A validated tumor control probability model based on a meta-analysis of low, intermediate, and high-risk prostate cancer patients treated by photon, proton, or carbon-ion radiotherapy

Understanding Relative Biological Effectiveness and Clinical Outcome of Prostate Cancer Therapy Using Particle Irradiation: Analysis of Tumor Control Probability With the Modified Microdosimetric Kinetic Model

PURPOSE

Recent experimental studies and clinical trial results might indicate that-at least for some indications-continued use of the mechanistic model for relative biological effectiveness (RBE) applied at carbon ion therapy facilities in Europe for several decades (LEM-I) may be unwarranted. We present a novel clinical framework for prostate cancer treatment planning and tumor control probability (TCP) prediction based on the modified microdosimetric kinetic model (mMKM) for particle therapy.

METHODS AND MATERIALS

Treatment plans of 91 patients with prostate tumors (proton: 46, carbon ions: 45) applying 66 GyRBE [RBE = 1.1 for protons and LEM-I, (α/β)x = 2.0 Gy, for carbon ions] in 20 fractions were recalculated using mMKM [(α/β)x = 3.1 Gy]). Based solely on the response data of photon-irradiated patient groups stratified according to risk and usage of androgen deprivation therapy, we derived parameters for an mMKM-based Poisson-TCP model. Subsequently, new carbon and helium ion plans, adhering to prescribed biological dose criteria, were generated. These were systematically compared with the clinical experience of Japanese centers employing an analogous fractionation scheme and existing proton plans.

RESULTS

mMKM predictions suggested significant biological dose deviation between the proton and carbon ion arms. Patients irradiated with protons received (3.25 ± 0.08)  GyRBEmMKM/Fx, whereas patients treated with carbon ions received(2.51 ± 0.05)  GyRBEmMKM/Fx. TCP predictions were (86 ± 3)% for protons and (52 ± 4)% for carbon ions, matching the clinical outcome of 85% and 50%. Newly optimized carbon ion plans, guided by the mMKM/TCP model, effectively replicated clinical data from Japanese centers. Using mMKM, helium ions exhibited similar target coverage as proton and carbon ions and improved rectum and bladder sparing compared with proton.

CONCLUSIONS

Our mMKM-based model for prostate cancer treatment planning and TCP prediction was validated against clinical data for proton and carbon ion therapy, and its application was extended to helium ion therapy. Based on the data presented in this work, mMKM seems to be a good candidate for clinical biological calculations in carbon ion therapy for prostate cancer.

ROE (Radiotherapy Outcomes Estimator): An open-source tool for optimizing radiotherapy prescriptions

BACKGROUND AND OBJECTIVES

Radiotherapy prescriptions currently derive from population-wide guidelines established through large clinical trials. We provide an open-source software tool for patient-specific prescription determination using personalized dose-response curves.

METHODS

We developed ROE, a plugin to the Computational Environment for Radiotherapy Research to visualize predicted tumor control and normal tissue complication simultaneously, as a function of prescription dose. ROE can be used natively with MATLAB and is additionally made accessible in GNU Octave and Python, eliminating the need for commercial licenses. It provides a curated library of published and validated predictive models and incorporates clinical restrictions on normal tissue outcomes. ROE additionally provides batch-mode tools to evaluate and select among different fractionation schemes and analyze radiotherapy outcomes across patient cohorts.

CONCLUSION

ROE is an open-source, GPL-copyrighted tool for interactive exploration of the dose-response relationship to aid in radiotherapy planning. We demonstrate its potential clinical relevance in (1) improving patient awareness by quantifying the risks and benefits of a given treatment protocol (2) assessing the potential for dose escalation across patient cohorts and (3) estimating accrual rates of new protocols.

Treatment planning of carbon ion radiotherapy for prostate cancer based on cellular experiments with PC3 human prostate cancer cells

[Purpose] Treatment plans for carbon ion radiotherapy (CIRT) in Japan are designed to uniformly deliver the prescribed clinical dose based on the radiosensitivity of human salivary gland (HSG) cells to the planning target volume (PTV). However, sensitivity to carbon beams varies between cell lines, that is, it should be checked that the clinical dose distribution based on the cell radiosensitivity of the treatment site is uniform within the PTV. [Methods] We modeled the linear energy transfer (LET) dependence of the linear-quadratic (LQ) coefficients specific to prostate cancer, which accounts for the majority of CIRT. This was achieved by irradiating prostate cancer cells (PC3) with X-rays from a 4 MV-Linac and carbon beams with different LETs of 11.1-214.3 keV/μm. By using the radiosensitivity of PC3 cells derived from cellular experiments, we reconstructed prostate-cancer-specific clinical dose distributions on patient computed tomography (CT). [Results] The LQ coefficient, α, of PC3 cells was larger than that of HSG cells at low (<50 keV/μm) LET and smaller at high (>50 keV/μm) LET, which was validated by cellular experiments performed on rectangular SOBPs. The reconstructed dose distribution on patient CT was sloped when 1 fraction incident from the one side of the patient was considered, but remained uniform from the sum of 12 fractions of the left-right opposing beams (as is used in clinical practice). [Conclusion] Our study reveals the inhomogeneity of clinical doses in single-field plans calculated using the PC3 radiosensitivity data. However, this inhomogeneity is compensated by using the combination of left-right opposing beams.

