified breast radiologists and the DL system (mean ΔAUROC, +0.04 in favor of the DL system). Radiologists' performance improved when their predictions were averaged with DL's predictions [mean ΔAUPRC (area under the precision-recall curve), +0.07]. We demonstrated the generalizability of the DL system using multiple datasets from Poland and the United States. An additional reader study on a Polish dataset showed that the DL system was as robust to distribution shift as radiologists. In subgroup analysis, we observed consistent results across different cancer subtypes and patient demographics. Using decision curve analysis, we showed that the DL system can reduce unnecessary biopsies in the range of clinically relevant risk thresholds. This would lead to avoiding biopsies yielding benign results in up to 20% of all patients with BI-RADS category 4 lesions. Last, we performed an error analysis, investigating situations where DL predictions were mostly incorrect. This exploratory work creates a foundation for deployment and prospective analysis of DL-based models for breast MRI.

With breast cancer ranking first among the most common malignant neoplasms in the world, new techniques of early detection are in even more demand than before. Our awareness of tumors' biology is expanding and may be used to treat patients more efficiently. A link between radiology and pathology was searched for in our study, as well as the answer to the question of whether a tumor type can be seen on contrast-enhanced mammography and if such knowledge may serve as part of precision medicine.

Many potential biomarkers in nephrology have been studied, but few are currently used in clinical practice. One is osteopontin (OPN). We compared urinary OPN concentrations in 80 participants: 67 patients with various biopsy-proven glomerulopathies (GNs)-immunoglobulin A nephropathy (IgAN, 29), membranous nephropathy (MN, 20) and lupus nephritis (LN, 18) and 13 with no GN. Follow-up included 48 participants. Machine learning was used to correlate OPN with other factors to classify patients by GN type. The resulting algorithm had an accuracy of 87% in differentiating IgAN from other GNs using urinary OPN levels only. A lesser effect for discriminating MN and LN was observed. However, the lower number of patients and the phenotypic heterogeneity of MN and LN might have affected those results. OPN was significantly higher in IgAN at baseline than in other GNs and therefore might be useful for identifying patients with IgAN. That observation did not apply to either patients with IgAN at follow-up or to patients with other GNs. OPN seems to be a valuable biomarker and should be validated in future studies. Machine learning is a powerful tool that, compared with traditional statistical methods, can be also applied to smaller datasets.

BACKGROUND This retrospective study included 103 patients diagnosed with rectal adenocarcinoma at a single center in Poland who underwent preoperative diffusion-weighted magnetic resonance imaging (DWI) and aimed to determine whether the apparent diffusion coefficient (ADC) was an imaging marker for tumor invasion and regional lymph node involvement. MATERIAL AND METHODS We analyzed primary staging magnetic resonance examinations of the rectum of 103 consecutive patients with histologically proven non-mucinous adenocarcinoma who underwent surgical treatment. In 85 patients, surgery was preceded by long-course chemoradiotherapy (n=18) or short-course radiotherapy (n=67). The following DWI parameters were measured: ADC mean, minimum, maximum, and standard deviation in the region of interest (ADC SD-in-ROI). Values were compared between subgroups based on histological parameters from the report: tumor stage, lymph node stage, differentiation grade, the presence of extranodal tumor deposits, angioinvasion, and perineural invasion. Statistical analysis was performed using the Mann-Whitney U test and the unilateral t test. RESULTS ADC mean values were lower for cases in which postoperative histopathological examination lymph node invasion (P=0.04) and tumor deposits were found (P=0.04). Minimal ADC value was higher in cases in which tumor deposits were not found (P=0.009). ADC SD-in-ROI values were lower in cases in which lymph nodes invasion was confirmed (P=0.014). There were no statistically significant differences for other parameters. CONCLUSIONS The ADC values in pre-treatment DWI in patients with rectal adenocarcinoma were correlated with tumor invasion and regional lymph node metastases. Therefore, ADC values from the pre-treatment MRI may help plan adjuvant therapy in patients with rectal adenocarcinoma.

