Tumour-informed liquid biopsies to monitor advanced melanoma patients under immune checkpoint inhibition.

Immune checkpoint inhibitors (ICI) have significantly improved overall survival in melanoma patients. However, 60% experience severe adverse events and early response markers are lacking. Circulating tumour DNA (ctDNA) is a promising biomarker for treatment-response and recurrence detection. The prospective PET/LIT study included 104 patients with palliative combined or adjuvant ICI. Tumour-informed sequencing panels to monitor 30 patient-specific variants were designed and 321 liquid biopsies of 87 patients sequenced. Mean sequencing depth after deduplication using UMIs was 6000x and the error rate of UMI-corrected reads was 2.47×10-4. Variant allele fractions correlated with PET/CT MTV (rho=0.69), S100 (rho=0.72), and LDH (rho=0.54). A decrease of allele fractions between T1 and T2 was associated with improved PFS and OS in the palliative cohort (p = 0.008 and p < 0.001). ctDNA was detected in 76.9% of adjuvant patients with relapse (n = 10/13), while all patients without progression (n = 9) remained ctDNA negative. Tumour-informed liquid biopsies are a reliable tool for monitoring treatment response and early relapse in melanoma patients with ICI.

Can Delta Radiomics Improve the Prediction of Best Overall Response, Progression-Free Survival, and Overall Survival of Melanoma Patients Treated with Immune Checkpoint Inhibitors?

BACKGROUND: The prevalence of metastatic melanoma is increasing, necessitating the identification of patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker based on the segmentation of all metastases at baseline and the first follow-up CT for the endpoints best overall response (BOR), progression-free survival (PFS), and overall survival (OS), encompassing various immunotherapies. Additionally, this study investigated whether reducing the number of segmented metastases per patient affects predictive capacity. METHODS: The total tumour load, excluding cerebral metastases, from 146 baseline and 146 first follow-up CTs of melanoma patients treated with first-line immunotherapy was volumetrically segmented. Twenty-one random forest models were trained and compared for the endpoints BOR; PFS at 6, 9, and 12 months; and OS at 6, 9, and 12 months, using as input either only clinical parameters, whole-tumour-load delta radiomics plus clinical parameters, or delta radiomics from the largest ten metastases plus clinical parameters. RESULTS: The whole-tumour-load delta radiomics model performed best for BOR (AUC 0.81); PFS at 6, 9, and 12 months (AUC 0.82, 0.80, and 0.77); and OS at 6 months (AUC 0.74). The model using delta radiomics from the largest ten metastases performed best for OS at 9 and 12 months (AUC 0.71 and 0.75). Although the radiomic models were numerically superior to the clinical model, statistical significance was not reached. CONCLUSIONS: The findings indicate that delta radiomics may offer additional value for predicting BOR, PFS, and OS in metastatic melanoma patients undergoing first-line immunotherapy. Despite its complexity, volumetric whole-tumour-load segmentation could be advantageous.

Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow.

PURPOSE: AI-assisted techniques for lesion registration and segmentation have the potential to make CT-based tumor follow-up assessment faster and less reader-dependent. However, empirical evidence on the advantages of AI-assisted volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans is lacking. The aim of this study was to assess the efficiency, quality, and inter-reader variability of an AI-assisted workflow for volumetric segmentation of lymph node and soft tissue metastases in follow-up CT scans. Three hypotheses were tested: (H1) Assessment time for follow-up lesion segmentation is reduced using an AI-assisted workflow. (H2) The quality of the AI-assisted segmentation is non-inferior to the quality of fully manual segmentation. (H3) The inter-reader variability of the resulting segmentations is reduced with AI assistance. MATERIALS AND METHODS: The study retrospectively analyzed 126 lymph nodes and 135 soft tissue metastases from 55 patients with stage IV melanoma. Three radiologists from two institutions performed both AI-assisted and manual segmentation, and the results were statistically analyzed and compared to a manual segmentation reference standard. RESULTS: AI-assisted segmentation reduced user interaction time significantly by 33% (222 s vs. 336 s), achieved similar Dice scores (0.80-0.84 vs. 0.81-0.82) and decreased inter-reader variability (median Dice 0.85-1.0 vs. 0.80-0.82; ICC 0.84 vs. 0.80), compared to manual segmentation. CONCLUSION: The findings of this study support the use of AI-assisted registration and volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans. The AI-assisted workflow achieved significant time savings, similar segmentation quality, and reduced inter-reader variability compared to manual segmentation.

