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Background: Accurate diagnosis and staging are essential for optimal treatment planning of prostate cancer. By combining functional and anatomical imaging, PSMA-PET/mpMRI offers a potential to improve lesion detection and enhance staging accuracy. This study aimed to evaluate the diagnostic performance of lesion detection and local staging of prostate cancer using combined PSMA-PET/mpMRI compared to standalone mpMRI or PSMA-PET.

Results: Fifty-five patients with intermediate- to high-risk prostate cancer scheduled for robot-assisted laparoscopic radical prostatectomy were included. All patients underwent [68Ga]PSMA-PET/mpMRI prior to surgery. Whole-mount histopathology and surgical report served as reference standard. Two radiologists independently evaluated mpMRI, while two nuclear medicine physicians assessed PSMA-PET. For the PSMA-PET/mpMRI analysis, a consensus evaluation was performed by a new set of readers in two teams, each comprising one radiologist and one nuclear medicine physician. Lesion localization was reported based on the PI-RADS v2.1 sector map and compared to histopathology. Among 130 histopathologically confirmed lesions, mean detection rates were 38% (49.5/130) for PSMA-PET/mpMRI, 32% (41/130) for mpMRI and 32% (41/130) for PSMA-PET. For clinically significant prostate cancer (csPC) (≥0.5 ml, ≥ISUP 2; 42 lesions), mean detection rates were 85% (35.5/42) for PSMA-PET/mpMRI, 75% (31.5/42) for mpMRI and 70% (29.5/42) for PSMA-PET. The mean false discovery rates were 8% (PSMA-PET/mpMRI), 15% (mpMRI) and 12% (PSMA-PET). The likelihood of extraprostatic extension (EPE) and seminal vesicle invasion (SVI) were scored using a 5-point Likert scale, where scores of 1–3 were classified as negative and scores of 4–5 were considered positive. Sensitivity for EPE was 32% for PSMA-PET/mpMRI, 37% for mpMRI and 7% for PSMA-PET, with a specificity of 100%, 96% and 98%, respectively. For SVI, sensitivity was 50% for PSMA-PET/mpMRI and 38% for mpMRI and PSMA-PET, with a specificity of 100%, 95% and 97% respectively.

Conclusions: PSMA-PET/mpMRI provided higher and a more consistent performance in localized prostate cancer detection and staging without increasing false-positive findings.

BACKGROUND AND PURPOSE: The study aims to evaluate dosimetric properties of hypofractionated treatment plans integrating focal boost, using registered whole-mount histopathology (WMHP) as reference standard.

METHODS: Fifteen men from the PAMP trial (EudraCT: 2015-005046-55) were included. Participants had ≥ 1 ISUP Grade group ≥ 4 lesion and underwent [⁶⁸Ga]prostate-specific membrane antigen (PSMA) positron emission tomography/multiparametric magnetic resonance imaging (PET/mpMRI) and [¹¹C]Acetate-PET/computed tomography before radical prostatectomy. Four radiation oncologists delineated gross tumor volumes (GTVs) on PSMA-PET/mpMRI. Sixty treatment plans were optimized, one per GTV and patient. Prostate planning target volumes were prescribed 42.7 Gy in seven fractions, with a simultaneous GTV boost up to 49.0 Gy, prioritizing organs at risk (OARs). Digital WMHP provided Gleason grading and was co-registered with in-vivo imaging. Target coverage for GTVs and voxels sharing Gleason patterns (GPs) was assessed via dose-volume histogram (DVH) analysis. Interobserver agreement in GTV-delineations was quantified with Fleiss' kappa.

RESULTS: The median GTV dose per plan (D50) ranged from 48.3 to 49.1 Gy. For voxels with the highest GP, D₅₀ was 42.9-49.2 Gy, exceeding 47.2 Gy in all except one plan. In lowest pattern voxels, D₅₀ was 42.5-49.3 Gy, and below 43.4 Gy in over half the plans. Significant positive correlations between Fleiss' kappa and DVH parameters appeared only for GP 5 regions, specifically for Fleiss' kappa and D₅₀ for two observers and the average D₅₀ across observers.

