Patient vulnerability in stereotactic arrhythmia radioablation (STAR): a preliminary ethical appraisal from the STOPSTORM.eu consortium.

This preliminary ethical appraisal from the STOPSTORM.eu consortium is meant to raise critical points that clinicians administering stereotactic arrhythmia radioablation should consider to meet the highest standards in medical ethics and thus promote quality of life of patients recruited for radiotherapy treatments at a stage in which they experience a significant degree of vulnerability.

From algorithms to action: improving patient care requires causality.

In cancer research there is much interest in building and validating outcome prediction models to support treatment decisions. However, because most outcome prediction models are developed and validated without regard to the causal aspects of treatment decision making, many published outcome prediction models may cause harm when used for decision making, despite being found accurate in validation studies. Guidelines on prediction model validation and the checklist for risk model endorsement by the American Joint Committee on Cancer do not protect against prediction models that are accurate during development and validation but harmful when used for decision making. We explain why this is the case and how to build and validate models that are useful for decision making.

Stereotactic Body and Conventional Radiotherapy for Painful Bone Metastases: A Systematic Review and Meta-Analysis.

Importance: Conventional external beam radiotherapy (cEBRT) and stereotactic body radiotherapy (SBRT) are commonly used treatment options for relieving metastatic bone pain. The effectiveness of SBRT compared with cEBRT in pain relief has been a subject of debate, and conflicting results have been reported. Objective: To compare the effectiveness associated with SBRT vs cEBRT for relieving metastatic bone pain. Data Sources: A structured search was performed in the PubMed, Embase, and Cochrane databases on June 5, 2023. Additionally, results were added from a new randomized clinical trial (RCT) and additional unpublished data from an already published RCT. Study Selection: Comparative studies reporting pain response after SBRT vs cEBRT in patients with painful bone metastases. Data Extraction and Synthesis: Two independent reviewers extracted data from eligible studies. Data were extracted for the intention-to-treat (ITT) and per-protocol (PP) populations. The study is reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Main Outcomes and Measures: Overall and complete pain response at 1, 3, and 6 months after radiotherapy, according to the study's definition. Relative risk ratios (RRs) with 95% CIs were calculated for each study. A random-effects model using a restricted maximum likelihood estimator was applied for meta-analysis. Results: There were 18 studies with 1685 patients included in the systematic review and 8 RCTs with 1090 patients were included in the meta-analysis. In 7 RCTs, overall pain response was defined according to the International Consensus on Palliative Radiotherapy Endpoints in clinical trials (ICPRE). The complete pain response was reported in 6 RCTs, all defined according to the ICPRE. The ITT meta-analyses showed that the overall pain response rates did not differ between cEBRT and SBRT at 1 (RR, 1.14; 95% CI, 0.99-1.30), 3 (RR, 1.19; 95% CI, 0.96-1.47), or 6 (RR, 1.22; 95% CI, 0.96-1.54) months. However, SBRT was associated with a higher complete pain response at 1 (RR, 1.43; 95% CI, 1.02-2.01), 3 (RR, 1.80; 95% CI, 1.16-2.78), and 6 (RR, 2.47; 95% CI, 1.24-4.91) months after radiotherapy. The PP meta-analyses showed comparable results. Conclusions and Relevance: In this systematic review and meta-analysis, patients with painful bone metastases experienced similar overall pain response after SBRT compared with cEBRT. More patients had complete pain alleviation after SBRT, suggesting that selected subgroups will benefit from SBRT.

A prediction model for response to immune checkpoint inhibition in advanced melanoma.

Predicting who will benefit from treatment with immune checkpoint inhibition (ICI) in patients with advanced melanoma is challenging. We developed a multivariable prediction model for response to ICI, using routinely available clinical data including primary melanoma characteristics. We used a population-based cohort of 3525 patients with advanced cutaneous melanoma treated with anti-PD-1-based therapy. Our prediction model for predicting response within 6 months after ICI initiation was internally validated with bootstrap resampling. Performance evaluation included calibration, discrimination and internal-external cross-validation. Included patients received anti-PD-1 monotherapy (n = 2366) or ipilimumab plus nivolumab (n = 1159) in any treatment line. The model included serum lactate dehydrogenase, World Health Organization performance score, type and line of ICI, disease stage and time to first distant recurrence-all at start of ICI-, and location and type of primary melanoma, the presence of satellites and/or in-transit metastases at primary diagnosis and sex. The over-optimism adjusted area under the receiver operating characteristic was 0.66 (95% CI: 0.64-0.66). The range of predicted response probabilities was 7%-81%. Based on these probabilities, patients were categorized into quartiles. Compared to the lowest response quartile, patients in the highest quartile had a significantly longer median progression-free survival (20.0 vs 2.8 months; P < .001) and median overall survival (62.0 vs 8.0 months; P < .001). Our prediction model, based on routinely available clinical variables and primary melanoma characteristics, predicts response to ICI in patients with advanced melanoma and discriminates well between treated patients with a very good and very poor prognosis.

