Impact of dose calculation models on radiotherapy outcomes and quality adjusted life years for lung cancer treatment: do we need to measure radiotherapy outcomes to tune the radiobiological parameters of a normal tissue complication probability model?

Impact of Dose Calculation Algorithms and Radiobiological Parameters on Prediction of Cardiopulmonary Complications in Left Breast Radiation Therapy

Background

Breast cancer requires evaluating treatment plans using dosimetric and biological parameters. Considering radiation dose distribution and tissue response, healthcare professionals can optimize treatment plans for better outcomes.

Objective

This study aimed to evaluate the effects of the different Dose Calculation Algorithms (DCAs) and Biologically Model-Related Parameters (BMRPs) on the prediction of cardiopulmonary complications due to left breast radiotherapy.

Material and Methods

In this practical study, the treatment plans of 21 female patients were simulated in the Monaco Treatment Planning System (TPS) with a prescribed dose of 50 Gy in 25 fractions. Dose distribution was extracted using the three DCAs [Pencil Beam (PB), Collapsed Cone (CC), and Monte Carlo (MC)]. Cardiopulmonary complications were predicted by Normal Tissue Complication Probability (NTCP) calculations using different dosimetric and biological parameters. The Lyman-Kutcher-Burman (LKB) and Relative-Seriality (RS) models were used to calculate NTCP. The endpoint for NTCP calculation was pneumonitis, pericarditis, and late cardiac mortality. The ANOVA test was used for statistical analysis.

Results

In calculating Tumor Control Probability (TCP), a statistically significant difference was observed between the results of DCAs in the Poisson model. The PB algorithm estimated NTCP as less than others for all Pneumonia BMRPs.

Conclusion

The impact of DCAs and BMRPs differs in the estimation of TCP and NTCP. DCAs have a stronger influence on TCP calculation, providing more effective results. On the other hand, BMRPs are more effective in estimating NTCP. Consequently, parameters for radiobiological indices should be cautiously used s to ensure the appropriate consideration of both DCAs and BMRPs.

Embedding machine learning based toxicity models within radiotherapy treatment plan optimization

Objective.This study addresses radiation-induced toxicity (RIT) challenges in radiotherapy (RT) by developing a personalized treatment planning framework. It leverages patient-specific data and dosimetric information to create an optimization model that limits adverse side effects using constraints learned from historical data.Approach.The study uses the optimization with constraint learning (OCL) framework, incorporating patient-specific factors into the optimization process. It consists of three steps: optimizing the baseline treatment plan using population-wide dosimetric constraints; training a machine learning (ML) model to estimate the patient's RIT for the baseline plan; and adapting the treatment plan to minimize RIT using ML-learned patient-specific constraints. Various predictive models, including classification trees, ensembles of trees, and neural networks, are applied to predict the probability of grade 2+ radiation pneumonitis (RP2+) for non-small cell lung (NSCLC) cancer patients three months post-RT. The methodology is assessed with four high RP2+ risk NSCLC patients, with the goal of optimizing the dose distribution to constrain the RP2+ outcome below a pre-specified threshold. Conventional and OCL-enhanced plans are compared based on dosimetric parameters and predicted RP2+ risk. Sensitivity analysis on risk thresholds and data uncertainty is performed using a toy NSCLC case.Main results.Experiments show the methodology's capacity to directly incorporate all predictive models into RT treatment planning. In the four patients studied, mean lung dose and V20 were reduced by an average of 1.78 Gy and 3.66%, resulting in an average RP2+ risk reduction from 95% to 42%. Notably, this reduction maintains tumor coverage, although in two cases, sparing the lung slightly increased spinal cord max-dose (0.23 and 0.79 Gy).Significance.By integrating patient-specific information into learned constraints, the study significantly reduces adverse side effects like RP2+ without compromising target coverage. This unified framework bridges the gap between predicting toxicities and optimizing treatment plans in personalized RT decision-making.

The use of the normal tissue non-complication probability (NTCP0) in the safety evaluations as a new alternative of assessing the side-effects of the radiation oncology treatments

PURPOSE

To encourage the use of the NTCP0 for evaluating safety as a new alternative of assessing the S-Es of the radiation oncology treatments; and the use of the 'NTCP0cal' methodology that calculates/estimates NTCP0.

