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Li R, Zhuang T, Montalvo S, Wang K, Parsons D, Zhang Y, Iyengar P, Wang J, Godley A, Cai B, Lin MH, Westover K. Adapt-On-Demand: A Novel Strategy for Personalized Adaptive Radiation Therapy for Locally Advanced Lung Cancer. Pract Radiat Oncol 2024; 14:e395-e406. [PMID: 38579986 DOI: 10.1016/j.prro.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 04/07/2024]
Abstract
PURPOSE Real-time adaptation of thoracic radiation plans is compelling because offline adaptive experiences show that tumor volumes and lung anatomy can change during therapy. We present and analyze a novel adaptive-on-demand (AOD) workflow combining online adaptive radiation therapy (o-ART) on the ETHOS system with image guided radiation therapy delivery on a Halcyon unit for conventional fractionated radiation therapy of locally advanced lung cancer (LALC). METHODS AND MATERIALS We analyzed 26 patients with LALC treated with the AOD workflow, adapting weekly. We timed segments of the workflow to evaluate efficiency in a real-world clinic. Target coverage and organ at risk (OAR) doses were compared between adaptive plans (ADP) and nonadaptive scheduled plans (SCH). Planning robustness was evaluated by the frequency of preplanning goals achieved in ADP plans, stratified by tumor volume change. RESULTS The AOD workflow was achievable within 30 minutes for most radiation fractions. Over the course of therapy, we observed an average 26.6% ± 23.3% reduction in internal target volume (ITV). Despite these changes, with o-ART, ITV and planning target volume (PTV) coverage (V100%) was 99.2% and 93.9% for all members of the cohort, respectively. This represented a 2.9% and 6.8% improvement over nonadaptive plans (P < .05), respectively. For tumors that grew >10%, V100% was 93.1% for o-ART and 76.4% for nonadaptive plans, representing a median 17.2% improvement in the PTV coverage (P < .05). In these plans, critical OAR constraints were met 94.1% of the time, whereas in nonadaptive plans, this figure was 81.9%. This represented reductions of 1.32 Gy, 1.34 Gy, or 1.75 Gy in the heart, esophagus, and lung, respectively. The effect was larger when tumors had shrunk more than 10%. Regardless of tumor volume alterations, the PTV/ITV coverage was achieved for all adaptive plans. Exceptional cases, where dose constraints were not met, were due to large initial tumor volumes or tumor growth. CONCLUSIONS The AOD workflow is efficient and robust in responding to anatomic changes in LALC patients, providing dosimetric advantages over standard therapy. Weekly adaptation was adequate to keep pace with changes. This approach is a feasible alternative to conventional offline replanning workflows for managing anatomy changes in LALC radiation therapy.
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Affiliation(s)
- Ruiqi Li
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas.
| | - Tingliang Zhuang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas.
| | - Steven Montalvo
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Kai Wang
- Department of Radiation Oncology, University of Maryland Medical Center, Baltimore, Maryland
| | - David Parsons
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Yuanyuan Zhang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Puneeth Iyengar
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas; Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York City, New York
| | - Jing Wang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Andrew Godley
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Bin Cai
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Mu-Han Lin
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Kenneth Westover
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
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Zheng X, Guo W, Wang Y, Zhang J, Zhang Y, Cheng C, Teng X, Lam S, Zhou T, Ma Z, Liu R, Wu H, Ge H, Cai J, Li B. Multi-omics to predict acute radiation esophagitis in patients with lung cancer treated with intensity-modulated radiation therapy. Eur J Med Res 2023; 28:126. [PMID: 36935504 PMCID: PMC10024847 DOI: 10.1186/s40001-023-01041-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 02/03/2023] [Indexed: 03/21/2023] Open
Abstract
PURPOSE The study aimed to predict acute radiation esophagitis (ARE) with grade ≥ 2 for patients with locally advanced lung cancer (LALC) treated with intensity-modulated radiation therapy (IMRT) using multi-omics features, including radiomics and dosiomics. METHODS 161 patients with stage IIIA-IIIB LALC who received chemoradiotherapy (CRT) or radiotherapy by IMRT with a prescribed dose from 45 to 70 Gy from 2015 to 2019 were enrolled retrospectively. All the toxicity gradings were given following the Common Terminology Criteria for Adverse Events V4.0. Multi-omics features, including radiomics, dosiomics (including dose-volume histogram dosimetric parameters), were extracted based on the planning CT image and three-dimensional dose distribution. All data were randomly divided into training cohorts (N = 107) and testing cohorts (N = 54). In the training cohorts, features with reliably high outcome relevance and low redundancy were selected under random patient subsampling. Four classification models (using clinical factors (CF) only, using radiomics features (RFs) only, dosiomics features (DFs) only, and the hybrid features (HFs) containing clinical factors, radiomics and dosiomics) were constructed employing the Ridge classifier using two-thirds of randomly selected patients as the training cohort. The remaining patient was treated as the testing cohort. A series of models were built with 30 times training-testing splits. Their performances were assessed using the area under the ROC curve (AUC) and accuracy. RESULTS Among all patients, 51 developed ARE grade ≥ 2, with an incidence of 31.7%. Next, 8990 radiomics and 213 dosiomics features were extracted, and 3, 6, 12, and 13 features remained after feature selection in the CF, DF, RF and DF models, respectively. The RF and HF models achieved similar classification performance, with the training and testing AUCs of 0.796 ± 0.023 (95% confidence interval (CI [0.79, 0.80])/0.744 ± 0.044 (95% CI [0.73, 0.76]) and 0.801 ± 0.022 (95% CI [0.79, 0.81]) (p = 0.74), respectively. The model performances using CF and DF features were poorer, with training and testing AUCs of 0.573 ± 0.026 (95% CI [0.56, 0.58])/ 0.509 ± 0.072 (95% CI [0.48, 0.53]) and 0.679 ± 0.027 (95% CI [0.67, 0.69])/0.604 ± 0.041 (95% CI [0.53, 0.63]) compared with the above two models (p < 0.001), respectively. CONCLUSIONS In LALC patients treated with CRT IMRT, the ARE grade ≥ 2 can be predicted using the pretreatment radiotherapy image features. To predict ARE, the multi-omics features had similar predictability with radiomics features; however, the dosiomics features and clinical factors had a limited classification performance.
