A Apte

Exploring feature-based approaches in PET images for predicting cancer treatment outcomes

Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patients response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

Elective clinical target volumes for conformal therapy in anorectal cancer: a radiation therapy oncology group consensus panel contouring atlas.

PURPOSE To develop a Radiation Therapy Oncology Group (RTOG) atlas of the elective clinical target volume (CTV) definitions to be used for planning pelvic intensity-modulated radiotherapy (IMRT) for anal and rectal cancers. METHODS AND MATERIALS The Gastrointestinal Committee of the RTOG established a task group (the nine physician co-authors) to develop this atlas. They responded to a questionnaire concerning three elective CTVs (CTVA: internal iliac, presacral, and perirectal nodal regions for both anal and rectal case planning; CTVB: external iliac nodal region for anal case planning and for selected rectal cases; CTVC: inguinal nodal region for anal case planning and for select rectal cases), and to outline these areas on individual computed tomographic images. The imaging files were shared via the Advanced Technology Consortium. A program developed by one of the co-authors (I.E.N.) used binomial maximum-likelihood estimates to generate a 95% group consensus contour. The computer-estimated consensus contours were then reviewed by the group and modified to provide a final contouring consensus atlas. RESULTS The panel achieved consensus CTV definitions to be used as guidelines for the adjuvant therapy of rectal cancer and definitive therapy for anal cancer. The most important difference from similar atlases for gynecologic or genitourinary cancer is mesorectal coverage. Detailed target volume contouring guidelines and images are discussed. CONCLUSION This report serves as a template for the definition of the elective CTVs to be used in IMRT planning for anal and rectal cancers, as part of prospective RTOG trials.

Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning

Multimodality imaging information is regularly used now in radiotherapy treatment planning for cancer patients. The authors are investigating methods to take advantage of all the imaging information available for joint target registration and segmentation, including multimodality images or multiple image sets from the same modality. In particular, the authors have developed variational methods based on multivalued level set deformable models for simultaneous 2D or 3D segmentation of multimodality images consisting of combinations of coregistered PET, CT, or MR data sets. The combined information is integrated to define the overall biophysical structure volume. The authors demonstrate the methods on three patient data sets, including a nonsmall cell lung cancer case with PET/CT, a cervix cancer case with PET/CT, and a prostate patient case with CT and MRI. CT, PET, and MR phantom data were also used for quantitative validation of the proposed multimodality segmentation approach. The corresponding Dice similarity coefficient (DSC) was 0.90±0.02(p<0.0001) with an estimated target volume error of 1.28±1.23% volume. Preliminary results indicate that concurrent multimodality segmentation methods can provide a feasible and accurate framework for combining imaging data from different modalities and are potentially useful tools for the delineation of biophysical structure volumes in radiotherapy treatment planning.

Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose–volume outcome relationships

Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another open-source tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose-volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearmans rank correlation and chi-square statistics, boxplots, nomograms, Kaplan-Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback.

Normal Tissue Complication Probability (NTCP) modeling of late rectal bleeding following external beam radiotherapy for prostate cancer: A Test of the QUANTEC-recommended NTCP model

Abstract Purpose/background. Validating a predictive model for late rectal bleeding following external beam treatment for prostate cancer would enable safer treatments or dose escalation. We tested the normal tissue complication probability (NTCP) model recommended in the recent QUANTEC review (quantitative analysis of normal tissue effects in the clinic). Material and methods. One hundred and sixty one prostate cancer patients were treated with 3D conformal radiotherapy for prostate cancer at the British Columbia Cancer Agency in a prospective protocol. The total prescription dose for all patients was 74 Gy, delivered in 2 Gy/fraction. 159 3D treatment planning datasets were available for analysis. Rectal dose volume histograms were extracted and fitted to a Lyman-Kutcher-Burman NTCP model. Results. Late rectal bleeding (>grade 2) was observed in 12/159 patients (7.5%). Multivariate logistic regression with dose-volume parameters (V50, V60, V70, etc.) was non-significant. Among clinical variables, only age was significant on a Kaplan-Meier log-rank test (p=0.007, with an optimal cut point of 77 years). Best-fit Lyman-Kutcher-Burman model parameters (with 95% confidence intervals) were: n = 0.068 (0.01, +infinity); m =0.14 (0.0, 0.86); and TD50 = 81 (27, 136) Gy. The peak values fall within the 95% QUANTEC confidence intervals. On this dataset, both models had only modest ability to predict complications: the best-fit model had a Spearmans rank correlation coefficient of rs = 0.099 (p = 0.11) and area under the receiver operating characteristic curve (AUC) of 0.62; the QUANTEC model had rs=0.096 (p= 0.11) and a corresponding AUC of 0.61. Although the QUANTEC model consistently predicted higher NTCP values, it could not be rejected according to the χ2 test (p = 0.44). Conclusions. Observed complications, and best-fit parameter estimates, were consistent with the QUANTEC-preferred NTCP model. However, predictive power was low, at least partly because the rectal dose distribution characteristics do not vary greatly within this patient cohort.

