Dataset Information

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

ABSTRACT: PET/CT imaging could improve delineation of rectal carcinoma gross tumor volume (GTV) and reduce interobserver variability. The objective of this work was to compare various functional volume delineation algorithms. We enrolled 31 consecutive patients with locally advanced rectal carcinoma. The FDG PET/CT and the high dose CT (CTRT) were performed in the radiation treatment position. For each patient, the anatomical GTVRT was delineated based on the CTRT and compared to six different functional/metabolic GTVPET derived from two automatic segmentation approaches (FLAB and a gradient-based method); a relative threshold (45% of the SUVmax) and an absolute threshold (SUV > 2.5), using two different commercially available software (Philips EBW4 and Segami OASIS). The spatial sizes and shapes of all volumes were compared using the conformity index (CI). All the delineated metabolic tumor volumes (MTVs) were significantly different. The MTVs were as follows (mean ± SD): GTVRT (40.6 ± 31.28ml); FLAB (21.36± 16.34 ml); the gradient-based method (18.97± 16.83ml); OASIS 45% (15.89 ± 12.68 ml); Philips 45% (14.52 ± 10.91 ml); OASIS 2.5 (41.6 2 ± 33.26 ml); Philips 2.5 (40 ± 31.27 ml). CI between these various volumes ranged from 0.40 to 0.90. The mean CI between the different MTVs and the GTVCT was < 0.4. Finally, the DICOM transfer of MTVs led to additional volume variations. In conclusion, we observed large and statistically significant variations in tumor volume delineation according to the segmentation algorithms and the software products. The manipulation of PET/CT images and MTVs, such as the DICOM transfer to the Radiation Oncology Department, induced additional volume variations.

SUBMITTER: Withofs N

PROVIDER: S-EPMC5711099 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background and objectives: Induction chemotherapy (ICT) before definitive chemoradiation (CRT) gives high response rates in locally advanced squamous cell carcinoma of the head and neck (LA-SCCHN). However, pre-ICT gross tumor volume (GTV) for radiotherapy (RT) planning is still recommended. As 18F-FDG PET/CT has an advantage of biological tumor information comparing to standard imaging methods, we aimed to evaluate the feasibility of 18F-FDG PET/CT-based post-ICT GTV delineation for RT planning in LA-SCCHN and to assess the prognostic value of PET parameters: maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Methods: 47 LA-SCCHN patients were treated with 3 cycles of ICT (docetaxel, cisplatin, and 5-fluorouracil) followed by CRT (70 Gy in 35 fractions with weekly cisplatin). Pre- and post-ICT PET/CT examinations were acquired. Planning CT was co-registered with post-ICT PET/CT and RT target volumes were contoured according to post-ICT PET. Post-ICT percentage decrease of SUVmax, MTV and TLG in primary tumor and metastatic regional lymphnodes (LN) was counted. Loco-regional failure patterns, 3-year progression free (PFS) and overall survival (OS) were evaluated. Results: 3-year PFS and OS rates for study population were 67% and 61% respectively. 31.9% of patients progressed loco-regionally. All progress was localized in high-to-intermediate dose (60?70 Gy) RT volumes and none in low dose (50 Gy) volumes. Decrease of SUVmax ? 74% (p = 0.04), MTV ? 68% (p = 0.03), TLG ? 76% (p = 0.03) in primary tumor, and LN TLG decrease ? 74% (p = 0.03) were associated with PFS. Decrease of primary tumor SUVmax ? 74% (p = 0.04), MTV ? 69% (p = 0.03), TLG ? 74% (p = 0.02) and LN TLG ? 73% (p = 0.02) were prognostic factors for OS. Conclusions: According to our results, 18F-FDG PET/CT-based post-ICT GTV delineation is feasible strategy without negative impacts on loco-regional control and survival. Percentage decrease of metabolic PET parameters SUVmax, MTV and TLG has a prognostic value in LA-SCCHN.

