The majority of clinical trials evaluating cancer treatments for objective response in solid tumors are using RECIST, or Response Evaluation Criteria in Solid Tumors. RECIST is crucial for evaluating the effectiveness of cancer therapies, but it’s not without its challenges.

In this blog, we detail RECIST, how it’s used in statistical analysis, the development of iRECIST for immunotherapy trials, statistical and clinical challenges with RECIST/iRECIST, and best practices for implementing RECIST/iRECIST in oncology trials.

 

What is RECIST 1.1 and why is it important in oncology?

RECIST (Response Evaluation Criteria in Solid Tumors) 1.1 is a standardized set of rules used to measure tumor response to treatment using imaging. It helps determine whether a tumor is shrinking, stable, or growing, which is crucial for evaluating the effectiveness of cancer therapies. As of today, the majority of clinical trials evaluating cancer treatments for objective response in solid tumors are using RECIST.

 

What are the key response assessments in RECIST 1.1?

The overall response for a given timepoint is the combination of target lesion response relying on unidimensional measurements, non-target lesion response, and presence/absence of new lesions.

• Complete Response (CR): Disappearance of all target lesions and non-target lesions and no new lesions. Any pathological lymph nodes must have a reduction in short axis to <10mm.
• Partial Response (PR): At least a 30% decrease in the sum of diameters of target lesions, taking as reference the baseline sum diameters, and no progression of non-target lesions and no new lesions.
• Stable Disease (SD): Neither sufficient shrinkage to qualify for PR nor sufficient increase to qualify for PD, taking as reference the smallest sum diameters while on study, and no new lesions.
• Progressive Disease (PD): At least a 20% increase in the sum of diameters and an absolute increase of ≥ 5mm, taking as reference the smallest sum of diameters on-study, or progression of non-target lesions or appearance of new lesions.
• Not Evaluable (NE)

 

How is RECIST used in statistical analysis?

RECIST is used to derive key endpoints like:

• Objective Response Rate (ORR)
• Disease Control Rate (DCR)
• Progression-Free Survival (PFS)
• Duration of Response (DOR)
• Time to Response (TTR)

 

RECIST 1.1 criteria state that confirmation of response (CR or PR) is required for non-randomized trials with a response primary endpoint to ensure responses identified are not the result of measurement error. However, in all other circumstances, i.e., in randomized trials (phase II or III) or studies where stable disease or progression are the primary endpoints, confirmation of response is not required since it will not add value to the interpretation of trial results.

The FDA generally expects a confirmed response for ORR in single-arm trials where it is the primary endpoint, especially for accelerated approval. The FDA/EMA may also request confirmation if ORR is a primary of key secondary endpoints or if imaging intervals are long. Nevertheless, this point should be discussed with Health Authorities as this additional confirmatory scan is usually requested 4 weeks later and the protocol might not plan for it; such analyses cannot be conducted ad hoc if the confirmatory assessment is not initially planned in the protocol.

 

What are the statistical and clinical challenges with RECIST 1.1?

Inter-reader variability

Despite the use of standardized RECIST 1.1 criteria for response, different radiologists may interpret imaging results differently, especially when measuring borderline lesions. This can introduce measurement bias and affect response classification (e.g., PR vs. SD) and therefore impact trial outcomes. As an example, the average discrepancy rate at the patient level was found to be 59.2% in lung cancer trials using RECIST 1.1.1

Lesion selection and measurement errors

• RECIST 1.1 limits the number of target lesions (up to 5 total, max 2 per organ).
• Importance of selecting the same target and non-target lesions to be followed across all timepoints otherwise patient level response will not be valid.
• Small errors in measuring lesion diameters can significantly impact response categorization.

Non-measurable disease

When the patient has only non-measurable disease, the increase must be substantial to lead to an overall response PD, which is relatively subjective.

Handling non-target and new lesions

Non-target lesions are assessed qualitatively, which introduces subjectivity.

The appearance of new lesions automatically triggers PD, even if the overall tumor burden is decreasing. Therefore, the finding of a new lesion should be unequivocal, i.e., not attributable to differences in scanning technique, change in imaging modality, or findings thought to represent something other than a tumor.

RECIST criteria are based on anatomical size, not functional or viable tumor volume

Focuses on unidimensional measurements, regardless of internal characteristics like necrosis or cavitation (common in lung or liver metastases).

Other criteria (e.g., Choi criteria for GISTs) may be more appropriate when necrosis is a key feature of response.

