1
|
Goldberg RM, Adams R, Buyse M, Eng C, Grothey A, André T, Sobrero AF, Lichtman SM, Benson AB, Punt CJA, Maughan T, Burzykowski T, Sommeijer D, Saad ED, Shi Q, Coart E, Chibaudel B, Koopman M, Schmoll HJ, Yoshino T, Taieb J, Tebbutt NC, Zalcberg J, Tabernero J, Van Cutsem E, Matheson A, de Gramont A. Clinical Trial Endpoints in Metastatic Cancer: Using Individual Participant Data to Inform Future Trials Methodology. J Natl Cancer Inst 2022; 114:819-828. [PMID: 34865086 PMCID: PMC9194619 DOI: 10.1093/jnci/djab218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/14/2021] [Accepted: 11/29/2021] [Indexed: 11/13/2022] Open
Abstract
Meta-analysis based on individual participant data (IPD) is a powerful methodology for synthesizing evidence by combining information drawn from multiple trials. Hitherto, its principal application has been in questions of clinical management, but an increasingly important use is in clarifying trials methodology, for instance in the selection of endpoints, as discussed in this review. In oncology, the Aide et Recherche en Cancérologie Digestive (ARCAD) Metastatic Colorectal Cancer Database is a leader in the use of IPD-based meta-analysis in methodological research. The ARCAD database contains IPD from more than 38 000 patients enrolled in 46 studies and continues to collect phase III trial data. Here, we review the principal findings of the ARCAD project in respect of endpoint selection and examine their implications for cancer trials. Analysis of the database has confirmed that progression-free survival (PFS) is no longer a valid surrogate endpoint predictive of overall survival in the first-line treatment of colorectal cancer. Nonetheless, PFS remains an endpoint of choice for most first-line trials in metastatic colorectal cancer and other solid tumors. Only substantial PFS effects are likely to translate into clinically meaningful benefits, and accordingly, we advocate an oncology research model designed to identify highly effective treatments in carefully defined patient groups. We also review the use of the ARCAD database in assessing clinical response including novel response metrics and prognostic markers. These studies demonstrate the value of IPD as a tool for methodological studies and provide a reference point for the expansion of this approach within clinical cancer research.
Collapse
Affiliation(s)
| | | | - Marc Buyse
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium
- Hasselt University, Hasselt, Belgium
| | - Cathy Eng
- Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
| | - Axel Grothey
- West Cancer Center and Research Institute, Germantown, TN, USA
| | | | | | | | - Al B Benson
- Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL, USA
| | | | - Tim Maughan
- Gray Institute of Radiation Oncology and Biology, University of Oxford, UK
| | - Tomasz Burzykowski
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium
- Hasselt University, Hasselt, Belgium
| | - Dirkje Sommeijer
- University of Amsterdam Academic Medical Centre and Flevohospital, Almere, the Netherlands
| | - Everardo D Saad
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium
- Dendrix Research, Sao Paulo, Brazil
| | | | - Elisabeth Coart
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium
| | | | | | | | | | - Julien Taieb
- Georges Pompidou European Hospital, Paris, France
| | | | - John Zalcberg
- Monash University, School of Public Health, Australia
| | - Josep Tabernero
- Vall d’Hebron Hospital Campus and Institute of Oncology (VHIO), Barcelona, Spain
| | | | | | - Aimery de Gramont
- Hôpital Franco-Britannique, Paris, France
- Fondation ARCAD , Paris, France
| |
Collapse
|
2
|
Ou FS, Tang J, An MW, Mandrekar SJ. Modeling tumor measurement data to predict overall survival (OS) in cancer clinical trials. Contemp Clin Trials Commun 2021; 23:100827. [PMID: 34430754 PMCID: PMC8365311 DOI: 10.1016/j.conctc.2021.100827] [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: 11/17/2020] [Revised: 06/07/2021] [Accepted: 07/26/2021] [Indexed: 11/16/2022] Open
Abstract
Introduction Longitudinal tumor measurements (TM) are commonly recorded in cancer clinical trials of solid tumors. To define patient response to treatment, the Response Evaluation Criteria in Solid Tumors (RECIST) categorizes the otherwise continuous measurements, which results in substantial information loss. We investigated two modeling approaches to incorporate all available cycle-by-cycle (continuous) TM to predict overall survival (OS) and compare the predictive accuracy of these two approaches to RECIST. Material and methods Joint modeling (JM) for longitudinal TM and OS and two-stage modeling with potential time-varying coefficients were utilized to predict OS using data from three trials with cycle-by-cycle TM. The JM approach incorporates TM data collected throughout the course of the clinical trial. The two-stage modeling approach incorporates information from early assessments (before 12 weeks) to predict subsequent OS outcome. The predictive accuracy was quantified by c-indices. Results Data from 577, 337, and 126 patients were included for the analysis (from two stage IV colorectal cancer trials (N9741, N9841) and an advanced non-small cell lung cancer trial (N0026), respectively). Both the JM and two-stage modeling reached a similar conclusion, i.e. the baseline covariates (age, gender, and race) were mostly not predictive of OS (p-value > 0.05). Quantities derived from TM were strong predictors of OS in the two colorectal cancer trials (p < 0.001 for both association in JM and two-stage modeling parameters); but less so in the lung cancer trial (p = 0.053 for association in JM and p = 0.024 and 0.160 for two-stage modeling parameters). The c-indices from the two-stage modeling were higher than those from a model using RECIST (range: 0.611–0.633 versus 0.586–0.590). The dynamic c-indices from the JM were in the range of 0.627–0.683 indicating good predictive accuracy. Conclusion Both modeling approaches provide highly interpretable and clinical meaningful results; the improved predictive performance compared with RECIST indicates the possibility of deriving better trial endpoints from these approaches. Two-stage modeling incorporating time-varying coefficients achieves better predictive accuracy than RECIST-alone. Two–stage modeling offers the possibility of alternative endpoint definition. Serial tumor measurements can be incorporated in OS prediction using joint modeling. Joint modeling can potentially guide individualized medicine.
