Use of articulated registration for response assessment of individual metastatic bone lesions

The purpose of this article is to review the status and limitations of anatomic tumor response metrics including the World Health Organization (WHO) criteria, the Response Evaluation Criteria in Solid Tumors (RECIST), and RECIST 1.1. This article also reviews qualitative and quantitative approaches to metabolic tumor response assessment with (18)F-FDG PET and proposes a draft framework for PET Response Criteria in Solid Tumors (PERCIST), version 1.0. PubMed searches, including searches for the terms RECIST, positron, WHO, FDG, cancer (including specific types), treatment response, region of interest, and derivative references, were performed. Abstracts and articles judged most relevant to the goals of this report were reviewed with emphasis on limitations and strengths of the anatomic and PET approaches to treatment response assessment. On the basis of these data and the authors' experience, draft criteria were formulated for PET tumor response to treatment. Approximately 3,000 potentially relevant references were screened. Anatomic imaging alone using standard WHO, RECIST, and RECIST 1.1 criteria is widely applied but still has limitations in response assessments. For example, despite effective treatment, changes in tumor size can be minimal in tumors such as lymphomas, sarcoma, hepatomas, mesothelioma, and gastrointestinal stromal tumor. CT tumor density, contrast enhancement, or MRI characteristics appear more informative than size but are not yet routinely applied. RECIST criteria may show progression of tumor more slowly than WHO criteria. RECIST 1.1 criteria (assessing a maximum of 5 tumor foci, vs. 10 in RECIST) result in a higher complete response rate than the original RECIST criteria, at least in lymph nodes. Variability appears greater in assessing progression than in assessing response. Qualitative and quantitative approaches to (18)F-FDG PET response assessment have been applied and require a consistent PET methodology to allow quantitative assessments. Statistically significant changes in tumor standardized uptake value (SUV) occur in careful test-retest studies of high-SUV tumors, with a change of 20% in SUV of a region 1 cm or larger in diameter; however, medically relevant beneficial changes are often associated with a 30% or greater decline. The more extensive the therapy, the greater the decline in SUV with most effective treatments. Important components of the proposed PERCIST criteria include assessing normal reference tissue values in a 3-cm-diameter region of interest in the liver, using a consistent PET protocol, using a fixed small region of interest about 1 cm(3) in volume (1.2-cm diameter) in the most active region of metabolically active tumors to minimize statistical variability, assessing tumor size, treating SUV lean measurements in the 1 (up to 5 optional) most metabolically active tumor focus as a continuous variable, requiring a 30% decline in SUV for "response," and deferring to RECIST 1.1 in cases that do not have (18)F-FDG avidity or are technically unsuitable. Criteria to define progression of tumor-absent new lesions are uncertain but are proposed. Anatomic imaging alone using standard WHO, RECIST, and RECIST 1.1 criteria have limitations, particularly in assessing the activity of newer cancer therapies that stabilize disease, whereas (18)F-FDG PET appears particularly valuable in such cases. The proposed PERCIST 1.0 criteria should serve as a starting point for use in clinical trials and in structured quantitative clinical reporting. Undoubtedly, subsequent revisions and enhancements will be required as validation studies are undertaken in varying diseases and treatments.

Historically, anatomical CT and MR images were used to delineate the gross tumour volumes (GTVs) for radiotherapy treatment planning. The capabilities offered by modern radiation therapy units and the widespread availability of combined PET/CT scanners stimulated the development of biological PET imaging-guided radiation therapy treatment planning with the aim to produce highly conformal radiation dose distribution to the tumour. One of the most difficult issues facing PET-based treatment planning is the accurate delineation of target regions from typical blurred and noisy functional images. The major problems encountered are image segmentation and imperfect system response function. Image segmentation is defined as the process of classifying the voxels of an image into a set of distinct classes. The difficulty in PET image segmentation is compounded by the low spatial resolution and high noise characteristics of PET images. Despite the difficulties and known limitations, several image segmentation approaches have been proposed and used in the clinical setting including thresholding, edge detection, region growing, clustering, stochastic models, deformable models, classifiers and several other approaches. A detailed description of the various approaches proposed in the literature is reviewed. Moreover, we also briefly discuss some important considerations and limitations of the widely used techniques to guide practitioners in the field of radiation oncology. The strategies followed for validation and comparative assessment of various PET segmentation approaches are described. Future opportunities and the current challenges facing the adoption of PET-guided delineation of target volumes and its role in basic and clinical research are also addressed.

Weight loss, tumor shrinkage, and tissue edema induce substantial modification of patient's anatomy during head and neck (HN) radiotherapy (RT) or chemo-radiotherapy. These modifications may impact on the dose distribution to both target volumes (TVs) and organs at risk (OARs). Adaptive radiotherapy (ART) where patients are re-imaged and re-planned several times during the treatment is a possible strategy to improve treatment delivery. It however requires the use of specific deformable registration (DR) algorithms that requires proper validation on a clinical material. Twelve voxel-based DR strategies were compared with a dataset of 5 patients imaged with computed tomography (CT) before and once during RT (on average after a mean dose of 36.8Gy): level-set (LS), level-set implemented in multi-resolution (LS(MR)), Demons' algorithm implemented in multi-resolution (D(MR)), D(MR) followed by LS (D(MR)-LS), fast free-form deformable registration via calculus of variations (F3CV) and F3CV followed by LS (F3CV-LS). The use of an edge-preserving denoising filter called "local M-smoothers" applied to the registered images and combined to all the aforesaid strategies was also tested (fLS, fLS(MR), fD(MR), fD(MR)-LS, fF3CV, fF3CV-LS). All these strategies were compared to a rigid registration based on mutual information (MI, fMI). Chronological and anti-chronological registrations were also studied. The various DR strategies were evaluated using a volume-based criterion (i.e. Dice similarity index, DSI) and a voxel-intensity criterion (i.e. correlation coefficient, CC) on a total of 18 different manually contoured volumes. For the DSI analysis, the best three strategies were D(MR), fD(MR)-LS, and fD(MR), with the median values of 0.86, 0.85 and 0.85, respectively; corresponding inter-quartile range (IQR) reached 9.6%, 10% and 10.2%. For the CC analysis, the best three strategies were fD(MR)-LS, D(MR)-LS and D(MR) with the median values of 0.97, 0.96 and 0.94, respectively; corresponding IQR reached 11%, 9% and 15%. Concerning the time-sequence analysis, the anti-chronological registration (all deformable strategies pooled) showed a better median DSI value (0.84 vs 0.83, p<0.001) and IQR (11.2% vs 12.4%). For CC, the anti-chronological registration (all deformable strategies pooled) had a slightly lower median value (0.91 vs 0.912, p<0.001) but a better IQR (16.4% vs 21%). The use of fD(MR)-LS is a good registration strategy for HN-ART as it is the best compromise in terms of median and IQR for both DSI and CC. Even though less robust in terms of CC, D(MR) is a good alternative. None of the time-sequence appears superior.