Radiotherapy is a localised treatment. The definition of tumour and target volumes for radiotherapy is vital to its successful execution. This requires the best possible characterisation of the location and extent of tumour. Diagnostic imaging, including help and advice from diagnostic specialists, is therefore essential for radiotherapy planning. There are three main volumes in radiotherapy planning. The first is the position and extent of gross tumour, i.e. what can be seen, palpated or imaged; this is known as the gross tumour volume (GTV). Developments in imaging have contributed to the definition of the GTV. The second volume contains the GTV, plus a margin for sub-clinical disease spread which therefore cannot be fully imaged; this is known as the clinical target volume (CTV). It is the most difficult because it cannot be accurately defined for an individual patient, but future developments in imaging, especially towards the molecular level, should allow more specific delineation of the CTV. The CTV is important because this volume must be adequately treated to achieve cure. The third volume, the planning target volume (PTV), allows for uncertainties in planning or treatment delivery. It is a geometric concept designed to ensure that the radiotherapy dose is actually delivered to the CTV. Radiotherapy planning must always consider critical normal tissue structures, known as organs at risk (ORs). In some specific circumstances, it is necessary to add a margin analogous to the PTV margin around an OR to ensure that the organ cannot receive a higher-than-safe dose; this gives a planning organ at risk volume. This applies to an organ such as the spinal cord, where damage to a small amount of normal tissue would produce a severe clinical manifestation. The concepts of GTV, CTV and PTV have been enormously helpful in developing modern radiotherapy. Attention to detail in radiotherapy planning is vital, and does affect outcomes: ‘the devil is in the detail’. Radiotherapy planning is also dependent on high quality imaging, and the better the imaging the better will be the outcomes from radiotherapy.

Several authors have reported data on the variation of Hounsfield numbers with electron density in CT scanners. The data can be fitted with a double straight line approach. For non-bone tissues (or phantom materials with similar atomic numbers) the data from all authors can be fitted to a single straight line. For bone-like materials the line varies between authors. The method used to measure electron density has a greater effect than the differences between scanners, or the kilovoltage used on a given scanner. The effect of variation of these slopes on the accuracy of radiotherapy treatment planning is analysed. For typical radiotherapy beams, to produce a 1% error in dosimetry would require errors of over 8% in bone electron density. Using a single pair of calibration lines for all the scanners reported would give dosimetric errors of under 0.8%. A formula is recommended as a default for use in planning systems in circumstances where no data are available for a particular scanner.

Background and purposeFor the first time, delivered dose to the rectum has been calculated and accumulated throughout the course of prostate radiotherapy using megavoltage computed tomography (MVCT) image guidance scans. Dosimetric parameters were linked with toxicity to test the hypothesis that delivered dose is a stronger predictor of toxicity than planned dose.Material and methodsDose–surface maps (DSMs) of the rectal wall were automatically generated from daily MVCT scans for 109 patients within the VoxTox research programme. Accumulated-DSMs, representing total delivered dose, and planned-DSMs, from planning CT data, were parametrised using Equivalent Uniform Dose (EUD) and ‘DSM dose-width’, the lateral dimension of an ellipse fitted to a discrete isodose cluster. Associations with 6 toxicity endpoints were assessed using receiver operator characteristic curve analysis.ResultsFor rectal bleeding, the area under the curve (AUC) was greater for accumulated dose than planned dose for DSM dose-widths up to 70 Gy. Accumulated 65 Gy DSM dose-width produced the strongest spatial correlation (AUC 0.664), while accumulated EUD generated the largest AUC overall (0.682). For proctitis, accumulated EUD was the only reportable predictor (AUC 0.673). Accumulated EUD was systematically lower than planned EUD.ConclusionsDosimetric parameters extracted from accumulated DSMs have demonstrated stronger correlations with rectal bleeding and proctitis, than planned DSMs.

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Although IMRT has been shown clinically to increase skin doses for some patients, it has also been shown that intensity modulated delivery does not, of itself, increase skin doses. The reason for this apparent difference is that inverse planning can result in solutions that give high fluence to tangential beam segments near the skin surface, in an attempt to counter the build-up region. In cases where the clinical target volume (CTV) stops short of the skin surface, but the planning target volume (PTV) does not, there is no clinical reason to treat the skin. The CTV-PTV margin exists purely to ensure that fields are large enough to allow for geometrical uncertainties. With an objective function based on the doses to the PTV, it is possible for a plan that gives excess fluence to the skin to have a lower objective function, and hence to be preferred in an optimization. We describe a technique of plan evaluation, based on analysis of a plan by recalculating several plans in which the isocentre has been offset by a distance equal to the CTV-PTV margin. We demonstrate that changes to a plan that reduce a PTV-based objective can give a worse dose distribution to the CTV when systematic and random set-up errors are accounted for, and increase skin dose. Several possible strategies for avoiding this problem are discussed, including the use of the skin as an organ at risk, modification of the PTV to avoid the skin, and the use of 'pretend bolus' applied in planning but not in treatment. The latter gave the best results. The possibility of using the evaluation method itself, as the basis of an objective function for optimization, is discussed.