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

AbstractPurposeDynamic 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.MethodsThirty 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.ResultsDCE-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.ConclusionsNo 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' ☆ ☆☆

AbstractPurposeAn overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy.Material and resultsRapid 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.ConclusionPersonalised 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

Retrospective assessment of MRI-based volumetric changes of normal tissues in glioma patients following radio(chemo)therapy

In glioma patients, linac-based photon beam irradiation is a widely applied therapy, which achieves highly conformal target volume coverage, but is also known to cause side-effects to adjacent areas of healthy tissue. Apart from subjective measures, such as quality of life assessment and neurocognitive function tests, objective methods to quantify tissue damage are needed to assess this impact. Magnetic resonance imaging (MRI) is a well-established method for brain tumor diagnoses as well as assessing treatment response. In this study, we retrospectively assessed volumetric changes of gray matter (GM) and white matter (WM) in glioma patients following photon irradiation using a heterogeneous MRI-dataset obtained in routine clinical practice at different sites with imaging parameters and magnetic field strengths. We found a significant reduction in WM volume at one year (p=0.01) and two years (p=0.008) post radio(chemo)therapy whereas corresponding GM volumes did not change significantly (p=0.05 and p=0.11, respectively). More importantly, we also found large variations in the segmented tissue volumes caused by the heterogeneous MR data, thus potentially masking more subtle tissue changes over time. On the basis of these observations, we present suggestions regarding data acquisitions in future prospective MR studies to assess such volumetric changes

The clinical target volume in lung, head-and-neck, and esophageal cancer: Lessons from pathological measurement and recurrence analysis

Radiotherapy research has achieved remarkable progress in target volume definition. Advances in medical imaging facilitate more precise localization of the gross tumor volume, alongside a more detailed understanding of the geometric uncertainties associated with treatment delivery that has enabled robust safety margins to be customized to the specific treatment scenario at hand. By contrast, the clinical target volume, meant to encompass gross tumor, as well as, adjacent sub-clinical disease, has evolved very little. It is more often defined by clinician experience and institutional convention than on a patient-specific basis. This disparity arises from the inherent invisibility of sub-clinical disease in current medical imaging. Its incidence and expanse can only be ascertained via indirect means. This article reviews two such strategies: histopathological measurements on resection specimen and analyses of locoregional recurrences after radiotherapy

Balancing radiation pneumonitis versus locoregional tumor control in non-small-cell lung cancer

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Successful immunotherapy and irradiation in a HIV-positive patient with metastatic Merkel cell carcinoma

This case report presents a HIV-positive 60-year old male with Merkel cell carcinoma of his right forearm and pulmonary sarcoidosis, who, after excisions and irradiations of the primary tumour site and subsequent lymph node metastases developed distant metastases. He received radiotherapy to symptomatic mediastinal lymph node metastases followed by Doxorubicin and, after two cycles, by the PD-1 inhibitor Pembrolizumab due to mixed response. Re-staging showed a para-mediastinal, radiotherapy-induced pneumonitis, which was treated by prednisolone due to clinical symptoms. In September 2017, the patient developed a solitary lymph node metastasis next to the right atrium, for which he received stereotactic radiotherapy. The systemic treatment with Pembrolizumab was replaced by the PD-L1 inhibitor Avelumab and is being continued since. The patient has been in complete remission for one year now and the HIV-infection is well-controlled. Keywords: Merkel cell carcinoma, Avelumab, Immunotherapy, Pembrolizumab, Immune checkpoint inhibition, Radiotherapy, HIV, Sarcoidosi

Comparison of 3D and 4D robustly optimized proton treatment plans for non-small cell lung cancer patients with tumour motion amplitudes larger than 5 mm

Background and purpose: There is no consensus about an ideal robust optimization (RO) strategy for proton therapy of targets with large intrafractional motion. We investigated the plan robustness of 3D and different 4D RO strategies. Materials and methods: For eight non-small cell lung cancer patients with clinical target volume (CTV) motion >5 mm, different RO approaches were investigated: 3DRO considering the average CT (AvgCT) with a target density override, 4DRO considering three/all 4DCT phases, and 4DRO considering the AvgCT and three/all 4DCT phases. Robustness against setup/range errors, interplay effects based on breathing and machine log file data for deliveries with/without rescanning, and interfractional anatomical changes were analyzed for target coverage and OAR sparing. Results: All nominal plans fulfilled the clinical requirements with individual CTV coverage differences <2pp; 4DRO without AvgCT generated the most conformal dose distributions. Robustness against setup/range errors was best for 4DRO with AvgCT (18% more passed error scenarios than 3DRO). Interplay effects caused fraction-wise median CTV coverage loss of 3pp and missed maximum dose constraints for heart and esophagus in 18% of scenarios. CTV coverage and OAR sparing fulfilled requirements in all cases when accumulating four interplay scenarios. Interfractional changes caused less target misses for RO with AvgCT compared to 4DRO without AvgCT (≤42%/33% vs. ≥56%/44% failed single/accumulated scenarios). Conclusions: All RO strategies provided acceptable plans with equally low robustness against interplay effects demanding other mitigation than rescanning to ensure fraction-wise target coverage. 4DRO considering three phases and the AvgCT provided best compromise on planning effort and robustness