Tumor Control Probability Model Research Articles

Dose-rate effects and tumor control probability in 177Lu-based targeted radionuclide therapy: a theoretical analysis

Objective.177Lu-based targeted radionuclide therapy (TRT) has become an important cancer treatment option in recent years, in particular in the treatment of advanced prostate cancer and metastasized neuroendocrine tumors. Although it is known from conventional radiotherapy that the temporal dynamics of the dose-rate can be of relevance for tumor cell survival, the analysis of TRT efficacy usually considers only the absorbed dose. Thus, the aim of this theoretical analysis is to shed light on the possible effects of the pattern of dose-rate in TRT on tumor control probability (TCP).Approach.For this purpose, TCP is studied numerically in a typical four-cycle treatment regime based on the mechanistic lethal-potentially lethal model and the Zaider-Minerbo model for TCP including repopulation of tumor cells.Main results.It is shown that the dose-rate pattern in TRT can have a substantial effect on TCP even though the absorbed dose in the tumor lesion is unchanged. These dose-rate effects are particularly evident when repair of potentially lethal lesions is slow.Significance.The results indicate that in some situations in the analysis of the efficacy of TRT it is necessary to consider the full dose-rate pattern instead of the absorbed dose alone. This can be highly relevant for optimization and further development of TRTs. In particular, it could be of relevancy in studying the efficacy of newly emerging treatment concepts that combine the use of TRT and drugs that inhibit DNA damage repair.

Development of a tumor control probability model for boron neutron capture therapy of head and neck cancer.

The tumor control probability (TCP) model has been used for estimating the response of the radiation (photon) therapy for a given treatment dose (distribution). In Taiwan, boron neutron capture therapy (BNCT) is still at the stage of the clinical trials without standard dose prescription. In this study, universal survival curve (USC) model was selected as the TCP model for BNCT. The tumor response and dose distribution from protocol I of the clinical trial of the recurrent head and neck (H&N) cancer conducted by Taipei Veterans General Hospital and National Tsing Hua University were used to verify the TCP model established in this study. The results showed that, using the USC model as a biological model of dose conversion, the TCP calculated by the generalized Equivalent Uniform Dose (gEUD)-based TCP model can be used to well correlate the relationship between the tumor response and dose distribution of the patients of recurrent H&N cancer. The result shows that 25% and 60% of TCP correspond to partial response and complete response of H&N cancer, respectively. This study also indicated that, when BNCT was used to treat recurrent H&N cancer, the minimum dose was an important factor on the efficacy of the treatment. Minimum dose of 18 Gy-w corresponds to at least 60% of TCP.

Integrating Overall Survival and Tumor Control Probability Models to Predict Local Progression After Brain Metastasis Radiosurgery

BackgroundStereotactic radiosurgery (SRS) for brain metastases is frequently prescribed to the maximum tolerated dose to minimize the probability of local progression. However, many patients die from extracranial disease prior to local progression and may not require maximally aggressive treatment. Recently, improvements in models of SRS tumor control probability (TCP) and overall survival (OS) have been made. We predicted that by combining models of OS and TCP, we could better predict the true risk of local progression after SRS than by employing TCP modeling alone. MethodsRecords of patients undergoing SRS at a single institution were reviewed retrospectively. Using established TCP and OS models, for each patient, the probability of one-year survival [p(OS)] was calculated, as was the probability of one-year local progression [p(LP)]) for each treated lesion. Joint-probability was used to combine the models [p(LP,OS)=p(LP)*p(OS)]. Analyses were conducted at the individual metastasis and whole-patient levels. Fine-Gray regression was used to model p(LP) or p(LP,OS) on the risk of local progression after SRS, with death as a competing risk. ResultsAt the patient level, one-year local progression was 0.08 (95%CI, 0.03-0.15), median p(LP,OS) was 0.13 (95%, CI 0.07-0.2), and median p(LP) was 0.29 (95% CI, 0.22-0.38). At the metastasis level, one-year local progression was 0.02 (95%CI, 0.01-0.04), median p(LP,OS) was 0.05 (95%, CI 0.02-0.07), and median p(LP) was 0.10 (95% CI, 0.07-0.13). p(LP,OS) was found to be significantly associated with the risk of local progression at the patient level (p=0.048) and metastasis level (p=0.007); however, p(LP) was not (p=0.16 and p=0.28, respectively). ConclusionSimultaneous modeling of OS and TCP more accurately predicted local progression than TCP modeling alone. Better understanding which patients with brain metastases are at risk of local progression after SRS may help personalize treatment to minimize risk without sacrificing efficacy.