Glomerular diseases (GNs) are responsible for approximately 20% of chronic kidney diseases. Glucocorticoid receptor gene ( NR3C1 ) single nucleotide polymorphisms (SNPs) are implicated in differences in predisposition to autoimmunity and steroid sensitivity. The aim of this study was to evaluate the frequency of the NR3C1 SNPs-rs6198, rs41423247 and rs17209237-in 72 IgA nephropathy (IgAN) and 38 membranous nephropathy (MN) patients compared to 175 healthy controls and to correlate the effectiveness of treatment in IgAN and MN groups defined as a reduction of proteinuria <1 g/24 h after 12 months of treatment. Real-time polymerase chain reactions and SNP array-based typing were used. We found significant rs41423247 association with MN ( p = 0.026); a significant association of rs17209237 with eGFR reduction after follow-up period in all patients with GNs ( p = 0.021) and with the degree of proteinuria after 1 year of therapy in all patients with a glomerulopathy ( p = 0.013) and IgAN ( p = 0.021); and in the same groups treated with steroids ( p = 0.021; p = 0.012). We also observed the association between rs41423247 and IgAN histopathologic findings ( p = 0.012). In conclusion, our results indicate that NR3C1 polymorphisms may influence treatment susceptibility and clinical outcome in IgAN and MN.

Purpose: Breast cancer is the most common cause of death from neoplastic disease in women. Among all breast anatomy types, glandular type is the most problematic concerning evaluation. While digital mammography still remains the basic diagnostic tool, one must be aware of its limitations in dense breasts. Although magnetic resonance imaging (MRI) has greatly improved sensitivity, its specificity is low. Moreover, there are contraindications for MRI for some patients, so a substitute has been searched for. This study was performed to check if contrast-enhanced spectral mammography (CESM) can be a viable option for patients with dense breasts., Material and Methods: The study involved 121 patients with abnormalities detected on base-line diagnostic imaging (ultrasound or mammography). The patients had subsequent examinations, both CESM and MRI performed within a maximum 2-month time interval. The sensitivity and specificity of both methods in the whole group as well as in specific breast structure types were measured and compared., Results: Contrast enhancement was visible in all 121 cases on MRI, while on CESM lack of enhancement was noted in 13 cases. All of those 13 lesions turned out to be benign. There were 40 (33%) benign and 81 (69%) malignant tumours. The analysed group included 53 (44%) glandular type breast patients, 39 (32%) mixed type, and 29 (23%) fatty type. Although MRI proved to be slightly more effective in dense breasts, both methods showed similar results in the whole study group., Conclusion: CESM can be used with confidence in patients with glandular breast type when MRI is not available or there are reported contraindications to MRI., Competing Interests: The authors report no conflict of interest., (Copyright © Polish Medical Society of Radiology 2021.)

Objectives: With the growing number of new breast cancer cases in women, new methods of imaging arise. Contrast enhanced spectral mammography (CESM) and magnetic resonance imaging (MRI) are comparable methods regarding sensitivity. The aim of this study is to check if analysis of background parenchymal enhancement on CESM can improve its usefulness., Material and Methods: A total of 64 patients with breast lesions found previously on ultrasound or mammography underwent MRI and CESM within less than one month. On MRI the contrast enhancement kinetics and visual BPE were evaluated. On CESM the enhancement of lesions was noted as well as a quantitative level of BPH. The gathered data was analysed in terms of patterns and relations., Results: A total of 66 lesions were identified both on MRI and CESM, including 11 (17%) benign and 55 (83%) malignant lesions. Among malignant lesions 13 (20%) were assessed as intraductal and 42 (64%) as infiltrating carcinomas. The study showed correlation between the level of enhancement on CESM and the type of kinetic curve on MRI and lesion enhance-ment on CESM as well as confirmed the fact that the BPE is a destimulant in both methods of imaging., Conclusions: Evaluation of BPE level on CESM can help reading radiologists to define a lesion as malignant with higher probability than based only on the qualitative lesion enhancement level.

Background/aim: Contrast enhanced spectral mammography (CESM) is a novel method of breast cancer diagnosis. Benign lesions are enhanced after contrast injection on both CESM and breast magnetic resonance imaging (MRI). Kinetic curves on breast MRI facilitate differentiation between benign and malignant lesions, while on CESM there is no such possibility and we need to asses lesions based only on their level of enhancement and its patterns. The aim of this study was to compare two subjective assessments of enhancement level on CESM with numerical values measured using the region of interests (ROIs) and to categorize obtained enhancement level values., Patients and Methods: Patients with suspicious findings on previously performed examinations were qualified for CESM. The qualitative assessment was based on the subjective classification of visible contrast enhancement as weak, medium or strong. The quantitative assessment was obtained by measurements of an average enhancement value and sigma value within the ellipsoidal shape ROI, inserted into the evaluated contrast enhancing lesion., Results: The study group included 151 patients with total of 195 lesions diagnosed. It was verified how the classification based on the threshold values of %RS (percentage signal difference between enhancing lesion and background) and SDNR (signal-difference-to-noise Ratio) corresponds to a subjective assessment., Conclusion: Quantitative assessment of contrast enhancement on CESM is helpful in making decisions whether a lesion requires a biopsy. This can reduce the number of unnecessary biopsy procedures and reduce the cost of diagnostics., (Copyright© 2020, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

