CT radiomics models are unable to predict new liver metastasis after successful thermal ablation of colorectal liver metastases.

BACKGROUND: Patients with colorectal liver metastases (CRLM) who undergo thermal ablation are at risk of developing new CRLM after ablation. Identification of these patients might enable individualized treatment. PURPOSE: To investigate whether an existing machine-learning model with radiomics features based on pre-ablation computed tomography (CT) images of patients with colorectal cancer can predict development of new CRLM. MATERIAL AND METHODS: In total, 94 patients with CRLM who were treated with thermal ablation were analyzed. Radiomics features were extracted from the healthy liver parenchyma of CT images in the portal venous phase, before thermal ablation. First, a previously developed radiomics model (Original model) was applied to the entire cohort to predict new CRLM after 6 and 24 months of follow-up. Next, new machine-learning models were developed (Radiomics, Clinical, and Combined), based on radiomics features, clinical features, or a combination of both. RESULTS: The external validation of the Original model reached an area under the curve (AUC) of 0.57 (95% confidence interval [CI]=0.56-0.58) and 0.52 (95% CI=0.51-0.53) for 6 and 24 months of follow-up. The new predictive radiomics models yielded a higher performance at 6 months compared to 24 months. For the prediction of CRLM at 6 months, the Combined model had slightly better performance (AUC=0.60; 95% CI=0.59-0.61) compared to the Radiomics and Clinical models (AUC=0.55-0.57), while all three models had a low performance for the prediction at 24 months (AUC=0.52-0.53). CONCLUSION: Both the Original and newly developed radiomics models were unable to predict new CLRM based on healthy liver parenchyma in patients who will undergo ablation for CRLM.

Eye-specific quantitative dynamic contrast-enhanced MRI analysis for patients with intraocular masses.

OBJECTIVE: Dynamic contrast enhanced (DCE)-MRI is currently not generally used for intraocular masses as lesions are small, have an inhomogeneous T1 and the eye is prone to motion. The aim of this paper is to address these eye-specific challenges, enabling accurate ocular DCE-MRI. MATERIALS & METHODS: DCE-MRI of 19 uveal melanoma (UM) patients was acquired using a fat-suppressed 3D spoiled gradient echo sequence with TWIST (time-resolved angiography with stochastic trajectories sequence). The analysis consisted of a two-step registration method to correct for both head and eye motion. A T1 map was calculated to convert signal intensities to concentrations. Subsequently, the Tofts model was fitted voxel wise to obtain Ktrans and ve. RESULTS: Registration significantly improved the concentration curve quality (p < 0.001). The T1 of melanotic lesions was significantly lower than amelanotic lesions (888 ms vs 1350 ms, p = 0.03). The average achieved B1+ in the lesions was 91%. The average Ktrans was 0.46 min-1 (range 0.13-1.0) and the average ve was 0.22 (range 0.10-0.51). CONCLUSION: Using this eye-specific analysis, DCE of intraocular masses is possible which might aid in the diagnosis, prognosis and follow-up of UM.

Optimal 68Ga-PSMA and 18F-PSMA PET window levelling for gross tumour volume delineation in primary prostate cancer.

PURPOSE: This study proposes optimal tracer-specific threshold-based window levels for PSMA PET-based intraprostatic gross tumour volume (GTV) contouring to reduce interobserver delineation variability. METHODS: Nine 68Ga-PSMA-11 and nine 18F-PSMA-1007 PET scans including GTV delineations of four expert teams (GTVmanual) and a majority-voted GTV (GTVmajority) were assessed with respect to a registered histopathological GTV (GTVhisto) as the gold standard reference. The standard uptake values (SUVs) per voxel were converted to a percentage (SUV%) relative to the SUVmax. The statistically optimised SUV% threshold (SOST) was defined as those that maximises accuracy for threshold-based contouring. A leave-one-out cross-validation receiver operating characteristic (ROC) curve analysis was performed to determine the SOST for each tracer. The SOST analysis was performed twice, first using the GTVhisto contour as training structure (GTVSOST-H) and second using the GTVmajority contour as training structure (GTVSOST-MA) to correct for any limited misregistration. The accuracy of both GTVSOST-H and GTVSOST-MA was calculated relative to GTVhisto in the 'leave-one-out' patient of each fold and compared with the accuracy of GTVmanual. RESULTS: ROC curve analysis for 68Ga-PSMA-11 PET revealed a median threshold of 25 SUV% (range, 22-27 SUV%) and 41 SUV% (40-43 SUV%) for GTVSOST-H and GTVSOST-MA, respectively. For 18F-PSMA-1007 PET, a median threshold of 42 SUV% (39-45 SUV%) for GTVSOST-H and 44 SUV% (42-45 SUV%) for GTVSOST-MA was found. A significant pairwise difference was observed when comparing the accuracy of the GTVSOST-H contours with the median accuracy of the GTVmanual contours (median, - 2.5%; IQR, - 26.5-0.2%; p = 0.020), whereas no significant pairwise difference was found for the GTVSOST-MA contours (median, - 0.3%; IQR, - 4.4-0.6%; p = 0.199). CONCLUSIONS: Threshold-based contouring using GTVmajority-trained SOSTs achieves an accuracy comparable with manual contours in delineating GTVhisto. The median SOSTs of 41 SUV% for 68Ga-PSMA-11 PET and 44 SUV% for 18F-PSMA-1007 PET form a base for tracer-specific window levelling. TRIAL REGISTRATION: Clinicaltrials.gov ; NCT03327675; 31-10-2017.

