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Background: The management of intraductal papilloma without atypia (IDP) in breast needle biopsy remains controversial. This study investigates the upgrade rate of IDP to carcinoma and clinical and radiologic features predictive of an upgrade. Methods: Patients with a diagnosis of IDP on image-guided (mammography, ultrasound, magnetic resonance imaging) core needle or vacuum-assisted biopsy and surgical excision of this lesion at a certified breast center between 2007 and 2017 were included in this institutional review board-approved retrospective study. Appropriate statistical tests were performed to assess clinical and radiologic characteristics associated with an upgrade to malignancy at excision. Results: For 60 women with 62 surgically removed IDPs, the upgrade rate to malignancy was 16.1% (10 upgrades, 4 invasive ductal carcinoma, 6 ductal carcinoma in situ). IDPs with upgrade to carcinoma showed a significantly greater distance to the nipple (63.5 vs. 36.8 mm; p = 0.012). No significant associations were found between upgrade to carcinoma and age, menopausal status, lesion size, microcalcifications, BI-RADS descriptors, initial BI-RADS category, and biopsy modality. Conclusion: The upgrade rate at excision for IDPs diagnosed with needle biopsy was higher than expected according to some guideline recommendations. Observation only might not be appropriate for all patients with IDP, particularly for those with peripheral IDP.
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.
A new genus of Baetidae is described from Southeast Asia, Procerobaetis gen. nov. It has a wide distribution reaching from Indonesia (Sumatra) to the Philippines. Two new species are described from Indonesia, P. leptobranchius gen. et sp. nov. and P. petersorum gen. et sp. nov., and one new species from the Philippines, P. freitagi gen. et sp. nov. Procerobaetis gen. nov. is characterized by having seven pairs of elongate, apically pointed gills. At least gills I and II are very slender with strongly extended points, which is unique in Baetidae. Similar gills were described from Leptophlebiidae. Procerobaetis gen. nov. is further characterized by having long, slender legs with extended, slender and slightly bent claws. The antennae posess remarkable spines at the outer, lateral margin, which are maximally developed on segments IX–XI of the flagellum. No spines are present on the posterior margins of abdominal tergites I–VI. COI sequences were obtained from all three of the new species. The genetic distances (Kimura 2-parameter) between these species are between 13% and 20%. Very limited genetic distances of 0% to 1% were found between specimens of the same species. The occurrence of two different species in the same area of Sumatra is discussed.
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.