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Cao Y, Aryal M, Li P, Lee C, Schipper M, You D, Jaworski E, Gharzai L, Shah J, Eisbruch A, Mierzwa M. Diffusion MRI correlation with p16 status and prediction for tumor progression in locally advanced head and neck cancer. Front Oncol 2023; 13:998186. [PMID: 38188292 PMCID: PMC10771284 DOI: 10.3389/fonc.2023.998186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/06/2023] [Indexed: 01/09/2024] Open
Abstract
Purpose To investigate p16 effects on diffusion image metrics and associations with tumor progression in patients with locally advanced head and neck cancers. Methods Diffusion images pretreatment and after 20 Gy (2wk) of RT were analyzed in patients with cT4/N3 p16+ oropharynx cancer (OPSCC) (N=51) and locoregionally advanced head and neck squamous cell carcinoma (LAHNSCC) (N=28), enrolled onto a prospective adaptive RT trial. Mean ADC values, subvolumes with ADC <1.2 um2/ms (TVLADC), and peak values of low (µL) and high (µH) components of ADC histograms in primary and total nodal gross tumor volumes were analyzed for prediction of freedom from local, distant, or any progression (FFLP, FFDP or FFLRDP) using multivariate Cox proportional-hazards model with clinical factors. P value with false discovery control <0.05 was considered as significant. Results With a mean follow up of 36 months, 18 of LAHNSCC patients and 16 of p16+ OPSCC patients had progression. After adjusting for p16, small µL and ADC values, and large TVLADC of primary tumors pre-RT were significantly associated with superior FFLRDP, FFLP and FFDP in the LAHNSCC (p<0.05), but no diffusion metrics were significant in p16+ oropharynx cancers. Post ad hoc analysis of the p16+ OPSCC only showed that large TVLADC of the total nodal burden pre-RT was significantly associated with inferior FFDP (p=0.05). Conclusion ADC metrics were associated with different progression patterns in the LAHNSCC and p16+ OPSCC, possibly explained by differences in cancer biology and morphology. A deep understanding of ADC metrics is warranted to establish imaging biomarkers for adaptive RT in HNSCC.
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Affiliation(s)
- Yue Cao
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - M. Aryal
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - P. Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - C. Lee
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - M. Schipper
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - D. You
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - E. Jaworski
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - L. Gharzai
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - J. Shah
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
- Department of Radiation Oncology, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States
| | - A. Eisbruch
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Michelle Mierzwa
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
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Trada Y, Keall P, Jameson M, Moses D, Lin P, Chlap P, Holloway L, Min M, Forstner D, Fowler A, Lee MT. Changes in serial multiparametric MRI and FDG-PET/CT functional imaging during radiation therapy can predict treatment response in patients with head and neck cancer. Eur Radiol 2023; 33:8788-8799. [PMID: 37405500 PMCID: PMC10667402 DOI: 10.1007/s00330-023-09843-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 04/03/2023] [Accepted: 04/14/2023] [Indexed: 07/06/2023]
Abstract
OBJECTIVES To test if tumour changes measured using combination of diffusion-weighted imaging (DWI) MRI and FDG-PET/CT performed serially during radiotherapy (RT) in mucosal head and neck carcinoma can predict treatment response. METHODS Fifty-five patients from two prospective imaging biomarker studies were analysed. FDG-PET/CT was performed at baseline, during RT (week 3), and post RT (3 months). DWI was performed at baseline, during RT (weeks 2, 3, 5, 6), and post RT (1 and 3 months). The ADCmean from DWI and FDG-PET parameters SUVmax, SUVmean, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were measured. Absolute and relative change (%∆) in DWI and PET parameters were correlated to 1-year local recurrence. Patients were categorised into favourable, mixed, and unfavourable imaging response using optimal cut-off (OC) values of DWI and FDG-PET parameters and correlated to local control. RESULTS The 1-year local, regional, and distant recurrence rates were 18.2% (10/55), 7.3% (4/55), and 12.7% (7/55), respectively. ∆Week 3 ADCmean (AUC 0.825, p = 0.003; OC ∆ > 24.4%) and ∆MTV (AUC 0.833, p = 0.001; OC ∆ > 50.4%) were the best predictors of local recurrence. Week 3 was the optimal time point for assessing DWI imaging response. Using a combination of ∆ADCmean and ∆MTV improved the strength of correlation to local recurrence (p ≤ 0.001). In patients who underwent both week 3 MRI and FDG-PET/CT, significant differences in local recurrence rates were seen between patients with favourable (0%), mixed (17%), and unfavourable (78%) combined imaging response. CONCLUSIONS Changes in mid-treatment DWI and FDG-PET/CT imaging can predict treatment response and could be utilised in the design of future adaptive clinical trials. CLINICAL RELEVANCE STATEMENT Our study shows the complementary information provided by two functional imaging modalities for mid-treatment response prediction in patients with head and neck cancer. KEY POINTS •FDG-PET/CT and DWI MRI changes in tumour during radiotherapy in head and neck cancer can predict treatment response. •Combination of FDG-PET/CT and DWI parameters improved correlation to clinical outcome. •Week 3 was the optimal time point for DWI MRI imaging response assessment.
