BOIN-ETC: A Bayesian optimal interval design considering efficacy and toxicity to identify the optimal dose combinations.

One of the primary objectives of a dose-finding trial for novel anti-cancer agent combination therapies, such as molecular targeted agents and immune-oncology therapies, is to identify optimal dose combinations that are tolerable and therapeutically beneficial for subjects in subsequent clinical trials. The goal differs from that of a dose-finding trial for traditional cytotoxic agents, in which the goal is to determine the maximum tolerated dose combinations. This paper proposes the new design, named 'BOIN-ETC' design, to identify optimal dose combinations based on both efficacy and toxicity outcomes using the waterfall approach. The BOIN-ETC design is model-assisted, so it is expected to be robust, and straightforward to implement in actual oncology dose-finding trials. These characteristics are quite valuable from a practical perspective. Simulation studies show that the BOIN-ETC design has advantages compared with the other approaches in the percentage of correct optimal dose combination selection and the average number of patients allocated to the optimal dose combinations across various realistic settings.

A basket trial design based on constrained hierarchical Bayesian model for latent subgroups.

It is well known a basket trial consisting of multiple cancer types has the potential of borrowing strength across the baskets defined by the cancer types, leading to an efficient design in terms of sample size and trial duration. The treatment effects in those baskets are often heterogeneous and categorized by the cancer types being sensitive or insensitive to the treatment. Hence, the assumption of exchangeability in many existing basket trials may be violated, and there is a need to design trials without this assumption. In this paper, we simplify the constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) for two classifiers to deal with the potential heterogeneity of treatment effects due to the single classifier of the cancer type. Different baskets are aggregated into subgroups using a latent subgroup modeling approach. The treatment effects are similar and exchangeable to facilitate information borrowing within each latent subgroup. Applying the simplified CHBM-LS approach to the real basket trials where baskets defined by only cancer types shows better performance than other available approaches. Further simulation study also demonstrates this CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios.

A generalized Bayesian optimal interval design for dose optimization in immunotherapy.

For novel immuno-oncology therapies, the primary purpose of a dose-finding trial is to identify an optimal dose (OD), defined as the tolerable dose having adequate efficacy and immune response under the unpredictable dose-outcome (toxicity, efficacy, and immune response) relationships. In addition, the multiple low or moderate-grade toxicities rather than dose-limiting toxicities (DLTs) and multiple levels of efficacy should be evaluated differently in dose-finding to determine true OD for developing novel immuno-oncology therapies. We proposed a generalized Bayesian optimal interval design for immunotherapy, simultaneously considering efficacy and toxicity grades and immune response outcomes. The proposed design, named gBOIN-ETI design, is model-assisted and easy to implement to develop immunotherapy efficiently. The operating characteristics of the gBOIN-ETI are compared with other dose-finding trial designs in oncology by simulation across various realistic settings. Our simulations show that the gBOIN-ETI design could outperform the other available approaches in terms of both the percentage of correct OD selection and the average number of patients allocated to the OD across various realistic trial settings.

Optimal biological dose selection in dose-finding trials with model-assisted designs based on efficacy and toxicity: a simulation study.

With the emergence of molecular targeted agents and immunotherapies in anti-cancer treatment, a concept of optimal biological dose (OBD), accounting for efficacy and toxicity in the framework of dose-finding, has been widely introduced into phase I oncology clinical trials. Various model-assisted designs with dose-escalation rules based jointly on toxicity and efficacy are now available to establish the OBD, where the OBD is generally selected at the end of the trial using all toxicity and efficacy data obtained from the entire cohort. Several measures to select the OBD and multiple methods to estimate the efficacy probability have been developed for the OBD selection, leading to many options in practice; however, their comparative performance is still uncertain, and practitioners need to take special care of which approaches would be the best for their applications. Therefore, we conducted a comprehensive simulation study to demonstrate the operating characteristics of the OBD selection approaches. The simulation study revealed key features of utility functions measuring the toxicity-efficacy trade-off and suggested that the measure used to select the OBD could vary depending on the choice of the dose-escalation procedure. Modelling the efficacy probability might lead to limited gains in OBD selection.