BACKGROUND Due to the decreased sensitivity of mammography in glandular breasts, new diagnostic modalities, like contrast-enhanced spectral mammography (CESM) and digital breast tomosynthesis (DBT) have been developed. The aim of this study was to compare qualitative enhancement levels on CESM with type of kinetic enhancement curves on MRI examination. MATERIAL AND METHODS Patients qualified for the CESM examination presented some diagnostic doubts - suspected multifocality, multicentricity, or having dense glandular breast tissue. The enhancement level on CESM was described as weak, medium, or strong. Enhancement on achieved MR images was assessed on the basis of enhancement kinetic curves. The level of enhancement on CESM was associated with enhancement curves type on MRI. All lesions detected on CESM and MRI were histopathologically verified. RESULTS The study involved 107 lesions diagnosed in 94 patients: 71 lesions (66%) appeared to be infiltrating on histopathological examination, 9 lesions (8%) were non-infiltrating cancers, and 27 lesions (25%) were benign. Data analysis revealed that lesions with wash-out curve on MRI most often presented strong enhancement on CESM, while in lesions with progressive enhancement curve, strong enhancement on CESM was the rarest. The relationship between enhancement level on CESM and curve type on contrast-enhanced MRI depends on the nature of the lesion. The type of MRI curve was found to be associated with enhancement level on CESM. CONCLUSIONS We compared subjective assessments of contrast enhancement on CESM with enhancement kinetic curves on MRI. The results showed that the level of enhancement on CESM and type of kinetic curves on MRI depends on the lesion type.

Objectives: Contrast-enhanced spectral mammography (CESM) is a novel method for breast cancer detection. The aim of this study is to check if there is a possibility of quantitative assessment of contrast enhancement in CESM and if there is any correlation between quantitative assessment of contrast enhancement in CESM and histopathology., Methods: A total of 167 female patients underwent CESM. All subjects previously had suspicious lesions found on mammography, breast ultrasound, or both. After imaging, the following parameters were evaluated: number of enhancing lesions in each breast and size and degree of enhancement of each lesion. Based on the collected data, the percentage signal difference between enhancing lesion and background (%RS) and signal-difference-to-noise ratio (SDNR) were measured for each lesion., Results: The number of lesions detected in the study population was 195. Among all diagnosed lesions, 120 (62%) were assessed to be infiltrating cancers, 16 (8%) non-infiltrating cancers, and 59 (30%) were benign. Thirteen (7%) lesions did not enhance in CESM; all non-enhancing lesions were confirmed to be benign under histopathological examination. Analysis of enhancement indices showed that signal values within lesions and signal values within background ROIs (regions of interest) were similar in CC (craniocaudal) and MLO (mediolateral) projections. Mean %RS values were correlated with the type of enhancing lesion, infiltrating cancers having the highest values, benign lesions the lowest., Conclusions: This work has demonstrated a significant correlation between the degree of lesion enhancement in CESM and malignancy. Quantitative analysis of enhancement levels in CESM can distinguish between invasive cancers and benign or in situ lesions., Key Points: • There is a possibility of quantitative assessment of contrast enhancement in CESM. • Correlation between quantitative assessment of contrast enhancement in CESM and histopathology was observed.

It has been recently established that the accuracy of structural parameters from X-ray refinement of crystal structures can be improved by using a bank of aspherical pseudoatoms instead of the classical spherical model of atomic form factors. This comes, however, at the cost of increased complexity of the underlying calculations. In order to facilitate the adoption of this more advanced electron density model by the broader community of crystallographers, a new software implementation called DiSCaMB , 'densities in structural chemistry and molecular biology', has been developed. It addresses the challenge of providing for high performance on modern computing architectures. With parallelization options for both multi-core processors and graphics processing units (using CUDA), the library features calculation of X-ray scattering factors and their derivatives with respect to structural parameters, gives access to intermediate steps of the scattering factor calculations (thus allowing for experimentation with modifications of the underlying electron density model), and provides tools for basic structural crystallographic operations. Permissively (MIT) licensed, DiSCaMB is an open-source C++ library that can be embedded in both academic and commercial tools for X-ray structure refinement.

