This study aimed to evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based radiomics for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients with locally advanced rectal adenocarcinoma.
Methods
Eligible studies developed MRI-based radiomics or deep learning models to predict pCR and reported sufficient data to reconstruct 2 × 2 contingency tables. Only validation cohorts were included in the quantitative synthesis. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 and the Radiomics Quality Score. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio were estimated using a bivariate random-effects model. Hierarchical summary receiver operating characteristic (HSROC) analysis was performed.
Results
Thirty-eight studies were included. The pooled sensitivity and specificity were 0.82 (95% CI, 0.71-0.90) and 0.86 (95% CI, 0.80-0.91), respectively. The pooled PLR and NLR were 6.0 (95% CI, 4.0-8.9) and 0.21 (95% CI, 0.12-0.35), corresponding to a diagnostic odds ratio of 29 (95% CI, 14-61). HSROC analysis showed an area under the curve of 0.846. Subgroup analyses suggested improved performance for deep learning and combined clinical–radiomic models.
Conclusion
MRI-based radiomics demonstrates good diagnostic accuracy for predicting pCR after neoadjuvant chemoradiotherapy in rectal cancer, although methodological heterogeneity and limited prospective validation remain challenges.
SungHFerlayJSiegelRL, 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(3):209‐249. doi:10.3322/caac.21660
2.
SauerRBeckerHHohenbergerW, et al.German Rectal Cancer Study G. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med. 2004;351(17):1731‐1740. doi:10.1056/NEJMoa040694
3.
MaasMNelemansPJValentiniV, et al.Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: A pooled analysis of individual patient data. Lancet Oncol. 2010;11(9):835‐844. doi:10.1016/S1470-2045(10)70172-8
4.
FokasELierschTFietkauR, et al.Tumor regression grading after preoperative chemoradiotherapy for locally advanced rectal carcinoma revisited: Updated results of the CAO/ARO/AIO-94 trial. J Clin Oncol. 2014;32(15):1554‐1562. doi:10.1200/JCO.2013.54.3769
5.
ZorcoloLRosmanASRestivoA, et al.Complete pathologic response after combined modality treatment for rectal cancer and long-term survival: A meta-analysis. Ann Surg Oncol. 2012;19(9):2822‐2832. doi:10.1245/s10434-011-2209-y
6.
van der ValkMJMHillingDEBastiaannetE, et al.Long-term outcomes of clinical complete responders after neoadjuvant treatment for rectal cancer in the International Watch & Wait Database (IWWD): An international multicentre registry study. Lancet. 2018;391(10139):2537‐2545. doi:10.1016/S0140-6736(18)31078-X
Habr-GamaAPerezRONadalinW, et al.Operative versus nonoperative treatment for stage 0 distal rectal cancer following chemoradiation therapy: Long-term results. Ann Surg. 2004;240(4):711‐717; discussion 7-8. doi:10.1097/01.sla.0000141194.27992.32
9.
Beets-TanRG. MRI in rectal cancer: The T stage and circumferential resection margin. Colorectal Dis. 2003;5(5):392‐395. doi:10.1046/j.1463-1318.2003.00518.x
10.
PatelUBTaylorFBlomqvistL, et al.Magnetic resonance imaging-detected tumor response for locally advanced rectal cancer predicts survival outcomes: MERCURY experience. J Clin Oncol. 2011;29(28):3753‐3760. doi:10.1200/JCO.2011.34.9068
11.
LambregtsDMCappendijkVCMaasMBeetsGLBeets-TanRG. Value of MRI and diffusion-weighted MRI for the diagnosis of locally recurrent rectal cancer. Eur Radiol. 2011;21(6):1250‐1258. doi:10.1007/s00330-010-2052-8
12.
LiCLuNHeZ, et al.A noninvasive tool based on magnetic resonance imaging radiomics for the preoperative prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer. Ann Surg Oncol. 2022;29(12):7685‐7693. doi:10.1245/s10434-022-12034-w
13.
ScapicchioCGabelloniMBarucciACioniDSabaLNeriE. A deep look into radiomics. Radiol Med. 2021;126(10):1296‐1311. doi:10.1007/s11547-021-01389-x
14.
GaneshanBMilesKA. Quantifying tumour heterogeneity with CT. Cancer Imaging. 2013;13(1):140‐149. doi:10.1102/1470-7330.2013.0015
15.
