Diagnostic performance of different methods for diagnosis of pseudoprogression after immunotherapy treatment: a systematic review and meta-analysis

Diagnostic performance of different methods for diagnosis of pseudoprogression after immunotherapy treatment: a systematic review and meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diagnostic performance of different methods for diagnosis of pseudoprogression after immunotherapy treatment: a systematic review and meta-analysis Zheng Zhu, Lin Li, Yanfeng Zhao, Xiaoyi Wang, Xinming Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6116450/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The purpose of this meta-analysis was to evaluate the diagnostic performance of different methods for pseudoprogression after immunotherapy treatment. Methods This systematic review adhered to the PRISMA for diagnostic test accuracy guidelines. The PubMed, Embase, and Cochrane Library databases were searched comprehensively for relevant studies up to October 01, 2023 according to specific inclusion and exclusion criteria. The quality of the included studies was assessed according to the quality assessment of diagnostic accuracy studies (QUADAS-2). After performing heterogeneity and threshold effect tests, pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated. Publication bias was evaluated visually and estimated by Deeks’ funnel plot. The area under the summary receiver operating characteristic (SROC) curve was calculated to demonstrate the diagnostic performance of modality. Results Five studies covering 250 lesions evaluating laboratory, image, and radiomics were included. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 77% (95% confidence interval [CI] 72%-81%), 70% (95% CI 66%-75%), 2.58 (95% CI 2.14–3.07), 0.33 (95% CI 0.27–0.40), and 7.88 (95% CI 5.59–10.52), respectively. The area under the SROC curve was 0.807. In addition, the SROC curve showed high sensitivities (0.77) and low false positive rates (0.33) suggested that the results were reliable. Furthermore, the Deeks’ funnel plot suggested no notable publication bias. No heterogeneity (I2 < 50%) was observed in the analysis of pooled studies. Conclusion Our review suggests that laboratory and images offered the optimal diagnostic performance of pseudoprogression after immunotherapy treatment. Meta-analysis Pseudoprogression Immunotherapy Immune Checkpoint inhibitors Diagnostic accuracy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Pseudoprogression, a phenomenon termed by a subset of patients treated with immune checkpoint inhibitors (ICIs) manifest an atypical pattern of tumor response either after an increase of tumor burden or appearance of new lesions, is classified as progressive disease by conventional response criteria[1; 2]. Along with increasing recognition of pseudoprogression and its importance, several studies evaluated its definitions, incidence and diagnostic value[ 3 – 5 ]. But a unifying diagnostic performance of different methods in pseudoprogression are lacking. Although studies have raised the need for robust data on pseudoprogression[ 6 ], to our knowledge, there is no evidence-based systematic summary of diagnostic value of pseudoprogression after immunotherapy treatment. There is an urgent need for prompt, accurate diagnosis of pseudoprogression to make informed clinical treatment decisions. Pseudoprogression can subside after several months of follow-up without further treatment [ 7 ], while tumor true progression is associated with neoangiogenesis to supply the rapidly growing tumor, much earlier differentiation from tumor progression is vital from a clinical standpoint. Current response criteria require confirmation such as additional follow up and/or histopathology at re-resection, which may result in delayed or inappropriate treatments. Confirmation of pseudoprogression requires subsequent imaging, imposing an ongoing challenge despite the development of newer criteria, such as the immune-related response criteria (irRC)[ 1 ]. ICI response assessment is challenging, as novel response patterns, such as pseudoprogression are not considered in the response evaluation criteria in solid tumors (RECIST 1.1). An increase in tumor volume could be based on either true progressive disease (TPD) or on influx of immune-competent cells (pseudoprogression). Early differentiation of pseudoprogression and TPD is highly relevant in daily clinical decision-making, and predictive biomarkers are needed for better patient selection. A systemic review of the diagnostic accuracy of different methods for diagnosis of pseudoprogression after immunotherapy is therefore needed to properly evaluate their diagnostic performance. We performed a meta-analysis of published original researches to determine the diagnostic performance of different methods such as laboratory, image, and radiomics in the diagnosis of pseudoprogression during treatment with immunotherapy. Methods Literature search The analysis was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines[8; 9]. A comprehensive search of several databases from each database inception to October 01, 2023, in any language was conducted. The databases included PubMed, Embase, and Cochrane Library. The keywords for the search were “pseudoprogression”, “immunotherapy”, and “immune checkpoint inhibitors”. Detailed search strategy was as follows: (Pseudoprogression) AND (((immunotherapy) OR immune checkpoint inhibitors) OR ((((((nivolumab) OR pembrolizumab) OR atezolizumab) OR avelumab) OR camrelizumab) OR durvalumab) OR (((programmed cell death 1 receptor) OR programmed cell death-1) OR PD-1 receptor) OR ((programmed death ligand 1) OR PD-L1 protein)). Articles in English were chosen. Meanwhile, we also widely scanned references cited in the retrieved articles to find other potentially eligible articles. Inclusion and Exclusion Criteria The inclusion criteria were as follows: retrospective or prospective studies; studies based on clinical research in humans and regarding the assessment of diagnostic performance for pseudoprogression; final diagnosis based on pathological or follow-up data; sufficient raw data was available to calculate true-positive (TP), false-positive (FP), false-negative (FN), and true-negative values (TN); in case of overlapping data, the study with the most cases. Review articles, abstracts, comments, case report, letters, and proceedings were excluded. Articles which hyperprogression (HP), progression (PD), progression-free survival (PFS) or overall survival (OS) was only studied were also excluded. Studies with overlapping patients (ie, when ≥ two studies reported the same patients, the study with the longer follow-up time was selected). Two authors (Z.Z. and Y.Z.) assessed and identified potential articles based on the inclusion and exclusion criteria independently, and disagreement was resolved by arbitration by the third author (X.W.). Methodological Quality and Risk of Bias Assessment Two authors (Z.Z. and Y.Z.) assessed all included studies [ 10 – 14 ] for methodological quality, including risk of bias and applicability, by use of the Scottish Intercollegiate Guidelines Network methodology checklist, adapted from Quality Assessment of Diagnostic Accuracy Studies 2 tool [ 15 ] for diagnostic studies. We assessed the following domains: patient selection, index test, reference standard, flow, and timing. Quality of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach[ 16 ]. Data Extraction From the included studies, two authors (Z.Z. and Y.Z.) independently extracted data from the included studies, with disagreement resolved by consensus with a third author (L.L.). Data extracted into standardized forms are following: study characteristics (ie, authors, year of publication, study design, and sample size), demographic and clinical characteristics of the modality (ie, type of modality such as laboratory, image, and radiomics), TP, FP, FN, TN, and outcome characteristics (ie, the number of patients with pseudoprogression and the diagnostic value of modality). Statistical Analysis We calculated measures of diagnostic test accuracy [sensitivity, specificity, likelihood ratios, and diagnostic odds ratios (DOR)] using a bivariate regression model, allowing for correlation between sensitivity and specificity. The SROC curves were used to calculate the area under the curve (AUC) of the combined model. The heterogeneity among the included studies was quantified using the Cochrane Q statistics and I 2 statistics. When the I 2 statistic exceeded 50%, the random-effect model was used to merge the diagnostic accuracy indicators. Otherwise, the fixed-effect model was used. Heterogeneity was assessed using the I 2 variable, which describes the proportion of variation in study results that can be attributed to heterogeneity[ 17 ]. I 2 values greater than 50% indicated substantial heterogeneity[ 18 ]. The heterogeneity test and the chi-square test were performed. The presence and quantity of statistical heterogeneity was assessed using the I² statistic, with significance set at P < 0.10. Publication bias of the included studies was assessed by Deek's funnel plot. All reported P values are two sided, and P values less than 0.05 were considered to indicate statistical significance. All statistical analyses were performed by using the robvis, meta, Rgraphviz, metamisc, meta4diag, INLA and metafor packages in R (version 4.1.1; R Foundation for Statistical Computing, Vienna, Austria) Results The study process is shown in Fig. 1 . The basic characteristic of the 5 studies we included are summarized in Table 1 . They were all retrospective studies, data including single institution (n = 4) and multiple centers (n = 1)[ 13 ]. Studies included patients with brain metastases in non–small cell lung cancer ( n = 1), metastatic melanoma (n = 3), and malignant solid tumors (n = 1). Of these, two studies were from Switzerland[10; 14], and the rest were from Australia[ 12 ], China[ 13 ], and the USA [ 11 ]. Methodological Quality of the Included Studies The overall risk of bias of the 5 studies (Fig. 2 ) that reported all diagnostic accuracy values was low to moderate. The majority (75%-100%) of the studies were judged to have low risk of bias in terms of index test, reference standard, flow, and timing.75% studies were judged to have high risk of bias in terms of patient selection. The majority of the studies were considered to have a low and moderate risk of bias on applicability to clinical practice in terms of reference standard, index test, and patient selection. Diagnostic Value of Modality in Pseudoprogression On the basis of the included studies (n = 5) with patients, the overall sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of diagnostic pseudoprogression was 77% (95% CI: 72%-81%), 70% (95% CI 66%-75%), 2.58 (95% CI 2.14–3.07), and 0.33 (95% CI 0.27–0.40), respectively (Fig. 3 ). No heterogeneity was observed ( I 2 = 1.39%; Q = 16.903, P = 0.392) when using random-effects model (REML). Pooled Analysis of Diagnostic Accuracy Analysis of the receiver operating characteristic area under the curve (Fig. 4 ) showed that area of diagnose pseudoprogression was 0.807 (95% CI, 0.675–0.834). Fagan’s nomogram showed that with the pretest probability of 31%, the posttest probability reached 54% and 13% for the positive and negative tests, respectively (Fig. 5 ). These results confirmed the high diagnostic efficiency of modality in the diagnosis of pseudoprogression. Fagan’s nomogram comprehensively considers PLR and NLR and adjusts the likelihood ratios according to the prior probability of diagnosis for different modalities. It shows that modality could be helpful in diagnosing pseudoprogression. Publication bias analysis Taking the inverse of the square root of effective sample size [1/root (ESS)] as the ordinate and DOR as the abscissa, the results of Deeks’ test showed that the P value of slope coefficient was 0.912, suggesting that there was no publication bias in the related methods (Fig. 6 ). Discussion To our knowledge, this is the first systematic review and meta-analysis examining methods for diagnostic performance of pseudoprogression after immunotherapy treatment, and reports the pooled analysis of sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio to inform a comprehensive evaluation of the relative diagnostic performance of methods for diagnosis pseudoprogression after immunotherapy treatment. In this meta-analysis, the sensitivity of overall diagnostic value of modality in pseudoprogression was 77% in those retrospective reports of patients with cancer undergoing immunotherapy treatment. The phenomenon of pseudoprogression may be related to factors such as delayed immune system activation, immune cell infiltration, and edema caused by local immune related inflammatory reactions [19; 20]. The mechanism behind pseudoprogression could be that tumors could have ongoing growth until the activation of effective antitumor immune responses develops [ 21 ]. Another explanation could be the infiltration of T cells into tumors, leading to a transient increase in tumor burden rather than true proliferation of tumor cells[ 22 ]. The second hypothesis was later confirmed on tumor biopsies from patients with melanoma experiencing transient progression on aCTLA-4 inhibitor, showing an acute inflammatory reaction with lymphocyte infiltration. The enrolled five studies evaluated the diagnostic performance of different methods included blood and imaging parameters even radiomics data, tumor heterogeneity is a typical feature widely studied in malignant tumors, closely related to tumor biological behavior and patient prognosis. The heterogeneity of tumors can be visualized through the roughness and irregularity caused by local spatial changes in image brightness in imaging, which is the basis for quantifying heterogeneity through texture analysis No heterogeneity was observed in the diagnostic performance indicators in enrolled studies, which varied in terms of the disease prevalence, study location, and most importantly the diagnostic methods. The diagnostic methods varied from laboratory to image even radiomics, the difference in diagnostic value between those methods are not focused on the present study since the number of enrolled studies are only five papers, even the subgroup study is not performed. More, the definition of pseudoprogression are varied from reference standard on previous publications [ 10 ] to their own definition [ 12 ], or from histopathology served as the standard [ 11 ] to radiographic observation [ 14 ]. Some pseudoprogression lesions were also irradiated or chemotherapy; therefore, there is also probably a component of radiation/chemotherapy-induced treatment effect in addition to the immunotherapy effect. Separating the effects of immunotherapy and radiation/chemo- therapy is difficult given the complementary roles of both treatments in routine clinical practice. In addition, pseudoprogressions were likely attributable for both immunotherapy and radiation, and it is difficult to differentiate due to the retrospective design of this study. Moreover, most of the pseudoprogresssion lesions were previously treated with radiotherapy, and we are unable to differentiate the attribution between radiation effect alone from immunotherapy effect alone, however the combination likely contributed for the overall pseudoprogression. Our study had limitations. First, we included only distinguish pseudoprogression from real progression related studies that were identified by using the term pseudoprogression and reported the detailed cutoff data of diagnosing pseudoprogression. Studies that did not have patients with pseudoprogression, the incidence of pseudoprogression, the clinical outcomes such as median overall survival time of pseudoprogression were not included, which may result in an apparent increase of pseudoprogression incidence. Although we did not exclude studies reporting the diagnostic blood data of pseudoprogression, all five studies meeting the criteria had at least laboratory, image or radiomics with diagnostic value with pseudoprogression. Second, subgroup study was not performed due to the enrolled number of studies. Third, we did not extract the time frame of pseudoprogression because that information was not fully presented in the included studies. It is an inborn limitation of systematic review. Though we have included all the immunotherapy at the time of the study, study may become available and add further knowledge in the near future, given the rapid advancement of immuno-oncology. The results of our study will provide a basis for future studies when sufficient newer data become available. In summary, in this review of all relevant published studies, we synthesized the pooled estimates of the diagnostic performance of diagnosis methods and found that laboratory and image offered excellent diagnostic performance for diagnosis of pseudoprogression after immunotherapy, there are still only a limited number of studies on those conditions. Therefore, more clinical studies are needed to establish the effectiveness of diagnostic value for pseudoprogression after immunotherapy. Declarations Contributors ZZ and YZ conceived and designed the study. ZZ and YZ screened literature and extracted data. LL assisted with data extraction. ZZ and YZ had access to and verified the data. YZ did the statistical analysis. ZZ, YZ, LL and XW interpreted the data. ZZ wrote the first draft of the manuscript, and all authors provided critical review and revision of the text and approved the final version. YZ and XZ had final responsibility for the decision to submit for publication. Funding Declaration: This project was supported by the National Natural Science Foundation of China (82102029) and Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2021-I2M-C&T-B-057) Declaration of Competing interests All authors declare no competing interests. Ethics declaration Not applicable. Clinical trial number : Not applicable. Ethics and Consent to Publish declarations: Not applicable. Ethics, Consent to Participate, and Consent to Publish declarations: Not applicable. Data Availability All data generated or analysed during this study are included in this published article’s supplementary information files. References Wolchok JD, Hoos A, O'Day S et al (2009) Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin Cancer Res 15:7412-7420 Nishino M (2016) Immune-related response evaluations during immune-checkpoint inhibitor therapy: establishing a "common language" for the new arena of cancer treatment. J Immunother Cancer 4:30 Park HJ, Kim KW, Pyo J et al (2020) Incidence of Pseudoprogression during Immune Checkpoint Inhibitor Therapy for Solid Tumors: A Systematic Review and Meta-Analysis. Radiology 297:87-96 Katz SI, Hammer M, Bagley SJ et al (2018) Radiologic Pseudoprogression during Anti-PD-1 Therapy for Advanced Non-Small Cell Lung Cancer. J Thorac Oncol 13:978-986 Nishino M, Dahlberg SE, Adeni AE et al (2017) Tumor Response Dynamics of Advanced Non-small Cell Lung Cancer Patients Treated with PD-1 Inhibitors: Imaging Markers for Treatment Outcome. Clin Cancer Res 23:5737-5744 Nishino M (2016) Pseudoprogression and Measurement Variability. J Clin Oncol 34:3480-3481 Hygino da Cruz LC, Jr., Rodriguez I, Domingues RC, Gasparetto EL, Sorensen AG (2011) Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. AJNR Am J Neuroradiol 32:1978-1985 McInnes MDF, Moher D, Thombs BD et al (2018) Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA 319:388-396 Page MJ, Moher D, Bossuyt PM et al (2021) PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ 372:n160 Akhoundova D, Hiltbrunner S, Mader C et al (2020) 18F-FET PET for Diagnosis of Pseudoprogression of Brain Metastases in Patients With Non-Small Cell Lung Cancer. Clin Nucl Med 45:113-117 Umemura Y, Wang D, Peck KK et al (2020) DCE-MRI perfusion predicts pseudoprogression in metastatic melanoma treated with immunotherapy. J Neurooncol 146:339-346 Lee JH, Long GV, Menzies AM et al (2018) Association Between Circulating Tumor DNA and Pseudoprogression in Patients With Metastatic Melanoma Treated With Anti-Programmed Cell Death 1 Antibodies. JAMA Oncol 4:717-721 He S, Feng Y, Lin Q et al (2021) CT-Based Peritumoral and Intratumoral Radiomics as Pretreatment Predictors of Atypical Responses to Immune Checkpoint Inhibitor Across Tumor Types: A Preliminary Multicenter Study. Front Oncol 11:729371 Basler L, Gabrys HS, Hogan SA et al (2020) Radiomics, Tumor Volume, and Blood Biomarkers for Early Prediction of Pseudoprogression in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibition. Clin Cancer Res 26:4414-4425 Whiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529-536 Murad MH, Montori VM, Ioannidis JP et al (2014) How to read a systematic review and meta-analysis and apply the results to patient care: users' guides to the medical literature. JAMA 312:171-179 Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21:1539-1558 Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557-560 Chiou VL, Burotto M (2015) Pseudoprogression and Immune-Related Response in Solid Tumors. J Clin Oncol 33:3541-3543 Wang Q, Gao J, Wu X (2018) Pseudoprogression and hyperprogression after checkpoint blockade. Int Immunopharmacol 58:125-135 Foller S, Oppel-Heuchel H, Grimm MO (2018) [Tumor assessment in immune checkpoint inhibitor therapy : Tumor response, progression and pseudoprogression]. Urologe A 57:1316-1325 Wei SC, Levine JH, Cogdill AP et al (2017) Distinct Cellular Mechanisms Underlie Anti-CTLA-4 and Anti-PD-1 Checkpoint Blockade. Cell 170:1120-1133 e1117 Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6116450","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":437678379,"identity":"4f52f936-deac-4128-b536-c95e7b6418fb","order_by":0,"name":"Zheng Zhu","email":"","orcid":"","institution":"National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Zhu","suffix":""},{"id":437678381,"identity":"e3db7d96-cd7e-47b0-9949-132f0b205bef","order_by":1,"name":"Lin Li","email":"","orcid":"","institution":"National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Li","suffix":""},{"id":437678382,"identity":"7eae22b3-01a8-46b7-b8f2-4381837c852a","order_by":2,"name":"Yanfeng Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYFACxocPJCoYeEjRwmxsYHGGRC1mEpVtpGiQn5HMIHFzXp2MOfsBxg8fc4jQwgjUYjhz22Eey54EZsmZ24hxlkT+gWTJbQd4DA4ksDHzEqOFTSKZ4fDfOXU8BucfEKmFRyKZsUGygZnH4AaxtkjwPGZmkDh2GKjlYTNxfpFvT2b/IVFTZ29wPvngh4/EaGEQSICxGBuIUQ8E/AeIVDgKRsEoGAUjFwAAZwoxR4jOpKwAAAAASUVORK5CYII=","orcid":"","institution":"National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Yanfeng","middleName":"","lastName":"Zhao","suffix":""},{"id":437678383,"identity":"4d5869af-165a-497c-a67c-f11fb543b03b","order_by":3,"name":"Xiaoyi Wang","email":"","orcid":"","institution":"National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyi","middleName":"","lastName":"Wang","suffix":""},{"id":437678384,"identity":"2cd1cd2b-351d-45af-a069-629cc2c45425","order_by":4,"name":"Xinming Zhao","email":"","orcid":"","institution":"National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xinming","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-02-27 00:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6116450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6116450/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79908285,"identity":"bc1189ea-649e-45c2-81ce-1e5d927a65a7","added_by":"auto","created_at":"2025-04-04 11:18:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":300440,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study selection procedure. Five retrospective studies were included in the meta-analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6116450/v1/f505f49aa74f85d4b1927028.png"},{"id":79909843,"identity":"83221577-b324-4751-80d2-1659cfd0d7a6","added_by":"auto","created_at":"2025-04-04 11:26:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":212373,"visible":true,"origin":"","legend":"\u003cp\u003eThe Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) results showing methodological quality of the study included. Green, red, and yellow bars proportionally indicate good, low, and unclear risk of bias, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6116450/v1/64c6b55ec3ab84dca1bcb682.png"},{"id":79910747,"identity":"1f00aade-b35d-410a-b3c5-b24ab1ea70fe","added_by":"auto","created_at":"2025-04-04 11:34:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6970820,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots to show the overall pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of diagnostic pseudoprogression. The pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of diagnostic pseudoprogressionwere 77%, 70%, 2.58, and 0.33, respectively. CI = confidence interval.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6116450/v1/99d762937c0a35fa2ff5ba5f.png"},{"id":79909846,"identity":"293b2743-e053-4246-aea5-90ae591a3774","added_by":"auto","created_at":"2025-04-04 11:26:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":299510,"visible":true,"origin":"","legend":"\u003cp\u003eSummary Receiver Operating Characteristic (SROC) curves showing performance of CT and MRI for diagnosis of HCC.\u003c/p\u003e\n\u003cp\u003eAbbreviations: AUC=area under the curve; SROC=summary receiver operating characteristic.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6116450/v1/f7933a60781125330c599c20.png"},{"id":79908287,"identity":"cbdcd846-a4a6-44d2-93fe-f7e7cddca330","added_by":"auto","created_at":"2025-04-04 11:18:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":305536,"visible":true,"origin":"","legend":"\u003cp\u003eFagan’s nomogram showing the posttest probability of pseudoprogression.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6116450/v1/f9102192b35d1f0f4ede6cc4.png"},{"id":79908283,"identity":"db10eab4-0c31-4f52-8152-9f87338bd7c9","added_by":"auto","created_at":"2025-04-04 11:18:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":265997,"visible":true,"origin":"","legend":"\u003cp\u003eDeeks’ funnel plot asymmetry test of related methods for the diagnosis of pseudoprogression to investigate reporting bias.