Intratumor genetic heterogeneity and head and neck cancer relapse.

BACKGROUND: Head and neck squamous cell carcinomas are treated by surgery, radiotherapy (RT), chemoradiotherapy (CRT) or combinations thereof, but locoregional recurrences (LRs) occur in 30-40% of treated patients. We have previously shown that in approximately half of the LRs after CRT, cancer driver mutations are not shared with the index tumor. AIM: To investigate two possible explanations for these genetically unrelated relapses, treatment-induced genetic changes and intratumor genetic heterogeneity. METHODS: To investigate treatment-induced clonal DNA changes, we compared copy number alterations (CNAs) and mutations between primary and recurrent xenografted tumors after treatment with (C)RT. Intratumor genetic heterogeneity was studied by multi-region sequencing on DNA from 31 biopsies of 11 surgically treated tumors. RESULTS: Induction of clonal DNA changes by (C)RT was not observed in the xenograft models. Multi-region sequencing demonstrated variations in CNA profiles between paired biopsies of individual tumors, with copy number heterogeneity scores varying from 0.027 to 0.333. In total, 32 cancer driver mutations could be identified and were shared in all biopsies of each tumor. Remarkably, multi-clonal mutations in these same cancer driver genes were observed in 6 of 11 tumors. Genetically distinct heterogeneous cell cultures could also be established from single tumors, with different biomarker profiles and drug sensitivities. CONCLUSION: Intratumor genetic heterogeneity at the level of the cancer driver mutations might explain the discordant mutational profiles in LRs after CRT, while there are no indications in xenograft models that these changes are induced by CRT.

Towards texture accurate slice interpolation of medical images using PixelMiner.

Quantitative image analysis models are used for medical imaging tasks such as registration, classification, object detection, and segmentation. For these models to be capable of making accurate predictions, they need valid and precise information. We propose PixelMiner, a convolution-based deep-learning model for interpolating computed tomography (CT) imaging slices. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy. PixelMiner was trained on a dataset of 7829 CT scans and validated using an external dataset. We demonstrated the model's effectiveness by using the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and the root mean squared error (RMSE) of extracted texture features. Additionally, we developed and used a new metric, the mean squared mapped feature error (MSMFE). The performance of PixelMiner was compared to four other interpolation methods: (tri-)linear, (tri-)cubic, windowed sinc (WS), and nearest neighbor (NN). PixelMiner produced texture with a significantly lowest average texture error compared to all other methods with a normalized root mean squared error (NRMSE) of 0.11 (p < .01), and the significantly highest reproducibility with a concordance correlation coefficient (CCC) ≥ 0.85 (p < .01). PixelMiner was not only shown to better preserve features but was also validated using an ablation study by removing auto-regression from the model and was shown to improve segmentations on interpolated slices.

The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review.

Basal cell carcinoma (BCC) is one of the most common types of cancer. The growing incidence worldwide and the need for fast, reliable and less invasive diagnostic techniques make a strong case for the application of different artificial intelligence techniques for detecting and classifying BCC and its subtypes. We report on the current evidence regarding the application of handcrafted and deep radiomics models used for the detection and classification of BCC in dermoscopy, optical coherence tomography and reflectance confocal microscopy. We reviewed all the articles that were published in the last 10 years in PubMed, Web of Science and EMBASE, and we found 15 articles that met the inclusion criteria. We included articles that are original, written in English, focussing on automated BCC detection in our target modalities and published within the last 10 years in the field of dermatology. The outcomes from the selected publications are presented in three categories depending on the imaging modality and to allow for comparison. The majority of articles (n = 12) presented different AI solutions for the detection and/or classification of BCC in dermoscopy images. The rest of the publications presented AI solutions in OCT images (n = 2) and RCM (n = 1). In addition, we provide future directions for the application of these techniques for the detection of BCC. In conclusion, the reviewed publications demonstrate the potential benefit of AI in the detection of BCC in dermoscopy, OCT and RCM.

Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review.