FengNWangKJiaoY. Integrating radiomics and machine learning for the diagnosis and prognosis of hepatocellular carcinoma. World J Gastrointest Oncol. 2025;17(7):106610. doi:10.4251/wjgo.v17.i7.106610
16.
HuangWQLinRXKeXHDengXHNiSXTangL. Radiomics in rectal cancer: Current status of use and advances in research. Front Oncol. 2024;14:1470824. doi:10.3389/fonc.2024.1470824
17.
HeJWangSXLiuP. Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: A systematic review and meta-analysis. Br J Radiol.2024;97(1159):1243‐1254. doi:10.1093/bjr/tqae098
18.
NourooziFKazemiHSAlinezhadA, et al.Artificial intelligence-based detection of neuropsychiatric lupus: An exploratory meta-analysis of neuroimaging and multimodal biomarker models. Clin Exp Med. 2026;26(1):125. doi:10.1007/s10238-025-02030-1
19.
FlaibanEOrhanKGoncalvesBCLopesSCostaALF. Radiomics in action: Multimodal synergies for imaging biomarkers. Bioengineering (Basel). 2025;12(11):1139. doi:10.3390/bioengineering12111139
20.
MeseIKocakB. Evaluating methodological quality in radiomics research using large language models: Added value of METRICS-E3 framework. Eur J Radiol. 2026;194:112519. doi:10.1016/j.ejrad.2025.112519
21.
WhitingPFRutjesAWWestwoodME, et al.QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529‐536. doi:10.7326/0003-4819-155-8-201110180-00009
22.
LambinPLeijenaarRTHDeistTM, et al.Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749‐762. doi:10.1038/nrclinonc.2017.141
23.
Alvarez-JimenezCAntunesJTDeSilvioT, et al.A novel structural modeling magnitude and orientation radiomic descriptor for evaluating response to neoadjuvant therapy in rectal cancers via MRI. NPJ Precis Oncol. 2025;9(1):215. doi:10.1038/s41698-025-01007-3
24.
AntunesJTOfshteynABeraK, et al.Radiomic features of primary rectal cancers on baseline T(2)-weighted MRI are associated with pathologic complete response to neoadjuvant chemoradiation: A multisite study. J Magn Reson Imaging. 2020;52(5):1531‐1541. doi:10.1002/jmri.27140
25.
AzamatSKaramanSAzamatIF, et al.Complete response evaluation of locally advanced rectal cancer to neoadjuvant chemoradiotherapy using textural features obtained from T2 weighted imaging and ADC maps. Curr Med Imaging. 2022;18(10):1061‐1069. doi:10.2174/1573405618666220303111026
26.
BegalJSaboEGoldbergNBittermanAKhouryW. Wavelets-based texture analysis of post neoadjuvant chemoradiotherapy magnetic resonance imaging as a tool for recognition of pathological complete response in rectal cancer, a retrospective study. J Clin Med. 2024;13(23):7383. doi:10.3390/jcm13237383
27.
BelliniDCarboneIRengoM, et al.Performance of machine learning and texture analysis for predicting response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer with 3T MRI. Tomography. 2022;8(4):2059‐2072. doi:10.3390/tomography8040173
28.
BoldriniLLenkowiczJOrlandiniLC, 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(1):78. doi:10.1186/s13014-022-02048-9
29.
BulensPCouwenbergAIntvenM, et al.Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics. Radiother Oncol. 2020;142:246‐252. doi:10.1016/j.radonc.2019.07.033
30.
ChenBYXieHLiY, et al.MRI-based Radiomics features to predict treatment response to neoadjuvant chemotherapy in locally advanced rectal cancer: A single center, prospective study. Front Oncol. 2022;12:801743. doi:10.3389/fonc.2022.801743
31.
ChengYLuoYHuY, et al.Multiparametric MRI-based radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Abdom Radiol (NY). 2021;46(11):5072‐5085. doi:10.1007/s00261-021-03219-0
32.
CrimiFD'AlessandroCZanonC, et al.A machine learning model based on MRI radiomics to predict response to chemoradiation among patients with rectal cancer. Life (Basel). 2024;14(12):1530. doi:10.3390/life14121530
33.
CuiYYangXShiZ, 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. doi:10.1007/s00330-018-5683-9
34.
FengLLiuZLiC, 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(1):e8‐e17. doi:10.1016/S2589-7500(21)00215-6
35.
HorvatNVeeraraghavanHKhanM, et al.MR imaging of rectal cancer: Radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology. 2018;287(3):833‐843. doi:10.1148/radiol.2018172300
36.
HorvatNVeeraraghavanHNahasCSR, et al.Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: An external validation study. Abdom Radiol (NY). 2022;47(8):2770‐2782. doi:10.1007/s00261-022-03572-8
37.
HuTGongJSunY, et al.Magnetic resonance imaging-based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study. MedComm. (2020). 2024;5(7):e609. doi:10.1002/mco2.609
38.
HuangHHanLGuoJ, et al.Multiphase and multiparameter MRI-based radiomics for prediction of tumor response to neoadjuvant therapy in locally advanced rectal cancer. Radiat Oncol. 2023;18(1):179. doi:10.1186/s13014-023-02368-4
39.
JangBSLimYJSongC, et al.Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging. Radiother Oncol. 2021;161:183‐190. doi:10.1016/j.radonc.2021.06.019
40.
JiangHGuoWYuZ, et al.A comprehensive prediction model based on MRI radiomics and clinical factors to predict tumor response after neoadjuvant chemoradiotherapy in rectal cancer. Acad Radiol. 2023;30(Suppl 1):S185‐SS98. doi:10.1016/j.acra.2023.04.032
41.
JinCYuHKeJ, et al.Predicting treatment response from longitudinal images using multi-task deep learning. Nat Commun. 2021;12(1):1851. doi:10.1038/s41467-021-22188-y
42.
LeeSLimJShinJKimSHwangH. Pathologic complete response prediction after neoadjuvant chemoradiation therapy for rectal cancer using radiomics and deep embedding network of MRI. Appl Sci. 2021;11(20):9494. doi:10.3390/app11209494
43.
LeeYDKimHGSeoMMoonSKParkSJYouMW. Machine learning-based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: Radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images. Int J Colorectal Dis. 2024;39(1):78. doi:10.1007/s00384-024-04651-6
44.
LuHYuanYLiuM, et al.Predicting pathological complete response following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer using merged model integrating MRI-based radiomics and deep learning data. BMC Med Imaging. 2024;24(1):289. doi:10.1186/s12880-024-01474-3
45.
MirandaJHorvatNAssuncaoANJr., et al.MRI-based radiomic score increased mrTRG accuracy in predicting rectal cancer response to neoadjuvant therapy. Abdom Radiol (NY). 2023;48(6):1911‐1920. doi:10.1007/s00261-023-03898-x
46.
NardoneVReginelliAGrassiR, 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(12):3004. doi:10.3390/cancers14123004
47.
PangXWangFZhangQ, 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:10.3389/fonc.2021.711747
48.
PengWWanLWangSZouSZhaoXZhangH. A multiple-time-scale comparative study for the added value of magnetic resonance imaging-based radiomics in predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Front Oncol. 2023;13:1234619. doi:10.3389/fonc.2023.1234619
49.
RengoMLandolfiFPicchiaS, et al.Rectal cancer response to neoadjuvant chemoradiotherapy evaluated with MRI: Development and validation of a classification algorithm. Eur J Radiol. 2022;147:110146. doi:10.1016/j.ejrad.2021.110146
50.
ShaishHAukermanAVanguriR, et al.Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: An international multicenter study. Eur Radiol. 2020;30(11):6263‐6273. doi:10.1007/s00330-020-06968-6
51.
ShinJSeoNBaekSE, et al.MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy. Radiology. 2022;303(2):351‐358. doi:10.1148/radiol.211986
52.
SuRWuSShenH, et al.Combining clinicopathology, IVIM-DWI and texture parameters for a nomogram to predict treatment response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer patients. Front Oncol. 2022;12:886101. doi:10.3389/fonc.2022.886101
53.
WanLZhangCZhaoQ, et al.Developing a prediction model based on MRI for pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Abdom Radiol (NY). 2019;44(9):2978‐2987. doi:10.1007/s00261-019-02129-6
54.
WanLPengWZouS, et al.MRI-based delta-radiomics are predictive of pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Acad Radiol. 2021;28(Suppl 1):S95‐S104. doi:10.1016/j.acra.2020.10.026
55.
WangYZhangLJiangY, et al.Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley additive exPlanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: A multicenter retrospective study. Quant Imaging Med Surg. 2024;14(7):4617‐4634. doi:10.21037/qims-24-7
56.
