FDG PET/CT for rectal carcinoma radiotherapy treatment planning: comparison of functional volume delineation algorithms and clinical challenges

Changes in treatment intent and target definition for preoperative radiotherapy after 18F-Fluorodeoxyglucose positron emission tomography in rectal cancer: A Meta-analysis

PURPOSE

To evaluate the impact of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) on changes in treatment plan and target definition for preoperative radiotherapy in patients with rectal cancer.

METHODS

Embase, PubMed, and Cochrane Library were searched up to November 2020 for all studies investigating the role of preoperative FDG PET in patients who underwent neoadjuvant radiotherapy before curative-intent surgery. The proportion of patients whose treatment plan (curative vs. palliative intent) or target definition was changed after FDG PET was analyzed. A random-effects model was used for pooled analysis. The change in target definition was compared between conventional radiological imaging-based target volume [gross tumor volume (GTV) or planning target volume (PTV)] and PET-based target volume (GTV or PTV) using the standardized mean difference (SMD) and 95% confidence interval (CI).

RESULTS

A total of 336 patients from twelve studies were included. In eight studies, PET changed either the treatment intent or target definition in 24.8% of patients (95% CI 15.1% to 37.9%, I² = 69%). In ten studies, the PET-based GTV was lower than the conventional imaging-based target volume (SMD -7.0, 95% CI -1.39 to -0.01). However, there was no significant difference between conventional imaging-based and PET-based PTV (SMD -0.07, 95% CI -0.75 to 0.62). In six studies evaluating the initial staging based on PET, the initial staging (nodal or metastasis status) was changed in 53 of 229 patients (23.1%). Newly detected or additional distant metastases were identified in 22 patients (9.6%) after FDG PET.

CONCLUSION

The use of FDG PET influences radiotherapy planning in a fourth of patients with rectal cancer. FDG PET can provide additive information for accurate tumor delineation, although PET-based PTV did not significantly change. These findings suggest that FDG PET may be beneficial to patients with rectal cancer before establishing a radiotherapy plan.

FDG-PET/CT and MR imaging for target volume delineation in rectal cancer radiotherapy treatment planning: a systematic review

Aim:

The aim of this systematic review was to synthesise and summarise evidence surrounding the clinical use of fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (FDG-PET/CT) and magnetic resonance imaging (MRI) for target volume delineation (TVD) in rectal cancer radiotherapy planning.

Methods:

PubMed, EMBASE, Cochrane library, CINAHL, Web of Science and Scopus databases and other sources were systematically queried using keywords and relevant synonyms. Eligible full-text studies were assessed for methodological quality using the QUADAS-2 tool.

Results:

Eight of the 1448 studies identified met the inclusion criteria. Findings showed that MRI significantly delineate larger tumour volumes (TVs) than FDG-PET/CT while diffusion-weighted magnetic resonance imaging (DW-MRI) defined smaller gross tumour volumes (GTVs) compared to T2 weighted-Magnetic Resonance Image. CT-based GTVs were found to be larger compared to FDG-PET/CT. FDG-PET/CT also identified new lesions in 15–17% patients and TVs extending outside the routinely used clinical standard CT TV in 29–83% patients. Between observers, delineated volumes were similar and consistent between MRI sequences, whereas interobserver agreement was significantly improved with FDG-PET/CT than CT.

Conclusion:

FDG-PET/CT and DW-MRI appear to delineate smaller rectal TVs and show improved interobserver variability. Overall, this study provides valuable insights into the amount of attention in the research literature that has been paid to imaging for TVD in rectal cancer.

Differences among [18F]FDG PET-derived parameters in lung cancer produced by three software packages

Investigation of differences in derived [18F]FDG PET metabolic and volumetric parameters among three different software programs in lung cancer. A retrospective analysis was performed on a group of 98 lung cancer patients who underwent a baseline [18F]FDG PET/CT study. To assess appropriate delineation methods, the NEMA phantom study was first performed using the following software: Philips EBW (Extended Brilliance Workstation), MIM Software and Rover. Based on this study, the best cut-off methods (dependent on tumour size) were selected, extracted and applied for lung cancer delineation. Several semiquantitative [18F]FDG parameters (SUVmax, SUVmean, TLG and MTV) were assessed and compared among the three software programs. The parameters were assessed based on body weight (BW), lean body mass (LBM) and Bq/mL. Statistically significant differences were found in SUVmean (LBM) between MIM Software and Rover (4.62 ± 2.15 vs 4.84 ± 1.20; p < 0.005), in SUVmean (Bq/mL) between Rover and Philips EBW (21,852.30 ± 21,821.23 vs 19,274.81 ± 13,340.28; p < 0.005) and Rover and MIM Software (21,852.30 ± 21,821.23 vs 19,399.40 ± 10,051.30; p < 0.005), and in MTV between MIM Software and Philips EBW (19.87 ± 25.83 vs 78.82 ± 228.00; p = 0.0489). This study showed statistically significant differences in the estimation of semiquantitative parameters using three independent image analysis tools. These findings are important for performing further diagnostic and treatment procedures in lung cancer patients.

