Review on radiomic analysis in
BRAF
FDG-PET
Immune checkpoint inhibition
Immunotherapy
Melanoma
NRAS
Radiomic
Journal
Cancer imaging : the official publication of the International Cancer Imaging Society
ISSN: 1470-7330
Titre abrégé: Cancer Imaging
Pays: England
ID NLM: 101172931
Informations de publication
Date de publication:
05 Jul 2024
05 Jul 2024
Historique:
received:
02
11
2023
accepted:
24
06
2024
medline:
6
7
2024
pubmed:
6
7
2024
entrez:
5
7
2024
Statut:
epublish
Résumé
Over the past decade, several strategies have revolutionized the clinical management of patients with cutaneous melanoma (CM), including immunotherapy and targeted tyrosine kinase inhibitor (TKI)-based therapies. Indeed, immune checkpoint inhibitors (ICIs), alone or in combination, represent the standard of care for patients with advanced disease without an actionable mutation. Notably BRAF combined with MEK inhibitors represent the therapeutic standard for disease disclosing BRAF mutation. At the same time, FDG PET/CT has become part of the routine staging and evaluation of patients with cutaneous melanoma. There is growing interest in using FDG PET/CT measurements to predict response to ICI therapy and/or target therapy. While semiquantitative values such as standardized uptake value (SUV) are limited for predicting outcome, new measures including tumor metabolic volume, total lesion glycolysis and radiomics seem promising as potential imaging biomarkers for nuclear medicine. The aim of this review, prepared by an interdisciplinary group of experts, is to take stock of the current literature on radiomics approaches that could improve outcomes in CM.
Identifiants
pubmed: 38970050
doi: 10.1186/s40644-024-00732-5
pii: 10.1186/s40644-024-00732-5
doi:
Substances chimiques
Fluorodeoxyglucose F18
0Z5B2CJX4D
Radiopharmaceuticals
0
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
87Informations de copyright
© 2024. The Author(s).
Références
Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359–386.
pubmed: 25220842
doi: 10.1002/ijc.29210
Matthews NH, Li W-Q, Qureshi AA, Weinstock MA, Cho E. Epidemiology of Melanoma. In: Ward WH, Farma JM, editors. Cutan Melanoma Etiol Ther [Internet]. Brisbane (AU): Codon Publications; 2017 [cited 2022 Feb 6]. http://www.ncbi.nlm.nih.gov/books/NBK481862/ .
Buja A, Bardin A, Damiani G, Zorzi M, De Toni C, Fusinato R, et al. Prognosis for cutaneous melanoma by Clinical and Pathological Profile: a Population-based study. Front Oncol. 2021;11:737399.
pubmed: 34868928
pmcid: 8634953
doi: 10.3389/fonc.2021.737399
Hartman RI, Lin JY. Cutaneous Melanoma-A review in detection, staging, and management. Hematol Oncol Clin North Am. 2019;33:25–38.
pubmed: 30497675
doi: 10.1016/j.hoc.2018.09.005
Keung EZ, Gershenwald JE. The eighth edition American Joint Committee on Cancer (AJCC) melanoma staging system: implications for melanoma treatment and care. Expert Rev Anticancer Ther. 2018;18:775–84.
pubmed: 29923435
pmcid: 7652033
doi: 10.1080/14737140.2018.1489246
Cherobin ACFP, Wainstein AJA, Colosimo EA, Goulart EMA, Bittencourt FV. Prognostic factors for metastasis in cutaneous melanoma. Bras Dermatol. 2018;93:19–26.
doi: 10.1590/abd1806-4841.20184779
Hodi FS, O’Day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB, et al. Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med. 2010;363:711–23.
pubmed: 20525992
pmcid: 3549297
doi: 10.1056/NEJMoa1003466
Robert C, Ribas A, Schachter J, Arance A, Grob J-J, Mortier L, et al. Pembrolizumab versus Ipilimumab in advanced melanoma (KEYNOTE-006): post-hoc 5-year results from an open-label, multicentre, randomised, controlled, phase 3 study. Lancet Oncol. 2019;20:1239–51.
pubmed: 31345627
doi: 10.1016/S1470-2045(19)30388-2
Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L, et al. Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med. 2015;372:320–30.
pubmed: 25399552
doi: 10.1056/NEJMoa1412082
Larkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Cowey CL, Lao CD, et al. Combined Nivolumab and Ipilimumab or Monotherapy in untreated melanoma. N Engl J Med. 2015;373:23–34.