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Affiliation(s)
- Xiaoli Zheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Wei Guo
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Yunhan Wang
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Yuanpeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Chen Cheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Saikit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Ruining Liu
- Department of Interventional Therapy, Henan Provincial People's Hospital, Zhengzhou, China
| | - Hui Wu
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
| | - Bing Li
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
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Piperdi H, Portal D, Neibart SS, Yue NJ, Jabbour SK, Reyhan M. Adaptive Radiation Therapy in the Treatment of Lung Cancer: An Overview of the Current State of the Field. Front Oncol 2021; 11:770382. [PMID: 34912715 PMCID: PMC8666420 DOI: 10.3389/fonc.2021.770382] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/09/2021] [Indexed: 12/25/2022] Open
Abstract
Lung cancer treatment is constantly evolving due to technological advances in the delivery of radiation therapy. Adaptive radiation therapy (ART) allows for modification of a treatment plan with the goal of improving the dose distribution to the patient due to anatomic or physiologic deviations from the initial simulation. The implementation of ART for lung cancer is widely varied with limited consensus on who to adapt, when to adapt, how to adapt, and what the actual benefits of adaptation are. ART for lung cancer presents significant challenges due to the nature of the moving target, tumor shrinkage, and complex dose accumulation because of plan adaptation. This article presents an overview of the current state of the field in ART for lung cancer, specifically, probing topics of: patient selection for the greatest benefit from adaptation, models which predict who and when to adapt plans, best timing for plan adaptation, optimized workflows for implementing ART including alternatives to re-simulation, the best radiation techniques for ART including magnetic resonance guided treatment, algorithms and quality assurance, and challenges and techniques for dose reconstruction. To date, the clinical workflow burden of ART is one of the major reasons limiting its widespread acceptance. However, the growing body of evidence demonstrates overwhelming support for reduced toxicity while improving tumor dose coverage by adapting plans mid-treatment, but this is offset by the limited knowledge about tumor control. Progress made in predictive modeling of on-treatment tumor shrinkage and toxicity, optimizing the timing of adaptation of the plan during the course of treatment, creating optimal workflows to minimize staffing burden, and utilizing deformable image registration represent ways the field is moving toward a more uniform implementation of ART.
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Affiliation(s)
- Huzaifa Piperdi
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Daniella Portal
- Rutgers Robert Wood Johnson Medical School, Rutgers, The State of New Jersey University, Piscataway, NJ, United States
| | - Shane S. Neibart
- Rutgers Robert Wood Johnson Medical School, Rutgers, The State of New Jersey University, Piscataway, NJ, United States
| | - Ning J. Yue
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Salma K. Jabbour
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
- Rutgers Robert Wood Johnson Medical School, Rutgers, The State of New Jersey University, Piscataway, NJ, United States
| | - Meral Reyhan
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
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Alam SR, Zhang P, Zhang SY, Chen I, Rimner A, Tyagi N, Hu YC, Lu W, Yorke ED, Deasy JO, Thor M. Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy. Int J Radiat Oncol Biol Phys 2021; 110:883-892. [PMID: 33453309 PMCID: PMC8180486 DOI: 10.1016/j.ijrobp.2021.01.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 12/30/2020] [Accepted: 01/07/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE Acute esophagitis (AE) is a common dose-limiting toxicity in radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). We developed an early AE prediction model from weekly accumulated esophagus dose and its associated local volumetric change. METHODS AND MATERIALS Fifty-one patients with LA-NSCLC underwent treatment with intensity modulated radiation therapy to 60 Gy in 2-Gy fractions with concurrent chemotherapy and weekly cone beam computed tomography (CBCT). Twenty-eight patients (55%) developed grade ≥2 AE (≥AE2) at a median of 4 weeks after the start of radiation therapy. For early ≥AE2 prediction, the esophagus on CBCT of the first 2 weeks was deformably registered to the planning computed tomography images, and weekly esophagus dose was accumulated. Week 1-to-week 2 (w1→w2) esophagus volume changes including maximum esophagus expansion (MEex%) and volumes with ≥x% local expansions (VEx%; x = 5, 10, 15) were calculated from the Jacobian map of deformation vector field gradients. Logistic regression model with 5-fold cross-validation was built using combinations of the accumulated mean esophagus doses (MED) and the esophagus change parameters with the lowest P value in univariate analysis. The model was validated on an additional 18 and 11 patients with weekly CBCT and magnetic resonance imaging (MRI), respectively, and compared with models using only planned mean dose (MEDPlan). Performance was assessed using area under the curve (AUC) and Hosmer-Lemeshow test (PHL). RESULTS Univariately, w1→w2 VE10% (P = .004), VE5% (P = .01) and MEex% (P = .02) significantly predicted ≥AE2. A model combining MEDW2 and w1→w2 VE10% had the best performance (AUC = 0.80; PHL = 0.43), whereas the MEDPlan model had a lower accuracy (AUC = 0.67; PHL = 0.26). The combined model also showed high accuracy in the CBCT (AUC = 0.78) and MRI validations (AUC = 0.75). CONCLUSIONS A CBCT-based, cross-validated, and internally validated model on MRI with a combination of accumulated esophagus dose and local volume change from the first 2 weeks of chemotherapy significantly improved AE prediction compared with conventional models using only the planned dose. This model could inform plan adaptation early to lower the risk of esophagitis.
