Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group – ESOI Joint Paper

Abstract

Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required.

THE ASSOCIATION BETWEEN B-RAF V600E GENE MUTATION WITH AGE, GRADING, AND CLINICAL RESPONSE IN COLORECTAL CANCER AT THE CENTER GENERAL HOSPITAL (RSUP) PROF. Dr. I GOESTI NGOERAH GDE NGOERAH IN BALI

“…Clinical progressive disease (PD): a tumor with an increase of more than 20% in the largest diameter or the appearance of new nodules is considered to be a progressive disease. (Fournier et al, 2022;Trimonika et al, 2018) The clinical response in this study was obtained from the results of adjuvant chemotherapy (Folfiri or folfox) with a negative response of 59.5% at 44/74 CRC (SD 2.3% and PD 97.7%) more when compared with a positive response of 40.5% at 30/74 CRC (100.00% CR and 0.00% PR).…”

“…These criteria were published in 2000 by an international collaboration. (Fournier et al, 2022;SMF Ilmu Bedah, 2018) The data obtained were entered in the master table and analyzed with the SPSS 25.0 computer program, with the following stages of analysis: descriptive analysis, where data analysis begins with conducting variable analysis by describing each variable studied and presenting it in the form of a frequency distribution table. To prove the existence of a association between the B-RAF V600E gene mutation and age, grading, clinical response, and statistical tests were carried out using the Fisher-exact test.…”

Predicting immunotherapy outcomes in patients with MSI tumors using NLR and CT global tumor volume

“…While Tumor Volume variation accounts for non-target and new lesions, RECIST variation does not. Interestingly, these lesions had been described to be at least as important as the target lesions determined by RECIST 1.1 in the occurrence of progressive disease ( 40 , 41 ). Therefore, Volume variation likely gives a more robust criteria for defining progression.…”

“…In line with this, the hypothesis of resistance mechanisms being organ specific or texture parameters of the lesions discriminating response vs relapse, needs to be explored. Therefore, the use of radiomics to extract radiographic characteristics from the tumor image to produce statistics related to the heterogeneity or contrast of the tumor region, coupled with the predictive power of artificial intelligence (AI) algorithms is proposed as a new novel non-invasive biomarker for immunotherapy response ( 41 – 43 ).…”

Imaging response assessment for oncology: An algorithmic approach

“…Using changes in size in sites of disease to determine treatment response of promising therapies continues to play an increasingly important role for drug discovery. One of the initial attempts to formalize this concept was an evaluation of simulated tumor size reproducibility by oncologists in 1976 and the effect of measurement variation on defining an objective response rate amongst patients in therapeutic clinical trials [1,2]. The World Health Organization (WHO) furthered this initiative with proposing a standardized response classification of treatment response differentiated by thresholds of change in tumor size, which included the categories: complete response, partial response, no change and progressive disease; as evaluated on clinical exam or radiograph [3].…”