C.M.L. Zegers

Radiomics: Extracting more information from medical images using advanced feature analysis

Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.

‘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.

A Comparative Study of the Hypoxia PET Tracers [18F]HX4, [18F]FAZA, and [18F]FMISO in a Preclinical Tumor Model

PURPOSE Several individual clinical and preclinical studies have shown the possibility of evaluating tumor hypoxia by using noninvasive positron emission tomography (PET). The current study compared 3 hypoxia PET tracers frequently used in the clinic, [(18)F]FMISO, [(18)F]FAZA, and [(18)F]HX4, in a preclinical tumor model. Tracer uptake was evaluated for the optimal time point for imaging, tumor-to-blood ratios (TBR), spatial reproducibility, and sensitivity to oxygen modification. METHODS AND MATERIALS PET/computed tomography (CT) images of rhabdomyosarcoma R1-bearing WAG/Rij rats were acquired at multiple time points post injection (p.i.) with one of the hypoxia tracers. TBR values were calculated, and reproducibility was investigated by voxel-to-voxel analysis, represented as correlation coefficients (R) or Dice similarity coefficient of the high-uptake volume. Tumor oxygen modifications were induced by exposure to either carbogen/nicotinamide treatment or 7% oxygen breathing. RESULTS TBR was stabilized and maximal at 2 hours p.i. for [(18)F]FAZA (4.0 ± 0.5) and at 3 hours p.i. for [(18)F]HX4 (7.2 ± 0.7), whereas [(18)F]FMISO showed a constant increasing TBR (9.0 ± 0.8 at 6 hours p.i.). High spatial reproducibility was observed by voxel-to-voxel comparisons and Dice similarity coefficient calculations on the 30% highest uptake volume for both [(18)F]FMISO (R = 0.86; Dice coefficient = 0.76) and [(18)F]HX4 (R = 0.76; Dice coefficient = 0.70), whereas [(18)F]FAZA was less reproducible (R = 0.52; Dice coefficient = 0.49). Modifying the hypoxic fraction resulted in enhanced mean standardized uptake values for both [(18)F]HX4 and [(18)F]FAZA upon 7% oxygen breathing. Only [(18)F]FMISO uptake was found to be reversible upon exposure to nicotinamide and carbogen. CONCLUSIONS This study indicates that each tracer has its own strengths and, depending on the question to be answered, a different tracer can be put forward.

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.

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.

TH-302 in Combination with Radiotherapy Enhances the Therapeutic Outcome and Is Associated with Pretreatment [18F]HX4 Hypoxia PET Imaging

Purpose: Conventional anticancer treatments are often impaired by the presence of hypoxia. TH-302 selectively targets hypoxic tumor regions, where it is converted into a cytotoxic agent. This study assessed the efficacy of the combination treatment of TH-302 and radiotherapy in two preclinical tumor models. The effect of oxygen modification on the combination treatment was evaluated and the effect of TH-302 on the hypoxic fraction (HF) was monitored using [18F]HX4-PET imaging and pimonidazole IHC stainings. Experimental Design: Rhabdomyosarcoma R1 and H460 NSCLC tumor-bearing animals were treated with TH-302 and radiotherapy (8 Gy, single dose). The tumor oxygenation status was altered by exposing animals to carbogen (95% oxygen) and nicotinamide, 21% or 7% oxygen breathing during the course of the treatment. Tumor growth and treatment toxicity were monitored until the tumor reached four times its start volume (T4×SV). Results: Both tumor models showed a growth delay after TH-302 treatment, which further increased when combined with radiotherapy (enhancement ratio rhabdomyosarcoma 1.23; H460 1.49). TH-302 decreases the HF in both models, consistent with its hypoxia-targeting mechanism of action. Treatment efficacy was dependent on tumor oxygenation; increasing the tumor oxygen status abolished the effect of TH-302, whereas enhancing the HF enlarged TH-302′s therapeutic effect. An association was observed in rhabdomyosarcoma tumors between the pretreatment HF as measured by [18F]HX4-PET imaging and the T4×SV. Conclusions: The combination of TH-302 and radiotherapy is promising and warrants clinical testing, preferably guided by the companion biomarker [18F]HX4 hypoxia PET imaging for patient selection. Clin Cancer Res; 21(13); 2984–92. ©2015 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.