Applications of Artificial Intelligence for Pediatric Cancer Imaging.

Artificial intelligence (AI) is transforming the medical imaging of adult patients. However, its utilization in pediatric oncology imaging remains constrained, in part due to the inherent scarcity of data associated with childhood cancers. Pediatric cancers are rare, and imaging technologies are evolving rapidly, leading to insufficient data of a particular type to effectively train these algorithms. The small market size of pediatric patients compared with adult patients could also contribute to this challenge, as market size is a driver of commercialization. This review provides an overview of the current state of AI applications for pediatric cancer imaging, including applications for medical image acquisition, processing, reconstruction, segmentation, diagnosis, staging, and treatment response monitoring. Although current developments are promising, impediments due to the diverse anatomies of growing children and nonstandardized imaging protocols have led to limited clinical translation thus far. Opportunities include leveraging reconstruction algorithms to achieve accelerated low-dose imaging and automating the generation of metric-based staging and treatment monitoring scores. Transfer learning of adult-based AI models to pediatric cancers, multiinstitutional data sharing, and ethical data privacy practices for pediatric patients with rare cancers will be keys to unlocking the full potential of AI for clinical translation and improving outcomes for these young patients.

Pre-treatment 18F-FDG-PET/CT parameters as biomarkers for progression free survival, best overall response and overall survival in metastatic melanoma patients undergoing first-line immunotherapy.

BACKGROUND: Checkpoint inhibitors have drastically improved the therapy of patients with advanced melanoma. 18F-FDG-PET/CT parameters might act as biomarkers for response and survival and thus can identify patients that do not benefit from immunotherapy. However, little literature exists on the association of baseline 18F-FDG-PET/CT parameters with progression free survival (PFS), best overall response (BOR), and overall survival (OS). MATERIALS AND METHODS: Using a whole tumor volume segmentation approach, we investigated in a retrospective registry study (n = 50) whether pre-treatment 18F-FDG-PET/CT parameters of three subgroups (tumor burden, tumor glucose uptake and non-tumoral hematopoietic tissue metabolism), can act as biomarkers for the primary endpoints PFS and BOR as well as for the secondary endpoint OS. RESULTS: Compared to the sole use of clinical parameters, baseline 18F-FDG-PET/CT parameters did not significantly improve a Cox proportional-hazard model for PFS (C-index/AIC: 0.70/225.17 and 0.68/223.54, respectively; p = 0.14). A binomial logistic regression analysis for BOR was not statistically significant (χ2(15) = 16.44, p = 0.35), with a low amount of explained variance (Nagelkerke's R2 = 0.38). Mean FDG uptake of the spleen contributed significantly to a Cox proportional-hazard model for OS (HR 3.55, p = 0.04). CONCLUSIONS: The present study could not confirm the capability of the pre-treatment 18F-FDG-PET/CT parameters tumor burden, tumor glucose uptake and non-tumoral hematopoietic tissue metabolism to act as biomarkers for PFS and BOR in metastatic melanoma patients receiving first-line immunotherapy. The documented potential of 18F-FDG uptake by immune-mediating tissues such as the spleen to act as a biomarker for OS has been reproduced.

Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study.

BACKGROUND: The aim of this study was to investigate whether the combination of radiomics and clinical parameters in a machine-learning model offers additive information compared with the use of only clinical parameters in predicting the best response, progression-free survival after six months, as well as overall survival after six and twelve months in patients with stage IV malignant melanoma undergoing first-line targeted therapy. METHODS: A baseline machine-learning model using clinical variables (demographic parameters and tumor markers) was compared with an extended model using clinical variables and radiomic features of the whole tumor burden, utilizing repeated five-fold cross-validation. Baseline CTs of 91 stage IV malignant melanoma patients, all treated in the same university hospital, were identified in the Central Malignant Melanoma Registry and all metastases were volumetrically segmented (n = 4727). RESULTS: Compared with the baseline model, the extended radiomics model did not add significantly more information to the best-response prediction (AUC [95% CI] 0.548 (0.188, 0.808) vs. 0.487 (0.139, 0.743)), the prediction of PFS after six months (AUC [95% CI] 0.699 (0.436, 0.958) vs. 0.604 (0.373, 0.867)), or the overall survival prediction after six and twelve months (AUC [95% CI] 0.685 (0.188, 0.967) vs. 0.766 (0.433, 1.000) and AUC [95% CI] 0.554 (0.163, 0.781) vs. 0.616 (0.271, 1.000), respectively). CONCLUSIONS: The results showed no additional value of baseline whole-body CT radiomics for best-response prediction, progression-free survival prediction for six months, or six-month and twelve-month overall survival prediction for stage IV melanoma patients receiving first-line targeted therapy. These results need to be validated in a larger cohort.

