1
|
Lin TA, Mao S, Anker C, Herman JM, Meyer JJ, Narang A, Hu C. Local Time-to-Event Endpoint Under-Reporting and Variability in Pancreatic Cancer Trials Involving Radiotherapy. Int J Radiat Oncol Biol Phys 2023; 117:e316-e317. [PMID: 37785136 DOI: 10.1016/j.ijrobp.2023.06.2351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The role of radiotherapy (RT) for pancreatic adenocarcinoma (PDAC) remains controversial, with recent studies showing conflicting results. Importantly, endpoints used to evaluate efficacy in recent RT trials for PDAC have been highly variable. As variability in time-to-event (TTE) endpoint definitions is demonstrated to influence outcomes in other cancers, it is critical that radiation oncologists develop consensus around optimal endpoint definitions to use in future PDAC trial design. Thus, we conducted a systematic review of PDAC trials involving RT to characterize the frequency and variability in local TTE endpoint reporting. MATERIALS/METHODS An electronic database search was conducted of PubMed, EMBASE, and Cochrane Library to identify phase 2 and 3 clinical trials published from 2010-2022 of localized PDAC involving RT that reported any TTE endpoint (e.g., local control). After excluding duplicates, two independent reviewers screened full-text manuscripts for inclusion. Trial characteristics and local TTE endpoints/definitions were tabulated. RESULTS Three hundred twenty references were screened and 79 trials were included, of which 73 (92%) were phase 2 and 26 (33%) were randomized. Twenty (25%) trials reported a local TTE endpoint; these were local control (LC; N = 6), local progression-free survival (LPFS; N = 4), freedom from local progression (N = 6), locoregional progression-free interval (N = 1), cumulative incidence of local recurrence (N = 1), time to failure of sustained LC (N = 1), and local disease-free survival (N = 1). LC (N = 6) had 5 unique definitions and was undefined once; 1 definition included death as an event. LPFS (N = 4) had 3 definitions; 2 did not consider death an event. Among trials with local TTE endpoints, 9 trials specified the definition of a local recurrence/progression. Four trials defined local recurrence based on RT volumes; one counted clinical evidence of recurrence (e.g., tumor bleed); and one counted a rise in tumor markers without evidence of distant metastases. The index time ("time-zero") was defined for local TTE endpoints in 10 trials, including start of RT (N = 4) or chemo (N = 1), end of RT (N = 1), diagnosis (N = 1), enrollment (N = 1), and time of surgery (N = 1). CONCLUSION Few pancreatic cancer trials involving RT report local TTE endpoints, with significant heterogeneity in endpoints used and their definitions. Development of consensus endpoint definitions will be critical for future PDAC trial design.
Collapse
Affiliation(s)
- T A Lin
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | - S Mao
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | - C Anker
- The University of Vermont Medical Center, Burlington, VT
| | - J M Herman
- Department of Radiation Medicine, Northwell Health Cancer Institute, New Hyde Park, NY
| | - J J Meyer
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | - A Narang
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | - C Hu
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD; Division of Quantitative Sciences, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| |
Collapse
|
3
|
Sørensen L, Igel C, Pai A, Balas I, Anker C, Lillholm M, Nielsen M. Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. Neuroimage Clin 2016; 13:470-482. [PMID: 28119818 PMCID: PMC5237821 DOI: 10.1016/j.nicl.2016.11.025] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2016] [Revised: 10/21/2016] [Accepted: 11/26/2016] [Indexed: 01/01/2023]
Abstract
This paper presents a brain T1-weighted structural magnetic resonance imaging (MRI) biomarker that combines several individual MRI biomarkers (cortical thickness measurements, volumetric measurements, hippocampal shape, and hippocampal texture). The method was developed, trained, and evaluated using two publicly available reference datasets: a standardized dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the imaging arm of the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL). In addition, the method was evaluated by participation in the Computer-Aided Diagnosis of Dementia (CADDementia) challenge. Cross-validation using ADNI and AIBL data resulted in a multi-class classification accuracy of 62.7% for the discrimination of healthy normal controls (NC), subjects with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD). This performance generalized to the CADDementia challenge where the method, trained using the ADNI and AIBL data, achieved a classification accuracy 63.0%. The obtained classification accuracy resulted in a first place in the challenge, and the method was significantly better (McNemar's test) than the bottom 24 methods out of the total of 29 methods contributed by 15 different teams in the challenge. The method was further investigated with learning curve and feature selection experiments using ADNI and AIBL data. The learning curve experiments suggested that neither more training data nor a more complex classifier would have improved the obtained results. The feature selection experiment showed that both common and uncommon individual MRI biomarkers contributed to the performance; hippocampal volume, ventricular volume, hippocampal texture, and parietal lobe thickness were the most important. This study highlights the need for both subtle, localized measurements and global measurements in order to discriminate NC, MCI, and AD simultaneously based on a single structural MRI scan. It is likely that additional non-structural MRI features are needed to further improve the obtained performance, especially to improve the discrimination between NC and MCI. The algorithm that won the CADDementia challenge is described and analyzed. Evaluation on data from ADNI, AIBL and the CADDementia challenge. Hippocampal texture is shown to be an important feature in the algorithm. Structural MRI intensity variations may include so far unused information. It is conjectured that additional features are needed in order to improve diagnostic performance.