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Affiliation(s)
- Sadegh R Alam
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Pengpeng Zhang
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Si-Yuan Zhang
- Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Ishita Chen
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Neelam Tyagi
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yu-Chi Hu
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Wei Lu
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ellen D Yorke
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joseph O Deasy
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maria Thor
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
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5
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Yu H, Lam KO, Green MD, Wu H, Yang L, Wang W, Jin J, Hu C, Wang Y, Jolly S, (Spring) Kong FM. Significance of radiation esophagitis: Conditional survival assessment in patients with non-small cell lung cancer. JOURNAL OF THE NATIONAL CANCER CENTER 2021; 1:31-38. [PMID: 39035770 PMCID: PMC11256695 DOI: 10.1016/j.jncc.2021.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 02/07/2021] [Accepted: 02/12/2021] [Indexed: 11/29/2022] Open
Abstract
Purpose This study aimed to examine the effect of radiation esophagitis (RE) and the dynamics of RE on subsequent survival in non-small cell lung cancer (NSCLC) patients who underwent radiotherapy. Experimental Design Patients with NSCLC treated with fractionated thoracic radiotherapy enrolled in prospective trials were eligible. RE was graded prospectively according to Common Terminology Criteria for Adverse Events (CTCAE) v3.0 per protocol requirement weekly during-RT and 1 month after RT. This study applied conditional survival assessment which has advantage over traditional survival analysis as it assesses the survival from the event instead of from the baseline. P-value less than 0.05 was considered to be significant. The primary endpoint is overall survival. Results A total of 177 patients were eligible, with a median follow-up of 5 years. The presence of RE, the maximum RE grade, the evolution of RE and the onset timing of RE events were all correlated with subsequent survival. At all conditional time points, patients first presented with RE grade1 (initial RE1) had significant inferior subsequent survival (multivariable HRs median: 1.63, all P-values<0.05); meanwhile those with RE progressed had significant inferior subsequent survival than those never develop RE (multivariable HRs median: 2.08, all P-values<0.05). Multivariable Cox proportional-hazards analysis showed significantly higher C-indexes for models with inclusion of RE events than those without (all P-values<0.05). Conclusion This study comprehensively evaluated the impact of RE with conditional survival assessment and demonstrated that RE is associated with inferior survival in NSCLC patients treated with RT.
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Affiliation(s)
- Hao Yu
- Biomedical Engineering, Shenzhen Polytechnic, Shenzhen, China
- BioHealth Informatics, School of Informatics and Computing, IUPUI, Indianapolis, IN, USA
| | - Ka-On Lam
- Department of Clinical Oncology, LKS Faculty of Medicine, the University of Hong Kong, Hong Kong, China
- Clinical Oncology Center, the University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Michael D. Green
- Radiation Oncology, Ann Arbor VA Health Care, Ann Arbor, MI, USA
- Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Huanmei Wu
- BioHealth Informatics, School of Informatics and Computing, IUPUI, Indianapolis, IN, USA
| | - Li Yang
- Clinical Oncology Center, the University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Weili Wang
- University Hospitals/Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Jianyue Jin
- University Hospitals/Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Chen Hu
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yang Wang
- Biomedical Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Shruti Jolly
- Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Feng-Ming (Spring) Kong
- Department of Clinical Oncology, LKS Faculty of Medicine, the University of Hong Kong, Hong Kong, China
- Clinical Oncology Center, the University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- University Hospitals/Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA
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Mohan V, Bruin NM, van de Kamer JB, Sonke JJ, Vogel WV. The increasing potential of nuclear medicine imaging for the evaluation and reduction of normal tissue toxicity from radiation treatments. Eur J Nucl Med Mol Imaging 2021; 48:3762-3775. [PMID: 33687522 PMCID: PMC8484246 DOI: 10.1007/s00259-021-05284-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 02/24/2021] [Indexed: 11/26/2022]
Abstract
Radiation therapy is an effective treatment modality for a variety of cancers. Despite several advances in delivery techniques, its main drawback remains the deposition of dose in normal tissues which can result in toxicity. Common practices of evaluating toxicity, using questionnaires and grading systems, provide little underlying information beyond subjective scores, and this can limit further optimization of treatment strategies. Nuclear medicine imaging techniques can be utilised to directly measure regional baseline function and function loss from internal/external radiation therapy within normal tissues in an in vivo setting with high spatial resolution. This can be correlated with dose delivered by radiotherapy techniques to establish objective dose-effect relationships, and can also be used in the treatment planning step to spare normal tissues more efficiently. Toxicity in radionuclide therapy typically occurs due to undesired off-target uptake in normal tissues. Molecular imaging using diagnostic analogues of therapeutic radionuclides can be used to test various interventional protective strategies that can potentially reduce this normal tissue uptake without compromising tumour uptake. We provide an overview of the existing literature on these applications of nuclear medicine imaging in diverse normal tissue types utilising various tracers, and discuss its future potential.
