Cited by 3,777Medical Imaging Techniques and ApplicationsRadiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and Treatment

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From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors

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Wahl, Richard L., Jacene, Heather, Kasamon, Yvette, Lodge, Martin A.·2009·Journal of Nuclear Medicine

DOI 10.2967/jnumed.108.057307PMC PMC2755245

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Abstract

UNLABELLED: The purpose of this article is to review the status and limitations of anatomic tumor response metrics including the World Health Organization (WHO) criteria, the Response Evaluation Criteria in Solid Tumors (RECIST), and RECIST 1.1. This article also reviews qualitative and quantitative approaches to metabolic tumor response assessment with (18)F-FDG PET and proposes a draft framework for PET Response Criteria in Solid Tumors (PERCIST), version 1.0. METHODS: PubMed searches, including searches for the terms RECIST, positron, WHO, FDG, cancer (including specific types), treatment response, region of interest, and derivative references, were performed. Abstracts and articles judged most relevant to the goals of this report were reviewed with emphasis on limitations and strengths of the anatomic and PET approaches to treatment response assessment. On the basis of these data and the authors' experience, draft criteria were formulated for PET tumor response to treatment. RESULTS: Approximately 3,000 potentially relevant references were screened. Anatomic imaging alone using standard WHO, RECIST, and RECIST 1.1 criteria is widely applied but still has limitations in response assessments. For example, despite effective treatment, changes in tumor size can be minimal in tumors such as lymphomas, sarcoma, hepatomas, mesothelioma, and gastrointestinal stromal tumor. CT tumor density, contrast enhancement, or MRI characteristics appear more informative than size but are not yet routinely applied. RECIST criteria may show progression of tumor more slowly than WHO criteria. RECIST 1.1 criteria (assessing a maximum of 5 tumor foci, vs. 10 in RECIST) result in a higher complete response rate than the original RECIST criteria, at least in lymph nodes. Variability appears greater in assessing progression than in assessing response. Qualitative and quantitative approaches to (18)F-FDG PET response assessment have been applied and require a consistent PET methodology to allow quantitative assessments. Statistically significant changes in tumor standardized uptake value (SUV) occur in careful test-retest studies of high-SUV tumors, with a change of 20% in SUV of a region 1 cm or larger in diameter; however, medically relevant beneficial changes are often associated with a 30% or greater decline. The more extensive the therapy, the greater the decline in SUV with most effective treatments. Important components of the proposed PERCIST criteria include assessing normal reference tissue values in a 3-cm-diameter region of interest in the liver, using a consistent PET protocol, using a fixed small region of interest about 1 cm(3) in volume (1.2-cm diameter) in the most active region of metabolically active tumors to minimize statistical variability, assessing tumor size, treating SUV lean measurements in the 1 (up to 5 optional) most metabolically active tumor focus as a continuous variable, requiring a 30% decline in SUV for "response," and deferring to RECIST 1.1 in cases that do not have (18)F-FDG avidity or are technically unsuitable. Criteria to define progression of tumor-absent new lesions are uncertain but are proposed. CONCLUSION: Anatomic imaging alone using standard WHO, RECIST, and RECIST 1.1 criteria have limitations, particularly in assessing the activity of newer cancer therapies that stabilize disease, whereas (18)F-FDG PET appears particularly valuable in such cases. The proposed PERCIST 1.0 criteria should serve as a starting point for use in clinical trials and in structured quantitative clinical reporting. Undoubtedly, subsequent revisions and enhancements will be required as validation studies are undertaken in varying diseases and treatments.