Proton Therapy in Breast Cancer: A Review of Potential Approaches for Patient Selection.

Proton radiotherapy may be a compelling technical option for the treatment of breast cancer due to its unique physical property known as the "Bragg peak." This feature offers distinct advantages, promising superior dose conformity within the tumor area and reduced radiation exposure to surrounding healthy tissues, enhancing the potential for better treatment outcomes. However, proton therapy is accompanied by inherent challenges, primarily higher costs and limited accessibility when compared to well-developed photon irradiation. Thus, in clinical practice, it is important for radiation oncologists to carefully select patients before recommendation of proton therapy to ensure the transformation of dosimetric benefits into tangible clinical benefits. Yet, the optimal indications for proton therapy in breast cancer patients remain uncertain. While there is no widely recognized methodology for patient selection, numerous attempts have been made in this direction. In this review, we intended to present an inspiring summarization and discussion about the current practices and exploration on the approaches of this treatment decision-making process in terms of treatment-related side-effects, tumor control, and cost-efficiency, including the normal tissue complication probability (NTCP) model, the tumor control probability (TCP) model, genomic biomarkers, cost-effectiveness analyses (CEAs), and so on. Additionally, we conducted an evaluation of the eligibility criteria in ongoing randomized controlled trials and analyzed their reference value in patient selection. We evaluated the pros and cons of various potential patient selection approaches and proposed possible directions for further optimization and exploration. In summary, while proton therapy holds significant promise in breast cancer treatment, its integration into clinical practice calls for a thoughtful, evidence-driven strategy. By continuously refining the patient selection criteria, we can harness the full potential of proton radiotherapy while ensuring maximum benefit for breast cancer patients.

Dose-response modeling and treatment plan assessment with a python software toolkit

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.

Radiobiological evaluation of hypo-fractionated breast cancer radiotherapy regimens in comparison with conventional approaches

Abstract Purpose: This study compares tumor control probability (TCP) and normal tissue complication probability (NTCP) across different hypo-fractionated (HypoRT) and conventional breast radiotherapy regimens using radiobiological models. Materials and methods: Computed tomography data from 30 patients with left breast-conserving surgery were used to evaluate three HypoRT regimens (39 Gy and 41·6 Gy in 13 fractions, and 40 Gy in 15 fractions) and a conventional regimen (50 Gy in 25 fractions). Dose-volume histograms (DVHs) were extracted for radiobiological calculations using Equivalent Uniform Dose (EUD) and Poisson models for TCP, and EUD and LKB (Lyman-Kutcher-Burman) models for NTCP. Results: Conventional treatment achieved significantly higher TCP (95%) than all HypoRT regimens (p < 0·001), with no significant differences between HypoRT regimens (p > 0·05. The 39 Gy/13 fraction regimen showed the lowest lung NTCP (p < 0·05). HypoRT regimens had significantly lower NTCP for the lungs and heart compared to the conventional regimen (p < 0·01). TCP and NTCP values from Poisson and LKB models were higher than those from the EUD model (p < 0·01). Conclusion: HypoRT regimens reduced NTCP, with the lowest values in the regime of 39 Gy/13 fractions regimen, though the conventional regimen had higher TCP.

Redefining the role of pulsed-dose-rate brachytherapy in cervical cancer treatment using a preplanned approach