A Bayesian optimal interval design for dose optimization with a randomization scheme based on pharmacokinetics outcomes in oncology.

The primary objective of an oncology dose-finding trial for novel therapies, such as molecularly targeted agents and immune-oncology therapies, is to identify the optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. Pharmacokinetic (PK) information is considered an appropriate indicator for evaluating the level of drug intervention in humans from a pharmacological perspective. Several novel anticancer agents have been shown to have significant exposure-efficacy relationships, and some PK information has been considered an important predictor of efficacy. This paper proposes a Bayesian optimal interval design for dose optimization with a randomization scheme based on PK outcomes in oncology. A simulation study shows that the proposed design has advantages compared to the other designs in the percentage of correct OD selection and the average number of patients allocated to OD in various realistic settings.

TITE-gBOIN-ET: Time-to-event generalized Bayesian optimal interval design to accelerate dose-finding accounting for ordinal graded efficacy and toxicity outcomes.

One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular-targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low or moderate-grade toxicities than dose-limiting toxicities. Besides, for efficacy, evaluating the overall response and long-term stable disease in solid tumors and considering the difference between complete remission and partial remission in lymphoma are preferable. It is also essential to accelerate early-stage trials to shorten the entire period of drug development. However, it is often challenging to make real-time adaptive decisions due to late-onset outcomes, fast accrual rates, and differences in outcome evaluation periods for efficacy and toxicity. To solve the issues, we propose a time-to-event generalized Bayesian optimal interval design to accelerate dose finding, accounting for efficacy and toxicity grades. The new design named "TITE-gBOIN-ET" design is model-assisted and straightforward to implement in actual oncology dose-finding trials. Simulation studies show that the TITE-gBOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment while having comparable or higher performance in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.

Assessment of Surrogate End Point Trends in Clinical Trials to Approve Oncology Drugs From 2001 to 2020 in Japan.

Importance: A surrogate end point (SEP) is an end point used in clinical trials as an alternative for measuring the true clinical benefit. The use of SEPs in trials shortens their duration. Objectives: To investigate the use of SEPs in clinical trials to support the approval of anticancer drugs and to determine whether confirmatory studies that use overall survival (OS) as an end point are being conducted in Japan. Design, Setting, and Participants: In this cross-sectional study, drug approvals and background information were obtained from publicly available information, such as the Pharmaceuticals and Medical Devices Agency website, for anticancer drugs approved in Japan from January 2001 to December 2020. Data analysis was performed from September 2021 to March 2022. Main Outcomes and Measures: Characteristics of approved oncology drugs in Japan, end points for pivotal clinical trials, and outcomes of confirmatory trials using OS as an end point following drug approval. Results: There were 299 anticancer drugs approved in Japan during the study period. Of these, 142 (47.5%) were molecular-targeted drugs, the most common of which targeted non-small cell lung cancer. There were 111 (37.1%) anticancer drugs with orphan designation. From 2001 to 2005, OS was used as an end point in 1 approval (3.6%); however, from 2006 to 2020, OS was used in 86 approvals (31.7%). Of the 212 anticancer drugs approved on the basis of SEPs, confirmatory studies with OS as the end point were conducted for only 37 approvals (17.5%); for the remaining 175 approvals, studies are under way for 35 approvals (16.5%), were waivered for 75 approvals (35.4%), and were not conducted for 65 approvals (30.7%). Furthermore, in 20 drug approvals (9.4%), the conducted confirmatory studies were not effective in determining the OS, but the drugs were approved following re-examination. Conclusions and Relevance: The findings of this study suggest that starting from 2005, the use of OS as an end point has increased in studies supporting the approval of anticancer drugs in Japan. However, even after 2005, approximately two-thirds of these approvals were SEP based. Postmarketing surveillance studies of the true end points are necessary to validate the use of SEPs.