A Framework for Assessing the Effect of Cardiac and Respiratory Motion for Stereotactic Arrhythmia Radioablation Using a Digital Phantom With a 17-Segment Model: A STOPSTORM.eu Consortium Study.

PURPOSE: The optimal motion management strategy for patients receiving stereotactic arrhythmia radioablation (STAR) for the treatment of ventricular tachycardia (VT) is not fully known. We developed a framework using a digital phantom to simulate cardiorespiratory motion in combination with different motion management strategies to gain insight into the effect of cardiorespiratory motion on STAR. METHODS AND MATERIALS: The 4-dimensional (4D) extended cardiac-torso (XCAT) phantom was expanded with the 17-segment left ventricular (LV) model, which allowed placement of STAR targets in standardized ventricular regions. Cardiac- and respiratory-binned 4D computed tomography (CT) scans were simulated for free-breathing, reduced free-breathing, respiratory-gating, and breath-hold scenarios. Respiratory motion of the heart was set to population-averaged values of patients with VT: 6, 2, and 1 mm in the superior-inferior, posterior-anterior, and left-right direction, respectively. Cardiac contraction was adjusted by reducing LV ejection fraction to 35%. Target displacement was evaluated for all segments using envelopes encompassing the cardiorespiratory motion. Envelopes incorporating only the diastole plus respiratory motion were created to simulate the scenario where cardiac motion is not fully captured on 4D respiratory CT scans used for radiation therapy planning. RESULTS: The average volume of the 17 segments was 6 cm3 (1-9 cm3). Cardiac contraction-relaxation resulted in maximum segment (centroid) motion of 4, 6, and 3.5 mm in the superior-inferior, posterior-anterior, and left-right direction, respectively. Cardiac contraction-relaxation resulted in a motion envelope increase of 49% (24%-79%) compared with individual segment volumes, whereas envelopes increased by 126% (79%-167%) if respiratory motion also was considered. Envelopes incorporating only the diastole and respiration motion covered on average 68% to 75% of the motion envelope. CONCLUSIONS: The developed LV-segmental XCAT framework showed that free-wall regions display the most cardiorespiratory displacement. Our framework supports the optimization of STAR by evaluating the effect of (cardio)respiratory motion and motion management strategies for patients with VT.

Different profiles of neurocognitive functioning in patients with brain metastases prior to brain radiotherapy.

OBJECTIVE: Patients with brain metastases (BrMs) are a heterogeneous population, with almost 50% experiencing cognitive impairment before brain radiotherapy. Defining pre-radiotherapy cognitive profiles will aid in understanding of the cognitive vulnerabilities and offer valuable insight and guidance for tailoring interventions. METHODS: The study population consisted of 58 adult patients with BrMs referred for radiotherapy. A semi-structured interview and comprehensive battery including 10 neuropsychological tests were used to assess subjective and objective cognitive performance prior to radiotherapy. RESULTS: A majority (69%) of patients report decline in cognitive performance compared to their premorbid level (i.e. pre-cancer). Objective testing revealed memory (52%), processing speed (33%) and emotion recognition (29%) deficits were most frequent. 21% of patients had no cognitive deficits while 55% had deficits (-1.5SD) in at least two cognitive domains. Hierarchical cluster analysis based on patient deficit profiles identified four clusters: (I) no or limited cognitive deficits selectively restricted to processing speed or executive function, (II) psychomotor speed deficits, (III) memory deficits and (IV) extensive cognitive deficits including memory. No patient or clinical-related (e.g. age, number of BrMs, previous treatment) differences were found between clusters. CONCLUSIONS: Patterns of cognitive performance in patients with BrMs are heterogeneous, with most experiencing at least some degree of neurocognitive dysfunction. We identified four meaningful cognitive clusters. Stability of these clusters over time and in different samples should be assessed to advance understanding of the cognitive vulnerability of this patient population.