Artificial Intelligence in Oncological Hybrid Imaging.

BACKGROUND: Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes. METHODS AND RESULTS: The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations. CONCLUSION: AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation. KEY POINTS: · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

Impact of MRI on target volume definition in head and neck cancer patients.

BACKGROUND: Target volume definition for curative radiochemotherapy in head and neck cancer is crucial since the predominant recurrence pattern is local. Additional diagnostic imaging like MRI is increasingly used, yet it is usually hampered by different patient positioning compared to radiotherapy. In this study, we investigated the impact of diagnostic MRI in treatment position for target volume delineation. METHODS: We prospectively analyzed patients who were suitable and agreed to undergo an MRI in treatment position with immobilization devices prior to radiotherapy planning from 2017 to 2019. Target volume delineation for the primary tumor was first performed using all available information except for the MRI and subsequently with additional consideration of the co-registered MRI. The derived volumes were compared by subjective visual judgment and by quantitative mathematical methods. RESULTS: Sixteen patients were included and underwent the planning CT, MRI and subsequent definitive radiochemotherapy. In 69% of the patients, there were visually relevant changes to the gross tumor volume (GTV) by use of the MRI. In 44%, the GTV_MRI would not have been covered completely by the planning target volume (PTV) of the CT-only contour. Yet, median Hausdorff und DSI values did not reflect these differences. The 3-year local control rate was 94%. CONCLUSIONS: Adding a diagnostic MRI in RT treatment position is feasible and results in relevant changes in target volumes in the majority of patients.

AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans.

Purpose: To develop a deep learning approach that enables ultra-low-dose, 1% of the standard clinical dosage (3 MBq/kg), ultrafast whole-body PET reconstruction in cancer imaging. Materials and Methods: In this Health Insurance Portability and Accountability Act-compliant study, serial fluorine 18-labeled fluorodeoxyglucose PET/MRI scans of pediatric patients with lymphoma were retrospectively collected from two cross-continental medical centers between July 2015 and March 2020. Global similarity between baseline and follow-up scans was used to develop Masked-LMCTrans, a longitudinal multimodality coattentional convolutional neural network (CNN) transformer that provides interaction and joint reasoning between serial PET/MRI scans from the same patient. Image quality of the reconstructed ultra-low-dose PET was evaluated in comparison with a simulated standard 1% PET image. The performance of Masked-LMCTrans was compared with that of CNNs with pure convolution operations (classic U-Net family), and the effect of different CNN encoders on feature representation was assessed. Statistical differences in the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and visual information fidelity (VIF) were assessed by two-sample testing with the Wilcoxon signed rank t test. Results: The study included 21 patients (mean age, 15 years ± 7 [SD]; 12 female) in the primary cohort and 10 patients (mean age, 13 years ± 4; six female) in the external test cohort. Masked-LMCTrans-reconstructed follow-up PET images demonstrated significantly less noise and more detailed structure compared with simulated 1% extremely ultra-low-dose PET images. SSIM, PSNR, and VIF were significantly higher for Masked-LMCTrans-reconstructed PET (P < .001), with improvements of 15.8%, 23.4%, and 186%, respectively. Conclusion: Masked-LMCTrans achieved high image quality reconstruction of 1% low-dose whole-body PET images.Keywords: Pediatrics, PET, Convolutional Neural Network (CNN), Dose Reduction Supplemental material is available for this article. © RSNA, 2023.

Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data.

