Complete pathological response to neoadjuvant treatment is associated with better survival outcomes in patients with soft tissue sarcoma: Results of a retrospective multicenter study.

BACKGROUND: Locally advanced soft tissue sarcoma (STS) management may include neoadjuvant or adjuvant treatment by radiotherapy (RT), chemotherapy (CT) or chemoradiotherapy (CRT) followed by wide surgical excision. While pathological complete response (pCR) to preoperative treatment is prognostic for survival in osteosarcomas, its significance for STS is unclear. We aimed to evaluate the prognostic significance of pCR to pre-operative treatment on 3-year disease-free survival (3y-DFS) in STS patients. METHODS: This is an observational, retrospective, international, study of adult patients with primary non-metastatic STS of the extremities and trunk wall, any grade, diagnosed between 2008 and 2012, treated with at least neoadjuvant treatment and surgical resection and observed for a minimum of 3 years after diagnosis. The primary objective was to evaluate the effect of pCR. (≤5% viable tumor cells or ≥95% necrosis/fibrosis) on 3y-DFS. Effect on local recurrence-free survival (LRFS), distant recurrence-free survival (MFS) overall survival (OS) at 3 years was also analyzed. Statistical univariate analysis utilized chi-square independence test and odds ratio confidence interval (CI) estimate, multivariate analysis was performed using LASSO. RESULTS: A total of 330 patients (median age 56 years old, range:19-95) treated by preoperative RT (67%), CT (15%) or CRT (18%) followed by surgery were included. pCR was achieved in 74/330 (22%) of patients, of which 56/74 (76%) had received RT. 3-yr DFS was observed in 76% of patients with pCR vs 61% without pCR (p < 0.001). Multivariate analysis showed that pCR is statistically associated with better MFS (95% CI, 1.054-3.417; p = 0.033), LRFS (95% CI, 1.226-5.916; p = 0.014), DFS (95% CI, 1.165-4.040; p = 0.015) and OS at 3 years (95% CI, 1.072-5.210; p = 0.033). CONCLUSIONS: In a wide, heterogeneous STS population we showed that pCR to preoperative treatment is prognostic for survival.

Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT.

Machine learning applications for personalized medicine are highly dependent on access to sufficient data. For personalized radiation oncology, datasets representing the variation in the entire cancer patient population need to be acquired and used to learn prediction models. Ethical and legal boundaries to ensure data privacy hamper collaboration between research institutes. We hypothesize that data sharing is possible without identifiable patient data leaving the radiation clinics and that building machine learning applications on distributed datasets is feasible. We developed and implemented an IT infrastructure in five radiation clinics across three countries (Belgium, Germany, and The Netherlands). We present here a proof-of-principle for future 'big data' infrastructures and distributed learning studies. Lung cancer patient data was collected in all five locations and stored in local databases. Exemplary support vector machine (SVM) models were learned using the Alternating Direction Method of Multipliers (ADMM) from the distributed databases to predict post-radiotherapy dyspnea grade [Formula: see text]. The discriminative performance was assessed by the area under the curve (AUC) in a five-fold cross-validation (learning on four sites and validating on the fifth). The performance of the distributed learning algorithm was compared to centralized learning where datasets of all institutes are jointly analyzed. The euroCAT infrastructure has been successfully implemented in five radiation clinics across three countries. SVM models can be learned on data distributed over all five clinics. Furthermore, the infrastructure provides a general framework to execute learning algorithms on distributed data. The ongoing expansion of the euroCAT network will facilitate machine learning in radiation oncology. The resulting access to larger datasets with sufficient variation will pave the way for generalizable prediction models and personalized medicine.

Quantification of CT-assessed radiation-induced lung damage in lung cancer patients treated with or without chemotherapy and cetuximab.

