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Ohri N, Jolly S, Cooper BT, Kabarriti R, Bodner WR, Klein J, Guha C, Viswanathan S, Shum E, Sabari JK, Cheng H, Gucalp RA, Castellucci E, Qin A, Gadgeel SM, Halmos B. Selective Personalized RadioImmunotherapy for Locally Advanced Non-Small-Cell Lung Cancer Trial (SPRINT). J Clin Oncol 2024; 42:562-570. [PMID: 37988638 DOI: 10.1200/jco.23.00627] [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: 03/21/2023] [Revised: 07/25/2023] [Accepted: 09/29/2023] [Indexed: 11/23/2023] Open
Abstract
PURPOSE Standard therapy for locally advanced non-small-cell lung cancer (LA-NSCLC) is concurrent chemoradiotherapy followed by adjuvant durvalumab. For biomarker-selected patients with LA-NSCLC, we hypothesized that sequential pembrolizumab and risk-adapted radiotherapy, without chemotherapy, would be well-tolerated and effective. METHODS Patients with stage III NSCLC or unresectable stage II NSCLC and an Eastern Cooperative Oncology Group performance status of 0-1 were eligible for this trial. Patients with a PD-L1 tumor proportion score (TPS) of ≥50% received three cycles of induction pembrolizumab (200 mg, once every 21 days), followed by a 20-fraction course of risk-adapted thoracic radiotherapy (55 Gy delivered to tumors or lymph nodes with metabolic volume exceeding 20 cc, 48 Gy delivered to smaller lesions), followed by consolidation pembrolizumab to complete a 1-year treatment course. The primary study end point was 1-year progression-free survival (PFS). Secondary end points included response rates after induction pembrolizumab, overall survival (OS), and adverse events. RESULTS Twenty-five patients with a PD-L1 TPS of ≥50% were enrolled. The median age was 71, most patients (88%) had stage IIIA or IIIB disease, and the median PD-L1 TPS was 75%. Two patients developed disease progression during induction pembrolizumab, and two patients discontinued pembrolizumab after one infusion because of immune-related adverse events. Using RECIST criteria, 12 patients (48%) exhibited a partial or complete response after induction pembrolizumab. Twenty-four patients (96%) received definitive thoracic radiotherapy. The 1-year PFS rate is 76%, satisfying our efficacy objective. One- and 2-year OS rates are 92% and 76%, respectively. The most common grade 3 adverse events were colitis (n = 2, 8%) and esophagitis (n = 2, 8%), and no higher-grade treatment-related adverse events have occurred. CONCLUSION Pembrolizumab and risk-adapted radiotherapy, without chemotherapy, are a promising treatment approach for patients with LA-NSCLC with a PD-L1 TPS of ≥50%.
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Affiliation(s)
- Nitin Ohri
- Department of Radiation Oncology, Montefiore Einstein Comprehensive Cancer Center, Bronx, NY
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Benjamin T Cooper
- Department of Radiation Oncology, Perlmutter Cancer Center, New York University Grossman School of Medicine, New York, NY
| | - Rafi Kabarriti
- Department of Radiation Oncology, Montefiore Einstein Comprehensive Cancer Center, Bronx, NY
| | - William R Bodner
- Department of Radiation Oncology, Montefiore Einstein Comprehensive Cancer Center, Bronx, NY
| | - Jonathan Klein
- Department of Radiation Oncology, Montefiore Einstein Comprehensive Cancer Center, Bronx, NY
| | - Chandan Guha
- Department of Radiation Oncology, Montefiore Einstein Comprehensive Cancer Center, Bronx, NY
| | - Shankar Viswanathan
- Department of Epidemiology and Population Health, Montefiore Einstein Comprehensive Cancer Center, Bronx, NY
| | - Elaine Shum
- Division of Medical Oncology, Department of Medicine, Perlmutter Cancer Center, New York University Grossman School of Medicine, New York, NY
| | - Joshua K Sabari
- Division of Medical Oncology, Department of Medicine, Perlmutter Cancer Center, New York University Grossman School of Medicine, New York, NY
| | - Haiying Cheng
- Department of Oncology, Montefiore Einstein Comprehensive Cancer Center, Bronx, NY
| | - Rasim A Gucalp
- Department of Oncology, Montefiore Einstein Comprehensive Cancer Center, Bronx, NY
| | - Enrico Castellucci
- Department of Oncology, Montefiore Einstein Comprehensive Cancer Center, Bronx, NY
| | - Angel Qin
- Department of Internal Medicine, Division of Hematology-Oncology, University of Michigan, Ann Arbor, MI
| | - Shirish M Gadgeel
- Department of Internal Medicine, Henry Ford Cancer Institute, Henry Ford Health System, Detroit, MI
| | - Balazs Halmos
- Department of Oncology, Montefiore Einstein Comprehensive Cancer Center, Bronx, NY