In oncology, intratumoural heterogeneity is closely linked with the efficacy of therapy, and can be partially characterized via tumour biopsies. Here we show that intratumoural heterogeneity can be characterized spatially via phenotype-specific, multi-view learning classifiers trained with data from dynamic positron emission tomography (PET) and multiparametric magnetic resonance imaging (MRI). Classifiers trained with PET-MRI data from mice with subcutaneous colon cancer quantified phenotypic changes resulting from an apoptosis-inducing targeted therapeutic and provided biologically relevant probability maps of tumour-tissue subtypes. When applied to retrospective PET-MRI data of patients with liver metastases from colorectal cancer, the trained classifiers characterized intratumoural tissue subregions in agreement with tumour histology. The spatial characterization of intratumoural heterogeneity in mice and patients via multimodal, multiparametric imaging aided by machine-learning may facilitate applications in precision oncology.

Artificial Intelligence in Oncological Hybrid Imaging.

BACKGROUND: Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes. METHODS AND RESULTS: The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations. CONCLUSION: AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation. KEY POINTS: · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making.. CITATION FORMAT: · Feuerecker B, Heimer M, Geyer T et al. Artificial Intelligence in Oncological Hybrid Imaging. Fortschr Röntgenstr 2023; 195: 105 - 114.

Impact of endorectal filling on interobserver variability of MRI based rectal primary tumor delineation.

Background: Online adaptive MR-guided radiotherapy allows for the reduction of safety margins in dose escalated treatment of rectal tumors. With the use of smaller margins, precise tumor delineation becomes more critical. In the present study we investigated the impact of rectal ultrasound gel filling on interobserver variability in delineation of primary rectal tumor volumes. Methods: Six patients with locally advanced rectal cancer were scanned on a 1.5 T MRI-Linac without (MRI_e) and with application of 100 cc of ultrasound gel transanally (MRI_f). Eight international radiation oncologists expert in the treatment of gastrointestinal cancers delineated the gross tumor volume (GTV) on both MRI scans. MRI_f scans were provided to the participating centers after MRI_e scans had been returned. Interobserver variability was analyzed by either comparing the observers' delineations with a reference delineation (approach 1) and by building all possible pairs between observers (approach 2). Dice Similarity Index (DICE) and 95 % Hausdorff-Distance (95 %HD) were calculated. Results: Rectal ultrasound gel filling was well tolerated by all patients. Overall, interobserver agreement was superior in MRI_f scans based on median DICE (0.81 vs 0.74, p < 0.005 for approach 1 and 0.76 vs 0.64, p < 0.0001 for approach 2) and 95 %HD (6.9 mm vs 4.2 mm for approach 1, p = 0.04 and 8.9 mm vs 6.1 mm, p = 0.04 for approach 2). Delineated median tumor volumes and inter-quartile ranges were 26.99 cc [18.01-50.34 cc] in MRI_e and 44.20 [19.72-61.59 cc] in MRI_f scans respectively, p = 0.012. Conclusions: Although limited by the small number of patients, in this study the application of rectal ultrasound gel resulted in higher interobserver agreement in rectal GTV delineation. The endorectal gel filling might be a useful tool for future dose escalation strategies.

Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC.

BACKGROUND: Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. METHODS: A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. RESULTS/CONCLUSIONS: Items with ≥ 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with ≤ 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified.

A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions.

We describe a publicly available dataset of annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies. 1014 whole body Fluorodeoxyglucose (FDG)-PET/CT datasets (501 studies of patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) and 513 studies without PET-positive malignant lesions (negative controls)) acquired between 2014 and 2018 were included. All examinations were acquired on a single, state-of-the-art PET/CT scanner. The imaging protocol consisted of a whole-body FDG-PET acquisition and a corresponding diagnostic CT scan. All FDG-avid lesions identified as malignant based on the clinical PET/CT report were manually segmented on PET images in a slice-per-slice (3D) manner. We provide the anonymized original DICOM files of all studies as well as the corresponding DICOM segmentation masks. In addition, we provide scripts for image processing and conversion to different file formats (NIfTI, mha, hdf5). Primary diagnosis, age and sex are provided as non-imaging information. We demonstrate how this dataset can be used for deep learning-based automated analysis of PET/CT data and provide the trained deep learning model.

Self-supervised learning for automated anatomical tracking in medical image data with minimal human labeling effort.