BACKGROUND AND PURPOSE: Prediction models for radiation-induced lung damage (RILD) are still unsatisfactory, with clinical toxicity endpoints that are difficult to quantify objectively. We therefore evaluated RILD more objectively, quantitatively and on a continuous scale measuring the lung tissue density changes per voxel. MATERIAL AND METHODS: Patients treated with radiotherapy (RT) alone, sequential and concurrent chemo-RT with and without the addition of cetuximab were studied. Follow-up computed tomography (CT) scans were co-registered using deformable registration to baseline CT scans. CT density changes were correlated to the RT dose delivered in every part of the lungs. RESULTS: One hundred and seventeen lung cancer patients were included. Mean dose to tumor was 60 Gy (range 45-79.2 Gy). Dose response curves showed a linear increase in the dose region between 0 and 65 Gy having a slope (based on coefficients of the multilevel model) expressed as a lung density increase per dose of 0.86 (95% CI 0.73-0.99), 1.31 (95% CI 1.19-1.43), 1.39 (95% CI 1.28-1.50) and 2.07 (95% CI 1.93-2.21) for patients treated only with RT (N=19), sequential chemo-RT (N=30), concurrent chemo-RT (N=49), and concurrent chemo-RT with cetuximab (N=19), respectively. CONCLUSIONS: CT density changes allow quantitative assessment of lung damage after fractionated RT, giving complementary information to standard used clinical endpoints. Patients receiving cetuximab showed a significantly larger dose response compared with other treatments.

Preclinical Assessment of Efficacy of Radiation Dose Painting Based on Intratumoral FDG-PET Uptake.

PURPOSE: We tested therapeutic efficacy of two dose painting strategies of applying higher radiation dose to tumor subvolumes with high FDG uptake (biologic target volume, BTV): dose escalation and dose redistribution. We also investigated whether tumor response was determined by the highest dose in BTV or the lowest dose in gross tumor volume (GTV). EXPERIMENTAL DESIGN: FDG uptake was evaluated in rat rhabdomyosarcomas prior to irradiation. BTV was defined as 30% of GTV with the highest (BTVhot) or lowest (BTVcold) uptake. To test efficacy of dose escalation, tumor response (time to reach two times starting tumor volume, TGTV2) to Hot Boost irradiation (40% higher dose to BTVhot) was compared with Cold Boost (40% higher dose to BTVcold), while mean dose to GTV remained 12 Gy. To test efficacy of dose redistribution, TGTV2 after Hot Boost was compared with uniform irradiation with the same mean dose (8 or 12 Gy). RESULTS: TGTV2 after 12 Gy delivered heterogeneously (Hot and Cold Boost) or uniformly were not significantly different: 20.2, 19.5, and 20.6 days, respectively. Dose redistribution (Hot Boost) with 8 Gy resulted in faster tumor regrowth as compared with uniform irradiation (13.3 vs. 17.1 days; P = 0.026). Further increase in dose gradient to 60% led to a more pronounced decrease in TGTV2 (10.9 days; P < 0.0001). CONCLUSIONS: Dose escalation effect was independent of FDG uptake in target tumor volume, while dose redistribution was detrimental in this tumor model for dose levels applied here. Our data are consistent with the hypothesis that tumor response depends on the minimum intratumoral dose.

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis.

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) R(D), dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) R(B), maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, R(B) was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values­which was used as a surrogate for textural feature interpretation­between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

What level of accuracy is achievable for preclinical dose painting studies on a clinical irradiation platform?