PET-based dose painting in non-small cell lung cancer: Comparing uniform dose escalation with boosting hypoxic and metabolically active sub-volumes

BACKGROUND AND PURPOSE We compared two imaging biomarkers for dose-escalation in patients with advanced non-small cell lung cancer (NSCLC). Treatment plans boosting metabolically active sub-volumes defined by FDG-PET or hypoxic sub-volumes defined by HX4-PET were compared with boosting the entire tumour. MATERIALS AND METHODS Ten NSCLC patients underwent FDG- and HX4-PET/CT scans prior to radiotherapy. Three isotoxic dose-escalation plans were compared per patient: plan A, boosting the primary tumour (PTVprim); plan B, boosting sub-volume with FDG >50% SUVmax (PTVFDG); plan C, boosting hypoxic volume with HX4 tumour-to-background >1.4 (PTVHX4). RESULTS Average boost volumes were 507 ± 466 cm(3) for PTVprim, 173 ± 127 cm(3) for PTVFDG and 114 ± 73 cm(3) for PTVHX4. The smaller PTVHX4 overlapped on average 87 ± 16% with PTVFDG. Prescribed dose was escalated to 87 ± 10 Gy for PTVprim, 107 ± 20 Gy for PTVFDG, and 117 ± 15 Gy for PTVHX4, with comparable doses to the relevant organs-at-risk (OAR). Treatment plans are available online (https://www.cancerdata.org/10.1016/j.radonc.2015.07.013). CONCLUSIONS Dose escalation based on metabolic sub-volumes, hypoxic sub-volumes and the entire tumour is feasible. Highest dose was achieved for hypoxia plans, without increasing dose to OAR. For most patients, boosting the metabolic sub-volume also resulted in boosting the hypoxic volume, although to a lower dose, but not vice versa.

Radiotherapy Combined with the Immunocytokine L19-IL2 Provides Long-lasting Antitumor Effects

Purpose: Radiotherapy modifies the tumor microenvironment and causes the release of tumor antigens, which can enhance the effect of immunotherapy. L19 targets the extra domain B (ED-B) of fibronectin, a marker for tumor neoangiogenesis, and can be used as immunocytokine when coupled to IL2. We hypothesize that radiotherapy in combination with L19-IL2 provides an enhanced antitumor effect, which is dependent on ED-B expression. Experimental Design: Mice were injected with syngeneic C51 colon carcinoma, Lewis lung carcinoma (LLC), or 4T1 mammary carcinoma cells. Tumor growth delay, underlying immunologic parameters, and treatment toxicity were evaluated after single-dose local tumor irradiation and systemic administration of L19-IL2 or equimolar controls. Results: ED-B expression was high, intermediate, and low for C51, LLC, and 4T1, respectively. The combination therapy showed (i) a long-lasting synergistic effect for the C51 model with 75% of tumors being cured, (ii) an additive effect for the LLC model, and (iii) no effect for the 4T1 model. The combination treatment resulted in a significantly increased cytotoxic (CD8+) T-cell population for both C51 and LLC. Depletion of CD8+ T cells abolished the benefit of the combination therapy. Conclusions: These data provide the first evidence for an increased therapeutic potential by combining radiotherapy with L19-IL2 in ED-B–positive tumors. This new opportunity in cancer treatment will be investigated in a phase I clinical study for patients with an oligometastatic solid tumor (NCT02086721). An animation summarizing our results is available at https://www.youtube.com/watch?v=xHbwQuCTkRc. Clin Cancer Res; 21(5); 1151–60. ©2014 AACR.

Decision support systems for personalized and participative radiation oncology.

Abstract A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models ‘learn’ using advanced and innovative information technologies (ideally in a distributed fashion — please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi‐faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re‐evaluated (through quality assurance procedures) in different patient datasets in order to refine and re‐optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine. Graphical abstract Figure. No caption available.