MRI-based qualitative, quantitative, and radiomics/deep learning methods for assessing treatment response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer

Abstract

Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is standard treatment for locally advanced rectal cancer (LARC). This approach allows a subset of patients to achieve a pathological complete response (pCR), thereby improving surgical outcomes, anal preservation rates, and disease-free survival. An accurate preoperative assessment of pCR is crucial for guiding treatment decisions. Magnetic resonance imaging (MRI), owing to its superior soft-tissue contrast and spatial resolution, has become the preferred noninvasive modality for assessing nCRT efficacy. Advances in functional MRI (fMRI) techniques include diffusion-weighted imaging, derived sequences, perfusion-weighted imaging, and neuro-fMRI. fMRI sequences provide not only a qualitative assessment but also quantitative parameters derived from various imaging principles, thereby significantly enhancing the clinical utility of MRI. Beyond conventional and functional MRI, this field is rapidly evolving with the integration of radiomics and deep learning approaches. Radiomics involves the high-throughput extraction of minimal quantitative features from medical images, which can reveal tumor heterogeneity and phenotypic characteristics that are invisible to the human eye. This review summarizes current research and future perspectives on MRI-based qualitative, quantitative, radiomic, and deep-learning approaches for assessing nCRT efficacy in patients with LARC.

Keywords

deep learning / diffusion-weighted imaging / locally advanced rectal cancer / magnetic resonance imaging / neoadjuvant chemoradiotherapy / perfusion-weighted imaging / Radiomics

References

[1] Siegel RL, Wagle NS, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2023. CA Cancer J Clin. 2023; 73(3): 233-254. https://doi.org/10.3322/caac.21772

[2] Glynne-Jones R, Wyrwicz L, Tiret E, et al. Rectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol Off J Eur Soc Med Oncol. 2017; 28(suppl_4): iv22-iv40. https://doi.org/10.1093/annonc/mdx224

[3] Benson AB, Venook AP, Al-Hawary MM, et al. Rectal cancer, version 2.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Cancer Netw JNCCN. 2022; 20(10): 1139-1167. https://doi.org/10.6004/jnccn.2022.0051

[4] Al-Rashid F, Robitaille S, Liberman AS, et al. Trajectory of change of low anterior resection syndrome over time after restorative proctectomy for rectal adenocarcinoma. Tech Coloproctology. 2022; 26(3): 195-203. https://doi.org/10.1007/s10151-021-02561-1

[5] Celentano V, Cohen R, Warusavitarne J, Faiz O, Chand M. Sexual dysfunction following rectal cancer surgery. Int J Colorectal Dis. 2017; 32(11): 1523-1530. https://doi.org/10.1007/s00384-017-2826-4

[6] Grass JK, Persiani R, Tirelli F, et al. Robotic versus transanal total mesorectal excision in sexual, anorectal, and urinary function: a multicenter, prospective, observational study. Int J Colorectal Dis. 2021; 36(12): 2749-2761. https://doi.org/10.1007/s00384-021-04030-5

[7] Denost Q, Laurent C, Capdepont M, Zerbib F, Rullier E. Risk factors for fecal incontinence after intersphincteric resection for rectal cancer. Dis Colon Rectum. 2011; 54(8): 963-968. https://doi.org/10.1097/DCR.0b013e31821d3677

[8] Hupkens BJP, Martens MH, Stoot JH, et al. Quality of life in rectal cancer patients after chemoradiation: watch-and-wait policy versus standard resection - a matched-controlled study. Dis Colon Rectum. 2017; 60(10): 1032-1040. https://doi.org/10.1097/DCR.0000000000000862

[9] Quezada-Diaz FF, Smith JJ, Jimenez-Rodriguez RM, et al. Patient-reported bowel function in patients with rectal cancer managed by a watch-and-wait strategy after neoadjuvant therapy: a case-control study. Dis Colon Rectum. 2020; 63(7): 897-902. https://doi.org/10.1097/DCR.0000000000001646

[10] Cerdan-Santacruz C, São Julião GP, Vailati BB, Corbi L, Habr-Gama A, Perez RO. Watch and wait approach for rectal cancer. J Clin Med. 2023; 12(8): 2873. https://doi.org/10.3390/jcm12082873

[11] Beets-Tan RG, Beets GL, Vliegen RF, et al. Accuracy of magnetic resonance imaging in prediction of tumour-free resection margin in rectal cancer surgery. Lancet Lond Engl. 2001; 357(9255): 497-504. https://doi.org/10.1016/s0140-6736(00)04040-x

[12] Zhang L, Jin Z, Yang F, et al. Added value of histogram analysis of intravoxel incoherent motion and diffusion kurtosis imaging for the evaluation of complete response to neoadjuvant therapy in locally advanced rectal cancer. Eur Radiol. 2024; 35(3): 1669-1678. https://doi.org/10.1007/s00330-024-11081-z

[13] Beets-Tan RGH, Lambregts DMJ, Maas M, 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(4): 1465-1475. https://doi.org/10.1007/s00330-017-5026-2

[14] Chen YG, Chen MQ, Guo YY, Li SC, Wu JX, Xu BH. Apparent diffusion coefficient predicts pathology complete response of rectal cancer treated with neoadjuvant chemoradiotherapy. PLOS One. 2016; 11(4):e0153944. https://doi.org/10.1371/journal.pone.0153944

[15] Iafrate F, Ciccarelli F, Masci GM, et al. Predictive role of diffusion-weighted MRI in the assessment of response to total neoadjuvant therapy in locally advanced rectal cancer. Eur Radiol. 2023; 33(2): 854-862. https://doi.org/10.1007/s00330-022-09086-7

[16] Intven M, Reerink O, Philippens MEP. Diffusion-weighted MRI in locally advanced rectal cancer : pathological response prediction after neo-adjuvant radiochemotherapy. Strahlenther Onkol Organ Dtsch Rontgengesellschaft Al. 2013; 189(2): 117-122. https://doi.org/10.1007/s00066-012-0270-5

[17] Zhong X, Zeng G, Zhang L, et al. Prediction of pathologic complete response to neoadjuvant chemoradiation in locally advanced rectal cancer. Front Oncol. 2024; 14:1361300. https://doi.org/10.3389/fonc.2024.1361300

[18] Yang F, Hill J, Abraham A, et al. Tumor volume predicts for pathologic complete response in rectal cancer patients treated with neoadjuvant chemoradiation. Am J Clin Oncol. 2022; 45(10): 405-409. https://doi.org/10.1097/COC.0000000000000942

[19] 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(2): 367-387. https://doi.org/10.1148/rg.2019180114

[20] Barbaro B, Vitale R, Leccisotti L, et al. Restaging locally advanced rectal cancer with MR imaging after chemoradiation therapy. Radiographics. 2010; 30(3): 699-716. https://doi.org/10.1148/rg.303095085

[21] Chandramohan A, Siddiqi UM, Mittal R, et al. Diffusion weighted imaging improves diagnostic ability of MRI for determining complete response to neoadjuvant therapy in locally advanced rectal cancer. Eur J Radiol Open. 2020; 7:100223. https://doi.org/10.1016/j.ejro.2020.100223

[22] Jang JK, Lee CM, Park SH, et al. How to combine diffusion-weighted and T2-weighted imaging for MRI assessment of pathologic complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer? Korean J Radiol. 2021; 22(9): 1451-1461. https://doi.org/10.3348/kjr.2020.1403

[23] Kalisz KR, Enzerra MD, Paspulati RM. MRI evaluation of the response of rectal cancer to neoadjuvant chemoradiation therapy. Radiographics. 2019; 39(2): 538-556. https://doi.org/10.1148/rg.2019180075

[24] Yacheva A, Dardanov D, Zlatareva D. The multipurpose usage of diffusion-weighted MRI in rectal cancer. Medicina (Kaunas). 2023; 59(12): 2162. https://doi.org/10.3390/medicina59122162

[25] Chung SR, Choi YJ, Suh CH, Lee JH, Baek JH. Diffusion-weighted magnetic resonance imaging for predicting response to chemoradiation therapy for head and neck squamous cell carcinoma: a systematic review. Korean J Radiol. 2019; 20(4): 649-661. https://doi.org/10.3348/kjr.2018.0446

[26] Partridge SC, Zhang Z, Newitt DC, et al. Diffusion-weighted MRI findings predict pathologic response in neoadjuvant treatment of breast cancer: the ACRIN 6698 multicenter trial. Radiology. 2018; 289(3): 618-627. https://doi.org/10.1148/radiol.2018180273

[27] Zhao DW, Fan WJ, Meng LL, et al. Comparison of the pre-treatment functional MRI metrics’ efficacy in predicting locoregionally advanced nasopharyngeal carcinoma response to induction chemotherapy. Cancer Imaging. 2021; 21(1): 59. https://doi.org/10.1186/s40644-021-00428-0

[28] Reig B, Lewin AA, Du L, et al. Breast MRI for evaluation of response to neoadjuvant therapy. Radiographics. 2021; 41(3): 665-679. https://doi.org/10.1148/rg.2021200134

[29] Chen K, She HL, Wu T, Hu F, Li T, Luo LP. Comparison of percentage changes in quantitative diffusion parameters for assessing pathological complete response to neoadjuvant therapy in locally advanced rectal cancer: a meta-analysis. Abdom Radiol N Y. 2021; 46(3): 894-908. https://doi.org/10.1007/s00261-020-02770-6

[30] Boraschi P, Cervelli R, Donati F, et al. Response assessment of locally advanced rectal cancer after neoadjuvant chemoradiotherapy: is apparent diffusion coefficient useful on 3 T magnetic resonance imaging? Colorectal Dis. 2023; 25(5): 905-915. https://doi.org/10.1111/codi.16483

[31] Yang L, Xia C, Zhao J, Zhou X, Wu B. The value of intravoxel incoherent motion and diffusion kurtosis imaging in the assessment of tumor regression grade and T stages after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer. Eur J Radiol. 2021; 136:109504. https://doi.org/10.1016/j.ejrad.2020.109504

[32] Li D, Cui Y, Hou L, et al. Diffusion kurtosis imaging-derived histogram metrics for prediction of resistance to neoadjuvant chemoradiotherapy in rectal adenocarcinoma: preliminary findings. Eur J Radiol. 2021; 144:109963. https://doi.org/10.1016/j.ejrad.2021.109963

[33] Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988; 168(2): 497-505. https://doi.org/10.1148/radiology.168.2.3393671

[34] Hu H, Jiang H, Wang S, Jiang H, Zhao S, Pan W. 3.0 T MRI IVIM-DWI for predicting the efficacy of neoadjuvant chemoradiation for locally advanced rectal cancer. Abdom Radiol N Y. 2021; 46(1): 134-143. https://doi.org/10.1007/s00261-020-02594-4

[35] Federau C, O'Brien K. Increased brain perfusion contrast with T₂-prepared intravoxel incoherent motion (T2prep IVIM) MRI. NMR Biomed. 2015; 28(1): 9-16. https://doi.org/10.1002/nbm.3223

[36] Federau C, Maeder P, O'Brien K, Browaeys P, Meuli R, Hagmann P. Quantitative measurement of brain perfusion with intravoxel incoherent motion MR imaging. Radiology. 2012; 265(3): 874-881. https://doi.org/10.1148/radiol.12120584

[37] Lemke A, Stieltjes B, Schad LR, Laun FB. Toward an optimal distribution of b values for intravoxel incoherent motion imaging. Magn Reson Imaging. 2011; 29(6): 766-776. https://doi.org/10.1016/j.mri.2011.03.004

[38] Zhang L, Yang LQ, Wen L, et al. Noninvasively evaluating the grading of glioma by multiparametric magnetic resonance imaging. Acad Radiol. 2021; 28(5): e137-e146. https://doi.org/10.1016/j.acra.2020.03.035

[39] Zhu HB, Zhang XY, Zhou XH, et al. Assessment of pathological complete response to preoperative chemoradiotherapy by means of multiple mathematical models of diffusion-weighted MRI in locally advanced rectal cancer: a prospective single-center study: MRI(DWI) in predicting pCR to CRT in rectal cancer. J Magn Reson Imaging. 2017; 46(1): 175-183. https://doi.org/10.1002/jmri.25567

[40] Tong T, Sun Y, Gollub MJ, et al. Dynamic contrast-enhanced MRI: use in predicting pathological complete response to neoadjuvant chemoradiation in locally advanced rectal cancer. J Magn Reson Imaging JMRI. 2015; 42(3): 673-680. https://doi.org/10.1002/jmri.24835

[41] Dijkhoff RAP, Beets-Tan RGH, Lambregts DMJ, Beets GL, Maas M. Value of DCE-MRI for staging and response evaluation in rectal cancer: a systematic review. Eur J Radiol. 2017; 95: 155-168. https://doi.org/10.1016/j.ejrad.2017.08.009

[42] Zou HH, Yu J, Wei Y, Wu JF, Xu Q. Response to neoadjuvant chemoradiotherapy for locally advanced rectum cancer: texture analysis of dynamic contrast-enhanced MRI. J Magn Reson Imaging JMRI. 2019; 49(3): 885-893. https://doi.org/10.1002/jmri.26254

[43] Keil VC, Mädler B, Gieseke J, et al. Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI. Magn Reson Imaging. 2017; 40: 83-90. https://doi.org/10.1016/j.mri.2017.04.006

[44] Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics. 2006; 7: 91. https://doi.org/10.1186/1471-2105-7-91

[45] Cui Y, Yang X, Shi Z, et al. 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. https://doi.org/10.1007/s00330-018-5683-9

[46] Shin J, Seo N, Baek SE, et al. MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy. Radiology. 2022; 303(2): 351-358. https://doi.org/10.1148/radiol.211986

[47] Wang J, Chen J, Zhou R, Gao Y, Li J. Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal cancer patients. BMC Cancer. 2022; 22(1): 420. https://doi.org/10.1186/s12885-022-09518-z

[48] Bibault JE, Giraud P, Housset M, et al. Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep. 2018; 8(1):12611. https://doi.org/10.1038/s41598-018-30657-6

[49] Zhou X, Yu Y, Feng Y, et al. Attention mechanism based multi-sequence MRI fusion improves prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Radiat Oncol Lond Engl. 2023; 18(1): 175. https://doi.org/10.1186/s13014-023-02352-y

[50] Zhang XY, Wang L, Zhu HT, et al. Predicting rectal cancer response to neoadjuvant chemoradiotherapy using deep learning of diffusion kurtosis MRI. Radiology. 2020; 296(1): 56-64. https://doi.org/10.1148/radiol.2020190936

[51] Poon STS, Hanna FWF, Lemarchand F, et al. Detecting adrenal lesions on 3D CT scans using a 2.5D deep learning model. Radiology and Imaging. Preprint posted online 2023. https://doi.org/10.1101/2023.02.22.23286184 medRxiv

[52] Wan L, Hu J, Chen S, et al. Prediction of lymph node metastasis in stage T1–2 rectal cancers with MRI-based deep learning. Eur Radiol. 2023; 33(5): 3638-3646. https://doi.org/10.1007/s00330-023-09450-1

[53] Shahzadi I, Zwanenburg A, Lattermann A, et al. Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models. Sci Rep. 2022; 12(1):10192. https://doi.org/10.1038/s41598-022-13967-8

[54] Zukauskaite R, Rumley CN, Hansen CR, et al. Delineation uncertainties of tumour volumes on MRI of head and neck cancer patients. Clin Transl Radiat Oncol. 2022; 36: 121-126. https://doi.org/10.1016/j.ctro.2022.08.005

[55] Borcoman E, Nandikolla A, Long G, Goel S, Le Tourneau C. Patterns of response and progression to immunotherapy. Am Soc Clin Oncol Educ Book Am Soc Clin Oncol Annu Meet. 2018; 38: 169-178. https://doi.org/10.1200/EDBK_200643

[56] Seymour L, Bogaerts J, Perrone A, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol. 2017; 18(3): e143-e152. https://doi.org/10.1016/S1470-2045(17)30074-8

[57] Ma Y, Wang Q, Dong Q, Zhan L, Zhang J. How to differentiate pseudoprogression from true progression in cancer patients treated with immunotherapy. Am J Cancer Res. 2019; 9(8): 1546-1553.