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Doot, R. K., Scheuermann, J. S., Christian, P. E., Karp, J. S., & Kinahan, P. E. (2010) · Medical Physics · doi:10.1118/1.3499298 · Cited by 82
Low-count whole-body PET with deep learning in a multicenter and externally validated study
Chaudhari, A. S., Mittra, E., Davidzon, G. A., Gulaka, P., Gandhi, H., Brown, A., Zhang, T., Srinivas, S., Gong, E., Zaharchuk, G., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00497-2 · Cited by 83
Quantitative implications of the updated EARL 2019 PET–CT performance standards
Kaalep, A., Burggraaff, C. N., Pieplenbosch, S., Verwer, E. E., Sera, T., Zijlstra, J., Hoekstra, O. S., Oprea-Lager, D. E., & Boellaard, R. (2019) · EJNMMI Physics · doi:10.1186/s40658-019-0257-8 · Cited by 79
Gastrointestinal stromal tumors: a comprehensive radiological review
Inoue, A., Ota, S., Yamasaki, M., Batsaikhan, B., Furukawa, A., & Watanabe, Y. (2022) · Japanese Journal of Radiology · doi:10.1007/s11604-022-01305-x · Cited by 79
“Two major response criteria, the European Organization for Research and Treatment of Cancer criteria using the maximum standardized uptake value [79] and the PET response criteria in solid tumors criteria employing the peak of the standardized uptake value corrected for lean body mass [(Lean body mass) = (Total weight…”
Method
Efficacy and safety of 225Ac-DOTATATE targeted alpha therapy in metastatic paragangliomas: a pilot study
Yadav, M., Ballal, S., Sahoo, R., & Bal, C. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05632-5 · Cited by 79
Variance of SUVs for FDG-PET/CT is Greater in Clinical Practice Than Under Ideal Study Settings
Kumar, V., Nath, K., Berman, C. G., Kim, J., Tanvetyanon, T., Chiappori, A. A., Gatenby, R. A., Gillies, R. J., & Eikman, E. A. (2013) · Clinical Nuclear Medicine · doi:10.1097/rlu.0b013e318279ffdf · Cited by 76
Immune effects of CDK4/6 inhibitors in patients with HR+/HER2− metastatic breast cancer: Relief from immunosuppression is associated with clinical response
Scirocchi, F., Scagnoli, S., Botticelli, A., Filippo, A. D., Napoletano, C., Zizzari, I. G., Strigari, L., Tomao, F., Cortesi, E., Rughetti, A., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104010 · Cited by 87
Breast cancer resistance to chemotherapy: When should we suspect it and how can we prevent it?
Prihantono, P., & Faruk, M. (2021) · Annals of Medicine and Surgery · doi:10.1016/j.amsu.2021.102793 · Cited by 84
“1, the Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) is available [30, 46].”
Background
Enhancing Radioiodine Incorporation into Radioiodine-Refractory Thyroid Cancer with MAPK Inhibition (ERRITI): A Single-Center Prospective Two-Arm Study
Weber, M., Kersting, D., Riemann, B., Brandenburg, T., Führer‐Sakel, D., Grünwald, F., Kreissl, M. C., Dralle, H., Weber, F., Schmid, K. W., et al. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-22-0437 · Cited by 87
“0 (24) criteria was assessed by single-session consensus of two readers.”
Background
Digital PET/CT allows for shorter acquisition protocols or reduced radiopharmaceutical dose in [18F]-FDG PET/CT
Alberts, I., Sachpekidis, C., Prenosil, G., Viscione, M., Bohn, K. P., Mingels, C., Shi, K., Ashar-Oromieh, A., & Rominger, A. (2021) · Annals of Nuclear Medicine · doi:10.1007/s12149-021-01588-6 · Cited by 70
English version of Japanese Clinical Practice Guidelines 2022 for gastrointestinal stromal tumor (GIST) issued by the Japan Society of Clinical Oncology
Hirota, S., Tateishi, U., Nakamoto, Y., Yamamoto, H., Sakurai, S., Kikuchi, H., Kanda, T., Kurokawa, Y., Cho, H., Nishida, T., et al. (2024) · International Journal of Clinical Oncology · doi:10.1007/s10147-024-02488-1 · Cited by 78
“…that after the start of drug therapy FDG-PET/CT should be performed at least 10 days apart, in consideration of the flare phenomenon (a phenomenon in which FDG accumulation increases after the start of drug therapy due to increased activity of immune cells, etc., regardless of the response) [35].”
