Blinded Independent Central Review (BICR) in New Therapeutic Lung Cancer Trials

A call for objectivity: Radiologists' proposed wishlist for response evaluation in solid tumors (RECIST 1.1)

The Response Evaluation in Solid Tumors (RECIST) 1.1 provides key guidance for performing imaging response assessment and defines image-based outcome metrics in oncology clinical trials, including progression free survival. In this framework, tumors identified on imaging are designated as either target lesions, non-target disease or new lesions and a structured categorical response is assigned at each imaging time point. While RECIST provides definitions for these categories, it specifically and objectively defines only the target disease. Predefined thresholds of size change provide unbiased metrics for determining objective response and disease progression of the target lesions. However, worsening of non-target disease or emergence of new lesions is given the same importance in determining disease progression despite these being qualitatively assessed and less rigorously defined. The subjective assessment of non-target and new disease contributes to reader variability, which can impact the quality of image interpretation and even the determination of progression free survival. The RECIST Working Group has made significant efforts in developing RECIST 1.1 beyond its initial publication, particularly in its application to targeted agents and immunotherapy. A review of the literature highlights that the Working Group has occasionally employed or adopted objective measures for assessing non-target and new lesions in their evaluation of RECIST-based outcome measures. Perhaps a prospective evaluation of these more objective definitions for non-target and new lesions within the framework of RECIST 1.1 might improve reader interpretation. Ideally, these changes could also better align with clinically meaningful outcome measures of patient survival or quality of life.

RECIST 1.1 assessments variability: a systematic pictorial review of blinded double reads

Reader variability is intrinsic to radiologic oncology assessments, necessitating measures to enhance consistency and accuracy. RECIST 1.1 criteria play a crucial role in mitigating this variability by standardizing evaluations, aiming to establish an accepted "truth" confirmed by histology or patient survival. Clinical trials utilize Blind Independent Centralized Review (BICR) techniques to manage variability, employing double reads and adjudicators to address inter-observer discordance effectively. It is essential to dissect the root causes of variability in response assessments, with a specific focus on the factors influencing RECIST evaluations. We propose proactive measures for radiologists to address variability sources such as radiologist expertise, image quality, and accessibility of contextual information, which significantly impact interpretation and assessment precision. Adherence to standardization and RECIST guidelines is pivotal in diminishing variability and ensuring uniform results across studies. Variability factors, including lesion selection, new lesion appearance, and confirmation bias, can have profound implications on assessment accuracy and interpretation, underscoring the importance of identifying and addressing these factors. Delving into the causes of variability aids in enhancing the accuracy and consistency of response assessments in oncology, underscoring the role of standardized evaluation protocols and mitigating risk factors that contribute to variability. Access to contextual information is crucial. CRITICAL RELEVANCE STATEMENT: By understanding the causes of diagnosis variability, we can enhance the accuracy and consistency of response assessments in oncology, ultimately improving patient care and clinical outcomes. KEY POINTS: Baseline lesion selection and detection of new lesions play a major role in the occurrence of discordance. Image interpretation is influenced by contextual information, the lack of which can lead to diagnostic uncertainty. Radiologists must be trained in RECIST criteria to reduce errors and variability.

Improvement of assessment in surrogate endpoint and safety outcome of single-arm trials for anticancer drugs

INTRODUCTION

Single-arm trials (SATs) and surrogate endpoints were adopted as pivotal evidence for accelerated approval of anticancer drugs for more than 30 years. However, concerns regarding clinical evidence quality in trials, particularly in the SATs of anticancer drugs have increasingly been raised. SAT may not always provide strong evidence due to the lack of control and endpoint of overall survival that is typically present in randomized controlled trials.

AREAS COVERED

Clinical trial endpoint adjudication is a crucial factor in surrogate outcome measurement to ensure the data quality of the clinical trial of anticancer drugs. In this review, we systematically discuss the characteristics of adjudications in assessments in surrogate endpoint and safety outcome respectively, which are essential for ensuring reliable and transparent outcomes. Endpoint adjudication effectively reduces potential bias and mitigates variance that may be introduced by investigators when analyzing the medical records for the surrogate endpoints. We analyze the advantages and disadvantages of each type of adjudicator and provide a summary of the roles of adjudicators.

EXPERT OPINION

By suggestion of improving data reliability and transparency in pivotal trials, this review aims to supply a strategy for better clinical investigation for anticancer drugs, ultimately leading to better patient outcomes.

Can we predict discordant RECIST 1.1 evaluations in double read clinical trials?

Background

In lung clinical trials with imaging, blinded independent central review with double reads is recommended to reduce evaluation bias and the Response Evaluation Criteria In Solid Tumor (RECIST) is still widely used. We retrospectively analyzed the inter-reader discrepancies rate over time, the risk factors for discrepancies related to baseline evaluations, and the potential of machine learning to predict inter-reader discrepancies.

Materials and methods

We retrospectively analyzed five BICR clinical trials for patients on immunotherapy or targeted therapy for lung cancer. Double reads of 1724 patients involving 17 radiologists were performed using RECIST 1.1. We evaluated the rate of discrepancies over time according to four endpoints: progressive disease declared (PDD), date of progressive disease (DOPD), best overall response (BOR), and date of the first response (DOFR). Risk factors associated with discrepancies were analyzed, two predictive models were evaluated.