[58] Sun R, Sundahl N, Hecht M, et al. Radiomics to predict outcomes and abscopal response of patients with cancer treated with immunotherapy combined with radiotherapy using a validated signature of CD8 cells. J Immunother Cancer. 2020; 8(2):e001429. https://doi.org/10.1136/jitc-2020-001429

[59] Hodi FS, Ballinger M, Lyons B, et al. Immune-modified response evaluation criteria In solid tumors (imRECIST): refining guidelines to assess the clinical benefit of cancer immunotherapy. J Clin Oncol Off J Am Soc Clin Oncol. 2018; 36(9): 850-858. https://doi.org/10.1200/JCO.2017.75.1644

[60] Li ZY, Wang XD, Li M, et al. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer. World J Gastroenterol. 2020; 26(19): 2388-2402. https://doi.org/10.3748/wjg.v26.i19.2388

[61] Chang L, Zhang X, He L, et al. Prognostic value of ctDNA detection in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy: a systematic review and meta-analysis. Oncologist. 2023; 28(12): e1198-e1208. https://doi.org/10.1093/oncolo/oyad151

[62] Shao L, Liu Z, Feng L, et al. Multiparametric MRI and whole slide image-based pretreatment prediction of pathological response to neoadjuvant chemoradiotherapy in rectal cancer: a multicenter radiopathomic study. Ann Surg Oncol. 2020; 27(11): 4296-4306. https://doi.org/10.1245/s10434-020-08659-4

[63] Gambacorta MA, Masciocchi C, Chiloiro G, et al. Timing to achieve the highest rate of pCR after preoperative radiochemotherapy in rectal cancer: a pooled analysis of 3085 patients from 7 randomized trials. Radiother Oncol J Eur Soc Ther Radiol Oncol. 2021; 154: 154-160. https://doi.org/10.1016/j.radonc.2020.09.026

[64] de Campos-Lobato LF, Geisler DP, da Luz Moreira A, Stocchi L, Dietz D, Kalady MF. Neoadjuvant therapy for rectal cancer: the impact of longer interval between chemoradiation and surgery. J Gastrointest Surg Off J Soc Surg Aliment Tract. 2011; 15(3): 444-450. https://doi.org/10.1007/s11605-010-1197-8

[65] Francois Y, Nemoz CJ, Baulieux J, et al. Influence of the interval between preoperative radiation therapy and surgery on downstaging and on the rate of sphincter-sparing surgery for rectal cancer: the lyon R90-01 randomized trial. J Clin Oncol Off J Am Soc Clin Oncol. 1999; 17(8):2396. https://doi.org/10.1200/JCO.1999.17.8.2396

[66] Hupkens BJP, Maas M, Martens MH, et al. Organ Preservation in Rectal Cancer After Chemoradiation: Should We Extend the Observation Period in Patients with a Clinical Near-Complete Response? Ann Surg Oncol. 2018; 25(1): 197-203. https://doi.org/10.1245/s10434-017-6213-8

[67] Probst CP, Becerra AZ, Aquina CT, et al. Extended intervals after neoadjuvant therapy in locally advanced rectal cancer: the key to improved tumor response and potential organ preservation. J Am Coll Surg. 2015; 221(2): 430-440. https://doi.org/10.1016/j.jamcollsurg.2015.04.010

[68] Luo D, Yang Y, Zhang R, Li Q, Li X. Effect of interval between neoadjuvant chemoradiotherapy and surgery on oncological outcomes in poor responders with locally advanced rectal cancer: a retrospective cohort study. Int J Surg Lond Engl. 2023; 109(7): 1993-2000. https://doi.org/10.1097/JS9.0000000000000438

[69] Huntington CR, Boselli D, Symanowski J, Hill JS, Crimaldi A, Salo JC. Optimal timing of surgical resection after radiation in locally advanced rectal adenocarcinoma: an analysis of the national cancer database. Ann Surg Oncol. 2016; 23(3): 877-887. https://doi.org/10.1245/s10434-015-4927-z

[70] Wang L, Fan J, Guo Y, et al. The optimal time interval between neoadjuvant chemoradiotherapy and surgery for patients with an unfavorable pathological response in locally advanced rectal cancer: a retrospective cohort study. Front Oncol. 2025; 15:1534148. https://doi.org/10.3389/fonc.2025.1534148

[71] Deidda S, Elmore U, Rosati R, et al. Association of delayed surgery with oncologic long-term outcomes in patients with locally advanced rectal cancer not responding to preoperative chemoradiation. JAMA Surg. 2021; 156(12): 1141-1149. https://doi.org/10.1001/jamasurg.2021.4566

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