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ObjectiveIn 2001, the National Institute for Health Research Cancer Research Network (NCRN) was established, leading to a rapid increase in clinical research activity across the English NHS. Using colorectal cancer (CRC) as an example, we test the hypothesis that high, sustained hospital-level participation in interventional clinical trials improves outcomes for all patients with CRC managed in those research-intensive hospitals.DesignData for patients diagnosed with CRC in England in 2001–2008 (n=209 968) were linked with data on accrual to NCRN CRC studies (n=30 998). Hospital Trusts were categorised by the proportion of patients accrued to interventional studies annually. Multivariable models investigated the relationship between 30-day postoperative mortality and 5-year survival and the level and duration of study participation.ResultsMost of the Trusts achieving high participation were district general hospitals and the effects were not limited to cancer ‘centres of excellence’, although such centres do make substantial contributions. Patients treated in Trusts with high research participation (≥16%) in their year of diagnosis had lower postoperative mortality (p<0.001) and improved survival (p<0.001) after adjustment for casemix and hospital-level variables. The effects increased with sustained research participation, with a reduction in postoperative mortality of 1.5% (6.5%–5%, p<2.2×10−6) and an improvement in survival (p<10−19; 5-year difference: 3.8% (41.0%–44.8%)) comparing high participation for ≥4 years with 0 years.ConclusionsThere is a strong independent association between survival and participation in interventional clinical studies for all patients with CRC treated in the hospital study participants. Improvement precedes and increases with the level and years of sustained participation.

Respiratory gating and tumor tracking for dynamic multileaf collimator delivery require accurate and real-time localization of the lung tumor position during treatment. Deriving tumor position from external surrogates such as abdominal surface motion may have large uncertainties due to the intra-and interfraction variations of the correlation between the external surrogates and internal tumor motion. Implanted fiducial markers can be used to track tumors fluoroscopically in real time with sufficient accuracy. However, it may not be a practical procedure when implanting fiducials bronchoscopically. In this work, a method is presented to track the lung tumor mass or relevant anatomic features projected in fluoroscopic images without implanted fiducial markers based on an optical flow algorithm. The algorithm generates the centroid position of the tracked target and ignores shape changes of the tumor mass shadow. The tracking starts with a segmented tumor projection in an initial image frame. Then, the optical flow between this and all incoming frames acquired during treatment delivery is computed as initial estimations of tumor centroid displacements. The tumor contour in the initial frame is transferred to the incoming frames based on the average of the motion vectors, and its positions in the incoming frames are determined by finetuning the contour positions using a template matching algorithm with a small search range. The tracking results were validated by comparing with clinician determined contours on each frame. The position difference in 95% of the frames was found to be less than 1.4 pixels ͑ϳ0.7 mm͒ in the best case and 2.8 pixels ͑ϳ1.4 mm͒ in the worst case for the five patients studied.

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Three types of iterative algorithms, algebraic inverse treatment planning (AITP), simultaneous iterative inverse treatment planning (SIITP), and iterative least-square inverse treatment planning (ILSITP), differentiated according to their updating sequences, were generalized to three dimension with true beam geometry and dose model. A rapid ray-tracing approach was developed to optimize the primary beam components. Instead of recalculating the dose matrix at each iteration, the dose distribution was generated by scaling up or down the dose matrix elements of the previous iteration. This significantly increased the calculation speed. The iterative algorithms started with an initial intensity profile for each beam, specified by a two-dimensional pixel beam map of M elements. The calculation volume was divided into N voxels, and the calculation was done by repeatedly comparing the calculated and desired doses and adjusting the values of the beam map elements to minimize an objective function. In AITP, the iteration is performed voxel by voxel. For each voxel, the dose discrepancy was evaluated and the contributing pencil beams were updated. In ILSITP and SIITP, the iteration proceeded pencil beam by pencil beam instead of voxel by voxel. In all cases, the iteration procedure was repeated until the best possible dose distribution was achieved. The algorithms were applied to two examples and the results showed that the iterative techniques were able to produce superior isodose distributions.

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