Siegel RL, Wagle NS, Cercek A, Smith RA, Jemal A (2023) Colorectal cancer statistics. A Cancer J Clin 73(3):233–254
Watanabe T, Muro K, Ajioka Y, Hashiguchi Y, Ito Y, Saito Y et al (2018) Japanese society for cancer of the colon and rectum (JSCCR) guidelines 2016 for the treatment of colorectal cancer. Int J Clin Oncol 23(1):1–34
Wan T, Zhang XF, Liang C, Liao CW, Li JY, Zhou YM (2019) The prognostic value of a pathologic complete response after neoadjuvant therapy for digestive cancer: systematic review and meta-analysis of 21 studies. Ann Surg Oncol 26(5):1412–1420
Maas M, Nelemans PJ, Valentini V, Das P, Rödel C, Kuo LJ et al (2010) Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol 11(9):835–844
Marijnen CA (2015) Organ preservation in rectal cancer: have all questions been answered? Lancet Oncol 16(1):e13-22
Smith JJ, Paty PB, Garcia-Aguilar J (2020) Watch and wait in rectal cancer or more wait and see? JAMA Surg 155(7):657–658
Article PubMed PubMed Central Google Scholar
LeBlanc JK (2007) Imaging and management of rectal cancer. Nat Clin Pract Gastroenterol Hepatol 4(12):665–676
Yan J, Zhang B, Zhang S, Cheng J, Liu X, Wang W et al (2021) Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients. NPJ Precision Oncol 5(1):72
Gollub MJ, Blazic I, Felder S, Knezevic A, Gonen M, Garcia-Aguilar J et al (2019) Value of adding dynamic contrast-enhanced MRI visual assessment to conventional MRI and clinical assessment in the diagnosis of complete tumour response to chemoradiotherapy for rectal cancer. Eur Radiol 29(3):1104–1113
Park SH, Cho SH, Choi SH, Jang JK, Kim MJ, Kim SH et al (2020) MRI assessment of complete response to preoperative chemoradiation therapy for rectal cancer: 2020 guide for practice from the korean society of abdominal radiology. Korean J Radiol 21(7):812–828
Article PubMed PubMed Central Google Scholar
Schurink NW, van Kranen SR, Roberti S, van Griethuysen JJM, Bogveradze N, Castagnoli F et al (2022) Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility. Eur Radiol 32(3):1506–1516
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures. They Are Data Radiol 278(2):563–577
Tomaszewski MR, Gillies RJ (2021) The biological meaning of radiomic features. Radiology 299(2):E256
Jia LL, Zheng QY, Tian JH, He DL, Zhao JX, Zhao LP et al (2022) Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: a systematic review and meta-analysis. Front Oncol 12:1026216
Article CAS PubMed PubMed Central Google Scholar
Park SH, Han K (2018) Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 286(3):800–809
Park SH, Kressel HY (2018) Connecting technological innovation in artificial intelligence to real-world medical practice through rigorous clinical validation: what peer-reviewed medical journals could do. J Korean Med Sci 33(22):e152
Article PubMed PubMed Central Google Scholar
Kirienko M, Sollini M, Ninatti G, Loiacono D, Giacomello E, Gozzi N et al (2021) Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI. Eur J Nucl Med Mol Imaging 48(12):3791–3804
Article PubMed PubMed Central Google Scholar
Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X et al (2020) Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 11(1):5088
Article CAS PubMed PubMed Central Google Scholar
Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N et al (2019) Deep learning and process understanding for data-driven Earth system science. Nature 566(7743):195–204
Article CAS PubMed Google Scholar
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clin Res ed) 372:n71
Sounderajah V, Ashrafian H, Rose S, Shah NH, Ghassemi M, Golub R et al (2021) A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med 27(10):1663–1665
Article CAS PubMed Google Scholar
Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–536
Yang B, Mallett S, Takwoingi Y, Davenport CF, Hyde CJ, Whiting PF et al (2021) QUADAS-C: a tool for assessing risk of bias in comparative diagnostic accuracy studies. Ann Intern Med 174(11):1592–1599
Altman DG, Bland JM (2003) Interaction revisited: the difference between two estimates. BMJ (Clin Res ed) 326(7382):219
Abbaspour S, Abdollahi H, Arabalibeik H, Barahman M, Arefpour AM, Fadavi P, Ay M, Mahdavi SR (2022) Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning. Abdom Radiol 47(11):3645–3659
Antunes JT, Ofshteyn A, Bera K, Wang EY, Brady JT, Willis JE et al (2020) Radiomic features of primary rectal cancers on baseline T-2-weighted MRI are associated with pathologic complete response to neoadjuvant chemoradiation: a multisite study. J Magn Reson Imaging 52(5):1531–1541
Article PubMed PubMed Central Google Scholar
Bibault JE, Giraud P, Housset M, Durdux C, Taieb J, Berger A et al (2018) Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep 8(1):12611
Article PubMed PubMed Central Google Scholar
Boldrini L, Lenkowicz J, Orlandini LC, Yin G, Cusumano D, Chiloiro G et al (2022) Applicability of a pathological complete response magnetic resonance-based radiomics model for locally advanced rectal cancer in intercontinental cohort. Radiat Oncol (London, England) 17(1):78
Bordron A, Rio E, Badic B, Miranda O, Pradier O, Hatt M, Visvikis D, Lucia F, Schick U, Bourbonne V (2022) External validation of a radiomics model for the prediction of complete response to neoadjuvant chemoradiotherapy in rectal cancer. Cancers 14(4):1079
Article PubMed PubMed Central Google Scholar
Bulens P, Couwenberg A, Intven M, Debucquoy A, Vandecaveye V, Van Cutsem E et al (2020) Predicting the tumor response to chemoradiotherapy for rectal cancer: model development and external validation using MRI radiomics. Radiother Oncol 142:246–252
Article CAS PubMed Google Scholar
Cheng Y, Luo Y, Hu Y, Zhang Z, Wang X, Yu Q et al (2021) Multiparametric MRI-based radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Abdom Radiol (New York) 46(11):5072–5085
Chiloiro G, Cusumano D, Romano A, Boldrini L, Nicolì G, Votta C, Tran HE, Barbaro B, Carano D, Valentini V, Gambacorta MA (2023) Delta radiomic analysis of mesorectum to predict treatment response and prognosis in locally advanced rectal cancer. Cancers 15(12):3082
Article PubMed PubMed Central Google Scholar
Cui Y, Yang X, Shi Z, Yang Z, Du X, Zhao Z et al (2019) Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol 29(3):1211–1220
Feng L, Liu Z, Li C, Li Z, Lou X, Shao L et al (2022) Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digital health 4(1):e8–e17
Article CAS PubMed Google Scholar
Ferrari R, Mancini-Terracciano C, Voena C, Rengo M, Zerunian M, Ciardiello A et al (2019) MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer. Eur J Radiol 118:1–9
Article CAS PubMed Google Scholar
Horvat N, Veeraraghavan H, Khan M, Blazic I, Zheng J, Capanu M et al (2018) MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 287(3):833–843
Horvat N, Veeraraghavan H, Nahas CSR, Bates DDB, Ferreira FR, Zheng J et al (2022) Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study. Abdom Radiol (New York) 47(8):2770–2782
Jang BS, Lim YJ, Song C, Jeon SH, Lee KW, Kang SB et al (2021) Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging. Radiother Oncol: J Eur Soc Ther Radiol Oncol 161:183–190
Jayaprakasam VS, Paroder V, Gibbs P, Bajwa R, Gangai N, Sosa RE et al (2022) MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer. Eur Radiol 32(2):971–980
留言 (0)