Radiotherapy for patients with esophageal cancer aged 80 years or older: A 16-year experience.

PURPOSE: Radiotherapy (RT) plays an important role in esophageal cancer (EC) patients aged ≥80 years. However, the survival modality and prognostic factors remain poorly understood. Thus, this study aimed to evaluate the tolerance and long-term overall survival (OS) of patients aged ≥80 years who were diagnosed with EC and underwent definitive RT. MATERIALS AND METHODS: A total of 213 consecutive patients with EC over 80 years old who were treated with curative intent RT between February 1999 and December 2015 at our institution were retrospectively reviewed. The clinical prognostic variables were analyzed against OS in univariate analyses using log-rank tests and in a multivariate model using Cox regression proportional hazards analysis. RESULT: The median patient age was 82 (range: 80-94) years. Atotal of 192 patients (90.1%) completed the definitive RT (median: 60 Gy, range: 50-72 Gy), and 11 patients had grade 4 or higher acute toxicity, including esophagitis, a cardiac event, infections, and sudden death. Atotal of 168 deaths (78.9%) were observed with a median follow up of 47 months (range: 0-153 months). The OS rates were 50.3%, 17.6%, and 13.2% at 1, 3, and 5 years, respectively. Multivariable analysis identified that tumors located in the cervical and upper thorax, a shorter tumor lesion, RT treatment of 50-60Gy, and a better response to treatment were the factors associated with longer OS. CONCLUSION: Definitive RT could be considered as an effective treatment for patients with EC who are older than 80 years, and 50-60 Gy seems to be a reasonable dose for these patients.

The Clinical Outcomes, Prognostic Factors and Nomogram Models for Primary Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy.

Purpose: Stereotactic body radiation therapy (SBRT) is a standard treatment for early primary lung cancer patients. However, there are few simple models for predicting the clinical outcomes of these patients. Our study analyzed the clinical outcomes, identified the prognostic factors, and developed prediction nomogram models for these patients. Materials and Methods: We retrospectively analyzed 114 patients with primary lung cancer treated with SBRT from 2012 to 2020 at our institutions and assessed patient's clinical outcomes and levels of toxicity. Kaplan-Meier analysis with a log-rank test was used to generate the survival curve. The cut-off values of continuous factors were calculated with the X-tile tool. Potential independent prognostic factors for clinical outcomes were explored using cox regression analysis. Nomograms for clinical outcomes prediction were established with identified factors and assessed by calibration curves. Results: The median overall survival (OS) was 40.6 months, with 3-year OS, local recurrence free survival (LRFS), distant disease-free survival (DDFS) and progression free survival (PFS) of 56.3%, 61.3%, 72.9% and 35.8%, respectively, with grade 3 or higher toxicity rate of 7%. The cox regression analysis revealed that the clinical stage, immobilization device, and the prescription dose covering 95% of the target area (D95) were independent prognostic factors associated with OS. Moreover, the clinical stage, and immobilization device were independent prognostic factors of LRFS and PFS. The smoking status, hemoglobin (Hb) and immobilization device were significant prognostic factors for DDFS. The nomograms and calibration curves incorporating the above factors indicated good predictive accuracy. Conclusions: SBRT is effective and safe for primary lung cancer. The prognostic factors associated with OS, LRFS, DDFS and PFS are proposed, and the nomograms we proposed are suitable for clinical outcomes prediction.

A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters.

PURPOSE: Stereotactic body radiotherapy (SBRT) is an important treatment modality for lung cancer patients, however, tumor local recurrence rate remains some challenge and there is no reliable prediction tool. This study aims to develop a prediction model of local control for lung cancer patients undergoing SBRT based on radiomics signature combining with clinical and dosimetric parameters. METHODS: The radiomics model, clinical model and combined model were developed by radiomics features, incorporating clinical and dosimetric parameters and radiomics signatures plus clinical and dosimetric parameters, respectively. Three models were established by logistic regression (LR), decision tree (DT) or support vector machine (SVM). The performance of models was assessed by receiver operating characteristic curve (ROC) and DeLong test. Furthermore, a nomogram was built and was assessed by calibration curve, Hosmer-Lemeshow and decision curve. RESULTS: The LR method was selected for model establishment. The radiomics model, clinical model and combined model showed favorite performance and calibration (Area under the ROC curve (AUC) 0.811, 0.845 and 0.911 in the training group, 0.702, 0.786 and 0.818 in the validation group, respectively). The performance of combined model was significantly superior than the other two models. In addition, Calibration curve and Hosmer-Lemeshow (training group: P = 0.898, validation group: P = 0.891) showed good calibration of combined nomogram and decision curve proved its clinical utility. CONCLUSIONS: The combined model based on radiomics features plus clinical and dosimetric parameters can improve the prediction of 1-year local control for lung cancer patients undergoing SBRT.