IntroductionThis study aims to determine predictive factors for cervical cancer patients who would benefit more from high-dose-rate (HDR) or pulsed-dose-rate (PDR) brachytherapy. MethodsThe sample included 50 patients treated with brachytherapy following external radiochemotherapy. PDR plans were compared to HDR preplans, with a focus on patients who may benefit from PDR using preplan metrics and clinical variables. The expected clinical effect was quantified using a tumor control probability model. ResultsResults showed PDR plans with 60 pulses to be optimal for achieving target clinical goals for D90CTVHR. A CTVHR volume of >67.5cc and/or D90CTVHR dose on the HDR preplan of <31.1 Gy was the strongest indicator for patient selection who would gain >3% increase in TCP with PDR. The process showed 96% accuracy, 88% sensitivity, and 98% specificity. Only 16% of patients showed a relevant benefit from PDR over HDR, with a mean D90CTVHR of 7 Gy higher and a mean TCP at 3 years of 4.8% higher for PDR. The benefit of PDR is highly influenced by the choice of alpha/beta ratio and repair halftime. ConclusionA small subset of cervical cancer patients may gain from PDR over HDR. CTVHR volume and preplan D90CTVHR doses may be useful in selecting patients for PDR brachytherapy.

MODL-27. INTEGRATING OVERALL SURVIVAL AND TUMOR CONTROL PROBABILITY MODELS TO PREDICT LOCAL PROGRESSION AFTER BRAIN METASTASIS RADIOSURGERY

Abstract PURPOSE Stereotactic radiosurgery (SRS) for brain metastases has competing goals of maximizing local control and minimizing toxicity. Tumor control probability (TCP) models predict decreasing risk of local progression (LP) with dose escalation. However, many patients die from extracranial disease prior to local progression and may not require maximally aggressive treatment, which increases risk of toxicity. Better tools to assess patients’ risk of LP after SRS could help practitioners personalize SRS treatment to maximize therapeutic ratio. Here we assess whether simultaneous modeling of TCP and overall survival (OS) can improve prediction of LP after SRS. METHODS Records of patients undergoing SRS at a single institution were reviewed retrospectively. Using established OS and TCP models, the probability of one-year survival [p(OS)] and probability of one-year LP [p(LP)]) were calculated for each patient and each metastasis, respectively. Joint-probability was used to combine the models [p(LP,OS)=p(LP)*p(OS)]. Analyses were conducted at the individual-metastasis and whole-patient levels. Fine-Gray regression was used to model the effect of p(LP) or p(LP, OS) on risk of local progression after SRS, with death as a competing risk. RESULTS Ninety-two patients and 391 lesions were identified. At the patient level, one-year LP was 0.08 (95%CI, 0.03-0.15), median p(LP,OS) was 0.13 (95%, CI 0.07-0.2), and median p(LP) was 0.29 (95% CI, 0.22-0.38). At the metastasis level, one-year LP was 0.02 (95%CI, 0.01-0.04), median p(LP,OS) was 0.05 (95%, CI 0.02-0.07), and median p(LP) was 0.10 (95% CI, 0.07-0.13). p(LP,OS) was significantly associated with the risk of local progression at the patient level (p=0.048) and metastasis level (p=0.007); however, p(LP) was not (p=0.16 and p=0.28, respectively). CONCLUSION This study suggests simultaneous OS and TCP modeling more accurately predicts LP than TCP modeling alone. Better understanding which patients are at risk of LP after SRS may help personalize treatment to minimize risk without sacrificing efficacy.

Model development of dose and volume predictors for esophagitis induced during chemoradiotherapy for lung cancer as a step towards radiobiological treatment planning

BackgroundCurrently, 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.MethodsEsophagus 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.ResultsThe 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.ConclusionsV20, 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.

Results From a Multi-Institutional Pilot Study of iContour, an Interactive Online Platform with Real-Time Feedback to Improve Contouring Education for Radiation Oncology Residents