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Affiliation(s)
- Yuvnik Trada
- Department of Radiation Oncology, Calvary Mater Newcastle, Edith St, Waratah, NSW, 2298, Australia.
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.
| | - Paul Keall
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- ACRF Image X Institute, University of Sydney, Sydney, NSW, Australia
| | - Michael Jameson
- GenesisCare St Vincents Hospital, Sydney, NSW, Australia
- St Vincents Clinical School, Faculty of Medicine, University of Sydney, Sydney, NSW, Australia
| | - Daniel Moses
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia
- Department of Medical Imaging, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Peter Lin
- Department of Nuclear Medicine and PET, Liverpool Hospital, Liverpool, NSW, Australia
- School of Medicine, Western Sydney University, Sydney, NSW, Australia
| | - Phillip Chlap
- Department of Radiation Oncology, Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
- South Western Clinical School, School of Medicine, University of New South Wales, Sydney, NSW, Australia
- Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Lois Holloway
- Department of Radiation Oncology, Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
- South Western Clinical School, School of Medicine, University of New South Wales, Sydney, NSW, Australia
- Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Myo Min
- University of Sunshine Coast, Birtinya, QLD, Australia
- Sunshine Coast University Hospital, Sunshine Coast, QLD, Australia
- Griffith University, Sunshine Coast, QLD, Australia
| | - Dion Forstner
- GenesisCare St Vincents Hospital, Sydney, NSW, Australia
- St Vincents Clinical School, Faculty of Medicine, University of Sydney, Sydney, NSW, Australia
| | - Allan Fowler
- Department of Radiation Oncology, Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
| | - Mark T Lee
- Department of Radiation Oncology, Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
- South Western Clinical School, School of Medicine, University of New South Wales, Sydney, NSW, Australia
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Boeke S, Winter RM, Leibfarth S, Krueger MA, Bowden G, Cotton J, Pichler BJ, Zips D, Thorwarth D. Machine learning identifies multi-parametric functional PET/MR imaging cluster to predict radiation resistance in preclinical head and neck cancer models. Eur J Nucl Med Mol Imaging 2023; 50:3084-3096. [PMID: 37148296 PMCID: PMC10382355 DOI: 10.1007/s00259-023-06254-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 04/25/2023] [Indexed: 05/08/2023]
Abstract
PURPOSE Tumor hypoxia and other microenvironmental factors are key determinants of treatment resistance. Hypoxia positron emission tomography (PET) and functional magnetic resonance imaging (MRI) are established prognostic imaging modalities to identify radiation resistance in head-and-neck cancer (HNC). The aim of this preclinical study was to develop a multi-parametric imaging parameter specifically for focal radiotherapy (RT) dose escalation using HNC xenografts of different radiation sensitivities. METHODS A total of eight human HNC xenograft models were implanted into 68 immunodeficient mice. Combined PET/MRI using dynamic [18F]-fluoromisonidazole (FMISO) hypoxia PET, diffusion-weighted (DW), and dynamic contrast-enhanced MRI was carried out before and after fractionated RT (10 × 2 Gy). Imaging data were analyzed on voxel-basis using principal component (PC) analysis for dynamic data and apparent diffusion coefficients (ADCs) for DW-MRI. A data- and hypothesis-driven machine learning model was trained to identify clusters of high-risk subvolumes (HRSs) from multi-dimensional (1-5D) pre-clinical imaging data before and after RT. The stratification potential of each 1D to 5D model with respect to radiation sensitivity was evaluated using Cohen's d-score and compared to classical features such as mean/peak/maximum standardized uptake values (SUVmean/peak/max) and tumor-to-muscle-ratios (TMRpeak/max) as well as minimum/valley/maximum/mean ADC. RESULTS Complete 5D imaging data were available for 42 animals. The final preclinical model for HRS identification at baseline yielding the highest stratification potential was defined in 3D imaging space based on ADC and two FMISO PCs ([Formula: see text]). In 1D imaging space, only clusters of ADC revealed significant stratification potential ([Formula: see text]). Among all classical features, only ADCvalley showed significant correlation to radiation resistance ([Formula: see text]). After 2 weeks of RT, FMISO_c1 showed significant correlation to radiation resistance ([Formula: see text]). CONCLUSION A quantitative imaging metric was described in a preclinical study indicating that radiation-resistant subvolumes in HNC may be detected by clusters of ADC and FMISO using combined PET/MRI which are potential targets for future functional image-guided RT dose-painting approaches and require clinical validation.
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Affiliation(s)
- Simon Boeke
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - René M Winter
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany
| | - Sara Leibfarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany
| | - Marcel A Krueger
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany
| | - Gregory Bowden
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany
| | - Jonathan Cotton
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany
| | - Bernd J Pichler
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Daniel Zips
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniela Thorwarth
- German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.
