Browsing by Author "Jolly, Shruti"
Now showing 1 - 10 of 12
Results Per Page
Sort Options
Item Central Airway Toxicity After High Dose Radiation: A Combined Analysis of Prospective Clinical Trials for Non-Small Cell Lung Cancer(Elsevier, 2020) Wang, Weili; Matuszak, Martha M.; Hu, Chen; Huang, Ke Colin; Chen, Eileen; Arenberg, Douglas; Curtis, Jeffrey L.; Jolly, Shruti; Jin, Jian-Yue; Machtay, Mitchell; Haken, Randall K.; Kong, Feng-Ming (Spring); Radiation Oncology, School of MedicinePurpose To study the dosimetric risk factors for radiation-induced proximal bronchial tree (PBT) toxicity in patients treated with radiation therapy for non-small cell lung cancer (NSCLC). Methods and Materials Patients with medically inoperable or unresectable NSCLC treated with conventionally fractionated 3-dimensional conformal radiation therapy (3DCRT) in prospective clinical trials were eligible for this study. Proximal bronchial tree (PBT) and PBT wall were contoured consistently per RTOG 1106 OAR-Atlas. The dose-volume histograms (DVHs) of physical prescription dose (DVHp) and biological effective dose (α/β = 2.5; DVH2.5) were generated, respectively. The primary endpoint was PBT toxicities, defined by CTCAE 4.0 under the terminology of bronchial stricture/atelectasis. Results Of 100 patients enrolled, with a median follow-up of 64 months (95% confidence interval [CI], 50-78), 73% received 70 Gy or greater and 17% developed PBT toxicity (grade 1, 8%; grade 2, 6%; grade 3, 0%; and grade 4, 3%). The median time interval between RT initiation and onset of PBT toxicity was 8.4 months (95% CI, 4.7-44.1). The combined DVHs showed that no patient with a PBT maximum physical dose <65 Gy developed any PBT toxicity. Cox proportional hazards analysis and receiver operating characteristic analysis demonstrated that V75 of PBT was the most significant dosimetric parameter for both grade 1+ (P = .035) and grade 2+ (P = .037) PBT toxicities. The dosimetric thresholds for V75 of PBT were 6.8% and 11.9% for grade 1+ and grade 2+ PBT toxicity, respectively. Conclusions V75 of PBT appeared be the most significant dosimetric parameter for PBT toxicity after conventionally fractionated thoracic 3DCRT. Constraining V75 of PBT can limit clinically significant PBT toxicity.Item Genetic Variations in the Transforming Growth Factor-β1 Pathway May Improve Predictive Power for Overall Survival in Non-small Cell Lung Cancer(Frontiers Media, 2021-07-07) Zhang, Hong; Wang, Weili; Pi, Wenhu; Bi, Nan; DesRosiers, Colleen; Kong, Fengchong; Cheng, Monica; Monica, Li; Yang, Li; Lautenschlaeger, Tim; Jolly, Shruti; Jin, Jianyue; Kong, Feng-Ming (Spring); Radiation Oncology, School of MedicinePurpose: Transforming growth factor-β1 (TGF-β1), a known immune suppressor, plays an important role in tumor progression and overall survival (OS) in many types of cancers. We hypothesized that genetic variations of single nucleotide polymorphisms (SNPs) in the TGF-β1 pathway can predict survival in patients with non-small cell lung cancer (NSCLC) after radiation therapy. Materials and Methods: Fourteen functional SNPs in the TGF-β1 pathway were measured in 166 patients with NSCLC enrolled in a multi-center clinical trial. Clinical factors, including age, gender, ethnicity, smoking status, stage group, histology, Karnofsky Performance Status, equivalent dose at 2 Gy fractions (EQD2), and the use of chemotherapy, were first tested under the univariate Cox's proportional hazards model. All significant clinical predictors were combined as a group of predictors named "Clinical." The significant SNPs under the Cox proportional hazards model were combined as a group of predictors named "SNP." The predictive powers of models using Clinical and Clinical + SNP were compared with the cross-validation concordance index (C-index) of random forest models. Results: Age, gender, stage group, smoking, histology, and EQD2 were identified as significant clinical predictors: Clinical. Among 14 SNPs, BMP2:rs235756 (HR = 0.63; 95% CI:0.42-0.93; p = 0.022), SMAD9:rs7333607 (HR = 2.79; 95% CI 1.22-6.41; p = 0.015), SMAD3:rs12102171 (HR = 0.68; 95% CI: 0.46-1.00; p = 0.050), and SMAD4: rs12456284 (HR = 0.63; 95% CI: 0.43-0.92; p = 0.016) were identified as powerful predictors of SNP. After adding SNP, the C-index of the model increased from 84.1 to 87.6% at 