Long-term survival of cancer patients compared to heart failure and stroke: A systematic review

<p>Abstract</p> <p>Background</p> <p>Cancer, heart failure and stroke are among the most common causes of death worldwide. Investigation of the prognostic impact of each disease is important, especially for a better understanding of competing risks. Aim of this study is to provide an overview of long term survival of cancer, heart failure and stroke patients based on the results of large population- and hospital-based studies.</p> <p>Methods</p> <p>Records for our study were identified by searches of Medline via Pubmed. We focused on observed and relative age- and sex-adjusted 5-year survival rates for cancer in general and for the four most common malignancies in developed countries, i.e. lung, breast, prostate and colorectal cancer, as well as for heart failure and stroke.</p> <p>Results</p> <p>Twenty studies were identified and included for analysis. Five-year observed survival was about 43% for all cancer entities, 40-68% for stroke and 26-52% for heart failure. Five-year age and sex adjusted relative survival was 50-57% for all cancer entities, about 50% for stroke and about 62% for heart failure. In regard to the four most common malignancies in developed countries 5-year relative survival was 12-18% for lung cancer, 73-89% for breast cancer, 50-99% for prostate cancer and about 43-63% for colorectal cancer. Trend analysis revealed a survival improvement over the last decades.</p> <p>Conclusions</p> <p>The results indicate that long term survival and prognosis of cancer is not necessarily worse than that of heart failure and stroke. However, a comparison of the prognostic impact of the different diseases is limited, corroborating the necessity for further systematic investigation of competing risks.</p

Intensity-modulated radiotherapy of nasopharyngeal carcinoma: a comparative treatment planning study of photons and protons

<p>Abstract</p> <p>Background</p> <p>The aim of this treatment planning study was to investigate the potential advantages of intensity-modulated (IM) proton therapy (IMPT) compared with IM photon therapy (IMRT) in nasopharyngeal carcinoma (NPC).</p> <p>Methods</p> <p>Eight NPC patients were chosen. The dose prescriptions in cobalt Gray equivalent (Gy_E) for gross tumor volumes of the primary tumor (GTV-T), planning target volumes of GTV-T and metastatic (PTV-TN) and elective (PTV-N) lymph node stations were 72.6 Gy_E, 66 Gy_E, and 52.8 Gy_E, respectively. For each patient, nine coplanar fields IMRT with step-and-shoot technique and 3D spot-scanned three coplanar fields IMPT plans were prepared. Both modalities were planned in 33 fractions to be delivered with a simultaneous integrated boost technique. All plans were prepared and optimized by using the research version of the inverse treatment planning system KonRad (DKFZ, Heidelberg).</p> <p>Results</p> <p>Both treatment techniques were equal in terms of averaged mean dose to target volumes. IMPT plans significantly improved the tumor coverage and conformation (<it>P </it>< 0.05) and they reduced the averaged mean dose to several organs at risk (OARs) by a factor of 2–3. The low-to-medium dose volumes (0.33–13.2 Gy_E) were more than doubled by IMRT plans.</p> <p>Conclusion</p> <p>In radiotherapy of NPC patients, three-field IMPT has greater potential than nine-field IMRT with respect to tumor coverage and reduction of the integral dose to OARs and non-specific normal tissues. The practicality of IMPT in NPC deserves further exploration when this technique becomes available on wider clinical scale.</p

Integrated-boost IMRT or 3-D-CRT using FET-PET based auto-contoured target volume delineation for glioblastoma multiforme - a dosimetric comparison

