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The International Lymphoma Radiation Oncology Group (ILROG) guidelines for using radiation therapy (RT) in hematologicalmalignancies are widely used in many countries. The emergency situation created by the COVID-19 pandemic may result in limitations of treatment resources. Furthermore, in recognition of the need to also reduce the exposure of patients and staff to potential infection with COVID-19, the ILROG task force has made recommendations for alternative radiation treatment schemes. The emphasis is on maintaining clinical efficacy and safety by increasing thedose per fractionwhile reducing the number of daily treatments. The guidance is informed by adhering to acceptable radiobiological parameters and clinical tolerability. The options for delaying or omitting RT in some hematological categories are also discussed.

Positron emission tomography (PET) imaging with 2-deoxy-2-[18F]-fluorodeoxyglucose (FDG) was proposed as prognostic marker in radiotherapy. Various uptake metrics and cut points were used, potentially leading to inflated effect estimates. Here, we performed a meta-analysis and systematic review of the prognostic value of pretreatment FDG-PET in head and neck squamous cell carcinoma (HNSCC) and non-small cell lung cancer (NSCLC), with tests for publication bias. Hazard ratio (HR) for overall survival (OS), disease free survival (DFS), and local control was extracted or derived from the 57 studies included. Test for publication bias was performed, and the number of statistical tests and cut-point optimizations were registered. Eggers regression related to correlation of SUVmax with OS/DFS yielded p = 0.08/p = 0.02 for HNSCC and p < 0.001/p = 0.014 for NSCLC. No outcomes showed significant correlation with SUVmax, when adjusting for publication bias effect, whereas all four showed a correlation in the conventional meta-analysis. The number of statistical tests and cut points were high with no indication of improvement over time. Our analysis showed significant evidence of publication bias leading to inflated estimates of the prognostic value of SUVmax. We suggest that improved management of these complexities, including predefined statistical analysis plans, are critical for a reliable assessment of FDG-PET.

BACKGROUND: The purpose of this study is to test if functional multiparametric imaging with 18F-FDG-PET/MRI correlates spatially with immunohistochemical biomarker status within a lesion of head and neck squamous cell carcinoma (HNSCC), and also whether a biopsy with the highest FDG uptake was more likely to have the highest PD-L1 expression or the highest percentage of vital tumour cells (VTC) compared with a random biopsy.METHODS: Thirty-one patients with HNSCC were scanned on an integrated PET/MRI scanner with FDG prior to surgery in this prospective study. Imaging was quantified with SUV, ADC and Ktrans. A 3D-morphometric MRI scan of the specimen was used to co-register the patient and the specimen scans. All specimens were sectioned in consecutive slices, and slices from six different locations were selected randomly from each tumour. Core biopsies were performed to construct TMA blocks for IHC staining with the ten predefined biomarkers. The spatial correlation was assessed with a partial correlation analysis.RESULTS: Twenty-eight patients with a total of 33 lesions were eligible for further analysis. There were significant correlations between the three imaging biomarkers and some of the IHC biomarkers. Moreover, a biopsy taken from the most FDG-avid part of the tumour did not have a statistically significantly higher probability of higher PD-L1 expression or VTC, compared with a random biopsy.CONCLUSION: We found statistically significant correlations between functional imaging parameters and key molecular cancer markers.