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Pennock M, Halmos B, Bodner W, Cheng H, Gucalp R, Ohri N. Exploring causes and consequences of early discontinuation of durvalumab after chemoradiotherapy for non-small cell lung cancer. Clin Transl Radiat Oncol 2023; 41:100643. [PMID: 37346274 PMCID: PMC10279777 DOI: 10.1016/j.ctro.2023.100643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/22/2023] [Accepted: 05/29/2023] [Indexed: 06/23/2023] Open
Abstract
Introduction For most locally advanced non-small cell lung cancer (LA-NSCLC) patients who complete definitive chemoradiotherapy (CRT) and do not experience disease progression, one year of adjuvant durvalumab is recommended. Here, we explore causes and consequences of early durvalumab discontinuation. Materials and Methods We reviewed patients treated for LA-NSCLC with definitive CRT who began adjuvant durvalumab between 2017 and 2021. Duration of durvalumab receipt and causes for early discontinuation were tabulated. Logistic regression models were utilized to evaluate predictors of early durvalumab discontinuation. Landmark analyses were performed to explore associations between early durvalumab discontinuation and clinical outcomes (progression-free survival (PFS), overall survival (OS)). Results Fifty-nine patients were included. Forty-one patients (69%) discontinued durvalumab early, most commonly for disease progression (n = 14) or lung toxicity (n = 10). Multivariable analysis revealed mean heart radiotherapy dose (MHD) was associated with risk of durvalumab discontinuation from progression (HR = 2.34 per 10 Gy, p = 0.052), and there was a trend suggesting an association between MHD and risk of durvalumab discontinuation from lung toxicity (HR = 2.16 per 10 Gy, p = 0.126). Median PFS duration following durvalumab initiation was 14 months, and median OS duration was 32 months. Landmark analyses that excluded patients with progression or death within one year of durvalumab initiation demonstrated improved outcomes for patients who completed one year of durvalumab (2-year PFS 100% v. 40%, p < 0.001; 2-year OS 100% v. 67%, p = 0.862). Improved outcomes were observed for patients who received MHD below the cohort median (9.3 Gy) compared to patients with higher MHD (median PFS 32 months v. 8 months, p < 0.001; 2-year OS 69% v. 44%, p = 0.088). Conclusion For LA-NSCLC patients treated with CRT followed by immunotherapy, extent of cardiac irradiation may be a risk factor for immunotherapy discontinuation, disease recurrence, and death.
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Affiliation(s)
- Michael Pennock
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Radiation Oncology, 1625 Poplar Street, Suite 101, Bronx, NY 10461, United States
| | - Balazs Halmos
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Oncology, 1695 Eastchester Road, Bronx, NY 10461-2374, United States
| | - William Bodner
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Radiation Oncology, 1625 Poplar Street, Suite 101, Bronx, NY 10461, United States
| | - Haiying Cheng
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Oncology, 1695 Eastchester Road, Bronx, NY 10461-2374, United States
| | - Rasim Gucalp
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Oncology, 1695 Eastchester Road, Bronx, NY 10461-2374, United States
| | - Nitin Ohri
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Radiation Oncology, 1625 Poplar Street, Suite 101, Bronx, NY 10461, United States
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Mattes MD, Suneja G, Haffty BG, Takita C, Katz MS, Ohri N, Deville C, Siker ML, Park HS. Overcoming Barriers to Radiation Oncology Access in Low-Resource Settings in the United States. Adv Radiat Oncol 2021; 6:100802. [PMID: 34693080 PMCID: PMC8515237 DOI: 10.1016/j.adro.2021.100802] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/26/2021] [Accepted: 08/30/2021] [Indexed: 11/28/2022] Open
Abstract
Providing high-quality radiation therapy in medically underserved, low-resource environments can be challenging in the United States. During the American Society of Radiation Oncology 2020 Annual Meeting, the American Society for Radiation Oncology Committee on Health Equity, Diversity, and Inclusion hosted 4 radiation oncologists from both academic and community practices in an educational session. Speakers discussed creative ways to overcome barriers to equitable cancer care and outcomes for their vulnerable patient populations in both rural and urban settings. Successful tactics have included applying for state-sponsored grants, lobbying hospital leadership for equipment upgrades, implementing quality improvement programs specifically targeting the needs of the patient population, studying novel hypofractionation schedules, monitoring toxicities using wearable devices, and expanding transportation options.