BackgroundMethodInfluential
The lung cancers: staging and response, CT, 18F-FDG PET/CT, MRI, DWI: review and new perspectives
Owens, C., Hindocha, S., Lee, R., Millard, T., & Sharma, B. (2023) · British Journal of Radiology · doi:10.1259/bjr.20220339 · Cited by 76
FDG-PET Predicts Neoadjuvant Therapy Response and Survival in Borderline Resectable/Locally Advanced Pancreatic Adenocarcinoma
Abdelrahman, A., Goenka, A., Alva-Ruiz, R., Yonkus, J., Leiting, J., Graham, R. P., Merrell, K., Thiels, C., Hallemeier, C., Warner, S., et al. (2022) · The Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2022.7041 · Cited by 68
“Major metabolic (PET) response was defined as FDG uptake of the tumor below hepatic FDG uptake and indistinguishable from background pancreatic tissue as previously described.(23) Minor metabolic response was defined as persistent or higher FDG activity than adjacent background tissues and compared with baseline FDG-P…”
Result
Imaging of Pheochromocytomas and Paragangliomas
Timmers, H. J. L. M., Taïeb, D., Pacák, K., & Lenders, J. W. M. (2024) · Endocrine Reviews · doi:10.1210/endrev/bnae001 · Cited by 102
“Currently, 2 sets of criteria to quantify metabolic response are available: PET Response Criteria in Solid Tumors (PERCIST) 1.0 (122) and the criteria developed by the European Organization for Research and Treatment of Cancer (EORTC) (123).”
Influential
Co-clinical FDG-PET Radiomic Signature in Predicting Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer
Roy, S., Whitehead, T., Li, S.-Q., Ademuyiwa, F., Wahl, R., Dehdashti, F., & Shoghi, K. (2021) · bioRxiv · doi:10.1007/s00259-021-05489-8 · Cited by 67
Self-citation
Repeatability of Quantitative Whole-Body 18F-FDG PET/CT Uptake Measures as Function of Uptake Interval and Lesion Selection in Non–Small Cell Lung Cancer Patients
Kramer, G. M., Frings, V., Hoetjes, N., Hoekstra, O. S., Smit, E. F., de Langen, A. J., & Boellaard, R. (2016) · Journal of Nuclear Medicine · doi:10.2967/jnumed.115.170225 · Cited by 65
Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction
Guo, R., Xue, S., Hu, J., Sari, H., Mingels, C., Zeimpekis, K., Prenosil, G., Wang, Y., Zhang, Y., Viscione, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33562-9 · Cited by 68
“Additionally, the proposed method needs further clinical validation in clinical applications, especially clinical quantitative scores (refer to quantitative clinical scores such as the PERCIST 56 or the Deauville-Score 57 , and so on).”
Method
Objective Task-Based Evaluation of Artificial Intelligence-Based Medical Imaging Methods
Jha, A. K., Myers, K. J., Obuchowski, N. A., Liu, Z., Rahman, M. A., Saboury, B., Rahmim, A., & Siegel, B. A. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.06.013 · Cited by 49
Effects of blood glucose level on 18F fluorodeoxyglucose (18F-FDG) uptake for PET/CT in normal organs: An analysis on 5623 patients
Sprinz, C., Zanon, M., Altmayer, S., Watte, G., Irion, K., Marchiori, E., & Hochhegger, B. (2018) · Scientific Reports · doi:10.1038/s41598-018-20529-4 · Cited by 59
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging
Yousefirizi, F., Decazes, P., Amyar, A., Ruan, S., Saboury, B., & Rahmim, A. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.09.010 · Cited by 76
“using liver or surrounding tissue) [109].”
Artificial intelligence-based PET denoising could allow a two-fold reduction in [18F]FDG PET acquisition time in digital PET/CT
Weyts, K., Lasnon, C., Ciappuccini, R., Lequesne, J., Corroyer-Dulmont, A., Quak, E., Clarisse, B., Roussel, L., Bardet, S., & Jaudet, C. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-05800-1 · Cited by 59
“Greater SUL biases occurred especially in lesions with a moderate size and uptake and mostly “non-target and non-evaluable lesions” according to PERCIST criteria [34, 35].”
Prediction of disease-free survival using relative change in FDG-uptake early during neoadjuvant chemoradiotherapy for potentially curable esophageal cancer: A prospective cohort study
Hagen, P. V., Heijl, M. V., van Berge Henegouwen, M. I., Boellaard, R., Bossuyt, P. M. M., Kate, F. J. W. T., Dekken, H. V., Hoekstra, O. S., Sloof, G. W., & Lanschot, J. J. B. V. (2017) · Diseases of the Esophagus · doi:10.1111/dote.12479 · Cited by 58
Prediction of 177Lu-DOTATATE PRRT Outcome Using Multimodality Imaging in Patients with Gastroenteropancreatic Neuroendocrine Tumors: Results from a Prospective Phase II LUMEN Study
Mileva, M., Marin, G., Levillain, H., Artigas, C., Bogaert, C. V., Marin, C., Danieli, R., Deleporte, A., Picchia, S., Stathopoulos, K., et al. (2023) · Journal of Nuclear Medicine · doi:10.2967/jnumed.123.265987 · Cited by 57
“18 F-FDG – positive target lesions were delineated on 18 F-FDG PET/CT images using a PERCIST-based threshold ( 17 ) to measure SUV max , SUV mean , meta-bolically active TV, and total lesion glycolysis (SUV mean 3 metabolically active TV).”