Generation of Radioresistant Prostate Cancer Cells

The development of in vitro isogenic models of radioresistance through exposure to fractionated radiation is an increasingly used approach to investigate the mechanisms of radioresistance in cancer cells. Owing to the complex nature of the biological effect of ionizing radiation, the generation and validation of these models requires the careful consideration of radiation exposure protocols and cellular endpoints. This chapter presents a protocol we used to derive and characterize an isogenic model of radioresistant prostate cancer cells. This protocol may be applicable to other cancer cell lines.

Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology

(1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models.

Assessment of RBE-Weighted Dose Models for Carbon Ion Therapy Toward Modernization of Clinical Practice at HIT: In Vitro, in Vivo, and in Patients

PURPOSE

Present-day treatment planning in carbon ion therapy is conducted with assumptions for a limited number of tissue types and models for effective dose. Here, we comprehensively assess relative biological effectiveness (RBE) in carbon ion therapy and associated models toward the modernization of current clinical practice in effective dose calculation.

METHODS

Using 2 human (A549, H460) and 2 mouse (B16, Renca) tumor cell lines, clonogenic cell survival assay was performed for examination of changes in RBE along the full range of clinical-like spread-out Bragg peak (SOBP) fields. Prediction power of the local effect model (LEM1 and LEM4) and the modified microdosimetric kinetic model (mMKM) was assessed. Experimentation and analysis were carried out in the frame of a multidimensional end point study for clinically relevant ranges of physical dose (D), dose-averaged linear energy transfer (LET_d), and base-line photon radio-sensitivity (α/β)_x. Additionally, predictions were compared against previously reported RBE measurements in vivo and surveyed in patient cases.

RESULTS

RBE model prediction performance varied among the investigated perspectives, with mMKM prediction exhibiting superior agreement with measurements both in vitro and in vivo across the 3 investigated end points. LEM1 and LEM4 performed their best in the highest LET conditions but yielded overestimations and underestimations in low/midrange LET conditions, respectively, as demonstrated by comparison with measurements. Additionally, the analysis of patient treatment plans revealed substantial variability across the investigated models (±20%-30% uncertainty), largely dependent on the selected model and absolute values for input tissue parameters α_x and β_x.

CONCLUSION

RBE dependencies in vitro, in vivo, and in silico were investigated with respect to various clinically relevant end points in the context of tumor-specific tissue radio-sensitivity assignment and accurate RBE modeling. Discovered model trends and performances advocate upgrading current treatment planning schemes in carbon ion therapy and call for verification via clinical outcome analysis with large patient cohorts.

Effect of Irradiation Time on Biological Effectiveness and Tumor Control Probability in Proton Therapy

PURPOSE

The biological effectiveness of proton beams may decrease with irradiation time because of sublethal damage repair (SLDR). The purpose of this study is to systematically evaluate this effect in hypofractionated proton therapy for various target sizes, depths, and prescribed doses per fraction.

METHODS AND MATERIALS

Plans with a single spread-out Bragg peak beam were created using a constant relative biological effectiveness (RBE) of 1.1 to cover targets of 6 different sizes located at 3 different depths in water. Biological doses of 2, 3, 5, 10, and 20 Gy (RBE) were prescribed to the targets. First, to investigate the depth variation of the biological effectiveness, the biological dose in instantaneous irradiation was recalculated based on the microdosimetric kinetic model. SLDR was then taken into account in the microdosimetric kinetic model during treatments to obtain the irradiation time-dependent biological effectiveness for irradiation time T of 5 to 60 minutes and beam interruption time τ of 0 to 60 minutes. The tumor control probabilities were calculated for single-fraction proton therapy fields of different Ts and τs, and the curative doses were evaluated at a tumor control probability of 90%.

RESULTS

The biological effectiveness decreased with longer T and τ and higher prescribed dose. The maximum decrease in the biological effectiveness was 21% with a 20 Gy (RBE) prescribed dose. In single-fraction proton therapy, the curative dose increased linearly by approximately 33% to 35% with the increase of T from 0 to 60 minutes.

CONCLUSIONS

The biological effectiveness varies largely with T and τ because of SLDR during treatments. This effect was pronounced for high prescribed doses per fraction. Thus, the effect of SLDR needs to be considered in hypofractionated and single-fraction proton therapies in relation to size and depth of the target.

MRI-based tumour control probability in skull-base chordomas treated with carbon-ion therapy

PURPOSE

To derive personalized tumour control probability (TCP) models, using diffusion-weighted (DW-) MRI for defining initial tumour cellular density in skull-base chordoma patients undergoing carbon-ion radiotherapy (CIRT).