Interobserver variability in target definition for stereotactic arrhythmia radioablation.

Background: Stereotactic arrhythmia radioablation (STAR) is a potential new therapy for patients with refractory ventricular tachycardia (VT). The arrhythmogenic substrate (target) is synthesized from clinical and electro-anatomical information. This study was designed to evaluate the baseline interobserver variability in target delineation for STAR. Methods: Delineation software designed for research purposes was used. The study was split into three phases. Firstly, electrophysiologists delineated a well-defined structure in three patients (spinal canal). Secondly, observers delineated the VT-target in three patients based on case descriptions. To evaluate baseline performance, a basic workflow approach was used, no advanced techniques were allowed. Thirdly, observers delineated three predefined segments from the 17-segment model. Interobserver variability was evaluated by assessing volumes, variation in distance to the median volume expressed by the root-mean-square of the standard deviation (RMS-SD) over the target volume, and the Dice-coefficient. Results: Ten electrophysiologists completed the study. For the first phase interobserver variability was low as indicated by low variation in distance to the median volume (RMS-SD range: 0.02-0.02 cm) and high Dice-coefficients (mean: 0.97 ± 0.01). In the second phase distance to the median volume was large (RMS-SD range: 0.52-1.02 cm) and the Dice-coefficients low (mean: 0.40 ± 0.15). In the third phase, similar results were observed (RMS-SD range: 0.51-1.55 cm, Dice-coefficient mean: 0.31 ± 0.21). Conclusions: Interobserver variability is high for manual delineation of the VT-target and ventricular segments. This evaluation of the baseline observer variation shows that there is a need for methods and tools to improve variability and allows for future comparison of interventions aiming to reduce observer variation, for STAR but possibly also for catheter ablation.

Evaluating Physiological MRI Parameters in Patients with Brain Metastases Undergoing Stereotactic Radiosurgery-A Preliminary Analysis and Case Report.

Brain metastases occur in ten to thirty percent of the adult cancer population. Treatment consists of different (palliative) options, including stereotactic radiosurgery (SRS). Sensitive MRI biomarkers are needed to better understand radiotherapy-related effects on cerebral physiology and the subsequent effects on neurocognitive functioning. In the current study, we used physiological imaging techniques to assess cerebral blood flow (CBF), oxygen extraction fraction (OEF), cerebral metabolic rate of oxygen (CMRO2) and cerebrovascular reactivity (CVR) before and three months after SRS in nine patients with brain metastases. The results showed improvement in OEF, CBF and CMRO2 within brain tissue that recovered from edema (all p ≤ 0.04), while CVR remained impacted. We observed a global post-radiotherapy increase in CBF in healthy-appearing brain tissue (p = 0.02). A repeated measures correlation analysis showed larger reductions within regions exposed to higher radiotherapy doses in CBF (rrm = -0.286, p < 0.001), CMRO2 (rrm = -0.254, p < 0.001), and CVR (rrm = -0.346, p < 0.001), but not in OEF (rrm = -0.004, p = 0.954). Case analyses illustrated the impact of brain metastases progression on the post-radiotherapy changes in both physiological MRI measures and cognitive performance. Our preliminary findings suggest no radiotherapy effects on physiological parameters occurred in healthy-appearing brain tissue within 3-months post-radiotherapy. Nevertheless, as radiotherapy can have late side effects, larger patient samples allowing meaningful grouping of patients and longer follow-ups are needed.

Stereotactic Arrhythmia Radioablation (STAR): Assessment of cardiac and respiratory heart motion in ventricular tachycardia patients - A STOPSTORM.eu consortium review.