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-6116450/v1/946c2082ff282732af3def16.png"},{"id":87412328,"identity":"377ac4ac-0124-4194-9e84-f5df88252405","added_by":"auto","created_at":"2025-07-23 14:02:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9236617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6116450/v1/77dec808-48d0-4fb6-8778-cfa2cd193ba6.pdf"},{"id":79908280,"identity":"74f53f94-2c8f-4486-85f3-3ae58770e684","added_by":"auto","created_at":"2025-04-04 11:18:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26357,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6116450/v1/331e2a4b8faaa3024644f42c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic performance of different methods for diagnosis of pseudoprogression after immunotherapy treatment: a systematic review and meta-analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePseudoprogression, a phenomenon termed by a subset of patients treated with immune checkpoint inhibitors (ICIs) manifest an atypical pattern of tumor response either after an increase of tumor burden or appearance of new lesions, is classified as progressive disease by conventional response criteria[1; 2]. Along with increasing recognition of pseudoprogression and its importance, several studies evaluated its definitions, incidence and diagnostic value[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. But a unifying diagnostic performance of different methods in pseudoprogression are lacking. Although studies have raised the need for robust data on pseudoprogression[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], to our knowledge, there is no evidence-based systematic summary of diagnostic value of pseudoprogression after immunotherapy treatment.\u003c/p\u003e \u003cp\u003eThere is an urgent need for prompt, accurate diagnosis of pseudoprogression to make informed clinical treatment decisions. Pseudoprogression can subside after several months of follow-up without further treatment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], while tumor true progression is associated with neoangiogenesis to supply the rapidly growing tumor, much earlier differentiation from tumor progression is vital from a clinical standpoint. Current response criteria require confirmation such as additional follow up and/or histopathology at re-resection, which may result in delayed or inappropriate treatments. Confirmation of pseudoprogression requires subsequent imaging, imposing an ongoing challenge despite the development of newer criteria, such as the immune-related response criteria (irRC)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eICI response assessment is challenging, as novel response patterns, such as pseudoprogression are not considered in the response evaluation criteria in solid tumors (RECIST 1.1). An increase in tumor volume could be based on either true progressive disease (TPD) or on influx of immune-competent cells (pseudoprogression). Early differentiation of pseudoprogression and TPD is highly relevant in daily clinical decision-making, and predictive biomarkers are needed for better patient selection.\u003c/p\u003e \u003cp\u003eA systemic review of the diagnostic accuracy of different methods for diagnosis of pseudoprogression after immunotherapy is therefore needed to properly evaluate their diagnostic performance. We performed a meta-analysis of published original researches to determine the diagnostic performance of different methods such as laboratory, image, and radiomics in the diagnosis of pseudoprogression during treatment with immunotherapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eLiterature search\u003c/p\u003e \u003cp\u003eThe analysis was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines[8; 9]. A comprehensive search of several databases from each database inception to October 01, 2023, in any language was conducted. The databases included PubMed, Embase, and Cochrane Library. The keywords for the search were \u0026ldquo;pseudoprogression\u0026rdquo;, \u0026ldquo;immunotherapy\u0026rdquo;, and \u0026ldquo;immune checkpoint inhibitors\u0026rdquo;. Detailed search strategy was as follows: (Pseudoprogression) AND (((immunotherapy) OR immune checkpoint inhibitors) OR ((((((nivolumab) OR pembrolizumab) OR atezolizumab) OR avelumab) OR camrelizumab) OR durvalumab) OR (((programmed cell death 1 receptor) OR programmed cell death-1) OR PD-1 receptor) OR ((programmed death ligand 1) OR PD-L1 protein)). Articles in English were chosen. Meanwhile, we also widely scanned references cited in the retrieved articles to find other potentially eligible articles.\u003c/p\u003e \u003cp\u003eInclusion and Exclusion Criteria\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: retrospective or prospective studies; studies based on clinical research in humans and regarding the assessment of diagnostic performance for pseudoprogression; final diagnosis based on pathological or follow-up data; sufficient raw data was available to calculate true-positive (TP), false-positive (FP), false-negative (FN), and true-negative values (TN); in case of overlapping data, the study with the most cases. Review articles, abstracts, comments, case report, letters, and proceedings were excluded. Articles which hyperprogression (HP), progression (PD), progression-free survival (PFS) or overall survival (OS) was only studied were also excluded. Studies with overlapping patients (ie, when \u0026ge;\u0026thinsp;two studies reported the same patients, the study with the longer follow-up time was selected). Two authors (Z.Z. and Y.Z.) assessed and identified potential articles based on the inclusion and exclusion criteria independently, and disagreement was resolved by arbitration by the third author (X.W.).