Evaluation of functional and metabolic tumor volume using voxel-wise analysis in childhood rhabdomyosarcoma.

BACKGROUND: Cross-sectional imaging-based morphological characteristics of pediatric rhabdomyosarcoma have failed to predict outcomes. OBJECTIVE: To evaluate the feasibility and possible value of generating tumor sub-volumes using voxel-wise analysis of metabolic and functional data from positron emission tomography/magnetic resonance imaging (PET/MR) or PET/computed tomography (CT) and MRI in rhabdomyosarcoma. MATERIALS AND METHODS: Thirty-four examinations in 17 patients who received PET/MRI or PET/CT plus MRI were analyzed. The volume of interest included total tumor volume before and after therapy. Apparent diffusion coefficients (ADC) and standard uptake values (SUV) were determined voxel-wise. Voxels were assigned to three different groups based on ADC and SUV: "viable tumor tissue," "intermediate tissue" or "possible necrosis." In a second approach, data were grouped into three clusters using the Gaussian mixture model. The ratio of these clusters to total tumor volume and changes due to chemotherapy were correlated with clinical and histopathological data. RESULTS: After chemotherapy, the proportion of voxels in the different groups changed significantly. A significant reduction of the proportion of voxels assigned to cluster 1 was found, from a mean of 36.4% to 2.5% (P < 0.001). There was a significant increase in the proportion of voxels in cluster 3 following chemotherapy from 24.8% to 81.6% (P = 0.02). The proportion of voxels in cluster 2 differed depending on the presence or absence of tumor recurrence, falling from 48% to 10% post-chemotherapy in the group with no tumor recurrence (P < 0.05) and from 29% to 23% (P > 0.05) in the group with tumor recurrence. CONCLUSION: Voxel-wise evaluation of multimodal data in rhabdomyosarcoma is feasible. Our initial results suggest that the different distribution of sub-volumes before and after therapy may have prognostic significance.

Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest.

OBJECTIVES: A deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without change of acquisition parameters. MATERIALS AND METHODS: Fifty patients who received VIBE postcontrast imaging of the chest at 1.5 T were included in this retrospective study. After acquisition of the standard VIBE (VIBES), a novel deep learning-based algorithm and a denoising algorithm were applied, resulting in enhanced images (VIBEDL). Two radiologists qualitatively evaluated both datasets independently, rating sharpness of soft tissue, vessels, bronchial structures, lymph nodes, artifacts, cardiac motion artifacts, noise levels and overall diagnostic confidence, using a Likert scale ranging from 1 to 4. In the presence of lung lesions, the largest lesion was rated regarding sharpness and diagnostic confidence using the same Likert scale as mentioned above. Additionally, the largest diameter of the lesion was measured. RESULTS: The sharpness of soft tissue, vessels, bronchial structures and lymph nodes as well as the diagnostic confidence, the extent of artifacts, the extent of cardiac motion artifacts and noise levels were rated superior in VIBEDL (all P < 0.001). There was no significant difference in the diameter or the localization of the largest lung lesion in VIBEDL compared to VIBES. Lesion sharpness as well as detectability was rated significantly better by both readers with VIBEDL (both P < 0.001). CONCLUSION: The application of a novel deep learning-based super-resolution approach in T1-weighted VIBE postcontrast imaging resulted in an improvement in image quality, noise levels and diagnostic confidence as well as in a shortened acquisition time.

Retrospective accuracy analysis of MRI based lesion size measurement in neuroblastic tumors: which sequence should we choose?

BACKGROUND: MR imaging of neuroblastic tumors is widely used for assessing the effect of chemotherapy on tumor size. However, there are some concerns that MRI might falsely estimate lesion diameters due to calcification and fibrosis. Therefore, the aim of our study was to compare neuroblastic tumor size based on MRI measurements to histopathology measurements of the resected specimens as standard of reference. METHODS: Inclusion criteria were diagnosis of a neuroblastic tumor, MR imaging within 100 days to surgery and gross total resection without fragmentation of the tumor between 2008 and 2019. Lesion diameters were measured by two radiologists according to RECIST 1.1 in axial plane in T2w turbo spin echo (TSE), diffusion-weighted imaging (DWI), and in T1w pre- and postcontrast sequences. Furthermore, the largest lesion size in three-dimensions was noted. The largest diameter of histopathology measurements of each specimen was used for comparison with MRI. RESULTS: Thirty-seven patients (mean age: 5 ± 4 years) with 38 lesions (neuroblastoma: n = 17; ganglioneuroblastoma: n = 11; ganglioneuroma: n = 10) were included in this retrospective study. There was excellent intra-class correlation coefficient between both readers for all sequences (> 0.9) Tumor dimensions of reader 1 based on axial MRI measurements were significantly smaller with the following median differences (cm): T1w precontrast - 1.4 (interquartile range (IQR): 1.8), T1w postcontrast - 1.0 (IQR: 1.9), T2w TSE: -1.0 (IQR: 1.6), and DWI -1.3 (IQR: 2.2) (p < 0.001 for all sequences). However, the evaluation revealed no significant differences between the three-dimensional measurements and histopathology measurements of the resected specimens regardless of the applied MRI sequence. CONCLUSIONS: Axial MRI based lesion size measurements are significantly smaller than histopathological measurements. However, there was no significant difference between three-dimensional measurements and histopathology measurements of the resected specimens. T2w TSE and T1w postcontrast images provided the lowest deviation and might consequently be preferred for measurements.