BACKGROUND AND OBJECTIVE: Tracking of anatomical structures in time-resolved medical image data plays an important role for various tasks such as volume change estimation or treatment planning. State-of-the-art deep learning techniques for automated tracking, while providing accurate results, require large amounts of human-labeled training data making their wide-spread use time- and resource-intensive. Our contribution in this work is the implementation and adaption of a self-supervised learning (SSL) framework that addresses this bottleneck of training data generation. METHODS: To this end we adapted and implemented an SSL framework that allows for automated anatomical tracking without the necessity for human-labeled training data. We evaluated this method by comparison to conventional- and deep learning optical flow (OF)-based tracking methods. We applied all methods on three different time-resolved medical image datasets (abdominal MRI, cardiac MRI, and echocardiography) and assessed their accuracy regarding tracking of pre-defined anatomical structures within and across individuals. RESULTS: We found that SSL-based tracking as well as OF-based methods provide accurate results for simple, rigid and smooth motion patterns. However, regarding more complex motion, e.g. non-rigid or discontinuous motion patterns in the cardiac region, and for cross-subject anatomical matching, SSL-based tracking showed markedly superior performance. CONCLUSION: We conclude that automated tracking of anatomical structures on time-resolved medical image data with minimal human labeling effort is feasible using SSL and can provide superior results compared to conventional and deep learning OF-based methods.

Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy.

BACKGROUND: This study investigated whether a machine-learning-based combination of radiomics and clinical parameters was superior to the use of clinical parameters alone in predicting therapy response after three months, and overall survival after six and twelve months, in stage-IV malignant melanoma patients undergoing immunotherapy with PD-1 checkpoint inhibitors and CTLA-4 checkpoint inhibitors. METHODS: A random forest model using clinical parameters (demographic variables and tumor markers = baseline model) was compared to a random forest model using clinical parameters and radiomics (extended model) via repeated 5-fold cross-validation. For this purpose, the baseline computed tomographies of 262 stage-IV malignant melanoma patients treated at a tertiary referral center were identified in the Central Malignant Melanoma Registry, and all visible metastases were three-dimensionally segmented (n = 6404). RESULTS: The extended model was not significantly superior compared to the baseline model for survival prediction after six and twelve months (AUC (95% CI): 0.664 (0.598, 0.729) vs. 0.620 (0.545, 0.692) and AUC (95% CI): 0.600 (0.526, 0.667) vs. 0.588 (0.481, 0.629), respectively). The extended model was not significantly superior compared to the baseline model for response prediction after three months (AUC (95% CI): 0.641 (0.581, 0.700) vs. 0.656 (0.587, 0.719)). CONCLUSIONS: The study indicated a potential, but non-significant, added value of radiomics for six-month and twelve-month survival prediction of stage-IV melanoma patients undergoing immunotherapy.

Local control and patient reported outcomes after online MR guided stereotactic body radiotherapy of liver metastases.

Introduction: Stereotactic body radiotherapy (SBRT) is used to treat liver metastases with the intention of ablation. High local control rates were shown. Magnetic resonance imaging guided radiotherapy (MRgRT) provides the opportunity of a marker-less liver SBRT treatment due to the high soft tissue contrast. We report herein on one of the largest cohorts of patients treated with online MRgRT of liver metastases focusing on oncological outcome, toxicity, patient reported outcome measures (PROMs), quality of life. Material and methods: Patients treated for liver metastases with online MR-guided SBRT at a 1,5 T MR-Linac (Unity, Elekta, Crawley, UK) between March 2019 and December 2021 were included in this prospective study. UK SABR guidelines were used for organs at risk constraints. Oncological endpoints such as survival parameters (overall survival, progression-free survival) and local control as well as patient reported acceptance and quality of life data (EORTC QLQ-C30 questionnaire) were assessed. For toxicity scoring the Common Toxicity Criteria Version 5 were used. Results: A total of 51 patients with 74 metastases were treated with a median of five fractions. The median applied BED GTV D98 was 84,1 Gy. Median follow-up was 15 months. Local control of the irradiated liver metastasis after 12 months was 89,6%, local control of the liver was 40,3%. Overall survival (OS) after 12 months was 85.1%. Progression free survival (PFS) after 12 months was 22,4%. Local control of the irradiated liver lesion was 100% after three years when a BED ≥100 Gy was reached. The number of treated lesions did not impact local control neither of the treated or of the hepatic control. Patient acceptance of online MRgSBRT was high. There were no acute grade ≥ 3 toxicities. Quality of life data showed no significant difference comparing baseline and follow-up data. Conclusion: Online MR guided radiotherapy is a noninvasive, well-tolerated and effective treatment for liver metastases. Further prospective trials with the goal to define patients who actually benefit most from an online adaptive workflow are currently ongoing.