Method
Tumor response assessment on imaging following immunotherapy
Berz, A. M., Dromain, C., Violi, N. V., Boughdad, S., & Durán, R. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.982983 · Cited by 62
Correction of scan time dependence of standard uptake values in oncological PET
van den Hoff, J., Lougovski, A., Schramm, G., Maus, J., Oehme, L., Petr, J., Beuthien-Baumann, B., Kotzerke, J., & Hofheinz, F. (2014) · EJNMMI Research · doi:10.1186/2191-219x-4-18 · Cited by 55
A method for model-free partial volume correction in oncological PET
Hofheinz, F., Langner, J., Petr, J., Beuthien-Baumann, B., Oehme, L., Steinbach, J., Kotzerke, J., & van den Hoff, J. (2012) · EJNMMI Research · doi:10.1186/2191-219x-2-16 · Cited by 55
A role of FDG-PET/CT for response evaluation in metastatic breast cancer?
Hildebrandt, M. G., Naghavi‐Behzad, M., & Vogsen, M. (2022) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2022.03.004 · Cited by 55
Advances in PET/CT Technology: An Update
Aide, N., Lasnon, C., Desmonts, C., Armstrong, I. S., Walker, M. D., & McGowan, D. R. (2021) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2021.10.005 · Cited by 55
FDG PET/CT-based response assessment in malignancies
Parihar, A. S., Dehdashti, F., & Wahl, R. L. (2023) · RadioGraphics · doi:10.1148/rg.220122 · Cited by 53
“The PERCIST were introduced by Wahl and colleagues (43) in 2009 with the aim of improving response assessment by using FDG PET/CT.”
InfluentialSelf-citation
Frontline and Relapsed Rhabdomyosarcoma (FaR-RMS) Clinical Trial: A Report from the European Paediatric Soft Tissue Sarcoma Study Group (EpSSG)
Chisholm, J., Mandeville, H., Adams, M., Minard-Collin, V., Rogers, T., Kelsey, A., Shipley, J., van Rijn, R., de Vries, I. S. A., van Ewijk, R., et al. (2024) · Cancers · doi:10.3390/cancers16050998 · Cited by 52
“The primary classification of FDG-PET response is with the use of PERCIST [92,93], including quantitative PET indices (standard uptake values (SUVs)).”
Method
Selective Personalized RadioImmunotherapy for Locally Advanced Non–Small-Cell Lung Cancer Trial (SPRINT)
Ohri, N., Jolly, S., Cooper, B. T., Kabarriti, R., Bodner, W. R., Klein, J., Guha, C., Shankar, V., Shum, E., Sabari, J. K., et al. (2023) · Journal of Clinical Oncology · doi:10.1200/jco.23.00627 · Cited by 70
“Responses on PET/CT performed after induction pem-brolizumab were assessed using CT measurements (RECIST 1.1 14 ) and on PET (PET Response Criteria in Solid Tumors [PERCIST] 15 ), where partial response is de ﬁ ned as ≥ 30% reduction of maximum standardized uptake value.”
MethodInfluential
Efficacy of [177Lu]Lu-DOTATATE in metastatic neuroendocrine neoplasms of different locations: data from the SEPTRALU study
Mitjavila, M., Jiménez-Fonseca, P., Belló, P., Pubul, V., Percovich, J. C., García-Burillo, A., Hernando, J., Arbizu, J., Rodeño, E., Estorch, M., et al. (2023) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-023-06166-8 · Cited by 52
“8), complete response defined as normalization of all SUVs, and progression defined as an increase in intensity > 30% or the appearance of new lesions, verified through morphological criteria during follow-up [23].”