BACKGROUND: The overall aim was to investigate the change over time in circulating cell free DNA (cfDNA) in patients with locally advanced non-small cell lung cancer (NSCLC) undergoing concurrent chemo-radiotherapy. Furthermore, to assess the possibility of detecting circulating cell free tumor DNA (ctDNA) using shallow whole genome sequencing (sWGS) and size selection.METHODS: Ten patients were included in a two-phase study. The first four patients had blood samples taken prior to a radiation therapy (RT) dose fraction and at 30 minutes, 1 hour and 2 hours after RT to estimate the short-term dynamics of cfDNA concentration after irradiation. The remaining six patients had one blood sample taken on six treatment days 30 minutes post treatment to measure cfDNA levels. Presence of ctDNA as indicated by chromosomal aberrations was investigated using sWGS. The sensitivity of this method was further enhanced using in silico size selection.RESULTS: cfDNA concentration from baseline to 120 min after therapy was stable within 95% tolerance limits of +/- 2 ng/ml cfDNA. Changes in cfDNA were observed during therapy with an apparent qualitative difference between adenocarcinoma (average increase of 0.69 ng/ml) and squamous cell carcinoma (average increase of 4.0 ng/ml). Tumor shrinkage on daily cone beam computer tomography scans during radiotherapy did not correlate with changes in concentration of cfDNA.CONCLUSION: Concentrations of cfDNA remain stable during the first 2 hours after an RT fraction. However, based on the sWGS profiles, ctDNA represented only a minor fraction of cfDNA in this group of patients. The detection sensitivity of genomic alterations in ctDNA strongly increases by applying size selection.

Background: Dose-painting has recently been investigated in early-phase trials in head-and-neck cancer (HNC) with the aim of improving local tumor control. At the same time proton therapy has been reported as potentially capable of decreasing toxicity. Here, we investigate whether protons could be applied in a dose-painting setting by comparing proton dose distributions with delivered photon plans from a phase-I trial of FDG-PET based dose-painting at our institution. Material and methods: Eleven oropharynx (5), hypopharynx (2) and larynx cancer (4) patients from the recently conducted phase I trial were used for comparison of proton and photon dose-painting techniques. Robust optimization (3.5%/3 mm) was used for proton plans. Plan robustness and difference in dose metrics to targets and organs at risk were evaluated. Results: The proton plans met target dose constraints, while having lower non-target dose than photon plans (body-minus-CTV, mean dose 3.9 Gy vs 7.2 Gy, p =.004). Despite the use of robust proton planning for plan max dose, photon plan max doses were more robust (p =.006). Max dose to medulla, brainstem and mandible were lower in the proton plans, while there was no significant difference in mean dose to submandibular- and parotid glands. Conclusion: Proton dose-painting for HNC seems feasible and can reduce the non-target dose overall, however not significantly to certain organs close to the target, such as the salivary glands. Max dose in proton plans had a lower robustness compared to photons, requiring caution to avoid unintended hot spots in consideration of the risk of mucosal toxicity.

Background: Isolated regional recurrences following head-neck squamous-cell carcinomas (HNSCC) are often accessible for curatively intended salvage treatment. Factors prognostic for outcome were investigated in a large cohort of HNSCC patients. Methods: In total, 1811 patients receiving curatively intended radiotherapy from 2007 to 2017 were reviewed and isolated cervical nodal recurrences were identified. Factors associated with survival and second recurrence were investigated using univariate and multivariate analyses. Results: Isolated regional recurrence was seen in 95/1811 (5.2%) patients. Eighty of 95 patients (84%) received salvage surgery. Two-year survival after isolated regional recurrence was 40%. Overall survival (OS) and time to second recurrence were associated with resection status of the salvage surgery and presence of extranodal spread (ENS), while p16-positive oropharyngeal squamous-cell carcinoma (OPSCC) was associated with better OS. Conclusion: Long-term survival after regional recurrence in HNSCC is possible. p16-positive OPSCC, complete salvage surgery, and lack of ENS are associated with better outcome.

