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Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Background: Breast cancer is the leading cause of cancer-related deaths in women, demanding new treatment options. With the advent of immune checkpoint blockade, immunotherapy emerged as a treatment option. In addition to lymphocytes, tumor-associated macrophages exert a significant, albeit controversial, impact on tumor development. Pro-inflammatory macrophages are thought to hinder, whereas anti-inflammatory macrophages promote tumor growth. However, molecular markers to identify prognostic macrophage populations remain elusive. Methods: We isolated two macrophage subsets, from 48 primary human breast tumors, distinguished by the expression of CD206. Their transcriptomes were analyzed via RNA-Seq, and potential prognostic macrophage markers were validated by PhenOptics in tissue microarrays of patients with invasive breast cancer. Results: Normal human breast tissue contained mainly CD206+ macrophages, while increased relative amounts of CD206− macrophages were observed in tumors. The presence of CD206+ macrophages correlated with a pronounced lymphocyte infiltrate and subsets of CD206+ macrophages, expressing SERPINH1 and collagen 1, or MORC4, were unexpectedly associated with improved survival of breast cancer patients. In contrast, MHCIIhi CD206− macrophages were linked with a poor survival prognosis. Conclusion: Our data highlight the heterogeneity of tumor-infiltrating macrophages and suggest the use of multiple phenotypic markers to predict the impact of macrophage subpopulations on cancer prognosis. We identified novel macrophage markers that correlate with the survival of patients with invasive mammary carcinoma.