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      Lactate dehydrogenase kinetics predict chemotherapy response in recurrent metastatic nasopharyngeal carcinoma

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          Abstract

          Background:

          Lactate dehydrogenase (LDH) is a known prognostic biomarker for the endemic variant of nasopharyngeal carcinoma (NPC). Here, we investigate whether serial changes in LDH level between chemotherapy (CT) cycles are associated with tumour response to CT.

          Methods:

          Patients with biopsy-proven, recurrent or treatment-naïve metastatic NPC (mNPC) were recruited. All patients had received at least two cycles of platinum-based doublet or triplet CT, with serial assessment of LDH prior to every cycle of chemotherapy (CT1–6). Patients harbouring conditions that affect LDH levels (IU/L) were excluded. Tumour response was assessed after every two cycles of CT by RECIST v1.1.

          Results:

          A total of 158 patients were analysed, including 77 with recurrent and 81 with treatment-naïve mNPC. High pre-CT LDH was associated with an inferior overall survival [hazard ratio 1.93 for ⩾240 versus <240 (1.34–2.77), p < 0.001], which is consistent with published literature. We found that both absolute LDH levels and LDH ratios (LDH CTn: LDH CTn–1) were associated with tumour response [partial response versus progressive disease: median value across CT1–6 = 168–190 versus 222–398 (absolute); 0.738–0.988 versus 1.039–1.406 (ratio)], albeit LDH ratio had a tighter variance between patients. Finally, we showed that an LDH ratio cut-off of 1.0 at CT1, CT3 and CT5 was predictive of progressive disease at CT2, CT4, CT6 [area under the curve of 0.73 (0.65–0.80)].

          Conclusion:

          Herein, we characterised the longitudinal variation of LDH in response to CT in mNPC. Our findings suggest the potential utility of interval LDH ratio to predict subsequent tumour response to CT.

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          Most cited references37

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          New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

          Assessment of the change in tumour burden is an important feature of the clinical evaluation of cancer therapeutics: both tumour shrinkage (objective response) and disease progression are useful endpoints in clinical trials. Since RECIST was published in 2000, many investigators, cooperative groups, industry and government authorities have adopted these criteria in the assessment of treatment outcomes. However, a number of questions and issues have arisen which have led to the development of a revised RECIST guideline (version 1.1). Evidence for changes, summarised in separate papers in this special issue, has come from assessment of a large data warehouse (>6500 patients), simulation studies and literature reviews. HIGHLIGHTS OF REVISED RECIST 1.1: Major changes include: Number of lesions to be assessed: based on evidence from numerous trial databases merged into a data warehouse for analysis purposes, the number of lesions required to assess tumour burden for response determination has been reduced from a maximum of 10 to a maximum of five total (and from five to two per organ, maximum). Assessment of pathological lymph nodes is now incorporated: nodes with a short axis of 15 mm are considered measurable and assessable as target lesions. The short axis measurement should be included in the sum of lesions in calculation of tumour response. Nodes that shrink to <10mm short axis are considered normal. Confirmation of response is required for trials with response primary endpoint but is no longer required in randomised studies since the control arm serves as appropriate means of interpretation of data. Disease progression is clarified in several aspects: in addition to the previous definition of progression in target disease of 20% increase in sum, a 5mm absolute increase is now required as well to guard against over calling PD when the total sum is very small. Furthermore, there is guidance offered on what constitutes 'unequivocal progression' of non-measurable/non-target disease, a source of confusion in the original RECIST guideline. Finally, a section on detection of new lesions, including the interpretation of FDG-PET scan assessment is included. Imaging guidance: the revised RECIST includes a new imaging appendix with updated recommendations on the optimal anatomical assessment of lesions. A key question considered by the RECIST Working Group in developing RECIST 1.1 was whether it was appropriate to move from anatomic unidimensional assessment of tumour burden to either volumetric anatomical assessment or to functional assessment with PET or MRI. It was concluded that, at present, there is not sufficient standardisation or evidence to abandon anatomical assessment of tumour burden. The only exception to this is in the use of FDG-PET imaging as an adjunct to determination of progression. As is detailed in the final paper in this special issue, the use of these promising newer approaches requires appropriate clinical validation studies.
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            pROC: an open-source package for R and S+ to analyze and compare ROC curves

            Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
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              Modeling Survival Data: Extending the Cox Model

              This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area. The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided. Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions. Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals.
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                Author and article information

                Contributors
                Journal
                Ther Adv Med Oncol
                Ther Adv Med Oncol
                TAM
                sptam
                Therapeutic Advances in Medical Oncology
                SAGE Publications (Sage UK: London, England )
                1758-8340
                1758-8359
                13 November 2020
                2020
                : 12
                : 1758835920970050
                Affiliations
                [1-1758835920970050]Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
                [2-1758835920970050]Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
                [3-1758835920970050]Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
                [4-1758835920970050]Division of Medical Sciences, National Cancer Centre Singapore, Singapore
                [5-1758835920970050]Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore
                [6-1758835920970050]Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
                [7-1758835920970050]Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
                [8-1758835920970050]Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
                [9-1758835920970050]Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
                [10-1758835920970050]Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
                [11-1758835920970050]Division of Medical Sciences, National Cancer Centre Singapore, Singapore
                [12-1758835920970050]Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
                [13-1758835920970050]Division of Medical Sciences, National Cancer Centre Singapore, Singapore
                [14-1758835920970050]Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
                [15-1758835920970050]Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore
                [16-1758835920970050]Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
                [17-1758835920970050]Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
                [18-1758835920970050]Department of Radiation Oncology, Chongqing University Cancer Hospital, 181 Han Yu Road, Chongqing, 400030, China
                [19-1758835920970050]Division of Radiation Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore
                [20-1758835920970050]Division of Medical Sciences, National Cancer Centre Singapore, Singapore
                [21-1758835920970050]Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore
                [22-1758835920970050]Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, 77 He Di Road, Nanning, 530021, China
                Author notes
                [*]

                These authors contributed equally to this work.

                [#]

                Co-senior authors.

                Author information
                https://orcid.org/0000-0002-1648-1473
                Article
                10.1177_1758835920970050
                10.1177/1758835920970050
                7672732
                33240398
                d5dbb22c-973a-42c9-9f39-3b0715b2b677
                © The Author(s), 2020

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 21 April 2020
                : 2 October 2020
                Funding
                Funded by: Chongqing Science and Technology Commission, FundRef https://doi.org/10.13039/501100002865;
                Award ID: cstc2017jcyj-yszx0001
                Funded by: National Medical Research Council, FundRef https://doi.org/10.13039/501100001349;
                Award ID: CSA/0027/2018
                Funded by: National Natural Science Foundation of China, FundRef https://doi.org/10.13039/501100001809;
                Award ID: 81972857
                Categories
                Original Research
                Custom metadata
                January-December 2020
                ts1

                biomarker kinetics,chemotherapy,lactate dehydrogenase,nasopharyngeal carcinoma,tumour response

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