Results

At the end of trials, the discrepancy rates between trials were not different. On average, the discrepancy rates were 21.0%, 41.0%, 28.8%, and 48.8% for PDD, DOPD, BOR, and DOFR, respectively. Over time, the discrepancy rate was higher for DOFR than DOPD, and the rates increased as the trial progressed, even after accrual was completed. It was rare for readers to not find any disease, for less than 7% of patients, at least one reader selected non-measurable disease only (NTL). Often the readers selected some of their target lesions (TLs) and NTLs in different organs, with ranges of 36.0-57.9% and 60.5-73.5% of patients, respectively. Rarely (4-8.1%) two readers selected all their TLs in different locations. Significant risk factors were different depending on the endpoint and the trial being considered. Prediction had a poor performance but the positive predictive value was higher than 80%. The best classification was obtained with BOR.

Conclusion

Predicting discordance rates necessitates having knowledge of patient accrual, patient survival, and the probability of discordances over time. In lung cancer trials, although risk factors for inter-reader discrepancies are known, they are weakly significant, the ability to predict discrepancies from baseline data is limited. To boost prediction accuracy, it would be necessary to enhance baseline-derived features or create new ones, considering other risk factors and looking into optimal reader associations.

Breaking down the RECIST 1.1 double read variability in lung trials: What do baseline assessments tell us?

Background

In clinical trials with imaging, Blinded Independent Central Review (BICR) with double reads ensures data blinding and reduces bias in drug evaluations. As double reads can cause discrepancies, evaluations require close monitoring which substantially increases clinical trial costs. We sought to document the variability of double reads at baseline, and variabilities across individual readers and lung trials.

Material and methods

We retrospectively analyzed data from five BICR clinical trials evaluating 1720 lung cancer patients treated with immunotherapy or targeted therapy. Fifteen radiologists were involved. The variability was analyzed using a set of 71 features derived from tumor selection, measurements, and disease location. We selected a subset of readers that evaluated ≥50 patients in ≥two trials, to compare individual reader’s selections. Finally, we evaluated inter-trial homogeneity using a subset of patients for whom both readers assessed the exact same disease locations. Significance level was 0.05. Multiple pair-wise comparisons of continuous variables and proportions were performed using one-way ANOVA and Marascuilo procedure, respectively.

Results

Across trials, on average per patient, target lesion (TL) number ranged 1.9 to 3.0, sum of tumor diameter (SOD) 57.1 to 91.9 mm. MeanSOD=83.7 mm. In four trials, MeanSOD of double reads was significantly different. Less than 10% of patients had TLs selected in completely different organs and 43.5% had at least one selected in different organs. Discrepancies in disease locations happened mainly in lymph nodes (20.1%) and bones (12.2%). Discrepancies in measurable disease happened mainly in lung (19.6%). Between individual readers, the MeanSOD and disease selection were significantly different (p<0.001). In inter-trials comparisons, on average per patient, the number of selected TLs ranged 2.1 to 2.8, MeanSOD 61.0 to 92.4 mm. Trials were significantly different in MeanSOD (p<0.0001) and average number of selected TLs (p=0.007). The proportion of patients having one of the top diseases was significantly different only between two trials for lung. Significant differences were observed for all other disease locations (p<0.05).

Conclusions

We found significant double read variabilities at baseline, evidence of reading patterns and a means to compare trials. Clinical trial reliability is influenced by the interplay of readers, patients and trial design.

Circulating immune response proteins predict the outcome following disease progression of osimertinib treated epidermal growth factor receptor-positive non-small cell lung cancer patients

Background

Lung cancer patients with sensitizing epidermal growth factor receptor (EGFR) mutations treated with osimertinib will eventually develop progressive disease (PD). The survival following PD varies greatly between patients, and no effective treatment strategy has been established. Furthermore, at the moment, no easily accessible and precise biomarker exists that can predict the survival after PD.

Methods

We analyzed blood samples drawn from non-small cell lung cancer patients harboring EGFR mutations that were treated with osimertinib. The levels of 92 circulating proteins were analyzed from plasma samples using a proximity extension assay (PEA). The results were evaluated with Gene Ontology (GO) enrichment analysis to reveal patterns of protein expression at progression while on osimertinib treatment.

Results

We found that the expression of 7 proteins were significantly altered at PD, compared to a sample taken at osimertinib response. GO enrichment analysis demonstrated that most of the significant proteins were related to the immune system, specifically the adaptive immune response. Defining two groups of patients, based on the levels of circulating immune response proteins at PD, revealed significant differences in the overall survival (OS) after PD [hazard ratio (HR) =3.04; 95% confidence interval (CI): 1.24-7.45; P=0.0046].

Conclusions

In this study, we discover novel circulating biomarkers that can predict the OS after PD on osimertinib. These findings support the recent acknowledgement of the immune system's importance in osimertinib resistance.

Advances in Lung Cancer Imaging and Therapy

This series of eight papers (five original articles, two reviews and one meta-analysis) is presented by international leaders covering various aspects of lung cancer management, starting with diagnostic imaging and analyzing the novel perspectives of therapy [...]