24 months and from 79.4 to 84.4% at 36 months. Conclusion: Genetic variations in the TGF-β1 pathway have the potential to improve the prediction accuracy for OS in patients with NSCLC.Item IDO Immune Status after Chemoradiation May Predict Survival in Lung Cancer Patients(American Association for Cancer Research, 2018-02-01) Wang, Weili; Huang, Lei; Jin, Jian-Yue; Jolly, Shruti; Zang, Yong; Wu, Huanmei; Yan, Li; Pi, Wenhu; Li, Lang; Mellor, Andrew L.; Kong, Feng-Ming Spring; Radiation Oncology, School of MedicineHost immunity influences the impact of radiotherapy (RT) in cancer, but mechanistic connections remain obscure. In this study, we investigated the relationship of indoleamine 2,3-dioxygenase (IDO) systemic activity on clinical outcomes in RT-treated non-small cell lung cancer (NSCLC). IDO-mediated production of kynurenine and the kynurenine:tryptophan ratio in patient blood serum were determined for stage III NSCLC patients at times before, during, and after RT administration and then correlated to overall survival (OS), progression-free survival, and disease progression rate in patients. We found the impact of RT on these serum IDO markers to be heterogeneous in patients. On average, kynurenine:tryptophan ratios were reduced during RT but restored after RT. Notably, both baseline levels of kynurenine:tryptophan and changes in the levels of kynurenine after RT were significantly associated with OS. When combined, favorable change and favorable baseline corresponded with very long-term OS (median OS was not reached after 57 months of median follow-up). Favorable change combined with unfavorable baseline still corresponded with a lack of distant metastases. Our results suggest that RT alters IDO-mediated immune status in NSCLC patients and that changes in this serum biomarker may be useful to predict outcomes and perhaps personalize RT dosage to improve survival.Significance: Radiotherapy appears to influence systemic IDO activity and to exert a significant impact on metastatic risk and overall survival, with possible implications for defining a biomarker to optimize radiation dose in patients to improve outcomes. Cancer Res; 78(3); 809-16. ©2017 AACR.Item Machine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non–Small Cell Lung Cancer(AACR, 2019-07) Yu, Hao; Wu, Huanmei; Wang, Weili; Jolly, Shruti; Jin, Jianyue; Hu, Chen; Kong, Feng-Ming (Spring); BioHealth Informatics, School of Informatics and ComputingPurpose: Radiation pneumonitis is an important adverse event in patients with non–small cell lung cancer (NSCLC) receiving thoracic radiotherapy. However, the risk of radiation pneumonitis grade ≥ 2 (RP2) has not been well predicted. This study hypothesized that inflammatory cytokines or the dynamic changes during radiotherapy can improve predictive accuracy for RP2. Experimental Design: Levels of 30 inflammatory cytokines and clinical information in patients with stages I–III NSCLC treated with radiotherapy were from our prospective studies. Statistical analysis was used to select predictive cytokine candidates and clinical covariates for adjustment. Machine learning algorithm was used to develop the generalized linear model for predicting risk RP2. Results: A total of 131 patients were eligible and 17 (13.0%) developed RP2. IL8 and CCL2 had significantly (Bonferroni) lower expression levels in patients with RP2 than without RP2. But none of the changes in cytokine levels during radiotherapy was significantly associated with RP2. The final predictive GLM model for RP2 was established, including IL8 and CCL2 at baseline level and two clinical variables. Nomogram was constructed based on the GLM model. The model's predicting ability was validated in the completely independent test set (AUC = 0.863, accuracy = 80.0%, sensitivity = 100%, specificity = 76.5%). Conclusions: By machine learning, this study has developed and validated a comprehensive model integrating inflammatory cytokines with clinical variables to predict RP2 before radiotherapy that provides an opportunity to guide clinicians.Item 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 MedicinePURPOSE: 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.Item Modern Radiation Further Improves Survival in Non-Small Cell Lung Cancer: An Analysis of 288,670 Patients(Ivyspring, 