Purpose: In current radiotherapy (RT) planning and delivery, population-based dose-volume constraints are used to limit the risk of toxicity from incidental irradiation of organs at risks (OARs). However, weighing tradeoffs between target coverage and doses to OARs (or prioritizing different OARs) in a quantitative way for each patient is challenging. We introduce a novel RT planning approach for patients with mediastinal Hodgkin lymphoma (HL) that aims to maximize overall outcome for each patient by optimizing on tumor control and mortality from late effects simultaneously. Material and Methods: We retrospectively analyzed 34 HL patients treated with conformal RT (3DCRT). We used published data to model recurrence and radiation-induced mortality from coronary heart disease and secondary lung and breast cancers. Patient-specific doses to the heart, lung, breast, and target were incorporated in the models as well as age, sex, and cardiac risk factors (CRFs). A preliminary plan of candidate beams was created for each patient in a commercial treatment planning system. From these candidate beams, outcome-optimized (O-OPT) plans for each patient were created with an in-house optimization code that minimized the individual risk of recurrence and mortality from late effects. O-OPT plans were compared to VMAT plans and clinical 3DCRT plans. Results: O-OPT plans generally had the lowest risk, followed by the clinical 3DCRT plans, then the VMAT plans with the highest risk with median (maximum) total risk values of 4.9 (11.1), 5.1 (17.7), and 7.6 (20.3)%, respectively (no CRFs). Compared to clinical 3DCRT plans, O-OPT planning reduced the total risk by at least 1% for 9/34 cases assuming no CRFs and 11/34 cases assuming presence of CRFs. Conclusions: We developed an individualized, outcome-optimized planning technique for HL. Some of the resulting plans were substantially different from clinical plans. The results varied depending on how risk models were defined or prioriti

Radiation oncology, a major treatment modality in the care of patients with malignant disease, is a technology- and computer-intensive medical specialty. As such, it should lend itself ideally to data science methods, where computer science, statistics, and clinical knowledge are combined to advance state-of-the-art care. Nevertheless, data science methods in radiation oncology research are still in their infancy and successful applications leading to improved patient care remain scarce. Here, we discuss data interoperability issues within and across organizational boundaries that hamper the introduction of big data and data science techniques in radiation oncology. At the semantic level, creating common underlying models and codification of the data, including the use of data elements with standardized definitions, an ontology, remains a work in progress. Methodological issues in data science and in the use of large population-based health data registries are identified. We show that data science methods and big data cannot replace randomized clinical trials in comparative effectiveness research by reviewing a series of instances where the outcomes of big data analyses and randomized trials are at odds. We also discuss the modern wave of machine learning and artificial intelligence as represented by deep learning and convolutional neural networks. Finally, we identify promising research avenues and remain optimistic that the data sources in radiation oncology can be linked to yield important insights in the near future. We argue that data science will be a valuable complement to, but not a replacement of, the traditional hypothesis-driven translational research chain and the randomized clinical trials that form the backbone of evidence-based medicine.

Purpose: Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% of patients with solid tumors. Radiation-induced toxicities of healthy organs accompany many RTs and represent one of the main limiting factors during dose delivery. The existing RT planning solutions generally discard spatial dose distribution information and lose the ability to recognize radiosensitive regions of healthy organs potentially linked to toxicity manifestation. This study proposes a universal deep learning-based algorithm for recognitions of consistent dose patterns and generation of toxicity risk maps for the abdominal area. Methods: We investigated whether convolutional neural networks (CNNs) can automatically associate abdominal computed tomography (CT) images and RT dose plans with post-RT toxicities without being provided segmentation of abdominal organs. The CNNs were also applied to study RT plans, where doses at specific anatomical regions were reduced/increased, with the aim to pinpoint critical regions sparing of which significantly reduces toxicity risks. The obtained risk maps were computed for individual anatomical regions inside the liver and statistically compared to the existing clinical studies. Results: A database of 122 liver stereotactic body RT (SBRT) executed at Stanford Hospital from July 2004 and November 2015 was assembled. All patients treated for primary liver cancer, mainly hepatocellular carcinoma and cholangiocarcinoma, with complete follow-ups were extracted from the database. The SBRT treatment doses ranged from 26 to 50 Gy delivered in 1–5 fractions for primary liver cancer. The patients were followed up for 1–68 months depending on the survival time. The CNNs were trained to recognize acute and late grade 3+ biliary stricture/obstruction, hepatic failure or decompensation, hepatobiliary infection, liver function test (LFT) elevation or/and portal vein thrombosis, named for convenience hepatobiliary (HB) toxicities. The