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Affiliation(s)
- V Mohan
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - N M Bruin
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - J B van de Kamer
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - J-J Sonke
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Wouter V Vogel
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
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Clinical response assessment on DW-MRI compared with FDG-PET/CT after neoadjuvant chemoradiotherapy in patients with oesophageal cancer. Eur J Nucl Med Mol Imaging 2020; 48:176-185. [PMID: 32572560 DOI: 10.1007/s00259-020-04917-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 06/07/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE In about 30% of patients treated with neoadjuvant chemoradiotherapy (nCRT) followed by surgical resection for locally advanced oesophageal cancer no vital tumour is found in the resection specimen. Accurate clinical response assessment is critical if deferral from surgery is considered in complete responders. Our study aimed to compare the performance of MRI and of FDG-PET/CT for the detection of residual disease after nCRT. METHODS Patients with oesophageal cancer eligible for nCRT and oesophagectomy were prospectively included. All patients underwent FDG-PET/CT and MRI before and between 6 and 8 weeks after nCRT. Two radiologists scored the MRI scans, and two nuclear medicine physicians scored the FDG-PET/CT scans using a 5-point score for residual disease. Histopathology after oesophagectomy represented the reference standard. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated for detection of residual tumour (ypT+), residual nodal disease (ypN+), and any residual disease (ypT+Nx/ypT0N+). RESULTS Seven out of 33 (21%) patients had a pathological complete response. The AUCs for individual readers to detect ypT+ were 0.71/0.70 on diffusion-weighted (DW)-MRI and 0.54/0.57 on FDG-PET/CT, and to detect ypN+ were 0.89/0.81 on DW-MRI and 0.75/0.71 on FDG-PET/CT. The AUCs/sensitivities/specificities for the individual readers to detect any residual disease were 0.74/92%/57% and 0.70/96%/43% on MRI; these were 0.49/69%/29% and 0.60/69%/43% on FDG-PET/CT, respectively. CONCLUSION MRI reached higher diagnostic accuracies than FDG-PET/CT for the detection of residual tumour in oesophageal cancer patients at 6 to 8 weeks after nCRT.
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Luna JM, Chao HH, Shinohara RT, Ungar LH, Cengel KA, Pryma DA, Chinniah C, Berman AT, Katz SI, Kontos D, Simone CB, Diffenderfer ES. Machine learning highlights the deficiency of conventional dosimetric constraints for prevention of high-grade radiation esophagitis in non-small cell lung cancer treated with chemoradiation. Clin Transl Radiat Oncol 2020; 22:69-75. [PMID: 32274426 PMCID: PMC7132156 DOI: 10.1016/j.ctro.2020.03.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 03/17/2020] [Accepted: 03/21/2020] [Indexed: 12/23/2022] Open
Abstract
A large cohort to predict radiation esophagitis in lung cancer patients was used. Modern machine learning models were implemented to predict radiation esophagitis. Previously published predictors of grade ≥ 3 radiation esophagitis may be unreliable.
Background and Purpose Radiation esophagitis is a clinically important toxicity seen with treatment for locally-advanced non-small cell lung cancer. There is considerable disagreement among prior studies in identifying predictors of radiation esophagitis. We apply machine learning algorithms to identify factors contributing to the development of radiation esophagitis to uncover previously unidentified criteria and more robust dosimetric factors. Materials and Methods We used machine learning approaches to identify predictors of grade ≥ 3 radiation esophagitis in a cohort of 202 consecutive locally-advanced non-small cell lung cancer patients treated with definitive chemoradiation from 2008 to 2016. We evaluated 35 clinical features per patient grouped into risk factors, comorbidities, imaging, stage, histology, radiotherapy, chemotherapy and dosimetry. Univariate and multivariate analyses were performed using a panel of 11 machine learning algorithms combined with predictive power assessments. Results All patients were treated to a median dose of 66.6 Gy at 1.8 Gy per fraction using photon (89.6%) and proton (10.4%) beam therapy, most often with concurrent chemotherapy (86.6%). 11.4% of patients developed grade ≥ 3 radiation esophagitis. On univariate analysis, no individual feature was found to predict radiation esophagitis (AUC range 0.45–0.55, p ≥ 0.07). In multivariate analysis, all machine learning algorithms exhibited poor predictive performance (AUC range 0.46–0.56, p ≥ 0.07). Conclusions Contemporary machine learning algorithms applied to our modern, relatively large institutional cohort could not identify any reliable predictors of grade ≥ 3 radiation esophagitis. Additional patients are needed, and novel patient-specific and treatment characteristics should be investigated to develop clinically meaningful methods to mitigate this survival altering toxicity.
