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image of Emerging MRI Biomarkers for Prognostication in Rectal Cancer

Abstract

Rectal cancer is a significant health concern with substantial morbidity and mortality rates. Magnetic Resonance Imaging (MRI) plays a crucial role in the diagnosis and management of rectal cancer, providing detailed anatomical and functional information. However, traditional MRI techniques have limitations in prognosticating tumor behavior and treatment response. The study emphasizes the importance of emerging techniques such as Diffusion-Weighted Imaging (DWI), mrDEC scoring system, Dynamic Contrast-Enhanced MRI (DCE-MRI), Radiomics, and Machine Learning. By examining recent research and clinical trials, we aim to offer a comprehensive overview of the current landscape, challenges, and future directions associated with the incorporation of these MRI biomarkers in predicting outcomes for rectal cancer patients. This review paper aims to provide an overview of the emerging MRI biomarkers that hold the potential for prognostication of rectal cancer.

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2024-10-29
2025-04-12

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References

  1. Di Costanzo G. Ascione R. Ponsiglione A. Tucci A.G. Dell’Aversana S. Iasiello F. Cavaglià E. Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: A review. Explor Target Antitumor Ther 2023 4 3 406 421 10.37349/etat.2023.00142 37455833
    [Google Scholar]
  2. Siegel R.L. Wagle N.S. Cercek A. Smith R.A. Jemal A. Colorectal cancer statistics, 2023. CA Cancer J. Clin. 2023 73 3 233 254 10.3322/caac.21772 36856579
    [Google Scholar]
  3. Fernandes M.C. Gollub M.J. Brown G. The importance of MRI for rectal cancer evaluation. Surg. Oncol. 2022 43 101739 10.1016/j.suronc.2022.101739 35339339
    [Google Scholar]
  4. Wong C. Fu Y. Li M. Mu S. Chu X. Fu J. Lin C. Zhang H. MRI-based artificial intelligence in rectal cancer. J. Magn. Reson. Imaging 2023 57 1 45 56 10.1002/jmri.28381 35993550
    [Google Scholar]
  5. Wang D. Xu J. Zhang Z. Li S. Zhang X. Zhou Y. Zhang X. Lu Y. Evaluation of rectal cancer circumferential resection margin using faster region-based convolutional neural network in high-resolution magnetic resonance images. Dis. Colon Rectum 2020 63 2 143 151 10.1097/DCR.0000000000001519 31842158
    [Google Scholar]
  6. Lotfollahzadeh S. Kashyap S. Tsoris A. Rectal cancer. StatPearls Treasure Island, FL StatPearls Publishing 2024
    [Google Scholar]
  7. Jeon J. Du M. Schoen R.E. Hoffmeister M. Newcomb P.A. Berndt S.I. Caan B. Campbell P.T. Chan A.T. Chang-Claude J. Giles G.G. Gong J. Harrison T.A. Huyghe J.R. Jacobs E.J. Li L. Lin Y. Le Marchand L. Potter J.D. Qu C. Bien S.A. Zubair N. Macinnis R.J. Buchanan D.D. Hopper J.L. Cao Y. Nishihara R. Rennert G. Slattery M.L. Thomas D.C. Woods M.O. Prentice R.L. Gruber S.B. Zheng Y. Brenner H. Hayes R.B. White E. Peters U. Hsu L. Determining risk of colorectal cancer and starting age of screening based on lifestyle, environmental, and genetic factors. Gastroenterology 2018 154 8 2152 2164.e19 10.1053/j.gastro.2018.02.021 29458155
    [Google Scholar]
  8. Müller M.F. Ibrahim A.E.K. Arends M.J. Molecular pathological classification of colorectal cancer. Virchows Arch. 2016 469 2 125 134 10.1007/s00428‑016‑1956‑3 27325016
    [Google Scholar]
  9. Aghagolzadeh P. Radpour R. New trends in molecular and cellular biomarker discovery for colorectal cancer. World J. Gastroenterol. 2016 22 25 5678 5693 10.3748/wjg.v22.i25.5678 27433083
    [Google Scholar]
  10. Kedrin D. Gala M.K. Genetics of the serrated pathway to colorectal cancer. Clin. Transl. Gastroenterol. 2015 6 4 e84 10.1038/ctg.2015.12 25856207
    [Google Scholar]
  11. Libra Vella N. Cannavò C. Scalisi A. Spandidos D.A. Toffoli G. Buonadonna A. Libra M. Stivala F. Role of genetic polymorphisms and mutations in colorectal cancer therapy (Review). Mol. Med. Rep. 2011 4 2 203 208 10.3892/mmr.2010.408 21468552
    [Google Scholar]
  12. Bosman F.T. Molecular pathology of colorectal cancer. Cytogenet. Genome Res. 1999 86 2 112 117 10.1159/000015362 10545700
    [Google Scholar]
  13. Weaver O. Leung J.W.T. Biomarkers and imaging of breast cancer. AJR Am. J. Roentgenol. 2018 210 2 271 278 10.2214/AJR.17.18708 29166151
    [Google Scholar]
  14. Zhang W. Guo Y. Jin Q. Radiomics and its feature selection: A review. Symmetry (Basel) 2023 15 10 1834 10.3390/sym15101834
    [Google Scholar]
  15. O’Connor J.P.B. Aboagye E.O. Adams J.E. Aerts H.J.W.L. Barrington S.F. Beer A.J. Boellaard R. Bohndiek S.E. Brady M. Brown G. Buckley D.L. Chenevert T.L. Clarke L.P. Collette S. Cook G.J. deSouza N.M. Dickson J.C. Dive C. Evelhoch J.L. Faivre-Finn C. Gallagher F.A. Gilbert F.J. Gillies R.J. Goh V. Griffiths J.R. Groves A.M. Halligan S. Harris A.L. Hawkes D.J. Hoekstra O.S. Huang E.P. Hutton B.F. Jackson E.F. Jayson G.C. Jones A. Koh D.M. Lacombe D. Lambin P. Lassau N. Leach M.O. Lee T.Y. Leen E.L. Lewis J.S. Liu Y. Lythgoe M.F. Manoharan P. Maxwell R.J. Miles K.A. Morgan B. Morris S. Ng T. Padhani A.R. Parker G.J.M. Partridge M. Pathak A.P. Peet A.C. Punwani S. Reynolds A.R. Robinson S.P. Shankar L.K. Sharma R.A. Soloviev D. Stroobants S. Sullivan D.C. Taylor S.A. Tofts P.S. Tozer G.M. van Herk M. Walker-Samuel S. Wason J. Williams K.J. Workman P. Yankeelov T.E. Brindle K.M. McShane L.M. Jackson A. Waterton J.C. Imaging biomarker roadmap for cancer studies. Nat. Rev. Clin. Oncol. 2017 14 3 169 186 10.1038/nrclinonc.2016.162 27725679
    [Google Scholar]