The Orthanc Ecosystem for Medical Imaging

This paper reviews the components of Orthanc, a free and open-source, highly versatile ecosystem for medical imaging. At the core of the Orthanc ecosystem, the Orthanc server is a lightweight vendor neutral archive that provides PACS managers with a powerful environment to automate and optimize the imaging flows that are very specific to each hospital. The Orthanc server can be extended with plugins that provide solutions for teleradiology, digital pathology, or enterprise-ready databases. It is shown how software developers and research engineers can easily develop external software or Web portals dealing with medical images, with minimal knowledge of the DICOM standard, thanks to the advanced programming interface of the Orthanc server. The paper concludes by introducing the Stone of Orthanc, an innovative toolkit for the cross-platform rendering of medical images.

Disease quantification on PET/CT images without explicit object delineation

PURPOSE

The derivation of quantitative information from images in a clinically practical way continues to face a major hurdle because of image segmentation challenges. This paper presents a novel approach, called automatic anatomy recognition-disease quantification (AAR-DQ), for disease quantification (DQ) on positron emission tomography/computed tomography (PET/CT) images. This approach explores how to decouple DQ methods from explicit dependence on object (e.g., organ) delineation through the use of only object recognition results from our recently developed automatic anatomy recognition (AAR) method to quantify disease burden.

METHOD

The AAR-DQ process starts off with the AAR approach for modeling anatomy and automatically recognizing objects on low-dose CT images of PET/CT acquisitions. It incorporates novel aspects of model building that relate to finding an optimal disease map for each organ. The parameters of the disease map are estimated from a set of training image data sets including normal subjects and patients with metastatic cancer. The result of recognition for an object on a patient image is the location of a fuzzy model for the object which is optimally adjusted for the image. The model is used as a fuzzy mask on the PET image for estimating a fuzzy disease map for the specific patient and subsequently for quantifying disease based on this map. This process handles blur arising in PET images from partial volume effect entirely through accurate fuzzy mapping to account for heterogeneity and gradation of disease content at the voxel level without explicitly performing correction for the partial volume effect. Disease quantification is performed from the fuzzy disease map in terms of total lesion glycolysis (TLG) and standardized uptake value (SUV) statistics. We also demonstrate that the method of disease quantification is applicable even when the "object" of interest is recognized manually with a simple and quick action such as interactively specifying a 3D box ROI. Depending on the degree of automaticity for object and lesion recognition on PET/CT, DQ can be performed at the object level either semi-automatically (DQ-MO) or automatically (DQ-AO), or at the lesion level either semi-automatically (DQ-ML) or automatically.

RESULTS

We utilized 67 data sets in total: 16 normal data sets used for model building, and 20 phantom data sets plus 31 patient data sets (with various types of metastatic cancer) used for testing the three methods DQ-AO, DQ-MO, and DQ-ML. The parameters of the disease map were estimated using the leave-one-out strategy. The organs of focus were left and right lungs and liver, and the disease quantities measured were TLG, SUV_Mean, and SUV_Max. On phantom data sets, overall error for the three parameters were approximately 6%, 3%, and 0%, respectively, with TLG error varying from 2% for large "lesions" (37 mm diameter) to 37% for small "lesions" (10 mm diameter). On patient data sets, for non-conspicuous lesions, those overall errors were approximately 19%, 14% and 0%; for conspicuous lesions, these overall errors were approximately 9%, 7%, 0%, respectively, with errors in estimation being generally smaller for liver than for lungs, although without statistical significance.

CONCLUSIONS

Accurate disease quantification on PET/CT images without performing explicit delineation of lesions is feasible following object recognition. Method DQ-MO generally yields more accurate results than DQ-AO although the difference is statistically not significant. Compared to current methods from the literature, almost all of which focus only on lesion-level DQ and not organ-level DQ, our results were comparable for large lesions and were superior for smaller lesions, with less demand on training data and computational resources. DQ-AO and even DQ-MO seem to have the potential for quantifying disease burden body-wide routinely via the AAR-DQ approach.