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Affiliation(s)
- Malcolm D Mattes
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey
| | - Gita Suneja
- Department of Radiation Oncology, University of Utah School Huntsman Cancer Institute, Salt Lake City, Utah
| | - Bruce G Haffty
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey
| | - Cristiane Takita
- Department of Radiation Oncology, University of Miami Sylvester Comprehensive Cancer Center, Miami, Florida
| | - Matthew S Katz
- Radiation Oncology Associates, Lowell General Hospital Cancer Center, Lowell, Massachusetts
| | - Nitin Ohri
- Department of Radiation Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York
| | - Curtiland Deville
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Malika L Siker
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Henry S Park
- Department of Radiation Oncology, Yale School of Medicine, New Haven, Connecticut
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Bertholet J, Vinogradskiy Y, Hu Y, Carlson DJ. Advances in Image-Guided Adaptive Radiation Therapy. Int J Radiat Oncol Biol Phys 2021; 110:625-628. [PMID: 34089669 DOI: 10.1016/j.ijrobp.2021.02.047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Jenny Bertholet
- Division of Medical Radiation Physics, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Yanle Hu
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona
| | - David J Carlson
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania.
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Binkley MS, Jeon YJ, Nesselbush M, Moding EJ, Nabet BY, Almanza D, Kunder C, Stehr H, Yoo CH, Rhee S, Xiang M, Chabon JJ, Hamilton E, Kurtz DM, Gojenola L, Owen SG, Ko RB, Shin JH, Maxim PG, Lui NS, Backhus LM, Berry MF, Shrager JB, Ramchandran KJ, Padda SK, Das M, Neal JW, Wakelee HA, Alizadeh AA, Loo BW, Diehn M. KEAP1/NFE2L2 Mutations Predict Lung Cancer Radiation Resistance That Can Be Targeted by Glutaminase Inhibition. Cancer Discov 2020; 10:1826-1841. [PMID: 33071215 PMCID: PMC7710558 DOI: 10.1158/2159-8290.cd-20-0282] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 08/12/2020] [Accepted: 09/16/2020] [Indexed: 11/16/2022]
Abstract
Tumor genotyping is not routinely performed in localized non-small cell lung cancer (NSCLC) due to lack of associations of mutations with outcome. Here, we analyze 232 consecutive patients with localized NSCLC and demonstrate that KEAP1 and NFE2L2 mutations are predictive of high rates of local recurrence (LR) after radiotherapy but not surgery. Half of LRs occurred in tumors with KEAP1/NFE2L2 mutations, indicating that they are major molecular drivers of clinical radioresistance. Next, we functionally evaluate KEAP1/NFE2L2 mutations in our radiotherapy cohort and demonstrate that only pathogenic mutations are associated with radioresistance. Furthermore, expression of NFE2L2 target genes does not predict LR, underscoring the utility of tumor genotyping. Finally, we show that glutaminase inhibition preferentially radiosensitizes KEAP1-mutant cells via depletion of glutathione and increased radiation-induced DNA damage. Our findings suggest that genotyping for KEAP1/NFE2L2 mutations could facilitate treatment personalization and provide a potential strategy for overcoming radioresistance conferred by these mutations. SIGNIFICANCE: This study shows that mutations in KEAP1 and NFE2L2 predict for LR after radiotherapy but not surgery in patients with NSCLC. Approximately half of all LRs are associated with these mutations and glutaminase inhibition may allow personalized radiosensitization of KEAP1/NFE2L2-mutant tumors.This article is highlighted in the In This Issue feature, p. 1775.
