MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study

European Radiology Experimental

Arianna Defeudis¹  Valentina Giannini¹  Daniele Regge¹  Giuliana Giannetto¹  Simone Mazzetti¹  Jovana Panic²  Monica Micilotta³  Stefano Cirillo³  Lorenzo Vassallo⁴  Marco Gatti⁴  Riccardo Faletti⁴ 

[1] Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS;Department of Surgical Sciences, University of Turin;Mauriziano hospital;Radiology Unit, Department of Surgical Sciences, University of Turin;

关键词: Artificial intelligence;    Machine learning;    Multiparametric magnetic resonance imaging;    Neoadjuvant therapy;    Rectal neoplasms;   

DOI  :  10.1186/s41747-022-00272-2

来源: DOAJ

【 摘 要 】

Abstract Background Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15–30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models. Methods Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers. Results Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation. Conclusion Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations.

【 授权许可】

Unknown