Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69:7–34.
Article
PubMed
Google Scholar
Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66:115–32.
Article
PubMed
Google Scholar
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66:7–30.
Article
PubMed
Google Scholar
Gunderson LL, Jessup JM, Sargent DJ, et al. Revised tumor and node categorization for rectal cancer based on surveillance, epidemiology, and end results and rectal pooled analysis outcomes. J Clin Oncol. 2010;28:256–63.
Article
PubMed
Google Scholar
Gunderson LL, Sargent DJ, Tepper JE, et al. Impact of T and N stage and treatment on survival and relapse in adjuvant rectal cancer: a pooled analysis. J Clin Oncol. 2004;22:1785–96.
Article
PubMed
Google Scholar
Gunderson LL, Jessup JM, Sargent DJ, et al. Revised TN categorization for colon cancer based on national survival outcomes data. J Clin Oncol. 2010;28:264–71.
Article
PubMed
Google Scholar
Dekker E, Tanis PJ, Vleugels JLA, et al. Colorectal cancer. Lancet. 2019;394:1467–80.
Article
PubMed
Google Scholar
Bailey CE, Hu CY, You YN, et al. Increasing disparities in the age-related incidences of colon and rectal cancers in the United States, 1975–2010. JAMA Surg. 2015;150:17–22.
Article
PubMed
PubMed Central
Google Scholar
Venook AP, Niedzwiecki D, Innocenti F, et al. Impact of primary (1º) tumor location on overall survival (OS) and progression-free survival (PFS) in patients (pts) with metastatic colorectal cancer (mCRC): Analysis of CALGB/SWOG 80405 (Alliance). J Clin Oncol. 2016;34:3504–3504.
Article
Google Scholar
Taieb J, Le Malicot K, Shi Q, et al. J Natl Cancer Inst. 2017;109:1.
Article
CAS
Google Scholar
Mala T, Bøhler G, Mathisen Ø, et al. Hepatic resection for colorectal metastases: can preoperative scoring predict patient outcome? World J Surg. 2002;26:1348–53.
Article
PubMed
Google Scholar
Lech G, Słotwiński R, Słodkowski M, et al. Colorectal cancer tumour markers and biomarkers: recent therapeutic advances. World J Gastroenterol. 2016;22:1745–55.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sinicrope FA, Sargent DJ. Clinical implications of microsatellite instability in sporadic colon cancers. Curr Opin Oncol. 2009;21:369–73.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sarli L, Bottarelli L, Bader G, et al. Association between recurrence of sporadic colorectal cancer, high level of microsatellite instability, and loss of heterozygosity at chromosome 18q. Dis Colon Rectum. 2004;47:1467–82.
Article
PubMed
Google Scholar
García-Figueiras R, Baleato-González S, Padhani AR, et al. Advanced imaging techniques in evaluation of colorectal cancer. Radiographics. 2018;38:740–65.
Article
PubMed
Google Scholar
Smith NJ, Bees N, Barbachano Y, et al. Preoperative computed tomography staging of nonmetastatic colon cancer predicts outcome: implications for clinical trials. Br J Cancer. 2007;96:1030–6.
Article
CAS
PubMed
PubMed Central
Google Scholar
Hundt W, Braunschweig R, Reiser M. Evaluation of spiral CT in staging of colon and rectum carcinoma. Eur Radiol. 1999;9:78–84.
Article
CAS
PubMed
Google Scholar
Engelmann BE, Loft A, Kjær A, et al. Positron emission tomography/computed tomography for optimized colon cancer staging and follow up. Scand J Gastroenterol. 2014;49:191–201.
Article
PubMed
Google Scholar
Nerad E, Lahaye MJ, Maas M, et al. Diagnostic accuracy of CT for local staging of colon cancer: a systematic review and meta-analysis. AJR Am J Roentgenol. 2016;207:984–95.
Article
PubMed
Google Scholar
Horvat N, Rocha C, Oliveira B, et al. MRI of rectal cancer: tumor staging, imaging techniques, and management. Radiographics. 2019;39:367–87.
Article
PubMed
Google Scholar
Liu LH, Lv H, Wang ZC, et al. Performance comparison between MRI and CT for local staging of sigmoid and descending colon cancer. Eur J Radiol. 2019;121:108741.
Article
PubMed
Google Scholar
Evans J, Patel U, Brown G. Rectal cancer: primary staging and assessment after chemoradiotherapy. Semin Radiat Oncol. 2011;21:169–77.
Article
PubMed
Google Scholar
Shan L: [(18)F]-Fluoro-2-deoxy-d-glucose-folate. In Molecular Imaging and Contrast Agent Database (MICAD). Bethesda (MD): National Center for Biotechnology Information (US); 2004
Lin M, Wong K, Ng WL, et al. Positron emission tomography and colorectal cancer. Crit Rev Oncol Hematol. 2011;77:30–47.
Article
PubMed
Google Scholar
Badic B, Desseroit MC, Hatt M, et al. Potential complementary value of noncontrast and contrast enhanced CT radiomics in colorectal cancers. Acad Radiol. 2019;26:469–79.
Article
PubMed
Google Scholar
Dai W, Mo S, Han L, et al. Prognostic and predictive value of radiomics signatures in stage I-III colon cancer. Clin Transl Med. 2020;10:288–93.
Article
PubMed
PubMed Central
Google Scholar
Li Y, Liu W, Pei Q, et al. Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer. Cancer Med. 2019;8:7244–52.
Article
PubMed
PubMed Central
Google Scholar
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–62.
Article
PubMed
Google Scholar
Reginelli A, Nardone V, Giacobbe G, et al. Radiomics as a new frontier of imaging for cancer prognosis: a narrative review. Diagnostics (Basel, Switzerland). 2021;11:1796.
CAS
PubMed Central
Google Scholar
Stanzione A, Verde F, Romeo V, et al. Radiomics and machine learning applications in rectal cancer: current update and future perspectives. World J Gastroenterol. 2021;27:5306–21.
Article
PubMed
PubMed Central
Google Scholar
Hou M, Sun JH. Emerging applications of radiomics in rectal cancer: state of the art and future perspectives. World J Gastroenterol. 2021;27:3802–14.
Article
PubMed
PubMed Central
Google Scholar
Jiang Y, Yuan Q, Lv W, et al. Radiomic signature of (18)F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits. Theranostics. 2018;8:5915–28.
Article
PubMed
PubMed Central
Google Scholar
Lv W, Yuan Q, Wang Q, et al. Radiomics analysis of PET and CT components of PET/CT imaging integrated with clinical parameters: application to prognosis for nasopharyngeal carcinoma. Mol Imaging Biol. 2019;21:954–64.
Article
CAS
PubMed
Google Scholar
Oikonomou A, Khalvati F, Tyrrell PN, et al. Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy. Sci Rep. 2018;8:4003.
Article
PubMed
PubMed Central
CAS
Google Scholar
Huang SY, Franc BL, Harnish RJ, et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. NPJ Breast Cancer. 2018;4:24.
Article
PubMed
PubMed Central
CAS
Google Scholar
Wang H, Zhao S, Li L, et al. Development and validation of an (18)F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma. Eur Radiol. 2020;30:5578–87.
Article
CAS
PubMed
Google Scholar
Staal FCR, van der Reijd DJ, Taghavi M, et al. Radiomics for the prediction of treatment outcome and survival in patients with colorectal cancer: a systematic review. Clin Colorectal Cancer. 2021;20:52–71.
Article
PubMed
Google Scholar
Bang JI, Ha S, Kang SB, et al. Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [(18)F]FDG PET/CT scans in locally advanced rectal cancer. Eur J Nucl Med Mol Imaging. 2016;43:422–31.
Article
CAS
PubMed
Google Scholar
Giannini V, Mazzetti S, Bertotto I, et al. Predicting locally advanced rectal cancer response to neoadjuvant therapy with (18)F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging. 2019;46:878–88.
