This paper shows how to significantly accelerate cone-beam CT reconstruction and 3D deformable image registration using the stream-processing model. We describe data-parallel designs for the Feldkamp, Davis and Kress (FDK) reconstruction algorithm, and the demons deformable registration algorithm, suitable for use on a commodity graphics processing unit. The streaming versions of these algorithms are implemented using the Brook programming environment and executed on an NVidia 8800 GPU. Performance results using CT data of a preserved swine lung indicate that the GPU-based implementations of the FDK and demons algorithms achieve a substantial speedup--up to 80 times for FDK and 70 times for demons when compared to an optimized reference implementation on a 2.8 GHz Intel processor. In addition, the accuracy of the GPU-based implementations was found to be excellent. Compared with CPU-based implementations, the RMS differences were less than 0.1 Hounsfield unit for reconstruction and less than 0.1 mm for deformable registration.

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Purpose The use of intensity-modulated radiation therapy (IMRT) in the treatment of soft tissue sarcoma (STS) of the extremity is increasing, but no large-scale direct comparison has been reported between conventional external-beam radiation therapy (EBRT) and IMRT. Methods Between January 1996 and December 2010, 319 consecutive adult patients with primary nonmetastatic extremity STS were treated with limb-sparing surgery and adjuvant radiotherapy (RT) at a single institution. Conventional EBRT was used in 154 patients and IMRT in 165 with similar dosing schedules. Median follow-up time for the cohort was 58 months. Results Treatment groups were comparable in terms of tumor location, histology, tumor size, depth, and use of chemotherapy. Patients treated with IMRT were older (P = .08), had more high-grade lesions (P = .05), close (< 1 mm) or positive margins (P = .04), preoperative radiation (P < .001), and nerve manipulation (P = .04). Median follow-up was 90 months for patients treated with conventional EBRT and 42 months for patients treated with IMRT. On multivariable analysis adjusting for patient age and tumor size, IMRT retained significance as an independent predictor of reduced LR (hazard ratio = 0.46; 95% CI, 0.24 to 0.89; P = .02). Conclusion Despite a preponderance of higher-risk features (especially close/positive margin) in the IMRT group, IMRT was associated with significantly reduced local recurrence compared with conventional EBRT for primary STS of the extremity.

Stereotactic body radiation therapy (SBRT) has demonstrated high local control rates in early stage non-small cell lung cancer patients who are not ideal surgical candidates. However, distant failure after SBRT is still common. For patients at high risk of early distant failure after SBRT treatment, additional systemic therapy may reduce the risk of distant relapse and improve overall survival. Therefore, a strategy that can correctly stratify patients at high risk of failure is needed. The field of radiomics holds great potential in predicting treatment outcomes by using high-throughput extraction of quantitative imaging features. The construction of predictive models in radiomics is typically based on a single objective such as overall accuracy or the area under the curve (AUC). However, because of imbalanced positive and negative events in the training datasets, a single objective may not be ideal to guide model construction. To overcome these limitations, we propose a multi-objective radiomics model that simultaneously considers sensitivity and specificity as objective functions. To design a more accurate and reliable model, an iterative multi-objective immune algorithm (IMIA) was proposed to optimize these objective functions. The multi-objective radiomics model is more sensitive than the single-objective model, while maintaining the same levels of specificity and AUC. The IMIA performs better than the traditional immune-inspired multi-objective algorithm.

Purpose To identify an anatomic structure predictive for acute (AUT) and late (LUT) urinary toxicity in patients with prostate cancer treated with low-dose-rate brachytherapy (LDR) with or without external beam radiation therapy (EBRT). Materials and Methods From 7/2002 to 1/2013, 927 patients with prostate cancer (median age, 66) underwent LDR brachytherapy using I-125 (n=753) or Pd-103 (n=174) as definitive treatment (n=478) and as a boost (n=449) followed by supplemental EBRT (median dose, 50.4 Gy). Structures contoured on Day 0 postimplant CT scan included prostate, urethra, bladder, and the bladder neck, defined as 5 mm around the urethra between the catheter balloon and the prostatic urethra. AUT and LUT were assessed using CTCAE v 4. Clinical and dosimetric factors associated with AUT and LUT were analyzed using Cox regression and receiver-operating characteristic analysis to calculate area under the receiver operator curve (ROC) (AUC). Results Grade ≥2 AUT and grade ≥2 LUT occurred in 520 (56%) patients and 154 (20%) patients, respectively. No grade 4 toxicities were observed. Bladder neck D2cc retained significant association with AUT (hazard ratio [HR], 1.03; 95% CI, 1.03–1.04) (P<.0001) and LUT (HR, 1.01; 95% CI, 1.00–1.03 (P=.014) on multivariable analysis. When comparing bladder neck with the standard dosimetric variables using ROC analysis (prostate V100>90%, D90>100%, V150>60%, urethra D20>130%), bladder neck D2cc>50% was shown to have the strongest prognostic power for AUT (AUC, 0.697; P<0.0001) and LUT (AUC, 0.620; P<.001). Conclusions Bladder neck D2cc>50% was the strongest predictor for grade ≥2 AUT and LUT in patients treated with LDR brachytherapy. These data support inclusion of bladder neck constraints into brachytherapy planning to decrease urinary toxicity.

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In this study, we investigate the use of imaging feature-based outcomes research (“radiomics”) combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5-fold cross validation with the area under the receiver operating characteristic curve (AUC)=0.65 (p=0.004), 0.73 (p=0.026), and 0.66 (p=0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC=0.68 (p=0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5-fold cross validation with AUC=0.60 (p=0.092) and 0.65 (p=0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5-fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.

Distant failure is the main cause of human cancer-related mortalities. To develop a model for predicting distant failure in non-small cell lung cancer (NSCLC) and cervix cancer (CC) patients, a shell feature, consisting of outer voxels around the tumor boundary, was constructed using pre-treatment positron emission tomography (PET) images from 48 NSCLC patients received stereotactic body radiation therapy and 52 CC patients underwent external beam radiation therapy and concurrent chemotherapy followed with high-dose-rate intracavitary brachytherapy. The hypothesis behind this feature is that non-invasive and invasive tumors may have different morphologic patterns in the tumor periphery, in turn reflecting the differences in radiological presentations in the PET images. The utility of the shell was evaluated by the support vector machine classifier in comparison with intensity, geometry, gray level co-occurrence matrix-based texture, neighborhood gray tone difference matrix-based texture, and a combination of these four features. The results were assessed in terms of accuracy, sensitivity, specificity, and AUC. Collectively, the shell feature showed better predictive performance than all the other features for distant failure prediction in both NSCLC and CC cohorts.