This software assistant aims at calculating the dose-response relations of tumors and normal tissues, or clinically assessing already determined values by other researchers. It can also indicate the optimal dose prescription by optimizing the expected treatment outcome. The software is developed solely in python programming language, and it employs PSFL license for its Graphical User Interface (GUI), NUMPY, MATPLOTLIB, and SCIPY libraries. It comprises of two components. The first is the Dose-response relations derivation component, which takes as input the dose volume histograms (DVHs) of patients and their recorded responses regarding a given clinical endpoint to determine the parameters of different tumor control probability (TCP) or normal tissue complication probability (NTCP) models. The second is the Treatment Plan Assessment component, which uses the DVHs of a plan and the dose-response parameters values of the involved tumors and organs at risk (OARs) to calculate their expected responses. Additionally, the overall probabilities of benefit (PB), injury (PI) and complication-free tumor control (P+) are calculated. The software calculates rapidly the corresponding generalized equivalent uniform doses (gEUD) and biologically effective uniform doses (D‾‾) for the Lyman-Kutcher-Burman (LKB), parallel volume (PV) and relative seriality (RS) models respectively, determining the model parameters. In the Dose-Response Relations Derivation component, the software plots the dose-response curves of the irradiated organ with the relevant confidence internals along with the data of the patients with and without toxicity. It also calculates the odds ratio (OR) and the area under the curve (AUC) of different dose metrics or model parameter values against the individual patient outcomes to determine their discrimination capacity. It also performs a goodness-of-fit evaluation of any model parameter set. The user has the option of viewing plots like Scatter, 3D surfaces, and Bootstrap plots. In the Treatment Plan Assessment part, the software calculates the TCP and NTCP values of the involved tumors and OARs, respectively. Furthermore, it plots the dose-response curves of the TCPs, NTCPs, PB, PI, and P+ for a range of prescription doses for different treatment plans. The presented software is ideal for efficiently conducting studies of radiobiological modeling. Furthermore, it is ideal for performing treatment plan assessment, comparison, and optimization studies.

Purpose: This study aims at determining the parameter values of three normal tissue complication probability (NTCP) models for the contralateral parotid gland, contralateral submandibular gland (SMG) and contralateral salivary glands regarding the endpoint of xerostomia 6-24 months after radiotherapy for oropharynx cancer.Methods: The treatment and outcome data of 231 patients with favorable risk, HPV-associated oropharyngeal squamous cell carcinoma are analyzed. 60 Gy intensity modulated radiotherapy was delivered to all the patients. The presence and severity of xerostomia was recorded (pre- and post- radiotherapy) by the PRO-CTCAE and the CTCAE scoring systems. In both scoring systems, patients with a change in symptom severity (from baseline) of >= 2 were considered responders.Results: Xerostomia was observed in 61.3 %, 39.2 %, 28.6 % and 27.0 % of the patients based on the PRO-CTCAE scoring system at 6-, 12-, 18- and 24-months post-RT, respectively. The AUCs of the contralateral salivary glands ranged between 0.58-0.64 in the LKB model with the gEUD ranging between 20.3 Gy and 24.7 Gy.Conclusions: Based on the PRO-CTCAE scores, mean dose < 22 Gy, V50 < 10 % for the contralateral salivary glands and mean dose < 18 Gy, V45 < 10 % for the contralateral parotid were found to significantly reduce by a factor of 2-3 the risk for radiation induced xerostomia that is observed at 6-24 months post-RT, respectively. Also, gEUD < 22 Gy to the contralateral salivary glands and < 18 Gy to the contralateral parotid was found to significantly reduce the risk for radiation induced xerostomia that is observed at 6-24 months post-RT by 2.0-2.3 times.

