Evaluation of variability in target volume delineation for newly diagnosed glioblastoma: a multi-institutional study from the Korean Radiation Oncology Group

The effect of editing clinical contours on deep-learning segmentation accuracy of the gross tumor volume in glioblastoma

Background and purpose

Deep-learning (DL) models for segmentation of the gross tumor volume (GTV) in radiotherapy are generally based on clinical delineations which suffer from inter-observer variability. The aim of this study was to compare performance of a DL-model based on clinical glioblastoma GTVs to a model based on a single-observer edited version of the same GTVs.

Materials and methods

The dataset included imaging data (Computed Tomography (CT), T1, contrast-T1 (T1C), and fluid-attenuated-inversion-recovery (FLAIR)) of 259 glioblastoma patients treated with post-operative radiotherapy between 2012 and 2019 at a single institute. The clinical GTVs were edited using all imaging data. The dataset was split into 207 cases for training/validation and 52 for testing.GTV segmentation models (nnUNet) were trained on clinical and edited GTVs separately and compared using Surface Dice with 1 mm tolerance (sDSC1mm). We also evaluated model performance with respect to extent of resection (EOR), and different imaging combinations (T1C/T1/FLAIR/CT, T1C/FLAIR/CT, T1C/FLAIR, T1C/CT, T1C/T1, T1C). A Wilcoxon test was used for significance testing.

Results

The median (range) sDSC1mm of the clinical-GTV-model and edited-GTV-model both evaluated with the edited contours, was 0.76 (0.43-0.94) vs. 0.92 (0.60-0.98) respectively (p < 0.001). sDSC1mm was not significantly different between patients with a biopsy, partial, and complete resection. T1C as single input performed as good as use of imaging combinations.

Conclusions

High segmentation accuracy was obtained by the DL-models. Editing of the clinical GTVs significantly increased DL performance with a relevant effect size. DL performance was robust for EOR and highly accurate using only T1C.

Robust AI-Driven Segmentation of Glioblastoma T1c and FLAIR MRI Series and the Low Variability of the MRIMath© Smart Manual Contouring Platform

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen-Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists' scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists' ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists' scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists' scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (<5 mm), volume measurements, kappa scores, and Bland-Altman differences that align closely with those measured by radiologists. Moreover, the inter-user variability between radiologists using the smart manual contouring platform was under 5% for T1c and under 10% for FLAIR images. These results underscore the MRIMath© platform's low inter-user variability and the high accuracy of its T1c and FLAIR AI models.

Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI

Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization.

Empirical planning target volume modeling for high precision MRI guided intracranial radiotherapy

Purpose

Magnetic resonance image-guided radiotherapy for intracranial indications is a promising advance; however, uncertainties remain for both target localization after translation-only MR setup and intrafraction motion. This investigation quantified these uncertainties and developed a population-based planning target volume (PTV) model to explore target and organ-at-risk (OAR) volumetric coverage tradeoffs.

Methods

Sixty-six patients, 49 with a primary brain tumor and 17 with a post-surgical resection cavity, treated on a 1.5T-based MR-linac across 1329 fractions were included. At each fraction, patients were setup by translation-only fusion of the online T1 MRI to the planning image. Each fusion was independently repeated offline accounting for rotations. The six degree-of-freedom difference between fusions was applied to transform the planning CTV at each fraction (CTV_fx). A PTV model parameterized by volumetric CTV_fx coverage, proportion of fractions, and proportion of patients was developed. Intrafraction motion was quantified in a 412 fraction subset as the fusion difference between post- and pre-irradiation T1 MRIs.

Results

For the left-right/anterior-posterior/superior-inferior axes, mean ± SD of the rotational fusion differences were 0.1 ± 0.8/0.1 ± 0.8/-0.2 ± 0.9°. Covering 98 % of the CTV_fx in 95 % of fractions in 95 % of patients required a 3 mm PTV margin. Margin reduction decreased PTV-OAR overlap; for example, the proportion of optic chiasm overlapped by the PTV was reduced up to 23.5 % by margin reduction from 4 mm to 3 mm.