In some tumor types or trials, modified criteria (e.g., mRECIST for hepatocellular carcinoma) are used, which do consider viable tumor (e.g., arterial enhancement) rather than total size.

RECIST does not capture atypical responses

Especially in immunotherapy, tumors respond differently compared with chemotherapy, raising questions about the assessment of changes in tumor burden. In particular, for immunotherapy, RECIST 1.1 may misclassify pseudoprogression as PD.

This has led to the development of iRECIST, but many trials still rely on RECIST 1.1.

Time-to-event endpoint challenges

PFS and DOR depend on accurate and timely assessments.

Delays in imaging or inconsistent scan intervals can lead to informative censoring or biased survival estimates.

Missing or incomplete data

Patients may miss scans or drop out, leading to missing data that complicates statistical modeling. Interval censoring can be used as sensitivity in that case.

Imputation is difficult due to the non-linear and categorical nature of RECIST outcomes.

Impact on interpretation

Low concordance between Independent Central Review and the Investigator would question the reliability of results.

 

Why was iRECIST developed and how does it differ from RECIST 1.1?

Traditional RECIST criteria may misclassify immune-related responses as progression. iRECIST was developed to:

• Reflect atypical response patterns in immunotherapy
• Allow continued treatment beyond initial progression
• Improve consistency in trial design and data interpretation

 

iRECIST is an adaptation of RECIST 1.1 designed for immunotherapy trials. It accounts for pseudoprogression, where tumors may initially appear to grow before shrinking due to immune cell infiltration. iRECIST introduces:

• Unconfirmed Progressive Disease (iUPD)
• Confirmed Progressive Disease (iCPD)

This two-step confirmation helps avoid prematurely stopping effective immunotherapy.

 

What are the statistical challenges with iRECIST?

Delayed treatment effects

Immunotherapies may show delayed clinical benefits, which violate the proportional hazards assumption used in standard survival analysis (e.g., Cox models). This can complicate sample size estimation, primary analysis, and, in particular, hazard ratio interpretation.

Pseudoprogression and confirmation requirements

iRECIST introduces iUPD and requires a follow-up scan to confirm progression as iCPD, which delays the determination of progression and requires more complex modelling of iPFS. This also introduces interval censoring and time-dependent bias.

The exact time of progression is not precisely known — it lies between the iUPD and iCPD scans. Uncertainty around the exact date of progression, which is already present with RECIST, is larger with iRECIST, given that the second scan is needed to confirm the PD. A specific method like the interval censoring method might be more appropriate than the Kaplan-Meier and Cox models.

Patients who survive long enough and/or are still in the study to get a confirmation scan are not randomly selected — they may be little healthier. This may introduce selection bias and time-dependent confounding.

Endpoint ambiguity

Common endpoints like PFS and ORR are harder to define, which can lead to inconsistent endpoint definitions across trials:

• Should PFS be based on iUPD or iCPD?
• How should iDOR be calculated?
• What if patients drop out before confirmation?
• SAP should clearly define the derivations

Data interpretation and trial comparability

Trials using iRECIST are not directly comparable to those using RECIST 1.1.

Meta-analyses and pooled analyses become more difficult.

The protocol/SAP may plan for both RECIST and iRECIST analyses, increasing complexity.

Increased risk of missing data

Patients may discontinue before confirmation scans for progression.

Imaging schedules may not align with iRECIST requirements: iRECIST requires a follow-up scan (typically within 4–8 weeks) after an initial iUPD to determine if the progression is real or pseudoprogression. However, in many clinical trials or treatment protocols, imaging is scheduled every 8–12 weeks, which may not fit with the expected confirmation window and increase the risk of missing data.

This leads to informative censoring and missing not at random (MNAR) data, which are hard to handle statistically.

Limited validation and standardization

iRECIST is still considered exploratory, especially for phase III trials (as per the guidelines).

There is no consensus on how to incorporate iRECIST endpoints into pivotal trials.

Validation requires large-scale data sharing, which is still limited.

 

Best practices for implementing RECIST/iRECIST in trials

• Follow published guidelines.
• Ensure the CRF appropriately collects the data (e.g., date of new lesions). Examples are available on the RECIST website.
• Ensure standardized imaging schedules and methods.
• Train radiologists and clinicians on RECIST/iRECIST criteria.
• Consider blinded independent central review to reduce variability, when relevant.
• Plan for additional scans to confirm progression with iRECIST.
• Ensure responses criteria used are clear in SAP, outputs, CSR, and manuscripts.