  16. Eisenhauer E.A. Therasse P. Bogaerts J. Schwartz L.H. Sargent D. Ford R. Dancey J. Arbuck S. Gwyther S. Mooney M. Rubinstein L. Shankar L. Dodd L. Kaplan R. Lacombe D. Verweij J. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur. J. Cancer 2009 45 2 228 247 10.1016/j.ejca.2008.10.026 19097774
    [Google Scholar]
  17. Gore J.C. Manning H.C. Quarles C.C. Waddell K.W. Yankeelov T.E. Magnetic resonance in the era of molecular imaging of cancer. Magn. Reson. Imaging 2011 29 5 587 600 10.1016/j.mri.2011.02.003 21524870
    [Google Scholar]
  18. 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 Suppl 1 122S 50S 10.2967/jnumed.108.057307
    [Google Scholar]
  19. Siddiqui M.R.S. Simillis C. Hunter C. Chand M. Bhoday J. Garant A. Vuong T. Artho G. Rasheed S. Tekkis P. Abulafi A.M. Brown G. A meta-analysis comparing the risk of metastases in patients with rectal cancer and MRI-detected extramural vascular invasion (mrEMVI) vs mrEMVI-negative cases. Br. J. Cancer 2017 116 12 1513 1519 10.1038/bjc.2017.99 28449006
    [Google Scholar]
  20. Lord A.C. D’Souza N. Pucher P.H. Moran B.J. Abulafi A.M. Wotherspoon A. Rasheed S. Brown G. Significance of extranodal tumour deposits in colorectal cancer: A systematic review and meta-analysis. Eur. J. Cancer 2017 82 92 102 10.1016/j.ejca.2017.05.027 28651160
    [Google Scholar]
  21. Peyravian N. Larki P. Gharib E. Nazemalhosseini-Mojarad E. Anaraki F. Young C. McClellan J. Ashrafian Bonab M. Asadzadeh-Aghdaei H. Zali M. The application of gene expression profiling in predictions of occult lymph node metastasis in colorectal cancer patients. Biomedicines 2018 6 1 27 10.3390/biomedicines6010027 29498671
    [Google Scholar]
  22. Keller D.S. Berho M. Perez R.O. Wexner S.D. Chand M. The multidisciplinary management of rectal cancer. Nat. Rev. Gastroenterol. Hepatol. 2020 17 7 414 429 10.1038/s41575‑020‑0275‑y 32203400
    [Google Scholar]
  23. Yang S.Y. Bae H. Seo N. Han K. Han Y.D. Cho M.S. Hur H. Min B.S. Kim N.K. Lee K.Y. Lim J.S. Pretreatment MRI-detected extramural venous invasion as a prognostic and predictive biomarker for neoadjuvant chemoradiotherapy in non-metastatic rectal cancer: A propensity score matched analysis. Eur. Radiol. 2023 34 6 3686 3698 10.1007/s00330‑023‑10300‑3 37994967
    [Google Scholar]
  24. Glynne-Jones R. Wyrwicz L. Tiret E. Brown G. Rödel C. Cervantes A. Arnold D. Rectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 2017 28 iv22 iv40 10.1093/annonc/mdx224 28881920
    [Google Scholar]
  25. Lambregts D.M.J. Bogveradze N. Blomqvist L.K. Fokas E. Garcia-Aguilar J. Glimelius B. Gollub M.J. Konishi T. Marijnen C.A.M. Nagtegaal I.D. Nilsson P.J. Perez R.O. Snaebjornsson P. Taylor S.A. Tolan D.J.M. Valentini V. West N.P. Wolthuis A. Lahaye M.J. Maas M. Beets G.L. Beets-Tan R.G.H. Current controversies in TNM for the radiological staging of rectal cancer and how to deal with them: Results of a global online survey and multidisciplinary expert consensus. Eur. Radiol. 2022 32 7 4991 5003 10.1007/s00330‑022‑08591‑z 35254485
    [Google Scholar]
  26. Gollub M.J. Lall C. Lalwani N. Rosenthal M.H. Current controversy, confusion, and imprecision in the use and interpretation of rectal MRI. Abdom. Radiol. (N.Y.) 2019 44 11 3549 3558 10.1007/s00261‑019‑01996‑3 31062058
    [Google Scholar]
  27. Horvat N. Carlos Tavares Rocha C. Clemente Oliveira B. Petkovska I. Gollub M.J. MRI of Rectal cancer: Tumor staging, imaging techniques, and management. Radiographics 2019 39 2 367 387 10.1148/rg.2019180114 30768361
    [Google Scholar]
  28. Srisajjakul S. Prapaisilp P. Bangchokdee S. Pitfalls in MRI of rectal cancer: What radiologists need to know and avoid. Clin. Imaging 2018 50 130 140 10.1016/j.clinimag.2017.11.012 29414101
    [Google Scholar]
  29. Brown G. Richards C.J. Bourne M.W. Newcombe R.G. Radcliffe A.G. Dallimore N.S. Williams G.T. Morphologic predictors of lymph node status in rectal cancer with use of high-spatial-resolution MR imaging with histopathologic comparison. Radiology 2003 227 2 371 377 10.1148/radiol.2272011747 12732695
    [Google Scholar]
  30. Zhang X.M. Zhang H.L. Yu D. Dai Y. Bi D. Prince M.R. Li C. 3-T MRI of rectal carcinoma: preoperative diagnosis, staging, and planning of sphincter-sparing surgery. AJR Am. J. Roentgenol. 2008 190 5 1271 1278 10.2214/AJR.07.2505 18430843
    [Google Scholar]
  31. Kim H. Lim J.S. Choi J.Y. Park J. Chung Y.E. Kim M.J. Choi E. Kim N.K. Kim K.W. Rectal cancer: Comparison of accuracy of local-regional staging with two- and three-dimensional preoperative 3-T MR imaging. Radiology 2010 254 2 485 492 10.1148/radiol.09090587 20093520
    [Google Scholar]
  32. Heo S.H. Kim J.W. Shin S.S. Jeong Y.Y. Kang H.K. Multimodal imaging evaluation in staging of rectal cancer. World J. Gastroenterol. 2014 20 15 4244 4255 10.3748/wjg.v20.i15.4244 24764662
    [Google Scholar]
  33. Yin J.D. Song L.R. Lu H.C. Zheng X. Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps. World J. Gastroenterol. 2020 26 17 2082 2096 10.3748/wjg.v26.i17.2082 32536776
    [Google Scholar]
  34. Beets-Tan R.G.H. Lambregts D.M.J. Maas M. Bipat S. Barbaro B. Curvo-Semedo L. Fenlon H.M. Gollub M.J. Gourtsoyianni S. Halligan S. Hoeffel C. Kim S.H. Laghi A. Maier A. Rafaelsen S.R. Stoker J. Taylor S.A. Torkzad M.R. Blomqvist L. Magnetic resonance imaging for clinical management of rectal cancer: Updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting. Eur. Radiol. 2018 28 4 1465 1475 10.1007/s00330‑017‑5026‑2 29043428
    [Google Scholar]
  35. Wang H. Chen X. Ding J. Deng S. Mao G. Tian S. Zhu X. Ao W. Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer. Abdom. Radiol. (N.Y.) 2022 48 2 471 485 10.1007/s00261‑022‑03759‑z 36508131
    [Google Scholar]
  36. Bogveradze N. Snaebjornsson P. Grotenhuis B.A. van Triest B. Lahaye M.J. Maas M. Beets G.L. Beets-Tan R.G.H. Lambregts D.M.J. MRI anatomy of the rectum: Key concepts important for rectal cancer staging and treatment planning. Insights Imaging 2023 14 1 13 10.1186/s13244‑022‑01348‑8 36652149
    [Google Scholar]
  37. Wang P.P. Deng C.L. Wu B. Magnetic resonance imaging-based artificial intelligence model in rectal cancer. World J. Gastroenterol. 2021 27 18 2122 2130 10.3748/wjg.v27.i18.2122 34025068
    [Google Scholar]
  38. Chen Y. Yang X. Wen Z. Liu Y. Lu B. Yu S. Xiao X. Association between high-resolution MRI-detected extramural vascular invasion and tumour microcirculation estimated by dynamic contrast-enhanced MRI in rectal cancer: Preliminary results. BMC Cancer 2019 19 1 498 10.1186/s12885‑019‑5732‑z 31133005
    [Google Scholar]
  39. Abramson R.G. Arlinghaus L.R. Dula A.N. Quarles C.C. Stokes A.M. Weis J.A. Whisenant J.G. Chekmenev E.Y. Zhukov I. Williams J.M. Yankeelov T.E. MR Imaging biomarkers in oncology clinical trials. Magn. Reson. Imaging Clin. N. Am. 2016 24 1 11 29 10.1016/j.mric.2015.08.002 26613873
    [Google Scholar]
  40. Reeder S.B. Emerging quantitative magnetic resonance imaging biomarkers of hepatic steatosis. Hepatology 2013 58 6 1877 1880 10.1002/hep.26543 23744793
    [Google Scholar]
  41. López-Campos F. Martín-Martín M. Fornell-Pérez R. García-Pérez J.C. Die-Trill J. Fuentes-Mateos R. López-Durán S. Domínguez-Rullán J. Ferreiro R. Riquelme-Oliveira A. Hervás-Morón A. Couñago F. Watch and wait approach in rectal cancer: Current controversies and future directions. World J. Gastroenterol. 2020 26 29 4218 4239 10.3748/wjg.v26.i29.4218 32848330
    [Google Scholar]
  42. Lambregts D.M.J. Boellaard T.N. Beets-Tan R.G.H. Response evaluation after neoadjuvant treatment for rectal cancer using modern MR imaging: A pictorial review. Insights Imaging 2019 10 1 15 10.1186/s13244‑019‑0706‑x 30758688
    [Google Scholar]
  43. Torkzad M.R. Påhlman L. Glimelius B. Magnetic resonance imaging (MRI) in rectal cancer: A comprehensive review. Insights Imaging 2010 1 4 245 267 10.1007/s13244‑010‑0037‑4 22347920
    [Google Scholar]
  44. Dieguez A. Rectal cancer staging: Focus on the prognostic significance of the findings described by high-resolution magnetic resonance imaging. Cancer Imaging 2013 13 2 277 297 10.1102/1470‑7330.2013.0028 23876415
    [Google Scholar]
  45. Taylor F.G.M. Quirke P. Heald R.J. Moran B. Blomqvist L. Swift I. Sebag-Montefiore D.J. Tekkis P. Brown G. Preoperative high-resolution magnetic resonance imaging can identify good prognosis stage I, II, and III rectal cancer best managed by surgery alone: A prospective, multicenter, European study. Ann. Surg. 2011 253 4 711 719 10.1097/SLA.0b013e31820b8d52 21475011
    [Google Scholar]
  46. Abramson R.G. McGhee C.R. Lakomkin N. Arteaga C.L. Pitfalls in RECIST data extraction for clinical trials. Acad. Radiol. 2015 22 6 779 786 10.1016/j.acra.2015.01.015 25794800
    [Google Scholar]
  47. Moreno C.C. Sullivan P.S. Mittal P.K. Rectal MRI for cancer staging and surveillance. Gastroenterol. Clin. North Am. 2018 47 3 537 552 10.1016/j.gtc.2018.04.005 30115436
    [Google Scholar]
  48. Zhu H.T. Zhang X.Y. Shi Y.J. Li X.T. Sun Y.S. Automatic segmentation of rectal tumor on diffusion‐weighted images by deep learning with U‐Net. J. Appl. Clin. Med. Phys. 2021 22 9 324 331 10.1002/acm2.13381 34343402
    [Google Scholar]
  49. Kluza E. Rozeboom E.D. Maas M. Martens M. Lambregts D.M.J. Slenter J. Beets G.L. Beets-Tan R.G.H. T2 weighted signal intensity evolution may predict pathological complete response after treatment for rectal cancer. Eur. Radiol. 2013 23 1 253 261 10.1007/s00330‑012‑2578‑z 22777621
    [Google Scholar]
  50. Jia H. Jiang X. Zhang K. Shang J. Zhang Y. Fang X. Gao F. Li N. Dong J. A nomogram of combining IVIM-DWI and MRI radiomics from the primary lesion of rectal adenocarcinoma to assess nonenlarged lymph node metastasis preoperatively. J. Magn. Reson. Imaging 2022 56 3 658 667 10.1002/jmri.28068 35090079
    [Google Scholar]
  51. Padhani A.R. Liu G. Mu-Koh D. Chenevert T.L. Thoeny H.C. Takahara T. Dzik-Jurasz A. Ross B.D. Van Cauteren M. Collins D. Hammoud D.A. Rustin G.J.S. Taouli B. Choyke P.L. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: Consensus and recommendations. Neoplasia 2009 11 2 102 125 10.1593/neo.81328 19186405
    [Google Scholar]
  52. Lindgren A. Anttila M. Arponen O. Hämäläinen K. Könönen M. Vanninen R. Sallinen H. Dynamic contrast-enhanced MRI to characterize angiogenesis in primary epithelial ovarian cancer: An exploratory study. Eur. J. Radiol. 2023 165 110925 10.1016/j.ejrad.2023.110925 37320880
    [Google Scholar]
  53. Thomassin-Naggara I. Soualhi N. Balvay D. Darai E. Cuenod C.A. Quantifying tumor vascular heterogeneity with DCE‐MRI in complex adnexal masses: A preliminary study. J. Magn. Reson. Imaging 2017 46 6 1776 1785 10.1002/jmri.25707 28370815
    [Google Scholar]
  54. Dijkhoff R.A.P. Beets-Tan R.G.H. Lambregts D.M.J. Beets G.L. Maas M. Value of DCE-MRI for staging and response evaluation in rectal cancer: A systematic review. Eur. J. Radiol. 2017 95 155 168 10.1016/j.ejrad.2017.08.009 28987662
    [Google Scholar]
  55. Blazic I.M. Campbell N.M. Gollub M.J. MRI for evaluation of treatment response in rectal cancer. Br. J. Radiol. 2016 89 1064 20150964 10.1259/bjr.20150964 27331883
    [Google Scholar]
  56. Dam C. Lund-Rasmussen V. Pløen J. Jakobsen A. Rafaelsen S.R. Computed tomography assessment of early response to neoadjuvant therapy in colon cancer. Dan. Med. J. 2015 62 7 A5103 26183044
    [Google Scholar]
  57. Borgheresi A. De Muzio F. Agostini A. Ottaviani L. Bruno A. Granata V. Fusco R. Danti G. Flammia F. Grassi R. Grassi F. Bruno F. Palumbo P. Barile A. Miele V. Giovagnoni A. Lymph nodes evaluation in rectal cancer: Where do we stand and future perspective. J. Clin. Med. 2022 11 9 2599 10.3390/jcm11092599 35566723
    [Google Scholar]
  58. Faletti R. Gatti M. Arezzo A. Stola S. Benedini M.C. Bergamasco L. Morino M. Fonio P. Preoperative staging of rectal cancer using magnetic resonance imaging: comparison with pathological staging. Minerva Surg. 2018 73 1 13 19 10.23736/S0026‑4733.17.07392‑8 28497665
    [Google Scholar]
  59. Valentini V. van Stiphout R.G.P.M. Lammering G. Gambacorta M.A. Barba M.C. Bebenek M. Bonnetain F. Bosset J.F. Bujko K. Cionini L. Gerard J.P. Rödel C. Sainato A. Sauer R. Minsky B.D. Collette L. Lambin P. Nomograms for predicting local recurrence, distant metastases, and overall survival for patients with locally advanced rectal cancer on the basis of European randomized clinical trials. J. Clin. Oncol. 2011 29 23 3163 3172 10.1200/JCO.2010.33.1595 21747092
    [Google Scholar]
  60. Rosa C. Caravatta L. Delli Pizzi A. Di Tommaso M. Cianci R. Gasparini L. Perrotti F. Solmita J. Sartori S. Zecca I.A.L. Di Nicola M. Basilico R. Genovesi D. Reproducibility of rectal tumor volume delineation using diffusion-weighted MRI: Agreement on volumes between observers. Cancer Radiother. 2019 23 3 216 221 10.1016/j.canrad.2018.10.004 31109840
    [Google Scholar]
  61. Arian A. Taher H. Suhail Najm Alareer H. Aghili M. Value of conventional MRI, DCE-MRI, and DWI-MRI in the discrimination of metastatic from non-metastatic lymph nodes in rectal cancer: A systematic review and meta-analysis study. Asian Pac. J. Cancer Prev. 2023 24 2 401 410 10.31557/APJCP.2023.24.2.401 36853286
    [Google Scholar]
  62. Heijnen L.A. Maas M. Beets-Tan R.G. Berkhof M. Lambregts D.M. Nelemans P.J. Riedl R. Beets G.L. Nodal staging in rectal cancer: Why is restaging after chemoradiation more accurate than primary nodal staging? Int. J. Colorectal Dis. 2016 31 6 1157 1162 10.1007/s00384‑016‑2576‑8 27055660
    [Google Scholar]
  63. Lord A.C. Corr A. Chandramohan A. Hodges N. Pring E. Airo-Farulla C. Moran B. Jenkins J.T. Di Fabio F. Brown G. Assessment of the 2020 NICE criteria for preoperative radiotherapy in patients with rectal cancer treated by surgery alone in comparison with proven MRI prognostic factors: A retrospective cohort study. Lancet Oncol. 2022 23 6 793 801 10.1016/S1470‑2045(22)00214‑5 35512720
    [Google Scholar]
  64. Smith N.J. Barbachano Y. Norman A.R. Swift R.I. Abulafi A.M. Brown G. Prognostic significance of magnetic resonance imaging-detected extramural vascular invasion in rectal cancer. Br. J. Surg. 2008 95 2 229 236 10.1002/bjs.5917 17932879
    [Google Scholar]
  65. Kim T.H. Woo S. Han S. Suh C.H. Vargas H.A. The diagnostic performance of MRI for detection of extramural venous invasion in colorectal cancer: A systematic review and meta-analysis of the literature. AJR Am. J. Roentgenol. 2019 213 3 575 585 10.2214/AJR.19.21112 31063424
    [Google Scholar]
  66. Shu Z. Mao D. Song Q. Xu Y. Pang P. Zhang Y. Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer. Eur. Radiol. 2022 32 2 1002 1013 10.1007/s00330‑021‑08242‑9 34482429
    [Google Scholar]
  67. Santiago I. Figueiredo N. Parés O. Matos C. MRI of rectal cancer—relevant anatomy and staging key points. Insights Imaging 2020 11 1 100 10.1186/s13244‑020‑00890‑7 32880782
    [Google Scholar]
  68. Yuval J.B. Thompson H.M. Firat C. Verheij F.S. Widmar M. Wei I.H. Pappou E. Smith J.J. Weiser M.R. Paty P.B. Nash G.M. Shia J. Gollub M.J. Garcia-Aguilar J. MRI at restaging after neoadjuvant therapy for rectal cancer overestimates circumferential resection margin proximity as determined by comparison with whole-mount pathology. Dis. Colon Rectum 2022 65 4 489 496 10.1097/DCR.0000000000002145 34803147
    [Google Scholar]
  69. Rao S.X. Zeng M.S. Chen C.Z. Li R.C. Zhang S.J. Xu J.M. Hou Y.Y. The value of diffusion-weighted imaging in combination with T2-weighted imaging for rectal cancer detection. Eur. J. Radiol. 2008 65 2 299 303 10.1016/j.ejrad.2007.04.001 17498902
    [Google Scholar]
  70. Park M.J. Kim S.H. Lee S.J. Jang K.M. Rhim H. Locally advanced rectal cancer: Added value of diffusion-weighted MR imaging for predicting tumor clearance of the mesorectal fascia after neoadjuvant chemotherapy and radiation therapy. Radiology 2011 260 3 771 780 10.1148/radiol.11102135 21846762
    [Google Scholar]
  71. Pikūnienė I. Saladžinskas Ž. Basevičius A. Strakšytė V. Žilinskas J. Ambrazienė R. MRI evaluation of rectal cancer lymph node staging using apparent diffusion coefficient. Cureus 2023 15 9 e45002 10.7759/cureus.45002 37701166
    [Google Scholar]
  72. Schurink N.W. Lambregts D.M.J. Beets-Tan R.G.H. Diffusion-weighted imaging in rectal cancer: current applications and future perspectives. Br. J. Radiol. 2019 92 1096 20180655 10.1259/bjr.20180655 30433814
    [Google Scholar]
  73. Su Y. Zhao H. Liu P. Zhang L. Jiao Y. Xu P. Lyu Z. Fu P. A nomogram model based on MRI and radiomic features developed and validated for the evaluation of lymph node metastasis in patients with rectal cancer. Abdom. Radiol. (N.Y.) 2022 47 12 4103 4114 10.1007/s00261‑022‑03672‑5 36102961
    [Google Scholar]
  74. Sathyakumar K. Chandramohan A. Masih D. Jesudasan M.R. Pulimood A. Eapen A. Best MRI predictors of complete response to neoadjuvant chemoradiation in locally advanced rectal cancer. Br. J. Radiol. 2016 89 1060 20150328 10.1259/bjr.20150328 26828967
    [Google Scholar]
  75. Lambregts D.M.J. Vandecaveye V. Barbaro B. Bakers F.C.H. Lambrecht M. Maas M. Haustermans K. Valentini V. Beets G.L. Beets-Tan R.G.H. Diffusion-weighted MRI for selection of complete responders after chemoradiation for locally advanced rectal cancer: A multicenter study. Ann. Surg. Oncol. 2011 18 8 2224 2231 10.1245/s10434‑011‑1607‑5 21347783
    [Google Scholar]
  76. Curvo-Semedo L. Lambregts D.M.J. Maas M. Beets G.L. Caseiro-Alves F. Beets-Tan R.G.H. Diffusion‐weighted MRI in rectal cancer: Apparent diffusion coefficient as a potential noninvasive marker of tumor aggressiveness. J. Magn. Reson. Imaging 2012 35 6 1365 1371 10.1002/jmri.23589 22271382
    [Google Scholar]
  77. Federau C. Meuli R. O’Brien K. Maeder P. Hagmann P. Perfusion measurement in brain gliomas with intravoxel incoherent motion MRI. AJNR Am. J. Neuroradiol. 2014 35 2 256 262 10.3174/ajnr.A3686 23928134
    [Google Scholar]
  78. Osman M.F. Ibrahim S.H. Ghoneim S.M.M. Ali R.M.M. Sedqi M.E.M. Gadalla A.A.E.H. Role of apparent diffusion coefficient in assessment of loco-regional nodal spread in cancer rectum: Correlative study with histopathological findings. Egypt. J. Radiol. Nucl. Med. 2023 54 1 48 10.1186/s43055‑023‑00995‑1
    [Google Scholar]
  79. Bassaneze T. Gonçalves J.E. Faria J.F. Palma R.T. Waisberg J. Quantitative aspects of diffusion-weighted magnetic resonance imaging in rectal cancer response to neoadjuvant therapy. Radiol. Oncol. 2017 51 3 270 276 10.1515/raon‑2017‑0025 28959163
    [Google Scholar]
  80. De Felice F. Magnante A.L. Musio D. Ciolina M. De Cecco C.N. Rengo M. Laghi A. Tombolini V. Diffusion-weighted magnetic resonance imaging in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy. Eur. J. Surg. Oncol. 2017 43 7 1324 1329 10.1016/j.ejso.2017.03.010 28363512
    [Google Scholar]
  81. Zhao M. Feng L. Zhao K. Cui Y. Li Z. Ke C. Yang X. Qiu Q. Lu W. Liang Y. Xie C. Wan X. Liu Z. An MRI-based scoring system for pretreatment risk stratification in locally advanced rectal cancer. Br. J. Cancer 2023 129 7 1095 1104 10.1038/s41416‑023‑02384‑x 37558922
    [Google Scholar]
  82. Thomassin-Naggara I. Balvay D. Aubert E. Daraï E. Rouzier R. Cuenod C.A. Bazot M. Quantitative dynamic contrast-enhanced MR imaging analysis of complex adnexal masses: A preliminary study. Eur. Radiol. 2012 22 4 738 745 10.1007/s00330‑011‑2329‑6 22105841
    [Google Scholar]
  83. Zahra M.A. Hollingsworth K.G. Sala E. Lomas D.J. Tan L.T. Dynamic contrast-enhanced MRI as a predictor of tumour response to radiotherapy. Lancet Oncol. 2007 8 1 63 74 10.1016/S1470‑2045(06)71012‑9 17196512
    [Google Scholar]
  84. Kang H. Lee H.Y. Lee K.S. Kim J.H. Imaging-based tumor treatment response evaluation: review of conventional, new, and emerging concepts. Korean J. Radiol. 2012 13 4 371 390 10.3348/kjr.2012.13.4.371 22778559
    [Google Scholar]
  85. O'Connor JP. Tofts PS. Miles KA. Parkes LM. Thompson G. Jackson A. Dynamic contrast-enhanced imaging techniques: CT and MRI. Br J Radiol 2011 84 Spec Iss 2 S112 20 10.1259/bjr/55166688
    [Google Scholar]
  86. Kelly R.J. Rajan A. Force J. Lopez-Chavez A. Keen C. Cao L. Yu Y. Choyke P. Turkbey B. Raffeld M. Xi L. Steinberg S.M. Wright J.J. Kummar S. Gutierrez M. Giaccone G. Evaluation of KRAS mutations, angiogenic biomarkers, and DCE-MRI in patients with advanced non-small-cell lung cancer receiving sorafenib. Clin. Cancer Res. 2011 17 5 1190 1199 10.1158/1078‑0432.CCR‑10‑2331 21224376
    [Google Scholar]
  87. Benjaminsen I.C. Brurberg K.G. Ruud E.B.M. Rofstad E.K. Assessment of extravascular extracellular space fraction in human melanoma xenografts by DCE-MRI and kinetic modeling. Magn. Reson. Imaging 2008 26 2 160 170 10.1016/j.mri.2007.06.003 17692490
    [Google Scholar]
  88. Chen J. Chen C. Xia C. Huang Z. Zuo P. Stemmer A. Song B. Quantitative free-breathing dynamic contrast-enhanced MRI in hepatocellular carcinoma using gadoxetic acid: Correlations with Ki67 proliferation status, histological grades, and microvascular density. Abdom. Radiol. (N.Y.) 2018 43 6 1393 1403 10.1007/s00261‑017‑1320‑3 28939963
    [Google Scholar]
  89. Dahut W.L. Madan R.A. Karakunnel J.J. Adelberg D. Gulley J.L. Turkbey I.B. Chau C.H. Spencer S.D. Mulquin M. Wright J. Parnes H.L. Steinberg S.M. Choyke P.L. Figg W.D. Phase II clinical trial of cediranib in patients with metastatic castration-resistant prostate cancer. BJU Int. 2013 111 8 1269 1280 10.1111/j.1464‑410X.2012.11667.x 23419134
    [Google Scholar]
  90. Yao W.W. Zhang H. Ding B. Fu T. Jia H. Pang L. Song L. Xu W. Song Q. Chen K. Pan Z. Rectal cancer: 3D dynamic contrast-enhanced MRI; Correlation with microvascular density and clinicopathological features. Radiol. Med. (Torino) 2011 116 3 366 374 10.1007/s11547‑011‑0628‑2 21298356
    [Google Scholar]
  91. Chen Y. Ding L. Zhang Z. Wu X. Que Y. Ma Y. Liu Y. Wen Z. Yang X. Lu B. Bao Y. Niu S. Yu S. Role of dynamic contrast-enhanced MRI in predicting severe acute radiation-induced rectal injury in patients with rectal cancer. Eur. Radiol. 2023 34 3 1471 1480 10.1007/s00330‑023‑10194‑1 37665390
    [Google Scholar]
