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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.
Simple Summary: Early and accurate diagnosis of breast cancer that has spread to other organs and tissues is crucial, as therapeutic decisions and outcome expectations might change. Computed tomography (CT) is often used to detect breast cancer’s spread, but this method has its weaknesses. The computer-assisted technique “radiomics” extracts grey-level patterns, so-called radiomic features, from medical images, which may reflect underlying biological processes. Our retrospective study therefore evaluated whether breast cancer spread can be predicted by radiomic features derived from iodine maps, an application on a new generation of CT scanners visualizing tissue blood flow. Based on 77 patients with newly diagnosed breast cancer, we found that this approach might indeed predict cancer spread to other organs/tissues. In the future, radiomics may serve as an additional tool for cancer detection and risk assessment.
Abstract: Dual-energy CT (DECT) iodine maps enable quantification of iodine concentrations as a marker for tissue vascularization. We investigated whether iodine map radiomic features derived from staging DECT enable prediction of breast cancer metastatic status, and whether textural differ- ences exist between primary breast cancers and metastases. Seventy-seven treatment-naïve patients with biopsy-proven breast cancers were included retrospectively (41 non-metastatic, 36 metastatic). Radiomic features including first-, second-, and higher-order metrics as well as shape descriptors were extracted from volumes of interest on iodine maps. Following principal component analysis, a multilayer perceptron artificial neural network (MLP-NN) was used for classification (70% of cases for training, 30% validation). Histopathology served as reference standard. MLP-NN predicted metastatic status with AUCs of up to 0.94, and accuracies of up to 92.6 in the training and 82.6 in the validation datasets. The separation of primary tumor and metastatic tissue yielded AUCs of up to 0.87, with accuracies of up to 82.8 in the training, and 85.7 in the validation dataset. DECT iodine map-based radiomic signatures may therefore predict metastatic status in breast cancer patients. In addition, microstructural differences between primary and metastatic breast cancer tissue may be reflected by differences in DECT radiomic features.