AIM: To identify the optimal STereotactic Arrhythmia Radioablation (STAR) strategy for individual patients, cardiorespiratory motion of the target volume in combination with different treatment methodologies needs to be evaluated. However, an authoritative overview of the amount of cardiorespiratory motion in ventricular tachycardia (VT) patients is missing. METHODS: In this STOPSTORM consortium study, we performed a literature review to gain insight into cardiorespiratory motion of target volumes for STAR. Motion data and target volumes were extracted and summarized. RESULTS: Out of the 232 studies screened, 56 provided data on cardiorespiratory motion, of which 8 provided motion amplitudes in VT patients (n = 94) and 10 described (cardiac/cardiorespiratory) internal target volumes (ITVs) obtained in VT patients (n = 59). Average cardiac motion of target volumes was < 5 mm in all directions, with maximum values of 8.0, 5.2 and 6.5 mm in Superior-Inferior (SI), Left-Right (LR), Anterior-Posterior (AP) direction, respectively. Cardiorespiratory motion of cardiac (sub)structures showed average motion between 5-8 mm in the SI direction, whereas, LR and AP motions were comparable to the cardiac motion of the target volumes. Cardiorespiratory ITVs were on average 120-284% of the gross target volume. Healthy subjects showed average cardiorespiratory motion of 10-17 mm in SI and 2.4-7 mm in the AP direction. CONCLUSION: This review suggests that despite growing numbers of patients being treated, detailed data on cardiorespiratory motion for STAR is still limited. Moreover, data comparison between studies is difficult due to inconsistency in parameters reported. Cardiorespiratory motion is highly patient-specific even under motion-compensation techniques. Therefore, individual motion management strategies during imaging, planning, and treatment for STAR are highly recommended.

Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks.

BACKGROUND: Respiratory-resolved four-dimensional magnetic resonance imaging (4D-MRI) provides essential motion information for accurate radiation treatments of mobile tumors. However, obtaining high-quality 4D-MRI suffers from long acquisition and reconstruction times. PURPOSE: To develop a deep learning architecture to quickly acquire and reconstruct high-quality 4D-MRI, enabling accurate motion quantification for MRI-guided radiotherapy (MRIgRT). METHODS: A small convolutional neural network called MODEST is proposed to reconstruct 4D-MRI by performing a spatial and temporal decomposition, omitting the need for 4D convolutions to use all the spatio-temporal information present in 4D-MRI. This network is trained on undersampled 4D-MRI after respiratory binning to reconstruct high-quality 4D-MRI obtained by compressed sensing reconstruction. The network is trained, validated, and tested on 4D-MRI of 28 lung cancer patients acquired with a T1-weighted golden-angle radial stack-of-stars (GA-SOS) sequence. The 4D-MRI of 18, 5, and 5 patients were used for training, validation, and testing. Network performances are evaluated on image quality measured by the structural similarity index (SSIM) and motion consistency by comparing the position of the lung-liver interface on undersampled 4D-MRI before and after respiratory binning. The network is compared to conventional architectures such as a U-Net, which has 30 times more trainable parameters. RESULTS: MODEST can reconstruct high-quality 4D-MRI with higher image quality than a U-Net, despite a thirty-fold reduction in trainable parameters. High-quality 4D-MRI can be obtained using MODEST in approximately 2.5 min, including acquisition, processing, and reconstruction. CONCLUSION: High-quality accelerated 4D-MRI can be obtained using MODEST, which is particularly interesting for MRIgRT.

Designing clinical trials based on modern imaging and metastasis-directed treatments in patients with oligometastatic breast cancer: a consensus recommendation from the EORTC Imaging and Breast Cancer Groups.

Breast cancer remains the most common cause of cancer death among women. Despite its considerable histological and molecular heterogeneity, those characteristics are not distinguished in most definitions of oligometastatic disease and clinical trials of oligometastatic breast cancer. After an exhaustive review of the literature covering all aspects of oligometastatic breast cancer, 35 experts from the European Organisation for Research and Treatment of Cancer Imaging and Breast Cancer Groups elaborated a Delphi questionnaire aimed at offering consensus recommendations, including oligometastatic breast cancer definition, optimal diagnostic pathways, and clinical trials required to evaluate the effect of diagnostic imaging strategies and metastasis-directed therapies. The main recommendations are the introduction of modern imaging methods in metastatic screening for an earlier diagnosis of oligometastatic breast cancer and the development of prospective trials also considering the histological and molecular complexity of breast cancer. Strategies for the randomisation of imaging methods and therapeutic approaches in different subsets of patients are also addressed.

Practice variation in re-resection for recurrent glioblastoma: A nationwide survey among Dutch neuro-oncology specialists.