Conclusions

An evidence-based PTV model was developed for brain cancer patients treated on the MR-linac. Informed by this model, we have clinically adopted a 3 mm PTV margin for conventionally fractionated intracranial patients.

Timing of radiotherapy in glioblastoma based on IMRT and STUPP chemo-radiation: may be no need to rush

OBJECTIVE

To investigate the effect of surgery to radiotherapy interval (SRI) on the prognosis of patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma.

METHODS

Retrospective analysis of the relationship between SRI and prognosis of patients with IDH wild-type glioblastoma who received postoperative intensity modulated radiotherapy (IMRT) in our center from July 2013 to July 2019. The patients were divided into SRI ≤ 42 days (regular group) and SRI > 42 days (delay group). Kaplan-Meier univariate analysis and Cox proportional hazard model were used to analyze whether SRI was an independent factor influencing the prognosis.

RESULTS

A total of 102 IDH wild-type glioblastoma were enrolled. Median follow-up was 35.9 months. The 1-, 2- and 3-year OS of "regular group" were 69.5%, 34.8%, 19.1%, and "delay group" were 69.8%, 26.1% and 13.4% respectively. Multivariate analysis showed that extent of resection (p = 0.041) was an independent prognostic factor for OS. SRI (p = 0.347), gender (p = 0.159), age (p = 0. 921), maximum diameter (p = 0.637) MGMT promoter methylation status (P = 0.630) and ki-67 expression (P = 0.974) had no effect on OS. Univariate analysis (p = 0.483) and multivariate analysis (p = 0.373) also showed that SRI had no effect on OS in glioblastoma who received gross total resection.

CONCLUSION

Appropriate extension in SRI has no negative effect on the OS of IDH wild-type glioblastoma. It is suggested that radiotherapy should be started after a good recovery from surgery. This conclusion needs further confirmed by long-term follow-up of a large sample.

Method to Minimize the Errors of AI: Quantifying and Exploiting Uncertainty of Deep Learning in Brain Tumor Segmentation

Despite the unprecedented success of deep learning in various fields, it has been recognized that clinical diagnosis requires extra caution when applying recent deep learning techniques because false prediction can result in severe consequences. In this study, we proposed a reliable deep learning framework that could minimize incorrect segmentation by quantifying and exploiting uncertainty measures. The proposed framework demonstrated the effectiveness of a public dataset: Multimodal Brain Tumor Segmentation Challenge 2018. By using this framework, segmentation performances, particularly for small lesions, were improved. Since the segmentation of small lesions is difficult but also clinically significant, this framework could be effectively applied to the medical imaging field.

Analyzing magnetic resonance imaging data from glioma patients using deep learning

The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain tumors has seen a significant rise in the clinical use of computational tools. The underlying technology to the vast majority of these tools are machine learning methods and, in particular, deep learning algorithms. This review offers clinical background information of key diagnostic biomarkers in the diagnosis of glioma, the most common primary brain tumor. It offers an overview of publicly available resources and datasets for developing new computational tools and image biomarkers, with emphasis on those related to the Multimodal Brain Tumor Segmentation (BraTS) Challenge. We further offer an overview of the state-of-the-art methods in glioma image segmentation, again with an emphasis on publicly available tools and deep learning algorithms that emerged in the context of the BraTS challenge.

The Definition and Delineation of the Target Area of Radiotherapy Based on the Recurrence Pattern of Glioblastoma After Temozolomide Chemoradiotherapy