18F FDG PET/CT and Novel Molecular Imaging for Directing Immunotherapy in Cancer
Hughes, D. J., Subesinghe, M., Taylor, B. P., Billè, A., Spicer, J. F., Papa, S., Goh, V., & Cook, G. (2022) · Radiology · doi:10.1148/radiol.212481 · Cited by 60
Joint EANM/SNMMI/ESTRO practice recommendations for the use of 2-[18F]FDG PET/CT external beam radiation treatment planning in lung cancer V1.0
Vaz, S. C., Adam, J. A., Bolton, R. C. D., Véra, P., van Elmpt, W., Herrmann, K., Hicks, R. J., Lievens, Y. N., Santos, A., Schöder, H. M., et al. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05624-5 · Cited by 61
“Metabolic response criteria, such as the PET Response Evaluation Criteria in Solid Tumours (PERCIST), that assess change in standardized uptake value (SUV) corrected for lean body mass (SUL) have also been used to quantify metabolic response to treatment in clinical trials [71].”
Method
Influences on PET Quantification and Interpretation
Rogasch, J. M. M., Hofheinz, F., van Heek, L., Voltin, C.-A., Boellaard, R., & Kobe, C. (2022) · Diagnostics · doi:10.3390/diagnostics12020451 · Cited by 57
Genomically matched therapy in advanced solid tumors: the randomized phase 2 ROME trial
Marchetti, P., Curigliano, G., Biffoni, M., Lonardi, S., Scagnoli, S., Fornaro, L., Guarneri, V., Giorgi, U. D., Ascierto, P. A., Blandino, G., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03918-x · Cited by 53
Noninvasive interrogation of CD8+ T cell effector function for monitoring early tumor responses to immunotherapy
Zhou, H., Wang, Y., Xu, H., Shen, X., Zhang, T., Zhou, X. M., Zeng, Y., Li, K., Zhang, L., Zhu, H., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci161065 · Cited by 77
“Wahl RL, et al. From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors.”
MethodInfluential
Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group – ESOI Joint Paper
Fournier, L., de Geus-Oei, L. D., Regge, D., Oprea-Lager, D., D'Anastasi, M., Bidaut, L., Bäuerle, T., Lopci, E., Cappello, G., Lecouvet, F., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2021.800547 · Cited by 49
“Positron Emission Tomography Response Criteria in Solid Tumours (PERCIST), introduced in 2009 (30, 101), enables response to be measured in 18F-fluorodeoxyglucose (18F-FDG) avid bone metastatic lesions based on their metabolic activity in the absence of any obvious anatomic changes.”
BackgroundInfluential
Effect of hyperglycemia on brain and liver 18F-FDG standardized uptake value (FDG SUV) measured by quantitative positron emission tomography (PET) imaging
Viglianti, B. L., Wong, K. K., Wimer, S. M., Parameswaran, A., Nan, B., Ky, C., Townsend, D. M., Rubello, D., Frey, K. A., & Gross, M. D. (2017) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2017.01.166 · Cited by 48
Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept
Salehjahromi, M., Karpinets, T. V., Sujit, S., Qayati, M., Chen, P., Aminu, M., Saad, M. B., Bandyopadhyay, R., Hong, L., Sheshadri, A., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101463 · Cited by 48
Molecular imaging predicts lack of T-DM1 response in advanced HER2-positive breast cancer (final results of ZEPHIR trial)
Mileva, M., de Vries, E. G. E., Guiot, T., Wimana, Z., Deleu, A.-L., Schröder, C. P., Lefebvre, Y., Paesmans, M., Stroobants, S. G., Huizing, M. T., et al. (2024) · npj Breast Cancer · doi:10.1038/s41523-023-00610-6 · Cited by 56
“For the late metabolic response assessment, a cutoff of a minimum 30% decrease in SUVmax was used 30 .”
Method
Evaluation of pediatric malignancies using total-body PET/CT with half-dose [18F]-FDG
Chen, W.-J., Liu, L., Li, Y.-H., Li, S., Li, Z., Zhang, W.-G., Zhang, X., Wu, R., Hu, D., Sun, H.-Y., et al. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-05893-8 · Cited by 48
The future of PSMA PET and WB MRI as next-generation imaging tools in prostate cancer
Wang, Y., Galante, J. R., Haroon, A., Wan, S., Afaq, A., Payne, H. A., Bomanji, J. B., Adeleke, S. M., & Kasivisvanathan, V. (2022) · Nature Reviews Urology · doi:10.1038/s41585-022-00618-w · Cited by 57
Efficacy and Safety of 177Lu-DOTATATE in Lung Neuroendocrine Tumors: A Bicenter study
Zidan, L., Iravani, A., Oleinikov, K., Ben-Haim, S., Gross, D., Meirovitz, A., Maimon, O., Akhurst, T., Michael, M., Hicks, R., et al. (2021) · Journal of Nuclear Medicine · doi:10.2967/jnumed.120.260760 · Cited by 48
“In patients who underwent baseline (18)F-FDG PET/CT, positive lesion was defined as uptake above the liver, and metabolic responses were assessed on the (18)F-FDG PET images qualitatively according to the PMCC criteria (supplemental materials) (15,16).”