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Affiliation(s)
- José Marcio Luna
- Department of Radiation Oncology, University of Pennsylvania, Perelman Center for Advanced Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Hann-Hsiang Chao
- Department of Radiation Oncology, Hunter Holmes McGuire Veterans Affairs Medical Center, 1201 Broad Rock Blvd, Richmond, VA 23249, United States
| | - Russel T Shinohara
- Department of Biostatistics and Epidemiology, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Lyle H Ungar
- Department of Computer and Information Science, University of Pennsylvania, 3330 Walnut St, Philadelphia, PA 19104, United States
| | - Keith A Cengel
- Department of Radiation Oncology, University of Pennsylvania, Perelman Center for Advanced Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Daniel A Pryma
- Department of Radiology, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, United States
| | | | - Abigail T Berman
- Department of Radiation Oncology, University of Pennsylvania, Perelman Center for Advanced Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Sharyn I Katz
- Department of Radiology, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, United States
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, United States
| | - Charles B Simone
- Department of Radiation Oncology, New York Proton Center, 225 East 126 St, New York, NY 10035, United States
| | - Eric S Diffenderfer
- Department of Radiation Oncology, University of Pennsylvania, Perelman Center for Advanced Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
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Zschaeck S, Li Y, Bütof R, Lili C, Hua W, Troost ECG, Beck M, Amthauer H, Kaul D, Kotzerke J, Baur ADJ, Ghadjar P, Baumann M, Krause M, Hofheinz F. Combined tumor plus nontumor interim FDG-PET parameters are prognostic for response to chemoradiation in squamous cell esophageal cancer. Int J Cancer 2020; 147:1427-1436. [PMID: 32010957 DOI: 10.1002/ijc.32897] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 01/09/2020] [Accepted: 01/13/2020] [Indexed: 11/06/2022]
Abstract
We have investigated the prognostic value of two novel interim 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) parameters in patients undergoing chemoradiation (CRT) for esophageal squamous cell carcinoma (ESCC): one tumor parameter (maximal standardized uptake ratio rSUR) and one normal tissue parameter (change of FDG uptake within irradiated nontumor-affected esophagus ∆SUVNTO ). PET data of 134 European and Chinese patients were analyzed. Parameter establishment was based on 36 patients undergoing preoperative CRT plus surgery, validation was performed in 98 patients receiving definitive CRT. Patients received PET imaging prior and during fourth week of CRT. Clinical parameters, baseline PET parameters, and interim PET parameters (rSUR and ∆SUVNTO ) were analyzed and compared to event-free survival (EFS), overall survival (OS), loco-regional control (LRC) and freedom from distant metastases (FFDM). Combining rSUR and ∆SUVNTO revealed a strong prognostic impact on EFS, OS, LRC and FFDM in patients undergoing preoperative CRT. In the definitive CRT cohort, univariate analysis with respect to EFS revealed several staging plus both previously established interim PET parameters as significant prognostic factors. Multivariate analyses revealed only rSUR and ∆SUVNTO as independent prognostic factors (p = 0.003, p = 0.008). Combination of these parameters with the cutoff established in preoperative CRT revealed excellent discrimination of patients with a long or short EFS (73% vs. 17% at 2 years, respectively) and significantly discriminated all other endpoints (OS, p < 0.001; LRC, p < 0.001; FFDM, p = 0.02), even in subgroups. Combined use of interim FDG-PET derived parameters ∆SUVNTO and rSUR seems to have predictive potential, allowing to select responders for definitive CRT and omission of surgery.
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Affiliation(s)
- Sebastian Zschaeck
- Department of Radiation Oncology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany
| | - Yimin Li
- Department of Radiation Oncology, Xiamen Cancer Hospital, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Rebecca Bütof
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner site Dresden, Dresden, Germany
| | - Chen Lili
- Department of Radiation Oncology, Xiamen Cancer Hospital, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Wu Hua
- Department of Nuclear Medicine, The Xiamen First Affiliated Hospital of Xiamen University, Xiamen, People's Republic of China
| | - Esther C G Troost
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner site Dresden, Dresden, Germany.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology OncoRay, Dresden, Germany
| | - Marcus Beck
- Department of Radiation Oncology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Holger Amthauer
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - David Kaul
- Department of Radiation Oncology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Jörg Kotzerke
- OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany.,Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Alexander D J Baur
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Klinik für Radiologie, Berlin, Germany
| | - Pirus Ghadjar
- Department of Radiation Oncology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Michael Baumann
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner site Dresden, Dresden, Germany.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology OncoRay, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mechthild Krause
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner site Dresden, Dresden, Germany.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology OncoRay, Dresden, Germany
| | - Frank Hofheinz
- Helmholtz-Zentrum Dresden-Rossendorf, PET Center, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
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10
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Grootjans W, de Geus-Oei LF, Bussink J. Image-guided adaptive radiotherapy in patients with locally advanced non-small cell lung cancer: the art of PET. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2018; 62:369-384. [PMID: 29869486 DOI: 10.23736/s1824-4785.18.03084-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
With a worldwide annual incidence of 1.8 million cases, lung cancer is the most diagnosed form of cancer in men and the third most diagnosed form of cancer in women. Histologically, 80-85% of all lung cancers can be categorized as non-small cell lung cancer (NSCLC). For patients with locally advanced NSCLC, standard of care is fractionated radiotherapy combined with chemotherapy. With the aim of improving clinical outcome of patients with locally advanced NSCLC, combined and intensified treatment approaches are increasingly being used. However, given the heterogeneity of this patient group with respect to tumor biology and subsequent treatment response, a personalized treatment approach is required to optimize therapeutic effect and minimize treatment induced toxicity. Medical imaging, in particular positron emission tomography (PET), before and during the course radiotherapy is increasingly being used to personalize radiotherapy. In this setting, PET imaging can be used to improve delineation of target volumes, employ molecularly-guided dose painting strategies, early response monitoring, prediction and monitoring of treatment-related toxicity. The concept of PET image-guided adaptive radiotherapy (IGART) is an interesting approach to personalize radiotherapy for patients with locally advanced NSCLC, which might ultimately contribute to improved clinical outcomes and reductions in frequency of treatment-related adverse events in this patient group. In this review, we provide a comprehensive overview of available clinical data supporting the use of PET imaging for IGART in patients with locally advanced NSCLC.
