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Abstract Background Currently, radiation therapy treatment planning system intends biological optimization that relies heavily upon plan metrics from tumor control probability (TCP) and normal tissue complication probability (NTCP) modeling. Implementation and expansion of TCP and NTCP models with alternative data is an important step towards reliable radiobiological treatment planning. In this retrospective single institution study, the treatment charts of 139 lung cancer patients treated with chemo-radiotherapy were reviewed and correlated dosimetric predictors with the incidence of esophagitis and established NTCP model of esophagitis grade 1 and 2 for lung cancer patients. Methods Esophagus is an organ at risk (OAR) in lung cancer radiotherapy (RT). Esophagitis is a common toxicity induced by RT. In this study, dose volume parameters Vx (Vx: percentage esophageal volume receiving ≥ x Gy) and mean esophagus dose (MED) as quantitative dose-volume metrics, the esophagitis grade 1 and 2 as endpoints, were reviewed and derived from the treatment planning system and the electronic medical record system. Statistical analysis of binary logistic regression and probit were performed to have correlated the probability of grade 1 and 2 esophagitis to MED and Vx. IBM SPSS software version 24 at 5% significant level (α = 0.05) was used in the statistical analysis. Results The probabilities of incidence of grade 1 and 2 esophagitis proportionally increased with increasing the values of Vx and MED. V20, V30, V40, V50 and MED are statistically significant good dosimetric predictors of esophagitis grade 1. 50% incidence probability (TD50) of MED for grade 1 and 2 esophagitis were determined. Lyman Kutcher Burman model parameters, such as, n, m and TD50, were fitted and compared with other published findings. Furthermore, the sigmoid shaped dose responding curve between probability of esophagitis grade 1 and MED were generated respecting to races, gender, age and smoking status. Conclusions V20, V30, V40 and V50 were added onto Quantitative Analysis of Normal Tissue Effects in the clinic, or QUANTEC group’s dose constrains of V35, V50, V70 and MED. Our findings may be useful as both validation of 3-Dimensional planning era models and also additional clinical guidelines in treatment planning and plan evaluation using radiobiology optimization.

Purpose: Total package time, or the time from diagnosis to completion of definitive treatment, has been associated with outcomes for a variety of tumor sites, but especially to head and neck (HN) cancer. Patients with HN cancer often undergo a complex diagnosis and treatment process involving multiple disciplines both within and outside of oncology. This complexity can lead to longer package times, and each involved discipline has the responsibility to maintain an efficient and effective process. Strategic intervention to improve package time must involve not only new technology or tools, but also “soft” components such as accountability, motivation, and leadership. This combination is necessary to truly optimize radiation therapy for HN cancer, leading to shorter total package times for these patients. Methods and Materials: Two interventions were strategically executed to improve radiation therapy workflow: upgrade of the treatment planning system and implementation of an automated patient management and accountability system. The radiation therapy–related timelines of 112 patients with HN cancer treated over 2 years were reviewed, and the average time differences were compared between the patient populations before and after the strategic interventions. Results: Purely upgrading the treatment planning system did not show significant improvements, but when combined with the patient management system, significant improvement in radiation-related package time can be noted for every time point. The overall reduction of radiation-related package time was statistically significant at 22.85 days (P = .002). Conclusions: On face value, the patient management system could be credited as responsible for the improvement, but on qualitative analysis, it is noted that the new system is only a tool that can be ignored or underused. Owing to the addition of important “soft” components such as accountability, motivation, and leadership, the patient management system was optimized and implemented in such a manner as to have the desired effect.