Precision Radiotherapy: 18F-FDG PET-based radiotherapy planning in Head and Neck cancers

Survival effect of pretreatment FDG-PET-CT on nasopharyngeal cancer

BACKGROUND/PURPOSE

Accurate staging is the first step for optimal treatment selection in patients with nasopharyngeal carcinoma (NPC). In this propensity-score-matched, population-based cohort study, we investigated the survival effects of pretreatment 8-fluorodeoxyglucose positron emission tomography-computed tomography (¹⁸FDG-PET-CT) on patients with NPC.

METHODS

We included patients with stage I-IVA NPC receiving radiotherapy or concurrent chemoradiotherapy and categorized them into two 1:1 propensity score-matched groups according to whether or not they underwent pretreatment ¹⁸FDG-PET-CT and compared their outcomes.

RESULTS

Of the 10,756 patients, propensity score matching yielded 4366 patients in each group. According to multivariable Cox regression analyses, the most prominent correlation between pretreatment ¹⁸FDG-PET-CT and all-cause death was observed in patients with stage II NPC (adjusted hazard ratio [aHR], 0.77; 95% confidence interval [CI], 0.60-0.90; P = .0433), followed by patients with stage III NPC (aHR, 0.81; 95% CI, 0.69-0.94; P = .0071) and patients with stage IVA NPC (aHR, 0.88; 95% CI, 0.79-0.97; P = .0091). This association was not significant in patients with stage I NPC (aHR, 1.20; 95% CI, 0.75-1.93; P = .4426).

CONCLUSION

Pretreatment ¹⁸FDG-PET-CT is associated with longer survival in patients with clinical stage II-IVA NPC but not in stage I NPC.

A Novel Radiomics-Based Tumor Volume Segmentation Algorithm for Lung Tumors in FDG-PET/CT after 3D Motion Correction—A Technical Feasibility and Stability Study

Positron emission tomography (PET) provides important additional information when applied in radiation therapy treatment planning. However, the optimal way to define tumors in PET images is still undetermined. As radiomics features are gaining more and more importance in PET image interpretation as well, we aimed to use textural features for an optimal differentiation between tumoral tissue and surrounding tissue to segment-target lesions based on three textural parameters found to be suitable in previous analysis (Kurtosis, Local Entropy and Long Zone Emphasis). Intended for use in radiation therapy planning, this algorithm was combined with a previously described motion-correction algorithm and validated in phantom data. In addition, feasibility was shown in five patients. The algorithms provided sufficient results for phantom and patient data. The stability of the results was analyzed in 20 consecutive measurements of phantom data. Results for textural feature-based algorithms were slightly worse than those of the threshold-based reference algorithm (mean standard deviation 1.2%—compared to 4.2% to 8.6%) However, the Entropy-based algorithm came the closest to the real volume of the phantom sphere of 6 ccm with a mean measured volume of 26.5 ccm. The threshold-based algorithm found a mean volume of 25.0 ccm. In conclusion, we showed a novel, radiomics-based tumor segmentation algorithm in FDG-PET with promising results in phantom studies concerning recovered lesion volume and reasonable results in stability in consecutive measurements. Segmentation based on Entropy was the most precise in comparison with sphere volume but showed the worst stability in consecutive measurements. Despite these promising results, further studies with larger patient cohorts and histopathological standards need to be performed for further validation of the presented algorithms and their applicability in clinical routines. In addition, their application in other tumor entities needs to be studied.