Browsing by Author "El Naqa, Issam"

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    Modeling Patient-Specific Dose-Function Response for Enhanced Characterization of Personalized Functional Damage
    (Elsevier, 2018-11-15) Rocky Owen, Daniel; Boonstra, Phillip S.; Viglianti, Benjamin L.; Balter, James M.; Schipper, Matthew J.; Jackson, William C.; El Naqa, Issam; Jolly, Shruti; Ten Haken, Randall K.; Spring Kong, Feng-Ming; Matuszak, Martha M.; Radiation Oncology, School of Medicine
    PURPOSE: Functional-guided radiation therapy (RT) plans have the potential to limit damage to normal tissue and reduce toxicity. Although functional imaging modalities have continued to improve, a limited understanding of the functional response to radiation and its application to personalized therapy has hindered clinical implementation. The purpose of this study was to retrospectively model the longitudinal, patient-specific dose-function response in non-small cell lung cancer patients treated with RT to better characterize the expected functional damage in future, unknown patients. METHODS AND MATERIALS: Perfusion single-photon emission computed tomography/computed tomography scans were obtained at baseline (n = 81), midtreatment (n = 74), 3 months post-treatment (n = 51), and 1 year post-treatment (n = 26) and retrospectively analyzed. Patients were treated with conventionally fractionated RT or stereotactic body RT. Normalized perfusion single-photon emission computed tomography voxel intensity was used as a surrogate for local lung function. A patient-specific logistic model was applied to each individual patient's dose-function response to characterize functional reduction at each imaging time point. Patient-specific model parameters were averaged to create a population-level logistic dose-response model. RESULTS: A significant longitudinal decrease in lung function was observed after RT by analyzing the voxelwise change in normalized perfusion intensity. Generated dose-function response models represent the expected voxelwise reduction in function, and the associated uncertainty, for an unknown patient receiving conventionally fractionated RT or stereotactic body RT. Differential treatment responses based on the functional status of the voxel at baseline suggest that initially higher functioning voxels are damaged at a higher rate than lower functioning voxels. CONCLUSIONS: This study modeled the patient-specific dose-function response in patients with non-small cell lung cancer during and after radiation treatment. The generated population-level dose-function response models were derived from individual patient assessment and have the potential to inform functional-guided treatment plans regarding the expected functional lung damage. This type of patient-specific modeling approach can be applied broadly to other functional response analyses to better capture intrapatient dependencies and characterize personalized functional damage.
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    A Multi-Objective Bayesian Networks Approach for Joint Prediction of Tumor Local Control and Radiation Pneumonitis in Non-Small-Cell Lung Cancer (NSCLC) for Response-Adapted Radiotherapy
    (Wiley, 2018) Luo, Yi; McShan, Daniel L.; Matuszak, Martha M.; Ray, Dipankar; Lawrence, Thodore S.; Jolly, Shruti; Kong, Feng-Ming; Ten Haken, Randall K.; El Naqa, Issam; Radiation Oncology, School of Medicine
    Purpose Individualization of therapeutic outcomes in NSCLC radiotherapy is likely to be compromised by the lack of proper balance of biophysical factors affecting both tumor local control (LC) and side effects such as radiation pneumonitis (RP), which are likely to be intertwined. Here, we compare the performance of separate and joint outcomes predictions for response‐adapted personalized treatment planning. Methods A total of 118 NSCLC patients treated on prospective protocols with 32 cases of local progression and 20 cases of RP grade 2 or higher (RP2) were studied. Sixty‐eight patients with 297 features before and during radiotherapy were used for discovery and 50 patients were reserved for independent testing. A multiobjective Bayesian network (MO‐BN) approach was developed to identify important features for joint LC/RP2 prediction using extended Markov blankets as inputs to develop a BN predictive structure. Cross‐validation (CV) was used to guide the MO‐BN structure learning. Area under the free‐response receiver operating characteristic (AU‐FROC) curve was used to evaluate joint prediction performance. Results Important features including single nucleotide polymorphisms (SNPs), micro RNAs, pretreatment cytokines, pretreatment PET radiomics together with lung and tumor gEUDs were selected and their biophysical inter‐relationships with radiation outcomes (LC and RP2) were identified in a pretreatment MO‐BN. The joint LC/RP2 prediction yielded an AU‐FROC of 0.80 (95% CI: 0.70–0.86) upon internal CV. This improved to 0.85 (0.75–0.91) with additional two SNPs, changes in one cytokine and two radiomics PET image features through the course of radiotherapy in a during‐treatment MO‐BN. This MO‐BN model outperformed combined single‐objective Bayesian networks (SO‐BNs) during‐treatment [0.78 (0.67–0.84)]. AU‐FROC values in the evaluation of the MO‐BN and individual SO‐BNs on the testing dataset were 0.77 and 0.68 for pretreatment, and 0.79 and 0.71 for during‐treatment, respectively. Conclusions MO‐BNs can reveal possible biophysical cross‐talks between competing radiotherapy clinical endpoints. The prediction is improved by providing additional during‐treatment information. The developed MO‐BNs can be an important component of decision support systems for personalized response‐adapted radiotherapy.