METHOD

Revisions of studies related to use of the NTCP in the evaluations of S-Es. Development of the first version of the Matlab application of our methodology, which provides three options, two of them employ the well-known aspects of a phenomenological model, or the relationship with the TNTCP; where NTCP0 = 100%-TNTCP; and the third option determines NTCP0 from an assumed NTCP discrete probabilistic distribution from the binomial distribution, where one of its parameters is automatically defined from a databased of the Disease locations Vs. Late complications.

RESULT

As result of revisions of some QUANTEC studies, we can say that: (1) The majority of current NTCP models are DVH-based; (2) The risk of toxicity is the way of evaluating the S-Es of the radiation oncology treatments; and (3) The NTCP are used mainly for evaluations of individual or principal complications or Endpoints of the radiation treatments. The 'NTCP0cal' Matlab application developed in this study has three calculation options. Two of the options provide additional graphical information about the distributions.

CONCLUSIONS

The NTCP0 is a new radiobiological concept, its introduction let to correct some current P + and UTCP formulations, and will allow evaluating S-Es in whatever activity involving ionizing radiation, like radiation treatments; and its phenomenological model function of dose prescribed (D = n*d) will allow calculating values of NTCP0 for a range of dose per fraction (d) in a treatment with a determined number of fractions (n), or for range of n for a constant d. The DVH is irrelevant for this model. For whatever radiation treatment given to a population of similar patients under similar circumstances, the NTCP0 is calculated as ratio of the number of patients without acute/late complications and total of them. When this number is unknown, then NTCP0 can be obtained using the 'NTCP0cal' application.

The radiobiological effect of using Acuros XB vs anisotropic analytical algorithm on hepatocellular carcinoma stereotactic body radiation therapy

The purpose of this work was to study the radiobiological effect of using Acuros XB (AXB) vs Analytic Anisotropic Algorithm (AAA) on hepatocellular carcinoma (HCC) stereotactic body radiation therapy (SBRT). Seventy SBRT volumetric modulated arc therapy (VMAT) plans for HCC were calculated using AAA and AXB respectively with the same treatment parameters. Published tumor control probability (TCP) and normal tissue complication probability (NTCP) models were used to quantify the effect of dosimetric difference between AAA and AXB on TCP, NTCP and uncomplicated tumor control probability (UTCP). There was an average decrease of 2.5% in 6-month TCP. Normal liver has the largest average decrease in NTCP which was 59.7%. Bowels followed with 26.6% average decrease in NTCP. Duodenum, stomach and esophagus had 10.2%, 5.1%, and 4.3% average decrease in NTCP. There was an average decrease of 1.8% and up to 7.2% in 6-month UTCP. There was an overall decrease in TCP, NTCP, and UTCP for HCC SBRT plans calculated using AXB compared to AAA which could be clinically significant.

Doses delivered by portal imaging quality assurance in routine practice of adjuvant breast radiotherapy worth to by monitored and compensated in some cases

Background

Imaging, in radiotherapy, has become a routine tool for repositioning of the target volume at each session. The repositioning precision, currently infracentimetric, evolves along with the irradiation techniques. This retrospective study aimed to identify practices and doses resulting from the use of high energy planar imaging (portal imaging) in daily practice.

Methods

A retrospective survey of portal images (PIs) was carried out over 10 years for 2,403 patients and for three linacs (1 Elekta SLi, 2 Varian Clinac) for postoperative mammary irradiations. Images were taken using a standardized number of monitor units (MU) for all patients. Due to the variable sensitivities of the detectors and the possibility of adjustment of the detector-patient distance, the number of MU were 3; 2 and 1 respectively, for Elekta SLi^®, Clinac 600^® and Clinac 2100^®. Then, a representative cumulated dose was calculated in simplified reference conditions (5 cm depth, beam of 10 cm × 10 cm, 6 MV), considering the total number of images taken during the whole treatment course. The consistency between the representative doses and the actual absorbed doses received by the patients was verified by simulating a series of typical cases with the treatment plan dose calculation system.