Comparison Between Simultaneous and Sequential Utilization of Safety and Efficacy for Optimal Dose Determination in Bayesian Model-Assisted Designs.

It has become quite common in recent early oncology trials to include both the dose-finding and the dose-expansion parts within the same study. This shift can be viewed as a seamless way of conducting the trials to obtain information on safety and efficacy hence identifying an optimal dose (OD) rather than just the maximum tolerated dose (MTD). One approach is to conduct a dose-finding part based solely on toxicity outcomes, followed by a dose expansion part to evaluate efficacy outcomes. Another approach employs only the dose-finding part, where the dose-finding decisions are made utilizing both the efficacy and toxicity outcomes of those enrolled patients. In this paper, we compared the two approaches through simulation studies under various realistic settings. The percentage of correct ODs selection, the average number of patients allocated to the ODs, and the average trial duration are reported in choosing the appropriate designs for their early-stage dose-finding trials, including expansion cohorts.

Incorporating historical information to improve dose optimization design with toxicity and efficacy endpoints: iBOIN-ET.

In modern oncology drug development, adaptive designs have been proposed to identify the recommended phase 2 dose. The conventional dose finding designs focus on the identification of maximum tolerated dose (MTD). However, designs ignoring efficacy could put patients under risk by pushing to the MTD. Especially in immuno-oncology and cell therapy, the complex dose-toxicity and dose-efficacy relationships make such MTD driven designs more questionable. Additionally, it is not uncommon to have data available from other studies that target on similar mechanism of action and patient population. Due to the high variability from phase I trial, it is beneficial to borrow historical study information into the design when available. This will help to increase the model efficiency and accuracy and provide dose specific recommendation rules to avoid toxic dose level and increase the chance of patient allocation at potential efficacious dose levels. In this paper, we propose iBOIN-ET design that uses prior distribution extracted from historical studies to minimize the probability of decision error. The proposed design utilizes the concept of skeleton from both toxicity and efficacy data, coupled with prior effective sample size to control the amount of historical information to be incorporated. Extensive simulation studies across a variety of realistic settings are reported including a comparison of iBOIN-ET design to other model based and assisted approaches. The proposed novel design demonstrates the superior performances in percentage of selecting the correct optimal dose (OD), average number of patients allocated to the correct OD, and overdosing control during dose escalation process.

An adaptive biomarker basket design in phase II oncology trials.

The phase II basket trial in oncology is a novel design that enables the simultaneous assessment of treatment effects of one anti-cancer targeted agent in multiple cancer types. Biomarkers could potentially associate with the clinical outcomes and re-define clinically meaningful treatment effects. It is therefore natural to develop a biomarker-based basket design to allow the prospective enrichment of the trials with the adaptive selection of the biomarker-positive (BM+) subjects who are most sensitive to the experimental treatment. We propose a two-stage phase II adaptive biomarker basket (ABB) design based on a potential predictive biomarker measured on a continuous scale. At Stage 1, the design incorporates a biomarker cutoff estimation procedure via a hierarchical Bayesian model with biomarker as a covariate (HBMbc). At Stage 2, the design enrolls only BM+ subjects, defined as those with the biomarker values exceeding the biomarker cutoff within each cancer type, and subsequently assesses the early efficacy and/or futility stopping through the pre-defined interim analyses. At the end of the trial, the response rate of all BM+ subjects for each cancer type can guide drug development, while the data from all subjects can be used to further model the relationship between the biomarker value and the clinical outcome for potential future research. The extensive simulation studies show that the ABB design could produce a good estimate of the biomarker cutoff to select BM+ subjects with high accuracy and could outperform the existing phase II basket biomarker cutoff design under various scenarios.

gBOIN-ET: The generalized Bayesian optimal interval design for optimal dose-finding accounting for ordinal graded efficacy and toxicity in early clinical trials.