Background: Despite current best treatment options, a glioblastoma almost inevitably recurs after primary treatment. However, in the absence of clear evidence, current guidelines on recurrent glioblastoma are not well-defined. Re-resection is one of the possible treatment modalities, though it can be challenging to identify those patients who will benefit. Therefore, treatment decisions are made based on multidisciplinary discussions. This study aimed to investigate the current practice variation between neuro-oncology specialists. Methods: In this nationwide study among Dutch neuro-oncology specialists, we surveyed possible practice variation. Via an online survey, 4 anonymized recurrent glioblastoma cases were presented to neurosurgeons, neuro-oncologists, medical oncologists, and radiation oncologists in The Netherlands using a standardized questionnaire on whether and why they would recommend a re-resection or not. The results were used to provide a qualitative analysis of the current practice in The Netherlands. Results: The survey was filled out by 56 respondents, of which 15 (27%) were neurosurgeons, 26 (46%) neuro-oncologists, 2 (4%) medical oncologists, and 13 (23%) radiation oncologists. In 2 of the 4 cases, there appeared to be clinical equipoise. Overall, neurosurgeons tended to recommend re-resection more frequently compared to the other specialists. Neurosurgeons and radiation oncologists showed opposite recommendations in 2 cases. Conclusions: This study showed that re-resection of recurrent glioblastoma is subject to practice variation both between and within neuro-oncology specialties. In the absence of unambiguous guidelines, we observed a relationship between preferred practice and specialty. Reduction of this practice variation is important; to achieve this, adequate prospective studies are essential.

Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis.

BACKGROUND: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT from fixed MRI acquisitions. However, MRI protocols may change over time or differ between centres resulting in low-quality sCT due to poor model generalisation. PURPOSE: investigating domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation. METHODS: CT and corresponding T1-weighted MRI with/without contrast, T2-weighted, and FLAIR MRI from 95 patients undergoing RT were collected, considering FLAIR the unseen sequence where to investigate generalisation. A "Baseline" generative adversarial network was trained with/without the FLAIR sequence to test how a model performs without DR. Image similarity and accuracy of sCT-based dose plans were assessed against CT to select the best-performing DR approach against the Baseline. RESULTS: The Baseline model had the poorest performance on FLAIR, with mean absolute error (MAE) = 106 ± 20.7 HU (mean ±σ). Performance on FLAIR significantly improved for the DR model with MAE = 99.0 ± 14.9 HU, but still inferior to the performance of the Baseline+FLAIR model (MAE = 72.6 ± 10.1 HU). Similarly, an improvement in γ-pass rate was obtained for DR vs Baseline. CONCLUSION: DR improved image similarity and dose accuracy on the unseen sequence compared to training only on acquired MRI. DR makes the model more robust, reducing the need for re-training when applying a model on sequences unseen and unavailable for retraining.

SynthRAD2023 Grand Challenge dataset: Generating synthetic CT for radiotherapy.

PURPOSE: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered cone-beam CT (CBCT) and magnetic resonance imaging (MRI) images to facilitate the development and evaluation of sCT generation for radiotherapy planning. ACQUISITION AND VALIDATION METHODS: The dataset consists of CT, CBCT, and MRI of 540 brains and 540 pelvic radiotherapy patients from three Dutch university medical centers. Subjects' ages ranged from 3 to 93 years, with a mean age of 60. Various scanner models and acquisition settings were used across patients from the three data-providing centers. Details are available in a comma separated value files provided with the datasets. DATA FORMAT AND USAGE NOTES: The data is available on Zenodo (https://doi.org/10.5281/zenodo.7260704, https://doi.org/10.5281/zenodo.7868168) under the SynthRAD2023 collection. The images for each subject are available in nifti format. POTENTIAL APPLICATIONS: This dataset will enable the evaluation and development of image synthesis algorithms for radiotherapy purposes on a realistic multi-center dataset with varying acquisition protocols. Synthetic CT generation has numerous applications in radiation therapy, including diagnosis, treatment planning, treatment monitoring, and surgical planning.

Real-time myocardial landmark tracking for MRI-guided cardiac radio-ablation using Gaussian Processes.