Results

The delivered doses differ significantly between the three linacs. The mean representative dose values by complete treatment were 0.695; 0.241 and 0.216 Gy, respectively, for SLi, Clinac 600 and Clinac 2100. However, 15 patients were exposed to a dose >2 Gy with a maximum dose of 5.05 Gy. The simulated doses were very similar to the representative doses.

Conclusions

A significant dose delivery was highlighted by this study. These representative doses are presently communicated weekly to the radiation oncologist for the radiation protection of their patients. Moreover, they should be taken into account in a possible study of long-term stochastic risks.

Analysis of Hepatocellular Carcinoma Stereotactic Body Radiation Therapy Dose Prescription Method Using Uncomplicated Tumor Control Probability Model

Purpose

This work was to establish an uncomplicated tumor control probability (UTCP) model using hepatocellular carcinoma (HCC) stereotactic body radiation therapy (SBRT) clinical data in our institution. The model was then used to analyze the current dose prescription method and to seek the opportunity for improvement.

Methods and Materials

A tumor control probability (TCP) model was generated based on local clinical data using the maximum likelihood method. A UTCP model was then formed by combining the established TCP model with the normal tissue complication probability model based on the study by Dawson et al. The authors investigated the dependence of maximum achievable UTCP on planning target volume equivalent uniform dose (EUD) at various ratio between planning target volume EUD and normal liver EUD (T/N EUD ratios). A new term uncomplicated tumor control efficiency (UTCE) was also introduced to analyze the outcome. A UTCE value of 1 implied that the theoretical maximum UTCP for the corresponding T/N EUD ratio was achieved.

Results

The UTCE of the HCC SBRT patients based on the current dose prescription method was found to be 0.93 ± 0.05. It was found that the UTCE could be increased to 0.99 ± 0.03 by using a new dose prescription scheme, for which the UTCP could be maximized while keeping the normal tissue complication probability value smaller than 5%.

Conclusions

The dose prescription method of the current HCC SBRT in our institution was analyzed using a UTCP model established based on local clinical data. It was shown that there could be a potential to increase the prescription dose of HCC SBRT. A new dose prescription scheme was proposed to achieve better UTCP. Additional clinical trials would be required to validate the proposed dose prescription scheme in the future.

Proposals of models for new formulations of the current complication-free cure (P+) and uncomplicated tumor control probability (UTCP) concepts, and total normal tissue complication probability of late complications

This study proposes phenomenological models for total normal tissue complication probability (TNTCP) and NTCP0. NTCP0 is a new acronym for reformulating the current complication-free cure (P+) and uncomplicated tumor control probability (UTCP) concepts, and TNTCP will reformulate the current NTCP involving multiple organs at risks. The current probabilistic concepts are incoherently formulated with mathematical operations of tumor control probability (TCP) and normal tissue complication probability (NTCP) that are associated with different stochastic processes and random variables. NTCP0 is equal to NTCP₀ (normal tissue non-complication probability) that is calculated as the ratio of a number of patients of a population without late complications and a total of them. As a cumulative distribution function (CDF) of late complications, TNTCP = sum(NTCP_i), where NTCP_i is the NTCP of the i^th late complication. TNTCP is also a new acronym, and the probabilistic complement of NTCP0, then NTCP0 = 100% - TNTCP. The NTCP0/TNTCP (D(d)) proposing models are based on the relationship between the NTCP0/TNTCP and total dose (D = n×d; where d = dose per fraction, and n = number of fractions). TNTCP(D) model will be correlated with LKB model (the normal CDF) that is an increasing function; and NTCP0(D) model with a decreasing function, which additionally will define clear limits of three possible regions for NTCP0: 0 and 100% deterministic, and a stochastic. These models are function D, which is widely used for characterizing radiation therapies.

Integration of radiobiological modeling and indices in comparative plan evaluation: A study comparing VMAT and 3D-CRT in patients with NSCLC

PURPOSE

The purpose of this article was to generate an algorithm to calculate radiobiological endpoints and composite indices and use them to compare volumetric modulated arc therapy (VMAT) and 3-dimensional conformal radiation therapy (3D-CRT) techniques in patients with locally advanced non-small cell lung cancer.

