18F-FDG-PET/MRI texture analysis in rectal cancer after neoadjuvant chemoradiotherapy.


Journal

Nuclear medicine communications
ISSN: 1473-5628
Titre abrégé: Nucl Med Commun
Pays: England
ID NLM: 8201017

Informations de publication

Date de publication:
01 Jul 2022
Historique:
pubmed: 27 4 2022
medline: 11 6 2022
entrez: 26 4 2022
Statut: ppublish

Résumé

Reliable markers to predict the response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) are lacking. We aimed to assess the ability of 18F-FDG PET/MRI to predict response to nCRT among patients undergoing curative-intent surgery. Patients with histological-confirmed LARC who underwent curative-intent surgery following nCRT and restaging with 18F-FDG PET/MRI were included. Statistical correlation between radiomic features extracted in PET, apparent diffusion coefficient (ADC) and T2w images and patients' histopathologic response to chemoradiotherapy using a multivariable logistic regression model ROC-analysis. Overall, 50 patients were included in the study. A pathological complete response was achieved in 28.0% of patients. Considering second-order textural features, nine parameters showed a statistically significant difference between the two groups in ADC images, six parameters in PET images and four parameters in T2w images. Combining all the features selected for the three techniques in the same multivariate ROC curve analysis, we obtained an area under ROC curve of 0.863 (95% CI, 0.760-0.966), showing a sensitivity, specificity and accuracy at the Youden's index of 100% (14/14), 64% (23/36) and 74% (37/50), respectively. PET/MRI texture analysis seems to represent a valuable tool in the identification of rectal cancer patients with a complete pathological response to nCRT.

Identifiants

pubmed: 35471653
doi: 10.1097/MNM.0000000000001570
pii: 00006231-202207000-00011
pmc: PMC9177153
doi:

Substances chimiques

Fluorodeoxyglucose F18 0Z5B2CJX4D

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

815-822

Informations de copyright

Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.

Références

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71:209–249.
Sauer R, Becker H, Hohenberger W, Rödel C, Wittekind C, Fietkau R, et al.; German Rectal Cancer Study Group. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med 2004; 351:1731–1740.
Kapiteijn E, Marijnen CA, Nagtegaal ID, Putter H, Steup WH, Wiggers T, et al.; Dutch Colorectal Cancer Group. Preoperative radiotherapy combined with total mesorectal excision for resectable rectal cancer. N Engl J Med 2001; 345:638–646.
Habr-Gama A, Perez RO, Nadalin W, Sabbaga J, Ribeiro U Jr, Silva e Sousa AH Jr, et al. Operative versus nonoperative treatment for stage 0 distal rectal cancer following chemoradiation therapy: long-term results. Ann Surg 2004; 240:711–717; discussion 717.
Capelli G, De Simone I, Spolverato G, Cinquini M, Moschetti I, Lonardi S, et al. Non-operative management versus total mesorectal excision for locally advanced rectal cancer with clinical complete response after neoadjuvant chemoradiotherapy: a GRADE approach by the rectal cancer guidelines writing group of the Italian Association of Medical Oncology (AIOM). J Gastrointest Surg 2020; 24:2150–2159.
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48:441–446.
Jalil O, Afaq A, Ganeshan B, Patel UB, Boone D, Endozo R, et al. Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal Dis 2017; 19:349–362.
Liu L, Liu Y, Xu L, Li Z, Lv H, Dong N, et al. Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer. J Magn Reson Imaging 2017; 45:1798–1808.
Cusumano D, Dinapoli N, Boldrini L, Chiloiro G, Gatta R, Masciocchi C, et al. Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer. Radiol Med 2018; 123:286–295.
De Cecco CN, Ganeshan B, Ciolina M, Rengo M, Meinel FG, Musio D, et al. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Invest Radiol 2015; 50:239–245.
De Cecco CN, Ciolina M, Caruso D, Rengo M, Ganeshan B, Meinel FG, et al. Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience. Abdom Radiol (NY) 2016; 41:1728–1735.
Shu Z, Fang S, Ye Q, Mao D, Cao H, Pang P, Gong X. Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images. Abdom Radiol (NY) 2019; 44:3775–3784.
Crimì F, Capelli G, Spolverato G, Bao QR, Florio A, Milite Rossi S, et al. MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). Radiol Medica 2020; 125:1216–1224
Giannini V, Mazzetti S, Bertotto I, Chiarenza C, Cauda S, Delmastro E, et al. Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging 2019; 46:878–888.
Ferri V, Vicente Lopez E, Quijano Collazo Y, Caruso R, Duran Gimenez Rico H, Ielpo B, et al. Quantitative analysis of 18-FDG-PET/MRI to assess pathological complete response following neoadjuvant radiochemotherapy in locally advanced rectal cancer. A prospective preliminary study. Acta Oncol 2019; 58:1246–1249.
Amorim BJ, Torrado-Carvajal A, Esfahani SA, Marcos SS, Vangel M, Stein D, et al. PET/MRI radiomics in rectal cancer: a pilot study on the correlation between PET- and MRI-derived image features with a clinical interpretation. Mol Imaging Biol 2020; 22:1438–1445.
Mandard AM, Dalibard F, Mandard JC, Marnay J, Henry-Amar M, Petiot JF, et al. Pathologic assessment of tumor regression after preoperative chemoradiotherapy of esophageal carcinoma. Clinicopathologic correlations. Cancer 1994; 73:2680–2686.
Eiber M, Martinez-Möller A, Souvatzoglou M, Holzapfel K, Pickhard A, Löffelbein D, et al. Value of a Dixon-based MR/PET attenuation correction sequence for the localization and evaluation of PET-positive lesions. Eur J Nucl Med Mol Imaging 2011; 38:1691–1701.
Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al.; European Association of Nuclear Medicine (EANM). FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging 2015; 42:328–354.
Pucciarelli S, Spolverato G. Invite comment on Pucciarelli and Spolverato: the fate of the rectum after organ sparing approach to rectal cancer. Tech Coloproctol 2019; 23:807–808.
Barina A, De Paoli A, Delrio P, Guerrieri M, Muratore A, Bianco F, et al. Rectal sparing approach after preoperative radio- and/or chemotherapy (RESARCH) in patients with rectal cancer: a multicentre observational study. Tech Coloproctol 2017; 21:633–640.
LINEE GUIDA NEOPLASIE DEL RETTO E ANO | AIOM. https://www.aiom.it/linee-guida-aiom-neoplasie-del-retto-e-ano-2019/ . Accessed 2 October 2020
Dattani M, Heald RJ, Goussous G, Broadhurst J, São Julião GP, Habr-Gama A, et al. Oncological and survival outcomes in watch and wait patients with a clinical complete response after neoadjuvant chemoradiotherapy for rectal cancer: a systematic review and pooled analysis. Ann Surg 2018; 268:955–967.
