Purpose: To compare CT, MRI, and [¹⁸F]-fluorodeoxyglucose positron emission tomography ([¹⁸F]-FDG PET/MRI) for nodal status, regarding quantity and location of metastatic locoregional lymph nodes in patients with newly diagnosed breast cancer. Materials and methods: One hundred eighty-two patients (mean age 52.7 ± 11.9 years) were included in this prospective double-center study. Patients underwent dedicated contrast-enhanced chest/abdomen/pelvis computed tomography (CT) and whole-body ([¹⁸F]-FDG PET/) magnet resonance imaging (MRI). Thoracal datasets were evaluated separately regarding quantity, lymph node station (axillary levels I–III, supraclavicular, internal mammary chain), and lesion character (benign vs. malign). Histopathology served as reference standard for patient-based analysis. Patient-based and lesion-based analyses were compared by a McNemar test. Sensitivity, specificity, positive and negative predictive values, and accuracy were assessed for all three imaging modalities. Results: On a patient-based analysis, PET/MRI correctly detected significantly more nodal positive patients than MRI (p < 0.0001) and CT (p < 0.0001). No statistically significant difference was seen between CT and MRI. PET/MRI detected 193 lesions in 75 patients (41.2%), while MRI detected 123 lesions in 56 patients (30.8%) and CT detected 104 lesions in 50 patients, respectively. Differences were statistically significant on a lesion-based analysis (PET/MRI vs. MRI, p < 0.0001; PET/MRI vs. CT, p < 0.0001; MRI vs. CT, p = 0.015). Subgroup analysis for different lymph node stations showed that PET/MRI detected significantly more lymph node metastases than MRI and CT in each location (axillary levels I–III, supraclavicular, mammary internal chain). MRI was superior to CT only in axillary level I (p = 0.0291). Conclusion: [¹⁸F]-FDG PET/MRI outperforms CT or MRI in detecting nodal involvement on a patient-based analysis and on a lesion-based analysis. Furthermore, PET/MRI was superior to CT or MRI in detecting lymph node metastases in all lymph node stations. Of all the tested imaging modalities, PET/MRI showed the highest sensitivity, whereas CT showed the lowest sensitivity, but was most specific.

Background: This study investigated the performance of simultaneous ¹⁸F-FDG PET/MRI of the breast as a platform for comprehensive radiomics analysis for breast cancer subtype analysis, hormone receptor status, proliferation rate and lymphonodular and distant metastatic spread. Methods: One hundred and twenty-four patients underwent simultaneous ¹⁸F-FDG PET/MRI. Breast tumors were segmented and radiomic features were extracted utilizing CERR software following the IBSI guidelines. LASSO regression was employed to select the most important radiomics features prior to model development. Five-fold cross validation was then utilized alongside support vector machines, resulting in predictive models for various combinations of imaging data series. Results: The highest AUC and accuracy for differentiation between luminal A and B was achieved by all MR sequences (AUC 0.98; accuracy 97.3). The best results in AUC for prediction of hormone receptor status and proliferation rate were found based on all MR and PET data (ER AUC 0.87, PR AUC 0.88, Ki-67 AUC 0.997). PET provided the best determination of grading (AUC 0.71), while all MR and PET analyses yielded the best results for lymphonodular and distant metastatic spread (0.81 and 0.99, respectively). Conclusion: ¹⁸F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for breast cancer phenotyping and tumor decoding, utilizing the perks of simultaneously acquired morphologic, functional and metabolic data.

Background: The aim of this study was to assess whether multiparametric ¹⁸F-FDG PET/MRI-based radiomics analysis is able to predict pathological complete response in breast cancer patients and hence potentially enhance pretherapeutic patient stratification. Methods: A total of 73 female patients (mean age 49 years; range 27–77 years) with newly diagnosed, therapy-naive breast cancer underwent simultaneous ¹⁸F-FDG PET/MRI and were included in this retrospective study. All PET/MRI datasets were imported to dedicated software (ITK-SNAP v. 3.6.0) for lesion annotation using a semi-automated method. Pretreatment biopsy specimens were used to determine tumor histology, tumor and nuclear grades, and immunohistochemical status. Histopathological results from surgical tumor specimens were used as the reference standard to distinguish between complete pathological response (pCR) and noncomplete pathological response. An elastic net was employed to select the most important radiomic features prior to model development. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for each model. Results: The best results in terms of AUCs and NPV for predicting complete pathological response in the entire cohort were obtained by the combination of all MR sequences and PET (0.8 and 79.5%, respectively), and no significant differences from the other models were observed. In further subgroup analyses, combining all MR and PET data, the best AUC (0.94) for predicting complete pathologic response was obtained in the HR+/HER2− group. No difference between results with/without the inclusion of PET characteristics was observed in the TN/HER2+ group, each leading to an AUC of 0.92 for all MR and all MR + PET datasets. Conclusion: ¹⁸F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for the prediction of pCR in breast cancer patients, especially in those with HR+/HER2− receptor status.