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Affiliation(s)
- Willem Grootjans
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands -
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
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11
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Bissonnette JP, Yap ML, Clarke K, Shessel A, Higgins J, Vines D, Atenafu EG, Becker N, Leavens C, Bezjak A, Jaffray DA, Sun A. Serial 4DCT/4DPET imaging to predict and monitor response for locally-advanced non-small cell lung cancer chemo-radiotherapy. Radiother Oncol 2018; 126:347-354. [DOI: 10.1016/j.radonc.2017.11.023] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 11/07/2017] [Accepted: 11/27/2017] [Indexed: 12/12/2022]
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12
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Hawkins PG, Boonstra PS, Hobson ST, Hayman JA, Ten Haken RK, Matuszak MM, Stanton P, Kalemkerian GP, Lawrence TS, Schipper MJ, Kong FMS, Jolly S. Prediction of Radiation Esophagitis in Non-Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels. Transl Oncol 2017; 11:102-108. [PMID: 29220828 PMCID: PMC6002355 DOI: 10.1016/j.tranon.2017.11.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 11/15/2017] [Indexed: 12/12/2022] Open
Abstract
Radiation 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.
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Affiliation(s)
- Peter G Hawkins
- Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, United States of America
| | - Philip S Boonstra
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, United States of America
| | - Stephen T Hobson
- Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, United States of America
| | - James A Hayman
- Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, United States of America
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, United States of America
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, United States of America
| | - Paul Stanton
- Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, United States of America
| | - Gregory P Kalemkerian
- Department of Internal Medicine, Division of Medical Oncology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, United States of America
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, United States of America
| | - Matthew J Schipper
- Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, United States of America; Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, United States of America
| | - Feng-Ming Spring Kong
- Department of Radiation Oncology, Indiana University, 535 Barnhill Drive, Indianapolis, IN 46202, United States of America
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, United States of America.
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13
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Niedzielski JS, Yang J, Mohan R, Titt U, Mirkovic D, Stingo F, Liao Z, Gomez DR, Martel MK, Briere TM, Court LE. Differences in Normal Tissue Response in the Esophagus Between Proton and Photon Radiation Therapy for Non-Small Cell Lung Cancer Using In Vivo Imaging Biomarkers. Int J Radiat Oncol Biol Phys 2017; 99:1013-1020. [PMID: 29063837 DOI: 10.1016/j.ijrobp.2017.07.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 06/26/2017] [Accepted: 07/01/2017] [Indexed: 01/21/2023]
Abstract
PURPOSE To determine whether there exists any significant difference in normal tissue toxicity between intensity modulated radiation therapy (IMRT) or proton therapy for the treatment of non-small cell lung cancer. METHODS AND MATERIALS A total of 134 study patients (n=49 treated with proton therapy, n=85 with IMRT) treated in a randomized trial had a previously validated esophageal toxicity imaging biomarker, esophageal expansion, quantified during radiation therapy, as well as esophagitis grade (Common Terminology Criteria for Adverse Events version 3.0), on a weekly basis during treatment. Differences between the 2 modalities were statically analyzed using the imaging biomarker metric value (Kruskal-Wallis analysis of variance), as well as the incidence and severity of esophagitis grade (χ2 and Fisher exact tests, respectively). The dose-response of the imaging biomarker was also compared between modalities using esophageal equivalent uniform dose, as well as delivered dose to an isotropic esophageal subvolume. RESULTS No statistically significant difference in the distribution of esophagitis grade, the incidence of grade ≥3 esophagitis (15 and 11 patients treated with IMRT and proton therapy, respectively), or the esophageal expansion imaging biomarker between cohorts (P>.05) was found. The distribution of imaging biomarker metric values had similar distributions between treatment arms, despite a slightly higher dose volume in the proton arm (P>.05). Imaging biomarker dose-response was similar between modalities for dose quantified as esophageal equivalent uniform dose and delivered esophageal subvolume dose. Regardless of treatment modality, there was high variability in imaging biomarker response, as well as esophagitis grade, for similar esophageal doses between patients. CONCLUSIONS There was no significant difference in esophageal toxicity from either proton- or photon-based radiation therapy as quantified by esophagitis grade or the esophageal expansion imaging biomarker.
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Affiliation(s)
- Joshua S Niedzielski
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas.
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas
| | - Radhe Mohan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas
| | - Uwe Titt
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas
| | - Dragan Mirkovic
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas
| | - Francesco Stingo
- Department of Statistics, Computer Science, Applications "G. Parenti," University of Florence, Florence, Italy
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - Daniel R Gomez
- Department of Radiation Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - Mary K Martel
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas
| | - Tina M Briere
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas
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14
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Jadvar H. The Use of Imaging in the Prediction and Assessment of Cancer Treatment Toxicity. Diagnostics (Basel) 2017; 7:diagnostics7030043. [PMID: 28726731 PMCID: PMC5617943 DOI: 10.3390/diagnostics7030043] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 07/15/2017] [Accepted: 07/17/2017] [Indexed: 01/15/2023] Open
Abstract
Multimodal imaging is commonly used in the management of patients with cancer. Imaging plays pivotal roles in the diagnosis, initial staging, treatment response assessment, restaging after treatment and the prognosis of many cancers. Indeed, it is difficult to imagine modern precision cancer care without the use of multimodal molecular imaging, which is advancing at a rapid pace with innovative developments in imaging sciences and an improved understanding of the complex biology of cancer. Cancer therapy often leads to undesirable toxicity, which can range from an asymptomatic subclinical state to severe end organ damage and even death. Imaging is helpful in the portrayal of the unwanted effects of cancer therapy and may assist with optimal clinical decision-making, clinical management, and overall improvements in the outcomes and quality of life for patients.