Project description:Background The aim of this work was to investigate the ability of building prognostic models in non-small cell lung cancer (NSCLC) using radiomic features from positron emission tomography and computed tomography with 2-deoxy-2-[fluorine-18]fluoro-d-glucose (18F-FDG PET/CT) images based on a “rough” volume of interest (VOI) containing the tumor instead of its accurate delineation, which is a significant time-consuming bottleneck of radiomics analyses. Methods A cohort of 138 patients with stage II–III NSCLC treated with radiochemotherapy recruited retrospectively (n = 87) and prospectively (n = 51) was used. Two approaches were compared: firstly, the radiomic features were extracted from the delineated primary tumor volumes in both PET (using the automated fuzzy locally adaptive Bayesian, FLAB) and CT (using a semi-automated approach with 3D Slicer™) components. Both delineations were carried out within previously manually defined “rough” VOIs containing the tumor and the surrounding tissues, which were exploited for the second approach: the same features were extracted from this alternative VOI. Both sets for features were then combined with the clinical variables and processed through the same machine learning (ML) pipelines using the retrospectively recruited patients as the training set and the prospectively recruited patients as the testing set. Logistic regression (LR), random forest (RF), and support vector machine (SVM), as well as their consensus through averaging the output probabilities, were considered for feature selection and modeling for overall survival (OS) prediction as a binary classification (either median OS or 6 months OS). The resulting models were compared in terms of balanced accuracy, sensitivity, and specificity. Results Overall, better performance was achieved using the features from delineated tumor volumes. This was observed consistently across ML algorithms and for the two clinical endpoints. However, the loss of performance was not significant, especially when a consensus of the three ML algorithms was considered (0.89 vs. 0.88 and 0.78 vs. 0.77). Conclusion Our findings suggest that it is feasible to achieve similar levels of prognostic accuracy in radiomics-based modeling by relying on a faster and easier VOI definition, skipping a time-consuming tumor delineation step, thus facilitating automation of the whole radiomics workflow. The associated cost is a loss of performance in the resulting models, although this loss can be greatly mitigated when a consensus of several models is relied upon.

Project description:BACKGROUND:To identify whether early metabolic responses as determined using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) during radiotherapy (RT) predict outcomes in patients with esophageal cancer. METHODS:Twenty-one patients with esophageal cancer who received pre-treatment 18F-FDG PET/CT (PET1) and inter-fractional 18F-FDG PET/CT (PET2) after 11 fractions of RT (median 23.1?Gy, 2.1?Gy per fraction) were retrospectively reviewed. The region of interest for each calculation was delineated using "PET Edge". We calculated PET parameters including maximum and mean standardized uptake values (SUVmax and SUVmean, respectively), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The relative changes (%) were calculated using the logarithmically transformed parameter values for the PET1 and PET2 scans. Multivariate analysis of locoregional recurrence and distant failures were performed using Cox regression analysis. After identifying statistically significant PET parameters for discriminating responders from non-responders, receiver operating characteristics curve analyses were used to assess the potentials of the studied PET parameters. RESULTS:After a median follow-up of 13?months, the 1-year overall and progression-free survival rates were 79.0% and 34.4%, respectively. Four patients developed locoregional recurrences (LRRs) and 8 had distant metastases (DMs). The 1-year overall LRR-free rate was 76.9% while the DM-free rate was 60.6%. The relative changes in MTV (?MTV) were significantly associated with LRR (p =?0.03). Conversely, the relative changes in SUVmean (?SUVmean) were associated with the risk of DM (p =?0.02). An ?MTV threshold of 1.14 yielded a sensitivity of 60%, specificity of 94%, and an accuracy of 86% for predicting an LRR. Additionally, a ?SUVmean threshold of a 35% decrease yielded a sensitivity of 67%, specificity of 83%, and accuracy of 76% for the prediction DM. TRIAL REGISTRATION:Retrospectively registered. CONCLUSIONS:Changes in tumor metabolism during RT could be used to predict treatment responses, recurrences, and prognoses in patients with esophageal cancer.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets