Table Info

Table 1.

Main studies on radiomics/AI imaging applications for staging, predicting treatment response, genotyping, and assessing high-risk pathological features and prognosis in the setting of RC management

References	Aim of the study	Study design	Sample size	Main outcome
[32]	To predict different stages of RC using texture analysis based on diffusion-weighted imaging (DWI) images and apparent diffusion coefficient (ADC) maps.	Retrospective, single center	115	Texture features extracted from DWI images and ADC maps are useful clues for predicting pathological T and N stages in RC.
[33]	To predict tumour pathological features of RC through a T2-weighted image (T2WI) radiomic-based model.	Retrospective, single center	152	T2WI-based radiomics model could serve as pretreatment biomarkers in predicting pathological features of RC.
[34]	To predict the pathological nodal stage of LARC by a radiomic method that uses collective features of multiple LNs in MRI images before and after neoadjuvant CRT (nCRT).	Retrospective, single center	215	Collective features from all rectal LNs perform better than tumour features for the prediction of the nodal stage of LARC.
[35]	To evaluate the predictive performance of radiomics nomogram for the diagnosis of synchronous liver metastases (SLM) in RC patients.	Retrospective, single center	169	The nomogram amalgamating the radiomics signature and clinical risk factors serve as an effective quantitative approach to predict the SLM of primary RC.
[36]	To investigate the value of T2WI radiomic-based MRI in predicting preoperative synchronous distant metastases (SDM) in patients with RC.	Retrospective, single center	177	The proposed clinical-radiomics combined model could be utilized as a noninvasive biomarker for identifying patients at high risk of SDM.
[37]	To evaluate radiomics models based on T2WI and DWI MRI for predicting pathological complete response (pCR) after nCRT in LARC and compare their performance with visual assessment by radiologists.	Retrospective, single center	898	MRI-based radiomics model showed better classification performance than experienced radiologists for diagnosing pCR in patients with LARC after nCRT.
[38]	To interrogate the mesorectal fat using MRI radiomics feature analysis in order to predict clinical outcomes in patients with LARC.	Retrospective, single center	236	Radiomics features of mesorectal fat can predict pCR and local and distant recurrence, as well as post-treatment T and N categories.
[39]	To develop and validate an AI radiopathomics integrated model to predict pCR in patients with LARC using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides.	Retrospective, multi-center	303	RAdioPathomics Integrated preDiction System (RAPIDS) was able to predict pCR to nCRT based on pretreatment radiopathomics images with high accuracy.
[40]	To develop and validate a DL model that could preoperatively predict the microsatellite instability (MSI) status of RC based on MRI.	Retrospective, single center	491	DL based on T2WI HR-MRI showed a good predictive performance for MSI status in RC patients.
[41]	To investigate whether DL-based segmentation is feasible in predicting Kirsten rat sarcoma viral oncogene homolog (KRAS)/neuroblastoma ras viral oncogene homolog (NRAS)/v-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutations of RC using MRI-based radiomics.	Retrospective, single center	202	3D V-Net architecture provided reliable RC segmentation on T2WI and DWI compared with expert-based segmentation, and auto segmentation was subjected to radiomics analysis in the prediction of KRAS/NRAS/BRAF mutation status and may produce a good prediction result.
[42]	To build and validate an MRI-based radiomics model to preoperatively evaluate TB in LARC.	Retrospective, multi-center	224	The novel MRI-based radiomics model combining multiple sequences is an effective and non-invasive approach for evaluating TB grade preoperatively in patients with LARC.
[43]	To perform distant metastases (DM) prediction through DL radiomics.	Retrospective, multi-center	235	MRI-based DL radiomics had the potential in predicting the DM of LARC patients receiving nCRT.

Declarations

Author contributions

GDC and RA: Conceptualization. AP and AGT: Methodology. SDA and FI: Data curation. RA: Writing—original draft. AP and EC: Writing—review & editing. GDC: Supervision.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

Not applicable.

Copyright

@ The Author(s) 2023.

References

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. [DOI] [PubMed]

Dekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB. Colorectal cancer. Lancet. 2019;394:1467–80. [DOI] [PubMed] [PMC]

Siegel RL, Miller KD, Goding Sauer A, Fedewa SA, Butterly LF, Anderson JC, et al. Colorectal cancer statistics, 2020. CA Cancer J Clin. 2020;70:145–64. [DOI] [PubMed]

Keller DS, Berho M, Perez RO, Wexner SD, Chand M. The multidisciplinary management of rectal cancer. Nat Rev Gastroenterol Hepatol. 2020;17:414–29. [DOI] [PubMed]

Glynne-Jones R, Wyrwicz L, Tiret E, Brown G, Rödel C, Cervantes A, et al.; ESMO Guidelines Committee. Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up^†. Ann Oncol. 2017;28:iv22–40. Erratum in: Ann Oncol. 2018;29:iv263. [DOI] [PubMed]

Benson AB, Venook AP, Al-Hawary MM, Arain MA, Chen YJ, Ciombor KK, et al. NCCN Guidelines insights: rectal cancer, version 6.2020. J Natl Compr Canc Netw. 2020;18:806–15. [DOI] [PubMed]

Horvat N, Carlos Tavares Rocha C, Clemente Oliveira B, Petkovska I, Gollub MJ. MRI of rectal cancer: tumor staging, imaging techniques, and management. Radiographics. 2019;39:367–87. [DOI] [PubMed] [PMC]

Beets-Tan RGH, Lambregts DMJ, Maas M, Bipat S, Barbaro B, Curvo-Semedo L, et al. 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;28:1465–75. Erratum in: Eur Radiol. 2018;28:2711. [DOI] [PubMed] [PMC]

Dieguez A. Rectal cancer staging: focus on the prognostic significance of the findings described by high-resolution magnetic resonance imaging. Cancer Imaging. 2013;13:277–97. [DOI] [PubMed] [PMC]

10.

Taylor FG, Quirke P, Heald RJ, Moran B, Blomqvist L, Swift I, et al.; MERCURY study group. 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;253:711–9. [DOI] [PubMed]

11.

Chen Y, Yang X, Wen Z, Liu Y, Lu B, Yu S, et al. 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;19:498. [DOI] [PubMed] [PMC]

12.

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;48:441–6. [DOI] [PubMed] [PMC]

13.

Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019;69:127–57. [DOI] [PubMed] [PMC]

14.

Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18:500–10. [DOI] [PubMed] [PMC]

15.

Ponsiglione A, Stanzione A, Cuocolo R, Ascione R, Gambardella M, De Giorgi M, et al. Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment. Eur Radiol. 2022;32:2629–38. [DOI] [PubMed]

16.

Ponsiglione A, Stanzione A, Spadarella G, Baran A, Cappellini LA, Lipman KG, et al. Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative. Eur Radiol. 2023;33:2239–47. [DOI] [PubMed] [PMC]

17.

Cheng J, Cui ZX, Huang W, Ke Z, Ying L, Wang H, et al. Learning data consistency and its application to dynamic MR imaging. IEEE Trans Med Imaging. 2021;40:3140–53. [DOI] [PubMed]

18.

Cuocolo R, Cipullo MB, Stanzione A, Romeo V, Green R, Cantoni V, et al. Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis. Eur Radiol. 2020;30:6877–87. [DOI] [PubMed]

19.

Ishida T, Kadoya N, Tanabe S, Ohashi H, Nemoto H, Dobashi S, et al. Evaluation of performance of pelvic CT-MR deformable image registration using two software programs. J Radiat Res. 2021;62:1076–82. [DOI] [PubMed]

20.

Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology. 2018;286:800–9. [DOI] [PubMed]

21.

Stanzione A, Gambardella M, Cuocolo R, Ponsiglione A, Romeo V, Imbriaco M. Prostate MRI radiomics: a systematic review and radiomic quality score assessment. Eur J Radiol. 2020;129:109095. Erratum in: Eur J Radiol. 2020;131:109208. [DOI] [PubMed]

22.

Martín Noguerol T, Paulano-Godino F, Martín-Valdivia MT, Menias CO, Luna A. Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology. J Am Coll Radiol. 2019;16:1239–47. [DOI] [PubMed]

23.

Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 2019;16:391–403. [DOI] [PubMed]

24.

Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563–77. [DOI] [PubMed] [PMC]

25.

Boldrini L, Cusumano D, Chiloiro G, Casà C, Masciocchi C, Lenkowicz J, et al. Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach. Radiol Med. 2019;124:145–53. [DOI] [PubMed] [PMC]

26.

Chiloiro G, Rodriguez-Carnero P, Lenkowicz J, Casà C, Masciocchi C, Boldrini L, et al. Delta radiomics can predict distant metastasis in locally advanced rectal cancer: the challenge to personalize the cure. Front Oncol. 2020;10:595012. [DOI] [PubMed] [PMC]

27.

Cusumano D, Boldrini L, Yadav P, Yu G, Musurunu B, Chiloiro G, et al. Delta radiomics for rectal cancer response prediction using low field magnetic resonance guided radiotherapy: an external validation. Phys Med. 2021;84:186–91. [DOI] [PubMed]

28.

Nardone V, Reginelli A, Grassi R, Boldrini L, Vacca G, D’Ippolito E, et al. Delta radiomics: a systematic review. Radiol Med. 2021;126:1571–83. [DOI] [PubMed]

29.

Nardone V, Reginelli A, Grassi R, Vacca G, Giacobbe G, Angrisani A, et al. Ability of delta radiomics to predict a complete pathological response in patients with loco-regional rectal cancer addressed to neoadjuvant chemo-radiation and surgery. Cancers (Basel). 2022;14:3004. [DOI] [PubMed] [PMC]

30.

Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics. 2017;37:2113–31. [DOI] [PubMed]

31.

Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017;37:505–15. [DOI] [PubMed] [PMC]

32.

Yin JD, Song LR, Lu HC, 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;26:2082–96. [DOI] [PubMed] [PMC]

33.

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;19:86. [DOI] [PubMed] [PMC]

34.

Zhu H, Zhang X, Li X, Shi Y, Zhu H, Sun Y. Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy. Chin J Cancer Res. 2019;31:984–92. [DOI] [PubMed] [PMC]

35.

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;9:5155–63. [DOI] [PubMed] [PMC]

36.

Liu H, Zhang C, Wang L, Luo R, Li J, Zheng H, et al. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol. 2019;29:4418–26. [DOI] [PubMed]

37.

Shin J, Seo N, Baek SE, Son NH, Lim JS, Kim NK, et al. MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy. Radiology. 2022;303:351–8. [DOI] [PubMed]

38.

Jayaprakasam VS, Paroder V, Gibbs P, Bajwa R, Gangai N, Sosa RE, et al. 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. 2022;32:971–80. [DOI] [PubMed] [PMC]

39.

Feng L, Liu Z, Li C, Li Z, Lou X, Shao L, et al. 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:e8–17. [DOI] [PubMed]

40.

Zhang W, Yin H, Huang Z, Zhao J, Zheng H, He D, et al. Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer. Cancer Med. 2021;10:4164–73. [DOI] [PubMed] [PMC]

41.

Zhang G, Chen L, Liu A, Pan X, Shu J, Han Y, et al. Comparable performance of deep learning-based to manual-based tumor segmentation in KRAS/NRAS/BRAF mutation prediction with MR-based radiomics in rectal cancer. Front Oncol. 2021;11:696706. [DOI] [PubMed] [PMC]

42.

Li Z, Chen F, Zhang S, Ma X, Xia Y, Shen F, et al. The feasibility of MRI-based radiomics model in presurgical evaluation of tumor budding in locally advanced rectal cancer. Abdom Radiol (NY). 2022;47:56–65. [DOI] [PubMed]

43.

Liu X, Zhang D, Liu Z, Li Z, Xie P, Sun K, et al. Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: a multicentre study. EBioMedicine. 2021;69:103442. [DOI] [PubMed] [PMC]

44.

Curvo-Semedo L. Rectal cancer: staging. Magn Reson Imaging Clin N Am. 2020;28:105–15. [DOI] [PubMed]

45.

Reginelli A, Clemente A, Sangiovanni A, Nardone V, Selvaggi F, Sciaudone G, et al. Endorectal ultrasound and magnetic resonance imaging for rectal cancer staging: a modern multimodality approach. J Clin Med. 2021;10:641. [DOI] [PubMed] [PMC]

46.

Lu HC, Wang F, Yin JD. Texture analysis based on sagittal fat-suppression and transverse T2-weighted magnetic resonance imaging for determining local invasion of rectal cancer. Front Oncol. 2020;10:1476. [DOI] [PubMed] [PMC]

47.

Granata V, Caruso D, Grassi R, Cappabianca S, Reginelli A, Rizzati R, et al. Structured reporting of rectal cancer staging and restaging: a consensus proposal. Cancers (Basel). 2021;13:2135. [DOI] [PubMed] [PMC]

48.

Lu H, Yuan Y, Zhou Z, Ma X, Shen F, Xia Y, et al. Assessment of MRI-based radiomics in preoperative T staging of rectal cancer: comparison between minimum and maximum delineation methods. Biomed Res Int. 2021;2021:5566885. [DOI] [PubMed] [PMC]

49.

Wu QY, Liu SL, Sun P, Li Y, Liu GW, Liu SS, et al. Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network. Chin Med J (Engl). 2021;134:821–8. [DOI] [PubMed] [PMC]

50.

Wang D, Xu J, Zhang Z, Li S, Zhang X, Zhou Y, et al. Evaluation of rectal cancer circumferential resection margin using faster region-based convolutional neural network in high-resolution magnetic resonance images. Dis Colon Rectum. 2020;63:143–51. [DOI] [PubMed]

51.

Ishihara S, Kawai K, Tanaka T, Kiyomatsu T, Hata K, Nozawa H, et al. Oncological outcomes of lateral pelvic lymph node metastasis in rectal cancer treated with preoperative chemoradiotherapy. Dis Colon Rectum. 2017;60:469–76. [DOI] [PubMed]

52.

Nougaret S, Jhaveri K, Kassam Z, Lall C, Kim DH. Rectal cancer MR staging: pearls and pitfalls at baseline examination. Abdom Radiol (NY). 2019;44:3536–48. [DOI] [PubMed]

53.

Yang L, Liu D, Fang X, Wang Z, Xing Y, Ma L, et al. Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis? Eur Radiol. 2019;29:6469–76. [DOI] [PubMed]

54.

Ding L, Liu GW, Zhao BC, Zhou YP, Li S, Zhang ZD, et al. Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer. Chin Med J (Engl). 2019;132:379–87. [DOI] [PubMed] [PMC]

55.

Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell. 2017;39:1137–49. [DOI] [PubMed]

56.

Shu Z, Fang S, Ding Z, Mao D, Cai R, Chen Y, et al. MRI-based radiomics nomogram to detect primary rectal cancer with synchronous liver metastases. Sci Rep. 2019;9:3374. [DOI] [PubMed] [PMC]

57.

Liang M, Cai Z, Zhang H, Huang C, Meng Y, Zhao L, et al. Machine learning-based analysis of rectal cancer MRI radiomics for prediction of metachronous liver metastasis. Acad Radiol. 2019;26:1495–504. [DOI] [PubMed]

58.

Barbaro B, Fiorucci C, Tebala C, Valentini V, Gambacorta MA, Vecchio FM, et al. Locally advanced rectal cancer: MR imaging in prediction of response after preoperative chemotherapy and radiation therapy. Radiology. 2009;250:730–9. [DOI] [PubMed]

59.

Boldrini L, Lenkowicz J, Orlandini LC, Yin G, Cusumano D, Chiloiro G, et al. Applicability of a pathological complete response magnetic resonance-based radiomics model for locally advanced rectal cancer in intercontinental cohort. Radiat Oncol. 2022;17:78. [DOI] [PubMed] [PMC]

60.

Tang B, Lenkowicz J, Peng Q, Boldrini L, Hou Q, Dinapoli N, et al. Local tuning of radiomics-based model for predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. BMC Med Imaging. 2022;22:44. [DOI] [PubMed] [PMC]

61.

Chiloiro G, Boldrini L, Preziosi F, Cusumano D, Yadav P, Romano A, et al. A predictive model of 2yDFS during MR-guided RT neoadjuvant chemoradiotherapy in locally advanced rectal cancer patients. Front Oncol. 2022;12:831712. [DOI] [PubMed] [PMC]

62.

Pang X, Wang F, Zhang Q, Li Y, Huang R, Yin X, et al. A pipeline for predicting the treatment response of neoadjuvant chemoradiotherapy for locally advanced rectal cancer using single MRI modality: combining deep segmentation network and radiomics analysis based on “suspicious region”. Front Oncol. 2021;11:711747. [DOI] [PubMed] [PMC]

63.

Aker M, Ganeshan B, Afaq A, Wan S, Groves AM, Arulampalam T. Magnetic resonance texture analysis in identifying complete pathological response to neoadjuvant treatment in locally advanced rectal cancer. Dis Colon Rectum. 2019;62:163–70. [DOI] [PubMed]

64.

Yang L, Qiu M, Xia C, Li Z, Wang Z, Zhou X, et al. 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;212:1279–86. [DOI] [PubMed]

65.

Wang J, Liu X, Hu B, Gao Y, Chen J, Li J. Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy. Abdom Radiol (NY). 2021;46:1805–15. [DOI] [PubMed]

66.

Liu Z, Zhang XY, Shi YJ, Wang L, Zhu HT, Tang Z, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res. 2017;23:7253–62. [DOI] [PubMed]

67.

Pinker K, Shitano F, Sala E, Do RK, Young RJ, Wibmer AG, et al. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging. 2018;47:604–20. [DOI] [PubMed] [PMC]

68.

Luchini C, Bibeau F, Ligtenberg MJL, Singh N, Nottegar A, Bosse T, et al. ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1/PD-L1 expression and tumour mutational burden: a systematic review-based approach. Ann Oncol. 2019;30:1232–43. [DOI] [PubMed]

69.

Khan K, Valeri N, Dearman C, Rao S, Watkins D, Starling N, et al. Targeting EGFR pathway in metastatic colorectal cancer- tumour heterogeniety and convergent evolution. Crit Rev Oncol Hematol. 2019;143:153–63. [DOI] [PubMed]

70.

Sorich MJ, Wiese MD, Rowland A, Kichenadasse G, McKinnon RA, Karapetis CS. Extended RAS mutations and anti-EGFR monoclonal antibody survival benefit in metastatic colorectal cancer: a meta-analysis of randomized, controlled trials. Ann Oncol. 2015;26:13–21. [DOI] [PubMed]

71.

Zhu K, Yan H, Wang R, Zhu H, Meng X, Xu X, et al. Mutations of KRAS and PIK3CA as independent predictors of distant metastases in colorectal cancer. Med Oncol. 2014;31:16. [DOI] [PubMed]

72.

Zhang Z, Shen L, Wang Y, Wang J, Zhang H, Xia F, et al. MRI radiomics signature as a potential biomarker for predicting KRAS status in locally advanced rectal cancer patients. Front Oncol. 2021;11:614052. [DOI] [PubMed] [PMC]

73.

Cui Y, Liu H, Ren J, Du X, Xin L, Li D, et al. Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer. Eur Radiol. 2020;30:1948–58. [DOI] [PubMed]

74.

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;52:51–9. [DOI] [PubMed] [PMC]

75.

Sinicrope FA, Sargent DJ. Molecular pathways: microsatellite instability in colorectal cancer: prognostic, predictive, and therapeutic implications. Clin Cancer Res. 2012;18:1506–12. [DOI] [PubMed] [PMC]

76.

Li Z, Dai H, Liu Y, Pan F, Yang Y, Zhang M. Radiomics analysis of multi-sequence MR images for predicting microsatellite instability status preoperatively in rectal cancer. Front Oncol. 2021;11:697497. Erratum in: Front Oncol. 2021;11:781636. [DOI] [PubMed] [PMC]

77.

Dawson H, Galuppini F, Träger P, Berger MD, Studer P, Brügger L, et al. Validation of the International Tumor Budding Consensus Conference 2016 recommendations on tumor budding in stage I-IV colorectal cancer. Hum Pathol. 2019;85:145–51. [DOI] [PubMed]

78.

Kim S, Huh JW, Lee WY, Yun SH, Kim HC, Cho YB, et al. Prognostic impact of lymphatic invasion, venous invasion, perineural invasion and tumor budding in rectal cancer treated with neoadjuvant chemoradiotherapy followed by total mesorectal excision. Dis Colon Rectum. 2022;[epub ahead of print]. [DOI] [PubMed]

79.

Massucco P, Fontana AP, Balbo Mussetto A, Desana B, Ricotti A, Gonella F, et al. MRI-detected extramural vascular invasion (mrEMVI) as the best predictive factor to identify candidates to total neoadjuvant therapy in locally advanced rectal cancer. J Surg Oncol. 2022;125:1024–31. [DOI] [PubMed]

80.

Xiao H, Yoon YS, Hong SM, Roh SA, Cho DH, Yu CS, et al. Poorly differentiated colorectal cancers: correlation of microsatellite instability with clinicopathologic features and survival. J Clin Pathol. 2013;140:341–7. [DOI] [PubMed]

81.

Meng X, Xia W, Xie P, Zhang R, Li W, Wang M, et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol. 2019;29:3200–9. [DOI] [PubMed]

82.

He B, Ji T, Zhang H, Zhu Y, Shu R, Zhao W, et al. MRI-based radiomics signature for tumor grading of rectal carcinoma using random forest model. J Cell Physiol. 2019;234:20501–9. [DOI] [PubMed]

83.

Lugli A, Zlobec I, Berger MD, Kirsch R, Nagtegaal ID. Tumour budding in solid cancers. Nat Rev Clin Oncol. 2021;18:101–15. [DOI] [PubMed]

84.

Kim TH, Woo S, Han S, Suh CH, Vargas HA. 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;213:575–85. [DOI] [PubMed] [PMC]

85.

Zhao L, Liang M, Wang S, Yang Y, Zhang H, Zhao X. Preoperative evaluation of extramural venous invasion in rectal cancer using radiomics analysis of relaxation maps from synthetic MRI. Abdom Radiol (NY). 2021;46:3815–25. [DOI] [PubMed]

86.

Yu X, Song W, Guo D, Liu H, Zhang H, He X, et al. Preoperative prediction of extramural venous invasion in rectal cancer: comparison of the diagnostic efficacy of radiomics models and quantitative dynamic contrast-enhanced magnetic resonance imaging. Front Oncol. 2020;10:459. [DOI] [PubMed] [PMC]

87.

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;32:1002–13. [DOI] [PubMed]

88.

Dhadda AS, Bessell EM, Scholefield J, Dickinson P, Zaitoun AM. Mandard tumour regression grade, perineural invasion, circumferential resection margin and post-chemoradiation nodal status strongly predict outcome in locally advanced rectal cancer treated with preoperative chemoradiotherapy. Clin Oncol (R Coll Radiol). 2014;26:197–202. [DOI] [PubMed]

89.

Zhang Y, He K, Guo Y, Liu X, Yang Q, Zhang C, et al. A novel multimodal radiomics model for preoperative prediction of lymphovascular invasion in rectal cancer. Front Oncol. 2020;10:457. [DOI] [PubMed] [PMC]

90.

Guo Y, Wang Q, Guo Y, Zhang Y, Fu Y, Zhang H. Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer. Sci Rep. 2021;11:9429. [DOI] [PubMed] [PMC]

91.

Chen J, Chen Y, Zheng D, Pang P, Zhang H, Zheng X, et al. Pretreatment MR-based radiomics nomogram as potential imaging biomarker for individualized assessment of perineural invasion status in rectal cancer. Abdom Radiol (NY). 2021;46:847–57. [DOI] [PubMed]

92.

O’Connor JP, Aboagye EO, Adams JE, Aerts HJ, Barrington SF, Beer AJ, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14:169–86. [DOI] [PubMed] [PMC]

93.

Socha J, Kępka L, Michalski W, Paciorek K, Bujko K. The risk of distant metastases in rectal cancer managed by a watch-and-wait strategy – a systematic review and meta-analysis. Radiother Oncol. 2020;144:1–6. [DOI] [PubMed]

94.

Tibermacine H, Rouanet P, Sbarra M, Forghani R, Reinhold C, Nougaret S; GRECCAR Study Group. Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches. Br J Surg. 2021;108:1243–50. [DOI] [PubMed]

95.

Cui Y, Yang W, Ren J, Li D, Du X, Zhang J, et al. Prognostic value of multiparametric MRI-based radiomics model: potential role for chemotherapeutic benefits in locally advanced rectal cancer. Radiother Oncol. 2021;154:161–9. [DOI] [PubMed]

96.

Meng Y, Zhang Y, Dong D, Li C, Liang X, Zhang C, et al. Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer. J Magn Reson Imaging. 2018;48:605–14. [DOI] [PubMed]

97.

Liu Z, Meng X, Zhang H, Li Z, Liu J, Sun K, et al. Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer. Nat Commun. 2020;11:4308. [DOI] [PubMed] [PMC]

98.

Wan L, Sun Z, Peng W, Wang S, Li J, Zhao Q, et al. Selecting candidates for organ-preserving strategies after neoadjuvant chemoradiotherapy for rectal cancer: development and validation of a model integrating MRI radiomics and pathomics. J Magn Reson Imaging. 2022;56:1130–42. [DOI] [PubMed]