Georgy Shakirin

Implementation and workflow for PET monitoring of therapeutic ion irradiation: a comparison of in-beam, in-room, and off-line techniques.

An independent assessment of the dose delivery in ion therapy can be performed using positron emission tomography (PET). For that a distribution of positron emitters which appear as the result of interaction between ions of the therapeutic beam and the irradiated tissue is measured during or after the irradiation. Three concepts for PET monitoring implemented in various therapy facilities are considered in this paper. The in-beam PET concept relies on the PET measurement performed simultaneously to the irradiation by means of a PET scanner which is completely integrated into the irradiation site. The in-room PET concept allows measurement immediately after irradiation by a standalone PET scanner which is installed very close to the irradiation site. In the off-line PET scenario the measurement is performed by means of a standalone PET/CT scanner 10-30 min after the irradiation. These three concepts were evaluated according to image quality criteria, integration costs, and their influence onto the workflow of radiotherapy. In-beam PET showed the best performance. However, the integration costs were estimated as very high for this modality. Moreover, the performance of in-beam PET depends heavily on type and duty cycle of the accelerator. The in-room PET is proposed for planned therapy facilities as a good compromise between the quality of measured data and integration efforts. For facilities which are close to the nuclear medicine departments off-line PET can be suggested under several circumstances.

Co-localisation of hypoxia and perfusion markers with parameters of glucose metabolism in human squamous cell carcinoma (hSCC) xenografts

Purpose: To examine relationships between tumour hypoxia, perfusion and metabolic microenvironment at the microregional level in three different human squamous cell carcinomas (hSCC). Materials and methods: Nude mice bearing FaDu, UT-SCC-15, and UT-SCC-5 hSCC were injected with pimonidazole hypoxia and Hoechst perfusion markers. Bioluminescence imaging was used to determine spatial distribution of glucose and lactate content in serial tumour sections. Metabolite levels were grouped in 10 concentration ranges. Images were co-registered and at each concentration range the proportion of area stained for pimonidazole and Hoechst was determined in 11–13 tumours per tumour line. Results: The spatial distribution of metabolites in pimonidazole hypoxic and Hoechst perfused areas is characterised by pronounced heterogeneity. In all three tumour lines glucose concentration decreased with increasing pimonidazole hypoxic fraction and increased with increasing perfused area at the microregional level. A weak albeit significant positive correlation between lactate concentration and pimonidazole hypoxic fraction was found only in UT-SCC-5. Lactate concentration consistently decreased with increasing perfused area in all three tumour lines. Conclusions: Both glucose consumption and supply may contribute to the microregional glucose levels. Microregional lactate accumulation in tumours may be governed by clearance potential. The extent of microregional hypoxia cannot be predicted from the lactate concentration indicating that both parameters need to be measured independently.

Dealing with geometric uncertainties in dose painting by numbers: introducing the ΔVH.

PURPOSE Dose painting by numbers lacks the conventional margin approach for geometric uncertainties. Moreover, the DVH is unable to assess the geometric accuracy of a non-uniform dose distribution because spatial information is lost. In this work we present tools for planning and evaluation of non-uniform treatment dose which take geometric uncertainties into account. METHODS AND MATERIALS The IMRT optimization functions in the Pinnacle treatment planning software were extended to allow non-uniform prescription dose distributions, e.g., derived from a PET image set. Also, explicit handling of systematic and random geometric uncertainties was incorporated in the functions, enabling confidence level based probabilistic treatment planning. For plan evaluation the concept of ΔVH was introduced, which is the volume histogram of the difference between planned and prescribed doses. Probability distributions for ΔVH points were estimated using Monte Carlo methods. As a demonstration of these methods, two examples are presented; one plan for a lung cancer patient and one for a tumor in the head-and-neck region. RESULTS Dose distributions were obtained using the PET SUV, while allowing for geometric uncertainties. Optimization was performed such that the ΔVH evaluation indicated a 90% confidence of having under-dosage less than 5% of prescription dose maximum in 99% of the tumor volume. This corresponds to the clinical target constraint for margin based planning with uniform dose prescription. CONCLUSIONS Clinical treatment planning tools were extended to allow non-uniform prescription. For planning we introduced confidence level based probabilistic optimization with non-uniform target dose, while confidence levels of ΔVH points summarize the probability of proper target coverage.

An in silico comparison between margin-based and probabilistic target-planning approaches in head and neck cancer patients.

BACKGROUND AND PURPOSE To apply target probabilistic planning (TPP) approach to intensity modulated radiotherapy (IMRT) plans for head and neck cancer (HNC) patients. MATERIAL AND METHODS Twenty plans of HNC patients were re-planned replacing the simultaneous integrated boost IMRT optimization objectives for minimum dose on the boost target and the elective volumes with research probabilistic objectives: the latter allow for explicit handling of systematic and random geometric uncertainties, enabling confidence level based probabilistic treatment planning. Monte-Carlo evaluations of geometrical errors were performed, with endpoints D98%, D2% and Dmean, calculated at a confidence level of 90%. The dose distribution was expanded outside the patient to prevent large bilateral elective treatment volumes ending up in air for probabilistic shifts. RESULTS TPP resulted in more regular isodoses and in reduced dose, on average, to organs at risk (OAR), up to more than 6Gy, while maintaining target coverage and keeping the maximum dose to limiting structures within requirements. In particular, when the surrounding OARs overlap with the planning target volume (PTV) but not with the clinical target volume (CTV), better results were achieved. CONCLUSION The TPP approach was evaluated in HNC patients, and proven to be an efficient tool for managing uncertainties.