One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low- or moderate-grade toxicities than dose-limiting toxicities. Besides, efficacy should be evaluated as an overall response and stable disease in solid tumors and the difference between complete remission and partial remission in lymphoma. This paper proposes the generalized Bayesian optimal interval design for dose-finding accounting for efficacy and toxicity grades. The new design, named "gBOIN-ET" design, is model-assisted, simple, and straightforward to implement in actual oncology dose-finding trials than model-based approaches. These characteristics are quite valuable in practice. A simulation study shows that the gBOIN-ET design has advantages compared with the other model-assisted designs in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.

[Introduction of Oncology Dose-Finding Trial Designs].

The primary objective of oncology dose-finding trials is to estimate the maximum tolerated dose(MTD)and determine the optimal dose(OD)for subsequent clinical trials by evaluating pharmacokinetics and pharmacodynamics of new drugs, treatment effects, and predictive markers. Oncology dose-finding trial designs can be categorized into 3 types based on their statistical bases and implementation approaches: algorithm-based, model-based, and model-assisted designs. In this paper, we introduce the characteristics of various oncology dose-finding trial designs according to the categories. First, oncology dose-finding trial designs solely based on toxicity for MTD determination are discussed, followed by oncology dose-finding trial designs based on efficacy and toxicity for identifying OD. Sequential enrollment, combination therapy, toxicity grade, and historical data are also briefly introduced.

TITE-gBOIN: Time-to-event Bayesian optimal interval design to accelerate dose-finding accounting for toxicity grades.

The new therapeutic agents, such as molecular targeted agents and immuno-oncology therapies, appear more likely to induce multiple toxicities at different grades than dose-limiting toxicities defined in traditional dose-finding trials. In addition, it is often challenging to make adaptive decisions on dose escalation and de-escalation on time because of the fast accrual rate and/or the late-onset toxicity outcomes, causing the potential suspension of the enrollment and the delay of the trials. To address these issues, we propose a time-to-event Bayesian optimal interval design to accelerate the dose-finding process utilizing toxicity grades based on both cumulative and pending toxicity outcomes. The proposed design, named "TITE-gBOIN" design, is a nonparametric and model-assisted design and has the virtues of robustness, simplicity and straightforward to implement in actual oncology dose-finding trials. A simulation study shows that the TITE-gBOIN design has a higher probability of selecting the MTDs correctly and allocating more patients to the MTDs across various realistic settings while reducing the trial duration significantly, therefore can accelerate early-stage dose-finding trials.

Constrained hierarchical Bayesian model for latent subgroups in basket trials with two classifiers.

The basket trial in oncology is a novel clinical trial design that enables the simultaneous assessment of one treatment in multiple cancer types. In addition to the usual basket classifier of the cancer types, many recent basket trials further contain other classifiers like biomarkers that potentially affect the clinical outcomes. In other words, the treatment effects in those baskets are often categorized by not only the cancer types but also the levels of other classifiers. Therefore, the assumption of exchangeability is often violated when some baskets are more sensitive to the targeted treatment, whereas others are less. In this article, we propose a constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) to deal with potential heterogeneity of treatment effects due to both the cancer type (first classifier) and another classifier (second classifier) in basket trials. Different baskets defined by multiple cancer types and multiple levels of the second classifier are aggregated into subgroups using a latent subgroup modeling approach. Within each latent subgroup, the treatment effects are similar and approximately exchangeable to borrow information. The CHBM-LS approach evaluates the treatment effect for each basket while allowing adaptive information borrowing across the baskets by identifying latent subgroups. The simulation study shows that the CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios with heterogeneous treatment effects across baskets.

Searching for potential surrogate endpoints of overall survival in clinical trials for patients with prostate cancer.