2019-01-01) Cheng, Monica; Jolly, Shruti; Quarshie, William O.; Kapadia, Nirav; Vigneau, Fawn D.; Kong, Feng-Ming Spring; Radiation Oncology, School of MedicineBackground: Radiation therapy plays an increasingly important role in the treatment of patients with non-small-cell lung cancer (NSCLC). The purpose of the present study is to assess the survival outcomes of radiotherapy treatment compared to other treatment modalities and to determine the potential role of advanced technologies in radiotherapy on improving survival. Methods: We used cancer incidence and survival data from the Surveillance, Epidemiology, and End Results database linked to U.S. Census data to compare survival outcomes of 288,670 patients with stage I-IV NSCLC treated between 1999 and 2008. The primary endpoint was overall survival. Results: Among the 288,670 patients diagnosed with stage I-IV NSCLC, 92,374 (32%) patients received radiotherapy-almost double the number receiving surgery (51,961, 18%). Compared to other treatment groups and across all stages of NSCLC, patients treated with radiotherapy showed greater median and overall survival than patients without radiation treatment (p < 0.0001). Radiotherapy had effectively improved overall survival regardless of age, gender, and histological categorization. Radiotherapy treatment received during the recent time period 2004 - 2008 is correlated with enhanced survival compared to the earlier time period 1999 - 2003. Conclusion: Radiation therapy was correlated with increased overall survival for all patients with primary NSCLC across stages. Combined surgery and radiotherapy treatment also correlates with improved survival, signaling the value of bimodal or multimodal treatments. Population-based increases in overall survival were seen in the recent time period, suggesting the potential role of advanced radiotherapeutic technologies in enhancing survival outcomes for lung cancer patients.Item 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 MedicinePurpose 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.Item Prediction of Radiation Esophagitis in Non-Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels(Elsevier, 2018-02) Hawkins, Peter G.; Boonstra, Philip S.; Hobson, Stephen T.; Hayman, James A.; Ten Haken, Randall K.; Matuszak, Martha M.; Stanton, Paul; Kalemkerian, Gregory P.; Lawrence, Theodore S.; Schipper, Matthew J.; Kong, Feng-Ming (Spring); Jolly, Shruti; Radiation Oncology, School of MedicineRadiation esophagitis (RE) is a common adverse event associated with radiotherapy for non-small cell lung cancer (NSCLC). While plasma cytokine levels have been correlated with other forms of radiation-induced toxicity, their association with RE has been less well studied. We analyzed data from 126 patients treated on 4 prospective clinical trials. Logistic regression models based on combinations of dosimetric factors [maximum dose to 2 cubic cm (D2cc) and generalized equivalent uniform dose (gEUD)], clinical variables, and pretreatment plasma levels of 30 cytokines were developed. Cross-validated estimates of area under the receiver operating characteristic curve (AUC) and log likelihood were used to assess prediction accuracy. Dose-only models predicted grade 3 RE with AUC values of 0.750 (D2cc) and 0.727 (gEUD). Combining clinical factors with D2cc increased the AUC to 0.779. Incorporating pretreatment cytokine measurements, modeled as direct associations with RE and as potential interactions with the dose-esophagitis association, produced AUC values of 0.758 and 0.773, respectively. D2cc and gEUD correlated with grade 3 RE with odds ratios (ORs) of 1.094/Gy and 1.096/Gy, respectively. Female gender was associated with a higher risk of RE, with ORs of 1.09 and 1.112 in the D2cc and gEUD models, respectively. Older age was associated with decreased risk of RE, with ORs of 0.992/year and 0.991/year in the D2cc and gEUD models, respectively. Combining clinical with dosimetric factors but not pretreatment cytokine levels yielded improved prediction of grade 3 RE compared to prediction by dose alone. Such multifactorial modeling may prove useful in directing radiation treatment planning.Item Principal component analysis identifies patterns of cytokine expression in non-small cell lung cancer patients undergoing definitive radiation therapy(PLOS, 