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4
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Romeo V, Stanzione A, Ugga L, Cuocolo R, Cocozza S, Quarantelli M, Chawla S, Farina D, Golay X, Parker G, Shukla-Dave A, Thoeny H, Vidiri A, Brunetti A, Surlan-Popovic K, Bisdas S. Clinical indications and acquisition protocol for the use of dynamic contrast-enhanced MRI in head and neck cancer squamous cell carcinoma: recommendations from an expert panel. Insights Imaging 2022; 13:198. [PMID: 36528678 PMCID: PMC9759606 DOI: 10.1186/s13244-022-01317-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/19/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The clinical role of perfusion-weighted MRI (PWI) in head and neck squamous cell carcinoma (HNSCC) remains to be defined. The aim of this study was to provide evidence-based recommendations for the use of PWI sequence in HNSCC with regard to clinical indications and acquisition parameters. METHODS Public databases were searched, and selected papers evaluated applying the Oxford criteria 2011. A questionnaire was prepared including statements on clinical indications of PWI as well as its acquisition technique and submitted to selected panelists who worked in anonymity using a modified Delphi approach. Each panelist was asked to rate each statement using a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Statements with scores equal or inferior to 5 assigned by at least two panelists were revised and re-submitted for the subsequent Delphi round to reach a final consensus. RESULTS Two Delphi rounds were conducted. The final questionnaire consisted of 6 statements on clinical indications of PWI and 9 statements on the acquisition technique of PWI. Four of 19 (21%) statements obtained scores equal or inferior to 5 by two panelists, all dealing with clinical indications. The Delphi process was considered concluded as reasons entered by panelists for lower scores were mainly related to the lack of robust evidence, so that no further modifications were suggested. CONCLUSIONS Evidence-based recommendations on the use of PWI have been provided by an independent panel of experts worldwide, encouraging a standardized use of PWI across university and research centers to produce more robust evidence.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", Naples, Italy
- Interdepartmental Research Center on Management and Innovation in Healthcare - CIRMIS, University of Naples Federico II, Naples, Italy
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Mario Quarantelli
- Biostructure and Bioimaging Institute, National Research Council, Naples, Italy
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine, the University of Pennsylvania, Philadelphia, PA, USA
| | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Xavier Golay
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Trust, London, UK
| | - Geoff Parker
- Department of Computer Science, Centre for Medical Image Computing, Queen Square Institute of Neurology, University College London, London, UK
| | - Amita Shukla-Dave
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Harriet Thoeny
- Department of Radiology, Cantonal Hospital Fribourg, University of Fribourg, Fribourg, Switzerland
| | - Antonello Vidiri
- Department of Radiology and Diagnostic Imaging, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | | | - Sotirios Bisdas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK.
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Trust, London, UK.
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5
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Mierzwa ML, Aryal M, Lee C, Schipper M, VanTil M, Rivera KM, Swiecicki PL, Casper KA, Malloy KM, Spector ME, Shuman AG, Chinn SB, Prince ME, Stucken CL, Rosko AJ, Lawrence TS, Brenner JC, Rosen B, Schonewolf CA, Shah J, Eisbruch A, Worden FP, Cao Y. Randomized Phase II Study of Physiologic MRI-Directed Adaptive Radiation Boost in Poor Prognosis Head and Neck Cancer. Clin Cancer Res 2022; 28:5049-5057. [PMID: 36107219 PMCID: PMC9773159 DOI: 10.1158/1078-0432.ccr-22-1522] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/06/2022] [Accepted: 09/13/2022] [Indexed: 01/24/2023]
Abstract
PURPOSE We conducted a randomized phase II multicenter clinical trial to test the hypothesis that physiologic MRI-based radiotherapy (RT) dose escalation would improve the outcome of patients with poor prognosis head and neck cancer. PATIENTS AND METHODS MRI was acquired at baseline and at RT fraction 10 to create low blood volume/apparent diffusion coefficient maps for RT boost subvolume definition in gross tumor volume. Patients were randomized to receive 70 Gy (standard RT) or 80 Gy to the boost subvolume (RT boost) with concurrent weekly platinum. The primary endpoint was disease-free survival (DFS) with significance defined at a one-sided 0.1 level, and secondary endpoints included locoregional failure (LRF), overall survival (OS), comparison of adverse events and patient reported outcomes (PRO). RESULTS Among 81 randomized patients, neither the primary endpoint of DFS (HR = 0.849, P = 0.31) nor OS (HR = 1.19, P = 0.66) was significantly improved in the RT boost arm. However, the incidence of LRF was significantly improved with the addition of the RT boost (HR = 0.43, P = 0.047). Two-year estimates [90% confidence interval (CI)] of the cumulative incidence of LRF were 40% (27%-53%) in the standard RT arm and 18% (10%-31%) in the RT boost arm. Two-year estimates (90% CI) for DFS were 48% (34%-60%) in the standard RT arm and 57% (43%-69%) in the RT boost arm. There were no significant differences in toxicity or longitudinal differences seen in EORTC QLQ30/HN35 subscales between treatment arms in linear mixed-effects models. CONCLUSIONS Physiologic MRI-based RT boost decreased LRF without a significant increase in grade 3+ toxicity or longitudinal PRO differences, but did not significantly improve DFS or OS. Additional improvements in systemic therapy are likely necessary to realize improvements in DFS and OS.