  92. Petrillo M. Fusco R. Catalano O. Sansone M. Avallone A. Delrio P. Pecori B. Tatangelo F. Petrillo A. MRI for assessing response to neoadjuvant therapy in locally advanced rectal cancer using DCE-MR and DW-MR data sets: A preliminary report. BioMed Res. Int. 2015 2015 1 8 10.1155/2015/514740 26413528
    [Google Scholar]
  93. Smith J.J. Garcia-Aguilar J. Advances and challenges in treatment of locally advanced rectal cancer. J. Clin. Oncol. 2015 33 16 1797 1808 10.1200/JCO.2014.60.1054 25918296
    [Google Scholar]
  94. Horvat N. Bates D.D.B. Petkovska I. Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review. Abdom. Radiol. (N.Y.) 2019 44 11 3764 3774 10.1007/s00261‑019‑02042‑y 31055615
    [Google Scholar]
  95. Lambin P. Leijenaar R.T.H. Deist T.M. Peerlings J. de Jong E.E.C. van Timmeren J. Sanduleanu S. Larue R.T.H.M. Even A.J.G. Jochems A. van Wijk Y. Woodruff H. van Soest J. Lustberg T. Roelofs E. van Elmpt W. Dekker A. Mottaghy F.M. Wildberger J.E. Walsh S. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 2017 14 12 749 762 10.1038/nrclinonc.2017.141 28975929
    [Google Scholar]
  96. Liu Z. Zhang X.Y. Shi Y.J. Wang L. Zhu H.T. Tang Z. Wang S. Li X.T. Tian J. Sun Y.S. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin. Cancer Res. 2017 23 23 7253 7262 10.1158/1078‑0432.CCR‑17‑1038 28939744
    [Google Scholar]
  97. Lu Z.H. Xia K.J. Jiang H. Jiang J.L. Wu M. Textural differences based on apparent diffusion coefficient maps for discriminating pT3 subclasses of rectal adenocarcinoma. World J. Clin. Cases 2021 9 24 6987 6998 10.12998/wjcc.v9.i24.6987 34540954
    [Google Scholar]
  98. Liang M. Cai Z. Zhang H. Huang C. Meng Y. Zhao L. Li D. Ma X. Zhao X. Machine learning-based analysis of rectal cancer MRI radiomics for prediction of metachronous liver metastasis. Acad. Radiol. 2019 26 11 1495 1504 10.1016/j.acra.2018.12.019 30711405
    [Google Scholar]
  99. Liu M. Ma X. Shen F. Xia Y. Jia Y. Lu J. MRI‐based radiomics nomogram to predict synchronous liver metastasis in primary rectal cancer patients. Cancer Med. 2020 9 14 5155 5163 10.1002/cam4.3185 32476295
    [Google Scholar]
  100. Christou N. Meyer J. Toso C. Ris F. Buchs N.C. Lateral lymph node dissection for low rectal cancer: Is it necessary? World J. Gastroenterol. 2019 25 31 4294 4299 10.3748/wjg.v25.i31.4294 31496614
    [Google Scholar]
  101. 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 12 6469 6476 10.1007/s00330‑019‑06328‑z 31278581
    [Google Scholar]
  102. Ma X. Shen F. Jia Y. Xia Y. Li Q. Lu J. MRI-based radiomics of rectal cancer: Preoperative assessment of the pathological features. BMC Med. Imaging 2019 19 1 86 10.1186/s12880‑019‑0392‑7 31747902
    [Google Scholar]
  103. Zhang S. Yu M. Chen D. Li P. Tang B. Li J. Role of MRI‑based radiomics in locally advanced rectal cancer (Review). Oncol. Rep. 2021 47 2 34 10.3892/or.2021.8245 34935061
    [Google Scholar]
  104. Staal F.C.R. van der Reijd D.J. Taghavi M. Lambregts D.M.J. Beets-Tan R.G.H. Maas M. Radiomics for the prediction of treatment outcome and survival in patients with colorectal cancer: A systematic review. Clin. Colorectal Cancer 2021 20 1 52 71 10.1016/j.clcc.2020.11.001 33349519
    [Google Scholar]
  105. Hou M. Sun J.H. Emerging applications of radiomics in rectal cancer: State of the art and future perspectives. World J. Gastroenterol. 2021 27 25 3802 3814 10.3748/wjg.v27.i25.3802 34321845
    [Google Scholar]
  106. Anukrishna P.R. Paul V. 2017 A review on feature selection for high dimensional data. International Conference on Inventive Systems and Control (ICISC) Coimbatore, India 2017 1 4 10.1109/ICISC.2017.8068746
    [Google Scholar]
  107. Alduailij M. Khan Q.W. Tahir M. Sardaraz M. Alduailij M. Malik F. Machine-learning-based DDoS attack detection using mutual information and random forest feature importance method. Symmetry (Basel) 2022 14 6 1095 10.3390/sym14061095
    [Google Scholar]
  108. Bulens P. Couwenberg A. Intven M. Debucquoy A. Vandecaveye V. Van Cutsem E. D’Hoore A. Wolthuis A. Mukherjee P. Gevaert O. Haustermans K. Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics. Radiother. Oncol. 2020 142 246 252 10.1016/j.radonc.2019.07.033 31431368
    [Google Scholar]
  109. Zhang Z. Shen L. Wang Y. Wang J. Zhang H. Xia F. Wan J. Zhang Z. MRI radiomics signature as a potential biomarker for predicting KRAS status in locally advanced rectal cancer patients. Front. Oncol. 2021 11 614052 10.3389/fonc.2021.614052 34026605
    [Google Scholar]
  110. Petresc B. Lebovici A. Caraiani C. Feier D.S. Graur F. Buruian M.M. Pre-treatment T2-WI based radiomics features for prediction of locally advanced rectal cancer non-response to neoadjuvant chemoradiotherapy: A preliminary study. Cancers (Basel) 2020 12 7 1894 10.3390/cancers12071894 32674345
    [Google Scholar]
  111. Gillies R.J. Kinahan P.E. Hricak H. Radiomics: Images are more than pictures, they are data. Radiology 2016 278 2 563 577 10.1148/radiol.2015151169 26579733
    [Google Scholar]
  112. Li Z.Y. Wang X.D. Li M. Liu X.J. Ye Z. Song B. Yuan F. Yuan Y. Xia C.C. Zhang X. Li Q. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer. World J. Gastroenterol. 2020 26 19 2388 2402 10.3748/wjg.v26.i19.2388 32476800
    [Google Scholar]
  113. Ma Y.Q. Wen Y. Liang H. Zhong J.G. Pang P.P. Magnetic resonance imaging-radiomics evaluation of response to chemotherapy for synchronous liver metastasis of colorectal cancer. World J. Gastroenterol. 2021 27 38 6465 6475 10.3748/wjg.v27.i38.6465 34720535
    [Google Scholar]
  114. Li Z. Huang H. Zhao Z. Ma W. Mao H. Liu F. Yang Y. Wang D. Lu Z. Development and validation of a nomogram based on DCE-MRI radiomics for predicting hypoxia-inducible factor 1α expression in locally advanced rectal cancer. Acad Radiol 2024 Online ahead of print 10.1016/j.acra.2024.05.015
    [Google Scholar]
  115. Oh J.E. Kim M.J. Lee J. Hur B.Y. Kim B. Kim D.Y. Baek J.Y. Chang H.J. Park S.C. Oh J.H. Cho S.A. Sohn D.K. Magnetic resonance-based texture analysis differentiating kras mutation status in rectal cancer. Cancer Res. Treat. 2020 52 1 51 59 10.4143/crt.2019.050 31096736
    [Google Scholar]
  116. Park H. Kim K.A. Jung J.H. Rhie J. Choi S.Y. MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer. Eur. Radiol. 2020 30 8 4201 4211 10.1007/s00330‑020‑06835‑4 32270317
    [Google Scholar]
  117. O’Sullivan N.J. Temperley H.C. Horan M.T. Corr A. Mehigan B.J. Larkin J.O. McCormick P.H. Kavanagh D.O. Meaney J.F.M. Kelly M.E. Radiogenomics: Contemporary applications in the management of rectal cancer. Cancers (Basel) 2023 15 24 5816 10.3390/cancers15245816 38136361
    [Google Scholar]
  118. Lo Gullo R. Daimiel I. Morris E.A. Pinker K. Combining molecular and imaging metrics in cancer: Radiogenomics. Insights Imaging 2020 11 1 1 10.1186/s13244‑019‑0795‑6 31901171
    [Google Scholar]
  119. Bi W.L. Hosny A. Schabath M.B. Giger M.L. Birkbak N.J. Mehrtash A. Allison T. Arnaout O. Abbosh C. Dunn I.F. Mak R.H. Tamimi R.M. Tempany C.M. Swanton C. Hoffmann U. Schwartz L.H. Gillies R.J. Huang R.Y. Aerts H.J.W.L. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J. Clin. 2019 69 2 127 157 10.3322/caac.21552 30720861
    [Google Scholar]
  120. Martín Noguerol T. Paulano-Godino F. Martín-Valdivia M.T. Menias C.O. Luna A. Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology. J. Am. Coll. Radiol. 2019 16 9 1239 1247 10.1016/j.jacr.2019.05.047 31492401
    [Google Scholar]
  121. Jin C. Yu H. Ke J. Ding P. Yi Y. Jiang X. Duan X. Tang J. Chang D.T. Wu X. Gao F. Li R. Predicting treatment response from longitudinal images using multi-task deep learning. Nat. Commun. 2021 12 1 1851 10.1038/s41467‑021‑22188‑y 33767170
    [Google Scholar]
  122. Jeong J.J. Tariq A. Adejumo T. Trivedi H. Gichoya J.W. Banerjee I. Systematic review of generative adversarial networks (GANs) for medical image classification and segmentation. J. Digit. Imaging 2022 35 2 137 152 10.1007/s10278‑021‑00556‑w 35022924
    [Google Scholar]
  123. Betge J. Pollheimer M.J. Lindtner R.A. Kornprat P. Schlemmer A. Rehak P. Vieth M. Hoefler G. Langner C. Intramural and extramural vascular invasion in colorectal cancer. Cancer 2012 118 3 628 638 10.1002/cncr.26310 21751188
    [Google Scholar]
  124. Memon S. Lynch A.C. Akhurst T. Ngan S.Y. Warrier S.K. Michael M. Heriot A.G. Systematic review of FDG-PET prediction of complete pathological response and survival in rectal cancer. Ann. Surg. Oncol. 2014 21 11 3598 3607 10.1245/s10434‑014‑3753‑z 24802909
    [Google Scholar]
  125. Kim J.H. Lee J.W. Park K. Ahn M.J. Moon J.W. Ham S.Y. Yi C.A. Dynamic contrast-enhanced MRI for response evaluation of non-small cell lung cancer in therapy with epidermal growth factor receptor tyrosine kinase inhibitors: A pilot study. Ann. Palliat. Med. 2021 10 2 1589 1598 10.21037/apm‑19‑622 33302635
    [Google Scholar]
  126. Yoon S.H. Park C.M. Park S.J. Yoon J.H. Hahn S. Goo J.M. Tumor heterogeneity in lung cancer: Assessment with dynamic contrast-enhanced MR imaging. Radiology 2016 280 3 940 948 10.1148/radiol.2016151367 27031994
    [Google Scholar]
  127. Li Z. Chen F. Zhang S. Ma X. Xia Y. Shen F. Lu Y. Shao C. The feasibility of MRI-based radiomics model in presurgical evaluation of tumor budding in locally advanced rectal cancer. Abdom. Radiol. (N.Y.) 2022 47 1 56 65 10.1007/s00261‑021‑03311‑5 34673995
    [Google Scholar]
  128. Aker M. Ganeshan B. Afaq A. Wan S. Groves A.M. Arulampalam T. Magnetic resonance texture analysis in identifying complete pathological response to neoadjuvant treatment in locally advanced rectal cancer. Dis. Colon Rectum 2019 62 2 163 170 10.1097/DCR.0000000000001224 30451764
    [Google Scholar]
  129. 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. AJR Am. J. Roentgenol. 2019 212 6 1279 1286 10.2214/AJR.18.20689 30860889
    [Google Scholar]
  130. Jacobs L. Intven M. van Lelyveld N. Philippens M. Burbach M. Seldenrijk K. Los M. Reerink O. Diffusion-weighted MRI for early prediction of treatment response on preoperative chemoradiotherapy for patients with locally advanced rectal cancer. Ann. Surg. 2016 263 3 522 528 10.1097/SLA.0000000000001311 26106836
    [Google Scholar]
  131. Liu H. Zhang C. Wang L. Luo R. Li J. Zheng H. Yin Q. Zhang Z. Duan S. Li X. Wang D. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur. Radiol. 2019 29 8 4418 4426 10.1007/s00330‑018‑5802‑7 30413955
    [Google Scholar]
  132. Yang J. Huang J. Han D. Ma X. Artificial intelligence applications in the treatment of colorectal cancer: A narrative review. Clin. Med. Insights Oncol. 2024 18 11795549231220320 10.1177/11795549231220320 38187459
    [Google Scholar]
  133. Horvat N. Veeraraghavan H. Khan M. Blazic I. Zheng J. Capanu M. Sala E. Garcia-Aguilar J. Gollub M.J. Petkovska I. MR imaging of rectal cancer: Radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 2018 287 3 833 843 10.1148/radiol.2018172300 29514017
    [Google Scholar]
  134. Hahn O.M. Yang C. Medved M. Karczmar G. Kistner E. Karrison T. Manchen E. Mitchell M. Ratain M.J. Stadler W.M. Dynamic contrast-enhanced magnetic resonance imaging pharmacodynamic biomarker study of sorafenib in metastatic renal carcinoma. J. Clin. Oncol. 2008 26 28 4572 4578 10.1200/JCO.2007.15.5655 18824708