A multi-institutional analysis of a general pelvis continuous Hounsfield unit synthetic CT software for radiotherapy.

PURPOSE: To validate a synthetic computed tomography (sCT) software with continuous HUs and large field-of-view (FOV) coverage for magnetic resonance imaging (MRI)-only workflow of general pelvis anatomy in radiotherapy (RT). METHODS: An sCT software for general pelvis anatomy (prostate, rectum, and female pelvis) has been developed by Philips Healthcare and includes continuous HUs assignment along with large FOV coverage. General pelvis sCTs were generated using a two-stack T1-weighted mDixon fast-field echo (FFE) sequence with a superior-inferior coverage of 36 cm. Seventy-seven prostate, 43 rectum, and 27 gynecological cases were scanned by three different institutions. mDixon image quality and sCTs were evaluated for soft tissue contrast by using a confidence level scale from 1 to 5 for bladder, prostate/rectum interface, mesorectum, and fiducial maker visibility. Dosimetric comparison was performed by recalculating the RT plans on the sCT after rigid registration. For 12 randomly selected cases, the mean absolute error (MAE) between sCT and CT was calculated to evaluate HU similarity, and the Pearson correlation coefficients (PCC) between the CT- and sCT-generated digitally reconstructed radiographs (DRRs) were obtained for quantitative comparison. To examine geometric accuracy of sCT as a reference for cone beam CT (CBCT), the difference between bone-based alignment of CBCT to CT and CBCT to sCT was obtained for 19 online-acquired CBCTs from three patients. RESULTS: Two-stack mDixon scans with large FOV did not show any image inhomogeneity or fat-water swap artifact. Fiducials, Foley catheter, and even rectal spacer were visible as dark signal on the sCT. Average visibility confidence level (average ± standard deviation) on the sCT was 5.0 ± 0.0, 4.6 ± 0.5, 3.8 ± 0.4, and 4.0 ± 1.1 for bladder, prostate/rectum interface, mesorectum and fiducial markers. Dosimetric accuracy showed on average < 1% difference with the CT-based plans for target and normal structures. The MAE of bone and soft tissue between the sCT and CT are 120.9 ± 15.4 HU, 33.4 ± 4.1 HU, respectively. Average PCC of all evaluated DRR pairs was 0.975. The average offset between CT and sCT as reference was (LR, AP, SI) = (0.19 ± 0.35, 0.14 ± 0.60, 0.44 ± 0.54) mm. CONCLUSIONS: The continuous HU sCT software-generated realistic sCTs and DRRs to enable MRI-only planning for general pelvis anatomy.

Histopathological Features of MRI-Invisible Regions of Prostate Cancer Lesions.