Radiotherapy is an important treatment for glioblastoma (GBM), but there is no consensus on the target delineation for GBM radiotherapy. The Radiation Therapy Oncology Group (RTOG) and European Organisation for Research and Treatment of Cancer (EORTC) each have their own rules. Our center adopted a target volume delineation plan based on our previous studies. This study focuses on the recurrence pattern of GBM patients whose target delineations did not intentionally include the T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity area outside of the gross tumor volume (GTV). We prospectively collected 162 GBM cases and retrospectively analysed the clinical data and continuous dynamic magnetic resonance images (MRI) of 55 patients with recurrent GBM. All patients received concurrent radiotherapy and chemotherapy with temozolomide (TMZ). The GTV that we defined includes the postoperative T1-weighted MRI enhancement area and resection cavity. Clinical target volume 1 (CTV1) and CTV2 were defined as GTVs with 1 and 2 cm margins, respectively. Planning target volume 1 (PTV1) and PTV2 were defined as CTV1 and CTV2 plus a 3 mm margin with prescribed doses of 60 and 54 Gy, respectively. The first recurrent contrast-enhanced T1-weighted MRI was introduced into the Varian Eclipse radiotherapy planning system and fused with the original planning computed tomography (CT) images to determine the recurrence pattern. The median follow-up time was 15.8 months. The median overall survival (OS) and progression-free survival (PFS) were 17.7 and 7.0 months, respectively. Among the patients, 44 had central recurrences, two had in-field recurrences, one had marginal recurrence occurred, 11 had distant recurrences, and three had subependymal recurrences. Five patients had multiple recurrence patterns. Compared to the EORTC protocol, target delineation that excludes the adjacent T2/FLAIR hyperintensity area reduces the brain volume exposed to high-dose radiation (P = 0.000) without an increased risk of marginal recurrence. Therefore, it is worthwhile to conduct a clinical trial investigating the feasibility of intentionally not including the T2/FLAIR hyperintensity region outside of the GTV.

Glioma consensus contouring recommendations from a MR-Linac International Consortium Research Group and evaluation of a CT-MRI and MRI-only workflow

Introduction

This study proposes contouring recommendations for radiation treatment planning target volumes and organs-at-risk (OARs) for both low grade and high grade gliomas.

Methods

Ten cases consisting of 5 glioblastomas and 5 grade II or III gliomas, including their respective gross tumor volume (GTV), clinical target volume (CTV), and OARs were each contoured by 6 experienced neuro-radiation oncologists from 5 international institutions. Each case was first contoured using only MRI sequences (MRI-only), and then re-contoured with the addition of a fused planning CT (CT-MRI). The level of agreement among all contours was assessed using simultaneous truth and performance level estimation (STAPLE) with the kappa statistic and Dice similarity coefficient.

Results

A high level of agreement was observed between the GTV and CTV contours in the MRI-only workflow with a mean kappa of 0.88 and 0.89, respectively, with no statistically significant differences compared to the CT-MRI workflow (p = 0.88 and p = 0.82 for GTV and CTV, respectively). Agreement in cochlea contours improved from a mean kappa of 0.39 to 0.41, to 0.69 to 0.71 with the addition of CT information (p < 0.0001 for both cochleae). Substantial to near perfect level of agreement was observed in all other contoured OARs with a mean kappa range of 0.60 to 0.90 in both MRI-only and CT-MRI workflows.

Conclusions

Consensus contouring recommendations for low grade and high grade gliomas were established using the results from the consensus STAPLE contours, which will serve as a basis for further study and clinical trials by the MR-Linac Consortium.

Electronic supplementary material

The online version of this article (10.1007/s11060-020-03605-6) contains supplementary material, which is available to authorized users.

Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps

OBJECTIVES

Glioblastoma multiforme (GBM) is a highly infiltrative primary brain tumour with an aggressive clinical course. Diffusion tensor imaging (DT-MRI or DTI) is a recently developed technique capable of visualising subclinical tumour spread into adjacent brain tissue. Tensor decomposition through p and q maps can be used for planning of treatment. Our objective was to develop a tool to automate the segmentation of DTI decomposed p and q maps in GBM patients in order to inform construction of radiotherapy target volumes.

METHODS

Chan-Vese level set model is applied to segment the p map using the q map as its initial starting point. The reason of choosing this model is because of the robustness of this model on either conventional MRI or only DTI. The method was applied on a data set consisting of 50 patients having their gross tumour volume delineated on their q map and Chan-Vese level set model uses these superimposed masks to incorporate the infiltrative edges.

RESULTS

The expansion of tumour boundary from q map to p map is clearly visible in all cases and the Dice coefficient (DC) showed a mean similarity of 74% across all 50 patients between the manually segmented ground truth p map and the level set automatic segmentation.

