Frank Hoebers

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

CD44 Expression Predicts Local Recurrence after Radiotherapy in Larynx Cancer

Purpose: To find molecular markers from expression profiling data to predict recurrence of laryngeal cancer after radiotherapy. Experimental Design: We generated gene expression data on pre-treatment biopsies from 52 larynx cancer patients. Patients developing a local recurrence were matched for T-stage, subsite, treatment, gender and age with non-recurrence patients. Candidate genes were then tested by immunohistochemistry on tumor material from a second series of 76 patients. Both series comprised early stage cancer treated with radiotherapy alone. Finally, gene expression data of eight larynx cancer cell lines with known radiosensitivity were analyzed. Results: Nineteen patients with a local recurrence were matched with 33 controls. Gene sets for hypoxia, proliferation and intrinsic radiosensitivity did not correlate with recurrence, whereas expression of the putative stem cell marker CD44 did. In a supervised analysis, probes for all three splice variants of CD44 on the array appeared in the top 10 most significantly correlated with local recurrence. Immunohistochemical analysis of CD44 expression on the independent validation series confirmed CD44s predictive potential. In 8 larynx cancer cell lines, CD44 gene expression did not correlate with intrinsic radiosensitivity although it did correlate significantly with plating efficiency, consistent with a relationship with stem cell content. Conclusions: CD44 was the only biological factor tested which significantly correlated with response to radiotherapy in early stage larynx cancer patients, both at the mRNA and protein levels. Further studies are needed to confirm this and to assess how general these findings are for other head and neck tumor stages and sites. Clin Cancer Res; 16(21); 5329–38. ©2010 AACR.

‘Rapid Learning health care in oncology’ – An approach towards decision support systems enabling customised radiotherapy’ ☆ ☆☆

PURPOSE An overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy. MATERIAL AND RESULTS Rapid Learning methodology is divided into four phases. In the data phase, diverse data are collected about past patients, treatments used, and outcomes. Innovative information technologies that support semantic interoperability enable distributed learning and data sharing without additional burden on health care professionals and without the need for data to leave the hospital. In the knowledge phase, prediction models are developed for new data and treatment outcomes by applying machine learning methods to data. In the application phase, this knowledge is applied in clinical practice via novel decision support systems or via extensions of existing models such as Tumour Control Probability models. In the evaluation phase, the predictability of treatment outcomes allows the new knowledge to be evaluated by comparing predicted and actual outcomes. CONCLUSION Personalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making.

Reirradiation for head-and-neck cancer: delicate balance between effectiveness and toxicity.

PURPOSE To analyze the effectiveness and toxicity of reirradiation (re-RT) for head-and-neck cancer. METHODS AND MATERIALS A retrospective data analysis was performed of 58 patients who underwent re-RT with curative intent. Re-RT was given as definitive treatment in 53% of patients, whereas salvage surgery preceded reirradiation in 47%. The median cumulative RT dose was 119 Gy (range, 76-140). Concurrent chemotherapy was administered with re-RT (CRT) in 57% of patients. Event-free survival was defined as survival without recurrence and without serious toxicity (≥Grade 3). RESULTS Median follow-up was 57 months (range, 9-140). Locoregional (LR) control was 50% at 2 and 5 years. The 2-year and 5-year overall survival (OS) was 42% and 34%. The following factors were associated with improved OS: postoperative re-RT (vs. primary re-RT), treatment with RT only (vs. CRT) and interval >3 years between previous RT and re-RT. For patients treated with postoperative re-RT and definitive re-RT, the 5-year OS was 49% and 20%, respectively. Patients treated with CRT had a 5-year OS of 13%. Serious (late) toxicity ≥Grade 3 was observed in 20 of 47 evaluable patients (43%). Three cases of treatment-related death were recorded. The 2- and 5-year serious toxicity-free interval was 59% and 55%, respectively. Associated with increased risk of serious toxicity were CRT and higher re-RT dose. The event-free survival rates at 2 and 5 years were 34% and 31%, respectively. CONCLUSIONS Re-RT in head-and-neck cancer is associated with poor survival rates of 13-20% in patients with inoperable disease treated with primary (chemo-) re-RT. For this subgroup, however, no other curative options are available. Long-term disease control and survival can be achieved in patients who receive re-RT as an adjunct to surgical resection. The rates of serious toxicity after re-RT are high, with an incidence of approximately 45% at 5 years. Approximately 1 in 3 patients survived re-RT without recurrence and severe complications.