 

Where can I learn more or access the guidelines?

Full RECIST guideline

Full iRECIST guideline

RECIST Questions and Clarifications

iRECIST

 

Final takeaways

RECIST 1.1 is the standard tool for evaluating tumor response in oncology trials, offering a consistent framework based on anatomical measurements. While it has brought uniformity to clinical research, it comes with some limitations — such as subjectivity in lesion selection and inability to capture atypical responses — especially with immunotherapies. To address these challenges, iRECIST was introduced as an adaptation that accounts for immune-related phenomena like pseudoprogression. However, it also brings statistical complexity and remains exploratory and is not yet fully reliable, with limited validation for pivotal trials.

This is precisely where Cytel can bring value to sponsors. By combining deep statistical expertise with operational insight, Cytel helps design and implement robust RECIST and iRECIST strategies — from endpoint definition to handling complex censoring and missing data. Cytel supports sponsors in navigating regulatory expectations, ensuring that trial results are both scientifically sound and submission-ready.

Oncology clinical trials are complex due to the nature of cancer progression, long follow-up times, start of further therapies, and ethical considerations. The estimand framework introduced in ICH E9(R1) provides a structured approach to align the clinical question with endpoints, intercurrent events, and analysis strategies.

 

Understanding the estimand framework in oncology

The estimand framework helps define what exactly a trial aims to measure, especially in the presence of intercurrent events (ICEs) that occur after treatment initiation and affect either the interpretation or existence of the outcome (like treatment discontinuation or new therapies).

Estimands need to be clearly defined in both the protocol and the Statistical Analysis Plan (SAP) using the five attributes outlined in the ICH E9(R1) addendum: population, variable (endpoint), treatment, intercurrent events and handling strategies, and population-level summary.

ICEs can complicate the estimation of treatment effects in oncology trials. Among these, the start of further anticancer therapy is particularly complex, especially when evaluating endpoints like Progression-Free Survival (PFS) and Overall Survival (OS).

Among all ICE handling strategies, two strategies are often used to handle the start of further anticancer therapy:

 

Hypothetical strategy

Estimate treatment effect in a world where further anticancer therapy would not exist.

• Implementation: Typically involves censoring patients at the time they start further anticancer therapy or using advanced statistical methods.
• Could be more meaningful from patient’s and prescriber’s perspective if subsequent therapies are not yet approved drugs and thus do not reflect clinical practice.
• May require additional data on baseline and/or time-dependent covariates to support modeling.

 

Treatment policy

Estimate treatment effect regardless of any further anticancer therapy, aiming to reflect real-world clinical practice.

• Implementation: Includes all events regardless of further anticancer therapy.
• Often considered as most relevant by regulatory authorities and other stakeholders if subsequent therapies are already approved and reflect clinical practice.
• Tend to dilute treatment effect.
• Assessments must continue beyond start of subsequent therapy.

 

Regulatory landscape

Historically, the FDA’s 2007 guidance leaned toward censoring at the start of new anticancer therapy — aligning with the hypothetical strategy. However, more recent guidance (2015, 2018) acknowledges both strategies, and the EMA’s 2012 guidance implicitly supports the treatment policy approach by recommending that progression should be considered even when observed after new anticancer treatment.

In Acute Myeloid Leukemia (AML), the FDA’s 2022 guidance is particularly clear: subsequent treatments like HSCT or anti-AML drugs should be considered part of the overall treatment regimen and not censored in the primary analysis.

 

When the hypothetical strategy may be preferable

In trials where conditions diverge significantly from routine clinical practice — such as early crossover or use of unapproved therapies — a hypothetical strategy may better capture the true clinical question.

Advanced methods like Rank Preserving Structural Failure Time (RPSFT) and Inverse Probability Censoring Weighting (IPCW) can help estimate what would have happened without treatment switching — but they come with assumptions and complexity.

 

Handling missing data in oncology

Effectively addressing missing data is essential for ensuring the reliability and integrity of statistical analyses in oncology trials. With regulatory agencies embracing the estimand framework, it’s essential to distinguish between ICEs and missing values, and to navigate their implications for primary and sensitivity analyses.

There is no consensus yet regarding how to handle missing tumor assessments in the primary analysis of PFS. Here’s a snapshot of key regulatory viewpoints:

According to the FDA’s 2018 guidance, “We recommend assigning the progression date to the earliest time when any progression is observed without prior missing assessments and censoring at the date when the last radiological assessment determined a lack of progression.”