Datamining approaches for modeling tumor control probability

Abstract Background. Tumor control probability (TCP) to radiotherapy is determined by complex interactions between tumor biology, tumor microenvironment, radiation dosimetry, and patient-related variables. The complexity of these heterogeneous variable interactions constitutes a challenge for building predictive models for routine clinical practice. We describe a datamining framework that can unravel the higher order relationships among dosimetric dose-volume prognostic variables, interrogate various radiobiological processes, and generalize to unseen data before when applied prospectively. Material and methods. Several datamining approaches are discussed that include dose-volume metrics, equivalent uniform dose, mechanistic Poisson model, and model building methods using statistical regression and machine learning techniques. Institutional datasets of non-small cell lung cancer (NSCLC) patients are used to demonstrate these methods. The performance of the different methods was evaluated using bivariate Spearman rank correlations (rs). Over-fitting was controlled via resampling methods. Results. Using a dataset of 56 patients with primary NCSLC tumors and 23 candidate variables, we estimated GTV volume and V75 to be the best model parameters for predicting TCP using statistical resampling and a logistic model. Using these variables, the support vector machine (SVM) kernel method provided superior performance for TCP prediction with an rs=0.68 on leave-one-out testing compared to logistic regression (rs=0.4), Poisson-based TCP (rs=0.33), and cell kill equivalent uniform dose model (rs=0.17). Conclusions. The prediction of treatment response can be improved by utilizing datamining approaches, which are able to unravel important non-linear complex interactions among model variables and have the capacity to predict on unseen data for prospective clinical applications.

Predicting Response in Lung Cancer from FDG-PET Uptake Characteristics

It was recognized recently that uptake characteristics of PET images could be used for planning and adapting radiotherapy treatment based on predicted outcome risks of individual patients. In this work, we are investigating PET uptake features as prognostic factors for patients with nonsmall cell lung cancer (NSCLC). Methods based on intensity-volume histogram (IVH) and extracted morphological features from regions of interest (ROI) such as shape deformations and texture heterogeneity are evaluated. Seventeen NSCLC patients who received 3D-CRT are analyzed. Sixteen patients with pre-radiotherapy FDG-PET were analyzed for local failure using the gross tumor volume (GTV) as ROI. Nine patients with post-radiotherapy FDG-PET were analyzed for pneumonitis. The lung minus GTV was selected as the ROI in this case. About thirty candidate variables were extracted from each case, which included: ROI volume, SUV descriptors, total lesion glycolosis (TLG), IVH variables, and local texture variability metrics. Model building approaches based on logistic regression and machine learning were evaluated and corresponding Spearman’s rank correlation (rs) was reported. Our preliminary results for local failure indicate that GTV and TLG had the highest correlation (rs= 0.476, p=0.031) while meanSUV, maxSUV, and local texture homogeneity showed modest association. A combined logistic model of TLG and V90 yielded rs=0.616 (p=0.009). This is slightly improved using a quadratic kernel (rs=0.644, p=0.006). In pneumonitis analysis, local contrast and homogeneity had the highest correlation with rs=0.725 and rs=-0.725 (p=0.014). These results imply intensity-volume effect in predicting local failure and significant local heterogeneity association in predicting onset of pneumonitis.