A novel concept for tumour targeting with radiation: Inverse dose-painting or targeting the “Low Drug Uptake Volume”

BACKGROUND AND PURPOSE We tested a novel treatment approach combining (1) targeting radioresistant hypoxic tumour cells with the hypoxia-activated prodrug TH-302 and (2) inverse radiation dose-painting to boost selectively non-hypoxic tumour sub-volumes having no/low drug uptake. MATERIAL AND METHODS 18F-HX4 hypoxia tracer uptake measured with a clinical PET/CT scanner was used as a surrogate of TH-302 activity in rhabdomyosarcomas growing in immunocompetent rats. Low or high drug uptake volume (LDUV/HDUV) was defined as 40% of the GTV with the lowest or highest 18F-HX4 uptake, respectively. Two hours post TH-302/saline administration, animals received either single dose radiotherapy (RT) uniformly (15 or 18.5Gy) or a dose-painted non-uniform radiation (15Gy) with 50% higher dose to LDUV or HDUV (18.5Gy). Treatment plans were created using Eclipse treatment planning system and radiation was delivered using VMAT. Tumour response was quantified as time to reach 3 times starting tumour volume. RESULTS Non-uniform RT boosting tumour sub-volume with low TH-302 uptake (LDUV) was superior to the same dose escalation to HDUV (p<0.0001) and uniform RT with the same mean dose 15Gy (p=0.0077). Noteworthy, dose escalation to LDUV required on average 3.5Gy lower dose to the GTV to achieve similar tumour response as uniform dose escalation. CONCLUSIONS The results support targeted dose escalation to non-hypoxic tumour sub-volume with no/low activity of hypoxia-activated prodrugs. This strategy applies on average a lower radiation dose and is as effective as uniform dose escalation to the entire tumour. It could be applied to other type of drugs provided that their distribution can be imaged.

Probabilistic evaluation of target dose deterioration in dose painting by numbers for stage II/III lung cancer

PURPOSE Non-small cell lung cancer is typically irradiated with 60-66 Gy in 2-Gy fractions. Local control could be improved by increasing dose to the more radiation-resistant areas (eg, based on the standardized uptake values of a pretreatment [(18)F]fluoro-deoxyglucose positron emission tomography scan). Such dose painting approaches, however, are poorly suited for a conventional planning target volume margin expansion; therefore, typically no margins are used. This study investigates dose deterioration of a dose painting by numbers (DPBN) approach resulting from geometrical uncertainties. METHODS AND MATERIALS For 9 DPBN plans of stage II/III non-small cell lung cancer patients, the boost dose was escalated up to 130 Gy (in 33 fractions) or until a dose-limiting constraint was reached. Then, using Monte Carlo methods, a probabilistic evaluation of dose endpoints for 99%, 98%, and 2% of gross tumor volume at a 90% confidence level was performed considering 8 different combinations of systematic (∑) and random (σ) geometric error distributions. RESULTS Important underdosages, because of geometric uncertainties, of up to 38 Gy with minimal image guidance occur, reducing to 8 Gy with the highest level of image guidance, for a patient where a maximum dose of 119 Gy could be achieved. The evaluation showed that systematic errors had the largest influence. The effects of the uncertainties are most evident where the dose or its gradient is high. CONCLUSIONS Probabilistic evaluation showed that the geometric uncertainties have a large effect and should be evaluated before approving DPBN plans.

A common approach to image reconstruction for different applications of Compton cameras

A Compton camera is a device with electronic collimation allowing detection of single gamma rays of a very wide energy range (0.2–15 MeV). Therefore, it is obvious to use a Compton camera for applications in astronomy [1], homeland security [2] and medical imaging [3] if wide energy range photons have to be measured.

Liver Tissue Classification in Patients with Hepatocellular Carcinoma by Fusing Structured and Rotationally Invariant Context Representation

This work addresses multi-class liver tissue classification from multi-parameter MRI in patients with hepatocellular carcinoma (HCC), and is among the first to do so. We propose a structured prediction framework to simultaneously classify parenchyma, blood vessels, viable tumor tissue, and necrosis, which overcomes limitations related to classifying these tissue classes individually and consecutively. A novel classification framework is introduced, based on the integration of multi-scale shape and appearance features to initiate the classification, which is iteratively refined by augmenting the feature space with both structured and rotationally invariant label context features. We study further the topic of rotationally invariant label context feature representations, and introduce a method for this purpose based on computing the energies of the spherical harmonic decompositions computed at different frequencies and radii. We test our method on full 3D multi-parameter MRI volumes from 47 patients with HCC and achieve promising results.

Combining Image and Non-image Clinical Data: An Infrastructure that Allows Machine Learning Studies in a Hospital Environment

Over the past years Machine Learning and Deep Learning techniques are showing their huge potential in medical research. However, this research is mainly done by using public or private datasets that were created for study purposes. Despite ensuring reproducibility, these datasets need to be constantly updated.

Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI

ObjectivesMagnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations.MethodsWe included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE.ResultsThe DLM detected meningiomas in 55 of 56 cases. Further, automated segmentations correlated strongly with manual segmentations: average Dice coefficients were 0.81 ± 0.10 (range, 0.46-0.93) for the total tumour volume (union of tumour volume in FLAIR and T1CE) and 0.78 ± 0.19 (range, 0.27-0.95) for contrast-enhancing tumour volume in T1CE.ConclusionsThe DLM yielded accurate automated detection and segmentation of meningioma tissue despite diverse scanner data and thereby may improve and facilitate therapy planning as well as monitoring of this highly frequent tumour entity.Key Points• Deep learning allows for accurate meningioma detection and segmentation• Deep learning helps clinicians to assess patients with meningiomas• Meningioma monitoring and treatment planning can be improved