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Affiliation(s)
- Hossein Jadvar
- Division of Nuclear Medicine, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
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15
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Niedzielski JS, Yang J, Stingo F, Liao Z, Gomez D, Mohan R, Martel M, Briere T, Court L. A Novel Methodology using CT Imaging Biomarkers to Quantify Radiation Sensitivity in the Esophagus with Application to Clinical Trials. Sci Rep 2017; 7:6034. [PMID: 28729729 PMCID: PMC5519548 DOI: 10.1038/s41598-017-05003-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Accepted: 05/23/2017] [Indexed: 12/25/2022] Open
Abstract
Personalized cancer therapy seeks to tailor treatment to an individual patient's biology. Therefore, a means to characterize radiosensitivity is necessary. In this study, we investigated radiosensitivity in the normal esophagus using an imaging biomarker of radiation-response and esophageal toxicity, esophageal expansion, as a method to quantify radiosensitivity in 134 non-small-cell lung cancer patients, by using K-Means clustering to group patients based on esophageal radiosensitivity. Patients within the cluster of higher response and lower dose were labelled as radiosensitive. This information was used as a variable in toxicity prediction modelling (lasso logistic regression). The resultant model performance was quantified and compared to toxicity prediction modelling without utilizing radiosensitivity information. The esophageal expansion-response was highly variable between patients, even for similar radiation doses. K-Means clustering was able to identify three patient subgroups of radiosensitivity: radiosensitive, radio-normal, and radioresistant groups. Inclusion of the radiosensitive variable improved lasso logistic regression models compared to model performance without radiosensitivity information. Esophageal radiosensitivity can be quantified using esophageal expansion and K-Means clustering to improve toxicity prediction modelling. Finally, this methodology may be applied in clinical trials to validate pre-treatment biomarkers of esophageal toxicity.
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Affiliation(s)
- Joshua S Niedzielski
- Department of Radiation Oncology, The University of Colorado-School of Medicine, Aurora, Colorado, USA. .,Department of Radiation Physics, The University of Texas-MD Anderson Cancer Center, Houston, Texas, USA. .,University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas, USA.
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas-MD Anderson Cancer Center, Houston, Texas, USA.,University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas, USA
| | - Francesco Stingo
- Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Florence, Italy.,University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas, USA
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas-MD Anderson Cancer Center, Houston, Texas, USA
| | - Daniel Gomez
- Department of Radiation Oncology, The University of Texas-MD Anderson Cancer Center, Houston, Texas, USA
| | - Radhe Mohan
- Department of Radiation Physics, The University of Texas-MD Anderson Cancer Center, Houston, Texas, USA.,University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas, USA
| | - Mary Martel
- Department of Radiation Physics, The University of Texas-MD Anderson Cancer Center, Houston, Texas, USA.,University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas, USA
| | - Tina Briere
- Department of Radiation Physics, The University of Texas-MD Anderson Cancer Center, Houston, Texas, USA.,University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas, USA
| | - Laurence Court
- Department of Radiation Physics, The University of Texas-MD Anderson Cancer Center, Houston, Texas, USA.,University of Texas-Houston Health Science Center, Graduate School of Biomedical Science, Houston, Texas, USA
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16
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Everitt S, Callahan J, Obeid E, Hicks RJ, Mac Manus M, Ball D. Acute radiation oesophagitis associated with 2-deoxy-2-[18F]fluoro-d-glucose uptake on positron emission tomography/CT during chemo-radiation therapy in patients with non-small-cell lung cancer. J Med Imaging Radiat Oncol 2017; 61:682-688. [PMID: 28608503 DOI: 10.1111/1754-9485.12631] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 04/22/2017] [Indexed: 12/25/2022]
Abstract
INTRODUCTION Acute radiation oesophagitis (ARO) is frequently experienced by patients receiving concurrent chemo-radiation therapy (cCRT) for non-small-cell lung cancer (NSCLC). We investigated ARO symptoms (CTCAE v3.0), radiation dose and oesophageal FDG PET/CT uptake. METHOD Candidates received cCRT (60 Gy, 2 Gy/fx) and sequential FDG PET/CT (baseline FDG0 , FDGwk2 and FDGwk4 ). Mean and maximum standardized uptake value (SUVmean and SUVmax) and radiation dose (Omean and Omax ) were calculated within the whole oesophagus and seven sub-regions (5-60 Gy). RESULTS Forty-four patients underwent FDG0 and FDGwk2 , and 41 (93%) received FDGwk4 , resulting in 129 PET/CT scans for analysis. Of 29 (66%) patients with ≥ grade 2 ARO, SUVmax (mean ± SD) increased from FDG0 to FDGwk4 (3.06 ± 0.69 to 3.83 ± 1.27, P = 0.0019) and FDGwk2 to FDGwk4 (3.10 ± 0.75 to 3.83 ± 1.27, P = 0.0046). Radiation dose (mean ± SD) was higher in grade ≥2 patients; Omean (47.5 ± 20 vs 53.9 ± 10.2, P = 0.0061), Omax (13.7 ± 9.6 vs 20.1 ± 10.6, P = 0.0009) and V40 Gy (8.0 ± 8.2 vs 11.9 ± 7.3, P = 0.0185). CONCLUSIONS FDGwk4 SUVmax and radiation dose were associated with ≥ grade 2 ARO. Compared to subjective assessments, future interim FDG PET/CT acquired for disease response assessment may also be utilized to objectively characterize ARO severity and image-guided oesophageal dose constraints.