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Affiliation(s)
- Michelle L Mierzwa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Madhava Aryal
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Matthew Schipper
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Monica VanTil
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | | | - Paul L. Swiecicki
- Department of Internal Medicine, Medical Oncology, University of Michigan, Ann Arbor, Michigan
| | - Keith A. Casper
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Kelly M. Malloy
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Matthew E. Spector
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Andrew G. Shuman
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Steven B. Chinn
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Mark E.P. Prince
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Chaz L. Stucken
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Andrew J. Rosko
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | | | - J Chad Brenner
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Benjamin Rosen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | | | - Jennifer Shah
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Avraham Eisbruch
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Francis P. Worden
- Department of Internal Medicine, Medical Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
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Cao Y, Haring CT, Brummel C, Bhambhani C, Aryal M, Lee C, Heft Neal M, Bhangale A, Gu W, Casper K, Malloy K, Sun Y, Shuman A, Prince ME, Spector ME, Chinn S, Shah J, Schonewolf C, McHugh JB, Mills RE, Tewari M, Worden FP, Swiecicki PL, Mierzwa M, Brenner JC. Early HPV ctDNA Kinetics and Imaging Biomarkers Predict Therapeutic Response in p16+ Oropharyngeal Squamous Cell Carcinoma. Clin Cancer Res 2022; 28:350-359. [PMID: 34702772 PMCID: PMC8785355 DOI: 10.1158/1078-0432.ccr-21-2338] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/11/2021] [Accepted: 10/20/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE In locally advanced p16+ oropharyngeal squamous cell carcinoma (OPSCC), (i) to investigate kinetics of human papillomavirus (HPV) circulating tumor DNA (ctDNA) and association with tumor progression after chemoradiation, and (ii) to compare the predictive value of ctDNA to imaging biomarkers of MRI and FDG-PET. EXPERIMENTAL DESIGN Serial blood samples were collected from patients with AJCC8 stage III OPSCC (n = 34) enrolled on a randomized trial: pretreatment; during chemoradiation at weeks 2, 4, and 7; and posttreatment. All patients also had dynamic-contrast-enhanced and diffusion-weighted MRI, as well as FDG-PET scans pre-chemoradiation and week 2 during chemoradiation. ctDNA values were analyzed for prediction of freedom from progression (FFP), and correlations with aggressive tumor subvolumes with low blood volume (TVLBV) and low apparent diffusion coefficient (TVLADC), and metabolic tumor volume (MTV) using Cox proportional hazards model and Spearman rank correlation. RESULTS Low pretreatment ctDNA and an early increase in ctDNA at week 2 compared with baseline were significantly associated with superior FFP (P < 0.02 and P < 0.05, respectively). At week 4 or 7, neither ctDNA counts nor clearance were significantly predictive of progression (P = 0.8). Pretreatment ctDNA values were significantly correlated with nodal TVLBV, TVLADC, and MTV pre-chemoradiation (P < 0.03), while the ctDNA values at week 2 were correlated with these imaging metrics in primary tumor. Multivariate analysis showed that ctDNA and the imaging metrics performed comparably to predict FFP. CONCLUSIONS Early ctDNA kinetics during definitive chemoradiation may predict therapy response in stage III OPSCC.
