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

Abstract

Our study investigated whether magnetic resonance imaging (MRI)-based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC). Radiomics features were extracted from the T2 weighted (T2W) and Apparent diffusion coefficient (ADC) images of 1070 LARC patients retrospectively and prospectively recruited from three hospitals. To create radiomic models for GR prediction, three classifications were utilized. The radiomic model with the best performance was integrated with important clinical MRI features to create the combined model. Finally, two clinical MRI features and ten radiomic features were chosen for GR prediction. The combined model, constructed with the tumor size, MR-detected extramural venous invasion, and radiomic signature generated by Support Vector Machine (SVM), showed promising discrimination of GR, with area under the curves of 0.799 (95% CI, 0.760–0.838), 0.797 (95% CI, 0.733–0.860), 0.754 (95% CI, 0.678–0.829), and 0.727 (95% CI, 0.641–0.813) in the training and three validation datasets, respectively. Decision curve analysis verified the clinical usefulness. Furthermore, according to Kaplan–Meier curves, patients with a high likelihood of GR as determined by the combined model had better disease-free survival than those with a low probability. This radiomics model was developed based on large-sample size, multicenter datasets, and prospective validation with high radiomics quality score, and also had clinical utility.

Keywords

locally advanced rectal cancer / neoadjuvant chemoradiotherapy / treatment response / radiomics analysis

References

1	H Sung, J Ferlay, RL Siegel, 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.
2	RL Siegel, KD Miller, HE Fuchs, A Jemal. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7-33.
3	AB Benson, AP Venook, MM Al-Hawary, et al. Rectal cancer, Version 2.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2022;20(10):1139-1167.
4	F Papaccio, S Rosello, M Huerta, et al. Neoadjuvant chemotherapy in locally advanced rectal cancer. Cancers (Basel). 2020;12(12):3611.
5	C Rodel, U Graeven, R Fietkau, et al. Oxaliplatin added to fluorouracil-based preoperative chemoradiotherapy and postoperative chemotherapy of locally advanced rectal cancer (the German CAO/ARO/AIO-04 study): final results of the multicentre, open-label, randomised, phase 3 trial. Lancet Oncol. 2015;16(8):979-989.
6	MJM van der Valk, DE Hilling, E Bastiaannet, 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.
7	AL Appelt, J Ploen, H Harling, et al. High-dose chemoradiotherapy and watchful waiting for distal rectal cancer: a prospective observational study. Lancet Oncol. 2015;16(8):919-927.
8	JJ Smith, P Strombom, OS Chow, et al. Assessment of a watch-and-wait strategy for rectal cancer in patients with a complete response after neoadjuvant therapy. JAMA Oncol. 2019;5(4):e185896.
9	C Rodel, T Liersch, H Becker, et al. Preoperative chemoradiotherapy and postoperative chemotherapy with fluorouracil and oxaliplatin versus fluorouracil alone in locally advanced rectal cancer: initial results of the German CAO/ARO/AIO-04 randomised phase 3 trial. Lancet Oncol. 2012;13(7):679-687.
10	JF Bosset, L Collette, G Calais, et al. Chemotherapy with preoperative radiotherapy in rectal cancer. N Engl J Med. 2006;355(11):1114-1123.
11	R Fusco, M Petrillo, V Granata, et al. Magnetic resonance imaging evaluation in neoadjuvant therapy of locally advanced rectal cancer: a systematic review. Radiol Oncol. 2017;51(3):252-262.
12	DDB Bates, ME Homsi, KJ Chang, N Lalwani, N Horvat, SP Sheedy. MRI for rectal cancer: staging, mrCRM, EMVI, lymph node staging and post-treatment response. Clin Colorectal Cancer. 2022;21(1):10-18.
13	N Horvat, H Veeraraghavan, M Khan, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology. 2018;287(3):833-843.
14	K Hammarstrom, I Imam, A Mezheyeuski, J Ekstrom, T Sjoblom, B Glimelius. A comprehensive evaluation of associations between routinely collected staging information and the response to (chemo)radiotherapy in rectal cancer. Cancers (Basel). 2020;13(1):16.
15	X Tang, W Jiang, H Li, et al. Predicting poor response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer: model constructed using pre-treatment MRI features of structured report template. Radiother Oncol. 2020;148:97-106.
16	Y Zhang, L Yan, Y Wu, M Xu, X Liu, G Guan. Worse treatment response to neoadjuvant chemoradiotherapy in young patients with locally advanced rectal cancer. BMC Cancer. 2020;20(1):854.
17	XY Zhang, S Wang, XT Li, et al. MRI of extramural venous invasion in locally advanced rectal cancer: relationship to tumor recurrence and overall survival. Radiology. 2018;289(3):677-685.
18	P Lambin, RTH Leijenaar, TM Deist, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749-762.
19	L Feng, Z Liu, C Li, 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.
20	Z Liu, X Meng, H Zhang, et al. Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer. Nat Commun. 2020;11(1):4308.
21	X Liu, D Zhang, Z Liu, 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.
22	Y Cui, W Yang, J Ren, et al. Prognostic value of multiparametric MRI-based radiomics model: potential role for chemotherapeutic benefits in locally advanced rectal cancer. Radiother Oncol. 2021;154:161-169.
23	Z Liu, XY Zhang, YJ Shi, et al. Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Clin Cancer Res. 2017;23(23):7253-7262.
24	P Bulens, A Couwenberg, M Intven, 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.
25	I Petkovska, F Tixier, EJ Ortiz, et al. Clinical utility of radiomics at baseline rectal MRI to predict complete response of rectal cancer after chemoradiation therapy. Abdom Radiol (NY). 2020;45(11):3608-3617.
26	Y Cui, X Yang, Z Shi, 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.
27	A Bordron, E Rio, B Badic, et al. External validation of a radiomics model for the prediction of complete response to neoadjuvant chemoradiotherapy in rectal cancer. Cancers (Basel). 2022;14(4):1079.
28	VS Jayaprakasam, V Paroder, P Gibbs, 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(2):971-980.
29	J Shin, N Seo, SE Baek, et al. MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy. Radiology. 2022;303(2):351-358.
30	Q Wei, Z Chen, Y Tang, et al. External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study. Eur Radiol. 2023;33(3):1906-1917.
31	V Giannini, S Mazzetti, I Bertotto, et al. Predicting locally advanced rectal cancer response to neoadjuvant therapy with (18)F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging. 2019;46(4):878-888.
32	Q Li, D Luo, J Zhu, et al. ACRNaCT trial protocol: efficacy of adjuvant chemotherapy in patients with clinical T3b/T4, N+ rectal Cancer undergoing Neoadjuvant Chemoradiotherapy: a pathology-oriented, prospective, multicenter, randomized, open-label, parallel group clinical trial. BMC Cancer. 2019;19(1):1117.
33	ZY Li, XD Wang, M Li, 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.
34	JJM van Griethuysen, DMJ Lambregts, S Trebeschi, et al. Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer. Abdom Radiol (NY). 2020;45(3):632-643.
35	B Petresc, A Lebovici, C Caraiani, DS Feier, F Graur, MM Buruian. 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.
36	H Shaish, A Aukerman, R Vanguri, 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.
37	Y Cheng, Y Luo, Y Hu, 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.
38	X Yi, Q Pei, Y Zhang, et al. MRI-based radiomics predicts tumor response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Front Oncol. 2019;9:552.
39	J Wang, X Liu, B Hu, Y Gao, J Chen, J Li. 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(5):1805-1815.
40	C Li, H Chen, B Zhang, et al. Radiomics signature based on support vector machines for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Cancers (Basel). 2023;15(21):5134.
41	RF Vliegen, GL Beets MF von Meyenfeldt, et al. Rectal cancer: mR imaging in local staging–is gadolinium-based contrast material helpful? Radiology. 2005;234(1):179-188.
42	MJ Gollub, S Arya, RG Beets-Tan, et al. Use of magnetic resonance imaging in rectal cancer patients: Society of Abdominal Radiology (SAR) rectal cancer disease-focused panel (DFP) recommendations 2017. Abdom Radiol (NY). 2018;43(11):2893-2902.
43	RGH Beets-Tan, DMJ Lambregts, M Maas, 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.
44	E Fokas, A Schlenska-Lange, B Polat, et al. Chemoradiotherapy plus induction or consolidation chemotherapy as total neoadjuvant therapy for patients with locally advanced rectal cancer: long-term results of the CAO/ARO/AIO-12 randomized clinical trial. JAMA Oncol. 2022;8(1):e215445.
45	M Song, S Li, H Wang, et al. MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer. Br J Cancer. 2022;127(2):249-257.
46	S Huang, N Cai, PP Pacheco, S Narrandes, Y Wang, W Xu. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics. 2018;15(1):41-51.
47	KE Emblem, MC Pinho, FG Zollner, et al. A generic support vector machine model for preoperative glioma survival associations. Radiology. 2015;275(1):228-234.
48	Y Cui, H Liu, J Ren, et al. Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer. Eur Radiol. 2020;30(4):1948-1958.
49	X Ma, F Shen, Y Jia, Y Xia, Q Li, J Lu. MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features. BMC Med Imaging. 2019;19(1):86.
50	M Liang, Z Cai, H Zhang, et al. Machine learning-based analysis of rectal cancer MRI radiomics for prediction of metachronous liver metastasis. Acad Radiol. 2019;26(11):1495-1504.
51	Q Wei, W Yuan, Z Jia, et al. Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma. Abdom Radiol (NY). 2023;48(2):458-470.
52	Y Zhang, J Liu, C Wu, J Peng, Y Wei, S Cui. Preoperative prediction of microsatellite instability in rectal cancer using five machine learning algorithms based on multiparametric MRI radiomics. Diagnostics (Basel). 2023;13(2):269.
53	Z Shu, D Mao, Q Song, Y Xu, P Pang, Y Zhang. Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer. Eur Radiol. 2022;32(2):1002-1013.
54	K Sirinukunwattana, E Domingo, SD Richman, et al. Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning. Gut. 2021;70(3):544-554.
55	L Cai, DMJ Lambregts, GL Beets, et al. An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre study. NPJ Precis Oncol. 2024;8(1):17.
56	X Jiang, H Zhao, OL Saldanha, et al. An MRI deep learning model predicts outcome in rectal cancer. Radiology. 2023;307(5):e222223.
57	DM Koh. Using deep learning for MRI to identify responders to chemoradiotherapy in rectal cancer. Radiology. 2020;296(1):65-66.
58	KA Tran, O Kondrashova, A Bradley, ED Williams, JV Pearson, N Waddell. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 2021;13(1):152.
59	SB Edge, CC Compton. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17(6):1471-1474.
60	B Miron, WRR Kursa. Feature selection with the boruta package. J Statist Software. 2010;36(11):13.