BACKGROUND: The purpose of this study was to investigate the correlation between overall survival (OS) and other clinical outcomes in patients with prostate cancer. Further, we conducted subgroup analysis in the correlation of OS. AIM: This study intended to investigate potential surrogate endpoints of OS for prostate cancer by examining the correlation between OS and the other endpoints. METHODS: We performed a systematic review through a literature search by computer-based searches of the Medline database (January 1965 and May 2014). RESULTS: The contents of 115 studies with endpoint as OS were analyzed in our study. Our results showed that 47.8% (55/115) of the studies used progression-free survival as an endpoint besides OS, followed by time to progression (43.5% [50/115]) and PSA response (40.9% [47/115]). Also, the relationship between OS and each surrogate endpoint was examined using the hazard ratio (HR) by a Bayesian hybrid model for random effect multivariate meta-analysis. Our results showed that the endpoint that had the highest correlation with OS was progression-free survival (PFS) with an estimated marginal correlation of 0.939 (95%CI: 0.900, 0.967). Furthermore, our stratified analysis identified PFS in castration-resistant prostate cancer patients (0.937), in sensitive patients (0.932), in none of chemotherapy patients (0.929), in first line of the chemotherapy (0.948), in patients who received no Docetaxel previously (0.942), in both symptomatic and asymptomatic patients (0.950), in patients who received only chemotherapy (0.956), and in phase III (0.960), time to progression (TTP) in castration-resistant prostate cancer (CRPC) patients (0.942), in metastasis patients (0.948), in both symptomatic and asymptomatic patients (0.953), in patients who received only chemotherapy (0.938), and in Phase III (0.927) as endpoints, which showed a lower limit for 95% CI of estimated marginal correlation ≥0.850 with overall survival. CONCLUSIONS: Our study suggests that PFS is a potential surrogate endpoint of OS in clinical trials for patients with prostate cancer. It also suggests potential surrogate endpoints for CRPC and locally advanced prostate cancer.

Safety and Antitumor Activity of Repeated ASP3026 Administration in Japanese Patients with Solid Tumors: A Phase I Study.

BACKGROUND AND OBJECTIVE: Anaplastic lymphoma kinase gene rearrangements (ALKr) resulting in EML4-ALK proteins occur in a subset of solid tumors and are targeted by ALK inhibitors. Given the development of drug resistance to ALK inhibitors, ALK inhibitors with different kinase selectivity are required. METHODS: This phase I, non-randomized, open-label study evaluated the dose-limiting toxicity (DLT), safety, pharmacokinetics, and antitumor activity of ASP3026, a second-generation ALK inhibitor, in Japanese patients with solid tumors. Between 1 June 2011 and 20 January 2014, 29 patients received different daily doses of ASP3026 in the escalation (25 mg, n = 3; 50 mg, n = 3; 75 mg, n = 3; 125 mg, n = 4; 200 mg, n = 3; or 325 mg, n = 7) and expansion (200 mg, n = 6) cohorts. RESULTS: Three patients had DLTs at the 325-mg dose: cataract exacerbation, increased aspartate transaminase and alanine transaminase, and impaired hepatic function (all Grade 3 severity). Thus, the maximum tolerated dose was 200 mg. The treatment-emergent adverse event incidence was 100%; the most common events were nausea (n = 8, 27.6%), decreased appetite (n = 10, 34.5%), and fatigue (n = 9, 31.0%) of mild or moderate severity. Six patients were positive for ALK protein and three had ALKr. Two patients achieved partial responses: one with Ewing sarcoma (75-mg dose group) and one with an ALKr-positive inflammatory myofibroblastic tumor (125-mg dose group). CONCLUSION: ASP3026 at a 200-mg dose may provide therapeutic benefit for patients with solid tumors, with a tolerable safety profile. CLINICAL TRIAL REGISTRATION: This study is registered at ClinicalTrials.gov under the identifier NCT01401504 on July 25, 2011.

A Comparison Between a Meta-analytic Approach and Power Prior Approach to Using Historical Control Information in Clinical Trials With Binary Endpoints.