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Affiliation(s)
- Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Catherine T Haring
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
| | - Collin Brummel
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
| | - Chandan Bhambhani
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Madhava Aryal
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Molly Heft Neal
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
| | - Apurva Bhangale
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
| | - Wenjin Gu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Keith Casper
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Kelly Malloy
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Yilun Sun
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Andrew Shuman
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Mark E Prince
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Matthew E Spector
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Steven Chinn
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Jennifer Shah
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Caitlin Schonewolf
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Jonathan B McHugh
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | - Ryan E Mills
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Muneesh Tewari
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Francis P Worden
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Paul L Swiecicki
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Michelle Mierzwa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - J Chad Brenner
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
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7
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Wang P, Wang X, Xu L, Yu J, Teng F. Prediction of the effects of radiation therapy in esophageal cancer using diffusion and perfusion MRI. Cancer Sci 2021; 112:5046-5054. [PMID: 34618997 PMCID: PMC8645758 DOI: 10.1111/cas.15156] [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: 08/09/2021] [Revised: 09/21/2021] [Accepted: 09/27/2021] [Indexed: 11/30/2022] Open
Abstract
Chemoradiation therapy (CRT) of locally advanced esophageal cancer (LAEC), although improving outcomes of patients, still results in 50% of local failure. An early prediction could identify patients at high risk of poor response for individualized adaptive treatment. We aimed to investigate physiological changes in LAEC using diffusion and perfusion magnetic resonance imaging (MRI) for early prediction of treatment response. In the study, 115 LAEC patients treated with CRT were enrolled (67 in the discovery cohort and 48 in the validation cohort). MRI scans were performed before radiotherapy (pre‐RT) and at week 3 during RT (mid‐RT). Gross tumor volume (GTV) of primary tumor was delineated on T2‐weighted images. Within the GTV, the hypercellularity volume (VHC) and high blood volume (VHBV) were defined based on the analysis of ADC and fractional plasma volume (Vp) histogram distributions within the tumors in the discovery cohort. The median GTVs were 28 cc ± 2.2 cc at pre‐RT and 16.7 cc ± 1.5 cc at mid‐RT. Respectively, VHC and VHBV decreased from 4.7 cc ± 0.7 cc and 5.7 cc ± 0.7 cc at pre‐RT to 2.8 cc ± 0.4 cc and 3.5 cc ± 0.5 cc at mid‐RT. Smaller VHC at mid‐RT (area under the curve [AUC] = 0.67, P = .05; AUC = 0.66, P = .05) and further decrease in VHC at mid‐RT (AUC = 0.7, P = .01; AUC = 0.69, P = .03) were associated with longer progression‐free survival (PFS) in both discovery and validation cohort. No significant predictive effects were shown in GTV and VHBV at any time point. In conclusion, we demonstrated that VHC represents aggressive subvolumes in LAEC. Further analysis will be carried out to confirm the correlations between the changes in image‐phenotype subvolumes and local failure to determine the radiation‐resistant tumor subvolumes, which may be useful for dose escalation.
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Affiliation(s)
- Peiliang Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Cheeloo college of medicine, Shandong University, Jinan, China
| | - Xin Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Liang Xu
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jinming Yu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Cheeloo college of medicine, Shandong University, Jinan, China.,Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Feifei Teng
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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8
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Thureau S, Briens A, Decazes P, Castelli J, Barateau A, Garcia R, Thariat J, de Crevoisier R. PET and MRI guided adaptive radiotherapy: Rational, feasibility and benefit. Cancer Radiother 2020; 24:635-644. [PMID: 32859466 DOI: 10.1016/j.canrad.2020.06.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023]
Abstract
Adaptive radiotherapy (ART) corresponds to various replanning strategies aiming to correct for anatomical variations occurring during the course of radiotherapy. The goal of the article was to report the rational, feasibility and benefit of using PET and/or MRI to guide this ART strategy in various tumor localizations. The anatomical modifications defined by scanner taking into account tumour mobility and volume variation are not always sufficient to optimise treatment. The contribution of functional imaging by PET or the precision of soft tissue by MRI makes it possible to consider optimized ART. Today, the most important data for both PET and MRI are for lung, head and neck, cervical and prostate cancers. PET and MRI guided ART appears feasible and safe, however in a very limited clinical experience. Phase I/II studies should be therefore performed, before proposing cost-effectiveness comparisons in randomized trials and before using the approach in routine practice.
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Affiliation(s)
- S Thureau
- Département de radiothérapie et de physique médicale, centre Henri-Becquerel, QuantIF EA 4108, université de Rouen, 76000 Rouen, France.
| | - A Briens
- Département de radiothérapie, centre Eugène-Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France
| | - P Decazes
- Département de médecine nucléaire, center Henri-Becquerel, QuantIF EA 4108, université de Rouen, Rouen, France
| | - J Castelli
- Département de radiothérapie, centre Eugène Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France; CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, université de Rennes, 35000 Rennes, France
| | - A Barateau
- Département de radiothérapie, centre Eugène Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France; CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, université de Rennes, 35000 Rennes, France
| | - R Garcia
- Service de physique médicale, institut Sainte-Catherine, 84918 Avignon, France
| | - J Thariat
- Department of radiation oncology, centre François-Baclesse, 14000 Caen, France; Laboratoire de physique corpusculaire IN2P3/ENSICAEN-UMR6534-Unicaen-Normandie université, 14000 Caen, France; ARCHADE Research Community, 14000 Caen, France
| | - R de Crevoisier
- Département de radiothérapie, centre Eugène-Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France; CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, université de Rennes, 35000 Rennes, France
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9
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Wang Y, Chen L, Ju L, Xiao Y, Wang X. Tumor mutational burden related classifier is predictive of response to PD-L1 blockade in locally advanced and metastatic urothelial carcinoma. Int Immunopharmacol 2020; 87:106818. [PMID: 32738594 DOI: 10.1016/j.intimp.2020.106818] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 07/16/2020] [Accepted: 07/16/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND Immunotherapy has made encouraging progress in the treatment of urothelial carcinoma, but only a small percentage of patients respond effectively to the immune checkpoint blockade (ICB). Our study aims to develop a classifier could effectively predict the response to ICB. METHODS Support vector machines recursive feature elimination (SVM-RFE) algorithm was used to feature selection, then compared nine common binary classification algorithms through machine learning, we selected the classifier with the highest prediction performance (LASSO logistics classifier). Ten-fold cross-validation was used to avoid the overfitting effect. RESULTS We developed a classifier on a urothelial carcinoma cohort treated with PD-L1 inhibitor Atzolizumab (IMvigor210 cohort, n = 272) and calculated a tumor mutational burden-related LASSO score (TLS) using the LASSO algorithm, which was significantly correlated with Tumor mutational burden (TMB) and neoantigen burden. We validated the efficacy of TLS in predicting prognosis and immunotherapy benefit in internal (IMvigor210) and external validation set (TCGA-BLCA, n = 406), respectively. CONCLUSIONS After in-depth analysis, we provide a new idea for stratified treatment of such patients, that is, patients with high TLS should use ICB and also may benefit from hormone therapy, while patients with low TLS respond poorly to ICB and maybe benefit from targeting TGFβ.