WenLLiuJHuP, et al.MRI-based radiomic models outperform radiologists in predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Acad Radiol. 2023;30(Suppl 1):S176‐SS84. doi:10.1016/j.acra.2022.12.037
57.
YardimciAHKocakBSelI, et al.Radiomics of locally advanced rectal cancer: Machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI. Jpn J Radiol. 2023;41(1):71‐82. doi:10.1007/s11604-022-01325-7
58.
YiXPeiQZhangY, et al.MRI-based radiomics predicts tumor response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Front Oncol. 2019;9:552. doi:10.3389/fonc.2019.00552
59.
ZhangXYWangLZhuHT, et al.Predicting rectal cancer response to neoadjuvant chemoradiotherapy using deep learning of diffusion kurtosis MRI. Radiology. 2020;296(1):56‐64. doi:10.1148/radiol.2020190936
60.
ZhuHTZhangXYShiYJLiXTSunYS. The conversion of MRI data with multiple b-values into signature-like pictures to predict treatment response for rectal cancer. J Magn Reson Imaging. 2022;56(2):562‐569. doi:10.1002/jmri.28033
61.
AgostiEMapelliKGrimodGPiazzaAFontanellaMMPancianiPP. MRI-based radiomics for non-invasive prediction of molecular biomarkers in gliomas. Cancers (Basel). 2026;18(3):491. doi:10.3390/cancers18030491
62.
CusumanoDDinapoliNBoldriniL, et al.Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer. Radiol Med. 2018;123(4):286‐295. doi:10.1007/s11547-017-0838-3
63.
HenryTSunRLerousseauM, et al.Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies. Sci Rep. 2022;12(1):17244. doi:10.1038/s41598-022-20931-z
64.
JaniszewskaMSteinSMetzger FilhoO, et al.The impact of tumor epithelial and microenvironmental heterogeneity on treatment responses in HER2+breast cancer. JCI Insight. 2021;6(11):e147617. doi:10.1172/jci.insight.147617
65.
YipSSAertsHJ. Applications and limitations of radiomics. Phys Med Biol. 2016;61(13):R150‐R166. doi:10.1088/0031-9155/61/13/R150
66.
TrebeschiSvan GriethuysenJJMLambregtsDMJ, et al.Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Sci Rep. 2017;7(1):5301. doi:10.1038/s41598-017-05728-9
67.
DuLYuanJGanM, et al.A comparative study between deep learning and radiomics models in grading liver tumors using hepatobiliary phase contrast-enhanced MR images. BMC Med Imaging. 2022;22(1):218. doi:10.1186/s12880-022-00946-8
68.
ChingJCFLamSLamCCH, et al.Integrating CT-based radiomic model with clinical features improves long-term prognostication in high-risk prostate cancer. Front Oncol. 2023;13:1060687. doi:10.3389/fonc.2023.1060687
69.
DemirciogluA. Reproducibility and interpretability in radiomics: A critical assessment. Diagn Interv Radiol. 2025;31(4):321‐328. doi:10.4274/dir.2024.242719
70.
HagiwaraAFujitaSOhnoYAokiS. Variability and standardization of quantitative imaging: Monoparametric to multiparametric quantification, radiomics, and artificial intelligence. Invest Radiol. 2020;55(9):601‐616. doi:10.1097/RLI.0000000000000666
71.
LeucutaDCUrda-CimpeanAEIstrateDDruganT. Risk of bias assessment of diagnostic accuracy studies using QUADAS 2 by large language models. Diagnostics (Basel). 2025;15(12):1451. doi:10.3390/diagnostics15121451
72.
CohenJFKorevaarDAAltmanDG, et al.STARD 2015 guidelines for reporting diagnostic accuracy studies: Explanation and elaboration. BMJ Open. 2016;6(11):e012799. doi:10.1136/bmjopen-2016-012799
73.
WhiteSJPhuaQSLuLYaxleyKLMcInnesMDFToMS. Heterogeneity in systematic reviews of medical imaging diagnostic test accuracy studies: A systematic review. JAMA Netw Open. 2024;7(2):e240649. doi:10.1001/jamanetworkopen.2024.0649
74.
BoldriniLCusumanoDChiloiroG, 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(2):145‐153. doi:10.1007/s11547-018-0951-y
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