Hybrid Cardiac Magnetic Resonance/Fluorodeoxyglucose Positron Emission Tomography to Differentiate Active From Chronic Cardiac Sarcoidosis.

OBJECTIVES: The purpose of this study was to investigate the diagnostic value of simultaneous hybrid cardiac magnetic resonance (CMR) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) for detection and differentiation of active (aCS) from chronic (cCS) cardiac sarcoidosis. BACKGROUND: Late gadolinium enhancement (LGE) CMR and FDG-PET are both established imaging techniques for the detection of CS. However, there are limited data regarding the value of a comprehensive simultaneous hybrid CMR/FDG-PET imaging approach that includes CMR mapping techniques. METHODS: Forty-three patients with biopsy-proven extracardiac sarcoidosis (median age: 48 years, interquartile range: 37-57 years, 65% male) were prospectively enrolled for evaluation of suspected CS. After dietary preparation for suppression of myocardial glucose metabolism, patients were evaluated on a 3-T hybrid PET/MR scanner. The CMR protocol included T1 and T2 mapping, myocardial function, and LGE imaging. We assumed aCS if PET and CMR (ie, LGE or T1/T2 mapping) were both positive (PET+/CMR+), cCS if PET was negative but CMR was positive (PET-/CMR+), and no CS if patients were CMR negative regardless of PET findings. RESULTS: Among the 43 patients, myocardial glucose uptake was suppressed successfully in 36 (84%). Hybrid CMR/FDG-PET revealed aCS in 13 patients (36%), cCS in 5 (14%), and no CS in 18 (50%). LGE was present in 14 patients (39%); T1 mapping was abnormal in 10 (27%) and T2 mapping abnormal in 2 (6%). CS was diagnosed based on abnormal T1 mapping in 4 out of 18 CS patients (22%) who were LGE negative. PET FDG uptake was present in 17 (47%) patients. CONCLUSIONS: Comprehensive simultaneous hybrid CMR/FDG-PET imaging is useful for the detection of CS and provides additional value for identifying active disease. Our results may have implications for enhanced diagnosis as well as improved identification of patients with aCS in whom anti-inflammatory therapy may be most beneficial.

Detection of lung lesions in breath-hold VIBE and free-breathing Spiral VIBE MRI compared to CT.

BACKGROUND: Detection of pulmonary nodules in MRI requires fast imaging strategies without respiratory motion impairment, such as single-breath-hold Cartesian VIBE. As patients with pulmonary diseases have limited breath-hold capacities, this study investigates the clinical feasibility of non-Cartesian Spiral VIBE under free-breathing compared to CT as the gold standard. METHODS: Prospective analysis of 27 oncological patients examined in PET/CT and PET/MR. A novel motion-robust 3D ultrashort-echo-time (UTE) MR sequence was evaluated in comparison with CT and conventional breath-hold MR. CT scans were performed under breath-hold in end-expiratory and end-inspiratory position (CT ex, CT in). MR data was acquired with non-contrast-enhanced breath-hold Cartesian VIBE followed by a free-breathing 3D UTE Spiral VIBE. Impact of respiratory motion on pulmonary evaluation was investigated by two readers in Cartesian VIBE, followed by UTE Spiral VIBE and CT ex and the reference standard of CT in. Diagnostic accuracy was calculated, and visual image quality assessed. RESULTS: Higher detection rate and sensitivity of pulmonary nodules in free-breathing UTE Spiral VIBE in comparison with breath-hold Cartesian VIBE were found for lesions > 10 mm (UTE Spiral VIBE/VIBE/CT ex): 93%/54%/100%; Lesions 5-10 mm: 67%/25%/ 92%; Lesions < 5 mm: 11%/11%/78%. Lobe-based analysis revealed sensitivities and specificities of 64%/96%/41% and 96%/93%/100% for UTE Spiral VIBE/VIBE/CT ex. CONCLUSION: Free-breathing UTE Spiral VIBE indicates higher sensitivity for detection of pulmonary nodules than breath-hold Cartesian VIBE and is a promising but time-consuming approach. However, sensitivity and specificity of inspiratory CT remain superior in comparison and should be preferred for detection of pulmonary lesions.