PET-based treatment response assessment typically measures the change in maximum standardized uptake value (SUV(max)), which is adversely affected by noise. Peak SUV (SUV(peak)) has been recommended ...as a more robust alternative, but its associated region of interest (ROI(peak)) is not uniquely defined. We investigated the impact of different ROI(peak) definitions on quantification of SUV(peak) and tumor response.
Seventeen patients with solid malignancies were treated with a multitargeted receptor tyrosine kinase inhibitor resulting in a variety of responses. Using the cellular proliferation marker 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT), whole-body PET/CT scans were acquired at baseline and during treatment. (18)F-FLT-avid lesions (∼2/patient) were segmented on PET images, and tumor response was assessed via the relative change in SUV(peak). For each tumor, 24 different SUV(peaks) were determined by changing ROI(peak) shape (circles vs. spheres), size (7.5-20 mm), and location (centered on SUV(max) vs. placed in highest-uptake region), encompassing different definitions from the literature. Within each tumor, variations in the 24 SUV(peaks) and tumor responses were measured using coefficient of variation (CV), standardized deviation (SD), and range. For each ROI(peak) definition, a population average SUV(peak) and tumor response were determined over all tumors.
A substantial variation in both SUV(peak) and tumor response resulted from changing the ROI(peak) definition. The variable ROI(peak) definition led to an intratumor SUV(peak) variation ranging from 49% above to 46% below the mean (CV, 17%) and an intratumor SUV(peak) response variation ranging from 49% above to 35% below the mean (SD, 9%). The variable ROI(peak) definition led to a population average SUV(peak) variation ranging from 24% above to 28% below the mean (CV, 14%) and a population average SUV(peak) response variation ranging from only 3% above to 3% below the mean (SD, 2%). The size of ROI(peak) caused more variation in intratumor response than did the location or shape of ROI(peak). Population average tumor response was independent of size, shape, and location of ROI(peak).
Quantification of individual tumor response using SUV(peak) is highly sensitive to the ROI(peak) definition, which can significantly affect the use of SUV(peak) for assessment of treatment response. Clinical trials are necessary to compare the efficacy of SUV(peak) and SUV(max) for quantification of response to therapy.
Abstract
Background. Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work ...is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Material and methods. Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45-60 minutes post-injection of 10 mCi of 18FFDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results. Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Conclusion. Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation.
Lung cancer, the most commonly diagnosed cancer worldwide, usually presents as solid pulmonary nodules (SPNs) on early diagnostic images. Classification of malignant disease at this early timepoint ...is critical for improving the success of surgical resection and increasing 5-year survival rates. 18F-fluorodeoxyglucose (18F-FDG) PET/CT has demonstrated value for SPNs diagnosis with high sensitivity to detect malignant SPNs, but lower specificity in diagnosing malignant SPNs in populations with endemic infectious lung disease. This study aimed to determine whether quantitative heterogeneity derived from various texture features on dual time FDG PET/CT images (DTPI) can differentiate between malignant and benign SPNs in patients from granuloma-endemic regions. Machine learning methods were employed to find optimal discrimination between malignant and benign nodules. Machine learning models trained by texture features on DTPI images achieved significant improvements over standard clinical metrics and visual interpretation for discriminating benign from malignant SPNs, especially by texture features on delayed FDG PET/CT images.
Summary Intensity-modulated radiation therapy (IMRT) is a conformal irradiation technique that enables steep dose gradients. In head and neck tumours this approach spares parotid-gland function ...without compromise to treatment efficacy. Anatomical and molecular imaging modalities may be used to tailor treatment by enabling proper selection and delineation of target volumes and organs at risk, which in turn lead to dose prescriptions that take into account the underlying tumour biology (eg, human papillomavirus status). Therefore, adaptations can be made throughout the course of radiotherapy, as required. Planned dose increases to parts of the target volumes may also be used to match the radiosensitivity of tumours (so-called dose-painting), assessed by molecular imaging. For swift implementation of tailored and adaptive IMRT, tools and procedures, such as accurate image acquisition and reconstruction, automatic segmentation of target volumes and organs at risk, non-rigid image and dose registration, and dose summation methods, need to be developed and properly validated.
Lung cancer usually presents as a solitary pulmonary nodule (SPN) on diagnostic imaging during the early stages of the disease. Since the early diagnosis of lung cancer is very important for ...treatment, the accurate diagnosis of SPNs has much importance. The aim of this study was to evaluate the discriminant power of dual time point imaging (DTPI) PET/CT in the differentiation of malignant and benign FDG-avid solitary pulmonary nodules by using neighborhood gray-tone difference matrix (NGTDM) texture features.
Retrospective analysis was carried out on 116 patients with SPNs (35 benign and 81 malignant) who had DTPI
F-FDG PET/CT between January 2005 and May 2015. Both PET and CT images were acquired at 1 h and 3 h after injection. The SUV
and NGTDM texture features (coarseness, contrast, and busyness) of each nodule were calculated on dual time point images. Patients were randomly divided into training and validation datasets. Receiver operating characteristic (ROC) curve analysis was performed on all texture features in the training dataset to calculate the optimal threshold for differentiating malignant SPNs from benign SPNs. For all the lesions in the testing dataset, two visual interpretation scores were determined by two nuclear medicine physicians based on the PET/CT images with and without reference to the texture features.