External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma

ABSTRACT Background. Oropharyngeal squamous cell carcinoma (OPSCC) is one of the fastest growing disease sites of head and neck cancers. A recently described radiomic signature, based exclusively on pre-treatment computed tomography (CT) imaging of the primary tumor volume, was found to be prognostic in independent cohorts of lung and head and neck cancer patients treated in the Netherlands. Here, we further validate this signature in a large and independent North American cohort of OPSCC patients, also considering CT artifacts. Methods. A total of 542 OPSCC patients were included for which we determined the prognostic index (PI) of the radiomic signature. We tested the signature model fit in a Cox regression and assessed model discrimination with Harrells c-index. Kaplan-Meier survival curves between high and low signature predictions were compared with a log-rank test. Validation was performed in the complete cohort (PMH1) and in the subset of patients without (PMH2) and with (PMH3) visible CT artifacts within the delineated tumor region. Results. We identified 267 (49%) patients without and 275 (51%) with visible CT artifacts. The calibration slope (β) on the PI in a Cox proportional hazards model was 1.27 (H0: β = 1, p = 0.152) in the PMH1 (n = 542), 0.855 (H0: β = 1, p = 0.524) in the PMH2 (n = 267) and 1.99 (H0: β = 1, p = 0.002) in the PMH3 (n = 275) cohort. Harrells c-index was 0.628 (p = 2.72e-9), 0.634 (p = 2.7e-6) and 0.647 (p = 5.35e-6) for the PMH1, PMH2 and PMH3 cohort, respectively. Kaplan-Meier survival curves were significantly different (p < 0.05) between high and low radiomic signature model predictions for all cohorts. Conclusion. Overall, the signature validated well using all CT images as-is, demonstrating a good model fit and preservation of discrimination. Even though CT artifacts were shown to be of influence, the signature had significant prognostic power regardless if patients with CT artifacts were included.

Hypoxia imaging with [18F]HX4 PET in NSCLC patients: Defining optimal imaging parameters

BACKGROUND AND PURPOSE [(18)F]HX4 is a promising hypoxia PET-tracer. Uptake, spatio-temporal stability and optimal acquisition parameters for [(18)F]HX4 PET imaging were evaluated in non-small cell lung cancer (NSCLC) patients. MATERIALS AND METHODS [(18)F]HX4 PET/CT images of 15 NSCLC patients were acquired 2h and 4h after injection (p.i.). Maximum standardized-uptake-value (SUV(max)), tumor-to-blood-ratio (TBR(max)), hypoxic fraction (HF) and contrast-to-noise-ratio (CNR) were determined for all lesions. To evaluate spatio-temporal stability, DICE-similarity and Pearson correlation coefficients were calculated. Optimal acquisition-duration was assessed by comparing 30, 20, 10 and 5 min acquisitions. RESULTS Considerable uptake (TBR >1.4) was observed in 18/25 target lesions. TBR(max) increased significantly from 2 h (1.6 ± 0.3) to 4 h p.i. (2.0 ± 0.6). Uptake patterns at 2 h and 4 h p.i. showed a strong correlation (R=0.77 ± 0.10) with a DICE similarity coefficient of 0.69 ± 0.08 for the 30% highest uptake volume. Reducing acquisition-time resulted in significant changes in SUV(max) and CNR. TBR(max) and HF were only affected for scan-times of 5 min. CONCLUSIONS The majority of NSCLC lesions showed considerable [(18)F]HX4 uptake. The heterogeneous uptake pattern was stable between 2 h and 4 h p.i. [(18)F]HX4 PET imaging at 4 h p.i. is superior to 2 h p.i. to reach highest contrast. Acquisition time may be reduced to 10 min without significant effects on TBR(max) and HF.

Adaptive and innovative Radiation Treatment FOR improving Cancer treatment outcomE (ARTFORCE); a randomized controlled phase II trial for individualized treatment of head and neck cancer

BackgroundFailure of locoregional control is the main cause of recurrence in advanced head and neck cancer. This multi-center trial aims to improve outcome in two ways. Firstly, by redistribution of the radiation dose to the metabolically most FDG-PET avid part of the tumour. Hereby, a biologically more effective dose distribution might be achieved while simultaneously sparing normal tissues. Secondly, by improving patient selection. Both cisplatin and Epidermal Growth Factor Receptor (EGFR) antibodies like Cetuximab in combination with Radiotherapy (RT) are effective in enhancing tumour response. However, it is unknown which patients will benefit from either agent in combination with irradiation. We will analyze the predictive value of biological markers and 89Zr-Cetuximab uptake for treatment outcome of chemoradiation with Cetuximab or cisplatin to improve patient selection.MethodsARTFORCE is a randomized phase II trial for 268 patients with a factorial 2 by 2 design: cisplatin versus Cetuximab and standard RT versus redistributed RT. Cisplatin is dosed weekly 40 mg/m2 for 6 weeks. Cetuximab is dosed 250mg/m2 weekly (loading dose 400 mg/m2) for 6 weeks. The standard RT regimen consists of elective RT up to 54.25 Gy with a simultaneous integrated boost (SIB) to 70 Gy in 35 fractions in 6 weeks. Redistributed adaptive RT consists of elective RT up to 54.25 Gy with a SIB between 64-80 Gy in 35 fractions in 6 weeks with redistributed dose to the gross tumour volume (GTV) and clinical target volume (CTV), and adaptation of treatment for anatomical changes in the third week of treatment.Patients with locally advanced, biopsy confirmed squamous cell carcinoma of the oropharynx, oral cavity or hypopharynx are eligible.Primary endpoints are: locoregional recurrence free survival at 2 years, correlation of the median 89Zr-cetuximab uptake and biological markers with treatment specific outcome, and toxicity. Secondary endpoints are quality of life, swallowing function preservation, progression free and overall survival.DiscussionThe objective of the ARTFORCE Head and Neck trial is to determine the predictive value of biological markers and 89Zr-Cetuximab uptake, as it is unknown how to select patients for the appropriate concurrent agent. Also we will determine if adaptive RT and dose redistribution improve locoregional control without increasing toxicity.ClinicalTrials.gov Identifier: NCT01504815