pubmed: 26027431
pmcid: 5698905
doi: 10.1056/NEJMoa1504030
Tawbi HA, Schadendorf D, Lipson EJ, Ascierto PA, Matamala L, Castillo Gutiérrez E, et al. Relatlimab and Nivolumab versus Nivolumab in Untreated Advanced Melanoma. N Engl J Med. 2022;386:24–34.
pubmed: 34986285
pmcid: 9844513
doi: 10.1056/NEJMoa2109970
Lebbé C, Weber JS, Maio M, Neyns B, Harmankaya K, Hamid O, et al. Survival follow-up and ipilimumab retreatment of patients with advanced melanoma who received ipilimumab in prior phase II studies. Ann Oncol off J Eur Soc Med Oncol. 2014;25:2277–84.
doi: 10.1093/annonc/mdu441
Robert C, Long GV, Brady B, Dutriaux C, Di Giacomo AM, Mortier L, et al. Five-year outcomes with nivolumab in patients with Wild-Type BRAF Advanced Melanoma. J Clin Oncol off J Am Soc Clin Oncol. 2020;38:3937–46.
doi: 10.1200/JCO.20.00995
LAG3-PD-. 1 combo impresses in Melanoma. Cancer Discov. 2021;11:1605–6.
doi: 10.1158/2159-8290.CD-NB2021-0347
Larkin J, Chiarion-Sileni V, Gonzalez R, Grob J-J, Rutkowski P, Lao CD, et al. Five-year survival with combined Nivolumab and Ipilimumab in Advanced Melanoma. N Engl J Med. 2019;381:1535–46.
pubmed: 31562797
doi: 10.1056/NEJMoa1910836
Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P, Larkin J, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med. 2011;364:2507–16.
pubmed: 21639808
pmcid: 3549296
doi: 10.1056/NEJMoa1103782
Hauschild A, Grob J-J, Demidov LV, Jouary T, Gutzmer R, Millward M, et al. Dabrafenib in BRAF-mutated metastatic melanoma: a multicentre, open-label, phase 3 randomised controlled trial. Lancet Lond Engl. 2012;380:358–65.
doi: 10.1016/S0140-6736(12)60868-X
Chapman PB, Robert C, Larkin J, Haanen JB, Ribas A, Hogg D, et al. Vemurafenib in patients with BRAFV600 mutation-positive metastatic melanoma: final overall survival results of the randomized BRIM-3 study. Ann Oncol. 2017;28:2581–7.
pubmed: 28961848
pmcid: 5834156
doi: 10.1093/annonc/mdx339
Hauschild A, Ascierto PA, Schadendorf D, Grob JJ, Ribas A, Kiecker F, et al. Long-term outcomes in patients with BRAF V600-mutant metastatic melanoma receiving dabrafenib monotherapy: analysis from phase 2 and 3 clinical trials. Eur J Cancer Oxf Engl 1990. 2020;125:114–20.
Larkin J, Ascierto PA, Dréno B, Atkinson V, Liszkay G, Maio M, et al. Combined vemurafenib and cobimetinib in BRAF-mutated melanoma. N Engl J Med. 2014;371:1867–76.
pubmed: 25265494
doi: 10.1056/NEJMoa1408868
Long GV, Stroyakovskiy D, Gogas H, Levchenko E, de Braud F, Larkin J, et al. Combined BRAF and MEK inhibition versus BRAF inhibition alone in melanoma. N Engl J Med. 2014;371:1877–88.
pubmed: 25265492
doi: 10.1056/NEJMoa1406037
Ascierto PA, Dréno B, Larkin J, Ribas A, Liszkay G, Maio M, et al. 5-Year outcomes with Cobimetinib plus Vemurafenib in BRAFV600 mutation-positive Advanced Melanoma: Extended follow-up of the coBRIM study. Clin Cancer Res off J Am Assoc Cancer Res. 2021;27:5225–35.
doi: 10.1158/1078-0432.CCR-21-0809
Dummer R, Flaherty KT, Robert C, Arance A, de Groot JWB, Garbe C, et al. COLUMBUS 5-Year update: a randomized, Open-Label, phase III trial of Encorafenib Plus Binimetinib Versus Vemurafenib or Encorafenib in patients with BRAF V600–Mutant melanoma. J Clin Oncol. 2022;40:4178–88.
pubmed: 35862871
pmcid: 9916040
doi: 10.1200/JCO.21.02659
Robert C, Grob JJ, Stroyakovskiy D, Karaszewska B, Hauschild A, Levchenko E, et al. Five-year outcomes with Dabrafenib plus Trametinib in Metastatic Melanoma. N Engl J Med. 2019;381:626–36.
pubmed: 31166680
doi: 10.1056/NEJMoa1904059
Wolchok JD, Chiarion-Sileni V, Gonzalez R, Grob J-J, Rutkowski P, Lao CD, et al. Long-term outcomes with Nivolumab Plus Ipilimumab or Nivolumab alone Versus Ipilimumab in patients with Advanced Melanoma. J Clin Oncol off J Am Soc Clin Oncol. 2022;40:127–37.
doi: 10.1200/JCO.21.02229
Ferrucci PF, Di Giacomo AM, Del Vecchio M, Atkinson V, Schmidt H, Schachter J, et al. KEYNOTE-022 part 3: a randomized, double-blind, phase 2 study of pembrolizumab, dabrafenib, and trametinib in BRAF-mutant melanoma. J Immunother Cancer. 2020;8:e001806.
pubmed: 33361337
pmcid: 7768966
doi: 10.1136/jitc-2020-001806
Ascierto PA, Stroyakovskiy D, Gogas H, Robert C, Lewis K, Protsenko S, et al. Overall survival with first-line atezolizumab in combination with vemurafenib and cobimetinib in BRAFV600 mutation-positive advanced melanoma (IMspire150): second interim analysis of a multicentre, randomised, phase 3 study. Lancet Oncol. 2023;24:33–44.
pubmed: 36460017
doi: 10.1016/S1470-2045(22)00687-8
Michielin O, Atkins MB, Koon HB, Dummer R, Ascierto PA. Evolving impact of long-term survival results on metastatic melanoma treatment. J Immunother Cancer. 2020;8:e000948.
pubmed: 33037115
pmcid: 7549477
doi: 10.1136/jitc-2020-000948
Huang M, Lou Y, Pellissier J, Burke T, Liu FX, Xu R, et al. Cost effectiveness of Pembrolizumab vs. Standard-of-care chemotherapy as first-line treatment for metastatic NSCLC that expresses high levels of PD-L1 in the United States. PharmacoEconomics. 2017;35:831–44.
pubmed: 28620848
pmcid: 5548835
doi: 10.1007/s40273-017-0527-z
Miguel LS, Lopes FV, Pinheiro B, Wang J, Xu R, Pellissier J, et al. Cost effectiveness of Pembrolizumab for Advanced Melanoma Treatment in Portugal. Value Health J Int Soc Pharmacoeconomics Outcomes Res. 2017;20:1065–73.
doi: 10.1016/j.jval.2017.05.009
Courtney PT, Yip AT, Cherry DR, Salans MA, Kumar A, Murphy JD. Cost-effectiveness of Nivolumab-Ipilimumab Combination Therapy for the treatment of Advanced Non-small Cell Lung Cancer. JAMA Netw Open. 2021;4:e218787.
pubmed: 33938936
pmcid: 8094011
doi: 10.1001/jamanetworkopen.2021.8787
Salaün P-Y, Abgral R, Malard O, Querellou-Lefranc S, Quere G, Wartski M, et al. Good clinical practice recommendations for the use of PET/CT in oncology. Eur J Nucl Med Mol Imaging. 2020;47:28–50.
pubmed: 31637482
doi: 10.1007/s00259-019-04553-8
Hicks RJ, Iravani A, Sandhu S. 18F-fluorodeoxyglucose Positron Emission Tomography/Computed tomography for assessing Tumor response to Immunotherapy in Solid tumors: Melanoma and Beyond. PET Clin. 2020;15:11–22.
pubmed: 31735298
doi: 10.1016/j.cpet.2019.08.007
Bisschop C, de Heer EC, Brouwers AH, Hospers G, a. P, Jalving M. Rational use of 18F-FDG PET/CT in patients with advanced cutaneous melanoma: a systematic review. Crit Rev Oncol Hematol. 2020;153:103044.
pubmed: 32673997
doi: 10.1016/j.critrevonc.2020.103044
Van de Wiele C, Juanito G, Vander BK, Lawal I, Sathekge M, Maes A, et al. Practical considerations when interpreting FDG PET/CT Imaging for Staging and Treatment Response Assessment in Melanoma patients. Semin Nucl Med. 2021;51:544–53.
pubmed: 34246450
doi: 10.1053/j.semnuclmed.2021.06.010
Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging. 2017;44:151–65.
pubmed: 27271051
doi: 10.1007/s00259-016-3427-0
Cook GJR, Goh V. A role for FDG PET Radiomics in Personalized Medicine? Semin Nucl Med. 2020;50:532–40.
pubmed: 33059822
doi: 10.1053/j.semnuclmed.2020.05.002
Reuzé S, Schernberg A, Orlhac F, Sun R, Chargari C, Dercle L, et al. Radiomics in Nuclear Medicine Applied to Radiation Therapy: methods, pitfalls, and challenges. Int J Radiat Oncol Biol Phys. 2018;102:1117–42.
pubmed: 30064704
doi: 10.1016/j.ijrobp.2018.05.022
Hatt M, Le Cheze C, Antonorsi N, Tixier F, Tankyevych O, Jaouen V, et al. Radiomics in PET/CT: current status and future AI-Based evolutions. Semin Nucl Med. 2021;51:126–33.
pubmed: 33509369
doi: 10.1053/j.semnuclmed.2020.09.002
Da-Ano R, Visvikis D, Hatt M. Harmonization strategies for multicenter radiomics investigations. Phys Med Biol. 2020.
Yang L, Xu P, Li M, Wang M, Peng M, Zhang Y et al. PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs. Front Oncol [Internet]. 2022 [cited 2023 Jun 4];12. https://www.frontiersin.org/articles/ https://doi.org/10.3389/fonc.2022.894323 .
Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2:36.
pubmed: 30426318
pmcid: 6234198
doi: 10.1186/s41747-018-0068-z
Bundschuh RA, Dinges J, Neumann L, Seyfried M, Zsótér N, Papp L, et al. Textural Parameters of Tumor Heterogeneity in
Lovinfosse P, Polus M, Van Daele D, Martinive P, Daenen F, Hatt M, et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Mol Imaging. 2018;45:365–75.
pubmed: 29046927
doi: 10.1007/s00259-017-3855-5
Lee S-T, Kovaleva N, Senko C, Kee D, Scott AM. Positron Emission Tomography/Computed Tomography Transformation of Oncology: Melanoma and skin malignancies. PET Clin. 2024;19:231–48.
pubmed: 38233284
doi: 10.1016/j.cpet.2023.12.009
Abgral R, Bourhis D, Salaun P-Y. Clinical perspectives for the use of total body PET/CT. Eur J Nucl Med Mol Imaging. 2021;48:1712–8.
pubmed: 33742236
doi: 10.1007/s00259-021-05293-4
Sah B-R, Owczarczyk K, Siddique M, Cook GJR, Goh V. Radiomics in esophageal and gastric cancer. Abdom Radiol N Y. 2019;44:2048–58.
doi: 10.1007/s00261-018-1724-8
Ito K, Schöder H, Teng R, Humm JL, Ni A, Wolchok JD, et al. Prognostic value of baseline metabolic tumor volume measured on 18F-fluorodeoxyglucose positron emission tomography/computed tomography in melanoma patients treated with ipilimumab therapy. Eur J Nucl Med Mol Imaging. 2019;46:930–9.
pubmed: 30488098
doi: 10.1007/s00259-018-4211-0
Hatt M, Le Rest CC, Tixier F, Badic B, Schick U, Visvikis D. Radiomics: Data are also images. J Nucl Med off Publ Soc Nucl Med. 2019;60:S38–44.
Seban R-D, Moya-Plana A, Antonios L, Yeh R, Marabelle A, Deutsch E, et al. Prognostic 18F-FDG PET biomarkers in metastatic mucosal and cutaneous melanoma treated with immune checkpoint inhibitors targeting PD-1 and CTLA-4. Eur J Nucl Med Mol Imaging. 2020;47:2301–12.
pubmed: 32206839
doi: 10.1007/s00259-020-04757-3
Ma Y, Xia R, Ma X, Judson-Torres RL, Zeng H. Mucosal melanoma: pathological evolution, pathway dependency and targeted therapy. Front Oncol. 2021;11:702287.
pubmed: 34350118
pmcid: 8327265
doi: 10.3389/fonc.2021.702287
Long GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma. Lancet Lond Engl. 2023;402:485–502.