Purpose: To examine the feasibility of automatic data extraction from clinical radiation therapy (RT) databases at four hospitals to investigate the impact of mean lung dose (MLD) and age on the risk of early respiratory-related death and early overall death for patients treated with RT for non-small-cell lung cancer (NSCLC). Material and methods: We included adult patients with NSCLC receiving curatively intended RT between 2002 and 2017 at four hospitals. A script was developed to automatically extract RT-related data. The cause of death for patients deceased within 180 days of the start of RT was retrospectively assessed. Using logistic regression, the risks of respiratory-related death and of overall death within 90 and 180 days were investigated using MLD and age as variables. Results: Altogether, 1785 patients were included in the analysis of early overall mortality and 1655 of early respiratory-related mortality. The respiratory-related mortalities within 90 and 180 days were 0.9% (15/1655) and 3.6% (60/1655). The overall mortalities within 90 and 180 days were 2.5% (45/1785) and 10.6% (190/1785). Higher MLD and older age were associated with an increased risk of respiratory-related death within 180 days and overall death within 90 and 180 days (all p<.05). For example, the risk of respiratory-related death within 180 days and their 95% confidence interval for patients aged 65 and 75 years with MLDs of 20 Gy was according to our logistic model 3.8% (2.6–5.0%) and 7.7% (5.5–10%), respectively. Conclusions: Automatic data extraction was successfully used to pool data from four hospitals. MLD and age were associated with the risk of respiratory-related death within 180 days of the start of RT and with overall death within 90 and 180 days. A model quantifying the risk of respiratory-related death within 180 days was formulated.

Aim: The aim was to validate promising radiomic features (RFs)1 on 18F-flourodeoxyglucose positron emission tomography/computed tomography-scans (18F-FDG PET/CT) of non-small cell lung cancer (NSCLC) patients undergoing definitive chemo-radiotherapy. Methods: 18F-FDG PET/CT scans performed for radiotherapy (RT) planning were retrieved. Auto-segmentation with visual adaption was used to define the primary tumour on PET images. Six pre-selected prognostic and reproducible PET texture -and shape-features were calculated using texture respectively shape analysis. The correlation between these RFs and metabolic active tumour volume (MTV)3, gross tumour volume (GTV)4 and maximum and mean of standardized uptake value (SUV)5 was tested with a Spearman's Rank test. The prognostic value of RFs was tested in a univariate cox regression analysis and a multivariate cox regression analysis with GTV, clinical stage and histology. P-value ≤ 0.05 were considered significant. Results: Image analysis was performed for 233 patients: 145 males and 88 females, mean age of 65.7 and clinical stage II-IV. Mean GTV was 129.87 cm3 (SD 130.30 cm3). Texture and shape-features correlated more strongly to MTV and GTV compared to SUV-measurements. Four RFs predicted PFS in the univariate analysis. No RFs predicted PFS in the multivariate analysis, whereas GTV and clinical stage predicted PFS (p = 0.001 and p = 0.008 respectively). Conclusion: The pre-selected RFs were insignificant in predicting PFS in combination with GTV, clinical stage and histology. These results might be due to variations in technical parameters. However, it is relevant to question whether RFs are stable enough to provide clinically useful information.

Objectives Knowledge of the detailed pattern of failure can be useful background knowledge in clinical decision making and potentially drive the development of new treatment strategies by increasing radiotherapy dose prescription to high-risk sub-regions of the target. Here, we analyze patterns of recurrence in patients with vulvar cancer treated with radiotherapy according to original planning target volumes and radiation dose delivered. Methods We analyzed dose-planning and post-treatment recurrence scans from patients with vulvar cancer treated at two institutions from January 2009 through October 2014. We delineated the recurrences and merged the dose-planning and recurrence scans for each patient by using deformable co-registration. We estimated the center of each recurrence on the merged scans with the goal of relating them to the original dose plan. Results We evaluated 157 patients who received radiotherapy for vulvar cancer. Median age was 68 years (range 29-91). Patients with International Federation of Gynecology and Obstetrics (FIGO) stage IA-IVB were included. Twenty-nine patients had recurrent disease; 156 patients had squamous cell carcinoma and one patient had adenosquamous carcinoma of the vulva. Among the 157 patients, 37 patients with recurrent disease had recurrence scans available for review, for a total of 80 recurrence sites; 53% of the recurrences were located in the region to which the highest dose (60-70 Gy) had been prescribed. Patients who received definitive radiotherapy developed failure primarily in the high-dose region (80.5%), whereas patients who received adjuvant radiotherapy had a more scattered failure pattern (p<0.0001). Among the latter group, 29.5% failed in the high-dose region. Conclusions Patients who received definitive versus adjuvant radiotherapy had different failure patterns, indicating that separate approaches are needed to improve both adjuvant and definitive radiotherapy for vulvar cancer.