The 2015 FDA NSCLC guidance offers case-based examples where progression events after two or more missed assessments are either censored or considered as events depending on the context, illustrating a cautious approach to ensure data robustness.

The 2012 EMA oncology guidelines advise against censoring for missed assessments: “The time of the progression or recurrence event is determined using the first date when there is documented evidence that the criteria have been met, even in situations where progression is observed after one or more missed visits, treatment discontinuation, or new anti-cancer treatment.”

Those different censoring rules can deeply impact PFS estimates, especially when early dropout rates are imbalanced between treatment arms.

Depending on the approach retained for the primary analysis, sensitivity analyses should be considered to assess the impact of missing tumor assessments. It may include a different set of censoring from the FDA guidance, but also interval censoring method.

 

Sensitivity and supplementary analyses

Understanding how different analyses relate to the primary estimand is critical for drawing robust and credible conclusions from clinical trial data. Two important analysis categories — sensitivity and supplementary analyses — serve distinct purposes and must be thoughtfully pre-specified in the SAP.

 

Sensitivity analyses: Testing the estimand’s foundations

According to ICH E9(R1), a sensitivity analysis is “a series of analyses conducted with the intent to explore the robustness of inferences from the main estimator to deviations from its underlying modeling assumptions and limitations in the data.”

Purpose: To verify that conclusions drawn from the primary analysis remain valid under alternative assumptions or data limitations. These analyses also probe key risks to inference, such as missing data or model specification.

Examples:

• Using an unstratified Cox model instead of a stratified one.
• Comparing investigator-assessed PFS with blinded independent central review (BICR)-assessed PFS.
• Applying alternative censoring rules (e.g., censoring after ≥2 missed tumor assessments), or interval-censored models for PFS.
• Using Restricted Mean Survival Time (RMST) to explore robustness under non-proportional hazards.

 

Supplementary analyses: Exploring beyond the estimand

While less explicitly defined, ICH E9(R1) describes supplementary analyses as: “Other analyses that are conducted in order to more fully investigate and understand the trial data.”

Purpose: To explore different strategies or assumptions that may be clinically or scientifically relevant.

Example: Using a different intercurrent event (ICE) strategy than the primary estimand.

 

Final takeaways

There’s no one-size-fits-all approach. Regulatory expectations continue to evolve, and sponsor decisions should balance regulatory guidelines, clinical practice norms, relevance to prescribers and patients, and feasibility of continued assessments.

Engaging in early discussions to align estimands with trial objectives and regulatory requirements is critical to ensuring efficient drug development and timely delivery to patients.

Blinded independent central review (BICR) is a process used in clinical trials, in which a group of independent experts review trial data, like radiographic images, to review assessments without access to information on patients’ treatment assignments. BICR of radiographic images is frequently conducted in oncology trials to address the potential bias of local evaluation by investigators (INV) of endpoints such as progression-free survival (PFS) and objective response rate (ORR).

 

What is the aim of BICR?

The BICR process serves several purposes. These include:

• Reducing bias: An investigator can be influenced in his or her assessment by prior knowledge of the treatment assignment in the case of an open-label study or patient toxicities. Blinded review enhances objectivity.
• Reducing measurement variability across sites and readers: Tasking a small number of central reviewers with expertise in a specific area with reviewing imaging may lead to more accurate and reliable assessments compared to local site reads. This is particularly important in multi-center trials.
• Ensure standardization: Centralized review ensures the standardized application of response criteria (e.g., RECIST 1.1, iRECIST).
• Improved data quality: Centralized monitoring allows for regular quality control, helping to identify issues such as inconsistent imaging techniques or poor-quality scans.
• Enhanced regulatory confidence: Regulatory agencies like the FDA and EMA often prefer or require BICR for pivotal oncology trials, in particular for open-label studies or those with higher bias risk. This strengthens the credibility of primary endpoints derived from tumor assessments.

 

What are the limitations of BICR?

While the BICR process can help achieve the above aims, there are limitations. For example:

• Operational complexity, time, and cost considerations: BICR requires a lot of coordination between multiple stakeholders (sponsors, imaging CROs, radiologists, adjudicators), resources, and logistics (e.g., reader’s training and data blinding, transfer, storage, and tracking).
• Informative censoring: BICR may introduce bias due to informative censoring, which results from having to censor unconfirmed locally determined progressions. Indeed, once a patient has progressed according to the local assessment, s/he might discontinue the study, and further imaging is unlikely to occur. As a result, determining the BICR progression time may be impossible. This type of censoring will be informative: patients who progress according to local review (but not according to central review) will be more likely to progress by the next scheduled scan than patients who have not been determined to progress by local review. One alternative to this issue is to request at least one additional scan beyond progression assessed by investigators. Even though it may be required in protocol, it may be difficult to implement.
• Regulatory agencies often expect BICR in pivotal oncology trials, in particular for open-label studies. However, inconsistencies between local and central results can complicate data interpretation and submission.