Lung cancer's radiomic phenotype may potentially inform clinical decision-making with respect to radical radiotherapy. At present there are no validated biomarkers available for the individualisation of radical radiotherapy in lung cancer and the mortality rate of this disease remains the highest of all other solid tumours. MEDLINE was searched using the terms 'radiomics' and 'lung cancer' according to the Preferred Reporting Items for Systematic Reviews and Met-Analyses (PRISMA) guidance. Radiomics studies were defined as those manuscripts describing the extraction and analysis of at least 10 quantifiable imaging features. Only those studies assessing disease control, survival or toxicity outcomes for patients with lung cancer following radical radiotherapy ± chemotherapy were included. Study titles and abstracts were reviewed by two independent reviewers. The Radiomics Quality Score was applied to the full text of included papers. Of 244 returned results, 44 studies met the eligibility criteria for inclusion. End points frequently reported were local (17%), regional (17%) and distant control (31%), overall survival (79%) and pulmonary toxicity (4%). Imaging features strongly associated with clinical outcomes include texture features belonging to the subclasses Gray level run length matrix, Gray level co-occurrence matrix and kurtosis. The median cohort size for model development was 100 (15-645); in the 11 studies with external validation in a separate independent population, the median cohort size was 84 (21-295). The median number of imaging features extracted was 184 (10-6538). The median Radiomics Quality Score was 11% (0-47). Patient-reported outcomes were not incorporated within any studies identified. No studies externally validated a radiomics signature in a registered prospective study. Imaging-derived indices attained through radiomic analyses could equip thoracic oncologists with biomarkers for treatment response, patterns of failure, normal tissue toxicity and survival in lung cancer. Based on routine scans, their non-invasive nature and cost-effectiveness are major advantages over conventional pathological assessment. Improved tools are required for the appraisal of radiomics studies, as significant barriers to clinical implementation remain, such as standardisation of input scan data, quality of reporting and external validation of signatures in randomised, interventional clinical trials.

Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability.

BACKGROUND: Radiomic features extracted from breast MRI have potential for diagnostic, prognostic, and predictive purposes. However, before they can be used as biomarkers in clinical decision support systems, features need to be repeatable and reproducible. OBJECTIVE: Identify repeatable radiomics features within breast tissue on prospectively collected MRI exams through multiple test-retest measurements. STUDY TYPE: Prospective. POPULATION: 11 healthy female volunteers. FIELD STRENGTH/SEQUENCE: 1.5 T; MRI exams, comprising T2-weighted turbo spin-echo (T2W) sequence, native T1-weighted turbo gradient-echo (T1W) sequence, diffusion-weighted imaging (DWI) sequence using b-values 0/150/800, and corresponding derived ADC maps. ASSESSMENT: 18 MRI exams (three test-retest settings, repeated on 2 days) per healthy volunteer were examined on an identical scanner using a fixed clinical breast protocol. For each scan, 91 features were extracted from the 3D manually segmented right breast using Pyradiomics, before and after image preprocessing. Image preprocessing consisted of 1) bias field correction (BFC); 2) z-score normalization with and without BFC; 3) grayscale discretization using 32 and 64 bins with and without BFC; and 4) z-score normalization + grayscale discretization using 32 and 64 bins with and without BFC. STATISTICAL TESTS: Features' repeatability was assessed using concordance correlation coefficient(CCC) for each pair, i.e. each MRI was compared to each of the remaining 17 MRI with a cut-off value of CCC > 0.90. RESULTS: Images without preprocessing produced the highest number of repeatable features for both T1W sequence and ADC maps with 15 of 91 (16.5%) and 8 of 91 (8.8%) repeatable features, respectively. Preprocessed images produced between 4 of 91 (4.4%) and 14 of 91 (15.4%), and 6 of 91 (6.6%) and 7 of 91 (7.7%) repeatable features, respectively for T1W and ADC maps. Z-score normalization produced highest number of repeatable features, 26 of 91 (28.6%) in T2W sequences, in these images, no preprocessing produced 11 of 91 (12.1%) repeatable features. DATA CONCLUSION: Radiomic features extracted from T1W, T2W sequences and ADC maps from breast MRI exams showed a varying number of repeatable features, depending on the sequence. Effects of different preprocessing procedures on repeatability of features were different for each sequence. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework.