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Affiliation(s)
- Yejinpeng Wang
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Liang Chen
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Lingao Ju
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China; Human Genetics Resource Preservation Center of Hubei Province, Wuhan, China; Human Genetics Resource Preservation Center of Wuhan University, Wuhan, China; Research Center of Wuhan for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan, China
| | - Yu Xiao
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China; Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China; Human Genetics Resource Preservation Center of Hubei Province, Wuhan, China; Human Genetics Resource Preservation Center of Wuhan University, Wuhan, China; Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China; Research Center of Wuhan for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan, China
| | - Xinghuan Wang
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China; Medical Research Institute, Wuhan University, Wuhan, China; Research Center of Wuhan for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan, China.
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10
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Capaldi DPI, Hristov DH, Kidd EA. Parametric Response Mapping of Coregistered Positron Emission Tomography and Dynamic Contrast Enhanced Computed Tomography to Identify Radioresistant Subvolumes in Locally Advanced Cervical Cancer. Int J Radiat Oncol Biol Phys 2020; 107:756-765. [PMID: 32251757 DOI: 10.1016/j.ijrobp.2020.03.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/06/2020] [Accepted: 03/19/2020] [Indexed: 01/31/2023]
Abstract
PURPOSE To identify subvolumes that may predict treatment response to definitive concurrent chemoradiation therapy using parametric response mapping (PRM) of coregistered positron emission tomography (PET) and dynamic contrast-enhanced (DCE) computed tomography (CT) in locally advanced cervical carcinoma. METHODS AND MATERIALS Pre- and midtreatment (after 23 ± 4 days of concurrent chemoradiation therapy) DCE CT and PET imaging were performed on 21 patients with cervical cancer who were enrolled in a pilot study to evaluate the prognostic value of CT perfusion for primary cervical cancer (NCT01805141). Three-dimensional coregistered maps of PET/CT standardized uptake value (SUV) and DCE CT blood flow (BF) were generated. PRM was performed using voxel-wise joint histogram analysis to classify voxels within the tumor as highly metabolic and perfused (SUVhiBFhi), highly metabolic and hypoxic (SUVhiBFlo), low metabolic activity and hypoxic (SUVloBFlo), or low metabolic activity and perfused (SUVloBFhi) tissue based on thresholds determined from population means of pretreatment PET SUV and DCE CT BF. Relationships between baseline pretreatment imaging metrics and relative changes in metabolic tumor volume (ΔMTV), calculated from before treatment and during treatment imaging, were determined using univariable and multivariable linear regression models. RESULTS The relative volume of three PRM subvolumes significantly changed during treatment (SUVhiBFhi: P = .04; SUVhiBFlo: P = .0008; SUVloBFhi: P = .02), whereas SUVloBFlo did not (P = .9). Pretreatment PET SUVmax (r = -.58, P = .006), PET SUVmean (ρ = -.59, P = .005), DCE CT BFmean (r = -.50, P = .02), tumor volume (ρ = -.65, P = .001) and PRM SUVhiBFhi (ρ = -.59, P = .004) were negatively correlated with ΔMTV, whereas PRM SUVloBFlo was positively related to ΔMTV (r = .77, P < .0001). In a multivariable model that predicted ΔMTV, PRM SUVloBFlo, which combines both PET/CT and DCE CT, was the only significant variable (β = 1.825, P = .03), dominating both imaging modalities independently. CONCLUSIONS PRM was applied in locally advanced cervical carcinoma treated definitively with chemoradiation, and radioresistant subvolumes were identified that correlated with changes in MTV and predicted treatment response. Identification of these subvolumes may assist in clinical decision making to tailor therapies, such as brachytherapy, in an effort to improve patient outcomes.
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Affiliation(s)
- Dante P I Capaldi
- Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, California
| | - Dimitre H Hristov
- Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, California
| | - Elizabeth A Kidd
- Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, California.