INTERPRETATION: The histologically confirmed tumor was only partially boosted. Regions with more aggressive disease received better coverage. These findings provide a rational for prioritizing OARs in treatment planning.

BACKGROUND: The delineation of intraprostatic lesions is vital for correct delivery of focal radiotherapy boost in patients with prostate cancer (PC). Errors in the delineation could translate into reduced tumour control and potentially increase the side effects. The purpose of this study is to compare PET-based delineation methods with histopathology.

MATERIALS AND METHODS: The study population consisted of 15 patients with confirmed high-risk PC intended for prostatectomy. [68Ga]-PSMA-PET/MR was performed prior to surgery. Prostate lesions identified in histopathology were transferred to the in vivo [68Ga]-PSMA-PET/MR coordinate system. Four radiation oncologists manually delineated intraprostatic lesions based on PET data. Various semi-automatic segmentation methods were employed, including absolute and relative thresholds, adaptive threshold, and multi-level Otsu threshold.

RESULTS: The gross tumour volumes (GTVs) delineated by the oncologists showed a moderate level of interobserver agreement with Dice similarity coefficient (DSC) of 0.68. In comparison with histopathology, manual delineations exhibited the highest median DSC and the lowest false discovery rate (FDR) among all approaches. Among semi-automatic approaches, GTVs generated using standardized uptake value (SUV) thresholds above 4 (SUV > 4) demonstrated the highest median DSC (0.41), with 0.51 median lesion coverage ratio, FDR of 0.66 and the 95th percentile of the Hausdorff distance (HD95%) of 8.22 mm.

INTERPRETATION: Manual delineations showed a moderate level of interobserver agreement. Compared to histopathology, manual delineations and SUV > 4 exhibited the highest DSC and the lowest HD95% values. The methods that resulted in a high lesion coverage were associated with a large overestimation of the size of the lesions.

Background: Should early response imaging predict tumor response to therapy, personalized treatment adaptations could be feasible to improve outcome or reduce the risk of adverse events. This prospective single-center observational study on 2-fluorine-18-fluoro-deoxy-glucose (2-[18F]FDG) positron-emission tomography/magnetic resonance imaging (PET/MRI) features aims to investigate the association between semantic 2-[18F]FDG-PET/MRI imaging parameters and outcome prediction in uterine cervical squamous cell carcinoma (CSCC) treated with radiotherapy.

Results: Eleven study participants with previously untreated CSCC were examined with 2-[18F]FDG-PET/MRI at baseline and approximately one week after start of curative radiotherapy. All study participants had at least 24 months clinical follow-up. Two patients relapsed during the follow-up period. Reduced tumor size according to visual assessment was present in 9/11 participants (median change in sum of largest diameters (SLD) − 10.4%; range − 2.5 to − 24.6%). The size reduction was less pronounced in the relapse group compared to the no relapse group, with median change in SLD − 4.9%, versus − 10.4%. None of the reductions qualified as significantly reduced or increased in size according to RECIST 1.1., hence all participants were at this stage classified as non-responders/stable disease. Median baseline functional tumor volume (FTV) for the relapse group was 126 cm3, while for the no relapse group 9.3 cm3. Median delta FTV in the relapse group was 50.7 cm3, representing an actual increase in metabolically active volume, while median delta FTV in the no relapse group was − 2.0 cm3. Median delta apparent diffusion coefficient (ADC) was lower in the relapse group versus the no relapse group (− 3.5 mm2/s vs. 71 mm2/s).

Conclusions: Early response assessment with 2-[18F]FDG-PET/MRI identified potentially predictive functional imaging biomarkers for prediction of radiotherapy outcome in CSCC, that could not be recognized with tumor measurements according to RECIST 1.1. These biomarkers (delta FTV and delta ADC) should be further evaluated.