The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning -the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS. Such techniques could be highly appealing due to the reuse of existing data, automation of clinical workflows, minimal invasiveness, three-dimensional volumetric characterization, and the promise of high accuracy and reproducibility of results and cost-effectiveness. Nevertheless, there are several challenges that quantitative imaging techniques face, and need to be addressed before the translation to clinical use. These challenges include, but are not limited to, the explainability of the models, the reproducibility of the quantitative imaging features, and their sensitivity to variations in image acquisition and reconstruction parameters. In this narrative review, we report on the status of quantitative medical image analysis using radiomics and deep learning, the challenges the field is facing, propose a framework for robust radiomics analysis, and discuss future prospects.

Exploring imaging features of molecular subtypes of large cell neuroendocrine carcinoma (LCNEC).

OBJECTIVES: Radiological characteristics and radiomics signatures can aid in differentiation between small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC). We investigated whether molecular subtypes of large cell neuroendocrine carcinoma (LCNEC), i.e. SCLC-like (with pRb loss) vs. NSCLC-like (with pRb expression), can be distinguished by imaging based on (1) imaging interpretation, (2) semantic features, and/or (3) a radiomics signature, designed to differentiate between SCLC and NSCLC. MATERIALS AND METHODS: Pulmonary oncologists and chest radiologists assessed chest CT-scans of 44 LCNEC patients for 'small cell-like' or 'non-small cell-like' appearance. The radiologists also scored semantic features of 50 LCNEC scans. Finally, a radiomics signature was trained on a dataset containing 48 SCLC and 76 NSCLC scans and validated on an external set of 58 SCLC and 40 NSCLC scans. This signature was applied on scans of 28 SCLC-like and 8 NSCLC-like LCNEC patients. RESULTS: Pulmonary oncologists and radiologists were unable to differentiate between molecular subtypes of LCNEC and no significant differences in semantic features were found. The area under the receiver operating characteristics curve of the radiomics signature in the validation set (SCLC vs. NSCLC) was 0.84 (95% confidence interval (CI) 0.77-0.92) and 0.58 (95% CI 0.29-0.86) in the LCNEC dataset (SCLC-like vs. NSCLC-like). CONCLUSION: LCNEC appears to have radiological characteristics of both SCLC and NSCLC, irrespective of pRb loss, compatible with the SCLC-like subtype. Imaging interpretation, semantic features and our radiomics signature designed to differentiate between SCLC and NSCLC were unable to separate molecular LCNEC subtypes, which underscores that LCNEC is a unique disease.

Can radiomics help to predict skeletal muscle response to chemotherapy in stage IV non-small cell lung cancer?

BACKGROUND: Muscle depletion negatively impacts treatment efficacy and survival rates in cancer. Prevention and timely treatment of muscle loss require prediction of patients at risk. We aimed to investigate the potential of skeletal muscle radiomic features to predict future muscle loss. METHODS: A total of 116 patients with stage IV non-small cell lung cancer included in a randomised controlled trial (NCT01171170) studying the effect of nitroglycerin added to paclitaxel-carboplatin-bevacizumab were enrolled. In this post hoc analysis, muscle cross-sectional area and radiomic features were extracted from computed tomography images obtained before initiation of chemotherapy and shortly after administration of the second cycle. For internal cross-validation, the cohort was randomly split in a training set and validation set 100 times. We used least absolute shrinkage and selection operator method to select features that were most significantly associated with muscle loss and an area under the curve (AUC) for model performance. RESULTS: Sixty-nine patients (59%) exhibited loss of skeletal muscle. One hundred ninety-three features were used to construct a prediction model for muscle loss. The average AUC was 0.49 (95% confidence interval [CI]: 0.36, 0.62). Differences in intensity and texture radiomic features over time were seen between patients with and without muscle loss. CONCLUSIONS: The present study shows that skeletal muscle radiomics did not predict future muscle loss during chemotherapy in non-small cell lung cancer. Differences in radiomic features over time might reflect myosteatosis. Future imaging analysis combined with muscle tissue analysis in patients and in experimental models is needed to unravel the biological processes linked to the radiomic features.

Decision Support Systems in Prostate Cancer Treatment: An Overview.