METHODS AND MATERIALS

The study included 25 patients with locally advanced non-small cell lung cancer treated with 3D-CRT at our center between January 1, 2010, and December 31, 2014. The planner generated VMAT plans using clones of the original computed tomography scans and regions of interest volumes, which did not include the original 3D plans. Both 3D-CRT and VMAT plans were generated using the same dose-volume constraint worksheet. The dose-volume histogram parameters for planning target volume and relevant organs at risk (OAR) were reviewed. The calculation engine was written in the R programming language; the user interface was developed with the "shiny" R Web library. Dose-volume histogram data were imported into the calculation engine and tumor control probability (TCP), normal tissue complication probability (NTCP), composite cardiopulmonary toxicity index (CPTI), morbidity index: MI = ∑_j = 1^{#ofrelevantOARs}(w_j ∗ NTCP_j), uncomplicated TCP (UTCP=TCP∗∏k=1#ofOARs1-NTCP_K100, and therapeutic gain (TG): ie, TG = TCP ∗ (100 - MI) were calculated.

RESULTS

TCP was better with 3D-CRT (12.62% vs 11.71%, P < .001), whereas VMAT demonstrated superior NTCP esophagus (4.45% vs 7.39%, P = .02). NTCP spinal cord (0.001% vs 0.009%, P = .001), and NTCP heart/perfusion defect (44.57% vs 56.42%, P = .016). There was no difference in NTCP lung (6.27% vs 7.62%, P = .221) and NTCP heart/pericarditis (0.001% vs 0.15%, P = .129) between 2 techniques. VMAT showed substantial improvement in morbidity index (11.06% vs. 14.31%, P = 0.01), CPTI (47.59% vs 59.41%, P = .03), TG (P = .035), and trend toward superiority in UTCP (5.89 vs 4.75, P=.057).

CONCLUSION

The study highlights the utility of the radiobiological algorithm and summary indices in comparative plan evaluation and demonstrates benefits of VMAT over 3D-CRT.

Agreement between gamma passing rates using computed tomography in radiotherapy and secondary cancer risk prediction from more advanced dose calculated models

BACKGROUND

During the past decades, in radiotherapy, the dose distributions were calculated using density correction methods with pencil beam as type 'a' algorithm. The objectives of this study are to assess and evaluate the impact of dose distribution shift on the predicted secondary cancer risk (SCR), using modern advanced dose calculation algorithms, point kernel, as type 'b', which consider change in lateral electrons transport.

METHODS

Clinical examples of pediatric cranio-spinal irradiation patients were evaluated. For each case, two radiotherapy treatment plans with were generated using the same prescribed dose to the target resulting in different number of monitor units (MUs) per field. The dose distributions were calculated, respectively, using both algorithms types. A gamma index (γ) analysis was used to compare dose distribution in the lung. The organ equivalent dose (OED) has been calculated with three different models, the linear, the linear-exponential and the plateau dose response curves. The excess absolute risk ratio (EAR) was also evaluated as (EAR = OED _{type 'b'} / OED _{type 'a'}).

RESULTS

The γ analysis results indicated an acceptable dose distribution agreement of 95% with 3%/3 mm. Although, the γ-maps displayed dose displacement >1 mm around the healthy lungs. Compared to type 'a', the OED values from type 'b' dose distributions' were about 8% to 16% higher, leading to an EAR ratio >1, ranged from 1.08 to 1.13 depending on SCR models.

CONCLUSIONS

The shift of dose calculation in radiotherapy, according to the algorithm, can significantly influence the SCR prediction and the plan optimization, since OEDs are calculated from DVH for a specific treatment. The agreement between dose distribution and SCR prediction depends on dose response models and epidemiological data. In addition, the γ passing rates of 3%/3 mm does not translate the difference, up to 15%, in the predictions of SCR resulting from alternative algorithms. Considering that modern algorithms are more accurate, showing more precisely the dose distributions, but that the prediction of absolute SCR is still very imprecise, only the EAR ratio could be used to rank radiotherapy plans.