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Affiliation(s)
- Michael S Binkley
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Young-Jun Jeon
- Stanford Cancer Institute, Stanford, California
- Department of Integrative Biotechnology, Sungkyunkwan University, Suwon, Republic of Korea
| | | | - Everett J Moding
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Barzin Y Nabet
- Department of Radiation Oncology, Stanford University, Stanford, California
- Stanford Cancer Institute, Stanford, California
| | - Diego Almanza
- Cancer Biology Program, Stanford University, Stanford, California
| | - Christian Kunder
- Department of Pathology, Stanford University, Stanford, California
| | - Henning Stehr
- Department of Pathology, Stanford University, Stanford, California
| | - Christopher H Yoo
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Siyeon Rhee
- Department of Biology, Stanford University, Stanford, California
| | - Michael Xiang
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California
| | | | - Emily Hamilton
- Cancer Biology Program, Stanford University, Stanford, California
| | - David M Kurtz
- Division of Oncology, Department of Medicine, Stanford University, Stanford, California
| | - Linda Gojenola
- Department of Pathology, Stanford University, Stanford, California
| | - Susie Grant Owen
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Ryan B Ko
- Department of Radiation Oncology, Stanford University, Stanford, California
| | | | - Peter G Maxim
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Natalie S Lui
- Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California
| | - Leah M Backhus
- Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California
| | - Mark F Berry
- Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California
| | - Joseph B Shrager
- Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California
| | - Kavitha J Ramchandran
- Stanford Cancer Institute, Stanford, California
- Division of Oncology, Department of Medicine, Stanford University, Stanford, California
| | - Sukhmani K Padda
- Stanford Cancer Institute, Stanford, California
- Division of Oncology, Department of Medicine, Stanford University, Stanford, California
| | - Millie Das
- Stanford Cancer Institute, Stanford, California
- Division of Oncology, Department of Medicine, Stanford University, Stanford, California
| | - Joel W Neal
- Stanford Cancer Institute, Stanford, California
- Division of Oncology, Department of Medicine, Stanford University, Stanford, California
| | - Heather A Wakelee
- Stanford Cancer Institute, Stanford, California
- Division of Oncology, Department of Medicine, Stanford University, Stanford, California
| | - Ash A Alizadeh
- Stanford Cancer Institute, Stanford, California
- Division of Oncology, Department of Medicine, Stanford University, Stanford, California
| | - Billy W Loo
- Department of Radiation Oncology, Stanford University, Stanford, California
- Stanford Cancer Institute, Stanford, California
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford University, Stanford, California.
- Stanford Cancer Institute, Stanford, California
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California
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6
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Ohri N, Halmos B, Bodner WR, Cheng H, Garg MK, Gucalp R, Guha C. Who Benefits the Most From Adjuvant Durvalumab After Chemoradiotherapy for Non-small Cell Lung Cancer? An Exploratory Analysis. Pract Radiat Oncol 2020; 11:e172-e179. [PMID: 33127337 DOI: 10.1016/j.prro.2020.09.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 08/24/2020] [Accepted: 09/14/2020] [Indexed: 12/27/2022]
Abstract
PURPOSE Adjuvant durvalumab is now recommended for most patients with locally advanced non-small cell lung cancer after concurrent chemoradiotherapy. Herein, we explore the clinical factors that may be associated with the benefit from adjuvant durvalumab. METHODS AND MATERIALS Patients with non-small cell lung cancer who were treated with definitive concurrent chemoradiotherapy at our institution between August 2013 and May 2019 were included in this analysis. Clinical and treatment characteristics were tested for associations with progression-free survival (PFS) in Cox models. Interaction terms were added to the PFS Cox models to explore factors that may modulate the effects of adjuvant durvalumab. PFS and overall survival (OS) rates were estimated using the Kaplan-Meier method, and comparisons between patient subgroups were performed using log rank testing. RESULTS A total of 105 patients met the eligibility criteria. Thirty-five patients (33%) received adjuvant durvalumab. Treatment with durvalumab was associated with significant improvement in PFS (1-year PFS: 67% vs 39%; log rank P = .006) and OS (1-year OS: 88% vs 76%; log rank P = .041). Exploratory analyses identified the neutrophil-to-lymphocyte ratio (NLR) after radiation therapy (RT) as a factor that may be associated with a benefit from durvalumab. For patients with post-RT NLR exceeding the cohort's median value of 4.3, receipt of adjuvant durvalumab was not associated with a significant PFS improvement (1-year PFS: 45% vs 36%; log rank P = .702). For patients with post-RT NLR <4.3, durvalumab receipt was associated with improved PFS (69% vs 41%; P = .009). High mean RT doses delivered to the heart and esophagus were associated with high post-RT NLR. CONCLUSIONS We identified low NLR after chemoradiotherapy as a factor that may be associated with a benefit from adjuvant durvalumab. Validation studies are warranted.