BACKGROUND: In the process of research and development of a new treatment, clinical trials are conducted to evaluate its safety and efficacy. Key to streamlining the process is to utilize appropriate historical information on an outcome of a control treatment when designing and analyzing a clinical trial. METHODS: For the use of such historical control information, there exist a meta-analytic approach and power prior approach. In this article, we evaluate their performance with regard to the type I error (TIE) rate and power through a simulation study where we analyze the data on a binary outcome of an experimental treatment and a control treatment from a new small-scale trial, along with the corresponding data of the control treatment from multiple historical trials. The reason is that the difference in the performance between the 2 approaches has not been clear. RESULTS: When historical trials were homogeneous, the power was higher in the power prior approach and the meta-analytic approach using a beta-binomial model with a less noninformative prior than the other approaches. However, when heterogeneous historical trials were mixed, the power was lower, or the TIE rates got inflated. CONCLUSIONS: To make use of historical control data, if importance is attached to control of the TIE rate, the meta-analytic approach using a normal-normal hierarchical model may be preferable to the power prior approach, whereas if attached to improvement of the power, this preference be reversed. Anyway, the best approach should be chosen by comparing the operational characteristics of the approaches.

A phase 1 study of oral ASP5878, a selective small-molecule inhibitor of fibroblast growth factor receptors 1-4, as a single dose and multiple doses in patients with solid malignancies.

ASP5878 is a selective small-molecule inhibitor of fibroblast growth factor receptors (FGFRs). This study investigated safety, tolerability, and antitumor effect of single and multiple oral doses of ASP5878 in patients with solid tumors. This phase 1, open label, first-in-human study comprised dose-escalation and dose-expansion parts. Primary objectives of the dose-escalation part were to identify the dose-limiting toxicity (DLT), maximum tolerated dose, and recommended dose of ASP5878 for the dose-expansion part. Nine dose cohorts of ASP5878 were evaluated (0.5─2 mg once daily; 2─40 mg twice daily [BID]). A single dose of ASP5878 was followed by a 2-day pharmacokinetic collection, and then either 28-day cycles of daily dosing (ASP5878 ≤ 10 mg BID) or 5-day dosing/2-day interruption (ASP5878 ≥ 20 mg BID). The primary objective of the dose-expansion part was to determine the safety of ASP5878 (16 mg BID) administered in 28-day cycles of 5-day dosing/2-day interruption in patients with urothelial carcinoma, hepatocellular carcinoma, or squamous cell lung carcinoma with FGFR genetic alterations. Safety was assessed by monitoring adverse events (AEs). Thirty-five patients were enrolled and 31 discontinued in the dose-escalation part; 51 patients were enrolled and 51 discontinued in the dose-expansion part. In the dose-escalation part, 66.7% of patients in the 20 mg BID 5-day dosing/2-day interruption group reported DLTs of hyperphosphatemia. The recommended dose for the dose-expansion part was 16 mg BID. Common AEs included retinal detachment, diarrhea, and increased alanine aminotransferase. One death occurred that was not related to ASP5878. ASP5878 was well tolerated with manageable toxicities including hyperphosphatemia.

TITE-BOIN-ET: Time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes.

One of the primary purposes of an oncology dose-finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de-escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late-onset outcomes. To solve these issues, we propose the time-to-event Bayesian optimal interval design to accelerate dose-finding based on cumulative and pending data of both efficacy and toxicity. The new design, named "TITE-BOIN-ET" design, is nonparametric and a model-assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose-finding trials compared with the model-based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE-BOIN-ET design has advantages compared with the model-based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE-BOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose-finding trials.

Late Onset of the Fabella Syndrome after Total Knee Arthroplasty.

Some differential diagnosis is thought due to knee pain after total knee arthroplasty (TKA) and fabella syndrome may cause post-TKA pain due to mechanical irritation. In this present case, a 64-year-old woman experienced lateral knee pain which was localized at the iliotibial ligament 8 years after the surgery. Fabella syndrome was diagnosed, and fabellectomy provided immediate resolution of the pain. The previous reports have revealed the symptoms occurred after 6 days to a year after total knee arthroplasty. This case widens the time span and the consideration of the fabella syndrome. The reason of this late onset symptom could be due to the enlargement of the fabella over time. We report that the differential diagnosis of fabella syndrome should be thought in symptoms of late onset knee pain after total knee arthroplasty.