Numerous studies have shown that variability in contouring by radiation oncologists is common and associated with poor clinical outcomes. Contouring is taught via an apprenticeship model during residency with inconsistent results. Currently, there is no standardized contouring curriculum. We hypothesized that an interactive online educational platform for learners to practice contouring and receive real-time visual feedback would be a useful curricular tool. The iContour platform displays anonymized DICOM data and allows for input and analysis of user contours in a web-based interface. Nine cases are available from the Head/Neck (H+N), Gynecologic (Gyn), and Gastrointestinal (GI) disease sites. The system presents users with a case and asks them to contour representative slices from 2-3 target volumes or OARs. Upon submission, users are shown several forms of feedback. These include immediate visual comparison with expert contours on the same dataset (all cases), customized feedback based on overlap with prespecified "avoidance" and "inclusion" structures that highlight common mistakes (Gyn cases), and tumor control probability models to estimate the clinical impact of a user's contour variations on patient outcomes (H+N cases). Some cases include short videos outlining anatomy and contouring principles which are shown before the user contours. A pilot study was performed to evaluate technical performance and educational utility. Pre- and post-surveys with Likert-type questions (1-5 scale from strongly disagree to strongly agree) were used to assess user satisfaction and preferences regarding feedback. A total of 9 residents participated (median PGY3, range PGY2-5) from 5 institutions in 2 countries. Each participant completed 2 cases from a single disease site (n = 3 each for H+N, Gyn and GI), one with educational videos and one without. 67% of users had completed a prior clinical rotation in their disease site. Overall, residents felt the system was a useful educational tool (mean Likert score 4.67 +/- 0.47) and were interested in using it during clinical rotations (4.89 +/- 0.31). Most participants (7/9) felt iContour was more useful than existing resources for contouring education. Residents unanimously (9/9) found direct visual comparison with expert contours the most useful type of feedback, and that cases with videos before contouring were more educational. The iContour platform is a useful educational tool for radiation oncology residents. Participants felt receiving immediate visual feedback on contours was a valuable learning experience. Short instructional videos before contouring can be utilized to provide "just in time" teaching. A randomized study to formally assess the platform's impact on contouring skills is planned.

Radiobiological Meta-Analysis of the Response of Prostate Cancer to Different Fractionations: Evaluation of the Linear-Quadratic Response at Large Doses and the Effect of Risk and ADT.

The purpose of this work was to investigate the response of prostate cancer to different radiotherapy schedules, including hypofractionation, to evaluate potential departures from the linear-quadratic (LQ) response, to obtain the best-fitting parameters for low-(LR), intermediate-(IR), and high-risk (HR) prostate cancer and to investigate the effect of ADT on the radiobiological response. We constructed a dataset of the dose-response containing 87 entries/16,536 patients (35/5181 LR, 32/8146 IR, 20/3209 HR), with doses per fraction ranging from 1.8 to 10 Gy. These data were fit to tumour control probability models based on the LQ model, linear-quadratic-linear (LQL) model, and a modification of the LQ (LQmod) model accounting for increasing radiosensitivity at large doses. Fits were performed with the maximum likelihood expectation methodology, and the Akaike information criterion (AIC) was used to compare the models. The AIC showed that the LQ model was superior to the LQL and LQmod models for all risks, except for IR, where the LQL model outperformed the other models. The analysis showed a low α/β for all risks: 2.0 Gy for LR (95% confidence interval: 1.7-2.3), 3.4 Gy for IR (3.0-4.0), and 2.8 Gy for HR (1.4-4.2). The best fits did not show proliferation for LR and showed moderate proliferation for IR/HR. The addition of ADT was consistent with a suppression of proliferation. In conclusion, the LQ model described the response of prostate cancer better than the alternative models. Only for IR, the LQL model outperformed the LQ model, pointing out a possible saturation of radiation damage with increasing dose. This study confirmed a low α/β for all risks.

EviGUIDE - a tool for evidence-based decision making in image-guided adaptive brachytherapy for cervical cancer

PurposeTo develop a novel decision-support system for radiation oncology that incorporates clinical, treatment and outcome data, as well as outcome models from a large clinical trial on magnetic resonance image-guided adaptive brachytherapy (MR-IGABT) for locally advanced cervical cancer (LACC). MethodsA system, called EviGUIDE, was developed that combines dosimetric information from the treatment planning system, patient and treatment characteristics, and established tumor control probability (TCP), and normal tissue complication probability (NTCP) models, to predict clinical outcome of radiotherapy treatment of LACC. Six Cox Proportional Hazards models based on data from 1341 patients of the EMBRACE-I study have been integrated. One TCP model for local tumor control, and five NTCP models for OAR morbidities. ResultsEviGUIDE incorporates TCP-NTCP graphs to help users visualize the clinical impact of different treatment plans and provides feedback on achievable doses based on a large reference population. It enables holistic assessment of the interplay between multiple clinical endpoints and tumour and treatment variables. Retrospective analysis of 45 patients treated with MR-IGABT showed that there exists a sub-cohort of patients (20%) with increased risk factors, that could greatly benefit from the quantitative and visual feedback. ConclusionA novel digital concept was developed that can enhance clinical decision- making and facilitate personalized treatment. It serves as a proof of concept for a new generation of decision support systems in radiation oncology, which incorporate outcome models and high-quality reference data, and aids the dissemination of evidence-based knowledge about optimal treatment and serve as a blueprint for other sites in radiation oncology.