BACKGROUND: Previous studies have reported tumor volume underestimation with multiparametric (mp)MRI in prostate cancer diagnosis. PURPOSE: To investigate why some parts of lesions are not visible on mpMRI by comparing their histopathology features to those of visible regions. STUDY TYPE: Retrospective. POPULATION: Thirty-four patients with biopsy-proven prostate cancer scheduled for prostatectomy (median 68.7 years). FIELD STRENGTH/SEQUENCE: T2 -weighted, diffusion-weighted imaging, T2 mapping, and dynamic contrast-enhanced MRI on two 3T systems and one 1.5T system. ASSESSMENT: Two readers delineated suspicious lesions on mpMRI. A pathologist delineated the lesions on histopathology. A patient-customized mold enabled the registration of histopathology and MRI. On histopathology we identified mpMRI visible and invisible lesions. Subsequently, within the visible lesions we identified regions that were visible and regions that were invisible on mpMRI. For each lesion and region the following characteristics were determined: size, location, International Society of Urological Pathology (ISUP) grade, and Gleason subpatterns (density [dense/intermediate], tumor morphology [homogeneous/heterogeneous], cribriform growth [yes/no]). STATISTICAL TESTS: With generalized linear mixed-effect modeling we investigated which features explain why a lesion or a region was invisible on MRI. We compared imaging values (T2 , ADC, and Ktrans ) for these features with one-way analysis of variance (ANOVA). RESULTS: Small, anterior, and ISUP grade 1-2 lesions (n = 34) were missed more frequent than large, posterior, ISUP grade ≥ 3 lesions (n = 35). Invisible regions on mpMRI had lower tumor density, heterogeneous tumor morphology, and were located in the transition zone. Both T2 and ADC values were higher in "intermediate" compared with "dense" regions (P = 0.002 and < 0.001) and in regions with heterogeneous compared with homogeneous morphology (P < 0.001 and 0.03). Ktrans was not significantly different (P = 0.24 and 0.99). DATA CONCLUSION: Regions of prostate cancer lesions that are invisible on mpMRI have different histopathology features than visible regions. This may have implications for monitoring during active surveillance and focal treatment strategies. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1235-1246.

Improved repeatability of dynamic contrast-enhanced MRI using the complex MRI signal to derive arterial input functions: a test-retest study in prostate cancer patients.

PURPOSE: The arterial input function (AIF) is a major source of uncertainty in tracer kinetic (TK) analysis of dynamic contrast-enhanced (DCE)-MRI data. The aim of this study was to investigate the repeatability of AIFs extracted from the complex signal and of the resulting TK parameters in prostate cancer patients. METHODS: Twenty-two patients with biopsy-proven prostate cancer underwent a 3T MRI exam twice. DCE-MRI data were acquired with a 3D spoiled gradient echo sequence. AIFs were extracted from the magnitude of the signal (AIFMAGN ), phase (AIFPHASE ), and complex signal (AIFCOMPLEX ). The Tofts model was applied to extract Ktrans , kep and ve . Repeatability of AIF curve characteristics and TK parameters was assessed with the within-subject coefficient of variation (wCV). RESULTS: The wCV for peak height and full width at half maximum for AIFCOMPLEX (7% and 8%) indicated an improved repeatability compared to AIFMAGN (12% and 12%) and AIFPHASE (12% and 7%). This translated in lower wCV values for Ktrans (11%) with AIFCOMPLEX in comparison to AIFMAGN (24%) and AIFPHASE (15%). For kep , the wCV was 16% with AIFMAGN , 13% with AIFPHASE , and 13% with AIFCOMPLEX . CONCLUSION: Repeatability of AIFPHASE and AIFCOMPLEX is higher than for AIFMAGN , resulting in a better repeatability of TK parameters. Thus, use of either AIFPHASE or AIFCOMPLEX improves the robustness of quantitative analysis of DCE-MRI in prostate cancer.

Quantitative 3T multiparametric MRI of benign and malignant prostatic tissue in patients with and without local recurrent prostate cancer after external-beam radiation therapy.