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Affiliation(s)
- Nitin Ohri
- Department of Radiation Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, The Bronx, New York.
| | - Balazs Halmos
- Department of Hematology/Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, The Bronx, New York
| | - William R Bodner
- Department of Radiation Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, The Bronx, New York
| | - Haiying Cheng
- Department of Hematology/Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, The Bronx, New York
| | - Madhur K Garg
- Department of Radiation Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, The Bronx, New York
| | - Rasim Gucalp
- Department of Hematology/Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, The Bronx, New York
| | - Chandan Guha
- Department of Radiation Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, The Bronx, New York
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Duan C, Chaovalitwongse WA, Bai F, Hippe DS, Wang S, Thammasorn P, Pierce LA, Liu X, You J, Miyaoka RS, Vesselle HJ, Kinahan PE, Rengan R, Zeng J, Bowen SR. Sensitivity analysis of FDG PET tumor voxel cluster radiomics and dosimetry for predicting mid-chemoradiation regional response of locally advanced lung cancer. Phys Med Biol 2020; 65:205007. [PMID: 33027064 PMCID: PMC7593986 DOI: 10.1088/1361-6560/abb0c7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We investigated the sensitivity of regional tumor response prediction to variability in voxel clustering techniques, imaging features, and machine learning algorithms in 25 patients with locally advanced non-small cell lung cancer (LA-NSCLC) enrolled on the FLARE-RT clinical trial. Metabolic tumor volumes (MTV) from pre-chemoradiation (PETpre) and mid-chemoradiation fluorodeoxyglucose-positron emission tomography (FDG PET) images (PETmid) were subdivided into K-means or hierarchical voxel clusters by standardized uptake values (SUV) and 3D-positions. MTV cluster separability was evaluated by CH index, and morphologic changes were captured by Dice similarity and centroid Euclidean distance. PETpre conventional features included SUVmean, MTV/MTV cluster size, and mean radiation dose. PETpre radiomics consisted of 41 intensity histogram and 3D texture features (PET Oncology Radiomics Test Suite) extracted from MTV or MTV clusters. Machine learning models (multiple linear regression, support vector regression, logistic regression, support vector machines) of conventional features or radiomic features were constructed to predict PETmid response. Leave-one-out-cross-validated root-mean-squared-error (RMSE) for continuous response regression (ΔSUVmean) and area-under-receiver-operating-characteristic-curve (AUC) for binary response classification were calculated. K-means MTV 2-clusters (MTVhi, MTVlo) achieved maximum CH index separability (Friedman p < 0.001). Between PETpre and PETmid, MTV cluster pairs overlapped (Dice 0.70-0.87) and migrated 0.6-1.1 cm. PETmid ΔSUVmean response prediction was superior in MTV and MTVlo (RMSE = 0.17-0.21) compared to MTVhi (RMSE = 0.42-0.52, Friedman p < 0.001). PETmid ΔSUVmean response class prediction performance trended higher in MTVlo (AUC = 0.83-0.88) compared to MTVhi (AUC = 0.44-0.58, Friedman p = 0.052). Models were more sensitive to MTV/MTV cluster regions (Friedman p = 0.026) than feature sets/algorithms (Wilcoxon signed-rank p = 0.36). Top-ranked radiomic features included GLZSM-LZHGE (large-zone-high-SUV), GTSDM-CP (cluster-prominence), GTSDM-CS (cluster-shade) and NGTDM-CNT (contrast). Top-ranked features were consistent between MTVhi and MTVlo cluster pairs but varied between MTVhi-MTVlo clusters, reflecting distinct regional radiomic phenotypes. Variability in tumor voxel cluster response prediction can inform robust radiomic target definition for risk-adaptive chemoradiation in patients with LA-NSCLC. FLARE-RT trial: NCT02773238.
