Abstract
Purpose: Male Breast Cancer (MBC) remains a poor understood disease. Prognostic factors are not well established and specific prognostic subgroups are warranted. Patients/methods: Retrospectively revision of 111 cases treated in the same Cancer Center. Blinded-central pathological revision with immunohistochemical (IHQ) analysis for estrogen (ER), progesterone (PR) and androgen (AR) receptors, HER2, ki67 and p53 was done. Cox regression model was used for uni/multivariate survival analysis. Two classifications of Female Breast Cancer (FBC) subgroups (based in ER, PR, HER2, 2000 classification, and in ER, PR, HER2, ki67, 2013 classification) were used to achieve their prognostic value in MBC patients. Hierarchical clustering was performed to define subgroups based on the six-IHQ panel. Results: According to FBC classifications, the majority of tumors were luminal: A (89.2%; 60.0%) and B (7.2%; 35.8%). Triple negative phenotype was infrequent (2.7%; 3.2%) and HER2 enriched, non-luminal, was rare (<= 1% in both). In multivariate analysis the poor prognostic factors were: size >2 cm (HR: 1.8; 95% CI: 1.0-3.4years, p = 0.049), absence of ER (HR: 4.9; 95% CI: 1.7-14.3years, p = 0.004) and presence of distant metastasis (HR: 5.3; 95% CI: 2.2-3.1years, p < 0.001). FBC subtypes were independent prognostic factors (p = 0.009, p = 0.046), but when analyzed only luminal groups, prognosis did not differ regardless the classification used (p > 0.20). Clustering defined different subgroups, that have prognostic value in multivariate analysis (p = 0.005), with better survival in ER/PR+, AR-, HER2- and ki67/p53 low group (median: 11.5 years; 95% CI: 6.2-16.8 years) and worst in PR-group (median: 4.5 years; 95% CI: 1.6 -7.8 years). Conclusion: FBC subtypes do not give the same prognostic information in MBC even in luminal groups. Two subgroups with distinct prognosis were identified in a common six-IHQ panel. Future studies must achieve their real prognostic value in these patients.

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Abstract

Abstract
Missing data consists in the lack of information in a dataset and since it directly influences classification performance, neglecting it is not a valid option. Over the years, several studies presented alternative imputation strategies to deal with the three missing data mechanisms, Missing Completely At Random, Missing At Random and Missing Not At Random. However, there are no studies regarding the influence of all these three mechanisms on the latest high-performance Artificial Intelligence techniques, such as Deep Learning. The goal of this work is to perform a comparison study between state-of-the-art imputation techniques and a Stacked Denoising Autoencoders approach. To that end, the missing data mechanisms were synthetically generated in 6 different ways; 8 different imputation techniques were implemented; and finally, 33 complete datasets from different open source repositories were selected. The obtained results showed that Support Vector Machines imputation ensures the best classification performance while Multiple Imputation by Chained Equations performs better in terms of imputation quality. © Springer Nature Switzerland AG 2018.

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Abstract
e13070 Background: Bone is one of the main sites of breast cancer metastasis. Staging of this kind of disease spread can be performed in locally advanced cases with PET-CT in conjunction with Bone Scintigraphy. The purpose of this work is to compare the efficiency of bone metastasis detection between PET-CT and bone scintigraphy. Methods: Prospective analysis of locally advanced breast cancer patients treated in a Comprehensive Cancer Center between 2014 and 2019 that performed PET-CT and Bone Scintigraphy in the staging. Interval between the two exams could not exceed 2 months. Clinical and pathological characteristics of the disease were collected from electronic files and independently clinical images reports were considered to evaluate the ability of each imaging modalities to identify bone disease. In discrepancy cases a re-analysis of the images by two independent nuclear physicians was performed to validate the findings. Results: We analyzed 204 cases. The majority of them had ductal carcinomas (72.5%), cT2/3 (70%), cN1/2(61.8%) and G2/3 (94.6%), luminal B- like, HER2 positive disease (49.2%). In this cohort, bone metastasis was documented in 52 (25.5%) patients. PET-CT presented 97.0% of accuracy, surpassing the 94.1% presented by Bone Scintigraphy. The latter failed to correctly detect bone metastasis in 11 (5.4%) patients and only outperformed PET-CT in 3 (1.5%) patients. The main difference between the two modalities was the non-detection of cranium lesions in PET-CT images. Conclusions: PET-CT showed higher efficiency in bone metastasis detection than Bone Scintigraphy, probably because it detects lytic lesions. The non-detection of cranium ones can be harmful and so modifications in the image acquisition are required to improve the quality of PET-CT, avoiding other exams in bone staging.