Objective.The high speed of cardiorespiratory motion introduces a unique challenge for cardiac stereotactic radio-ablation (STAR) treatments with the MR-linac. Such treatments require tracking myocardial landmarks with a maximum latency of 100 ms, which includes the acquisition of the required data. The aim of this study is to present a new method that allows to track myocardial landmarks from few readouts of MRI data, thereby achieving a latency sufficient for STAR treatments.Approach.We present a tracking framework that requires only few readouts of k-space data as input, which can be acquired at least an order of magnitude faster than MR-images. Combined with the real-time tracking speed of a probabilistic machine learning framework called Gaussian Processes, this allows to track myocardial landmarks with a sufficiently low latency for cardiac STAR guidance, including both the acquisition of required data, and the tracking inference.Main results.The framework is demonstrated in 2D on a motion phantom, andin vivoon volunteers and a ventricular tachycardia (arrhythmia) patient. Moreover, the feasibility of an extension to 3D was demonstrated byin silico3D experiments with a digital motion phantom. The framework was compared with template matching-a reference, image-based, method-and linear regression methods. Results indicate an order of magnitude lower total latency (<10 ms) for the proposed framework in comparison with alternative methods. The root-mean-square-distances and mean end-point-distance with the reference tracking method was less than 0.8 mm for all experiments, showing excellent (sub-voxel) agreement.Significance.The high accuracy in combination with a total latency of less than 10 ms-including data acquisition and processing-make the proposed method a suitable candidate for tracking during STAR treatments. Additionally, the probabilistic nature of the Gaussian Processes also gives access to real-time prediction uncertainties, which could prove useful for real-time quality assurance during treatments.

The impact of schizophrenia spectrum disorder, bipolar disorder and borderline personality disorder on radiotherapy treatment and overall survival in cancer patients: A matched pair analysis.

Introduction: The effect of a psychiatric disorder (PD) on the choice of radiotherapy regimens and subsequent cancer control outcomes is largely unknown. In this study, we evaluated differences in radiotherapy regimens and overall survival (OS) between cancer patients with a PD in comparison with a control population of patients without a PD. Methods: Referred patients with a PD (i.e. schizophrenia spectrum disorder, bipolar disorder or borderline personality disorder) were included through a text-based search of the electronic patient database of all the patients that received radiotherapy between 2015 and 2019 at a single centre. Each patient was matched to a patient without a PD. Matching was based on cancer type, staging, performance score (WHO/KPS), non-radiotherapeutic cancer treatment, gender and age. Outcomes were the amount of fractions received, total dose, and OS. Results: 88 patients with PD were identified; 44 patients with schizophrenia spectrum disorder, 34 with bipolar disorder, and 10 with borderline personality disorder. Matched patients without a PD showed similar baseline characteristics. No statistically significant difference was observed regarding the number of fractions with a median of 16 (interquartile range [IQR] 3-23) versus 16 (IQR 3-25), respectively (p = 0.47). Additionally, no difference in total dose was found. Kaplan-Meier curves showed a statistically significant difference in OS between the patients with a PD versus those without a PD, with 3-year OS rates of 47 % versus 61 %, respectively (hazard ratio 1.57, 95 % confidence interval 1.05-2.35, p = 0.03). No clear differences in causes of death were observed. Conclusion: Cancer patients referred for radiotherapy with schizophrenia spectrum disorder, bipolar disorder or borderline personality disorder receive similar radiotherapy schedules for a variety of tumour types but attain worse survival.

CT radiomics compared to a clinical model for predicting checkpoint inhibitor treatment outcomes in patients with advanced melanoma.

INTRODUCTION: Predicting checkpoint inhibitors treatment outcomes in melanoma is a relevant task, due to the unpredictable and potentially fatal toxicity and high costs for society. However, accurate biomarkers for treatment outcomes are lacking. Radiomics are a technique to quantitatively capture tumour characteristics on readily available computed tomography (CT) imaging. The purpose of this study was to investigate the added value of radiomics for predicting clinical benefit from checkpoint inhibitors in melanoma in a large, multicenter cohort. METHODS: Patients who received first-line anti-PD1±anti-CTLA4 treatment for advanced cutaneous melanoma were retrospectively identified from nine participating hospitals. For every patient, up to five representative lesions were segmented on baseline CT, and radiomics features were extracted. A machine learning pipeline was trained on the radiomics features to predict clinical benefit, defined as stable disease for more than 6 months or response per RECIST 1.1 criteria. This approach was evaluated using a leave-one-centre-out cross validation and compared to a model based on previously discovered clinical predictors. Lastly, a combination model was built on the radiomics and clinical model. RESULTS: A total of 620 patients were included, of which 59.2% experienced clinical benefit. The radiomics model achieved an area under the receiver operator characteristic curve (AUROC) of 0.607 [95% CI, 0.562-0.652], lower than that of the clinical model (AUROC=0.646 [95% CI, 0.600-0.692]). The combination model yielded no improvement over the clinical model in terms of discrimination (AUROC=0.636 [95% CI, 0.592-0.680]) or calibration. The output of the radiomics model was significantly correlated with three out of five input variables of the clinical model (p < 0.001). DISCUSSION: The radiomics model achieved a moderate predictive value of clinical benefit, which was statistically significant. However, a radiomics approach was unable to add value to a simpler clinical model, most likely due to the overlap in predictive information learned by both models. Future research should focus on the application of deep learning, spectral CT-derived radiomics, and a multimodal approach for accurately predicting benefit to checkpoint inhibitor treatment in advanced melanoma.