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Affiliation(s)
- Chunyan Duan
- Department of Mechanical Engineering, Tongji University School of Mechanical Engineering, Shanghai China
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - W. Art Chaovalitwongse
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Fangyun Bai
- Department of Management Science and Engineering, Tongji University School of Economics and Management, Shanghai China
- Department of Industrial, Manufacturing, & Systems Engineering, University of Texas at Arlington College of Engineering, Arlington, TX
| | - Daniel S. Hippe
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Shouyi Wang
- Department of Industrial, Manufacturing, & Systems Engineering, University of Texas at Arlington College of Engineering, Arlington, TX
| | - Phawis Thammasorn
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Larry A. Pierce
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Xiao Liu
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Jianxin You
- Department of Management Science and Engineering, Tongji University School of Economics and Management, Shanghai China
| | - Robert S. Miyaoka
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Hubert J. Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Paul E. Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - Stephen R. Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
- Department of Radiology, University of Washington School of Medicine, Seattle WA
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Cardiac Irradiation Predicts Activity Decline in Patients Receiving Concurrent Chemoradiation for Locally Advanced Lung Cancer. Int J Radiat Oncol Biol Phys 2020; 108:597-601. [PMID: 32497682 DOI: 10.1016/j.ijrobp.2020.05.042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 03/11/2020] [Accepted: 05/23/2020] [Indexed: 11/23/2022]
Abstract
PURPOSE Many patients with lung cancer are inactive due to their disease and underlying comorbidities, and activity levels can decline further during cancer therapy. Here we explore dosimetric predictors of activity decline in a cohort of patients who underwent continuous activity monitoring during definitive concurrent chemoradiotherapy (CRT) for locally advanced lung cancer. METHODS AND MATERIALS We identified patients who participated in prospective clinical trials involving the use of a commercial fitness tracker throughout the course of CRT. For each patient, we applied linear regression to log-transformed daily step counts to compute the weekly rate of activity change from 1 week before radiation therapy (RT) initiation to 2 weeks after RT completion. Clinical and dosimetric factors were tested as predictors of activity change using linear regressions. RESULTS Forty-six patient met the eligibility criteria. Median age was 66 years (range, 38-90). Pretreatment Eastern Cooperative Oncology Group performance status was 0, 1, and 2 for 17%, 70%, and 13%, respectively. Mean lung dose ranged from 5.0 to 23.5 Gy, mean esophagus dose from 1.1 to 39.6 Gy, and mean heart dose from 0.6 to 31.5 Gy. Median daily step count average was 5861 (interquartile range, 3540-8282) before RT and 3422 (interquartile range, 2364-5395) 2 weeks after RT completion. Rate of activity change was not significantly associated with age, performance status, or mean RT dose received by lungs or esophagus. In multivariate analysis, mean heart dose was significantly associated with rate of activity decline, with a 3.1% reduction in step count per week for every 10 Gy increase in mean heart dose (95% confidence interval: 0.5-5.7, P = .023). CONCLUSIONS Extent of cardiac irradiation is associated with the rate of physical activity decline during CRT for lung cancer. Our novel finding contributes to the growing body of evidence that adverse effects of cardiac irradiation may be manifested at early time points.