<p>Abstract</p> <p>Background</p> <p>Biological brain tumor imaging using O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET)-PET combined with inverse treatment planning for locally restricted dose escalation in patients with glioblastoma multiforme seems to be a promising approach.</p> <p>The aim of this study was to compare inverse with forward treatment planning for an integrated boost dose application in patients suffering from a glioblastoma multiforme, while biological target volumes are based on FET-PET and MRI data sets.</p> <p>Methods</p> <p>In 16 glioblastoma patients an intensity-modulated radiotherapy technique comprising an integrated boost (IB-IMRT) and a 3-dimensional conventional radiotherapy (3D-CRT) technique were generated for dosimetric comparison. FET-PET, MRI and treatment planning CT (P-CT) were co-registrated. The integrated boost volume (PTV1) was auto-contoured using a cut-off tumor-to-brain ratio (TBR) of ≥ 1.6 from FET-PET. PTV2 delineation was MRI-based. The total dose was prescribed to 72 and 60 Gy for PTV1 and PTV2, using daily fractions of 2.4 and 2 Gy.</p> <p>Results</p> <p>After auto-contouring of PTV1 a marked target shape complexity had an impact on the dosimetric outcome. Patients with 3-4 PTV1 subvolumes vs. a single volume revealed a significant decrease in mean dose (67.7 vs. 70.6 Gy). From convex to complex shaped PTV1 mean doses decreased from 71.3 Gy to 67.7 Gy. The homogeneity and conformity for PTV1 and PTV2 was significantly improved with IB-IMRT. With the use of IB-IMRT the minimum dose within PTV1 (61.1 vs. 57.4 Gy) and PTV2 (51.4 vs. 40.9 Gy) increased significantly, and the mean EUD for PTV2 was improved (59.9 vs. 55.3 Gy, p < 0.01). The EUD for PTV1 was only slightly improved (68.3 vs. 67.3 Gy). The EUD for the brain was equal with both planning techniques.</p> <p>Conclusion</p> <p>In the presented planning study the integrated boost concept based on inversely planned IB-IMRT is feasible. The FET-PET-based automatically contoured PTV1 can lead to very complex geometric configurations, limiting the achievable mean dose in the boost volume. With IB-IMRT a better homogeneity and conformity, compared to 3D-CRT, could be achieved.</p

Pareto navigation – systematic multicriteria-based IMRT treatment plan determination

Background and purpose Inherently, IMRT treatment planning involves compromising between different planning goals. Multi-criteria IMRT planning directly addresses this compromising and thus makes it more systematic. Usually, several plans are computed from which the planner selects the most promising following a certain procedure. Applying Pareto navigation for this selection step simultaneously increases the variety of planning options and eases the identification of the most promising plan. Material and methods Pareto navigation is an interactive multi-criteria optimization method that consists of the two navigation mechanisms “selection” and “restriction”. The former allows the formulation of wishes whereas the latter allows the exclusion of unwanted plans. They are realized as optimization problems on the so-called plan bundle – a set constructed from precomputed plans. They can be approximately reformulated so that their solution time is a small fraction of a second. Thus, the user can be provided with immediate feedback regarding his or her decisions

Trade-off bounds and their effect in multi-criteria IMRT planning

One approach to multi-criteria IMRT planning is to automatically calculate a data set of Pareto-optimal plans for a given planning problem in a first phase, and then interactively explore the solution space and decide for the clinically best treatment plan in a second phase. The challenge of computing the plan data set is to assure that all clinically meaningful plans are covered and that as many as possible clinically irrelevant plans are excluded to keep computation times within reasonable limits. In this work, we focus on the approximation of the clinically relevant part of the Pareto surface, the process that consititutes the first phase. It is possible that two plans on the Parteto surface have a very small, clinically insignificant difference in one criterion and a significant difference in one other criterion. For such cases, only the plan that is clinically clearly superior should be included into the data set. To achieve this during the Pareto surface approximation, we propose to introduce bounds that restrict the relative quality between plans, so called tradeoff bounds. We show how to integrate these trade-off bounds into the approximation scheme and study their effects

Intensity-Modulated Radiotherapy - A Large Scale Multi-Criteria Programming Problem

Radiation therapy planning is always a tight rope walk between dangerous insufficient dose in the target volume and life threatening overdosing of organs at risk. Finding ideal balances between these inherently contradictory goals challenges dosimetrists and physicians in their daily practice. Today’s planning systems are typically based on a single evaluation function that measures the quality of a radiation treatment plan. Unfortunately, such a one dimensional approach cannot satisfactorily map the different backgrounds of physicians and the patient dependent necessities. So, too often a time consuming iteration process between evaluation of dose distribution and redefinition of the evaluation function is needed. In this paper we propose a generic multi-criteria approach based on Pareto’s solution concept. For each entity of interest - target volume or organ at risk a structure dependent evaluation function is defined measuring deviations from ideal doses that are calculated from statistical functions. A reasonable bunch of clinically meaningful Pareto optimal solutions are stored in a data base, which can be interactively searched by physicians. The system guarantees dynamical planning as well as the discussion of tradeoffs between different entities. Mathematically, we model the upcoming inverse problem as a multi-criteria linear programming problem. Because of the large scale nature of the problem it is not possible to solve the problem in a 3D-setting without adaptive reduction by appropriate approximation schemes. Our approach is twofold: First, the discretization of the continuous problem is based on an adaptive hierarchical clustering process which is used for a local refinement of constraints during the optimization procedure. Second, the set of Pareto optimal solutions is approximated by an adaptive grid of representatives that are found by a hybrid process of calculating extreme compromises and interpolation methods