Background and purpose: Dose escalation in external radiotherapy of prostate cancer shows promising results in terms of biochemical disease-free survival. Boost volume delineation guidelines are sparse which may cause high interobserver variability. The aim of this research was to characterize gross tumor volume (GTV) delineations based on multiparametric magnetic resonance imaging (mpMRI) and prostate specific membrane antigen-positron emission tomography (PSMA-PET) in relation to histopathology-validated Gleason grade 4 and 5 regions.

Material and methods: The study participants were examined with [68Ga]PSMA-PET/mpMRI prior to radical prostatectomy. Four radiation oncologists delineated GTVs in 15 study participants, on four different image types; T2-weighted (T2w), diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE) and PSMA-PET scans separately. The simultaneous truth and performance level estimation (STAPLE) algorithm was used to generate combined GTVs. GTVs were subsequently compared to histopathology. We analysed how Dice similarity coefficient (DSC) and lesion coverage are affected by using single versus multiple image types as well as by adding a clinical target volume (CTV) margin.

Results: Median DSC (STAPLE) for different GTVs varied between 0.33 and 0.52. GTVPSMA-PET/mpMRI generated the highest median lesion coverage at 0.66. Combining different image types achieved similar lesion coverage as adding a CTV margin to contours from a single image type, while reducing non-malignant tissue inclusion within the target volume.

Conclusion: The combined use of mpMRI or PSMA-PET/mpMRI shows promise, achieving higher DSC and lesion coverage while minimizing non-malignant tissue inclusion, in comparison to the use of a single image type with an added CTV margin.

The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily increasing, however most proposed methods were trained and validated on private datasets of a single contrast from a single scanner. Such solutions might not perform equally well on other datasets, limiting their general usability and therefore value. Additionally, functional evaluations of sCTs such as dosimetric comparisons with CT-based dose calculations better show the impact of the methods, but the evaluations are more labor intensive than pixel-wise metrics.

To improve the generalization of an sCT model, we propose to incorporate a pre-trained DL model to pre-process the input MR images by generating artificial proton density, T1 and T2 maps (i.e. contrast-independent quantitative maps), which are then used for sCT generation. Using a dataset of only T2w MR images, the robustness towards input MR contrasts of this approach is compared to a model that was trained using the MR images directly. We evaluate the generated sCTs using pixel-wise metrics and calculating mean radiological depths, as an approximation of the mean delivered dose. On T2w images acquired with the same settings as the training dataset, there was no significant difference between the performance of the models. However, when evaluated on T1w images, and a wide range of other contrasts and scanners from both public and private datasets, our approach outperforms the baseline model. Using a dataset of T2w MR images, our proposed model implements synthetic quantitative maps to generate sCT images, improving the generalization towards other contrasts. Our code and trained models are publicly available.

Objective: PET/CT and multiparametric MRI (mpMRI) are important diagnostic tools in clinically significant prostate cancer (csPC). The aim of this study was to compare csPC detection rates with [⁶⁸Ga]PSMA-11-PET (PSMA)-PET, [¹¹C] Acetate (ACE)-PET, and mpMRI with histopathology as reference, to identify the most suitable imaging modalities for subsequent hybrid imaging. An additional aim was to compare inter-reader variability to assess reproducibility.

Methods: During 2016–2019, all study participants were examined with PSMA-PET/mpMRI and ACE-PET/CT prior to radical prostatectomy. PSMA-PET, ACE-PET and mpMRI were evaluated separately by two observers, and were compared with histopathology-defined csPC. Statistical analyses included two-sided McNemar test and index of specific agreement.

Results: Fifty-five study participants were included, with 130 histopathological intraprostatic lesions >0.05 cc. Of these, 32% (42/130) were classified as csPC with ISUP grade ≥2 and volume >0.5 cc. PSMA-PET and mpMRI showed no difference in performance (P = 0.48), with mean csPC detection rate of 70% (29.5/42) and 74% (31/42), respectively, while with ACE-PET the mean csPC detection rate was 37% (15.5/42). Interobserver agreement was higher with PSMA-PET compared to mpMRI [79% (26/33) vs 67% (24/38)]. Including all detected lesions from each pair of observers, the detection rate increased to 90% (38/42) with mpMRI, and 79% (33/42) with PSMA-PET.