\u003c/p\u003e \u003cp\u003eMethodological Quality and Risk of Bias Assessment\u003c/p\u003e \u003cp\u003eTwo authors (Z.Z. and Y.Z.) assessed all included studies [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] for methodological quality, including risk of bias and applicability, by use of the Scottish Intercollegiate Guidelines Network methodology checklist, adapted from Quality Assessment of Diagnostic Accuracy Studies 2 tool [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] for diagnostic studies. We assessed the following domains: patient selection, index test, reference standard, flow, and timing. Quality of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eData Extraction\u003c/p\u003e \u003cp\u003eFrom the included studies, two authors (Z.Z. and Y.Z.) independently extracted data from the included studies, with disagreement resolved by consensus with a third author (L.L.). Data extracted into standardized forms are following: study characteristics (ie, authors, year of publication, study design, and sample size), demographic and clinical characteristics of the modality (ie, type of modality such as laboratory, image, and radiomics), TP, FP, FN, TN, and outcome characteristics (ie, the number of patients with pseudoprogression and the diagnostic value of modality).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe calculated measures of diagnostic test accuracy [sensitivity, specificity, likelihood ratios, and diagnostic odds ratios (DOR)] using a bivariate regression model, allowing for correlation between sensitivity and specificity. The SROC curves were used to calculate the area under the curve (AUC) of the combined model. The heterogeneity among the included studies was quantified using the Cochrane Q statistics and \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e statistics. When the \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e statistic exceeded 50%, the random-effect model was used to merge the diagnostic accuracy indicators. Otherwise, the fixed-effect model was used. Heterogeneity was assessed using the \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e variable, which describes the proportion of variation in study results that can be attributed to heterogeneity[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e values greater than 50% indicated substantial heterogeneity[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The heterogeneity test and the chi-square test were performed. The presence and quantity of statistical heterogeneity was assessed using the \u003cem\u003eI\u0026sup2;\u003c/em\u003e statistic, with significance set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10. Publication bias of the included studies was assessed by Deek's funnel plot. All reported \u003cem\u003eP\u003c/em\u003e values are two sided, and \u003cem\u003eP\u003c/em\u003e values less than 0.05 were considered to indicate statistical significance. All statistical analyses were performed by using the robvis, meta, Rgraphviz, metamisc, meta4diag, INLA and metafor packages in R (version 4.1.1; R Foundation for Statistical Computing, Vienna, Austria)\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study process is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The basic characteristic of the 5 studies we included are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. They were all retrospective studies, data including single institution (n\u0026thinsp;=\u0026thinsp;4) and multiple centers (n\u0026thinsp;=\u0026thinsp;1)[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies included patients with brain metastases in non\u0026ndash;small cell lung cancer (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1), metastatic melanoma (n\u0026thinsp;=\u0026thinsp;3), and malignant solid tumors (n\u0026thinsp;=\u0026thinsp;1). Of these, two studies were from Switzerland[10; 14], and the rest were from Australia[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], China[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], and the USA [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eMethodological Quality of the Included Studies\u003c/p\u003e\n\u003cp\u003eThe overall risk of bias of the 5 studies (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) that reported all diagnostic accuracy values was low to moderate. The majority (75%-100%) of the studies were judged to have low risk of bias in terms of index test, reference standard, flow, and timing.75% studies were judged to have high risk of bias in terms of patient selection. The majority of the studies were considered to have a low and moderate risk of bias on applicability to clinical practice in terms of reference standard, index test, and patient selection.\u003c/p\u003e\n\u003cp\u003eDiagnostic Value of Modality in Pseudoprogression\u003c/p\u003e\n\u003cp\u003eOn the basis of the included studies (n\u0026thinsp;=\u0026thinsp;5) with patients, the overall sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of diagnostic pseudoprogression was 77% (95% CI: 72%-81%), 70% (95% CI 66%-75%), 2.58 (95% CI 2.14\u0026ndash;3.07), and 0.33 (95% CI 0.27\u0026ndash;0.40), respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNo heterogeneity was observed (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.39%; Q\u0026thinsp;=\u0026thinsp;16.903, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.392) when using random-effects model (REML).\u003c/p\u003e\n\u003cp\u003ePooled Analysis of Diagnostic Accuracy\u003c/p\u003e\n\u003cp\u003eAnalysis of the receiver operating characteristic area under the curve (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that\u003c/p\u003e\n\u003cp\u003earea of diagnose pseudoprogression was 0.807 (95% CI, 0.675\u0026ndash;0.834).\u003c/p\u003e\n\u003cp\u003eFagan\u0026rsquo;s nomogram showed that with the pretest probability of 31%, the posttest probability reached 54% and 13% for the positive and negative tests, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). These results confirmed the high diagnostic efficiency of modality in the diagnosis of pseudoprogression. Fagan\u0026rsquo;s nomogram comprehensively considers PLR and NLR and adjusts the likelihood ratios according to the prior probability of diagnosis for different modalities. It shows that modality could be helpful in diagnosing pseudoprogression.\u003c/p\u003e\n\u003cp\u003ePublication bias analysis\u003c/p\u003e\n\u003cp\u003eTaking the inverse of the square root of effective sample size [1/root (ESS)] as the ordinate and DOR as the abscissa, the results of Deeks\u0026rsquo; test showed that the \u003cem\u003eP\u003c/em\u003e value of slope coefficient was 0.912, suggesting that there was no publication bias in the related methods (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first systematic review and meta-analysis examining methods for diagnostic performance of pseudoprogression after immunotherapy treatment, and reports the pooled analysis of sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio to inform a comprehensive evaluation of the relative diagnostic performance of methods for diagnosis pseudoprogression after immunotherapy treatment. In this meta-analysis, the sensitivity of overall diagnostic value of modality in pseudoprogression was 77% in those retrospective reports of patients with cancer undergoing immunotherapy treatment.