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Brodin NP, Tomé WA, Abraham T, Ohri N. 18F-Fluorodeoxyglucose PET in Locally Advanced Non-small Cell Lung Cancer: From Predicting Outcomes to Guiding Therapy. PET Clin 2020; 15:55-63. [PMID: 31735302 DOI: 10.1016/j.cpet.2019.08.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
PET using 18-fluorodeoxyglucose (FDG) has become an important part of the work-up for non-small cell lung cancer (NSCLC). This article summarizes advancements in using FDG-PET for patients with locally advanced NSCLC treated with definitive radiation therapy (RT). This article discusses prognostication of outcome based on pretreatment or midtreatment PET metrics, using textural image features to predict treatment outcomes, and using PET to define RT target volumes and inform RT dose modifications. The role of PET is evolving and is moving toward using quantitative image information, with the overarching goal of individualizing therapy to improve outcomes for patients with NSCLC.
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Affiliation(s)
- N Patrik Brodin
- Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY 10461, USA.
| | - Wolfgang A Tomé
- Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY 10461, USA; Department of Neurology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Tony Abraham
- Department of Radiology (Nuclear Medicine), Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Nitin Ohri
- Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY 10461, USA
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Binkley MS, Koenig JL, Kashyap M, Xiang M, Liu Y, Sodji Q, Maxim PG, Diehn M, Loo BW, Gensheimer MF. Predicting per-lesion local recurrence in locally advanced non-small cell lung cancer following definitive radiation therapy using pre- and mid-treatment metabolic tumor volume. Radiat Oncol 2020; 15:114. [PMID: 32429982 PMCID: PMC7238662 DOI: 10.1186/s13014-020-01546-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 04/22/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We evaluated whether pre- and mid-treatment metabolic tumor volume (MTV) predicts per lesion local recurrence (LR) in patients treated with definitive radiation therapy (RT, dose≥60 Gy) for locally advanced non-small cell lung cancer (NSCLC). METHODS We retrospectively reviewed records of patients with stage III NSCLC treated from 2006 to 2018 with pre- and mid-RT PET-CT. We measured the MTV of treated lesions on the pre-RT (MTVpre) and mid-RT (MTVmid) PET-CT. LR was defined per lesion as recurrence within the planning target volume. Receiver operating characteristic (ROC) curves, cumulative incidence rates, and uni- and multivariable (MVA) competing risk regressions were used to evaluate the association between MTV and LR. RESULTS We identified 111 patients with 387 lesions (112 lung tumors and 275 lymph nodes). Median age was 68 years, 69.4% were male, 46.8% had adenocarcinoma, 39.6% had squamous cell carcinoma, and 95.5% received concurrent chemotherapy. Median follow-up was 38.7 months. 3-year overall survival was 42.3%. 3-year cumulative incidence of LR was 26.8% per patient and 11.9% per lesion. Both MTVpre and MTVmid were predictive of LR by ROC (AUC = 0.71 and 0.76, respectively) and were significantly associated with LR on MVA (P = 0.004 and P = 7.1e-5, respectively). Among lesions at lower risk of LR based on MTVpre, higher MTVmid was associated with LR (P = 0.001). CONCLUSION Per-lesion, larger MTVpre and MTVmid predicted for increased risk of LR. MTVmid was more highly predictive of LR than MTVpre and if validated may allow for further discrimination of high-risk lesions at mid-RT informing dose painting strategies.
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Affiliation(s)
- Michael S Binkley
- Department of Radiation Oncology, Stanford University School of Medicine and Stanford Cancer Institute, 875 Blake Wilbur Dr MC 5847, Stanford, CA, 94305, USA
| | - Julie L Koenig
- Department of Radiation Oncology, Stanford University School of Medicine and Stanford Cancer Institute, 875 Blake Wilbur Dr MC 5847, Stanford, CA, 94305, USA
| | - Mehr Kashyap
- Department of Radiation Oncology, Stanford University School of Medicine and Stanford Cancer Institute, 875 Blake Wilbur Dr MC 5847, Stanford, CA, 94305, USA
| | - Michael Xiang
- Department of Radiation Oncology, Stanford University School of Medicine and Stanford Cancer Institute, 875 Blake Wilbur Dr MC 5847, Stanford, CA, 94305, USA
| | - Yufei Liu
- Department of Radiation Oncology, Stanford University School of Medicine and Stanford Cancer Institute, 875 Blake Wilbur Dr MC 5847, Stanford, CA, 94305, USA
| | - Quaovi Sodji
- Department of Radiation Oncology, Stanford University School of Medicine and Stanford Cancer Institute, 875 Blake Wilbur Dr MC 5847, Stanford, CA, 94305, USA
| | - Peter G Maxim
- Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford University School of Medicine and Stanford Cancer Institute, 875 Blake Wilbur Dr MC 5847, Stanford, CA, 94305, USA.