Simulating the Potential of Model-Based Individualized Prescriptions for Ultracentral Lung Tumors

The use of stereotactic body radiation therapy for ultracentral lung tumors is limited by increased toxicity. We hypothesized that using published normal tissue complication probability (NTCP) and tumor control probability (TCP) models could improve the therapeutic ratio between tumor control and toxicity. A proposed model-based approach was applied to virtually replan early-stage non-small cell lung cancer (NSCLC) tumors. The analysis included 63 patients with ultracentral NSCLC tumors treated at our center between 2008 and 2017. Along with current clinical constraints, additional NTCP model-based criteria, including for grade 3+ radiation pneumonitis (RP3+) and grade 2+ esophagitis, were implemented using 4 different fractionation schemes. Scaled dose distributions resulting in the highest TCP without violating constraints were selected (optimal plan [Planopt]). Planopt predictions were compared with the observed local control and toxicities. The observed 2-year local control rate was 72% (95% CI, 57%-88%) compared with 87% (range, 6%-93%) for Planopt TCP. Thirty-nine patients had Planopt with TCP > 80%, and 14 patients had Planopt TCP < 50%. The Planopt NTCPs for RP3+ were reduced by nearly half compared with patients' observed RP3+. The RP3+ NTCP was the most frequent reason for TCP of Planopt < 80% (14/24 patients), followed by grade 2+ esophagitis NTCP (5/24 patients) due to larger tumors (>40 cc vs ≤40 cc; P=.002) or a shorter tumor to esophagus distance (≥5 cm vs <5 cm; P < .001). We demonstrated the potential for model-based prescriptions to yield higher TCP while respecting NTCP for patients with ultracentral NSCLC. Individualizing treatments based on NTCP- and TCP-driven simulations halved the predicted relative to the observed rates of RP3+. Our simulations also identified patients whose TCP could not be improved without violating NTCP due to larger tumors or a near tumor to esophagus proximity.

Isotoxic dose escalated radiotherapy for glioblastoma based on diffusion-weighted MRI and tumor control probability-an in-silico study.

Glioblastoma (GBM) is the most common malignant primary brain tumor with local recurrence after radiotherapy (RT), the most common mode of failure. Standard RT practice applies the prescription dose uniformly across tumor volume disregarding radiological tumor heterogeneity. We present a novel strategy using diffusion-weighted (DW-) MRI to calculate the cellular density within the gross tumor volume (GTV) in order to facilitate dose escalation to a biological target volume (BTV) to improve tumor control probability (TCP). The pre-treatment apparent diffusion coefficient (ADC) maps derived from DW-MRI of ten GBM patients treated with radical chemoradiotherapy were used to calculate the local cellular density based on published data. Then, a TCP model was used to calculate TCP maps from the derived cell density values. The dose was escalated using a simultaneous integrated boost (SIB) to the BTV, defined as the voxels for which the expected pre-boost TCP was in the lowest quartile of the TCP range for each patient. The SIB dose was chosen so that the TCP in the BTV increased to match the average TCP of the whole tumor. By applying a SIB of between 3.60 Gy and 16.80 Gy isotoxically to the BTV, the cohort's calculated TCP increased by a mean of 8.44% (ranging from 7.19 to 16.84%). The radiation dose to organ at risk is still under their tolerance. Our findings indicate that TCPs of GBM patients could be increased by escalating radiation doses to intratumoral locations guided by the patient's biology (i.e., cellularity), moreover offering the possibility for personalized RT GBM treatments. A personalized and voxel level SIB radiotherapy method for GBM is proposed using DW-MRI, which can increase the tumor control probability and maintain organ at risk dose constraints.

Validation of esophageal cancer treatment methods from 3D-CRT, IMRT, and Rapid Arc plans using custom Python software to compare radiobiological plans to normal tissue integral dosage.