BACKGROUND: Post-radiotherapy locally recurrent prostate cancer (PCa) patients are candidates for focal salvage treatment. Multiparametric MRI (mp-MRI) is attractive for tumor localization. However, radiotherapy-induced tissue changes complicate image interpretation. To develop focal salvage strategies, accurate tumor localization and distinction from benign tissue is necessary. PURPOSE: To quantitatively characterize radio-recurrent tumor and benign radiation-induced changes using mp-MRI, and investigate which sequences optimize the distinction between tumor and benign surroundings. STUDY TYPE: Prospective case-control. SUBJECTS: Thirty-three patients with biochemical failure after external-beam radiotherapy (cases), 35 patients without post-radiotherapy recurrent disease (controls), and 13 patients with primary PCa (untreated). FIELD STRENGTH/SEQUENCES: 3T; quantitative mp-MRI: T2 -mapping, ADC, and Ktrans and kep maps. ASSESSMENT: Quantitative image-analysis of prostatic regions, within and between cases, controls, and untreated patients. STATISTICAL TESTS: Within-groups: nonparametric Friedman analysis of variance with post-hoc Wilcoxon signed-rank tests; between-groups: Mann-Whitney tests. All with Bonferroni corrections. Generalized linear mixed modeling to ascertain the contribution of each map and location to tumor likelihood. RESULTS: Benign imaging values were comparable between cases and controls (P = 0.15 for ADC in the central gland up to 0.91 for kep in the peripheral zone), both with similarly high peri-urethral Ktrans and kep values (min-1 ) (median [range]: Ktrans = 0.22 [0.14-0.43] and 0.22 [0.14-0.36], P = 0.60, kep = 0.43 [0.24-0.57] and 0.48 [0.32-0.67], P = 0.05). After radiotherapy, benign central gland values were significantly decreased for all maps (P ≤ 0.001) as well as T2 , Ktrans , and kep of benign peripheral zone (all with P ≤ 0.002). All imaging maps distinguished recurrent tumor from benign peripheral zone, but only ADC, Ktrans , and kep were able to distinguish it from benign central gland. Recurrent tumor and peri-urethral Ktrans values were not significantly different (P = 0.81), but kep values were (P < 0.001). Combining all quantitative maps and voxel location resulted in an optimal distinction between tumor and benign voxels. DATA CONCLUSION: Mp-MRI can distinguish recurrent tumor from benign tissue. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:269-278.

Quantitative 3-T multi-parametric MRI and step-section pathology of recurrent prostate cancer patients after radiation therapy.

OBJECTIVES: Diagnosis of radio-recurrent prostate cancer using multi-parametric MRI (mp-MRI) can be challenging due to the presence of radiation effects. We aim to characterize imaging of prostate tissue after radiation therapy (RT), using histopathology as ground truth, and to investigate the visibility of tumor lesions on mp-MRI. METHODS: Tumor delineated histopathology slides from salvage radical prostatectomy patients, primarily treated with RT, were registered to MRI. Median T2-weighted, ADC, Ktrans, and kep values in tumor and other regions were calculated. Two radiologists independently performed mp-MRI-based tumor delineations which were compared with the true pathological extent. General linear mixed-effect modeling was used to establish the contribution of each imaging modality and combinations thereof in distinguishing tumor and benign voxels. RESULTS: Nineteen of the 21 included patients had tumor in the available histopathology slides. Recurrence was predominantly multifocal with large tumor foci seen after external beam radiotherapy, whereas these were small and sparse after low-dose-rate brachytherapy. MRI-based delineations missed small foci and slightly underestimated tumor extent. The combination of T2-weighted, ADC, Ktrans, and kep had the best performance in distinguishing tumor and benign voxels. CONCLUSIONS: Using high-resolution histopathology delineations, the real tumor extent and size were found to be underestimated on MRI. mp-MRI obtained the best performance in identifying tumor voxels. Appropriate margins around the visible tumor-suspected region should be included when designing focal salvage strategies. Recurrent tumor delineation guidelines are warranted. KEY POINTS: • Compared to the use of individual sequences, multi-parametric MRI obtained the best performance in distinguishing recurrent tumor from benign voxels. • Delineations based on mp-MRI miss smaller foci and slightly underestimate tumor volume of local recurrent prostate cancer. • Focal salvage strategies should include appropriate margins around the visible tumor.

Performance of a fast and high-resolution multi-echo spin-echo sequence for prostate T2 mapping across multiple systems.

PURPOSE: To evaluate the performance of a multi-echo spin-echo sequence with k-t undersampling scheme (k-t T2 ) in prostate cancer. METHODS: Phantom experiments were performed at five systems to estimate the bias, short-term repeatability, and reproducibility across all systems expressed with the within-subject coefficient of variation (wCV). Monthly measurements were performed on two systems for long-term repeatability estimation. To evaluate clinical repeatability, two T2 maps (voxel size 0.8 × 0.8 × 3 mm3 ; 5 min) were acquired at separate visits on one system for 13 prostate cancer patients. Repeatability was assessed per patient in relation to spatial resolution. T2 values were compared for tumor, peripheral zone, and transition zone. RESULTS: Phantom measurements showed a small bias (median = -0.9 ms) and good short-term repeatability (median wCV = 0.5%). Long-term repeatability was 0.9 and 1.1% and reproducibility between systems was 1.7%. The median bias observed in patients was -1.1 ms. At voxel level, the median wCV was 15%, dropping to 4% for structures of 0.5 cm3 . The median tumor T2 values (79 ms) were significantly lower (P < 0.001) than in the peripheral zone (149 ms), but overlapped with the transition zone (91 ms). CONCLUSIONS: Reproducible T2 mapping of the prostate is feasible with good spatial resolution in a clinically reasonable scan time, allowing reliable measurement of T2 in structures as small as 0.5 cm3 . Magn Reson Med 79:1586-1594, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Impact of contrast agent injection duration on dynamic contrast-enhanced MRI quantification in prostate cancer.