Conclusion: PSMA-PET and mpMRI showed high csPC detection rates and superior performance compared to ACE-PET. The interobserver agreement indicates higher reproducibility with PSMA-PET. The combined result of all observers in both PSMA-PET and mpMRI showed the highest detection rate, suggesting an added value of a hybrid imaging approach.

BACKGROUND: Perirectal spacers may be beneficial to reduce rectal side effects from radiotherapy (RT). Here, we present the impact of a hyaluronic acid (HA) perirectal spacer on rectal dose as well as spacer stability, long-term gastrointestinal (GI) and genitourinary (GU) toxicity and patient-reported outcome (PRO).

METHODS: In this phase II study 81 patients with low- and intermediate-risk prostate cancer received transrectal injections with HA before external beam RT (78 Gy in 39 fractions). The HA spacer was evaluated with MRI four times; before (MR0) and after HA-injection (MR1), at the middle (MR2) and at the end (MR3) of RT. GI and GU toxicity was assessed by physician for up to five years according to the RTOG scale. PROs were collected using the Swedish National Prostate Cancer Registry and Prostate cancer symptom scale questionnaires.

RESULTS: There was a significant reduction in rectal V70% (54.6 Gy) and V90% (70.2 Gy) between MR0 and MR1, as well as between MR0 to MR2 and MR3. From MR1 to MR2/MR3, HA thickness decreased with 28%/32% and CTV-rectum space with 19%/17% in the middle level. The cumulative late grade ≥ 2 GI toxicity at 5 years was 5% and the proportion of PRO moderate or severe overall bowel problems at 5 years follow-up was 12%. Cumulative late grade ≥ 2 GU toxicity at 5 years was 12% and moderate or severe overall urinary problems at 5 years were 10%.

CONCLUSION: We show that the HA spacer reduced rectal dose and long-term toxicity.

PURPOSE: During the acquisition of MRI data, patient-, sequence-, or hardware-related factors can introduce artefacts that degrade image quality. Four of the most significant tasks for improving MRI image quality have been bias field correction, super-resolution, motion-, and noise correction. Machine learning has achieved outstanding results in improving MR image quality for these tasks individually, yet multi-task methods are rarely explored.

METHODS: In this study, we developed a model to simultaneously correct for all four aforementioned artefacts using multi-task learning. Two different datasets were collected, one consisting of brain scans while the other pelvic scans, which were used to train separate models, implementing their corresponding artefact augmentations. Additionally, we explored a novel loss function that does not only aim to reconstruct the individual pixel values, but also the image gradients, to produce sharper, more realistic results. The difference between the evaluated methods was tested for significance using a Friedman test of equivalence followed by a Nemenyi post-hoc test.

RESULTS: Our proposed model generally outperformed other commonly-used correction methods for individual artefacts, consistently achieving equal or superior results in at least one of the evaluation metrics. For images with multiple simultaneous artefacts, we show that the performance of using a combination of models, trained to correct individual artefacts depends heavily on the order that they were applied. This is not an issue for our proposed multi-task model. The model trained using our novel convolutional loss function always outperformed the model trained with a mean squared error loss, when evaluated using Visual Information Fidelity, a quality metric connected to perceptual quality.

CONCLUSION: We trained two models for multi-task MRI artefact correction of brain, and pelvic scans. We used a novel loss function that significantly improves the image quality of the outputs over using mean squared error. The approach performs well on real world data, and it provides insight into which artefacts it detects and corrects for. Our proposed model and source code were made publicly available.

Objective: In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation.

Approach: We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task.

Main results: We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were-(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0.

Significance: We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.