Feasibility and first results of the 'Trials-within-Cohorts' (TwiCs) design in patients undergoing radiotherapy for lung cancer.

Background: 'Trials-within-Cohorts' (TwiCs), previously known as 'cohort multiple randomized controlled trials' is a pragmatic trial design, supporting an efficient and representative recruitment of patients for (future) trials. To our knowledge, the 'COhort for Lung cancer Outcome Reporting and trial inclusion' (COLOR) is the first TwiCs in lung cancer patients. In this study we aimed to assess the feasibility and first year results of COLOR.Material and Methods: All patients diagnosed with lung cancer referred to the Radiotherapy department were eligible to participate in the ongoing prospective COLOR study. At inclusion, written informed consent was requested for use of patient data, participation in patient-reported outcomes (PROs), and willingness to participate in (future) trials. Feasibility was studied by assessing participation and comparing baseline PROs to EORTC reference values. First-year results of PROs at baseline and 3 months after inclusion were evaluated separately for stereotactic body radiotherapy (SBRT) and conventional radiotherapy patients.Results: Of the 338 eligible patients between July 2020 and July 2021, 169 (50%) participated. Among these, 127 (75%) gave informed consent to PROs participation and 110 (65%) were willing to participate in (future) trials. The inclusion percentage dropped from 77% to 33% when the information procedure was switched from in-person to by phone (due to COVID-19 pandemic measures). Baseline PROs for physical and cognitive functioning were comparable in COLOR patients compared to the EORTC reference values. No significant changes in PROs were observed 3 months after inclusion, except for a slight increase in pain scores in the SBRT group (n = 97).Conclusions: The TwiCs-design appears feasible in lung cancer patients with fair participation rates (although negatively impacted by the COVID-19 pandemic). With a planned expansion to other centers, the COLOR-study is expected to enable multiple (randomized) evaluations of experimental interventions with important advantages for recruitment, generalizability, and long-term outcome data collection.

Time interval from primary melanoma to first distant recurrence in relation to patient outcomes in advanced melanoma.

Since the introduction of BRAF(/MEK) inhibition and immune checkpoint inhibition (ICI), the prognosis of advanced melanoma has greatly improved. Melanoma is known for its remarkably long time to first distant recurrence (TFDR), which can be decades in some patients and is partly attributed to immune-surveillance. We investigated the relationship between TFDR and patient outcomes after systemic treatment for advanced melanoma. We selected patients undergoing first-line systemic therapy for advanced melanoma from the nationwide Dutch Melanoma Treatment Registry. The association between TFDR and progression-free survival (PFS) and overall survival (OS) was assessed by Cox proportional hazard regression models. The TFDR was modeled categorically, linearly, and flexibly using restricted cubic splines. Patients received anti-PD-1-based treatment (n = 1844) or BRAF(/MEK) inhibition (n = 1618). For ICI-treated patients with a TFDR <2 years, median OS was 25.0 months, compared to 37.3 months for a TFDR >5 years (P = .014). Patients treated with BRAF(/MEK) inhibition with a longer TFDR also had a significantly longer median OS (8.6 months for TFDR <2 years compared to 11.1 months for >5 years, P = .004). The hazard of dying rapidly decreased with increasing TFDR until approximately 5 years (HR 0.87), after which the hazard of dying further decreased with increasing TFDR, but less strongly (HR 0.82 for a TFDR of 10 years and HR 0.79 for a TFDR of 15 years). Results were similar when stratifying for type of treatment. Advanced melanoma patients with longer TFDR have a prolonged PFS and OS, irrespective of being treated with first-line ICI or targeted therapy.