In the training dataset, the AUCs of delayed busyness, delayed coarseness, early busyness, and early SUV
were 0.87, 0.85, 0.75 and 0.75, respectively. In the validation dataset, the AUCs of visual interpretations with and without texture features were 0.89 and 0.80, respectively.
Compared to SUV
or visual interpretation, NGTDM texture features derived from DTPI PET/CT images can be used as good predictors of SPN malignancy. Improvement in discriminating benign from malignant nodules using SUVmax and visual interpretation can be achieved by adding busyness extracted from delayed PET/CT images.
•Clinical target volume definition in radiotherapy is challenging.•The contribution of computational methods is discussed.•Goals are automation, consistency, and ultimately improvements.•Image ...segmentation algorithms can automate the process in parts.•Mathematical models may quantitatively describe tumor progression patterns.
Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume (GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definition and PTV margins have improved through the development of novel imaging techniques and better image guidance, respectively. CTV definition is sometimes considered the weakest element in the planning process. CTV definition is particularly complex since the extension of microscopic disease cannot be seen using currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowledge of the patterns of tumor progression. While CTV delineation has largely been considered the domain of radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses the contributions that medical physics and computer science can make by developing computational methods to support CTV definition. First, we overview the role of image segmentation algorithms, which may in part automate CTV delineation through segmentation of lymph node stations or normal tissues representing anatomical boundaries of microscopic tumor progression. The recent success of deep convolutional neural networks has also enabled learning entire CTV delineations from examples. Second, we discuss the use of mathematical models of tumor progression for CTV definition, using as example the application of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consistent with neuroanatomy. We further consider statistical machine learning models to quantify lymphatic metastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we discuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well as general limitations of the CTV concept in the case of infiltrating tumors without natural boundaries.
Patients with metastatic melanoma often receive
F-FDG PET/CT scans on different scanners throughout their monitoring period. In this study, we quantified the impact of scanner harmonization on ...longitudinal changes in PET standardized uptake values using various harmonization and normalization methods, including an anthropomorphic PET phantom. Twenty metastatic melanoma patients received at least two FDG PET/CT scans, each on two different scanners with an average of 4 months (range: 2-8) between. Scans from a General Electric (GE) Discovery 710 PET CT
were harmonized to the GE Discovery VCT using image reconstruction settings matching recovery coefficients in an anthropomorphic phantom with bone equivalent inserts and wall-less synthetic lesions. In patient images, SUV
was measured for each melanoma lesion and time-point. Lesions were classified as progressing, stable, or responding based on pre-defined threshold of ±30% change in SUV
. For comparison, harmonization was also performed using simpler methods, including harmonization using a NEMA phantom, post-reconstruction filtering, reference region normalization of SUV
, and use of SUV
instead of SUV
In the 20 patients, 90 lesions across two time-points were available for treatment response assessment. Treatment response classification changed in 47% (42/90) of cases after harmonization with anthropomorphic phantom. Before harmonization, 37% (33/90) of the lesions were classified as stable (changing less than 30% between two time-points), while the fraction of stable lesions increased to 58% (52/90) after harmonization. Harmonization with the NEMA phantom agreed with harmonization with the anthropomorphic phantom in 91% (82/90) of cases. Post-reconstruction filtering agreed with anthropomorphic phantom-based harmonization in 83% (75/90) cases. The utilization of reference regions for normalization or SUV
was unable to correct for changes as identified by the anthropomorphic phantom-based harmonization. Overall, PET scanner harmonization has a major impact on individual lesion treatment response classification in metastatic melanoma patients. Harmonization using the NEMA phantom yielded similar results to harmonization using anthropomorphic phantom, while the only acceptable post-reconstruction technique was post-reconstruction filtering. Phantom-based harmonization is therefore strongly recommended when comparing lesion uptake across time-points when the images have been acquired on different PET scanners.
Quantitative PET in the 2020s: a roadmap Meikle, Steven R; Sossi, Vesna; Roncali, Emilie ...
Physics in medicine & biology,
03/2021, Letnik:
66, Številka:
6
Journal Article
Recenzirano
Odprti dostop
Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for ...reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.
Abstract Accurate delineation of the primary tumor and of involved lymph nodes is a key requisite for successful curative radiotherapy in non-small cell lung cancer (NSCLC). In recent years, it has ...become clear that the incorporation of FDG PET-CT scan information into the related processes of patient selection and radiotherapy planning has lead to significant improvements for patients with NSCLC. The use of FDG PET-CT information in radiotherapy planning allows better target volume definition, reduces inter-observer variability and encourages selective irradiation of involved mediastinal lymph nodes. PET-CT also opens the door for innovative radiotherapy delivery and the development of new concepts. However, care must be taken to avoid a variety of technical pitfalls and specific education is necessary, for clinicians and physicists alike.
Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management ...of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.
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