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, California, USA.
| | - Billy W Loo
- Department of Radiation Oncology, Stanford University School of Medicine and Stanford Cancer Institute, 875 Blake Wilbur Dr MC 5847, Stanford, CA, 94305, USA.
| | - Michael F Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine and Stanford Cancer Institute, 875 Blake Wilbur Dr MC 5847, Stanford, CA, 94305, USA.
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Bowen SR, Hippe DS, Chaovalitwongse WA, Duan C, Thammasorn P, Liu X, Miyaoka RS, Vesselle HJ, Kinahan PE, Rengan R, Zeng J. Voxel Forecast for Precision Oncology: Predicting Spatially Variant and Multiscale Cancer Therapy Response on Longitudinal Quantitative Molecular Imaging. Clin Cancer Res 2019; 25:5027-5037. [PMID: 31142507 DOI: 10.1158/1078-0432.ccr-18-3908] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 03/28/2019] [Accepted: 05/17/2019] [Indexed: 12/25/2022]
Abstract
PURPOSE Prediction of spatially variant response to cancer therapies can inform risk-adaptive management within precision oncology. We developed the "Voxel Forecast" multiscale regression framework for predicting spatially variant tumor response to chemoradiotherapy on fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging. EXPERIMENTAL DESIGN Twenty-five patients with locally advanced non-small cell lung cancer, enrolled on the FLARE-RT phase II trial (NCT02773238), underwent FDG PET/CT imaging prior to (PETpre) and during week 3 (PETmid) of concurrent chemoradiotherapy. Voxel Forecast was designed to predict tumor voxel standardized uptake value (SUV) on PETmid from baseline patient-level and voxel-level covariates using a custom generalized least squares (GLS) algorithm. Matérn covariance matrices were fit to patient- specific empirical variograms of distance-dependent intervoxel correlation. Regression coefficients from variogram-based weights and corresponding standard errors were estimated using the jackknife technique. The framework was validated using statistical simulations of known spatially variant tumor response. Mean absolute prediction errors (MAEs) of Voxel Forecast models were calculated under leave-one-patient-out cross-validation. RESULTS Patient-level forecasts resulted in tumor voxel SUV MAE on PETmid of 1.5 g/mL while combined patient- and voxel-level forecasts achieved lower MAE of 1.0 g/mL (P < 0.0001). PETpre voxel SUV was the most important predictor of PETmid voxel SUV. Patients with a greater percentage of under-responding tumor voxels were classified as PETmid nonresponders (P = 0.030) with worse overall survival prognosis (P < 0.001). CONCLUSIONS Voxel Forecast multiscale regression provides a statistical framework to predict voxel-wise response patterns during therapy. Voxel Forecast can be extended to predict spatially variant response on multimodal quantitative imaging and may eventually guide optimized spatial-temporal dose distributions for precision cancer therapy.
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Affiliation(s)
- Stephen R Bowen
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington. .,Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
| | - Daniel S Hippe
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - W Art Chaovalitwongse
- Department of Industrial Engineering, University of Arkansas, Fayetteville, Arkansas
| | - Chunyan Duan
- Department of Industrial Engineering, University of Arkansas, Fayetteville, Arkansas.,Department of Management Science and Engineering, Tongji University, Shanghai, China
| | - Phawis Thammasorn
- Department of Industrial Engineering, University of Arkansas, Fayetteville, Arkansas
| | - Xiao Liu
- Department of Industrial Engineering, University of Arkansas, Fayetteville, Arkansas
| | - Robert S Miyaoka
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Hubert J Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Paul E Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
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Radiation Therapy in Non-small-Cell Lung Cancer. Radiat Oncol 2019. [DOI: 10.1007/978-3-319-52619-5_34-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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