Article
CAS
PubMed
Google Scholar
Li H, Boimel P, Janopaul-Naylor J, et al. Deep convolutional neural networks for imaging data based survival analysis of rectal cancer. Proc IEEE Int Symp Biomed Imaging. 2019;2019:846–9.
PubMed
PubMed Central
Google Scholar
van Helden EJ, Vacher YJL, van Wieringen WN, et al. Radiomics analysis of pre-treatment [(18)F]FDG PET/CT for patients with metastatic colorectal cancer undergoing palliative systemic treatment. Eur J Nucl Med Mol Imaging. 2018;45:2307–17.
Article
PubMed
PubMed Central
Google Scholar
Rahmim A, Bak-Fredslund KP, Ashrafinia S, et al. Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features. Eur J Radiol. 2019;113:101–9.
Article
PubMed
PubMed Central
Google Scholar
Nakajo M, Kajiya Y, Tani A, et al. A pilot study for texture analysis of (18)F-FDG and (18)F-FLT-PET/CT to predict tumor recurrence of patients with colorectal cancer who received surgery. Eur J Nucl Med Mol Imaging. 2017;44:2158–68.
Article
PubMed
Google Scholar
Chen SW, Shen WC, Chen WT, et al. Metabolic imaging phenotype using radiomics of [(18)F]FDG PET/CT associated with genetic alterations of colorectal cancer. Mol Imaging Biol. 2019;21:183–90.
Article
CAS
PubMed
Google Scholar
Yushkevich PA, Piven J, Hazlett HC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage (Orlando, Fla). 2006;31:1116–28.
Google Scholar
Ha S, Choi H, Paeng JC, et al. Radiomics in oncological PET/CT: a methodological overview. Nucl Med Mol Imaging. 2019;53:14–29.
Article
PubMed
PubMed Central
Google Scholar
Tixier F, Le Rest CC, Hatt M, 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. 2011;52:369–78.
Article
PubMed
Google Scholar
Pfaehler E, van Sluis J, Merema BBJ, 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.
Article
CAS
PubMed
PubMed Central
Google Scholar
Welch ML, McIntosh C, Haibe-Kains B, et al. Vulnerabilities of radiomic signature development: the need for safeguards. Radiother Oncol. 2019;130:2–9.
Article
PubMed
Google Scholar
Dou TH, Coroller TP, van Griethuysen JJM, et al. Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC. PLoS ONE. 2018;13:e0206108.
Article
PubMed
PubMed Central
CAS
Google Scholar
Yuan R, Shi S, Chen J, et al. Radiomics in RayPlus: a web-based tool for texture analysis in medical images. J Digit Imaging. 2019;32:269–75.
Article
PubMed
Google Scholar
van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104–7.
Article
PubMed
PubMed Central
CAS
Google Scholar
Zwanenburg A, Vallieres M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295:328–38.
Article
PubMed
Google Scholar
Ishwaran H, Kogalur UB, Chen X, et al. Random survival forests for high-dimensional data. 2011;4:115–32.
Google Scholar
Lovinfosse P, Polus M, Van Daele D, 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.
Article
PubMed
Google Scholar
Chagpar R, Xing Y, Chiang YJ, et al. Adherence to stage-specific treatment guidelines for patients with colon cancer. J Clin Oncol. 2012;30:972–9.
Article
PubMed
PubMed Central
Google Scholar
Hari DM, Leung AM, Lee JH, et al. AJCC Cancer Staging Manual 7th edition criteria for colon cancer: do the complex modifications improve prognostic assessment?. J Am Coll Surg. 2013; 217:181–190.
Webber C, Gospodarowicz M, Sobin LH, et al. Improving the TNM classification: findings from a 10-year continuous literature review. Int J Cancer. 2014;135:371–8.
Article
CAS
PubMed
Google Scholar
Mayerhoefer ME, Materka A, Langs G, et al. Introduction to Radiomics. J Nucl Med. 2020;61:488–95.
Article
CAS
PubMed
Google Scholar
Sollini M, Antunovic L, Chiti A, et al. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging. 2019;46:2656–72.
Article
PubMed
PubMed Central
Google Scholar