BACKGROUND: A multifactorial decision support system (mDSS) is a tool designed to improve the clinical decision-making process, while using clinical inputs for an individual patient to generate case-specific advice. The study provides an overview of the literature to analyze current available mDSS focused on prostate cancer (PCa), in order to better understand the availability of decision support tools as well as where the current literature is lacking. METHODS: We performed a MEDLINE literature search in July 2018. We divided the included studies into different sections: diagnostic, which aids in detection or staging of PCa; treatment, supporting the decision between treatment modalities; and patient, which focusses on informing the patient. We manually screened and excluded studies that did not contain an mDSS concerning prostate cancer and study proposals. RESULTS: Our search resulted in twelve diagnostic mDSS; six treatment mDSS; two patient mDSS; and eight papers that could improve mDSS. CONCLUSIONS: Diagnosis mDSS is well represented in the literature as well as treatment mDSS considering external-beam radiotherapy; however, there is a lack of mDSS for other treatment modalities. The development of patient decision aids is a new field of research, and few successes have been made for PCa patients. These tools can improve personalized medicine but need to overcome a number of difficulties to be successful and require more research.

Assessment of conversational pragmatics: A screening tool for pragmatic language impairment in a control population of children aged 6-12 years.

Pragmatics can be defined as the way in which language is used to communicate in a given social context. Although there is a lack of a standardized assessment, healthcare professionals find themselves confronted with pragmatic language skill impairments in children with neurodevelopmental disorders or brain injuries. The characterization of language use causes problems in social interactions, which has clinical implications in daily life. However, this is still underestimated because there is currently no quick, easy-to-use screening device to rank these deficits. We have developed a pragmatic deficits screening chart that has been tested on a control population of children aged 6-12 years. The chart comprises 26 items exploring seven areas of pragmatics (intentionality, governance of exchange, organization of information, adaptation strategies, conversational implicit language, nonverbal skills, and paralinguistic aspects). Parents select one of four possible answers to describe how frequently their child demonstrates each type of behavior ("never, rarely, sometimes, often"). We distributed 1666 charts; 760 were returned, of which 552 could be analyzed. Internal consistency as measured with Cronbach's alpha coefficient (0.88) was satisfactory. There was no influence of age on total score, nor of the department/type of schooling. The population distribution was non-Gaussian so the results are presented in percentiles. We propose a first-line screening tool that is quick and easy to complete by family, which facilitates referral to specialists for further investigations into the etiological implications of pragmatic language impairment.

Evaluation of third treatment week as temporal window for assessing responsiveness on repeated FDG-PET-CT scans in Non-Small Cell Lung Cancer patients.

PURPOSE: Early assessment of tumour response to treatment with repeated FDG-PET-CT imaging has potential for treatment adaptation but it is unclear what the optimal time window for this evaluation is. Previous studies indicate that changes in SUVmean and the effective radiosensitivity (αeff, accounting for uptake variations and accumulated dose until the second FDG-PET-CT scan) are predictive of 2-year overall survival (OS) when imaging is performed before radiotherapy and during the second week. This study aims to investigate if multiple FDG-PET-derived quantities determined during the third treatment week have stronger predictive power. METHODS: Twenty-eight lung cancer patients were imaged with FDG-PET-CT before radiotherapy (PET1) and during the third week (PET2). SUVmean, SUVmax, SUVpeak, MTV41%-50% (Metabolic Tumour Volume), TLG41%-50% (Total Lesion Glycolysis) in PET1 and PET2 and their change (), as well as average αeff (α¯eff) and the negative fraction of αeff values [Formula: see text] ) were determined. Correlations were sought between FDG-PET-derived quantities and OS with ROC analysis. RESULTS: Neither SUVmean, SUVmax, SUVpeak in PET1 and PET2 (AUC = 0.5-0.6), nor their changes (AUC = 0.5-0.6) were significant for outcome prediction purposes. Lack of correlation with OS was also found for α¯eff (AUC = 0.5) and [Formula: see text] (AUC = 0.5). Threshold-based quantities (MTV41%-50%, TLG41%-50%) and their changes had AUC = 0.5-0.7. P-values were in all cases ≫0.05. CONCLUSIONS: The poor OS predictive power of the quantities determined from repeated FDG-PET-CT images indicates that the third week of treatment might not be suitable for treatment response assessment. Comparatively, the second week during the treatment appears to be a better time window.

Dynamics of rectal balloon implant shrinkage in prostate VMAT : Influence on anorectal dose and late rectal complication risk.