Purpose: To compare breast magnetic resonance imaging (MRI), thoracal MRI, thoracal ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET)/MRI and axillary sonography for the detection of axillary lymph node metastases in women with newly diagnosed breast cancer. Materials and Methods: This prospective double-center study included patients with newly diagnosed breast cancer between March 2018 and December 2019. Patients underwent thoracal (¹⁸F-FDG PET/)MRI, axillary sonography, and dedicated prone breast MRI. Datasets were evaluated separately regarding nodal status (nodal+ vs. nodal-). Histopathology served as reference standard in all patients. The diagnostic performance of breast MRI, thoracal MRI, thoracal PET/MRI and axillary sonography in detecting nodal positive patients was tested by creating receiver-operating-characteristic curves (ROC) with a calculated area under the curve (AUC). Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for all four modalities. A McNemar test was used to assess differences. Results: 112 female patients (mean age 53.04 ± 12.6 years) were evaluated. Thoracal PET/MRI showed the highest ROC-AUC with a value of 0.892. The AUC for breast MRI, thoracal MRI and sonography were 0.782, 0.814 and 0.834, respectively. Differences between thoracal PET/MRI and axillary sonography, thoracal MRI and breast MRI were statistically significant (PET/MRI vs. axillary sonography, P = 0.01; PET/MRI vs. thoracal MRI, P = 0.02; PET/MRI vs. breast MRI, P = 0.03). PET/MRI showed the highest sensitivity (81.8%, 36/44) (95%-CI: 67.29-91.81%) while axillary sonography had the highest specificity (98.5%, 65/66), 95%-CI: 91.84-99.96%). Conclusion:¹⁸F-FDG PET/MRI outperforms axillary sonography, breast MRI and thoracal MRI in determining the axillary lymph node status. In a clinical setting, the combination of ¹⁸F-FDG PET/MRI and axillary sonography might be considered to provide even more accuracy in diagnosis.

Background: In addition to its high prognostic value, the involvement of axillary lymph nodes in breast cancer patients also plays an important role in therapy planning. Therefore, an imaging modality that can determine nodal status with high accuracy in primary breast cancer patients is desirable. Purpose: To investigate if machine-learning prediction models based on simple assessable imaging features in MRI (magnetic resonance imaging) or PET (positron emission tomography)/MRI are able to determine nodal status in newly diagnosed breast cancer patients with comparable performance as experienced radiologists, if such models can be adjusted to achieve low rates of false negatives such that invasive procedures could potentially be omitted, and if a clinical framework for decision-support based on simple imaging features can be derived from these models. Methods: 303 participants from three centres prospectively underwent dedicated whole-body ¹⁸F-FDG (¹⁸F-fluorodeoxyglucose) PET/MRI between August 2017 and September 2020. Imaging datasets were evaluated regarding axillary lymph node metastases based on morphologic and metabolic features. Predictive models were developed for MRI and PET/MRI separately using random forest classifiers on data of two centers and were tested on data of the third center. Results: The diagnostic accuracy for MRI features was 87.5% both for radiologists and for machine learning algorithm. For PET/MRI the diagnostic accuracy was 89.3% for the radiologists and 91.2% for the machine learning algorithm with no significant differences in diagnostic performance of radiologists and the machine learning algorithm in MRI (P = 0.671) and PET/MRI (P = 0.683). Most important lymph node feature was tracer uptake, followed by lymph node size. With an adjusted threshold, a sensitivity of 96.2% was achieved by the random forest classifier, whereas specificity, positive predictive value, negative predictive value and accuracy were 68.2%, 78.1%, 93.8% and 83.3%. A decision tree based on three simple imaging features could be established for MRI and PET/MRI. Conclusion: Applying a high sensitivity threshold to the random forest results could potentially avoid invasive procedures such as sentinel lymph node biopsy in 68.2% of the patients.