MATERIALS AND METHODS

67 patients affected by skull-base chordoma were enrolled for a standardized CIRT treatment (70.4 Gy (RBE) prescription dose). Local control information was clinically assessed. For 20 of them, apparent diffusion coefficient (ADC) maps were computed from DW-MRI and then converted into cellular density. Radiosensitivity parameters (α, β) were estimated from the available data through an optimization procedure, taking advantage of a relationship observed between local control and the dose received by at least the 98% of the gross tumour volume. These parameters were fed into two poissonian TCP models, based on the LQ model, being the first (TCP_LIT) computed from literature parameters and the second (TCP_ADC) enriched by a personalized initial cellular density derived from ADC maps.

RESULTS

The inclusion of the cellular density derived from ADC into TCP_ADC yielded slightly higher dose values at which TCP = 0.5 (D₅₀ = 38.91 Gy (RBE)) with respect to TCP_LIT (D₅₀34.16 Gy (RBE)). This suggested a more conservative approach, even if the prognostic power of TCP_ADC and TCP_LIT, tested with respect to local control, was equivalent in terms of sensitivity (0.867) and specificity (0.600).

CONCLUSIONS

Both TCP_ADC and TCP_LIT exhibited good agreement with a clinically validated information of local control, the former providing more conservative predictions.

Radiobiological parameters in a tumour control probability model for prostate cancer LDR brachytherapy

To provide recommendations for the selection of radiobiological parameters for prostate cancer treatment planning. Recommendations were based on validation of the previously published values, parameter estimation and a consideration of their sensitivity within a tumour control probability (TCP) model using clinical outcomes data from low-dose-rate (LDR) brachytherapy. The proposed TCP model incorporated radiosensitivity (α) heterogeneity and a non-uniform distribution of clonogens. The clinical outcomes data included 849 prostate cancer patients treated with LDR brachytherapy at four Australian centres between 1995 and 2012. Phoenix definition of biochemical failure was used. Validation of the published values from four selected literature and parameter estimation was performed with a maximum likelihood estimation method. Each parameter was varied to evaluate the change in calculated TCP to quantify the sensitivity of the model to its radiobiological parameters. Using a previously published parameter set and a total clonogen number of 196 000 provided TCP estimates that best described the patient cohort. Fitting of all parameters with a maximum likelihood estimation was not possible. Variations in prostate TCP ranged from 0.004% to 0.67% per 1% change in each parameter. The largest variation was caused by the log-normal distribution parameters for α (mean, [Formula: see text], and standard deviation, σ _α ). Based on the results using the clinical cohort data, we recommend a previously published dataset is used for future application of the TCP model with inclusion of a patient-specific, non-uniform clonogen density distribution which could be derived from multiparametric imaging. The reduction in uncertainties in these parameters will improve the confidence in using biological models for clinical radiotherapy planning.

Towards a Clinical Decision Support System for External Beam Radiation Oncology Prostate Cancer Patients: Proton vs. Photon Radiotherapy? A Radiobiological Study of Robustness and Stability

We present a methodology which can be utilized to select proton or photon radiotherapy in prostate cancer patients. Four state-of-the-art competing treatment modalities were compared (by way of an in silico trial) for a cohort of 25 prostate cancer patients, with and without correction strategies for prostate displacements. Metrics measured from clinical image guidance systems were used. Three correction strategies were investigated; no-correction, extended-no-action-limit, and online-correction. Clinical efficacy was estimated via radiobiological models incorporating robustness (how probable a given treatment plan was delivered) and stability (the consistency between the probable best and worst delivered treatments at the 95% confidence limit). The results obtained at the cohort level enabled the determination of a threshold for likely clinical benefit at the individual level. Depending on the imaging system and correction strategy; 24%, 32% and 44% of patients were identified as suitable candidates for proton therapy. For the constraints of this study: Intensity-modulated proton therapy with online-correction was on average the most effective modality. Irrespective of the imaging system, each treatment modality is similar in terms of robustness, with and without the correction strategies. Conversely, there is substantial variation in stability between the treatment modalities, which is greatly reduced by correction strategies. This study provides a ‘proof-of-concept’ methodology to enable the prospective identification of individual patients that will most likely (above a certain threshold) benefit from proton therapy.

Prostate Cancer Radiation Therapy: What Do Clinicians Have to Know?

Radiotherapy (RT) for prostate cancer (PC) has steadily evolved over the last decades, with improving biochemical disease-free survival. Recently population based research also revealed an association between overall survival and doses ≥ 75.6 Gray (Gy) in men with intermediate- and high-risk PC. Examples of improved RT techniques are image-guided RT, intensity-modulated RT, volumetric modulated arc therapy, and stereotactic ablative body RT, which could facilitate further dose escalation. Brachytherapy is an internal form of RT that also developed substantially. New devices such as rectum spacers and balloons have been developed to spare rectal structures. Newer techniques like protons and carbon ions have the intrinsic characteristics maximising the dose on the tumour while minimising the effect on the surrounding healthy tissue, but clinical data are needed for confirmation in randomised phase III trials. Furthermore, it provides an overview of an important discussion issue in PC treatment between urologists and radiation oncologists: the comparison between radical prostatectomy and RT. Current literature reveals that all possible treatment modalities have the same cure rate, but a different toxicity pattern. We recommend proposing the possible different treatment modalities with their own advantages and side-effects to the individual patient. Clinicians and patients should make treatment decisions together (shared decision-making) while using patient decision aids.