PET imaging in adaptive radiotherapy of gastrointestinal tumors

INTRODUCTION

Radiotherapy is a cornerstone in the multimodality treatment of several gastrointestinal (GI) tumors. Positron-emission tomography (PET) has an established role in the diagnosis, response assessment and (re-)staging of these tumors. Nevertheless, the value of PET in adaptive radiotherapy remains unclear. This review focuses on the role of PET in adaptive radiotherapy, i.e. during the treatment course and in the delineation process.

EVIDENCE ACQUISITION

The MEDLINE database was searched for the terms ("Radiotherapy"[Mesh] AND "Positron-Emission Tomography"[Mesh] AND one of the site-specific keywords, yielding a total of 1710 articles. After abstract selection, 27 papers were identified for esophageal neoplasms, 1 for gastric neoplasms, 9 for pancreatic neoplasms, 6 for liver neoplasms, 1 for biliary tract neoplasms, none for colonic neoplasms, 15 for rectal neoplasms and 12 for anus neoplasms.

EVIDENCE SYNTHESIS

The use of PET for truly adaptive radiotherapy during treatment for GI tumors has barely been investigated, in contrast to the potential of the PET-defined metabolic tumor volume for optimization of the target volume. The optimized target definition seems useful for treatment individualization such as focal boosting strategies in esophageal, pancreatic and anorectal cancer. Nevertheless, for all GI tumors, further investigation is needed.

CONCLUSIONS

In general, too little data are available to conclude on the role of PET imaging during radiotherapy for ART strategies in GI cancer. On the other hand, based on the available evidence, the use of biological imaging for target volume adaptation seems promising and could pave the road towards individualized treatment strategies.

Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges

Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving ¹⁸F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.

Junior E, Machado AM, Mamede M. Individualized threshold for tumor segmentation in 18F-FDG PET/CT imaging: The key for response evaluation of neoadjuvant chemoradiation therapy in patients with rectal cancer?

Summary Introduction: The standard treatment for locally advanced rectal cancer (RC) consists of neoadjuvant chemoradiation followed by radical surgery. Regardless the extensive use of SUVmax in 18F-FDG PET tumor uptake as representation of tumor glycolytic consumption, there is a trend to apply metabolic volume instead. Thus, the aim of the present study was to evaluate a noninvasive method for tumor segmentation using the 18F-FDG PET imaging in order to predict response to neoadjuvant chemoradiation therapy in patients with rectal cancer. Method: The sample consisted of stage II and III rectal cancer patients undergoing 18F-FDG PET/CT examination before and eight weeks after neoadjuvant therapy. An individualized tumor segmentation methodology was applied to generate tumor volumes (SUV2SD) and compare with standard SUVmax and fixed threshold (SUV40%, SUV50% and SUV60%) pre- and post-therapy. Therapeutic response was assessed in the resected specimens using Dworak's protocol recommendations. Several variables were generated and compared with the histopathological results. Results: Seventeen (17) patients were included and analyzed. Significant differences were observed between responders (Dworak 3 and 4) and non-responders for SUVmax-2 (p<0.01), SUV2SD-2 (p<0.05), SUV40%-2 (p<0.05), SUV50%-2 (p<0.05) and SUV60%-2 (p<0.05). ROC analyses showed significant areas under the curve (p<0.01) for the proposed methodology with sensitivity and specificity varying from 60% to 83% and 73% to 82%, respectively. Conclusion: The present study confirmed the predictive power of the variables using a noninvasive individualized methodology for tumor segmentation based on 18F-FDG PET/CT imaging for response evaluation in patients with rectal cancer after neoadjuvant chemoradiation therapy.

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

PURPOSE

The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application.

APPROACH

A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed.

FINDINGS

A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol.

CONCLUSIONS

Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.

18F-FLT and 18F-FDG PET/CT in Predicting Response to Chemoradiotherapy in Nasopharyngeal Carcinoma: Preliminary Results

The purpose of this study was to explore the feasibility of ¹⁸F-Fluorothymidine (¹⁸F-FLT) and ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) in predicting treatment response of nasopharyngeal carcinoma (NPC). Patients with NPC of Stage II-IVB were prospectively enrolled, receiving 2 cycles of neoadjuvant chemotherapy (NACT), followed by concurrent chemoradiotherapy. Each patient underwent pretreatment and post-NACT FLT PET/CT and FDG PET/CT. Standard uptake values (SUV) and tumor volume were measured. Tumor response to NACT was evaluated before radiotherapy by MRI (magnetic resonance imaging), and tumor regression at the end of radiotherapy was evaluated at 55 Gy, according to RECIST 1.1 Criteria. Finally, 20 patients were consecutively enrolled. At the end of radiotherapy, 7 patients reached complete regression while others were partial regression. After 2 cycles of NACT both FLT and FDG parameters declined remarkably. Parameters of FDG PET were more strongly correlated to tumor regression than those of FLT PET.70% SUVmax was the best threshold to define contouring margin around the target. Some residual lesions after NACT showed by MRI were negative in PET/CT. Preliminary results showed both ¹⁸F-FDG and ¹⁸F-FLT PET have the potential to monitor and predict tumor regression.