The volume transfer constant Ktrans , which describes the leakage of contrast agent (CA) from vasculature into tissue, is the most commonly reported quantitative parameter for dynamic contrast-enhanced (DCE-) MRI. However, the variation in reported Ktrans values between studies from different institutes is large. One of the primary sources of uncertainty is quantification of the arterial input function (AIF). The aim of this study is to determine the influence of the CA injection duration on the AIF and tracer kinetic analysis (TKA) parameters (i.e. Ktrans , kep and ve ). Thirty-one patients with prostate cancer received two DCE-MRI examinations with an injection duration of 5 s in the first examination and a prolonged injection duration in the second examination, varying between 7.5 s and 30 s. The DCE examination was carried out on a 3.0 T MRI scanner using a transversal T1 -weighted 3D spoiled gradient echo sequence (300 s duration, dynamic scan time of 2.5 s). Data of 29 of the 31 were further analysed. AIFs were determined from the phase signal in the left and right femoral arteries. Ktrans , kep and ve were estimated with the standard Tofts model for regions of healthy peripheral zone and tumour tissue. We observed a significantly smaller peak height and increased width in the AIF for injection durations of 15 s and longer. However, we did not find significant differences in Ktrans , kep or ve for the studied injection durations. The study demonstrates that the TKA parameters Ktrans , kep and ve , measured in the prostate, do not show a significant change as a function of injection duration.

Establishing implantation uncertainties for focal brachytherapy with I-125 seeds for the treatment of localized prostate cancer.

BACKGROUND: The efficacy of focal continuous low dose-rate brachytherapy (CLDR-BT) for prostate cancer requires that appropriate margins are applied to ensure robust target coverage. In this study we propose a method to establish such margins by emulating a focal treatment in patients treated with CLDR-BT to the entire gland. MATERIAL AND METHODS: In 15 patients with localized prostate cancer, prostate volumes and dominant intra-prostatic lesions were delineated on pre-treatment magnetic resonance imaging (MRI). Delineations and MRI were registered to trans-rectal ultrasound images in the operating theater. The patients received CLDR-BT treatment to the total prostate volume. The implantation consisted of two parts: an experimental focal plan covering the dominant intra-prostatic lesion (F-GTV), followed by a plan containing additional seeds to achieve entire prostate coverage. Isodose surfaces were reconstructed using follow-up computed tomography (CT). The focal dose was emulated by reconstructing seeds from the focal plan only. The distance to agreement between planned and delivered isodose surfaces and F-GTV coverage was determined to calculate the margin required for robust treatment. RESULTS: If patients had been treated only focally, the target volume would have been reduced from an average of 40.9 cm3 for the entire prostate to 5.8 cm3 for the focal plan. The D90 for the F-GTV in the focal plan was 195±60 Gy, the V100 was 94% [range 71-100%]. The maximum distance (cd95) between the planned and delivered isodose contours was 0.48 cm. CONCLUSIONS: This study provides an estimate of 0.5 cm for the margin required for robust coverage of a focal target volume prior to actually implementing a focal treatment protocol.

(31) P MR spectroscopic imaging combined with (1) H MR spectroscopic imaging in the human prostate using a double tuned endorectal coil at 7T.

PURPOSE: Improved diagnostic sensitivity could be obtained in cancer detection and staging when individual compounds of the choline pool can be detected. Therefore, a novel coil design is proposed, providing the ability to acquire both (1) H and (31) P magnetic resonance spectroscopic imaging (MRSI) in patients with prostate cancer. METHODS: A two-element (1) H/(31) P endorectal coil was designed by adjusting a commercially available 3T endorectal coil. The two-element coil setup was interfaced as a transceiver to a whole body 7T MR scanner. Simulations and phantom measurements were performed to compare the efficiency of the coil. (1) H MRSI and (31) P MRSI were acquired in vivo in prostate cancer patients. RESULTS: The efficiency of the (1) H/(31) P coil is comparable to the dual channel (1) H coil previously published. Individually distinguishable phospholipid metabolites in the in vivo (31) P spectra were: phosphoethanolamine, phosphocholine, phosphate, glycerophosphoethanolamine, glycerophosphocholine, phosphocreatine, and adenosine triposphate. (1) H MRSI was performed within the same scan session, visualizing choline, polyamines, creatine, and citrate. CONCLUSION: (1) H MRSI and (31) P MRSI can be acquired in the human prostate at 7T within the same scan session using an endorectal coil matched and tuned for (1) H (quadrature) and (31) P (linear) without the need of cable traps and with negligible efficiency losses in the (1) H and (31) P channel.