doi: 10.1016/S0140-6736(23)00821-8
Ayati N, Sadeghi R, Kiamanesh Z, Lee ST, Zakavi SR, Scott AM. The value of 18F-FDG PET/CT for predicting or monitoring immunotherapy response in patients with metastatic melanoma: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging. 2021;48:428–48.
pubmed: 32728798
doi: 10.1007/s00259-020-04967-9
Nakamoto R, Zaba LC, Liang T, Reddy SA, Davidzon G, Aparici CM, et al. Prognostic value of bone marrow metabolism on pretreatment 18F-FDG PET/CT in patients with metastatic melanoma treated with Anti-PD-1 therapy. J Nucl Med off Publ Soc Nucl Med. 2021;62:1380–3.
Schweighofer-Zwink G, Manafi-Farid R, Kölblinger P, Hehenwarter L, Harsini S, Pirich C, et al. Prognostic value of 2-[18F]FDG PET-CT in metastatic melanoma patients receiving immunotherapy. Eur J Radiol. 2022;146:110107.
pubmed: 34922117
doi: 10.1016/j.ejrad.2021.110107
Ito K, Teng R, Schöder H, Humm JL, Ni A, Michaud L, et al. 18F-FDG PET/CT for Monitoring of Ipilimumab Therapy in patients with metastatic melanoma. J Nucl Med off Publ Soc Nucl Med. 2019;60:335–41.
Annovazzi A, Ferraresi V, Rea S, Russillo M, Renna D, Carpano S, et al. Prognostic value of total metabolic tumour volume and therapy-response assessment by [18F]FDG PET/CT in patients with metastatic melanoma treated with BRAF/MEK inhibitors. Eur Radiol. 2022;32:3398–407.
pubmed: 34779873
doi: 10.1007/s00330-021-08355-1
Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50:S122–50.
doi: 10.2967/jnumed.108.057307
Iravani A, Wallace R, Lo SN, Galligan A, Weppler AM, Hicks RJ, et al. FDG PET/CT prognostic markers in patients with Advanced Melanoma treated with Ipilimumab and Nivolumab. Radiology. 2023;307:e221180.
pubmed: 36853183
doi: 10.1148/radiol.221180
Flaus A, Habouzit V, De Leiris N, Vuillez JP, Leccia MT, Perrot JL, et al. FDG PET biomarkers for prediction of survival in metastatic melanoma prior to anti-PD1 immunotherapy. Sci Rep. 2021;11:18795.
pubmed: 34552135
pmcid: 8458464
doi: 10.1038/s41598-021-98310-3
Da-Ano R, Masson I, Lucia F, Doré M, Robin P, Alfieri J, et al. Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Sci Rep. 2020;10:10248.
pubmed: 32581221
pmcid: 7314795
doi: 10.1038/s41598-020-66110-w
Svedman FC, Pillas D, Taylor A, Kaur M, Linder R, Hansson J. Stage-specific survival and recurrence in patients with cutaneous malignant melanoma in Europe - a systematic review of the literature. Clin Epidemiol. 2016;8:109–22.
pubmed: 27307765
pmcid: 4887072
doi: 10.2147/CLEP.S99021
Forschner A, Eichner F, Amaral T, Keim U, Garbe C, Eigentler TK. Improvement of overall survival in stage IV melanoma patients during 2011–2014: analysis of real-world data in 441 patients of the German Central Malignant Melanoma Registry (CMMR). J Cancer Res Clin Oncol. 2017;143:533–40.
pubmed: 27878363
doi: 10.1007/s00432-016-2309-y
Kunz M. The genetic basis of new treatment modalities in melanoma. Curr Drug Targets. 2015;16:233–48.
pubmed: 25654738
doi: 10.2174/1389450116666150204112138
Saadani H, van der Hiel B, Aalbersberg EA, Zavrakidis I, Haanen JBAG, Hoekstra OS, et al. Metabolic biomarker-based BRAFV600 Mutation Association and Prediction in Melanoma. J Nucl Med off Publ Soc Nucl Med. 2019;60:1545–52.
Demircioğlu A. Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics. Insights Imaging. 2021;12:172.
pubmed: 34817740
pmcid: 8613324
doi: 10.1186/s13244-021-01115-1
Olthof S-C, Krumm P, Weichold O, Eigentler T, Bösmüller H, la Fougère C, et al. CT texture analysis compared to Positron Emission Tomography (PET) and mutational status in resected melanoma metastases. Eur J Radiol. 2020;131:109242.
pubmed: 32942199
doi: 10.1016/j.ejrad.2020.109242
Aoude LG, Wong BZY, Bonazzi VF, Brosda S, Walters SB, Koufariotis LT, et al. Radiomics biomarkers correlate with CD8 expression and predict Immune signatures in Melanoma patients. Mol Cancer Res MCR. 2021;19:950–6.
pubmed: 33811161
doi: 10.1158/1541-7786.MCR-20-1038
Dimitrakopoulou-Strauss A. Monitoring of patients with metastatic melanoma treated with immune checkpoint inhibitors using PET-CT. Cancer Immunol Immunother CII. 2019;68:813–22.
pubmed: 30123922
doi: 10.1007/s00262-018-2229-6
Brooks FJ, Grigsby PW. The effect of small Tumor volumes upon intra-tumoral Tracer Uptake Heterogeneity studies. J Nucl Med off Publ Soc Nucl Med. 2014;55:37–42.
Brooks FJ, Grigsby PW. Low-order non-spatial effects dominate second-order spatial effects in the texture quantifier analysis of 18F-FDG-PET images. PLoS ONE. 2015;10:e0116574.
pubmed: 25714472
pmcid: 4340651
doi: 10.1371/journal.pone.0116574
Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The image Biomarker Standardization Initiative: standardized quantitative Radiomics for High-Throughput Image-based phenotyping. Radiology. 2020;295:328–38.
pubmed: 32154773
doi: 10.1148/radiol.2020191145
Keller H, Shek T, Driscoll B, Xu Y, Nghiem B, Nehmeh S, et al. Noise-based image harmonization significantly increases repeatability and reproducibility of Radiomics Features in PET images: a Phantom Study. Tomogr Ann Arbor Mich. 2022;8:1113–28.
Dittrich D, Pyka T, Scheidhauer K, Lütje S, Essler M, Bundschuh RA. Textural features in FDG-PET/CT can predict outcome in melanoma patients to treatment with Vemurafenib and Ipililumab. Nukl Nucl Med. 2020;59:228–34.
doi: 10.1055/a-1140-5458
Van de Wiele C, Kruse V, Smeets P, Sathekge M, Maes A. Predictive and prognostic value of metabolic tumour volume and total lesion glycolysis in solid tumours. Eur J Nucl Med Mol Imaging. 2013;40:290–301.
pubmed: 23151913
doi: 10.1007/s00259-012-2280-z
Guezennec C, Bourhis D, Orlhac F, Robin P, Corre J-B, Delcroix O, et al. Inter-observer and segmentation method variability of textural analysis in pre-therapeutic FDG PET/CT in head and neck cancer. PLoS ONE. 2019;14:e0214299.
pubmed: 30921388
pmcid: 6438585
doi: 10.1371/journal.pone.0214299
Jardim S, António J, Mora C. Image thresholding approaches for medical image segmentation - short literature review. Procedia Comput Sci. 2023;219:1485–92.
doi: 10.1016/j.procs.2023.01.439
Lasnon C, Enilorac B, Popotte H, Aide N. Impact of the EARL harmonization program on automatic delineation of metabolic active tumour volumes (MATVs). EJNMMI Res. 2017;7:30.
pubmed: 28361349
pmcid: 5374086
doi: 10.1186/s13550-017-0279-y
Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for Radiomic feature calculation in Multimodality Imaging to accelerate advances in the characterization of Tumor Heterogeneity. Cancer Res. 2018;78:4786–9.
pubmed: 29959149
doi: 10.1158/0008-5472.CAN-18-0125
Hatt M, Majdoub M, Vallières M, Tixier F, Le Rest CC, Groheux D, et al. 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J Nucl Med off Publ Soc Nucl Med. 2015;56:38–44.
Orlhac F, Soussan M, Maisonobe J-A, Garcia CA, Vanderlinden B, Buvat I. Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med off Publ Soc Nucl Med. 2014;55:414–22.
Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges J-P, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med off Publ Soc Nucl Med. 2011;52:369–78.
Crandall JP, Fraum TJ, Lee M, Jiang L, Grigsby P, Wahl RL. Repeatability of 18F-FDG PET Radiomic features in Cervical Cancer. J Nucl Med off Publ Soc Nucl Med. 2021;62:707–15.
Pfaehler E, van Sluis J, Merema BBJ, van Ooijen P, Berendsen RCM, van Velden FHP, et al. Experimental multicenter and multivendor evaluation of the performance of PET radiomic features using 3-Dimensionally printed Phantom inserts. J Nucl Med. 2020;61:469–76.
pubmed: 31420497
pmcid: 7067530
doi: 10.2967/jnumed.119.229724
Orlhac F, Soussan M, Chouahnia K, Martinod E, Buvat I. 18F-FDG PET-Derived Textural Indices reflect tissue-specific Uptake Pattern in Non-small Cell Lung Cancer. PLoS ONE. 2015;10:e0145063.
pubmed: 26669541
pmcid: 4682929
doi: 10.1371/journal.pone.0145063
Vagenas TP, Economopoulos TL, Sachpekidis C, Dimitrakopoulou-Strauss A, Pan L, Provata A et al. A decision support system for the identification of metastases of Metastatic Melanoma using whole-body FDG PET/CT images. IEEE J Biomed Health Inf. 2022;PP.
Anne-Leen D, Machaba S, Alex M, Bart DS, Laurence B, Mike S, et al. Principal component analysis of texture features derived from FDG PET images of melanoma lesions. EJNMMI Phys. 2022;9:64.
pubmed: 36107331
pmcid: 9478000
doi: 10.1186/s40658-022-00491-x
Chang E, Joel MZ, Chang HY, Du J, Khanna O, Omuro A, et al. Comparison of radiomic feature aggregation methods for patients with multiple tumors. Sci Rep. 2021;11:9758.
pubmed: 33963236
pmcid: 8105371
doi: 10.1038/s41598-021-89114-6
Peisen F, Hänsch A, Hering A, Brendlin AS, Afat S, Nikolaou K, et al. Combination of whole-body baseline CT Radiomics and Clinical parameters to predict response and survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy. Cancers. 2022;14:2992.
pubmed: 35740659
pmcid: 9221470
doi: 10.3390/cancers14122992
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045–57.
pubmed: 23884657
pmcid: 3824915
doi: 10.1007/s10278-013-9622-7
Lee H, Palm J, Grimes SM, Ji HP. The Cancer Genome Atlas Clinical Explorer: a web and mobile interface for identifying clinical-genomic driver associations. Genome Med. 2015;7:112.
pubmed: 26507825
pmcid: 4624593
doi: 10.1186/s13073-015-0226-3
Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Phys Med Biol. 2016;61:R150–166.
pubmed: 27269645
pmcid: 4927328
doi: 10.1088/0031-9155/61/13/R150
Bogowicz M, Leijenaar RTH, Tanadini-Lang S, Riesterer O, Pruschy M, Studer G, et al. Post-radiochemotherapy PET radiomics in head and neck cancer - the influence of radiomics implementation on the reproducibility of local control tumor models. Radiother Oncol J Eur Soc Ther Radiol Oncol. 2017;125:385–91.
doi: 10.1016/j.radonc.2017.10.023
Da-Ano R, Visvikis D, Hatt M. Harmonization strategies for multicenter radiomics investigations. Phys Med Biol. 2020;65:24TR02.
pubmed: 32688357
doi: 10.1088/1361-6560/aba798
Kothari S, Phan JH, Stokes TH, Osunkoya AO, Young AN, Wang MD. Removing batch effects from histopathological images for enhanced cancer diagnosis. IEEE J Biomed Health Inf. 2014;18:765–72.
doi: 10.1109/JBHI.2013.2276766
Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, et al. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage. 2018;167:104–20.
pubmed: 29155184
doi: 10.1016/j.neuroimage.2017.11.024
Müller C, Schillert A, Röthemeier C, Trégouët D-A, Proust C, Binder H, et al. Removing batch effects from Longitudinal Gene expression - quantile normalization plus ComBat as Best Approach for microarray Transcriptome Data. PLoS ONE. 2016;11:e0156594.
pubmed: 27272489
pmcid: 4896498
doi: 10.1371/journal.pone.0156594
Stein CK, Qu P, Epstein J, Buros A, Rosenthal A, Crowley J, et al. Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat. BMC Bioinformatics. 2015;16:63.
pubmed: 25887219
pmcid: 4355992
doi: 10.1186/s12859-015-0478-3