Purpose: We propose and simulate a model-based methodology to incorporate heterogeneous treatment benefit of proton therapy (PrT) versus photon therapy into randomized trial designs. We use radiation-induced pneumonitis (RP) as an exemplar. The aim is to obtain an unbiased estimate of how predicted difference in normal tissue complications probability (DNTCP) converts into clinical outcome on the patient level. Materials and Methods: DNTCP data from in silico treatment plans for photon therapy and PrT for patients with locally advanced lung cancer as well as randomly sampled clinical risk factors were included in simulations of trial outcomes. The model used at point of analysis of the trials was an iQUANTEC model. Trial outcomes were examined with Cox proportional hazards models, both in case of a correctly specified model and in a scenario where there is discrepancy between the dose metric used for DNTCP and the dose metric associated with the "true" clinical outcome, that is, when the model is misspecified. We investigated how outcomes from such a randomized trial may feed into a model-based estimate of the patient-level benefit from PrT, by creating patient-specific predicted benefit probability distributions. Results: Simulated trials showed benefit in accordance with that expected when the NTCP model was equal to the model for simulating outcome. When the model was misspecified, the benefit changed and we observed a reversal when the driver of outcome was high-dose dependent while the NTCP model was mean-dose dependent. By converting trial results into probability distributions, we demonstrated large heterogeneity in predicted benefit, and provided a randomized measure of the precision of individual benefit estimates. Conclusions: The design allows for quantifying the benefit of PrT referral, based on the combination of NTCP models, clinical risk factors, and traditional randomization. A misspecified model can be detected through a lower-than-expected hazar

Background: Selecting patients for early clinical trials is a challenging process and clinicians lack sufficient tools to predict overall survival (OS). Circulating cell-free DNA (cfDNA) has recently been shown to be a promising prognostic biomarker. The aim of this study was to investigate whether baseline cfDNA measurement could improve the prognostic information of the Royal Marsden Hospital (RMH) score. Methods: Solid tumour patients referred for phase I trials were included in the Copenhagen Personalized Oncology (CoPPO) programme. Baseline characteristics were collected prospectively, including the RMH prognostic score, Eastern Cooperative Oncology Group (ECOG) performance status and concentration of cfDNA per millilitre plasma. Cox proportional hazards model was used to assess the prognostic value of baseline variables. Results: Plasma cfDNA concentration was quantifiable in 302 patients out of a total of 419 included in the study period of 2 years and 5 months. The RMH score was confirmed to be associated with OS. Cell-free DNA was shown to be an independent prognostic marker of OS and improved the risk model, including RMH, performance status and age. Furthermore, both plasma cfDNA concentration and RMH score were associated with treatment allocation (p < 0.00001). Conclusion: Our model based on RMH score, age, ECOG performance status and cfDNA improved prediction of OS and constitutes a clinically valuable tool when selecting patients for early clinical trials. An interactive version of the prognostic model is published on http://bit.ly/phase1survival.