Accurate prostate tumour detection with multiparametric magnetic resonance imaging: dependence on histological properties.

BACKGROUND: To benefit most of focal treatment of prostate tumours, detection with high precision of all tumour voxels is needed. Although diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have good diagnostic performance, perfect tumour detection is challenging. In this study, we investigated the variation in prostate tissue characteristics Gleason score (GS), cell density (CD) and microvessel density (MVD) to explain the limitations in tumour voxel detection with a MRI-based logistic regression model. MATERIAL AND METHODS: Twelve radical prostatectomy patients underwent a pre-operative 3.0T DWI and DCE-MRI exam. The MRI scans were used to calculate voxel-wise tumour probability with a logistic regression model for the peripheral zone (PZ) of the prostate. Tumour probability maps were correlated and validated with whole-mount histology. Additionally, from the whole-mount histological sections CD, MVD and GS were retrieved for every single voxel. GS, CD and MVD of true- and false-positive voxels and of true- and false-negative voxels were compared using Mann-Whitney U-tests. RESULTS: False-negative tumour voxels had significantly lower CD and MVD (p < 0.0001) and were similar to non-tumour PZ. True-positive detected tumour voxels had high CDs and MVDs (p < 0.0001). In addition, tumour voxels with higher GS showed a trend towards more frequent detection (p = 0.06). Tumour voxels with GS ≥ 3 + 4 showed higher CD and MVD compared to tumour voxels with GS 3 + 3 (p < 0.0001). CONCLUSION: Tumour voxels with low CD and MVD resemble healthy tissue and are limiting tumour voxel detection using DWI and DCE-MRI. Nevertheless, the most aggressive tumour voxels, containing high CD, MVD and GS, are more likely to be detected and can therefore be treated with high dose using focal therapy or focal boosting.

Quantitative MRI on MR-Linacs: Towards Biological Image-Guided Adaptive Radiotherapy.

Recognizing the potential of quantitative imaging biomarkers (QIBs) in radiotherapy, many studies have investigated the prognostic value of quantitative MRI (qMRI). With the introduction of MRI-guided radiotherapy systems, the practical challenges of repeated imaging have been substantially reduced. Since patients are treated inside an MRI scanner, acquisition of qMRI can be done during each fraction with limited or no prolongation of the fraction duration. In this review paper, we identify the steps that need been taken to move from MR as an imaging technique to a useful biomarker for MRI-guided radiotherapy (MRgRT).

Evaluation of Deep Learning Clinical Target Volumes Auto-Contouring for Magnetic Resonance Imaging-Guided Online Adaptive Treatment of Rectal Cancer.

Purpose: Segmentation of clinical target volumes (CTV) on medical images can be time-consuming and is prone to interobserver variation (IOV). This is a problem for online adaptive radiation therapy, where CTV segmentation must be performed every treatment fraction, leading to longer treatment times and logistic challenges. Deep learning (DL)-based auto-contouring has the potential to speed up CTV contouring, but its current clinical use is limited. One reason for this is that it can be time-consuming to verify the accuracy of CTV contours produced using auto-contouring, and there is a risk of bias being introduced. To be accepted by clinicians, auto-contouring must be trustworthy. Therefore, there is a need for a comprehensive commissioning framework when introducing DL-based auto-contouring in clinical practice. We present such a framework and apply it to an in-house developed DL model for auto-contouring of the CTV in rectal cancer patients treated with MRI-guided online adaptive radiation therapy. Methods and Materials: The framework for evaluating DL-based auto-contouring consisted of 3 steps: (1) Quantitative evaluation of the model's performance and comparison with IOV; (2) Expert observations and corrections; and (3) Evaluation of the impact on expected volumetric target coverage. These steps were performed on independent data sets. The framework was applied to an in-house trained nnU-Net model, using the data of 44 rectal cancer patients treated at our institution. Results: The framework established that the model's performance after expert corrections was comparable to IOV, and although the model introduced a bias, this had no relevant impact on clinical practice. Additionally, we found a substantial time gain without reducing quality as determined by volumetric target coverage. Conclusions: Our framework provides a comprehensive evaluation of the performance and clinical usability of target auto-contouring models. Based on the results, we conclude that the model is eligible for clinical use.