WE‐E‐AUD C‐07: A Robust Approach for Estimating Tumor Volume Change During Radiotherapy of Lung Cancer

Purpose:Radiotherapytreatment of lungcancer patients is complicated by changes in tumor position due to breathing and changes in size due to regression. Accurate quantification of these changes during the course of treatment would likely improve tumor response and reduce toxicity risks. We are investigating robust methods for tracking and estimating tumor volume changes between treatment fractions. Method and Materials: We have developed a registration—assisted segmentation approach based on the level‐set deformable model, in which pre‐treatment contours are propagated and adapted to fractions times at selected respiratory phases. At any time‐point during treatment, a reference respiratory phase is selected and corresponding 3D‐CT volumes are reconstructed from 4D‐CT acquisition data. Images are then globally aligned using an efficient registration algorithm. Pre‐treatment planning contours are copied to selected time‐points. In our tumor regression analysis, the GTV contour was used to initialize the level set algorithm in‐place and the PTV contour was used to narrowband the region, thus improving the algorithms convergence. The feasibility of the proposed approach was investigated on a set of patients with repeated 4D scans acquired at three time‐intervals. Results: Our preliminary analysis indicates that the proposed approach can properly capture the boundary of the shrinking tumor region or split regions due to its topological adaptation ability. On an initial cohort of four NSCLC patients, the estimated tumor volume reductions ranged between 3–46% with a median of 8% by mid‐treatment and between 26–51% with a median of 34% by the end of treatment.Conclusion: We have demonstrated a new approach for tracking tumor regression during the course of radiotherapytreatment of NSCLC patients based on a novel level‐set segmentation algorithm. This approach provides us with a semi‐automated tool for quantifying tumor shrinkage and allows accurate estimates of ‘true’ accumulated dose to the tumor. Supported by CA85181 grant.

SU‐E‐T‐620: Computational Boundary Sampling to Accelerate IMRT Optimization

PURPOSE To reduce the time and memory requirements of Intensity Modulated Radiation Therapy (IMRT) treatment planning. METHODS We propose a new sampling method, called Computational Boundary Sampling (CBS) for IMRT optimization, which samples all the boundary voxels and a certain percentage of inner voxels of each region of interest (ROI). Within CBS, we developed a grid-based sampling method for choosing inner voxels. In this method, each region is first evenly gridded and then sampling points are randomly selected from each sub-volume. We also developed a supporting theory to quantify the solution quality of CBS. We compared a variant of CBS that always keeps boundary voxels and a variant of CBS that does not. Finally, we quantified the impact of CBS on 10 different anonymized, clinical treatment cases using a prioritized prescription optimization method, including compute time, required memory and objective function values. RESULTS (1) We have found that the D95 of the targets are generally 4% larger when boundary voxels are included. (2) Grid sampling, compared to completely random sampling, yields more uniformly distributed sampling, with better solution quality, and less variance between independent runs, using the same or less time. (3) We have compared our original IMRT optimization solver without sampling and the solver combined with CBS sampling. The result showed that CBS can reduce the solution time and memory consumption by up to 20x with < 2% change in dosimetric variables. CONCLUSIONS We have proposed a new sampling method (CBS), along with corresponding new techniques including boundary sampling and grid sampling, to improve time and space efficiency of IMRT optimization. A corresponding theory is developed to quantify the error bound. Experimental results have shown that our new methods significantly reduce solution time and memory costs with negligible impact on resulting plan quality.

SU‐GG‐J‐114: A Graphical Tool for Assessing Margin Definition from Daily Deformations

Purpose: Estimating the proper margins for the planned target volume (PTV) could be a challenging task in cases where the organ undergoes significant changes during the course of radiotherapy treatment. This is practically the case in stomach lymphoma, where the stomach can change significantly from day to day. A common practice is to add a constant l–2cm margin isotropically around the tumor volume based on planning CT scan. This might lead to some portions of stomach being under dosed or surrounding normal structures over‐dosed. The purpose of this work is to develop a tool that utilizes information from daily images to aid localization of these deformations and guide estimations of proper margins. Method and Materials: In this work, the stomach volume for each treatment fraction was delineated manually over the course of 6 weeks of fractionated treatment using daily Tomotherapy Mega voltage CT (MVCT) images from three patients. A software tool was developed to aid tracking variations in shape during therapy and record estimates of the probability by which an organ spends at any particular place. Accordingly, plots of estimated tumor coverage and excess irradiated normal tissue volumes as a function of margin could be generated.Results: Estimated stomach volumes for these cases were 429.2±67.7, 383.0±66.6, and 294.1±27.7 cc. Plots of tumor coverage and excess irradiated volume were created for each case. It was observed that with a margin of 1.5 cm over the original stomach, the entire union volume was covered. This margin resulted in about 500–800 cc of excess volume being irradiated for the three test cases. Conclusions: This work presented a new tool for visualization and quantification of daily deformations for isotropic and anisotropic margin definition guidance.