    [Google Scholar]
  135. Just N. Improving tumour heterogeneity MRI assessment with histograms. Br. J. Cancer 2014 111 12 2205 2213 10.1038/bjc.2014.512 25268373
    [Google Scholar]
  136. Cui Y. Yang X. Shi Z. Yang Z. Du X. Zhao Z. Cheng X. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur. Radiol. 2019 29 3 1211 1220 10.1007/s00330‑018‑5683‑9 30128616
    [Google Scholar]
  137. Liu Z. Meng X. Zhang H. Li Z. Liu J. Sun K. Meng Y. Dai W. Xie P. Ding Y. Wang M. Cai G. Tian J. Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer. Nat. Commun. 2020 11 1 4308 10.1038/s41467‑020‑18162‑9 32855399
    [Google Scholar]
  138. Pei Q. Yi X. Chen C. Pang P. Fu Y. Lei G. Chen C. Tan F. Gong G. Li Q. Zai H. Chen B.T. Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer. Eur. Radiol. 2022 32 1 714 724 10.1007/s00330‑021‑08167‑3 34258636
    [Google Scholar]
  139. Jiang X. Zhao H. Saldanha O.L. Nebelung S. Kuhl C. Amygdalos I. Lang S.A. Wu X. Meng X. Truhn D. Kather J.N. Ke J. An MRI deep learning model predicts outcome in rectal cancer. Radiology 2023 307 5 e222223 10.1148/radiol.222223 37278629
    [Google Scholar]
  140. Reuzé S. Schernberg A. Orlhac F. Sun R. Chargari C. Dercle L. Deutsch E. Buvat I. Robert C. Radiomics in nuclear medicine applied to radiation therapy: Methods, pitfalls, and challenges. Int. J. Radiat. Oncol. Biol. Phys. 2018 102 4 1117 1142 10.1016/j.ijrobp.2018.05.022 30064704
    [Google Scholar]
  141. Crimì F. Capelli G. Spolverato G. Bao Q.R. Florio A. Milite Rossi S. Cecchin D. Albertoni L. Campi C. Pucciarelli S. Stramare R. 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. Med. (Torino) 2020 125 12 1216 1224 10.1007/s11547‑020‑01215‑w 32410063
    [Google Scholar]
  142. Bane O. Gee M.S. Menys A. Dillman J.R. Taouli B. Emerging imaging biomarkers in Crohn disease. Top. Magn. Reson. Imaging 2021 30 1 31 41 10.1097/RMR.0000000000000264 33528210
    [Google Scholar]
  143. Wu J. Zhang C. Xue T. Freeman B. Tenenbaum J. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. Adv. Neural Inf. Process. Syst. 2016 29
    [Google Scholar]
  144. Mao W. Zhou J. Zhang H. Qiu L. Tan H. Hu Y. Shi H. Relationship between KRAS mutations and dual time point 18F-FDG PET/CT imaging in colorectal liver metastases. Abdom. Radiol. (N.Y.) 2019 44 6 2059 2066 10.1007/s00261‑018‑1740‑8 30143816
    [Google Scholar]
  145. Badic B. Tixier F. Cheze Le Rest C. Hatt M. Visvikis D. Radiogenomics in colorectal cancer. Cancers (Basel) 2021 13 5 973 10.3390/cancers13050973 33652647
    [Google Scholar]
  146. Feng L. Liu Z. Li C. Li Z. Lou X. Shao L. Wang Y. Huang Y. Chen H. Pang X. Liu S. He F. Zheng J. Meng X. Xie P. Yang G. Ding Y. Wei M. Yun J. Hung M.C. Zhou W. Wahl D.R. Lan P. Tian J. Wan X. 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 Digit. Health 2022 4 1 e8 e17 10.1016/S2589‑7500(21)00215‑6 34952679
    [Google Scholar]
  147. Pinker K. Shitano F. Sala E. Do R.K. Young R.J. Wibmer A.G. Hricak H. Sutton E.J. Morris E.A. Background, current role, and potential applications of radiogenomics. J. Magn. Reson. Imaging 2018 47 3 604 620 10.1002/jmri.25870 29095543
    [Google Scholar]
  148. Yao X. Zhu X. Deng S. Zhu S. Mao G. Hu J. Xu W. Wu S. Ao W. MRI-based radiomics for preoperative prediction of recurrence and metastasis in rectal cancer. Abdom. Radiol. (N.Y.) 2024 49 4 1306 1319 10.1007/s00261‑024‑04205‑y 38407804
    [Google Scholar]
  149. Zhou X. Yi Y. Liu Z. Zhou Z. Lai B. Sun K. Li L. Huang L. Feng Y. Cao W. Tian J. Radiomics-based preoperative prediction of lymph node status following neoadjuvant therapy in locally advanced rectal cancer. Front. Oncol. 2020 10 604 10.3389/fonc.2020.00604 32477930
    [Google Scholar]
  150. Li Z. Zhang J. Zhong Q. Feng Z. Shi Y. Xu L. Zhang R. Yu F. Lv B. Yang T. Huang C. Cui F. Chen F. Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: A retrospective multicenter study. Eur. Radiol. 2022 33 3 1835 1843 10.1007/s00330‑022‑09160‑0 36282309
    [Google Scholar]
  151. Giannini V. Mazzetti S. Bertotto I. Chiarenza C. Cauda S. Delmastro E. Bracco C. Di Dia A. Leone F. Medico E. Pisacane A. Ribero D. Stasi M. Regge D. 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 4 878 888 10.1007/s00259‑018‑4250‑6 30637502
    [Google Scholar]
  152. Niu Y. Yu X. Wen L. Bi F. Jian L. Liu S. Yang Y. Zhang Y. Lu Q. Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer. Front. Oncol. 2023 13 1230698 10.3389/fonc.2023.1230698 38074652
    [Google Scholar]
  153. Li J. Zhou Y. Wang X. Yu Y. Zhou X. Luan K. Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict lymph node metastasis in T3 stage rectal carcinoma. Cancer Manag. Res. 2021 13 2983 2993 10.2147/CMAR.S298907 33833581
    [Google Scholar]
  154. Feng F. Liu Y. Bao J. Hong R. Hu S. Hu C. Multiregional-based magnetic resonance imaging radiomics model for predicting tumor deposits in resectable rectal cancer. Abdom. Radiol. (N.Y.) 2023 48 11 3310 3321 10.1007/s00261‑023‑04013‑w 37578553
    [Google Scholar]
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Emerging MRI Biomarkers for Prognostication in Rectal Cancer
Bentham Science Publishers ; https://doi.org/10.2174/0115733947344693241018081807
/content/journals/cctr/10.2174/0115733947344693241018081807
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  • Article Type:     Review Article
Keywords: prognostication ; rectal cancer ; DCE-MRI ; MRI ; DWI ; Emerging MRI biomarkers

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