Literature DB >> 32133275

Objective response rate assessment in oncology: Current situation and future expectations.

Nuri Faruk Aykan1, Tahsin Özatlı2.   

Abstract

The tumor objective response rate (ORR) is an important parameter to demonstrate the efficacy of a treatment in oncology. The ORR is valuable for clinical decision making in routine practice and a significant end-point for reporting the results of clinical trials. World Health Organization and Response Evaluation Criteria in Solid Tumors (RECIST) are anatomic response criteria developed mainly for cytotoxic chemotherapy. These criteria are based on the visual assessment of tumor size in morphological images provided by computed tomography (CT) or magnetic resonance imaging. Anatomic response criteria may not be optimal for biologic agents, some disease sites, and some regional therapies. Consequently, modifications of RECIST, Choi criteria and Morphologic response criteria were developed based on the concept of the evaluation of viable tumors. Despite its limitations, RECIST v1.1 is validated in prospective studies, is widely accepted by regulatory agencies and has recently shown good performance for targeted cancer agents. Finally, some alternatives of RECIST were developed as immune-specific response criteria for checkpoint inhibitors. Immune RECIST criteria are based essentially on defining true progressive disease after a confirmatory imaging. Some graphical methods may be useful to show longitudinal change in the tumor burden over time. Tumor tissue is a tridimensional heterogenous mass, and tumor shrinkage is not always symmetrical; thus, metabolic response assessments using positron emission tomography (PET) or PET/CT may reflect the viability of cancer cells or functional changes evolving after anticancer treatments. The metabolic response can show the benefit of a treatment earlier than anatomic shrinkage, possibly preventing delays in drug approval. Computer-assisted automated volumetric assessments, quantitative multimodality imaging in radiology, new tracers in nuclear medicine and finally artificial intelligence have great potential in future evaluations. ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.

Entities:  

Keywords:  Depth of response; Early tumor shrinkage; Immune Response Evaluation Criteria in Solid Tumors criteria; Objective response rate; Response Evaluation Criteria in Solid Tumors; Spider plot; Swimmer plot; Tumor shrinkage; Waterfall plot; World Health Organization criteria

Year:  2020        PMID: 32133275      PMCID: PMC7046919          DOI: 10.5306/wjco.v11.i2.53

Source DB:  PubMed          Journal:  World J Clin Oncol        ISSN: 2218-4333


  16 in total

1.  Validation of Real-World Data-based Endpoint Measures of Cancer Treatment Outcomes.

Authors:  Qian Li; Hansi Zhang; Zhaoyi Chen; Yi Guo; Thomas J George; Yong Chen; Fei Wang; Jiang Bian
Journal:  AMIA Annu Symp Proc       Date:  2022-02-21

2.  Evaluation of atrial anatomical remodeling in atrial fibrillation with machine-learned morphological features.

Authors:  Fanli Zhou; Zhidong Yuan; Xianglin Liu; Keyan Yu; Bowei Li; Xingyan Li; Xin Liu; Guanxun Cheng
Journal:  Int J Comput Assist Radiol Surg       Date:  2022-10-22       Impact factor: 3.421

Review 3.  Strategies for Testing Intervention Matching Schemes in Cancer.

Authors:  Nicholas J Schork; Laura H Goetz; James Lowey; Jeffrey Trent
Journal:  Clin Pharmacol Ther       Date:  2020-07-24       Impact factor: 6.875

Review 4.  Positron Emission Tomography-Based Response to Target and Immunotherapies in Oncology.

Authors:  Maria Isabella Donegani; Giulia Ferrarazzo; Stefano Marra; Alberto Miceli; Stefano Raffa; Matteo Bauckneht; Silvia Morbelli
Journal:  Medicina (Kaunas)       Date:  2020-07-24       Impact factor: 2.430

5.  Characterizing tumor shrinkage as a measure of clinical benefit for immune checkpoint inhibitors.

Authors:  Thomas Kelleher; Junliang Cai; Nicholas Aj Botwood; Dominic F Labriola
Journal:  J Immunother Cancer       Date:  2021-02       Impact factor: 13.751

6.  Trends in Phase II Trials for Cancer Therapies.

Authors:  Faruque Azam; Alexei Vazquez
Journal:  Cancers (Basel)       Date:  2021-01-07       Impact factor: 6.639

7.  Role in staging and prognostic value of pretherapeutic F-18 FDG PET/CT in patients with gastric MALT lymphoma without high-grade transformation.

Authors:  Yong-Jin Park; Seung Hyup Hyun; Seung Hwan Moon; Kyung-Han Lee; Byung Hoon Min; Jun Haeng Lee; Won Seog Kim; Seok Jin Kim; Joon Young Choi
Journal:  Sci Rep       Date:  2021-04-29       Impact factor: 4.379

Review 8.  A review of the application of machine learning in molecular imaging.

Authors:  Lin Yin; Zhen Cao; Kun Wang; Jie Tian; Xing Yang; Jianhua Zhang
Journal:  Ann Transl Med       Date:  2021-05

9.  The use of external control data for predictions and futility interim analyses in clinical trials.

Authors:  Steffen Ventz; Leah Comment; Bill Louv; Rifaquat Rahman; Patrick Y Wen; Brian M Alexander; Lorenzo Trippa
Journal:  Neuro Oncol       Date:  2022-02-01       Impact factor: 13.029

Review 10.  A narrative review: depth of response as a predictor of the long-term outcomes for solid tumors.

Authors:  Xiaohui Xie; Xin Li; Wenxiu Yao
Journal:  Transl Cancer Res       Date:  2021-02       Impact factor: 1.241

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