Determinants of treatment waiting times for head and neck cancer in the Netherlands and their relation to survival

INTRODUCTION Waiting to start treatment has been shown to be associated with tumor progression and upstaging in head and neck squamous cell carcinomas (HNSCCs). This diminishes the chance of cure and might lead to unnecessary mortality. We investigated the association between waiting times and survival in the Netherlands and assessed which factors were associated to longer waiting times. METHODS Patient (age, sex, socioeconomic status (SES), tumor (site, stage) and treatment (type, of institute of diagnosis/treatment) characteristics for patients with HNSCC who underwent treatment were extracted from the Netherlands Cancer Registry (NCR) for 2005-2011. Waiting time was defined as the number of days between histopathological diagnosis and start of treatment. Univariable and multivariable Cox regression was used to evaluate survival. RESULTS In total, 13,140 patients were included, who had a median waiting time of 37days. Patients who were more likely to wait longer were men, patients with a low SES, oropharynx tumors, stage IV tumors, patients to be treated with radiotherapy or chemoradiation, and patients referred for treatment to a Head and Neck Oncology Center (HNOC) from another hospital. The 5-year overall survival was 58% for all patients. Our multivariable Cox regression model showed that longer waiting time, was significantly related to a higher hazard of dying (p<0.0001). CONCLUSION This is the first large population-based study showing that longer waiting time for surgery, radiotherapy or chemoradiation is a significant negative prognostic factor for HNSCC patients.

In Vivo Quantification of Hypoxic and Metabolic Status of NSCLC Tumors Using [18F]HX4 and [18F]FDG-PET/CT Imaging

Purpose: Increased tumor metabolism and hypoxia are related to poor prognosis in solid tumors, including non–small cell lung cancer (NSCLC). PET imaging is a noninvasive technique that is frequently used to visualize and quantify tumor metabolism and hypoxia. The aim of this study was to perform an extensive comparison of tumor metabolism using 2[18F]fluoro-2-deoxy-d-glucose (FDG)-PET and hypoxia using HX4-PET imaging. Experimental Design: FDG- and HX4-PET/CT images of 25 patients with NSCLC were coregistered. At a global tumor level, HX4 and FDG parameters were extracted from the gross tumor volume (GTV). The HX4 high-fraction (HX4-HF) and HX4 high-volume (HX4-HV) were defined using a tumor-to-blood ratio > 1.4. For FDG high-fraction (FDG-HF) and FDG high-volume (FDG-HV), a standardized uptake value (SUV) > 50% of SUVmax was used. We evaluated the spatial correlation between HX4 and FDG uptake within the tumor, to quantify the (mis)match between volumes with a high FDG and high HX4 uptake. Results: At a tumor level, significant correlations were observed between FDG and HX4 parameters. For the primary GTV, the HX4-HF was three times smaller compared with the FDG-HF. In 53% of the primary lesions, less than 1 cm3 of the HX4-HV was outside the FDG–HV; for 37%, this volume was 1.9 to 12 cm3. Remarkably, a distinct uptake pattern was observed in 11%, with large hypoxic volumes localized outside the FDG-HV. Conclusion: Hypoxic tumor volumes are smaller than metabolic active volumes. Approximately half of the lesions showed a good spatial correlation between the PET tracers. In the other cases, a (partial) mismatch was observed. The addition of HX4-PET imaging has the potential to individualize patient treatment. Clin Cancer Res; 20(24); 6389–97. ©2014 AACR.

Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine.

ABSTRACT Background. Trials are vital in informing routine clinical care; however, current designs have major deficiencies. An overview of the various challenges that face modern clinical research and the methods that can be exploited to solve these challenges, in the context of personalised cancer treatment in the 21st century is provided. Aim. The purpose of this manuscript, without intending to be comprehensive, is to spark thought whilst presenting and discussing two important and complementary alternatives to traditional evidence-based medicine, specifically rapid learning health care and cohort multiple randomised controlled trial design. Rapid learning health care is an approach that proposes to extract and apply knowledge from routine clinical care data rather than exclusively depending on clinical trial evidence, (please watch the animation: http://youtu.be/ZDJFOxpwqEA). The cohort multiple randomised controlled trial design is a pragmatic method which has been proposed to help overcome the weaknesses of conventional randomised trials, taking advantage of the standardised follow-up approaches more and more used in routine patient care. This approach is particularly useful when the new intervention is a priori attractive for the patient (i.e. proton therapy, patient decision aids or expensive medications), when the outcomes are easily collected, and when there is no need of a placebo arm. Discussion. Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge.