Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer

• Published in: Radiotherapy and oncology Vol. 132; no. C; pp. 100 - 108
• Main Authors: Tang, Zhenchao, Zhang, Xiao-Yan, Liu, Zhenyu, Li, Xiao-Ting, Shi, Yan-Jie, Wang, Shou, Fang, Mengjie, Shen, Chen, Dong, Enqing, Sun, Ying-Shi, Tian, Jie
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.03.2019; Elsevier
• Subjects: Chemoradiotherapy - methods; Decision support; Diffusion Magnetic Resonance Imaging - methods; Diffusion weighted imaging; Female; Humans; Locally advanced rectal cancer; Male; Middle Aged; Neoadjuvant chemoradiotherapy; Neoadjuvant Therapy - methods; Organ-preserving strategies; Predictive Value of Tests; Rectal Neoplasms - diagnostic imaging; Rectal Neoplasms - drug therapy; Rectal Neoplasms - radiotherapy; Rectal Neoplasms - therapy; ROC Curve; Treatment Outcome; Organ-preserving strategies; Locally advanced rectal cancer; Diffusion weighted imaging; Neoadjuvant chemoradiotherapy; Decision support
• ISSN: 0167-8140; 1879-0887
• DOI: 10.1016/j.radonc.2018.11.007

•LARC down-staging to ypT0-1N0 after neoadjuvant chemoradiotherapy achieves pGR.•pGR may receive organ-preserving treatment instead of total mesorectal excision.•Quantitative DWI analysis obtains high positive predict values in predicting pGR.•Quantitative DWI combined with clinical predictors could accurately predict pGR.•DWI analysis holds great potential in decision support of organ-preserving strategy. Locally advanced rectal cancer (LARC) patients showing pathological good response (pGR) of down-staging to ypT0-1N0 after neoadjuvant chemoradiotherapy (nCRT) may receive organ-preserving treatment instead of total mesorectal excision (TME). In the current study, quantitative analysis of diffusion weighted imaging (DWI) is conducted to predict pGR patients in order to provide decision support for organ-preserving strategies. 222 LARC patients receiving nCRT and TME are enrolled from Beijing Cancer Hospital and allocated into training (152) and validation (70) set. Three pGR prediction models are constructed in the training set, including DWI prediction model based on quantitative DWI features, clinical prediction model based on clinical characteristics, and combined prediction model integrating DWI and clinical predictors. Prediction performances are assessed by area under receiver operating characteristic curve (AUC), classification accuracy (ACC), positive and negative predictive values (PPV and NPV). The DWI (AUC = 0.866, ACC = 91.43%) and combined (AUC = 0.890, ACC = 90%) prediction model obtains good prediction performance in the independent validation set. Nevertheless, the clinical prediction model performs worse than the other two models (AUC = 0.631, ACC = 75.71% in validation set). Calibration analysis indicates that the pGR probability predicted by the combined prediction model is close to perfect prediction. Decision curve analysis reveals that the LARC patients will acquire clinical benefit if receiving organ-preserving strategy according to combined prediction model. Combination of quantitative DWI analysis and clinical characteristics holds great potential in identifying the pGR patients and providing decision support for organ-preserving strategies after nCRT treatment.