Method
Impact of Different Standardized Uptake Value Measures on PET-Based Quantification of Treatment Response
Vanderhoek, M., Perlman, S. B., & Jeraj, R. (2013) · Journal of Nuclear Medicine · doi:10.2967/jnumed.112.113332 · Cited by 47
Real-world efficacy and safety of tofacitinib treatment in Asian patients with ulcerative colitis
Kojima, K., Watanabe, K., Kawai, M., Yagi, S., Kaku, K., Ikenouchi, M., Sato, T., Kamikozuru, K., Yokoyama, Y., Takagawa, T., et al. (2024) · World Journal of Gastroenterology · doi:10.3748/wjg.v30.i13.1871 · Cited by 45
“Figure 2 displays the flowchart illustrating trial progression.”
Whole-body uptake classification and prostate cancer staging in 68Ga-PSMA-11 PET/CT using dual-tracer learning
Capobianco, N., Sibille, L., Chantadisai, M., Gafita, A., Langbein, T., Platsch, G., Solari, E. L., Shah, V. P., Spottiswoode, B. S., Eiber, M., et al. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05473-2 · Cited by 49
Response Monitoring in Metastatic Breast Cancer: A Prospective Study Comparing 18F-FDG PET/CT with Conventional CT
Vogsen, M., Harbo, F., Jakobsen, N., Nissen, H. J., Dahlsgaard-Wallenius, S. E., Gerke, O., Jensen, J. D., Asmussen, J., Jylling, A., Braad, P., et al. (2022) · Journal of Nuclear Medicine · doi:10.2967/jnumed.121.263358 · Cited by 44
The Role and Potential of 18F-FDG PET/CT in Malignant Melanoma: Prognostication, Monitoring Response to Targeted and Immunotherapy, and Radiomics
Filippi, L., Bianconi, F., Schillaci, O., Spanu, A., & Palumbo, B. (2022) · Diagnostics · doi:10.3390/diagnostics12040929 · Cited by 44
“[47] 2009 PERCIST Complete regression of all 18F-FDG-avid sites SULpeak reduction in at least 30% in the target lesions Increase in SULpeak of at least 30% or new lesions None of the previously mentioned conditions”
Background
Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [18F]FDG PET/CT features
de Jesus, F. M., Yin, Y., Mantzorou-Kyriaki, E., Kahle, X., de Haas, R. D., Yakar, D., Glaudemans, A., Noordzij, W., Kwee, T., & Nijland, M. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05626-3 · Cited by 44
“In a first phase, lesions were preselected in line with PERCIST recommendations with a semiau-tomatic segmentation tool on Hermes Hybrid 3D software (Hermes Medical Solutions AB, Stockholm, Sweden) by FMJ and blinded for histopathologic diagnosis (Figure 1) [28].”
Method
[18F]FDG PET/CT: EANM procedure guidelines for tumour imaging: version 3.0
Boellaard, R., Herrmann, K., Barrington, S. F., Cranston, I. C., Mottaghy, F. M., Hoekstra, C., Beyer, T., Pike, L., Willemsen, A., Graham, M. M., et al. (2025) · The EANM Journal · doi:10.1016/j.eanmj.2025.100006 · Cited by 43
ESR Essentials: staging and restaging with FDG-PET/CT in oncology—practice recommendations by the European Society for Hybrid, Molecular and Translational Imaging
Ebner, R., Sheikh, G., Brendel, M., Ricke, J., & Cyran, C. (2024) · European Radiology · doi:10.1007/s00330-024-11094-8 · Cited by 43
“…approach not only enhances the evaluation of therapeutic effectiveness, especially in cases where anatomical changes are minimal but also underscores the importance of structured reporting for referring clinicians, ensuring they receive clear and actionable information for patient management [5].”
