RESUMEN
PURPOSE: In Peptide Receptor Radionuclide Therapy (PRRT) with [177Lu]Lu-DOTATATE of gastro-entero-pancreatic neuroendocrine tumours (GEP NETs) a question remains open about the potential benefits of personalised dosimetry. This observational prospective study examines the association of individualized dosimetry with progression free survival (PFS) in G1-G2 GEP NETs patients following the standard [177Lu]Lu-DOTATATE therapeutic regimen. METHODS: The analysis was conducted on 42 patients administered 4 times, and on 165 lesions. Dosimetry was performed after the first and the forth cycle, with two SPECT/CT scans at day 1 and 7 after administration. Global mean Tumour absorbed Dose of each patient (GTD) was calculated after cycle 1 and 4 as the sum of lesion doses weighted by lesion mass, normalized by the global tumour mass. Cumulative GTD_TOT was calculated as the mean between cycle 1 (GTD_1) and 4 (GTD_4) multiplied by 4. Patients were followed-up for median 32.8 (range 18-45.5) months, through blood tests and contrast enhanced CT (ceCT). This study assessed the correlation between global tumour dose (GTD) and PFS longer or shorter than 24 months. After a ROC analysis, we stratified patients according to the best cut-off value for two additional statistical analyses. At last a multivariate analysis was carried out for PFS > / < 24 months. RESULTS: The median follow-up interval was 33 months, ranging from 18 to 45.5 months. The median PFS was 42 months. The progression free survival rate at 20 months was 90.5%. GTD_1 and GTD_TOT were statistically associated with PFS > / < 24 m (p = 0.026 and p = 0.03 respectively). The stratification of patients on GTD_1 lower or higher than the best cut-off value at 10.6 Gy provided significantly different median PFS of 21 months versus non reached, i.e. longer than 45.5 months (p = 0.004), with a hazard ratio of 8.6, (95% C.I.: [2 - 37]). Using GTD_TOT with the best cut-off at 43 Gy, the same PFS values were obtained as after cycle 1 (p = 0.035). At multivariate analysis, a decrease in GTD_1 and, with lower impact, a higher global tumour volume were significantly associated with PFS < 24 months. We calculated the Tumour Control Probability of obtaining PFS > 24 months as a function of GTD_1. DISCUSSION: Several statistical analyses seem to confirm that simple tumour dosimetry with 2 SPECT/CT scans after the first administration allows to predict PFS values after 4 × 7.4 GBq administrations of 177Lu[Lu]-DOTATATE in G1-G2 GEP NETs. This result qualitatively confirms recent findings by a Belgian and a French study. However, dosimetric thresholds are different. This probably comes from different cohort baseline characteristics, since the median PFS in our study (42 m) was longer than in the other studies (28 m and 31 m). CONCLUSION: Tumour dosimetry after the first administration of [177Lu]Lu-DOTATATE offers an important prognostic value in the clinical decision-making process, especially for the future as alternative emitters or administration schedule may become available.
RESUMEN
[This corrects the article DOI: 10.1371/journal.pone.0232639.].
RESUMEN
INTRODUCTION: In this study, we investigate the role of radiomics for prediction of overall survival (OS), locoregional recurrence (LRR) and distant metastases (DM) in stage III and IV HNSCC patients treated by chemoradiotherapy. We hypothesize that radiomic analysis of (peri-)tumoral tissue may detect invasion of surrounding tissues indicating a higher chance of locoregional recurrence and distant metastasis. METHODS: Two comprehensive data sources were used: the Dutch Cancer Society Database (Alp 7072, DESIGN) and "Big Data To Decide" (BD2Decide). The gross tumor volumes (GTV) were delineated on contrast-enhanced CT. Radiomic features were extracted using the RadiomiX Discovery Toolbox (OncoRadiomics, Liege, Belgium). Clinical patient features such as age, gender, performance status etc. were collected. Two machine learning methods were chosen for their ability to handle censored data: Cox proportional hazards regression and random survival forest (RSF). Multivariable clinical and radiomic Cox/ RSF models were generated based on significance in univariable cox regression/ RSF analyses on the held out data in the training dataset. Features were selected according to a decreasing hazard ratio for Cox and relative importance for RSF. RESULTS: A total of 444 patients with radiotherapy planning CT-scans were included in this study: 301 head and neck squamous cell carcinoma (HNSCC) patients in the training cohort (DESIGN) and 143 patients in the validation cohort (BD2DECIDE). We found that the highest performing model was a clinical model that was able to predict distant metastasis in oropharyngeal cancer cases with an external validation C-index of 0.74 and 0.65 with the RSF and Cox models respectively. Peritumoral radiomics based prediction models performed poorly in the external validation, with C-index values ranging from 0.32 to 0.61 utilizing both feature selection and model generation methods. CONCLUSION: Our results suggest that radiomic features from the peritumoral regions are not useful for the prediction of time to OS, LR and DM.