\u003c/p\u003e \u003cp\u003eThe phenomenon of pseudoprogression may be related to factors such as delayed immune system activation, immune cell infiltration, and edema caused by local immune related inflammatory reactions [19; 20]. The mechanism behind pseudoprogression could be that tumors could have ongoing growth until the activation of effective antitumor immune responses develops [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Another explanation could be the infiltration of T cells into tumors, leading to a transient increase in tumor burden rather than true proliferation of tumor cells[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The second hypothesis was later confirmed on tumor biopsies from patients with melanoma experiencing transient progression on aCTLA-4 inhibitor, showing an acute inflammatory reaction with lymphocyte infiltration. The enrolled five studies evaluated the diagnostic performance of different methods included blood and imaging parameters even radiomics data, tumor heterogeneity is a typical feature widely studied in malignant tumors, closely related to tumor biological behavior and patient prognosis. The heterogeneity of tumors can be visualized through the roughness and irregularity caused by local spatial changes in image brightness in imaging, which is the basis for quantifying heterogeneity through texture analysis\u003c/p\u003e \u003cp\u003eNo heterogeneity was observed in the diagnostic performance indicators in enrolled studies, which varied in terms of the disease prevalence, study location, and most importantly the diagnostic methods. The diagnostic methods varied from laboratory to image even radiomics, the difference in diagnostic value between those methods are not focused on the present study since the number of enrolled studies are only five papers, even the subgroup study is not performed. More, the definition of pseudoprogression are varied from reference standard on previous publications [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] to their own definition [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], or from histopathology served as the standard [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] to radiographic observation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome pseudoprogression lesions were also irradiated or chemotherapy; therefore, there is also probably a component of radiation/chemotherapy-induced treatment effect in addition to the immunotherapy effect. Separating the effects of immunotherapy and radiation/chemo- therapy is difficult given the complementary roles of both treatments in routine clinical practice. In addition, pseudoprogressions were likely attributable for both immunotherapy and radiation, and it is difficult to differentiate due to the retrospective design of this study. Moreover, most of the pseudoprogresssion lesions were previously treated with radiotherapy, and we are unable to differentiate the attribution between radiation effect alone from immunotherapy effect alone, however the combination likely contributed for the overall pseudoprogression.\u003c/p\u003e \u003cp\u003eOur study had limitations. First, we included only distinguish pseudoprogression from real progression related studies that were identified by using the term pseudoprogression and reported the detailed cutoff data of diagnosing pseudoprogression. Studies that did not have patients with pseudoprogression, the incidence of pseudoprogression, the clinical outcomes such as median overall survival time of pseudoprogression were not included, which may result in an apparent increase of pseudoprogression incidence. Although we did not exclude studies reporting the diagnostic blood data of pseudoprogression, all five studies meeting the criteria had at least laboratory, image or radiomics with diagnostic value with pseudoprogression. Second, subgroup study was not performed due to the enrolled number of studies. Third, we did not extract the time frame of pseudoprogression because that information was not fully presented in the included studies. It is an inborn limitation of systematic review. Though we have included all the immunotherapy at the time of the study, study may become available and add further knowledge in the near future, given the rapid advancement of immuno-oncology. The results of our study will provide a basis for future studies when sufficient newer data become available.\u003c/p\u003e \u003cp\u003eIn summary, in this review of all relevant published studies, we synthesized the pooled estimates of the diagnostic performance of diagnosis methods and found that laboratory and image offered excellent diagnostic performance for diagnosis of pseudoprogression after immunotherapy, there are still only a limited number of studies on those conditions. Therefore, more clinical studies are needed to establish the effectiveness of diagnostic value for pseudoprogression after immunotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZZ and YZ conceived and designed the study. ZZ and YZ screened literature and extracted data. LL assisted with data extraction. ZZ and YZ had access to and verified the data. YZ did the statistical analysis. ZZ, YZ, LL and XW interpreted the data. ZZ wrote the first draft of the manuscript, and all authors provided critical review and revision of the text and approved the final version. YZ and XZ had final responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by the National Natural Science Foundation of China (82102029) and Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2021-I2M-C\u0026amp;T-B-057)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Publish declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article\u0026rsquo;s supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWolchok JD, Hoos A, O\u0026apos;Day S et al (2009) Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin Cancer Res 15:7412-7420\u003c/li\u003e\n\u003cli\u003eNishino M (2016) Immune-related response evaluations during immune-checkpoint inhibitor therapy: establishing a \u0026quot;common language\u0026quot; for the new arena of cancer treatment. J Immunother Cancer 