2017-09-21) Ellsworth, Susannah G.; Rabatic, Bryan M.; Chen, Jie; Zhao, Jing; Campbell, Jeffrey; Wang, Weili; Pi, Wenhu; Stanton, Paul; Matuszak, Martha; Jolly, Shruti; Miller, Amy; Kong, Feng-Ming; Radiation Oncology, School of MedicineBackground/Purpose Radiation treatment (RT) stimulates the release of many immunohumoral factors, complicating the identification of clinically significant cytokine expression patterns. This study used principal component analysis (PCA) to analyze cytokines in non-small cell lung cancer (NSCLC) patients undergoing RT and explore differences in changes after hypofractionated stereotactic body radiation therapy (SBRT) and conventionally fractionated RT (CFRT) without or with chemotherapy. Methods The dataset included 141 NSCLC patients treated on prospective clinical protocols; PCA was based on the 128 patients who had complete CK values at baseline and during treatment. Patients underwent SBRT (n = 16), CFRT (n = 18), or CFRT (n = 107) with concurrent chemotherapy (ChRT). Levels of 30 cytokines were measured from prospectively collected platelet-poor plasma samples at baseline, during RT, and after RT. PCA was used to study variations in cytokine levels in patients at each time point. Results Median patient age was 66, and 22.7% of patients were female. PCA showed that sCD40l, fractalkine/C3, IP10, VEGF, IL-1a, IL-10, and GMCSF were responsible for most variability in baseline cytokine levels. During treatment, sCD40l, IP10, MIP-1b, fractalkine, IFN-r, and VEGF accounted for most changes in cytokine levels. In SBRT patients, the most important players were sCD40l, IP10, and MIP-1b, whereas fractalkine exhibited greater variability in CFRT alone patients. ChRT patients exhibited variability in IFN-γ and VEGF in addition to IP10, MIP-1b, and sCD40l. Conclusions PCA can identify potentially significant patterns of cytokine expression after fractionated RT. Our PCA showed that inflammatory cytokines dominate post-treatment cytokine profiles, and the changes differ after SBRT versus CFRT, with vs without chemotherapy. Further studies are planned to validate these findings and determine the clinical significance of the cytokine profiles identified by PCA.Item Radiation-induced lung toxicity in non-small-cell lung cancer: Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship(Elsevier, 2017-10) Hawkins, Peter G.; Boonstra, Philip S.; Hobson, Stephen; Hearn, Jason W.D.; Hayman, James A.; Haken, Randall K. Ten; Matuszak, Martha M.; Stanton, Paul; Kalemkerian, Gregory P.; Ramnath, Nithya; Lawrence, Theodore S.; Schipper, Matthew J.; Kong, Feng-Ming (Spring); Jolly, Shruti; Radiation Oncology, School of MedicineBACKGROUND AND PURPOSE: Current methods to estimate risk of radiation-induced lung toxicity (RILT) rely on dosimetric parameters. We aimed to improve prognostication by incorporating clinical and cytokine data, and to investigate how these factors may interact with the effect of mean lung dose (MLD) on RILT. MATERIALS AND METHODS: Data from 125 patients treated from 2004 to 2013 with definitive radiotherapy for stages I-III NSCLC on four prospective clinical trials were analyzed. Plasma levels of 30 cytokines were measured pretreatment, and at 2 and 4weeks midtreatment. Penalized logistic regression models based on combinations of MLD, clinical factors, and cytokine levels were developed. Cross-validated estimates of log-likelihood and area under the receiver operating characteristic curve (AUC) were used to assess accuracy. RESULTS: In prognosticating grade 3 or greater RILT by MLD alone, cross-validated log-likelihood and AUC were -28.2 and 0.637, respectively. Incorporating clinical features and baseline cytokine levels increased log-likelihood to -27.6 and AUC to 0.669. Midtreatment cytokine data did not further increase log-likelihood or AUC. Of the 30 cytokines measured, higher levels of 13 decreased the effect of MLD on RILT, corresponding to a lower odds ratio for RILT per Gy MLD, while higher levels of 4 increased the association. CONCLUSIONS: Although the added prognostic benefit from cytokine data in our model was modest, understanding how clinical and biologic factors interact with the MLD-RILT relationship represents a novel framework for understanding and investigating the multiple factors contributing to radiation-induced toxicity.