Shortening Acquisition Time and Improving Image Quality for Pelvic MRI Using Deep Learning Reconstruction for Diffusion-Weighted Imaging at 1.5 T.

RATIONALE AND OBJECTIVES: To determine the impact on acquisition time reduction and image quality of a deep learning (DL) reconstruction for accelerated diffusion-weighted imaging (DWI) of the pelvis at 1.5 T compared to standard DWI. MATERIALS AND METHODS: A total of 55 patients (mean age, 61 ± 13 years; range, 27-89; 20 men, 35 women) were consecutively included in this retrospective, monocentric study between February and November 2022. Inclusion criteria were (1) standard DWI (DWIS) in clinically indicated magnetic resonance imaging (MRI) at 1.5 T and (2) DL-reconstructed DWI (DWIDL). All patients were examined using the institution's standard MRI protocol according to their diagnosis including DWI with two different b-values (0 and 800 s/mm2) and calculation of apparent diffusion coefficient (ADC) maps. Image quality was qualitatively assessed by four radiologists using a visual 5-point Likert scale (5 = best) for the following criteria: overall image quality, noise level, extent of artifacts, sharpness, and diagnostic confidence. The qualitative scores for DWIS and DWIDL were compared with the Wilcoxon signed-rank test. RESULTS: The overall image quality was evaluated to be significantly superior in DWIDL compared to DWIS for b = 0 s/mm2, b = 800 s/mm2, and ADC maps by all readers (P < .05). The extent of noise was evaluated to be significantly less in DWIDL compared to DWIS for b = 0 s/mm2, b = 800 s/mm2, and ADC maps by all readers (P < .001). No significant differences were found regarding artifacts, lesion detectability, sharpness of organs, and diagnostic confidence (P > .05). Acquisition time for DWIS was 2:06 minutes, and simulated acquisition time for DWIDL was 1:12 minutes. CONCLUSION: DL image reconstruction improves image quality, and simulation results suggest that a reduction in acquisition time for diffusion-weighted MRI of the pelvis at 1.5 T is possible.

Feasibility and possible value of quantitative semi-automated diffusion weighted imaging volumetry of neuroblastic tumors.

BACKGROUND: To assess the feasibility and possible value of semi-automated diffusion weighted imaging (DWI) volumetry of whole neuroblastic tumors with apparent diffusion coefficient (ADC) map evaluation after neoadjuvant chemotherapy. METHODS: Pediatric patients who underwent surgical resection of neuroblastic tumors at our institution from 2013 to 2019 and who received a preoperative MRI scan with DWI after chemotherapy were included. Tumor volume was assessed with a semi-automated approach in DWI using a dedicated software prototype. Quantitative ADC values were calculated automatically of the total tumor volume after manual exclusion of necrosis. Manual segmentation in T1 weighted and T2 weighted sequences was used as reference standard for tumor volume comparison. The Student's t test was used for parametric data while the Wilcoxon rank sum test and the Kruskal-Wallis test were applied for non-parametric data. RESULTS: Twenty seven patients with 28 lesions (neuroblastoma (NB): n = 19, ganglioneuroblastoma (GNB): n = 7, ganglioneuroma (GN): n = 2) could be evaluated. Mean patient age was 4.5 ± 3.2 years. Median volume of standard volumetry (T1w or T2w) was 50.2 ml (interquartile range (IQR): 91.9 ml) vs. 45.1 ml (IQR: 98.4 ml) of DWI (p = 0.145). Mean ADC values (× 10- 6 mm2/s) of the total tumor volume (without necrosis) were 1187 ± 301 in NB vs. 1552 ± 114 in GNB/GN (p = 0.037). The 5th percentile of ADC values of NB (614 ± 275) and GNB/GN (1053 ± 362) provided the most significant difference (p = 0.007) with an area under the curve of 0.848 (p < 0.001). CONCLUSIONS: Quantitative semi-automated DWI volumetry is feasible in neuroblastic tumors with integrated analysis of tissue characteristics by providing automatically calculated ADC values of the whole tumor as well as an ADC heatmap. The 5th percentile of the ADC values of the whole tumor volume proved to be the most significant parameter for differentiation of the histopathological subtypes in our patient cohort and further investigation seems to be worthwhile.