In breast cancer radiotherapy, significant discrepancies in dose delivery can contribute to underdosage of the tumor or overdosage of normal tissue, which is potentially related to a reduction of local tumor control and an increase of side effects. To study the impact of these factors in breast cancer radiotherapy, a meta analysis of the clinical data reported by Mavroidis et al. (2002) in Acta Oncol (41:471–85), showing the patient setup and breathing uncertainties characterizing three different irradiation techniques, were employed. The uncertainties in dose delivery are simulated based on fifteen breast cancer patients (5 mastectomized, 5 resected with negative node involvement (R-) and 5 resected with positive node involvement (R+)), who were treated by three different irradiation techniques, respectively. The positioning and breathing effects were taken into consideration in the determination of the real dose distributions delivered to the CTV and lung in each patient. The combined frequency distributions of the positioning and breathing distributions were obtained by convolution. For each patient the effectiveness of the dose distribution applied is calculated by the Poisson and relative seriality models and a set of parameters that describe the dose-response relations of the target and lung. The three representative radiation techniques are compared based on radiobiological measures by using the complication-free tumor control probability, P + and the biologically effective uniform dose, D̿ concepts. For the Mastectomy case, the average P + values of the planned and delivered dose distributions are 93.8% for a D̿ CTV of 51.8 Gy and 85.0% for a D̿ CTV of 50.3 Gy, respectively. The respective total control probabilities, P B values are 94.8% and 92.5%, whereas the corresponding total complication probabilities, P I values are 0.9% and 7.4%. For the R- case, the average P + values are 89.4% for a D̿ CTV of 48.9 Gy and 88.6% for a D̿ CTV of 49.0 Gy, respectively. The respective P B values are 89.8% and 89.9%, whereas the corresponding P I values are 0.4% and 1.2%. For the R+ case, the average P + values are 86.1% for a D̿ CTV of 49.2 Gy and 85.5% for a D̿ CTV of 49.1 Gy, respectively. The respective P B values are 90.2% and 90.1%, whereas the corresponding P I values are 4.1% and 4.6%. The combined effects of positioning uncertainties and breathing can introduce a significant deviation between the planned and delivered dose distributions in lung in breast cancer radiotherapy. The positioning and breathing uncertainties do not affect much the dose distribution to the CTV. The simulated delivered dose distributions show larger lung complication probabilities than the treatment plans. This means that in clinical practice the true expected complications are underestimated. Radiation pneumonitis of Grade 1–2 is more frequent and any radiotherapy optimization should use this as a more clinically relevant endpoint.

Accurately determined dose-response relations of the different tumors and normal tissues should be estimated and used in the clinic. The aim of this study is to demonstrate developed tools that are necessary for determining the dose-response parameters of tumors and normal tissues, for clinically verifying already published parameter sets using local patient materials and for making use of all this information in the optimization and comparison of different treatment plans and radiation techniques. One of the software modules (the Parameter Determination Module) is designed to determine the dose-response parameters of tumors and normal tissues. This is accomplished by performing a maximum likelihood fitting to calculate the best estimates and confidence intervals of the parameters used by different radiobiological models. Another module of this software (the Parameter Validation Module) concerns the validation and compatibility of external or reported dose-response parameters describing tumor control and normal tissue complications. This is accomplished by associating the expected response rates, which are calculated using different models and published parameter sets, with the clinical follow-up records of the local patient population. Finally, the last module of the software (the Radiobiological Plan Evaluation Module) is used for estimating and optimizing the effectiveness a treatment plan in terms of complication-free tumor control, P + . The use of the Parameter Determination Module is demonstrated by deriving the dose-response relation of proximal esophagus from head & neck cancer radiotherapy. The application of the Parameter Validation Module is illustrated by verifying the clinical compatibility of those dose-response parameters with the examined treatment methodologies. The Radiobiological Plan Evaluation Module is demonstrated by evaluating and optimizing the effectiveness of head & neck cancer treatment plans. The results of the radiobiological evaluation are compared against dosimetric criteria. The presented toolkit appears to be very convenient and efficient for clinical implementation of radiobiological modeling. It can also be used for the development of a clinical data and health information database for assisting the performance of epidemiological studies and the collaboration between different institutions within research and clinical frameworks.