4:30\u003c/li\u003e\n\u003cli\u003ePark HJ, Kim KW, Pyo J et al (2020) Incidence of Pseudoprogression during Immune Checkpoint Inhibitor Therapy for Solid Tumors: A Systematic Review and Meta-Analysis. Radiology 297:87-96\u003c/li\u003e\n\u003cli\u003eKatz SI, Hammer M, Bagley SJ et al (2018) Radiologic Pseudoprogression during Anti-PD-1 Therapy for Advanced Non-Small Cell Lung Cancer. J Thorac Oncol 13:978-986\u003c/li\u003e\n\u003cli\u003eNishino M, Dahlberg SE, Adeni AE et al (2017) Tumor Response Dynamics of Advanced Non-small Cell Lung Cancer Patients Treated with PD-1 Inhibitors: Imaging Markers for Treatment Outcome. Clin Cancer Res 23:5737-5744\u003c/li\u003e\n\u003cli\u003eNishino M (2016) Pseudoprogression and Measurement Variability. J Clin Oncol 34:3480-3481\u003c/li\u003e\n\u003cli\u003eHygino da Cruz LC, Jr., Rodriguez I, Domingues RC, Gasparetto EL, Sorensen AG (2011) Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. AJNR Am J Neuroradiol 32:1978-1985\u003c/li\u003e\n\u003cli\u003eMcInnes MDF, Moher D, Thombs BD et al (2018) Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA 319:388-396\u003c/li\u003e\n\u003cli\u003ePage MJ, Moher D, Bossuyt PM et al (2021) PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ 372:n160\u003c/li\u003e\n\u003cli\u003eAkhoundova D, Hiltbrunner S, Mader C et al (2020) 18F-FET PET for Diagnosis of Pseudoprogression of Brain Metastases in Patients With Non-Small Cell Lung Cancer. Clin Nucl Med 45:113-117\u003c/li\u003e\n\u003cli\u003eUmemura Y, Wang D, Peck KK et al (2020) DCE-MRI perfusion predicts pseudoprogression in metastatic melanoma treated with immunotherapy. J Neurooncol 146:339-346\u003c/li\u003e\n\u003cli\u003eLee JH, Long GV, Menzies AM et al (2018) Association Between Circulating Tumor DNA and Pseudoprogression in Patients With Metastatic Melanoma Treated With Anti-Programmed Cell Death 1 Antibodies. JAMA Oncol 4:717-721\u003c/li\u003e\n\u003cli\u003eHe S, Feng Y, Lin Q et al (2021) CT-Based Peritumoral and Intratumoral Radiomics as Pretreatment Predictors of Atypical Responses to Immune Checkpoint Inhibitor Across Tumor Types: A Preliminary Multicenter Study. Front Oncol 11:729371\u003c/li\u003e\n\u003cli\u003eBasler L, Gabrys HS, Hogan SA et al (2020) Radiomics, Tumor Volume, and Blood Biomarkers for Early Prediction of Pseudoprogression in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibition. Clin Cancer Res 26:4414-4425\u003c/li\u003e\n\u003cli\u003eWhiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529-536\u003c/li\u003e\n\u003cli\u003eMurad MH, Montori VM, Ioannidis JP et al (2014) How to read a systematic review and meta-analysis and apply the results to patient care: users\u0026apos; guides to the medical literature. JAMA 312:171-179\u003c/li\u003e\n\u003cli\u003eHiggins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21:1539-1558\u003c/li\u003e\n\u003cli\u003eHiggins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557-560\u003c/li\u003e\n\u003cli\u003eChiou VL, Burotto M (2015) Pseudoprogression and Immune-Related Response in Solid Tumors. J Clin Oncol 33:3541-3543\u003c/li\u003e\n\u003cli\u003eWang Q, Gao J, Wu X (2018) Pseudoprogression and hyperprogression after checkpoint blockade. Int Immunopharmacol 58:125-135\u003c/li\u003e\n\u003cli\u003eFoller S, Oppel-Heuchel H, Grimm MO (2018) [Tumor assessment in immune checkpoint inhibitor therapy : Tumor response, progression and pseudoprogression]. Urologe A 57:1316-1325\u003c/li\u003e\n\u003cli\u003eWei SC, Levine JH, Cogdill AP et al (2017) Distinct Cellular Mechanisms Underlie Anti-CTLA-4 and Anti-PD-1 Checkpoint Blockade. Cell 170:1120-1133 e1117\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Meta-analysis, Pseudoprogression, Immunotherapy, Immune Checkpoint inhibitors, Diagnostic accuracy","lastPublishedDoi":"10.21203/rs.3.rs-6116450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6116450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe purpose of this meta-analysis was to evaluate the diagnostic performance of different methods for pseudoprogression after immunotherapy treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis systematic review adhered to the PRISMA for diagnostic test accuracy guidelines. The PubMed, Embase, and Cochrane Library databases were searched comprehensively for relevant studies up to October 01, 2023 according to specific inclusion and exclusion criteria. The quality of the included studies was assessed according to the quality assessment of diagnostic accuracy studies (QUADAS-2). After performing heterogeneity and threshold effect tests, pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated. Publication bias was evaluated visually and estimated by Deeks\u0026rsquo; funnel plot. The area under the summary receiver operating characteristic (SROC) curve was calculated to demonstrate the diagnostic performance of modality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFive studies covering 250 lesions evaluating laboratory, image, and radiomics were included. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 77% (95% confidence interval [CI] 72%-81%), 70% (95% CI 66%-75%), 2.58 (95% CI 2.14\u0026ndash;3.07), 0.33 (95% CI 0.27\u0026ndash;0.40), and 7.88 (95% CI 5.59\u0026ndash;10.52), respectively. The area under the SROC curve was 0.807. In addition, the SROC curve showed high sensitivities (0.77) and low false positive rates (0.33) suggested that the results were reliable. Furthermore, the Deeks\u0026rsquo; funnel plot suggested no notable publication bias. No heterogeneity (I2\u0026thinsp;\u0026lt;\u0026thinsp;50%) was observed in the analysis of pooled studies.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur review suggests that laboratory and images offered the optimal diagnostic performance of pseudoprogression after immunotherapy treatment.\u003c/p\u003e","manuscriptTitle":"Diagnostic performance of different methods for diagnosis of pseudoprogression after immunotherapy treatment: a systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-04 11:18:11","doi":"10.21203/rs.3.rs-6116450/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd496e6c-80c6-40e9-9413-50e4ad4f8d96","owner":[],"postedDate":"April 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-23T13:53:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-04 11:18:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6116450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6116450","identity":"rs-6116450","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}