The efficacy and safety of definitive chemoradiotherapy are considered feasible with sufficient laryngeal preservation.

This study sought to develop and validate a prognostic model for non-lung cancer death (NLCD) in elderly patients with non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT). Patients aged ≥65 diagnosed with NSCLC (Tis-4N0M0), tumor diameter ≤5 cm and SBRT between 1998 and 2015 were retrospectively registered from two independent institutions. One institution was used for model development (arm D, 353 patients) and the other for validation (arm V, 401 patients). To identify risk factors for NLCD, multiple regression analysis on age, sex, performance status (PS), body mass index (BMI), Charlson comorbidity index (CCI), tumor diameter, histology and T-stage was performed on arm D. A score calculated using the regression coefficient was assigned to each factor and three risk groups were defined based on total score. Scores of 1.0 (BMI ≤18.4), 1.5 (age ≥ 5), 1.5 (PS ≥2), 2.5 (CCI 1 or 2) and 3 (CCI ≥3) were assigned, and risk groups were designated as low (total ≤ 3), intermediate (3.5 or 4) and high (≥4.5). The cumulative incidences of NLCD at 5 years in the low, intermediate and high-risk groups were 6.8, 23 and 40% in arm D, and 23, 19 and 44% in arm V, respectively. The AUC index at 5 years was 0.705 (arm D) and 0.632 (arm V). The proposed scoring system showed usefulness in predicting a high risk of NLCD in elderly patients treated with SBRT for NSCLC.

Cystic brain necrosis (CBN) is a rare form of BN. It typically occurs as a very late complication, and no standard treatment has been established. We report a case of a 59-year-old man who developed CBN 10 years after radiation therapy for metastatic brain tumors. The therapy consisted of whole brain radiotherapy followed by linac-based stereotactic radiosurgery as a boost. Initially, the CBN continued to expand despite treatment with corticosteroids and bevacizumab. Therefore, we resected the tumor and implanted an Ommaya reservoir, which successfully stabilized the lesion. Although the prognosis of patients with brain metastases is generally poor, some patients, like the one reported here, achieve long survival. Therefore, we should follow such cases carefully, considering the possibility of developing CBN as a late complication.

Purpose To validate the clinical applicability of knowledge‐based (KB) planning in single‐isocenter volumetric‐modulated arc therapy (VMAT) for multiple brain metastases using the k ‐fold cross‐validation (CV) method. Methods This study comprised 60 consecutive patients with multiple brain metastases treated with single‐isocenter VMAT (28 Gy in five fractions). The patients were divided randomly into five groups (Groups 1–5). The data of Groups 1–4 were used as the training and validation dataset and those of Group 5 were used as the testing dataset. Four KB models were created from three of the training and validation datasets and then applied to the remaining Groups as the fourfold CV phase. As the testing phase, the final KB model was applied to Group 5 and the dose distributions were calculated with a single optimization process. The dose‐volume indices (DVIs), modified Ian Paddick Conformity Index (mIPCI), modulation complexity scores for VMAT plans (MCSv), and the total number of monitor units (MUs) of the final KB plan were compared to those of the clinical plan (CL) using a paired Wilcoxon signed‐rank test. Results In the fourfold CV phase, no significant differences were observed in the DVIs among the four KB plans (KBPs). In the testing phase, the final KB plan was statistically equivalent to the CL, except for planning target volumes (PTVs) D 2% and D 50% . The differences between the CL and KBP in terms of the PTV D 99.5% , normal brain, and D max to all organs at risk (OARs) were not significant. The KBP achieved a lower total number of MUs and higher MCSv than the CL with no significant difference. Conclusions We demonstrated that a KB model in a single‐isocenter VMAT for multiple brain metastases was equivalent in dose distribution, MCSv, and total number of MUs to a CL with a single optimization.

The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively (P = 0.045). The cumulative incidence of recurrence (CIR) was significantly higher in the STAS high-risk group when using the radiomics model than that in the low-risk group (44% vs. 4% at 5 years; P = 0.002) in patients who underwent limited resection in the testing cohort. In contrast, the 5-year CIR was not significantly different among patients who underwent lobectomy (17% vs. 11%; P = 0.469). In conclusion, the machine learning model for STAS prediction based on peritumoral radiomics features performed better than the C/T ratio model.