CONCLUSION

Automated segmentation of the tumour infiltration boundary using DTI and tensor decomposition is possible using Chan-Vese level set methods to expand q map to p map. We have provided initial validation of this technique against manual contours performed by experienced clinicians.

ADVANCES IN KNOWLEDGE

This novel automated technique to generate p maps has the potential to individualise radiation treatment volumes and act as a decision support tool for the treating oncologist.

Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation

Image registration and segmentation are the two most studied problems in medical image analysis. Deep learning algorithms have recently gained a lot of attention due to their success and state-of-the-art results in variety of problems and communities. In this paper, we propose a novel, efficient, and multi-task algorithm that addresses the problems of image registration and brain tumor segmentation jointly. Our method exploits the dependencies between these tasks through a natural coupling of their interdependencies during inference. In particular, the similarity constraints are relaxed within the tumor regions using an efficient and relatively simple formulation. We evaluated the performance of our formulation both quantitatively and qualitatively for registration and segmentation problems on two publicly available datasets (BraTS 2018 and OASIS 3), reporting competitive results with other recent state-of-the-art methods. Moreover, our proposed framework reports significant amelioration (p < 0.005) for the registration performance inside the tumor locations, providing a generic method that does not need any predefined conditions (e.g., absence of abnormalities) about the volumes to be registered. Our implementation is publicly available online at https://github.com/TheoEst/joint_registration_tumor_segmentation.

Report of first recurrent glioma patients examined with PET-MRI prior to re-irradiation

Background and purpose

The advantage of combined PET-MRI over sequential PET and MRI is the high spatial conformity and the absence of time delay between the examinations. The benefit of this technique for planning of re-irradiation (re-RT) treatment is unkown yet. Imaging data from a phase 1 trial of re-RT for recurrent glioma was analysed to assess whether planning target volumes and treatment margins in glioma re-RT can be adjusted by PET-MRI with rater independent PET based biological tumour volumes (BTVs).

Patients and methods

Combined PET-MRI with the tracer O-(2-¹⁸F-fluoroethyl)-l-tyrosine (¹⁸F-FET) prior to re-RT was performed in recurrent glioma patients in a phase I trial. GTVs including all regions suspicious of tumour on contrast enhanced MRI were delineated by three experienced radiation oncologists and included into MRI based consensus GTVs (_MRGTVs). BTVs were semi-automatically delineated with a fixed threshold of 1.6 x background activity. Corresponding BTVs and _MRGTVs were fused into union volume _PET-MRGTVs. The Sørensen–Dice coefficient and the conformity index were used to assess the geometric overlap of the BTVs with the _MRGTVs. A recurrence pattern analysis was performed based on the original planning target volumes (PTVs = GTV + 10 mm margin or 5 mm in one case) and the _PET-MRGTVs with margins of 10, 8, 5 and 3 mm.

Results

Seven recurrent glioma patients, who received PET-MRI prior to re-RT, were included into the present planning study. At the time of re-RT, patients were in median 54 years old and had a median Karnofsky Performance Status (KPS) score of 80. Median post-recurrence survival after the beginning of re-RT was 13 months. Concomitant bevacizumab therapy was applied in six patients and one patient received chemoradiation with temozolomide. Median GTV volumes of the three radiation oncologists were 35.0, 37.5 and 40.5 cubic centimeters (cc) and median _MRGTV volume 41.8 cc. Median BTV volume was 36.6 cc and median _PET-MRGTV volume 59.3 cc. The median Sørensen–Dice coefficient for the comparison between _MRGTV and BTV was 0.61 and the median conformity index 0.44. Recurrence pattern analysis revealed two central, two in-field and one distant recurrence within both, the original PTV, as well as the _PET-MRGTV with a reduced margin of 3 mm.

Conclusion

PET-MRI provides radiation treatment planning imaging with high spatial and timely conformity for high-grade glioma patients treated with re-RT with potential advancements for target volume delineation. Prospective randomised trials are warranted to further investigate the treatment benefits of PET-MRI based re-RT planning.

