Predicting outcomes in radiation oncology--multifactorial decision support systems

Particle radiosurgery: a new frontier of physics in medicine

Radiosurgery was introduced over half a century ago for treatment of intracranial lesions. In more recent years, stereotactic radiotherapy has rapidly advanced and is now commonly used for treatments of both cranial and extracranial lesions with high doses delivered in a few, down to a single fraction. The results of a workshop on Particle radiosurgery: A new frontier of physics in medicine held at Obergurgl, Austria during August 25-29 2013 are summarized in this issue with an overview presented in this paper. The focus was laid on particle radiosurgery but the content also includes current practice in x-ray radiosurgery and the overarching research in radiobiology and motion management for extracranial lesions. The results and discussions showed that especially research in radiobiology of high-dose charged-particles and motion management are necessary for the success of particle radiosurgery.

An umbrella protocol for standardized data collection (SDC) in rectal cancer: a prospective uniform naming and procedure convention to support personalized medicine

Predictive models allow treating physicians to deliver tailored treatment moving from prescription by consensus to prescription by numbers. The main features of an umbrella protocol for standardizing data and procedures to create a consistent dataset useful to obtain a trustful analysis for a Decision Support System for rectal cancer are reported.

A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making

BACKGROUND

Decision Support Systems, based on statistical prediction models, have the potential to change the way medicine is being practiced, but their application is currently hampered by the astonishing lack of impact studies. Showing the theoretical benefit of using these models could stimulate conductance of such studies. In addition, it would pave the way for developing more advanced models, based on genomics, proteomics and imaging information, to further improve the performance of the models.

PURPOSE

In this prospective single-center study, previously developed and validated statistical models were used to predict the two-year survival (2yrS), dyspnea (DPN), and dysphagia (DPH) outcomes for lung cancer patients treated with chemo radiation. These predictions were compared to probabilities provided by doctors and guideline-based recommendations currently used. We hypothesized that model predictions would significantly outperform predictions from doctors.

MATERIALS AND METHODS

Experienced radiation oncologists (ROs) predicted all outcomes at two timepoints: (1) after the first consultation of the patient, and (2) after the radiation treatment plan was made. Differences in the performances of doctors and models were assessed using Area Under the Curve (AUC) analysis.

RESULTS

A total number of 155 patients were included. At timepoint #1 the differences in AUCs between the ROs and the models were 0.15, 0.17, and 0.20 (for 2yrS, DPN, and DPH, respectively), with p-values of 0.02, 0.07, and 0.03. Comparable differences at timepoint #2 were not statistically significant due to the limited number of patients. Comparison to guideline-based recommendations also favored the models.

CONCLUSION

The models substantially outperformed ROs' predictions and guideline-based recommendations currently used in clinical practice. Identification of risk groups on the basis of the models facilitates individualized treatment, and should be further investigated in clinical impact studies.

New perspectives in treatment decision for integrated management of rectal cancer: multimodal research for multimodal treatments

Rectal cancer management improved results in the last thirty-five applying new integrated treatment options. Preoperative radiochemotherapy or radiotherapy alone joined to the modern surgery gaining significant improvement of outcomes. Nevertheless, a definitive conclusion about superiority of one on the other in term of survival and toxicity is still lacking, and further improvement is in general required and seems obtainable. The need for a wide sharing of the accumulated knowledge is represented by the consensus conferences that over the years summarizes the state of the art for the management of rectal cancer. One of the most promising opportunities comes from the attempt of characterization of the tumor heterogeneity. An always-increasing number of new parameters come from different sources including genomic, imaging, pathological features and many others. The need of new informatics technologies able to handle and continuously incorporate new inputs derived from the evidences is also imperative. The combined use of large shared databases and "learning models" could allow generating and rapidly testing new hypotheses, providing further survival improvement in the next years.

Understanding the tumor microenvironment and radioresistance by combining functional imaging with global gene expression

The objective of this review is to present an argument for performing joint analyses between functional imaging with global gene expression studies. The reason for making this link is that tumor microenvironmental influences on functional imaging can be uncovered. Such knowledge can lead to (1) more informed decisions regarding how to use functional imaging to guide therapy and (2) discovery of new therapeutic targets. As such, this approach could lead to identification of patients who need aggressive treatment tailored toward the phenotype of their tumor vs those who could be spared treatment that carries risk for more normal tissue complications. Only a handful of papers have been published on this topic thus far, but all show substantial promise.