The aim was to develop in-house software that is able to calculate and generate the biological plan evaluation of the esophagus treatment plan using the Niemierko model for normal tissue complication probability and tumor control probability. The Niemierko model can be applied for esophagus cancer treatment plan to estimate the tumor control probability (TCP) and the normal tissue complication probability (NTCP) using different planning techniques. The equivalent uniform dose (EUD) and effective volume parameters were compared with organ at risk. Subsequently, EUD and TCP parameter were compared with tumor volume for all five different planning techniques. Ten cases for esophageal cancer were included in this study. For each patient, five treatment plans were generated. The Anisotropic analytical algorithms (AAA) were used for dose calculation for the three-dimensional conformal radiation therapy (3D-CRT), intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) techniques. The in-house developed radiobiological plan evaluation software using python programming is used for this study which takes a dose volume histogram (DVH) text file as an input file for biological plan evaluation. EUD, NTCP, TCP and effective volume were calculated from the Niemierko model using the in-house developed python based software and compared with treatment monitor units (MU) with all five different treatment plan. The best technique is quantified as benchmarked out of other different qualities of treatment. The four field 3D-CRT treatment plan is found to be the best suited from the perspective of biological plan index evaluation among the other planning techniques.

Estimation of treatment efficiency of head-and-neck cancer based on tumour control probability model

External beam radiotherapy is widely used for the treatment of the locally advanced head-and-neck cancer (LAHNC). Analysis of the developed treatment plans based on tumour control probability (TCP) models could help to estimate expected treatment results of the developed plans and to find optimal treatment schemes with respect to total dose, fractional dose and overall treatment time (OTT). In this study, the simultaneous integrated boost VMAT (SIB-VMAT) plans and sequential boost VMAT (SEQ-VMAT) plans were developed based on the anatomical data of 11 patients. Methods and Material: The data of 11 patients with LAHNC (larynx, oropharynx and oral cavity) were used. For each patient two treatment plans were developed, SIB-VMAT (70 Gy to tumour, 50 Gy to lymph nodes, 25 fractions) and SEQ-VMAT (70 Gy to tumour, 50 Gy to lymph nodes, 35 fractions). The developed plans were analysed using the Niemierko’s TCP model with Maciejewski’s parameters (TCD50 = 70.26 Gy at 49-days OTT) taking into account dose-volume histograms and OTT. Results: The developed plans resulted in high clinical treatment volume (CTV) conformity (98%-98%) for all patients, except one. The average TCP value of SIB-VMAT was equal to 99.9% due to very short OTT. The average value of TCP for SEQ-VMAT was equal to 61.0%. For one patient, the both SIB-VMAT and SEQ-VMAT plans showed zero expected efficiency due to CTV coverage 95%-95%. Conclusions: Use of TCP models allows analysis of treatment plans for each particular patient and development of different treatment schemes with increase of the total dose value, fractional dose and shortening of OTT.

Dose-Escalated 2-Fraction Spine Stereotactic Body Radiation Therapy: 28 Gy Versus 24 Gy in 2 Daily Fractions

Stereotactic body radiation therapy (SBRT) for spine metastases improves pain response rates compared with conventional external beam radiation therapy; however, the optimal fractionation schedule is unclear. We report local control and toxicity outcomes after dose-escalated 2-fraction spine SBRT. A prospectively maintained institutional database of over 600 patients and 1400 vertebral segments treated with spine SBRT was reviewed to identify those prescribed 28 or 24 Gy in 2 daily fractions. The primary endpoint was magnetic resonance imaging based local failure (LF), and secondary endpoints included overall survival and vertebral compression fracture (VCF). A total of947 treated vertebral segments in 482 patients were identified, of which 301 segments in 159 patients received 28 Gy, and 646 segments in 323 patients received 24 Gy in 2 fractions. Median follow-up per patient was 23.5 months, and median overall survival was 49.1 months. In the 28 Gy cohort, the 6-, 12-, and 24-month cumulative incidences of LF were 3.5%, 5.4%, and 11.1%, respectively, versus 6.0%, 12.5%, and 17.6% in the 24 Gy cohort, respectively (P=.008). On multivariable analysis, 24 Gy (hazard ratio [HR], 1.525; 95% confidence interval, 1.039-2.238; P=.031), paraspinal disease extension (HR, 1.422; 95% confidence interval, 1.010-2.002; P=.044), and epidural extension in either radioresistant or radiosensitive histologies (HR, 2.117 and 1.227, respectively; P=.003) were prognostic for higher rates of LF. Risk of VCF was 5.5%, 7.6%, and 10.7% at 6, 12, and 24 months, respectively, and was similar between cohorts (P=.573). Spinal malalignment (P < .001), baseline VCF (P=.003), junctional spine location (P=.030), and greater minimum dose to 90% of planning target volume were prognostic for higher rates of VCF. Dose escalation to 28 Gy in 2 daily fractions was associated with improved local control without increasing the risk of VCF. The 2-year local control rates are consistent with those predicted by the HypofractionatedTreatment Effects in the Clinic spine tumor control probability model, and these data will inform a proposed dose escalation randomized trial.