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11
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Vidiri A, Gangemi E, Ruberto E, Pasqualoni R, Sciuto R, Sanguineti G, Farneti A, Benevolo M, Rollo F, Sperati F, Spasiano F, Pellini R, Marzi S. Correlation between histogram-based DCE-MRI parameters and 18F-FDG PET values in oropharyngeal squamous cell carcinoma: Evaluation in primary tumors and metastatic nodes. PLoS One 2020; 15:e0229611. [PMID: 32119697 PMCID: PMC7051076 DOI: 10.1371/journal.pone.0229611] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Accepted: 02/10/2020] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES To investigate the correlation between histogram-based Dynamic Contrast-Enhanced magnetic resonance imaging (DCE-MRI) parameters and positron emission tomography with 18F-fluorodeoxyglucose (18F-FDG-PET) values in oropharyngeal squamous cell carcinoma (OPSCC), both in primary tumors (PTs) and in metastatic lymph nodes (LNs). METHODS 52 patients with a new pathologically-confirmed OPSCC were included in the present retrospective cohort study. Imaging including DCE-MRI and 18F-FDG PET/CT scans were acquired in all patients. Both PTs and the largest LN, if present, were volumetrically contoured. Quantitative parameters, including the transfer constants, Ktrans and Kep, and the volume of extravascular extracellular space, ve, were calculated from DCE-MRI. The percentiles (P), P10, P25, P50, P75, P90, and skewness, kurtosis and entropy were obtained from the histogram-based analysis of each perfusion parameter. Standardized uptake values (SUV), SUVmax, SUVpeak, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were calculated applying a SUV threshold of 40%. The correlations between all variables were investigated with the Spearman-rank correlation test. To exclude false positive results under multiple testing, the Benjamini-Hockberg procedure was applied. RESULTS No significant correlations were found between any parameters in PTs, while significant associations emerged between Ktrans and 18F-FDG PET parameters in LNs. CONCLUSIONS Evident relationships emerged between DCE-MRI and 18F-FDG PET parameters in OPSCC LNs, while no association was found in PTs. The complex relationships between perfusion and metabolic biomarkers should be interpreted separately for primary tumors and lymph-nodes. A multiparametric approach to analyze PTs and LNs before treatment is advisable in head and neck squamous cell carcinoma (HNSCC).
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Affiliation(s)
- Antonello Vidiri
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Emma Gangemi
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
- Departmental Faculty of Medicine and Surgery, Center for Integrated Research, University Campus Bio-Medico of Rome, Rome, Italy
- * E-mail:
| | - Emanuela Ruberto
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Rosella Pasqualoni
- Department of Nuclear Medicine, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Rosa Sciuto
- Department of Nuclear Medicine, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Giuseppe Sanguineti
- Department of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Alessia Farneti
- Department of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Maria Benevolo
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Rollo
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Sperati
- Biostatistics-Scientific Direction, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Filomena Spasiano
- Department of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Raul Pellini
- Department of Otolaryngology & Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Simona Marzi
- Medical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, Rome, Italy
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12
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Aryal MP, Lee C, Hawkins PG, Chapman C, Eisbruch A, Mierzwa M, Cao Y. Real-Time Quantitative Assessment of Accuracy and Precision of Blood Volume Derived from DCE-MRI in Individual Patients During a Clinical Trial. ACTA ACUST UNITED AC 2020; 5:61-67. [PMID: 30854443 PMCID: PMC6403042 DOI: 10.18383/j.tom.2018.00029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Accuracy and precision of quantitative imaging (QI) metrics should be assessed in real time in each patient during a clinical trial to support QI-based decision-making. We developed a framework for real-time quantitative assessment of QI metrics and evaluated accuracy and precision of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI)–derived blood volume (BV) in a clinical trial for head and neck cancers. Patients underwent DCE-MRI before and after 2 weeks of radiation therapy (2wkRT). A mean as a reference value and a repeatability coefficient (RC) of BV values established from n patients in cerebellum volumes of interest (VOIs), which were normal and affected little by therapy, served as accuracy and precision measurements. The BV maps of a new patient were called accurate and precise if the values in cerebellum VOIs and the difference between the 2 scans agreed with the respective mean and RC with 95% confidence. The new data could be used to update reference values. Otherwise, the data were flagged for further evaluation before use in the trial. BV maps from 62 patients enrolled on the trial were evaluated. Mean BV values were 2.21 (±0.14) mL/100 g pre-RT and 2.22 (±0.17) mL/100 g at 2wkRT; relative RC was 15.9%. The BV maps from 3 patients were identified to be inaccurate and imprecise before use in the clinical trial. Our framework of real-time quantitative assessment of QI metrics during a clinical trial can be translated to different QI metrics and organ-sites for supporting QI-based decision-making that warrants success of a clinical trial.