Systematic analysis of 18F-FDG PET and metabolism, proliferation and hypoxia markers for classification of head and neck tumors

Background

Quantification of molecular cell processes is important for prognostication and treatment individualization of head and neck cancer (HNC). However, individual tumor comparison can show discord in upregulation similarities when analyzing multiple biological mechanisms. Elaborate tumor characterization, integrating multiple pathways reflecting intrinsic and microenvironmental properties, may be beneficial to group most uniform tumors for treatment modification schemes. The goal of this study was to systematically analyze if immunohistochemical (IHC) assessment of molecular markers, involved in treatment resistance, and ¹⁸F-FDG PET parameters could accurately distinguish separate HNC tumors.

Methods

Several imaging parameters and texture features for ¹⁸F-FDG small-animal PET and immunohistochemical markers related to metabolism, hypoxia, proliferation and tumor blood perfusion were assessed within groups of BALB/c nu/nu mice xenografted with 14 human HNC models. Classification methods were used to predict tumor line based on sets of parameters.

Results

We found that ¹⁸F-FDG PET could not differentiate between the tumor lines. On the contrary, combined IHC parameters could accurately allocate individual tumors to the correct model. From 9 analyzed IHC parameters, a cluster of 6 random parameters already classified 70.3% correctly. Combining all PET/IHC characteristics resulted in the highest tumor line classification accuracy (81.0%; cross validation 82.0%), which was just 2.2% higher (p = 5.2×10^-32) than the performance of the IHC parameter/feature based model.

Conclusions

With a select set of IHC markers representing cellular processes of metabolism, proliferation, hypoxia and perfusion, one can reliably distinguish between HNC tumor lines. Addition of ¹⁸F-FDG PET improves classification accuracy of IHC to a significant yet minor degree. These results may form a basis for development of tumor characterization models for treatment allocation purposes.

Concept for individualized patient allocation: ReCompare--remote comparison of particle and photon treatment plans

Background

Identifying those patients who have a higher chance to be cured with fewer side effects by particle beam therapy than by state-of-the-art photon therapy is essential to guarantee a fair and sufficient access to specialized radiotherapy. The individualized identification requires initiatives by particle as well as non-particle radiotherapy centers to form networks, to establish procedures for the decision process, and to implement means for the remote exchange of relevant patient information. In this work, we want to contribute a practical concept that addresses these requirements.

Methods

We proposed a concept for individualized patient allocation to photon or particle beam therapy at a non-particle radiotherapy institution that bases on remote treatment plan comparison. We translated this concept into the web-based software tool ReCompare (REmote COMparison of PARticlE and photon treatment plans).

Results

We substantiated the feasibility of the proposed concept by demonstrating remote exchange of treatment plans between radiotherapy institutions and the direct comparison of photon and particle treatment plans in photon treatment planning systems. ReCompare worked with several tested standard treatment planning systems, ensured patient data protection, and integrated in the clinical workflow.

Conclusions

Our concept supports non-particle radiotherapy institutions with the patient-specific treatment decision on the optimal irradiation modality by providing expertise from a particle therapy center. The software tool ReCompare may help to improve and standardize this personalized treatment decision. It will be available from our website when proton therapy is operational at our facility.

Investigating the potential impact of four-dimensional computed tomography (4DCT) on toxicity, outcomes and dose escalation for radical lung cancer radiotherapy

AIMS

To investigate the potential dosimetric and clinical benefits predicted by using four-dimensional computed tomography (4DCT) compared with 3DCT in the planning of radical radiotherapy for non-small cell lung cancer.

MATERIALS AND METHODS

Twenty patients were planned using free breathing 4DCT then retrospectively delineated on three-dimensional helical scan sets (3DCT). Beam arrangement and total dose (55 Gy in 20 fractions) were matched for 3D and 4D plans. Plans were compared for differences in planning target volume (PTV) geometrics and normal tissue complication probability (NTCP) for organs at risk using dose volume histograms. Tumour control probability and NTCP were modelled using the Lyman-Kutcher-Burman (LKB) model. This was compared with a predictive clinical algorithm (Maastro), which is based on patient characteristics, including: age, performance status, smoking history, lung function, tumour staging and concomitant chemotherapy, to predict survival and toxicity outcomes. Potential therapeutic gains were investigated by applying isotoxic dose escalation to both plans using constraints for mean lung dose (18 Gy), oesophageal maximum (70 Gy) and spinal cord maximum (48 Gy).