Clinical Impact of Contouring Variability for Prostate Cancer Tumor Boost.

PURPOSE: The focal radiotherapy (RT) boost technique was shown in a phase III randomized controlled trial (RCT) to improve prostate cancer outcomes without increasing toxicity. This technique relies on the accurate delineation of prostate tumors on MRI. A recent prospective study evaluated radiation oncologists' accuracy when asked to delineate prostate tumors on MRI and demonstrated high variability in tumor contours. We sought to evaluate the impact of contour variability and inaccuracy on predicted clinical outcomes. We hypothesized that radiation oncologists' contour inaccuracies would yield meaningfully worse clinical outcomes. MATERIALS & METHODS: 45 radiation oncologists and 2 expert radiologists contoured prostate tumors on 30 patient cases. Of these cases, those with CT simulation or diagnostic CT available were selected for analysis. A knowledge-based planning model was developed to generate focal RT boost plans for each contour per the RCT protocol. Probability of biochemical failure (BF) was determined using a model from the RCT. The primary metric evaluated was delta BF (ΔBF = Participant BF - Expert BF). An absolute increase in BF ≥5% was considered clinically meaningful. RESULTS: 8 patient cases and 394 target volumes for focal RT boost planning were included in this analysis. In general, participant plans were associated with worse predicted clinical outcomes compared to the expert plan, with an average absolute increase in BF of 4.3%. 37% of participant plans were noted to have an absolute increase in BF of 5% or more. CONCLUSION: Radiation oncologists' attempts to contour tumor targets for focal RT boost are frequently inaccurate enough to yield meaningfully inferior clinical outcomes for patients.

Safety and Tolerability of Online Adaptive High-Field Magnetic Resonance-Guided Radiotherapy.

Importance: In 2018, the first online adaptive magnetic resonance (MR)-guided radiotherapy (MRgRT) system using a 1.5-T MR-equipped linear accelerator (1.5-T MR-Linac) was clinically introduced. This system enables online adaptive radiotherapy, in which the radiation plan is adapted to size and shape changes of targets at each treatment session based on daily MR-visualized anatomy. Objective: To evaluate safety, tolerability, and technical feasibility of treatment with a 1.5-T MR-Linac, specifically focusing on the subset of patients treated with an online adaptive strategy (ie, the adapt-to-shape [ATS] approach). Design, Setting, and Participants: This cohort study included adults with solid tumors treated with a 1.5-T MR-Linac enrolled in Multi Outcome Evaluation for Radiation Therapy Using the MR-Linac (MOMENTUM), a large prospective international study of MRgRT between February 2019 and October 2021. Included were adults with solid tumors treated with a 1.5-T MR-Linac. Data were collected in Canada, Denmark, The Netherlands, United Kingdom, and the US. Data were analyzed in August 2023. Exposure: All patients underwent MRgRT using a 1.5-T MR-Linac. Radiation prescriptions were consistent with institutional standards of care. Main Outcomes and Measures: Patterns of care, tolerability, and technical feasibility (ie, treatment completed as planned). Acute high-grade radiotherapy-related toxic effects (ie, grade 3 or higher toxic effects according to Common Terminology Criteria for Adverse Events version 5.0) occurring within the first 3 months after treatment delivery. Results: In total, 1793 treatment courses (1772 patients) were included (median patient age, 69 years [range, 22-91 years]; 1384 male [77.2%]). Among 41 different treatment sites, common sites were prostate (745 [41.6%]), metastatic lymph nodes (233 [13.0%]), and brain (189 [10.5%]). ATS was used in 1050 courses (58.6%). MRgRT was completed as planned in 1720 treatment courses (95.9%). Patient withdrawal caused 5 patients (0.3%) to discontinue treatment. The incidence of radiotherapy-related grade 3 toxic effects was 1.4% (95% CI, 0.9%-2.0%) in the entire cohort and 0.4% (95% CI, 0.1%-1.0%) in the subset of patients treated with ATS. There were no radiotherapy-related grade 4 or 5 toxic effects. Conclusions and Relevance: In this cohort study of patients treated on a 1.5-T MR-Linac, radiotherapy was safe and well tolerated. Online adaptation of the radiation plan at each treatment session to account for anatomic variations was associated with a low risk of acute grade 3 toxic effects.