Tumor Control Probability Modeling and Systematic Review of Oligometastatic Prostate Cancer Treated with Stereotactic Ablative Radiotherapy

<h3>Purpose/Objective(s)</h3> Oligometastatic prostate adenocarcinoma is increasingly treated with stereotactic ablative body radiotherapy (SABR), and treatment efficacy requires achieving adequate local control (LC). Reported dose and fractionation schemes vary and evidence supporting a consensus treatment schema is lacking. We sought to define a dose-response curve based on meta-analysis of reported SABR regimens for prostate cancer oligometastases. <h3>Materials/Methods</h3> A systematic literature review was performed through PubMed using MESH terms "neoplasm metastasis," "radiotherapy," and "prostatic neoplasms." Inclusion criteria included radiologically- or biopsy- identified prostate cancer metastases, histologically confirmed prostate cancer diagnosis, metastasis-directed treatment with SABR, follow-up with radiologic assessment of treated metastases, and reporting of SABR dose and fractionation. Review articles were excluded. Doses were converted to equivalent doses of 2 Gy per fraction (EQD₂) with alpha/beta ratio of 1.5. 90% and 95% tumor control probability (TCP) at 6 months, 1 year, and 2 years was calculated using the Poisson model, with TCP = (1/2)<i>^e</i>^{[2γ50 (1-D/D50)/ln2]} and fit determined by least squares analysis in Proc NLIN of a data management and decision management software. The basic parameters of each model are D₅₀, which is the equivalent dose for a LC rate of 50%; and γ, which is the maximum normalized slope (gradient) of the dose response curve. <h3>Results</h3> We screened 733 studies and 16 studies met inclusion criteria. 1,214 metastatic prostate lesions were treated with SABR. The median EQD₂ was 230 Gy (IQR 202.9-286.7) with a median prescription dose of 30 Gy (IQR 24-33.8) in 3 fractions (range 1-10). There was poor model fit at 6 months and 1 year (p>0.05). At 2 years, the median LC was 95.7% (IQR 93.3%-100%). Chi-square test for our model fit was <0.0001, with estimated D₅₀ of 56.35 (95% CI 12.18-100.50) and γ of 0.30 (95% CI -0.05-0.65). TCP of 90% may be achieved with EQD₂ of 179.0 Gy, and TCP of 95% with EQD₂ of 225.8 Gy. This corresponds to a prescription of 41.1 Gy in 3 fractions or 52.3 Gy in 5 fractions for 90% TCP and 46.5 Gy in 3 fractions or 59.2 Gy in 5 fractions for 95% TCP at 2 years. <h3>Conclusion</h3> Although significant dose and fractionation variation is present, excellent TCP for oligometastatic prostate cancer treated with SABR was reported. Our study was limited by availability of prescription doses, rather than dosimetric data, and an individual patient-level meta-analysis to expand on this work is planned. There are many areas of need for continued research as SABR is increasingly utilized for oligometastatic prostate cancer, including durability of LC with longer follow-up, integration of novel imaging techniques into disease assessment, and the impact of systemic therapy on LC assessment.