RESULTS

4DCT based plans had lower PTV volumes, a lower dose to organs at risk and lower predicted NTCP rates on LKB modelling (P < 0.006). The clinical algorithm showed no difference for predicted 2-year survival and dyspnoea rates between the groups, but did predict for lower oesophageal toxicity with 4DCT plans (P = 0.001). There was no correlation between LKB modelling and the clinical algorithm for lung toxicity or survival. Dose escalation was possible in 15/20 cases, with a mean increase in dose by a factor of 1.19 (10.45 Gy) using 4DCT compared with 3DCT plans.

CONCLUSIONS

4DCT can theoretically improve therapeutic ratio and dose escalation based on dosimetric parameters and mathematical modelling. However, when individual characteristics are incorporated, this gain may be less evident in terms of survival and dyspnoea rates. 4DCT allows potential for isotoxic dose escalation, which may lead to improved local control and better overall survival.

International data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data mining

Extensive, multifactorial data sharing is a crucial prerequisite for current and future (radiotherapy) research. However, the cost, time and effort to achieve this are often a roadblock. We present an open-source based data-sharing infrastructure between two radiotherapy departments, allowing seamless exchange of de-identified, automatically translated clinical and biomedical treatment data.

Interview: Lung cancer: a very challenging disease

Philippe Lambin speaks to Alisa Crisp, Commissioning Editor: Philippe Lambin is a clinician, a radiation oncologist, and a pioneer in translational research with a focus on hypoxia and Decision Support Systems. He has a PhD in radiation biology and is professor at the University of Maastricht (radiation oncology) and at the University of Eindhoven (functional imaging). He is co-author of more than 282 peer-reviewed scientific papers (Hirsch Index: 42), co-inventor of more than 13 patents (filed or submitted) and (co)promotor of more then 27 completed PhDs. Moreover, Professor Lambin has extensive experience with clinical trials. He is one of the international experts in the Flims workshop ‘Methods in Clinical Cancer Research’ organized jointly by the Federation of European Cancer Societies, American Association for Cancer Research and American Society of Clinical Oncology and he is leading several clinical trials. He is also a member of the scientific committee of Koningin Wilhelmina Fonds (the main Dutch funding body in cancer research) and the advice committee on proton therapy of College Voor Zorgverzekeringen (Dutch medical insurance). He is currently involved in several successful European grants (e.g., Centre for Disruptive Photonic Technologies, Biocare, Euroxy, Metoxia, euroCAT and Eureca) and one NIH grant (in radiomics). His main areas of interest are directed towards translational research in radiation biology with a specific focus on tumor hypoxia, functional imaging (computed tomography–PET) and lung and head and neck cancer.

Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer

BACKGROUND

Maximum, mean and peak SUV of primary tumor at baseline FDG-PET scans, have often been found predictive for overall survival in non-small cell lung cancer (NSCLC) patients. In this study we further investigated the prognostic power of advanced metabolic metrics derived from intensity volume histograms (IVH) extracted from PET imaging.

METHODS

A cohort of 220 NSCLC patients (mean age, 66.6 years; 149 men, 71 women), stages I-IIIB, treated with radiotherapy with curative intent were included (NCT00522639). Each patient underwent standardized pre-treatment CT-PET imaging. Primary GTV was delineated by an experienced radiation oncologist on CT-PET images. Common PET descriptors such as maximum, mean and peak SUV, and metabolic tumor volume (MTV) were quantified. Advanced descriptors of metabolic activity were quantified by IVH. These comprised five groups of features: absolute and relative volume above relative intensity threshold (AVRI and RVRI), absolute and relative volume above absolute intensity threshold (AVAI and RVAI), and absolute intensity above relative volume threshold (AIRV). MTV was derived from the IVH curves for volumes with SUV above 2.5, 3 and 4, and of 40% and 50% maximum SUV. Univariable analysis using Cox Proportional Hazard Regression was performed for overall survival assessment.

RESULTS

Relative volume above higher SUV (80%) was an independent predictor of OS (p = 0.05). None of the possible surrogates for MTV based on volumes above SUV of 3, 40% and 50% of maximum SUV showed significant associations with OS [p (AVAI3) = 0.10, p (AVAI4) = 0.22, p (AVRI40%) = 0.15, p (AVRI50%) = 0.17]. Maximum and peak SUV (r = 0.99) revealed no prognostic value for OS [p (maximum SUV) = 0.20, p (peak SUV) = 0.22].

CONCLUSIONS

New methods using more advanced imaging features extracted from PET were analyzed. Best prognostic value for OS of NSCLC patients was found for relative portions of the tumor above higher uptakes (80% SUV).

Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability

PURPOSE

Besides basic measurements as maximum standardized uptake value (SUV)max or SUVmean derived from 18F-FDG positron emission tomography (PET) scans, more advanced quantitative imaging features (i.e. "Radiomics" features) are increasingly investigated for treatment monitoring, outcome prediction, or as potential biomarkers. With these prospected applications of Radiomics features, it is a requisite that they provide robust and reliable measurements. The aim of our study was therefore to perform an integrated stability analysis of a large number of PET-derived features in non-small cell lung carcinoma (NSCLC), based on both a test-retest and an inter-observer setup.

METHODS

Eleven NSCLC patients were included in the test-retest cohort. Patients underwent repeated PET imaging within a one day interval, before any treatment was delivered. Lesions were delineated by applying a threshold of 50% of the maximum uptake value within the tumor. Twenty-three NSCLC patients were included in the inter-observer cohort. Patients underwent a diagnostic whole body PET-computed tomography (CT). Lesions were manually delineated based on fused PET-CT, using a standardized clinical delineation protocol. Delineation was performed independently by five observers, blinded to each other. Fifteen first order statistics, 39 descriptors of intensity volume histograms, eight geometric features and 44 textural features were extracted. For every feature, test-retest and inter-observer stability was assessed with the intra-class correlation coefficient (ICC) and the coefficient of variability, normalized to mean and range. Similarity between test-retest and inter-observer stability rankings of features was assessed with Spearman's rank correlation coefficient.

RESULTS

Results showed that the majority of assessed features had both a high test-retest (71%) and inter-observer (91%) stability in terms of their ICC. Overall, features more stable in repeated PET imaging were also found to be more robust against inter-observer variability.

CONCLUSION

Results suggest that further research of quantitative imaging features is warranted with respect to more advanced applications of PET imaging as being used for treatment monitoring, outcome prediction or imaging biomarkers.

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.

Cardiac comorbidity is an independent risk factor for radiation-induced lung toxicity in lung cancer patients

PURPOSE

To test the hypothesis that cardiac comorbidity before the start of radiotherapy (RT) is associated with an increased risk of radiation-induced lung toxicity (RILT) in lung cancer patients.

MATERIAL AND METHODS

A retrospective analysis was performed of a prospective cohort of 259 patients with locoregional lung cancer treated with definitive radio(chemo)therapy between 2007 and 2011 (ClinicalTrials.gov Identifiers: NCT00572325 and NCT00573040). We defined RILT as dyspnea CTCv.3.0 grade ≥2 within 6 months after RT, and cardiac comorbidity as a recorded treatment of a cardiac pathology at a cardiology department. Univariate and multivariate analyses, as well as external validation, were performed. The model-performance measure was the area under the receiver operating characteristic curve (AUC).

RESULTS

Prior to RT, 75/259 (28.9%) patients had cardiac comorbidity, 44% of whom (33/75) developed RILT. The odds ratio of developing RILT for patients with cardiac comorbidity was 2.58 (p<0.01). The cross-validated AUC of a model with cardiac comorbidity, tumor location, forced expiratory volume in 1s, sequential chemotherapy and pretreatment dyspnea score was 0.72 (p<0.001) on the training set, and 0.67 (p<0.001) on the validation set.

CONCLUSION

Cardiac comorbidity is an important risk factor for developing RILT after definite radio(chemo)therapy of lung cancer patients.

Direct and bystander radiation effects: a biophysical model and clinical perspectives

In planning treatment for each new patient, radiation oncologists pay attention to the aspects that they control. Thus their attention is usually focused on volume and dose. The dilemma for the physician is how to protract the treatment in a way that maximizes control of the tumor and minimizes normal tissue injury. The initial radiation-induced damage to DNA may be a biological indicator of the quantity of energy transferred to the DNA. However, until now the biophysical models proposed cannot explain either the early or the late adverse effects of radiation, and a more general theory appears to be required. The bystander component of tumor cell death after radiotherapy measured in many experimental works highlights the importance of confirming these observations in a clinical situation.

Characterization of tumor heterogeneity using dynamic contrast enhanced CT and FDG-PET in non-small cell lung cancer

PURPOSE

Dynamic contrast-enhanced CT (DCE-CT) quantifies vasculature properties of tumors, whereas static FDG-PET/CT defines metabolic activity. Both imaging modalities are capable of showing intra-tumor heterogeneity. We investigated differences in vasculature properties within primary non-small cell lung cancer (NSCLC) tumors measured by DCE-CT and metabolic activity from FDG-PET/CT.