NRG brain tumor specialists consensus guidelines for glioblastoma contouring

INTRODUCTION

NRG protocols for glioblastoma allow for clinical target volume (CTV) reductions at natural barriers; however, literature examining CTV contouring and the relevant white matter pathways is lacking. This study proposes consensus CTV guidelines, with a focus on areas of controversy while highlighting common errors in glioblastoma target delineation.

METHODS

Ten academic radiation oncologists specializing in brain tumor treatment contoured CTVs on four glioblastoma cases. CTV expansions were based on NRG trial guidelines. Contour consensus was assessed and summarized by kappa statistics. A meeting was held to discuss the mathematically averaged contours and form consensus contours and recommendations.

RESULTS

Contours of the cavity plus enhancement (mean kappa 0.69) and T2-FLAIR signal (mean kappa 0.74) showed moderate to substantial agreement. Experts were asked to trim off anatomic barriers while respecting pathways of spread to develop their CTVs. Submitted CTV_4600 (mean kappa 0.80) and CTV_6000 (mean kappa 0.81) contours showed substantial to near perfect agreement. Simultaneous truth and performance level estimation (STAPLE) contours were then reviewed and modified by group consensus. Anatomic trimming reduced the amount of total brain tissue planned for radiation targeting by a 13.6% (range 8.7-17.9%) mean proportional reduction. Areas for close scrutiny of target delineation were described, with accompanying recommendations.

CONCLUSIONS

Consensus contouring guidelines were established based on expert contours. Careful delineation of anatomic pathways and barriers to spread can spare radiation to uninvolved tissue without compromising target coverage. Further study is necessary to accurately define optimal target volumes beyond isometric expansion techniques for individual patients.

Multi-institutional study of the variability in target delineation for six targets commonly treated with radiosurgery

BACKGROUND

Although accurate delineation of the target is a key factor of success in radiosurgery there are no consensus guidelines for target contouring.

AIM

The aim of the present study was therefore to quantify the variability in target delineation and discuss the potential clinical implications, for six targets regarded as common in stereotactic radiosurgery.

MATERIAL AND METHODS

Twelve Gamma Knife centers participated in the study by contouring the targets and organs at risks and performing the treatment plans. Analysis of target delineation variability was based on metrics defined based on agreement volumes derived from overlapping structures following a previously developed method. The 50% agreement volume (AV₅₀), the common and the encompassing volumes as well as the Agreement Volume Index (AVI) were determined.

RESULTS

Results showed that the lowest AVI (0.16) was found for one of the analyzed metastases (range of delineated volumes 1.27-3.33 cm³). AVI for the other two metastases was 0.62 and 0.37, respectively. Corresponding AVIs for the cavernous sinus meningioma, pituitary adenoma and vestibular schwannoma were 0.22, 0.37 and 0.50.

CONCLUSIONS

This study showed that the variability in the contouring was much higher than expected and therefore further work in standardizing the contouring practice in radiosurgery is warranted.

Role of amino-acid PET in high-grade gliomas: limitations and perspectives

Positron emission tomography (PET) using radiolabeled amino-acids was recently recommended by the Response Assessment in Neuro-Oncology (RANO) working group as an additional tool in the diagnostic assessment of brain tumors. The aim of this review is to summarize available literature data on the role of amino-acid PET imaging in high-grade gliomas (HGGs), with regard to diagnosis, treatment planning and follow-up of these tumors. Indeed, amino-acid PET applications are multiple throughout the evolution of HGGs. However, certain limitations such as lack of specificity, uncertain value for grading and prognostication or the limited data for treatment monitoring should to be taken into account, the latter of which are further developed in this review. Notwithstanding these limitations, amino-acid PET is becoming increasingly accessible in many nuclear medicine centers. Larger prospective cohort prospective studies are thus needed in order to increase the clinical value of this modality and enable its extended use to the largest number of patients.

Variability of Gross Tumor Volume Delineation for Stereotactic Body Radiotherapy of the Lung With Tri-60Co Magnetic Resonance Image-Guided Radiotherapy System (ViewRay): A Comparative Study With Magnetic Resonance- and Computed Tomography-Based Target Delineation

Introduction:

To evaluate the intra-/interobserver variability of gross target volumes between delineation based on magnetic resonance imaging and computed tomography in patients simulated for stereotactic body radiotherapy for primary lung cancer and lung metastasis.