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Affiliation(s)
- Sarah Everitt
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.,Department of Medical Imaging & Radiation Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Jason Callahan
- Department of Medical Imaging & Radiation Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, Victoria, Australia.,Centre for Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Eman Obeid
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Rodney J Hicks
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.,Centre for Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Michael Mac Manus
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - David Ball
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
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17
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Niedzielski JS, Yang J, Liao Z, Gomez DR, Stingo F, Mohan R, Martel MK, Briere TM, Court LE. (18)F-Fluorodeoxyglucose Positron Emission Tomography Can Quantify and Predict Esophageal Injury During Radiation Therapy. Int J Radiat Oncol Biol Phys 2016; 96:670-8. [PMID: 27681764 PMCID: PMC5117825 DOI: 10.1016/j.ijrobp.2016.07.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 07/05/2016] [Accepted: 07/13/2016] [Indexed: 12/25/2022]
Abstract
PURPOSE We sought to investigate the ability of mid-treatment (18)F-fluorodeoxyglucose positron emission tomography (PET) studies to objectively and spatially quantify esophageal injury in vivo from radiation therapy for non-small cell lung cancer. METHODS AND MATERIALS This retrospective study was approved by the local institutional review board, with written informed consent obtained before enrollment. We normalized (18)F-fluorodeoxyglucose PET uptake to each patient's low-irradiated region (<5 Gy) of the esophagus, as a radiation response measure. Spatially localized metrics of normalized uptake (normalized standard uptake value [nSUV]) were derived for 79 patients undergoing concurrent chemoradiation therapy for non-small cell lung cancer. We used nSUV metrics to classify esophagitis grade at the time of the PET study, as well as maximum severity by treatment completion, according to National Cancer Institute Common Terminology Criteria for Adverse Events, using multivariate least absolute shrinkage and selection operator (LASSO) logistic regression and repeated 3-fold cross validation (training, validation, and test folds). This 3-fold cross-validation LASSO model procedure was used to predict toxicity progression from 43 asymptomatic patients during the PET study. Dose-volume metrics were also tested in both the multivariate classification and the symptom progression prediction analyses. Classification performance was quantified with the area under the curve (AUC) from receiver operating characteristic analysis on the test set from the 3-fold analyses. RESULTS Statistical analysis showed increasing nSUV is related to esophagitis severity. Axial-averaged maximum nSUV for 1 esophageal slice and esophageal length with at least 40% of axial-averaged nSUV both had AUCs of 0.85 for classifying grade 2 or higher esophagitis at the time of the PET study and AUCs of 0.91 and 0.92, respectively, for maximum grade 2 or higher by treatment completion. Symptom progression was predicted with an AUC of 0.75. Dose metrics performed poorly at classifying esophagitis (AUC of 0.52, grade 2 or higher mid treatment) or predicting symptom progression (AUC of 0.67). CONCLUSIONS Normalized uptake can objectively, locally, and noninvasively quantify esophagitis during radiation therapy and predict eventual symptoms from asymptomatic patients. Normalized uptake may provide patient-specific dose-response information not discernible from dose.
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Affiliation(s)
- Joshua S Niedzielski
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas Houston Graduate School of Biomedical Science, Houston, Texas.
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas Houston Graduate School of Biomedical Science, Houston, Texas
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Daniel R Gomez
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Francesco Stingo
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Radhe Mohan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas Houston Graduate School of Biomedical Science, Houston, Texas
| | - Mary K Martel
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas Houston Graduate School of Biomedical Science, Houston, Texas
| | - Tina M Briere
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas Houston Graduate School of Biomedical Science, Houston, Texas
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; University of Texas Houston Graduate School of Biomedical Science, Houston, Texas
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Adaptive Dose Escalation using Serial Four-dimensional Positron Emission Tomography/Computed Tomography Scans during Radiotherapy for Locally Advanced Non-small Cell Lung Cancer. Clin Oncol (R Coll Radiol) 2016; 28:e199-e205. [PMID: 27637725 DOI: 10.1016/j.clon.2016.08.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Revised: 04/20/2016] [Accepted: 06/24/2016] [Indexed: 11/23/2022]
Abstract
AIMS Computed tomography (CT)-based radiotherapy dose escalation for locally advanced non-small cell lung cancer (LA-NSCLC) has had limited success. In this planning study, we investigated the potential for adaptive dose escalation using respiratory-gated 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography scans (4DPET/4DCT) acquired before and during a course of chemoradiotherapy (CRT). MATERIALS AND METHODS We prospectively enrolled patients with LA-NSCLC receiving curative intent CRT. Radiotherapy was delivered using intensity-modulated radiotherapy (IMRT) using the week 0 4DCT scan. Three alternative, dose-escalated IMRT plans were developed offline based on the week 0, 2 and 4 4DPET/4DCT scans. The FDG-avid primary (PET-T) and nodal disease (PET-N) volumes defined by the 50% of maximum standard uptake value threshold were dose escalated to as high as possible while respecting organ at risk constraints. RESULTS Thirty-two patients were recruited, 27 completing all scans. Twenty-five patients (93%) were boosted successfully above the clinical plan doses at week 0, 23 (85%) at week 2 and 20 (74%) at week 4. The median dose received by 95% of the planning target volume (D95) at week 0, 2 and 4 to PET-T were 74.4 Gy, 75.3 Gy and 74.1 Gy and to PET-N were 74.3 Gy, 71.0 Gy and 69.5 Gy. CONCLUSIONS Using 18F-FDG-4DPET/4DCT, it is feasible to dose escalate both primary and nodal disease in most patients. Choosing week 0 images to plan a course with an integrated boost to PET-avid disease allows for more patients to be successfully dose escalated with the highest boost dose.
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van Diessen JN, Chen C, van den Heuvel MM, Belderbos JS, Sonke JJ. Differential analysis of local and regional failure in locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy. Radiother Oncol 2016; 118:447-52. [DOI: 10.1016/j.radonc.2016.02.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Revised: 02/02/2016] [Accepted: 02/04/2016] [Indexed: 12/25/2022]
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