METHODS

Thirty three NSCLC patients were analyzed prior to treatment. FDG-PET/CT and DCE-CT were co-registered. The tumor was delineated and metabolic activity was segmented on the FDG-PET/CT in two regions: low (<50% maximum SUV) and high (≥50% maximum SUV) metabolic uptake. Blood flow, blood volume and permeability were calculated using a maximum slope, deconvolution algorithm and a Patlak model. Correlations were assessed between perfusion parameters for the regions of interest.

RESULTS

DCE-CT provided additional information on vasculature and tumor heterogeneity that was not correlated to metabolic tumor activity. There was no significant difference between low and high metabolic active regions for any of the DCE-CT parameters. Furthermore, only moderate correlations between maximum SUV and DCE-CT parameters were observed.

CONCLUSIONS

No direct correlation was observed between FDG-uptake and parameters extracted from DCE-CT. DCE-CT may provide complementary information to the characterization of primary NSCLC tumors over FDG-PET/CT imaging.

'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.

Different recurrence pattern after neoadjuvant chemoradiotherapy compared to surgery alone in esophageal cancer patients

PURPOSE

To evaluate the rate and pattern of recurrences after neoadjuvant chemoradiotherapy (CRT) in esophageal cancer patients.

METHODS

We described survival and differences in recurrences from a single center between neoadjuvant CRT (carboplatin/paclitaxel and 41.4 Gy) and surgery alone for the period 2000-2011. To reduce bias, we performed a propensity score matched analysis.

RESULTS

A total of 204 patients were analyzed, 75 treated with neoadjuvant CRT and 129 with surgery alone. The pathologic response to neoadjuvant CRT was 69% with a complete response rate of 25%. After matching, baseline characteristics between the groups (both n = 75) were equally distributed. The 3- and 5-year disease-free survival was 53 and 42% in the neoadjuvant CRT group compared with 24 and 18% in the surgery-alone group (P = 0.011). After 3 and 5 years' CRT, patients had an estimated locoregional recurrence-free survival of 83 and 73% compared with 52 and 49% in the surgery-alone group (P = 0.015). The distant recurrence-free survival was comparable in both groups. Locoregional recurrences were located less in the paraesophageal lymph nodes in the CRT group than in the surgery-alone group, 9 versus 21%, respectively (P = 0.041). With respect to differences in distant recurrences, we observed more skeletal recurrences in the surgery-alone group compared to CRT, 12 versus 1% (P = 0.009).

CONCLUSIONS

The neoadjuvant CRT regimen we used offers a significant improvement in outcome, with a different recurrence pattern compared with surgery alone. This effect is probably due to both the pathologic complete response and eradication of micrometastases in CRT group.

Predicting outcome by images?

Features from CT, MRI, and PET scans are related to survival of patients with non-small cell lung carcinoma. Individualized image-based tissue characterization allows a whole body view of all tumor deposits and organs at risk. The time is ripe to embark on huge international studies aiming to validate and implement this technology in clinical practice.

Prediction of response to radiotherapy in the treatment of esophageal cancer using stem cell markers

BACKGROUND AND PURPOSE

In this study, we investigated whether cancer stem cell marker expressing cells can be identified that predict for the response of esophageal cancer (EC) to CRT.

MATERIALS AND METHODS

EC cell-lines OE-33 and OE-21 were used to assess in vitro, stem cell activity, proliferative capacity and radiation response. Xenograft tumors were generated using NOD/SCID mice to assess in vivo proliferative capacity and tumor hypoxia. Archival and fresh EC biopsy tissue was used to confirm our in vitro and in vivo results.

RESULTS

We showed that the CD44+/CD24- subpopulation of EC cells exerts a higher proliferation rate and sphere forming potential and is more radioresistant in vitro, when compared to unselected or CD44+/CD24+ cells. Moreover, CD44+/CD24- cells formed xenograft tumors faster and were often located in hypoxic tumor areas. In a study of archival pre-neoadjuvant CRT biopsy material from EC adenocarcinoma patients (N=27), this population could only be identified in 50% (9/18) of reduced-responders to neoadjuvant CRT, but never (0/9) in the complete responders (P=0.009).

CONCLUSION

These results warrant further investigation into the possible clinical benefit of CD44+/CD24- as a predictive marker in EC patients for the response to chemoradiation.