Collapse
Affiliation(s)
- Lauge Sørensen
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
| | - Christian Igel
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark
| | - Akshay Pai
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
| | - Ioana Balas
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark
| | | | - Martin Lillholm
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
| | | |
Collapse
|
6
|
Ahmed I, Biswas A, Krishnamurthy S, Julka P, Rath G, Back M, Huang D, Gzell C, Chen J, Kastelan M, Gaur P, Wheeler H, Badiyan SN, Robinson CG, Simpson JR, Tran DD, Rich KM, Dowling JL, Chicoine MR, Leuthardt EC, Kim AH, Huang J, Michaelsen SR, Christensen IJ, Grunnet K, Stockhausen MT, Broholm H, Kosteljanetz M, Poulsen HS, Tieu M, Lovblom E, Macnamara M, Mason W, Rodin D, Tai E, Ubhi K, Laperriere N, Millar BA, Menard C, Perkins B, Chung C, Clarke J, Molinaro A, Phillips J, Butowski N, Chang S, Perry A, Costello J, DeSilva A, Rabbitt J, Prados M, Cohen AL, Anker C, Shrieve D, Hall B, Salzman K, Jensen R, Colman H, Farber O, Weinberg U, Palti Y, Fisher B, Chen H, Macdonald D, Lesser G, Coons S, Brachman D, Ryu S, Werner-Wasik M, Bahary JP, Chakravarti A, Mehta M, Gupta T, Nair V, Epari S, Godasastri J, Moiyadi A, Shetty P, Juvekar S, Jalali R, Herrlinger U, Schafer N, Steinbach J, Weyerbrock A, Hau P, Goldbrunner R, Kohnen R, Urbach H, Stummer W, Glas M, Houillier C, Ghesquieres H, Chabrot C, Soussain C, Ahle G, Choquet S, Faurie P, Bay JO, Vargaftig J, Gaultier C, Nicolas-Virelizier E, Hoang-Xuan K, Iskanderani O, Izar F, Benouaich-Amiel A, Filleron T, Moyal E, Iweha C, Jain S, Melian E, Sethi A, Albain K, Shafer D, Emami B, Kong XT, Green S, Filka E, Green R, Yong W, Nghiemphu P, Cloughesy T, Lai A, Mallick S, Biswas A, Roy S, Purkait S, Gupta S, Julka PK, Rath GK, Marosi C, Thaler J, Ay C, Kaider A, Reitter EM, Haselbock J, Preusser M, Flechl B, Zielinski C, Pabinger I, Miyatake SI, Furuse M, Miyata T, Yoritsune E, Kawabata S, Kuroiwa T, Muragaki Y, Maruyama T, Iseki H, Akimoto J, Ikuta S, Nitta M, Maebayashi K, Saito T, Okada Y, Kaneko S, Matsumura A, Kuroiwa T, Karasawa K, Nakazato Y, Kayama T, Nabors LB, Fink KL, Mikkelsen T, Grujicic D, Tarnawski R, Nam DH, Mazurkiewicz M, Salacz M, Ashby L, Thurzo L, Zagonel V, Depenni R, Perry JR, Henslee-Downey J, Picard M, Reardon DA, Nambudiri N, Nayak L, LaFrankie D, Wen P, Ney D, Carlson J, Damek D, Blatchford P, Gaspar L, Kavanagh B, Waziri A, Lillehei K, Reddy K, Chen C, Rashed I, Melian E, Sethi A, Barton K, Anderson D, Prabhu V, Rusch R, Belongia M, Maheshwari M, Firat S, Schiff D, Desjardins A, Cloughesy T, Mikkelsen T, Glantz M, Chamberlain M, Reardon DA, Wen P, Shapiro W, Gopal S, Judy K, Patel S, Mahapatra A, Shan J, Gupta D, Shih K, Bacha JA, Brown D, Garner WJ, Steino A, Schwart R, Kanekal S, Li M, Lopez L, Burris HA, Soderberg-Naucler C, Rahbar A, Stragliotto G, Song AJ, Kumar AMS, Murphy ES, Tekautz T, Suh JH, Recinos V, Chao ST, Spoor J, Korami K, Kloezeman J, Balvers R, Dirven C, Lamfers M, Leenstra S, Sumrall A, Haggstrom D, Crimaldi A, Symanowski J, Giglio P, Asher A, Burri S, Sunkersett G, Khatib Z, Prajapati CM, Magalona EE, Mariano M, Sih IM, Torcuator R, Taal W, Oosterkamp H, Walenkamp A, Beerenpoot L, Hanse M, Buter J, Honkoop A, Boerman D, de Vos F, Jansen R, van der Berkmortel F, Brandsma D, Enting R, Kros J, Bromberg J, van Heuvel I, Smits M, van der Holt R, Vernhout R, van den Bent M, Weinberg U, Farber O, Palti Y, Wick W, Suarez C, Rodon J, Desjardins A, Forsyth P, Gueorguieva I, Cleverly A, Burkholder T, Desaiah D, Lahn M, Zach L, Guez D, Last D, Daniels D, Nissim O, Grober Y, Hoffmann C, Nass D, Talianski A, Spiegelmann R, Cohen Z, Mardor Y. MEDICAL RADIATION THERAPIES. Neuro Oncol 2013; 15:iii75-iii84. [PMCID: PMC3823894 DOI: 10.1093/neuonc/not179] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023] Open
|