PURPOSE: To assess the effect of a shrinking rectal balloon implant (RBI) on the anorectal dose and complication risk during the course of moderately hypofractionated prostate radiotherapy. METHODS: In 15 patients with localized prostate cancer, an RBI was implanted. A weekly kilovolt cone-beam computed tomography (CBCT) scan was acquired to measure the dynamics of RBI volume and prostate-rectum separation. The absolute anorectal volume encompassed by the 2 Gy equieffective 75 Gy isodose (V75Gy) was recalculated as well as the mean anorectal dose. The increase in estimated risk of grade 2-3 late rectal bleeding (LRB) between the start and end of treatment was predicted using nomograms. The observed acute and late toxicities were evaluated. RESULTS: A significant shrinkage of RBI volumes was observed, with an average volume of 70.4% of baseline at the end of the treatment. Although the prostate-rectum separation significantly decreased over time, it remained at least 1 cm. No significant increase in V75Gy of the anorectum was observed, except in one patient whose RBI had completely deflated in the third week of treatment. No correlation between mean anorectal dose and balloon deflation was found. The increase in predicted LRB risk was not significant, except in the one patient whose RBI completely deflated. The observed toxicities confirmed these findings. CONCLUSIONS: Despite significant decrease in RBI volume the high-dose rectal volume and the predicted LRB risk were unaffected due to a persistent spacing between the prostate and the anterior rectal wall.

PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE.

AIM: Identifying the best care for a patient can be extremely challenging. To support the creation of multifactorial Decision Support Systems (DSSs), we propose an Umbrella Protocol, focusing on prostate cancer. MATERIALS & METHODS: The PRODIGE project consisted of a workflow for standardizing data, and procedures, to create a consistent dataset useful to elaborate DSSs. Techniques from classical statistics and machine learning will be adopted. The general protocol accepted by our Ethical Committee can be downloaded from cancerdata.org . RESULTS: A standardized knowledge sharing process has been implemented by using a semi-formal ontology for the representation of relevant clinical variables. CONCLUSION: The development of DSSs, based on standardized knowledge, could be a tool to achieve a personalized decision-making.

Pharmacokinetic modeling of a novel hypoxia PET tracer [18F]HX4 in patients with non-small cell lung cancer.

BACKGROUND: [18F]HX4 is a promising new PET tracer developed to identify hypoxic areas in tumor tissue. This study analyzes [18F]HX4 kinetics and assesses the performance of simplified methods for quantification of [18F]HX4 uptake. To this end, eight patients with non-small cell lung cancer received dynamic PET scans at three different time points (0, 120, and 240 min) after injection of 426 ± 72 MBq [18F]HX4, each lasting 30 min. Several compartment models were fitted to time activity curves (TAC) derived from various areas within tumor tissue using image-derived input functions. RESULTS: Best fits were obtained using the reversible two-tissue compartment model with blood volume parameter (2T4k+VB). Simplified measures correlated well with VT estimates (tumor-to-blood ratio (TBr) R 2 = 0.96, tumor-to-muscle ratio R 2 = 0.94, standardized uptake value R 2 = 0.89). CONCLUSIONS: [18F]HX4 shows reversible kinetics in tumor tissue: 2T4k+VB. TBr based on static imaging at 2 or 4 h can be used for quantification of [18F]HX4 uptake.

How do changes in the mtDNA and mitochondrial dysfunction influence cancer and cancer therapy? Challenges, opportunities and models.

Several mutations in nuclear genes encoding for mitochondrial components have been associated with an increased cancer risk or are even causative, e.g. succinate dehydrogenase (SDHB, SDHC and SDHD genes) and iso-citrate dehydrogenase (IDH1 and IDH2 genes). Recently, studies have suggested an eminent role for mitochondrial DNA (mtDNA) mutations in the development of a wide variety of cancers. Various studies associated mtDNA abnormalities, including mutations, deletions, inversions and copy number alterations, with mitochondrial dysfunction. This might, explain the hampered cellular bioenergetics in many cancer cell types. Germline (e.g. m.10398A>G; m.6253T>C) and somatic mtDNA mutations as well as differences in mtDNA copy number seem to be associated with cancer risk. It seems that mtDNA can contribute as driver or as complementary gene mutation according to the multiple-hit model. This can enhance the mutagenic/clonogenic potential of the cell as observed for m.8993T>G or influences the metastatic potential in later stages of cancer progression. Alternatively, other mtDNA variations will be innocent passenger mutations in a tumor and therefore do not contribute to the tumorigenic or metastatic potential. In this review, we discuss how reported mtDNA variations interfere with cancer treatment and what implications this has on current successful pharmaceutical interventions. Mutations in MT-ND4 and mtDNA depletion have been reported to be involved in cisplatin resistance. Pharmaceutical impairment of OXPHOS by metformin can increase the efficiency of radiotherapy. To study mitochondrial dysfunction in cancer, different cellular models (like ρ(0) cells or cybrids), in vivo murine models (xenografts and specific mtDNA mouse models in combination with a spontaneous cancer mouse model) and small animal models (e.g. Danio rerio) could be potentially interesting to use. For future research, we foresee that unraveling mtDNA variations can contribute to personalized therapy for specific cancer types and improve the outcome of the disease.

