Doris Leithner

Rationale To assess the performance of ⁶⁸Ga-PSMA PET/CT in detecting biochemically recurrent prostate cancer and evaluate associations of PET imaging findings with alterations on genomic profiling. Methods ⁶⁸Ga-PSMA PET/CT scans of 603 prostate cancer patients with biochemical recurrence or persistently elevated PSA and without known metastatic disease were retrospectively included (mean PSA 1.0 ng/mL; range, 0.08-5). All scans were analysed for presence of recurrence by two experienced nuclear medicine physicians independently; discrepancies were resolved by an additional expert reader. A subset of patients (n = 239) had undergone tumour genetic profiling using the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) sequencing assay. PET positivity and positive predictive value, inter-reader agreement, as well as associations between patient characteristics, location of recurrence, time to PET confirmed biochemical recurrence (PCBR), SUVmax, and most frequent gene alterations were investigated. Results The overall recurrence detection rate of ⁶⁸Ga-PSMA PET/CT was 59.2% (357/603), with a positive predictive value of 0.98 (95% CI, 0.94–0.99), and a sensitivity of 0.60 (95% CI, 0.54–0.66). When analysed by PSA level, 43.8% of patients with PSA < 0.5 ng/mL, 64.1% with PSA 0.5–0.99 ng/mL, 73.6% with PSA 1.0-1.99 ng/mL, and 87% with PSA > 2.0 ng/mL were PET positive. Inter-reader agreement was excellent with κ-coefficient of 0.884. SUVmax was significantly higher in patients with higher PSA (P < 0.001), and extra-pelvic nodal metastases (P < 0.001). Patients with persistently elevated PSA, higher TNM stage, higher Gleason score, and without RT at baseline (all P < 0.001) had shorter time to PCBR. In patients with genetic alteration available, TP53 mutations were significantly associated with time to PCBR (alteration, n = 38; wildtype, n = 200; median time 4.3 versus 5.3 years, P = 0.04), and borderline significantly associated with SUVmax (median 11 versus 7, n = 33 and 121, P = 0.054). Conclusion ⁶⁸Ga-PSMA PET/CT can detect prostate cancer recurrence potentially even among patients with a low PSA level. TP53 alterations on genetic analysis were associated with shorter time to PCBR and borderline associated with higher SUVmax, suggesting that an integrated diagnostic approach may identify more aggressive disease.

Rationale: To determine whether ComBat harmonization improves ¹⁸F-FDG-PET radiomics-based tissue classification in pooled PET/MR and PET/CT datasets. Methods: Two-hundred patients who had undergone ¹⁸F-FDG-PET/MR (two scanners/vendors; 50 patients each) or -PET/CT (two scanners/vendors; 50 patients each) were retrospectively included. Grey-level histogram (GLH), co-occurrence matrix (GLCM), run-length matrix (GLRLM), size-zone matrix (GLSZM), and neighborhood grey-tone difference matrix (NGTDM) radiomic features were calculated for volumes of interest in the disease-free liver, spleen, and bone marrow. For individual feature classes and a multi-class radiomic signature, tissue classification was performed on ComBat-harmonized and unharmonized pooled data, using a multi-layer perceptron neural network. Results: Median accuracies in training/validation datasets were: GLH, 69.5/68.3% (harmonized) vs. 59.5/58.9% (unharmonized); GLCM, 92.1/86.1% vs. 53.6/50.0%; GLRLM, 84.8/82.8% vs. 62.4/58.3%; GLSZM, 87.6/85.6% vs. 56.2/52.8%; NGTDM, 79.5/77.2% vs. 54.8/53.9%, and radiomic signature, 86.9/84.4% vs. 62.9/58.3%. Conclusion: ComBat harmonization may be useful for multi-center ¹⁸F-FDG-PET radiomics studies using pooled PET/MR and PET/CT data.

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 differences 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.