Materials and Methods:

Twenty-five patients (27 lesions) who underwent computed tomography and magnetic resonance simulation with the MR-⁶⁰Co system (ViewRay) were included in the study. Gross target volumes were delineated on the magnetic resonance imaging (GTV_MR) and computed tomography (GTV_CT) images by 2 radiation oncologists (RO1 and RO2). Volumes of all contours were measured. Levels of intraobserver (GTV_MR_RO vs GTV_CT_RO) and interobserver (GTV_MR_RO1 vs GTV_MR_RO2; GTV_CT_RO1 vs GTV_CT_RO2) agreement were evaluated using the generalized κ statistics and the paired t test.

Results:

No significant volumetric difference was observed between all 4 comparisons (GTV_MR_RO1 vs GTV_CT_RO1, GTV_MR_RO2 vs GTV_CT_RO2, GTV_MR_RO1 vs GTV_MR_RO2, and GTV_CT_RO1 vs GTV_CT_RO2; P > .05), with mean volumes of GTVs ranging 5 to 6 cm³. The levels of agreement between those 4 comparisons were all substantial with mean κ values of 0.64, 0.66, 0.74, and 0.63, respectively. However, the interobserver agreement level was significantly higher for GTV_CT compared to GTV_MR (P <.001). The mean κ values significantly increased in all 4 comparisons for tumors >5 cm³ compared to tumors ≤5 cm³ (all P < .05).

Conclusion:

No significant differences in volumes between magnetic resonance- and computed tomograpghy-based Gross target volumes were found among 2 ROs. Magnetic resonance-based GTV delineation for lung stereotactic body radiotherapy also demonstrated acceptable interobserver agreement. Tumors >5 cm³ show higher intra-/interobserver agreement compared to tumors <5 cm³. More experience should be accumulated to reduce variability in magnetic resonance-based Gross target volumes delineation in lung stereotactic body radiotherapy.

A proliferation saturation index to predict radiation response and personalize radiotherapy fractionation

BACKGROUND

Although altered protocols that challenge conventional radiation fractionation have been tested in prospective clinical trials, we still have limited understanding of how to select the most appropriate fractionation schedule for individual patients. Currently, the prescription of definitive radiotherapy is based on the primary site and stage, without regard to patient-specific tumor or host factors that may influence outcome. We hypothesize that the proportion of radiosensitive proliferating cells is dependent on the saturation of the tumor carrying capacity. This may serve as a prognostic factor for personalized radiotherapy (RT) fractionation.

METHODS

We introduce a proliferation saturation index (PSI), which is defined as the ratio of tumor volume to the host-influenced tumor carrying capacity. Carrying capacity is as a conceptual measure of the maximum volume that can be supported by the current tumor environment including oxygen and nutrient availability, immune surveillance and acidity. PSI is estimated from two temporally separated routine pre-radiotherapy computed tomography scans and a deterministic logistic tumor growth model. We introduce the patient-specific pre-treatment PSI into a model of tumor growth and radiotherapy response, and fit the model to retrospective data of four non-small cell lung cancer patients treated exclusively with standard fractionation. We then simulate both a clinical trial hyperfractionation protocol and daily fractionations, with equal biologically effective dose, to compare tumor volume reduction as a function of pretreatment PSI.

RESULTS

With tumor doubling time and radiosensitivity assumed constant across patients, a patient-specific pretreatment PSI is sufficient to fit individual patient response data (R(2) = 0.98). PSI varies greatly between patients (coefficient of variation >128 %) and correlates inversely with radiotherapy response. For this study, our simulations suggest that only patients with intermediate PSI (0.45-0.9) are likely to truly benefit from hyperfractionation. For up to 20 % uncertainties in tumor growth rate, radiosensitivity, and noise in radiological data, the absolute estimation error of pretreatment PSI is <10 % for more than 75 % of patients.

CONCLUSIONS

Routine radiological images can be used to calculate individual PSI, which may serve as a prognostic factor for radiation response. This provides a new paradigm and rationale to select personalized RT dose-fractionation.