Why prostate tumour delineation based on apparent diffusion coefficient is challenging: an exploration of the tissue microanatomy.

BACKGROUND: Focal boosting of prostate tumours to improve outcome, requires accurate tumour delineation. For this, the apparent diffusion coefficient (ADC) derived from diffusion-weighted MR imaging (DWI) seems a useful tool. On voxel level, the relationship between ADC and histological presence of tumour is, however, ambiguous. Therefore, in this study the relationship between ADC and histological variables was investigated on voxel level to understand the strengths and limitations of DWI for prostate tumour delineation. MATERIAL AND METHODS: Twelve radical prostatectomy patients underwent a pre-operative 3.0T DWI exam and the ADC was calculated. From whole-mount histological sections cell density and glandular area were retrieved for every voxel. The distribution of all variables was described for tumour, peripheral zone (PZ) and central gland (CG) on regional and voxel level. Correlations between variables and differences between regions were calculated. RESULTS: Large heterogeneity of ADC on voxel level was observed within tumours, between tumours and between patients. This heterogeneity was reflected by the distribution of cell density and glandular area. On regional level, tumour was different from PZ having higher cell density (p = 0.007), less glandular area (p = 0.017) and lower ADCs (p = 0.017). ADC was correlated with glandular area (r = 0.402) and tumour volume (r = -0.608), but not with Gleason score. ADC tended to decrease with increasing cell density (r = -0.327, p = 0.073). On voxel level, correlations between ADC and histological variables varied among patients, for cell density ranging from r = -0.439 to r = 0.261 and for glandular area from r = 0.593 to r = 0.207. CONCLUSIONS: The variation in ADC can to a certain extent be explained by the variation in cell density and glandular area. The ADC is highly heterogeneous, which reflects the heterogeneity of malignant and benign prostate tissue. This heterogeneity might however obscure small tumours or parts of tumours. Therefore, DWI has to be used in the context of multiparametric MRI.

Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy.

BACKGROUND: Segmentation of the Gross Tumor Volume (GTV) is a crucial step in the brachytherapy (BT) treatment planning workflow. Currently, radiation oncologists segment the GTV manually, which is time-consuming. The time pressure is particularly critical for BT because during the segmentation process the patient waits immobilized in bed with the applicator in place. Automatic segmentation algorithms can potentially reduce both the clinical workload and the patient burden. Although deep learning based automatic segmentation algorithms have been extensively developed for organs at risk, automatic segmentation of the targets is less common. The aim of this study was to automatically segment the cervical cancer GTV on BT MRI images using a state-of-the-art automatic segmentation framework and assess its performance. METHODS: A cohort of 195 cervical cancer patients treated between August 2012 and December 2021 was retrospectively collected. A total of 524 separate BT fractions were included and the axial T2-weighted (T2w) MRI sequence was used for this project. The 3D nnU-Net was used as the automatic segmentation framework. The automatic segmentations were compared with the manual segmentations used for clinical practice with Sørensen-Dice coefficient (Dice), 95th Hausdorff distance (95th HD) and mean surface distance (MSD). The dosimetric impact was defined as the difference in D98 (ΔD98) and D90 (ΔD90) between the manual segmentations and the automatic segmentations, evaluated using the clinical dose distribution. The performance of the network was also compared separately depending on FIGO stage and on GTV volume. RESULTS: The network achieved a median Dice of 0.73 (interquartile range (IQR) = 0.50-0.80), median 95th HD of 6.8 mm (IQR = 4.2-12.5 mm) and median MSD of 1.4 mm (IQR = 0.90-2.8 mm). The median ΔD90 and ΔD98 were 0.18 Gy (IQR = -1.38-1.19 Gy) and 0.20 Gy (IQR =-1.10-0.95 Gy) respectively. No significant differences in geometric or dosimetric performance were observed between tumors with different FIGO stages, however significantly improved Dice and dosimetric performance was found for larger tumors. CONCLUSIONS: The nnU-Net framework achieved state-of-the-art performance in the segmentation of the cervical cancer GTV on BT MRI images. Reasonable median performance was achieved geometrically and dosimetrically but with high variability among patients.