CONTOURING AND PLANNING

In IGRT, pretreatment images constitute the inputs to the treatment planning process, which is vulnerable to the GIGO effect (garbage in, garbage out). For this reason, a series of scientific articles on CT have been raising increasing concern about image quality.40 However, the higher the dose contributing to the image, the less apparent is image noise and the easier it is to perceive low-contrast structures. In view of possible cancer risk, CT dose reduction strategies and scanning protocols have been suggested to match CT dose with the necessary image quality by many organizations.41–43 In contrast to therapeutic irradiation, the CT planning doses are too small to judge whether cancer patients benefit from the reduced dose strategies. Hence, it is not of great concern to these patients and their attending radiation oncologists. The acquisition of optimal CT data set fused with other imaging modalities, such as MRI and PET, to allow the radiation oncologist to accurately contour the tumor targets, is always the subject of the RT procedure.

Various studies indicate that inconsistencies in anatomy contouring may be larger than errors in the other steps of the treatment planning and delivery process.44 They also could potentially lead to reduction in the dose to the tumor, increased locoregional recurrence and worse survival. Up to now, a large number of contouring consensus guidelines have been established, and structured training on image interpretation has been offered. In addition, semiautomatic and automatic contouring methods, such as probabilistic atlases and machine-learning technologies, have been proposed to minimize manual input and increase consistency in delineating target volume. Various margins are recommended to generate clinical target volume (CTV) or planning target volume (PTV). Frequent image guidance is suggested allowing margin reduction to several millimeters and dose escalation while maintaining sparing of healthy tissue. Standards for quality assurance (QA) of IGRT devices and reconstruction algorithms are implemented to quantify image quality in adaptive RT (ART) and online/offline planning process, improving positioning or registration accuracy.

However, traditional CT images show respiratory motion as artifacts, and target volumes based on these images may be distorted. Four-dimensional (4D) CT (4DCT) technology allows clinicians to view volumetric CT images changing over time for the observation of intra-fractional target motion and assessment of lung function in RT and to predict treatment response even better than static pretreatment images. Although a prospective 4DCT simulation can allow radiation exposure to be minimized to almost the same as that in helical CT, the measured effective dose was 28.7–33.2 mSv during a retrospective 4DCT simulation, approximately four times higher than that for helical CT.45From the radiological protection point of view, it is important to optimize the 4DCT scan protocol to minimize patient exposure.


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To enhance target definition and RT planning, IGRT takes full advantage of these imaging advances. It markedly impacts the amount of radiation dose and volume of irradiated tissue as well as cancer risk. Large clinical studies have highlighted that patients who received RT had a higher risk of SMNs.46 SMNs usually occur near volumes irradiated to intermediate doses (~2–50 Gy) or in volumes receiving full-dose radiation.47 Although LSS shows that a linear relationship exists between cancer and dose from ~0.1 to ~2.0 Sv, there are great uncertainties at higher doses (Figure 2). The bell-shaped Gray model (linear–quadratic–exponential model) hypothesizes that the relative increase in risk declines above a threshold dose (thyroid cancer, ~20 Gy) in terms of a balance between cell mutation and killing; a plateau model based on various biological parameters, such as cell sterilization, proliferation, and carcinogenic effects, speculates that there is a plateau beyond a threshold dose (breast cancer, ~10 Gy).48 However, several epidemiological studies indicate that SMN risks increase significantly from 1 to 45 Gy for stomach and pancreas, but 1–60 Gy for bladder and rectum or 1–15 Gy for kidney.46 It seems that the risk continues to rise as a linear function of dose.47

In IGRT, especially for planning process, many factors such as fractionation schedule, linear accelerator (linac), RT techniques, beam energy, and dose/dose rate may affect SMN risks. Carcinoma and sarcoma risks are decreased by ~10% and ~15% per gray with increasing fractionation dose.49 The out-of-field dose is likely to be highly facility dependent due to leakage and scattering from head and accessories.50 Intensity-modulated techniques increase the risks because of more monitor units (MUs), increasing leakage and scattering radiation; dynamic mode or cross-plane motion also increases SMN risks compared to segmental or in-plane mode; more fields are correlated with higher risks.51–53Higher energies (>6 MV) produce larger scattered X-rays and small but significant neutrons due to photonuclear interactions with thresholds of 6–13 MeV for most materials.54 Although the model-dependent SMN risks have been presenting in many literatures according to the Gy, plateau, or linear dose–response model, large discrepancies exist among them.55

Clinically, choosing the least toxic radiation modality is of utmost importance. Previous studies have also demonstrated that increased risks for RT-related SMNs vary in different organs/tissues (Figure 3);56–61 both size and shape of the PTV influence SMN.62,63 While providing equally good PTV coverage and following the specific organ-at-risk (OAR) constraints, the probability of SMN incidence should be carefully examined and weighed against the possibility of developing acute side effects for each patient individually as well as treatment efficacy. According to the target characteristics, a major decrease in the volume receiving a moderate radiation dose by any strategy (e.g., patient positioning, linac choice, and shielding strategies) is the appropriate way to substantially decrease the second cancer risk while designing the treatment plan. In addition, clinical trials have demonstrated that plan quality is associated with survival and local tumor control.64 There have been intense research activities in planning quality evaluation using machine-learning approaches based on prior plans.65–67 Further, automatic treatment planning solutions such as knowledge-based planning and multicriteria optimization have been proposed to improve the plan quality and workflow, including automatic learning-based beam angle selection and automatic optimized intensity for individual patients based on their unique anatomy.

(To view a larger version of Figure 3, click here.)