Can metformin improve 'the tomorrow' of patients treated for oesophageal cancer?

INTRODUCTION: Recent studies suggest that the use of metformin is associated with reduced cancer incidence and improved prognosis in patients with oesophageal cancer. We explored the relationship between the use of metformin and outcome (pathologic response rate, distant metastasis-free and overall survival) in our mono-institutional cohort of patients treated for oesophageal cancer. MATERIAL AND METHODS: Between 2008 and 2014, a total of 196 patients with oesophageal cancer (ages ranged from 37 to 82 years) eligible for curative treatment entered the study. Patients were categorized as non-diabetic (n = 172), diabetic not taking metformin (n = 5) or diabetic taking metformin (n = 19). The majority of patients were treated with trimodality therapy (n = 189). Pathologic response was graded according to Mandard's tumour regression score at the time of surgery. Distant metastasis-free and overall survival were calculated using the Kaplan-Meier method with log rank comparisons performed to determine significance. RESULTS: The overall pathologic complete response rate for the study population was 26%. It was 25% for patients not using metformin and 39% for diabetics taking metformin (p = 0.260). The two-year overall survival rate for the whole group was 59%. Use of metformin was associated with a significantly better distant metastasis-free survival rate (p = 0.040) or overall survival rate (p = 0.012). Multivariate analysis using Cox regression found that metformin treatment significantly prolonged survival (p = 0.043). CONCLUSION: In our population-based study, the use of metformin was associated with an improved overall and distant metastasis-free survival rate in patients with oesophageal cancer. These data are complementary to one other clinical study and warrant further prospective study.

Different prognostic models for different patient populations: validation of a new prognostic model for patients with oropharyngeal cancer in Western Europe.

OBJECTIVE: The presence of human papillomavirus (HPV) infection in oropharyngeal squamous cell carcinoma (OPSCC) is a major determinant in prognostic risk modelling. Recently, a prognostic model was proposed in which HPV status, comorbidity and nodal stage were the most important prognostic factors to determine high-, intermediate- and low-risk survival groups. Here, we report on the validation of this model using an independent single-institutional cohort. METHODS: A total number of 235 patients curatively treated for OPSCC in the period 2000-2011 at the MUMC (Maastricht University Medical Center, The Netherlands) were included. The presence of an oncogenic HPV infection was determined by p16 immunostaining, followed by a high-risk HPV DNA PCR on the p16-positive cases. The model variables included were HPV status, comorbidity and nodal stage. As a measure of model performance, the Harrell's Concordance index (Harrell's C-index) was used. RESULTS: The 5-year overall survival (OS) estimates were 84.6%, 54.5% and 28.7% in the low-, intermediate- and high-risk group, respectively. The difference between the survival curves was highly significant (P<0.001). The Harrell's C-index was 0.69 (95% confidence interval (CI): 0.63-0.75). CONCLUSION: In this study a previously developed prognostic risk model was validated. This model will help to personalise treatment in OPSCC patients. This model is publicly available at www.predictcancer.org.

Organizational development trajectory of a large academic radiotherapy department set up similarly to a prospective clinical trial: the MAASTRO experience.

