High dose proton and photon-based radiation therapy for 213 liver lesions: a multi-institutional dosimetric comparison with a clinical perspective.

BACKGROUND: Stereotactic radiotherapy (SRT) and Proton therapy (PT) are both options in the management of liver lesions. Limited clinical-dosimetric comparison are available. Moreover, dose-constraint routinely used in liver PT and SRT considers only the liver spared, while optimization strategies to limit the liver damaged are poorly reported. METHODS: Primary endpoint was to assess and compare liver sparing of four contemporary RT techniques. Secondary endpoints were freedom from local recurrence (FFLR), overall survival (OS), acute and late toxicity. We hypothesize that Focal Liver Reaction (FLR) is determined by a similar biologic dose. FLR was delineated on follow-up MRI. Mean C.I. was computed for all the schedules used. A so-called Fall-off Volume (FOV) was defined as the area of healthy liver (liver-PTV) receiving more than the isotoxic dose. Fall-off Volume Ratio (FOVR) was defined as ratio between FOV and PTV. RESULTS: 213 lesions were identified. Mean best fitting isodose (isotoxic doses) for FLR were 18Gy, 21.5 Gy and 28.5 Gy for 3, 5 and 15 fractions. Among photons, an advantage in terms of healthy liver sparing was found for Vmat FFF with 5mm jaws (p = 0.013) and Cyberknife (p = 0.03). FOV and FOVR resulted lower for PT (p < 0.001). Three years FFLR resulted 83%. Classic Radiation induced liver disease (RILD, any grade) affected 2 patients. CONCLUSIONS: Cyberknife and V-MAT FFF with 5mm jaws spare more liver than V-MAT FF with 10 mm jaws. PT spare more liver compared to photons. FOV and FOVR allows a quantitative analysis of healthy tissue sparing performance showing also the quality of plan in terms of dose fall-off.

Artificial Intelligence-suggested Predictive Model of Survival in Patients Treated With Stereotactic Radiotherapy for Early Lung Cancer.

BACKGROUND/AIM: Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT) are still unavailable. The aim of this work was to build a predictive model of OS in this setting. PATIENTS AND METHODS: Clinical variables of patients treated in three Institutions with SBRT for stage I NSCLC were retrospectively collected into a reference cohort A (107 patients) and 2 comparative cohorts B1 (32 patients) and B2 (38 patients). A predictive model was built using Cox regression (CR) and artificial neural networks (ANN) on reference cohort A and then tested on comparative cohorts. RESULTS: Cohort B1 patients were older and with worse chronic obstructive pulmonary disease (COPD) than cohort A. Cohort B2 patients were heavier smokers but had lower Charlson Comorbidity Index (CCI). At CR analysis for cohort A, only ECOG Performance Status 0-1 and absence of previous neoplasms correlated with better OS. The model was enhanced combining ANN and CR findings. The reference cohort was divided into prognostic Group 1 (0-2 score) and Group 2 (3-9 score) to assess model's predictions on OS: grouping was close to statistical significance (p=0.081). One and 2-year OS resulted higher for Group 1, lower for Group 2. In comparative cohorts, the model successfully predicted two groups of patients with divergent OS trends: higher for Group 1 and lower for Group 2. CONCLUSION: The produced model is a relevant tool to clinically stratify SBRT candidates into prognostic groups, even when applied to different cohorts. ANN are a valuable resource, providing useful data to build a prognostic model that deserves to be validated prospectively.