Appelt AL, Pløen J, Harling H, Jensen FS, Jensen LH, Jørgensen JC, et al. High-dose chemoradiotherapy and watchful waiting for distal rectal cancer: a prospective observational study. Lancet Oncol 2015; 16:919–927.
Hiotis SP, Weber SM, Cohen AM, Minsky BD, Paty PB, Guillem JG, et al. Assessing the predictive value of clinical complete response to neoadjuvant therapy for rectal cancer: an analysis of 488 patients. J Am Coll Surg 2002; 194:131–135; discussion 135.
van der Valk MJM, Hilling DE, Bastiaannet E, Meershoek-Klein Kranenbarg E, Beets GL, Figueiredo NL, et al.; IWWD Consortium. Long-term outcomes of clinical complete responders after neoadjuvant treatment for rectal cancer in the International Watch & Wait Database (IWWD): an international multicentre registry study. Lancet 2018; 391:2537–2545.
Yang L, Qiu M, Xia C, Li Z, Wang Z, Zhou X, Wu B. Value of high-resolution DWI in combination with texture analysis for the evaluation of tumor response after preoperative chemoradiotherapy for locally advanced rectal cancer. Am J Roentgenol 2019; 212:1279–1286.
Shi L, Zhang Y, Nie K, Sun X, Niu T, Yue N, et al. Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI. Magn Reson Imaging 2019; 61:33–40.
Shayesteh SP, Alikhassi A, Fard Esfahani A, Miraie M, Geramifar P, Bitarafan-Rajabi A, et al. Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients. Phys Med 2019; 62:111–119.
Ferrari R, Mancini-Terracciano C, Voena C, Rengo M, Zerunian M, Ciardiello A, et al. MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer. Eur J Radiol 2019; 118:1–9.
Shayesteh SP, Alikhassi A, Farhan F, Ghalehtaki R, Soltanabadi M, Haddad P, et al. Prediction of response to neoadjuvant chemoradiotherapy by MRI-based machine learning texture analysis in rectal cancer patients. J Gastrointest Cancer 2020; 51:601–609.
van Griethuysen JJM, Lambregts DMJ, Trebeschi S, Lahaye MJ, Bakers FCH, Vliegen RFA, et al. Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer. Abdom Radiol (NY) 2020; 45:632–643.
Antunes JT, Ofshteyn A, Bera K, Wang EY, Brady JT, Willis JE, et al. Radiomic features of primary rectal cancers on baseline T2-weighted MRI are associated with pathologic complete response to neoadjuvant chemoradiation: a multisite study. J Magn Reson Imaging 2020; 52:1531–1541.
Nardone V, Reginelli A, Scala F, Carbone SF, Mazzei MA, Sebaste L, et al. Magnetic-resonance-imaging texture analysis predicts early progression in rectal cancer patients undergoing neoadjuvant chemoradiation. Gastroenterol Res Pract 2019; 2019:8505798.
Horvat N, Veeraraghavan H, Pelossof RA, Fernandes MC, Arora A, Khan M, et al. Radiogenomics of rectal adenocarcinoma in the era of precision medicine: a pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations. Eur J Radiol 2019; 113:174–181.
Oh JE, Kim MJ, Lee J, Hur BY, Kim B, Kim DY, et al. Magnetic resonance-based texture analysis differentiating KRAS mutation status in rectal cancer. Cancer Res Treat 2020; 52:51–59.
Xu Y, Xu Q, Ma Y, Duan J, Zhang H, Liu T, et al. Characterizing MRI features of rectal cancers with different KRAS status. BMC Cancer 2019; 19:1111.
Yang L, Liu D, Fang X, Wang Z, Xing Y, Ma L, Wu B. Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis? Eur Radiol 2019; 29:6469–6476.
Crimì F, Spolverato G, Lacognata C, Garieri M, Cecchin D, Urso ED, et al. 18F-FDG PET/MRI for rectal cancer TNM restaging after preoperative chemoradiotherapy: initial experience. Dis Colon Rectum 2020; 63:310–318.
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14:749–762.
Park JE, Kim HS, Kim D, Park SY, Kim JY, Cho SJ, Kim JH. A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features. BMC Cancer 2020; 20:29.

Auteurs

Giulia Capelli (G)

General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova.

Cristina Campi (C)

Department of Mathematics, University of Genova, Genova.

Quoc Riccardo Bao (QR)

General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova.

Francesco Morra (F)

Institute of Radiology, Department of Medicine, University of Padova.

Carmelo Lacognata (C)

Radiology Department, Azienda Ospedaliera di Padova.

Pietro Zucchetta (P)

Nuclear Medicine Unit, Department of Medicine, University of Padova, Padova, Italy.

Diego Cecchin (D)

Nuclear Medicine Unit, Department of Medicine, University of Padova, Padova, Italy.

Salvatore Pucciarelli (S)

General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova.

Gaya Spolverato (G)

General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova.

Filippo Crimì (F)

Institute of Radiology, Department of Medicine, University of Padova.

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