Collapse
Affiliation(s)
- Fang-Shu Ou
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Jun Tang
- Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA, USA
| | - Ming-Wen An
- Department of Mathematics and Statistics, Vassar College, Poughkeepsie, NY, USA
| | | |
Collapse
|
3
|
Gao Y, Liang Z, Xing Y, Zhang H, Pomeroy M, Lu S, Ma J, Lu H, Moore W. Characterization of tissue-specific pre-log Bayesian CT reconstruction by texture-dose relationship. Med Phys 2020; 47:5032-5047. [PMID: 32786070 DOI: 10.1002/mp.14449] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 06/21/2020] [Accepted: 08/04/2020] [Indexed: 12/18/2022] Open
Abstract
PURPOSE Tissue textures have been recognized as biomarkers for various clinical tasks. In computed tomography (CT) image reconstruction, it is important but challenging to preserve the texture when lowering x-ray exposure from full- toward low-/ultra-low dose level. Therefore, this paper aims to explore the texture-dose relationship within one tissue-specific pre-log Bayesian CT reconstruction algorithm. METHODS To enhance the texture in ultra-low dose CT (ULdCT) reconstruction, this paper presents a Bayesian type algorithm. A shifted Poisson model is adapted to describe the statistical properties of pre-log data, and a tissue-specific Markov random field prior (MRFt) is used to incorporate tissue texture from previous full-dose CT, thus called SP-MRFt algorithm. Utilizing the SP-MRFt algorithm, we investigated tissue texture degradation as a function of x-ray dose levels from full dose (100 mAs/120 kVp) to ultralow dose (1 mAs/120 kVp) by using quantitative texture-based evaluation metrics. RESULTS Experimental results show the SP-MRFt algorithm outperforms conventional filtered back projection (FBP) and post-log domain penalized weighted least square MRFt (PWLS-MRFt) in terms of noise suppression and texture preservation. Comparable results are also obtained with shifted Poisson model with 7 × 7 Huber MRF weights (SP-Huber7). The investigation on texture-dose relationship shows that the quantified texture measures drop monotonically as dose level decreases, and interestingly a turning point is observed on the texture-dose response curve. CONCLUSIONS This important observation implies that there exists a minimum dose level, at which a given CT scanner (hardware configuration and image reconstruction software) can achieve without compromising clinical tasks. Moreover, the experiment results show that the variance of electronic noise has higher impact than the mean to the texture-dose relationship.
Collapse
Affiliation(s)
- Yongfeng Gao
- Department of Radiology, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Zhengrong Liang
- Departments of Radiology, Biomedical Engineering, Computer Science, and Electrical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Yuxiang Xing
- Department of Engineering Physics, Tsinghua University, Beijing, 100871, China
| | - Hao Zhang
- Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Marc Pomeroy
- Departments of Radiology and Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY, 11794, USA
| | - Siming Lu
- Departments of Radiology and Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY, 11794, USA
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Hongbing Lu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China
| | - William Moore
- Department of Radiology, New York University, New York, NY, 10016, USA
| |
Collapse
|
4
|
Gao Y, Liang Z, Zhang H, Yang J, Ferretti J, Bilfinger T, Yaddanapudi K, Schweitzer M, Bhattacharji P, Moore W. A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020; 4:441-449. [PMID: 33907724 PMCID: PMC8075295 DOI: 10.1109/trpms.2019.2957459] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.
Collapse
Affiliation(s)
- Yongfeng Gao
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA
| | - Zhengrong Liang
- Departments of Radiology, Biomedical Engineering, Computer Science,
and Electrical Engineering, Stony Brook University, Stony Brook, NY 11794,
USA
| | - Hao Zhang
- Departments of Radiology and Biomedical Engineering, Stony Brook
University, Stony Brook, NY 11794, USA and now with the Department of
Radiation Oncology, Stanford University, Stanford, CA 94035, USA
| | - Jie Yang
- Department of Family, Population and Preventive Medicine, Stony
Brook University, Stony Brook, NY 11794, USA
| | - John Ferretti
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA
| | - Thomas Bilfinger
- Department of Surgery, Stony Brook University, Stony Brook, NY
11794, USA)
| | | | - Mark Schweitzer
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA
| | - Priya Bhattacharji
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA, and now with the Department of Radiology, New York University,
New York, NY 10016, USA
| | - William Moore
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA, and now with the Department of Radiology, New York University,
New York, NY 10016, USA
| |
Collapse
|