Advancements made over the past decades in both molecular imaging and radiotherapy planning and delivery have enabled studies that explore the efficacy of heterogeneous radiation treatment ("dose painting") of solid cancers based on biological information provided by different imaging modalities. In addition to clinical trials, preclinical studies may help contribute to identifying promising dose painting strategies. The goal of this current study was twofold: to develop a reproducible positioning and set-up verification protocol for a rat tumor model to be imaged and treated on a clinical platform, and to assess the dosimetric accuracy of dose planning and delivery for both uniform and positron emission tomography-computed tomography (PET-CT) based heterogeneous dose distributions. We employed a syngeneic rat rhabdomyosarcoma model, which was irradiated by volumetric modulated arc therapy (VMAT) with uniform or heterogeneous 6 MV photon dose distributions. Mean dose to the gross tumor volume (GTV) as a whole was kept at 12 Gy for all treatment arms. For the nonuniform plans, the dose was redistributed to treat the 30% of the GTV representing the biological target volume (BTV) with a dose 40% higher than the rest of the GTV (GTV - BTV) (~15 Gy was delivered to the BTV vs. ~10.7 Gy was delivered to the GTV - BTV). Cone beam computed tomography (CBCT) images acquired for each rat prior to irradiation were used to correctly reposition the tumor and calculate the delivered 3D dose. Film quality assurance was performed using a water-equivalent rat phantom. A comparison between CT or CBCT doses and film measurements resulted in passing rates >98% with a gamma criterion of 3%/2 mm using 2D dose images. Moreover, between the CT and CBCT calculated doses for both uniform and heterogeneous plans, we observed maximum differences of <2% for mean dose to the tumor and mean dose to the biological target volumes. In conclusion, we have developed a robust method for dose painting in a rat tumor model on a clinical platform, with a high accuracy achieved in the delivery of complex dose distributions. Our work demonstrates the technical feasibility of this approach and enables future investigations on the therapeutic effect of preclinical dose painting strategies using a state-of-the-art clinical platform.

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.

'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.

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.

Intravascular enhancement with identical iodine delivery rate using different iodine contrast media in a circulation phantom.

PURPOSE: Both iodine delivery rate (IDR) and iodine concentration are decisive factors for vascular enhancement in computed tomographic angiography. It is unclear, however, whether the use of high-iodine concentration contrast media is beneficial to lower iodine concentrations when IDR is kept identical. This study evaluates the effect of using different iodine concentrations on intravascular attenuation in a circulation phantom while maintaining a constant IDR. MATERIALS AND METHODS: A circulation phantom with a low-pressure venous compartment and a high-pressure arterial compartment simulating physiological circulation parameters was used (heart rate, 60 beats per minute; stroke volume, 60 mL; blood pressure, 120/80 mm Hg). Maintaining a constant IDR (2.0 g/s) and a constant total iodine load (20 g), prewarmed (37°C) contrast media with differing iodine concentrations (240-400 mg/mL) were injected into the phantom using a double-headed power injector. Serial computed tomographic scans at the level of the ascending aorta (AA), the descending aorta (DA), and the left main coronary artery (LM) were obtained. Total amount of contrast volume (milliliters), iodine delivery (grams of iodine), peak flow rate (milliliter per second), and intravascular pressure (pounds per square inch) were monitored using a dedicated data acquisition program. Attenuation values in the AA, the DA, and the LM were constantly measured (Hounsfield unit [HU]). In addition, time-enhancement curves, aortic peak enhancement, and time to peak were determined. RESULTS: All contrast injection protocols resulted in similar attenuation values: the AA (516 [11] to 531 [37] HU), the DA (514 [17] to 531 [32] HU), and the LM (490 [10] to 507 [17] HU). No significant differences were found between the AA, the DA, and the LM for either peak enhancement (all P > 0.05) or mean time to peak (AA, 19.4 [0.58] to 20.1 [1.05] seconds; DA, 21.1 [1.0] to 21.4 [1.15] seconds; LM, 19.8 [0.58] to 20.1 [1.05] seconds). CONCLUSIONS: This phantom study demonstrates that constant injection parameters (IDR, overall iodine load) lead to robust enhancement patterns, regardless of the contrast material used. Higher iodine concentration itself does not lead to higher attenuation levels. These results may stimulate a shift in paradigm toward clinical usage of contrast media with lower iodine concentrations (eg, 240 mg iodine/mL) in individual tailored contrast protocols. The use of low-iodine concentration contrast media is desirable because of the lower viscosity and the resulting lower injection pressure.

Predicting outcomes in radiation oncology--multifactorial decision support systems.

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.