Dose-Response Prediction for SBRT of Prostate Cancer Based on Measured Cell Survival Curves Using a Unified Multi-Activation Model

<h3>Purpose/Objective(s)</h3> Hypofractionated external beam radiotherapy with 10 to 30 fractions (fx) and stereotactic body radiation therapy (SBRT) for prostate cancer (PC) have similar rates of cancer control and toxicity at 5 years as compared to conventional treatment. There is a lack of a radiobiological modeling from lab experiments to guide this transition. By assuming a unified multi-activation (UMA) model of lab-measured radiosensitivity (RS) of the cell lines to the entire dose range, we explored the clinical dose-response, modeling tumor-control probability (TCP) and normal-tissue complication probability (NTCP). <h3>Materials/Methods</h3> Published cell survival curves (CSC) with experimental error bars for PC of 5 DU145 CSCs, 3 CP3 CSCs, 6 LNCaP + 2 LnCaP with BCL2 overexpressed or down-regulated CSCs, 3 wild-type (WT)-22RV1 CSCs, 3 post-radiation radioresistant (RR)-22RV1 CSCs, 2 hypoxia-22RV1 CSCs, 3 urothelial HCV-29 CSCs and 4 large intestine (LI) or small intestine (SI) stem cell and organoid CSCs were fitted with a UMA model by using a logit transformation and c²-regression to obtain the model parameters of g and n and their deviations. Cell survival at any fractional dose (=total dose/fx) and TCP and NTCP for the total number of cancer cells or normal tissue cells corresponding to a grade of toxicity were analytically determined from the parameters. <h3>Results</h3> All of the CSCs had been excellently fitted with R² > 0.95 and Q-factor > 0.2 excepting for two in-vivo LI/SI crypt CSCs each with an outline point. We identified radiobiological parameters and predicted TCP/NTCP curves and listed the data for the 8Gyx5 SBRT of PC in Table 1 that are comparable with the results listed in the HyTEC papers. The 0% TCP to RR or hypoxia PC and 100% NTCP to SI are similar to conventional EBRT. The Dg reflects the major uncertainties in the lab-measured RS. D50, the total dose at 50% TCP or NTCP, correlated with RS and number of cells. Cell heterogeneity greatly changed RS and shapes of TCP and NTCP curves. <h3>Conclusion</h3> These findings may be useful for the design and evaluation of new clinical treatment schemes in PC translating lab work into clinical application.

Intensity Modulation in Electron FLASH Radiotherapy

<h3>Purpose/Objective(s)</h3> Combining methods of conformal dose delivery with ultra-high dose rate (UHDR) beams is advantageous for realizing the benefits of the FLASH effect in clinical treatments. We hypothesize that deployment of intensity modulation in passive electron FLASH radiotherapy through treatment plan optimization is feasible. <h3>Materials/Methods</h3> A beam model of an electron FLASH irradiator (delivering ∼300Gy/s at the isocenter) in the decimal ElectronRT treatment planning system (TPS) was validated with film measured lateral and percent-depth-dose (PDD) profiles. Plans were developed comparing collimated open fields and intensity modulated electron beams achieved with cylindrical passive metal compensators that vary in spacing and size. Homogeneity and conformity were quantified for plans developed in a water phantom and anonymized patient cases considering constraints in prescribed dose to the target volume and minimizing dose to organs-at-risk (OAR). <h3>Results</h3> The film measured profiles and TPS beam model agreed on average to within 1% and 2% for lateral profile and PDD, respectively. For a large 15 × 15 cm² field in a water phantom the intensity modulation improved the beam flatness (∼30% to <5%) and penumbra (35 mm to 15mm) at 3 cm depth while retaining UHDR in the treatment field with a 38% reduction in central axis output while still retaining the UHDR conditions (∼180Gy/sec). The PDD exhibited <2% difference along the central axis and minimal changes to symmetry, shift, and practical range. In the rib metastasis case, the unmodulated and modulated plans treat the tumor volume with a homogeneity index (HI), improvement by 0.2. In the facial orbital plan, the conformity index improved by 8% with an improvement in HI by 0.05 and comparable dose to OARs. <h3>Conclusion</h3> Intensity modulation of electrons FLASH is achievable and improves homogeneity of prescribed dose with proper treatment constraints while reducing hot spots for clinical plans. Depending on the treatment case intensity modulation can also generate superior dose distributions while retaining the UHDR conditions to best exploit the FLASH effect. Future study will focus on demonstrating dosimetric benefits quantified by tumor control probability and normal tissue complication probability models in a larger number of relevant cases.