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Affiliation(s)
| | | | | | | | | | | | - Yue Cao
- Departments of Radiation Oncology.,Radiology; and.,Biomedical Engineering, University of Michigan, Ann Arbor, MI
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13
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Cao Y, Aryal M, Li P, Lee C, Schipper M, Hawkins PG, Chapman C, Owen D, Dragovic AF, Swiecicki P, Casper K, Worden F, Lawrence TS, Eisbruch A, Mierzwa M. Predictive Values of MRI and PET Derived Quantitative Parameters for Patterns of Failure in Both p16+ and p16- High Risk Head and Neck Cancer. Front Oncol 2019; 9:1118. [PMID: 31799173 PMCID: PMC6874128 DOI: 10.3389/fonc.2019.01118] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 10/08/2019] [Indexed: 01/19/2023] Open
Abstract
Purpose: FDG-PET adds to clinical factors, such tumor stage and p16 status, in predicting local (LF), regional (RF), and distant failure (DF) in poor prognosis locally advanced head and neck cancer (HNC) treated with chemoradiation. We hypothesized that MRI-based quantitative imaging (QI) metrics could add to clinical predictors of treatment failure more significantly than FDG-PET metrics. Materials and methods: Fifty four patients with poor prognosis HNCs who were enrolled in an IRB approved prospective adaptive chemoradiotherapy trial were analyzed. MRI-derived gross tumor volume (GTV), blood volume (BV), and apparent diffusion coefficient (ADC) pre-treatment and mid-treatment (fraction 10), as well as pre-treatment FDG PET metrics, were analyzed in primary and individual nodal tumors. Cox proportional hazards models for prediction of LRF and DF free survival were used to test the additional value of QI metrics over dominant clinical predictors. Results: The mean ADC pre-RT and its change rate mid-treatment were significantly higher and lower in p16- than p16+ primary tumors, respectively. A Cox model identified that high mean ADC pre-RT had a high hazard for LF and RF in p16- but not p16+ tumors (p = 0.015). Most interesting, persisting subvolumes of low BV (TVbv) in primary and nodal tumors mid-treatment had high-risk for DF (p < 0.05). Also, total nodal GTV mid-treatment, mean/max SUV of FDG in all nodal tumors, and total nodal TLG were predictive for DF (p < 0.05). When including clinical stage (T4/N3) and total nodal GTV in the model, all nodal PET parameters had a p-value of >0.3, and only TVbv of primary tumors had a p-value of 0.06. Conclusion: MRI-defined biomarkers, especially persisting subvolumes of low BV, add predictive value to clinical variables and compare favorably with FDG-PET imaging markers. MRI could be well-integrated into the radiation therapy workflow for treatment planning, response assessment, and adaptive therapy.
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Affiliation(s)
- Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States.,Department of Radiology, University of Michigan, Ann Arbor, MI, United States.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Madhava Aryal
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Pin Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Matthew Schipper
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States.,Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Peter G Hawkins
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Christina Chapman
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States.,Department of Radiation Oncology, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States
| | - Dawn Owen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Aleksandar F Dragovic
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Paul Swiecicki
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Keith Casper
- Department of Otolaryngology, University of Michigan, Ann Arbor, MI, United States
| | - Francis Worden
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Avraham Eisbruch
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Michelle Mierzwa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
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14
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État des lieux de la radiothérapie adaptative en 2019 : de la mise en place à l’utilisation clinique. Cancer Radiother 2019; 23:581-591. [DOI: 10.1016/j.canrad.2019.07.142] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 07/12/2019] [Indexed: 12/20/2022]
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15
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Matuszak MM, Kashani R, Green M, Lee C, Cao Y, Owen D, Jolly S, Mierzwa M. Functional Adaptation in Radiation Therapy. Semin Radiat Oncol 2019; 29:236-244. [PMID: 31027641 DOI: 10.1016/j.semradonc.2019.02.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The promise of adaptive therapy to improve outcomes in radiation oncology has been an area of interest and research in the community for many years. One of the sources of data that can be used to drive adaptive therapy is functional information about the tumor or normal tissues. This avenue of adaptation includes many potential sources of data including global markers and functional imaging. Global markers can be assessments derived from blood measurements, patient functional testing, and circulating tumor material and functional imaging data comprises spatial physiological information from various imaging studies such as positron emission tomography, magnetic resonance imaging, and single photon emission computed tomography. The goal of functional adaptation is to use these functional data to adapt radiation therapy to improve patient outcomes. While functional adaptation holds a lot of promise, there are challenges such as quantifying and minimizing uncertainties, streamlining clinical implementation, determining the ideal way to incorporate information within treatment plan optimization, and proving the clinical benefit through trials. This paper will discuss the types of functional information currently being used for adaptation, highlight several areas where functional adaptation has been studied, and introduce some of the barriers to more widespread clinical implementation.
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Affiliation(s)
- Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI.
| | - Rojano Kashani
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Michael Green
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Dawn Owen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Michelle Mierzwa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
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16
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Hamming-Vrieze O, Navran A, Al-Mamgani A, Vogel WV. Biological PET-guided adaptive radiotherapy for dose escalation in head and neck cancer: a systematic review. 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; 62:349-368. [DOI: 10.23736/s1824-4785.18.03087-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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