OBJECTIVE: To simultaneously improve patient care processes and clinical research activities by starting a hypothesis-driven reorganization trajectory mimicking the rigorous methodology of a prospective clinical trial. METHODS: The design of this reorganization trajectory was based on the model of a prospective trial. It consisted of (1) listing problems and analysing their potential causes, (2) defining interventions, (3) defining end points and (4) measuring the effect of the interventions (i.e. at baseline and after 1 and 2 years). The primary end point for patient care was the number of organizational root causes of incidents/near incidents; for clinical research, it was the number of patients in trials. There were several secondary end points. We analysed the data using two sample z-tests, χ(2) test, a Mann-Whitney U test and the one-way analysis of variance with Bonferroni correction. RESULTS: The number of organizational root causes was reduced by 27% (p < 0.001). There was no effect on the percentage of patients included in trials. CONCLUSION: The reorganizational trajectory was successful for the primary end point of patient care and had no effect on clinical research. Some confounding events hampered our ability to draw strong conclusions. Nevertheless, the transparency of this approach can give medical professionals more confidence in moving forward with other organizational changes in the same way. ADVANCES IN KNOWLEDGE: This article is novel because managerial interventions were set up similarly to a prospective clinical trial. This study is the first of its kind in radiotherapy, and this approach can contribute to discussions about the effectiveness of managerial interventions.

Nomograms to predict survival and the risk for developing local or distant recurrence in patients with rectal cancer treated with optional short-term radiotherapy.

BACKGROUND: In many European countries, short-term 5 × 5 Gy radiotherapy has become the standard preoperative treatment of patients with resectable rectal cancer. Individualized risk assessment might allow a better selection of patients who will benefit from postoperative treatment and intensified follow-up. PATIENTS AND METHODS: From patient's data from three European rectal cancer trials (N = 2881), we developed multivariate cox nomograms reflecting the risk for local recurrence (LR), distant metastases (DM) and overall survival (OS). Evaluated variables were age, gender, tumour distance from the anal verge, the use of radiotherapy, surgical technique (total mesorectal excision/conventional surgery), surgery type (low anterior resection/abdominoperineal resection), time from randomization to surgery, residual disease (R0 versus R1 + 2), pT-stage, pN-stage and surgical complications. RESULTS: Pathological T- and N-status are of vital importance for an accurate prediction of LR, DM and OS. Short-course radiotherapy reduces the rate of LR. The developed nomograms are capable of predicting events with a validation c-index of 0.79 (LR), 0.76 (DM) and 0.75 (OS). The proposed stratification in risk groups allowed significant distinction between Kaplan-Meier curves for outcome. CONCLUSION: The developed nomograms can contribute to better individual risk prediction for LR, DM and OS for patients operated on rectal cancer. The practicality of the defined risk groups makes decision support in the consulting room feasible, assisting physicians to select patients for adjuvant therapy or intensified follow-up.

Image-guided stereotactic ablative radiotherapy for the liver: a safe and effective treatment.

AIMS: Stereotactic ablative body radiotherapy (SABR) is a non-invasive treatment option for inoperable patients or patients with irresectable liver tumors. Outcome and toxicity were evaluated retrospectively in this single-institution patient cohort. PATIENTS AND METHODS: Between 2010 and 2014, 39 lesions were irradiated in 33 consecutive patients (18 male, 15 female, median age of 68 years). All the lesions were liver metastases (n = 34) or primary hepatocellular carcinomas (n = 5). The patients had undergone four-dimensional respiration-correlated PET-CT for treatment simulation to capture tumor motion. We analyzed local control with a focus on CT-based response at three months, one year and two years after treatment, looking at overall survival and the progression pattern. RESULTS: All patients were treated with hypofractionated image-guided stereotactic radiotherapy. The equivalent dose in 2 Gy fractions varied from 62.5 Gy to 150 Gy, delivered in 3-10 fractions (median dose 93.8 Gy, alpha/beta = 10). The CT-based regression pattern three months after radiotherapy revealed partial regression in 72.7% of patients with a complete remission in 27.3% of the cases. The site of first progression was predominantly distant. One- and two-year overall survival rates were 85.4% and 68.8%, respectively. No toxicity of grade 2 or higher according to the NCI Common Terminology Criteria for Adverse Events v4.0 was observed. CONCLUSION: SABR is a safe and efficient treatment for selected inoperable patients or irresectable tumors of the liver. Future studies should combine SABR with systemic treatment acting in synergy with radiation, such as immunological interventions or hypoxic cell radiosensitizers to prevent distant relapse.