BACKGROUND AND PURPOSE: Treatment planning of radiotherapy (RT) for left-sided breast cancer is a challenging case. Several competing concerns are incorporated at present through protocol-defined dose-volume constraints, e.g. cardiac exposure and target coverage. Such constraints are limited by neglecting patient-specific risk factors (RFs). We propose an alternative RT planning method based solely on bioeffect models to minimize the estimated risks of breast cancer recurrence (BCR) and radiation-induced mortality endpoints considering patient-specific factors.METHODS AND MATERIALS: Thirty-nine patients with left-sided breast cancer treated with comprehensive post-lumpectomy loco-regional conformal RT were included. An in-house particle swarm optimization (PSO) engine was used to choose fields from a large set of predefined fields and optimize monitor units to minimize the total risk of BCR and mortality caused by radiation-induced ischaemic heart disease (IHD), secondary lung cancer (SLC) and secondary breast cancer (SBC). Risk models included patient age, smoking status and cardiac risk and were developed using published multi-institutional data.RESULTS: For the clinical plans the normal tissue complication probability, i.e. summed risk of IHD, SLC and SBC, was <3.7% and the risk of BCR was <6.1% for all patients. Median total decrease in mortality or recurrence achieved with individualized PSO plans was 0.4% (range, 0.06-2.0%)/0.5% (range, 0.11-2.2%) without/with risk factors.CONCLUSIONS: Inverse RT plan optimization using bioeffect probability models allows individualization according to patient-specific risk factors. The modelled benefit when compared to clinical plans is, however, modest in most patients, demonstrating that current clinical plans are close to optimal. Larger gains may be achievable with morbidity endpoints rather than mortality.

Background: The systematic use of a Patient-Reported Outcome (PRO) as symptom monitoring during cancer treatment and follow-up has the potential to increase symptom awareness, secure timely management of side effects, improve health-related quality of life and improve data quality. This study was conducted to identify the patients' experience during chemoradiotherapy for squamous cell carcinoma of the head and neck (HNSCC) and to investigate how these symptoms correspond with different PRO questionnaires. Material and methods: Semi-structured interviews on acute side effects were performed until saturation with HNSCC patients treated with high-dose radiotherapy (RT) ± concomitant chemotherapy. The symptoms were thematically grouped in organ classes in accordance with Medical Dictionary for Regulatory Activities (MedDRA). PRO questionnaires validated for patients with HNSCC during RT were identified in the literature and were compared to the patients' symptoms. Results: Thirteen patients were interviewed. The most frequently mentioned symptoms were oral pain, decreased appetite, dysphagia, dry mouth, fatigue and hoarseness, in order of frequency. A comparison between the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire for Head and Neck Cancer (EORTC QLQ-H&N35), the Functional Assessment of Cancer Therapy General and Head and Neck (FACT-H&N), the M.D. Anderson Symptom Inventory Head and Neck questionnaire (MDASI-HN), selected items from the Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) and the symptoms described by the patients showed that the PROs do not cover the same symptoms, and no specific questionnaire covers all patient's experiences. Conclusion: We find, that questionnaires applied in the field of PRO among patients with HNSCC undergoing RT may not fully comprise the experiences of patients and we recommend, that experiences of patients must be included i

Intratumor heterogeneity may contribute to the ambiguous clinical results on PD-L1 status as a predictor for immunotherapy response in patients with HNSCC. This decreases the utility of PD-L1 expression from single tumour biopsies as a predictive biomarker. In this prospective study, intratumor heterogeneity of PD-L1 expression in HNSCC was investigated with both Tumour Proportion Score (TPS) and Combined Positive Score (CPS). Thirty-three whole surgical specimens from 28 patients with HNSCC were included. PD-L1 expression in six random core biopsies from each surgical specimen was used to assess the concordance between multiple biopsies and the negative predictive value of a single negative core biopsy. With 1% cut off, 36% of the specimens were concordant with TPS and 52% with CPS. With a 50% cut-off value the concordance was 70% with TPS and 55% with CPS. Defining a tumour as positive if just a single-one of the biopsies was positive, the negative predictive value (NPV) of a single negative core biopsy was 38.9 and 0% (1% cut off), and 79.9% and 62.8% (50% cut off) for TPS and CPS, respectively. In conclusion, PD-L1 positivity varies markedly within the tumour, both with TPS and CPS, challenging the utility of this biomarker.