Background/aim: It is possible that the degree of enhancement on contrast-enhanced spectral mammography (CESM), a new diagnostic method, might provide prognostic information for breast cancer patients. Therefore, in a group of 82 breast cancer patients, we analyzed the prognostic significance of degree and pattern of enhancement on CESM as well as its relation to: (a) breast cancer immunophenotype (based on ER/PR/HER2 status) (b) podoplanin expression in cancer stroma (lymphatic vessel density plus podoplanin-positivity of cancer-associated fibroblasts), and (c) other histological parameters., Materials and Methods: For each tumor the intensity of enhancement on CESM was qualitatively assessed as strong or weak/medium, while the pattern - as homogenous and heterogenous., Results: Herein we report, for the first time, that strong and heterogenous enhancement on CESM was related to unfavorable disease-free survival of breast cancer patients (p=0.005). Moreover, the strong enhancement was more frequent in large and node-positive tumors (pT>1, pN>0) (p=0.002), as well as in carcinomas with podoplanin-sparse stroma (p=0.008)., Conclusion: Intensity and pattern of enhancement on CESM might provide (together with the results of other diagnostic imaging methods) not only the confirmation of presence or absence of tumor, but also prognostic information., (Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background: Transcriptional regulation in multi-cellular organisms is a complex process involving multiple modular regulatory elements for each gene. Building whole-genome models of transcriptional networks requires mapping all relevant enhancers and then linking them to target genes. Previous methods of enhancer identification based either on sequence information or on epigenetic marks have different limitations stemming from incompleteness of each of these datasets taken separately., Results: In this work we present a new approach for discovery of regulatory elements based on the combination of sequence motifs and epigenetic marks measured with ChIP-Seq. Our method uses supervised learning approaches to train a model describing the dependence of enhancer activity on sequence features and histone marks. Our results indicate that using combination of features provides superior results to previous approaches based on either one of the datasets. While histone modifications remain the dominant feature for accurate predictions, the models based on sequence motifs have advantages in their general applicability to different tissues. Additionally, we assess the relevance of different sequence motifs in prediction accuracy showing that even tissue-specific enhancer activity depends on multiple motifs., Conclusions: Based on our results, we conclude that it is worthwhile to include sequence motif data into computational approaches to active enhancer prediction and also that classifiers trained on a specific set of enhancers can generalize with significant accuracy beyond the training set.

Reverse transcriptase (RT) is a viral enzyme crucial for HIV-1 replication. Currently, 12 drugs are targeted against the RT. The low fidelity of the RT-mediated transcription leads to the quick accumulation of drug-resistance mutations. The sequence-resistance relationship remains only partially understood. Using publicly available data collected from over 15 years of HIV proteome research, we have created a general and predictive rule-based model of HIV-1 resistance to eight RT inhibitors. Our rough set-based model considers changes in the physicochemical properties of a mutated sequence as compared to the wild-type strain. Thanks to the application of the Monte Carlo feature selection method, the model takes into account only the properties that significantly contribute to the resistance phenomenon. The obtained results show that drug-resistance is determined in more complex way than believed. We confirmed the importance of many resistance-associated sites, found some sites to be less relevant than formerly postulated and-more importantly-identified several previously neglected sites as potentially relevant. By mapping some of the newly discovered sites on the 3D structure of the RT, we were able to suggest possible molecular-mechanisms of drug-resistance. Importantly, our model has the ability to generalize predictions to the previously unseen cases. The study is an example of how computational biology methods can increase our understanding of the HIV-1 resistome.

This study presents a model for mezoscopic molecular dynamics simulations with objects of different scale and properties e.e. atoms, pseudoatoms, rigid and pseudo-elastic bodies, described by the external coordinates and internal degrees of freedom. The Lagrangian approach is used to derive equations of motion and a quaternion representation is used for the description of the dynamics of rigid and pseudo-elastic molecular elements. Stability of the LQMD algorithm was tested for a 10-base pair deoxynucleotide. The total energy, momentum and angular momentum are conserved for time-steps up to 20 fs.