Objectives: To compare radiation dose and image quality of single-energy (SECT) and dual-energy (DECT) head and neck CT examinations performed with second- and third-generation dual-source CT (DSCT) in matched patient cohorts. Methods: 200 patients (mean age 55.1 ± 16.9 years) who underwent venous phase head and neck CT with a vendor-preset protocol were retrospectively divided into four equal groups (n = 50) matched by gender and BMI: second (Group A, SECT, 100-kV; Group B, DECT, 80/Sn140-kV), and third-generation DSCT (Group C, SECT, 100-kV; Group D, DECT, 90/Sn150-kV). Assessment of radiation dose was performed for an average scan length of 27 cm. Contrast-to-noise ratio measurements and dose-independent figure-of-merit calculations of the submandibular gland, thyroid, internal jugular vein, and common carotid artery were analyzed quantitatively. Qualitative image parameters were evaluated regarding overall image quality, artifacts and reader confidence using 5-point Likert scales. Results: Effective radiation dose (ED) was not significantly different between SECT and DECT acquisition for each scanner generation (p = 0.10). Significantly lower effective radiation dose (p < 0.01) values were observed for third-generation DSCT groups C (1.1 ± 0.2 mSv) and D (1.0 ± 0.3 mSv) compared to second-generation DSCT groups A (1.8 ± 0.1 mSv) and B (1.6 ± 0.2 mSv). Figure-of-merit/contrast-to-noise ratio analysis revealed superior results for third-generation DECT Group D compared to all other groups. Qualitative image parameters showed non-significant differences between all groups (p > 0.06). Conclusion: Contrast-enhanced head and neck DECT can be performed with second- and third-generation DSCT systems without radiation penalty or impaired image quality compared with SECT, while third-generation DSCT is the most dose efficient acquisition method. Advances in knowledge: Differences in radiation dose between SECT and DECT of the dose-vulnerable head and neck region using DSCT systems have not been evaluated so far. Therefore, this study directly compares radiation dose and image quality of standard SECT and DECT protocols of second- and third-generation DSCT platforms.

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR−), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR− breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR− breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.

We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77–0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75–0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.

Background: To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes. Methods: One hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n = 49; luminal B, n = 8; HER2-enriched, n = 11; triple negative, n = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n = 17; luminal B, n = 17; triple negative, n = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference. Results: In the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%). Conclusions: In this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.

Objective:To evaluate low-tube-voltage 90-kVp CT pulmonary angiography (CTPA) with advanced modeled iterative reconstruction algorithm (Admire) compared to 120-kVp equivalent dual-energy (DE) acqui...

Objective: To investigate the impact of low-tube-voltage 90-kVp acquisition combined with advanced modeled iterative reconstruction algorithm (Admire) on radiation exposure, image quality, artifacts, and assessment of stenosis in carotid and intracranial CT angiography (CTA). Methods: Dual-energy CTA studies of 43 patients performed on a third-generation 192-slice dual-source CT were retrospectively evaluated. Intraindividual comparison of 90-kVp and linearly blended 120-kVp equivalent image series (M_0.6, 60% 90-kVp, 40% Sn-150-kVp) was performed. Contrast-to-noise and signal-to-noise ratios of common carotid artery, internal carotid artery, middle cerebral artery, and basilar artery were calculated. Qualitative image analysis included evaluation of artifacts and suitability for angiographical assessment at shoulder level, carotid bifurcation, siphon, and intracranial by three independent radiologists. Detection and quantification of carotid stenosis were performed. Radiation dose was expressed as dose–length product (DLP). Results: Contrast-to-noise values of all arteries were significantly increased in 90-kVp compared to M_0.6 (p < 0.001). Suitability for angiographical evaluation was rated excellent with low artifacts for all levels in both image series. Both 90-kVp and M_0.6 showed excellent accordance for detection and grading of carotid stenosis with almost perfect interobserver agreement (carotid stenoses in 32 of 129 segments; intraclass correlation coefficient, 0.94). dose–length product was reduced by 40.3% in 90-kVp (110.6 ± 32.1 vs 185.4 ± 47.5 mGy·cm, p < 0.001). Conclusion: 90-kVp carotid and intracranial CTA with Admire provides increased quantitative and similarly good qualitative image quality, while reducing radiation exposure substantially compared to M_0.6. Diagnostic performance for arterial stenosis detection and quantification remained excellent. Advances in knowledge: 90-kVp carotid and intracranial CTA with an advanced iterative reconstruction algorithm results in excellent image quality and reduction of radiation exposure without limiting diagnostic performance.