EARL compliance and imaging optimisation on the Biograph Vision Quadra PET/CT using phantom and clinical data
van Sluis, J., van Snick, J. H., Brouwers, A., Noordzij, W., Dierckx, R., Borra, R., Slart, R., Lammertsma, A., Glaudemans, A., Boellaard, R., et al. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-05919-1 · Cited by 43
“, to determine treatment-response, which requires standardised imaging procedures, especially in a multicentre setting [8].”
Background
A CGA/EGFR/GATA2 positive feedback circuit confers chemoresistance in gastric cancer
Cao, T., Lu, Y.-Y., Wang, Q., Qin, H.-Q., Li, H.-W., Guo, H., Ge, M., Glass, S. E., Singh, B., Zhang, W.-Y., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci154074 · Cited by 43
“Response Evaluation Criteria in Solid Tumors (RECIST, version Chemoresistance is usually monitored through radiological assessment in clinical practice (44).”
Abbreviated scan protocols to capture 18F-FDG kinetics for long axial FOV PET scanners
Viswanath, V., Sari, H., Pantel, A., Conti, M., Daube-Witherspoon, M., Mingels, C., Alberts, I., Eriksson, L., Shi, K., Rominger, A., et al. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-05747-3 · Cited by 43
Bone Metastases Are Measurable: The Role of Whole-Body MRI and Positron Emission Tomography
Oprea‐Lager, D. E., Cysouw, M. C. F., Boellaard, R., Deroose, C. M., de Geus‐Oei, L., Lopci, E., Bidaut, L. M., Herrmann, K., Fournier, L. S., Bäuerle, T., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.772530 · Cited by 49
“In 2009, the PET Response Criteria in Solid Tumors (PERCIST) were introduced for (18)F-FDG PET (77).”
MethodInfluential
Early molecular imaging response assessment based on determination of total viable tumor burden in [68Ga]Ga-PSMA-11 PET/CT independently predicts overall survival in [177Lu]Lu-PSMA-617 radioligand therapy
Rosar, F., Wenner, F. N., Khreish, F., Dewes, S., Wagenpfeil, G., Hoffmann, M. A., Schreckenberger, M., Bartholomä, M. D., & Ezziddin, S. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05594-8 · Cited by 45
Artificial Intelligence-based Radiomics in the Era of Immuno-oncology
Kang, C. Y., Duarte, S., Kim, H. S., Kim, E., Park, J., Lee, A. D., Kim, Y., Kim, L., Cho, S., Oh, Y. G., et al. (2022) · The Oncologist · doi:10.1093/oncolo/oyac036 · Cited by 48
A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT
Zhang, X., Chen, L., Jiang, H., He, X., Feng, L., Ni, M., Ma, M., Wang, J., Zhang, T., Wu, S., et al. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05572-0 · Cited by 43

Authors: Wahl, R. L., Jacene, H., Kasamon, Y., & Lodge, M. A.
Affiliations: Johns Hopkins University (US)
Corresponding author: Wahl, Richard L.
ORCID: Wahl, Richard L.
Jacene, Heather
Kasamon, Yvette
Lodge, Martin A.
Venue: Journal of Nuclear Medicine
Publisher: Society of Nuclear Medicine
Published: 2009-04-29
Volume: 50
Issue: Suppl 1
Pages: 122S-150S
ISSN: 0161-5505 (ISSN-L)
2159-662X
DOI: 10.2967/jnumed.108.057307
PMC: PMC2755245
OpenAlex: W2155263737
Cited by: 3,777
References: 157
Citations by year: 2026: 180 · 2025: 200 · 2024: 197 · 2023: 230 · 2022: 248 · 2021: 309 · 2020: 271 · 2019: 259 · 2018: 225 · 2017: 274 · 2016: 246 · 2015: 245 · 2014: 221 · 2013: 230 · 2012: 192
FWCI: 21.01
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Journal of Nuclear Medicine (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Cancer Institute (CA006973, P30CA006973)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
Keywords: RECIST, PERCIST, 18F-FDG PET, tumor response assessment, standardized uptake value, WHO criteria, RECIST 1.1, region of interest, treatment response assessment, solid tumors, lymphoma, sarcoma, hepatocellular carcinoma, mesothelioma, gastrointestinal stromal tumor, test-retest variability
MeSH terms: Forecasting, Humans, Neoplasms, Prognosis, World Health Organization, Treatment Outcome, Outcome Assessment, Health Care, Practice Guidelines as Topic, Radiopharmaceuticals, Fluorodeoxyglucose F18, Internationality, Positron-Emission Tomography

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From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors

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