 

How is BICR data used?

As the primary endpoint (e.g., PFS-BICR): All patients need to be reviewed by BICR either in real time or by regular batch to be agreed upon.

For sensitivity analysis: All patients need to be reviewed by BICR either in real time or by regular batch (to be agreed upon) or as retrospective BICR. Retrospective BICR can be implemented only if the trial is positive based on local assessments (INV). Ideally, images should be collected and archived even after progression, to allow for retrospective BICR, if needed.

As an audit tool: In such context, only a random subset of patients can be reviewed and concordance between BICR and INV assessed on this subset of patients.

 

Practical aspects in BICR implementation

Imaging Charter

The Imaging Charter needs to be written in accordance with the protocol to ensure consistent methodology between investigators and independent review assessments.

Readers involved need to be specified in the charter; in general, two primary readers and one adjudicator are involved.

Adjudication paradigms should be detailed, in particular:

• Are the primary readers compared at the patient level or visit level?
• Which criteria are used to consider whether assessments from primary readers are different? For example:
  • At the patient level: the date of progression and/or best response are different
  • At the visit level: the sum of diameters on target lesions > xx mm

The choice of criteria impacts the number of cases that go to adjudication. Indeed, studies have shown that discrepancies between two readers can be substantial. For example, in lung cancer trials using RECIST 1.1, the average discrepancy rate at the patient level was around 59.2%,1 with adjudications often required to resolve differences. These discrepancies may stem from medically justifiable differences in interpretation or from errors, both of which can affect trial outcomes. Training and monitoring by a Central Imaging vendor can mitigate a large portion of the commonly encountered reading errors and therefore reduce variability.

• If adjudication occurs, which reader is the accepted one? (e.g., one of the primary readers (forced adjudication) and/or a new reader by adjudication (open adjudication, less common)).
• If adjudication does not occur, which reader is the accepted one? (e.g., reader 1 as default).
• The timing of readers and adjudication should be defined in the Charter (e.g., real-time review or review only once the patient discontinues the treatment, adjudication only once per patient or once per study, etc.).

 

Data transfers

The independent review must remain independent; imaging results should not be shared from the site to the BICR or vice versa.

The timing/frequency of the transfer to be defined:

• If only adjudication data are included, there might be a greater backlog for the tracking of BICR events.

Data reconciliation. Review of general consistency between the INV and BICR:

• Check whether patient populations, set of scans, visits, dates, and method of assessments (if needed) are consistent between BICR and INV datasets.
• Visits with Investigator Tumor Assessment but Missing BICR Assessment

Concordance between the INV and BICR results can be assessed during data review and at time of analysis.

• For PFS: Concordance of Occurrence and Timing of disease progression
• For ORR: Concordance of occurrence of Complete Response/Partial Response

 

Impact of tracking events and prediction of analysis timing

When the primary endpoint is PFS-BICR, interim or primary analyses are triggered by the number of BICR-assessed events. In such context:

• Monitoring BICR events may be more challenging than INV events. Indeed, it is hardly ever real-time event monitoring, there is some backlog for the review and adjudication time is to be considered.
• For event projections:
  • PFS-INV can be used as a first surrogate; if observed concordance is relatively high, it should provide relatively good estimate. The study team could consider estimating the BICR-assessed number of events based on an estimated ratio of investigator-assessed events (e.g., xx% of INV events).
  • Adjudication must be initiated early, to ensure that the number of BICR-assessed events are accurate. However, in some instances, the study may have pending adjudications at the time of data transfer for event projection.
  • BICR readers should read the full set of scans done up to a certain cutoff date and then transfer the data. Predictions can also be made for each reader separately if the adjudication is not yet completed.

 

Final takeaways

BICR enhances the objectivity and regulatory credibility of oncology trial endpoints by minimizing bias and standardizing radiographic assessments.

However, its implementation introduces operational challenges, may add complexity around data analysis with the risk of informative censoring, data interpretation in case of poor concordance between INV and BICR and prediction of analysis timing.