Imaging of Tumor Metabolism Using Positron Emission Tomography (PET)

Molecular imaging employing PET/CT enables in vivo visualization, characterization, and measurement of biologic processes in tumors at a molecular and cellular level. Using specific metabolic tracers, information about the integrated function of multiple transporters and enzymes involved in tumor metabolic pathways can be depicted, and the tracers can be directly applied as biomarkers of tumor biology. In this review, we discuss the role of F-18-fluorodeoxyglucose (FDG) as an in vivo glycolytic marker which reflects alterations of glucose metabolism in cancer cells. This functional molecular imaging technique offers a complementary approach to anatomic imaging such as computed tomography (CT) and magnetic resonance imaging (MRI) and has found widespread application as a diagnostic modality in oncology to monitor tumor biology, optimize the therapeutic management, and guide patient care. Moreover, emerging methods for PET imaging of further biologic processes relevant to cancer are reviewed, with a focus on tumor hypoxia and aberrant tumor perfusion. Hypoxic tumors are associated with poor disease control and increased resistance to cytotoxic and radiation treatment. In vivo imaging of hypoxia, perfusion, and mismatch of metabolism and perfusion has the potential to identify specific features of tumor microenvironment associated with poor treatment outcome and, thus, contribute to personalized treatment approaches.

Diffusion volume (DV) measurement in endometrial and cervical cancer: A new MRI parameter in the evaluation of the tumor grading and the risk classification

PURPOSE

A new MRI parameter representative of active tumor burden is proposed: diffusion volume (DV), defined as the sum of all the voxels within a tumor with apparent diffusion coefficient (ADC) values within a specific range. The aims of the study were: (a) to calculate DV on ADC maps in patients with cervical/endometrial cancer; (b) to correlate DV with histological grade (G) and risk classification; (c) to evaluate intra/inter-observer agreement of DV calculation.

MATERIALS AND METHODS

Fifty-three patients with endometrial (n=28) and cervical (n=25) cancers underwent pelvic MRI with DWI sequences. Both endometrial and cervical tumors were classified on the basis of G (G1/G2/G3) and FIGO staging (low/medium/high-risk). A semi-automated segmentation procedure was used to calculate the DV. A freehand closed ROI outlined the whole visible tumor on the most representative slice of ADC maps defined as the slice with the maximum diameter of the solid neoplastic component. Successively, two thresholds were generated on the basis of the mean and standard deviation (SD) of the ADC values: lower threshold (LT="mean minus three SD") and higher threshold (HT="mean plus one SD"). The closed ROI was expanded in 3D, including all the contiguous voxels with ADC values in the range LT-HT × 10-3mm(2)/s. A Kruskal-Wallis test was used to assess the differences in DV among G and risk groups. Intra-/inter-observer variability for DV measurement was analyzed according to the method of Bland and Altman and the intraclass-correlation-coefficient (ICC).

RESULTS

DV values were significantly different among G and risk groups in both endometrial (p<0.05) and cervical cancers (p ≤ 0.01). For endometrial cancer, DV of G1 (mean ± sd: 2.81 ± 3.21 cc) neoplasms were significantly lower than G2 (9.44 ± 9.58 cc) and G3 (11.96 ± 8.0 cc) ones; moreover, DV of low risk cancers (5.23 ± 8.0 cc) were significantly lower than medium (7.28 ± 4.3 cc) and high risk (14.7 ± 9.9 cc) ones. For cervical cancer, DV of G1 (0.31 ± 0.13 cc) neoplasms was significantly lower than G3 (40.68 ± 45.65 cc) ones; moreover, DV of low risk neoplasms (6.98 ± 8.08 cc) was significantly lower than medium (21.7 ± 17.13 cc) and high risk (62.9 ± 51.12 cc) ones and DV of medium risk neoplasms was significantly lower than high risk ones. The intra-/inter-observer variability for DV measurement showed an excellent correlation for both cancers (ICC ≥ 0.86).

CONCLUSIONS

DV is an accurate index for the assessment of G and risk classification of cervical/endometrial cancers with low intra-/inter-observer variability.