Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer

Abstract Perineural invasion (PNI) as a grossly underreported independent risk predictor in rectal cancer is hard to identify preoperatively. We aim to predict PNI status in rectal cancer using multi-modality radiomics. In total, 396 radiomics features were extracted from T2-weighted images (T2WIs),...

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Autores principales:	Yu Guo, Quan Wang, Yan Guo, Yiying Zhang, Yu Fu, Huimao Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/93abc9a59f9e486f9b9e07a90eb38c14
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spelling	oai:doaj.org-article:93abc9a59f9e486f9b9e07a90eb38c142021-12-02T15:38:10ZPreoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer10.1038/s41598-021-88831-22045-2322https://doaj.org/article/93abc9a59f9e486f9b9e07a90eb38c142021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88831-2https://doaj.org/toc/2045-2322Abstract Perineural invasion (PNI) as a grossly underreported independent risk predictor in rectal cancer is hard to identify preoperatively. We aim to predict PNI status in rectal cancer using multi-modality radiomics. In total, 396 radiomics features were extracted from T2-weighted images (T2WIs), diffusion-weighted images (DWIs), and portal venous phase of contrast-enhanced CT (CE-CT) respectively of 94 consecutive patients with histologically confirmed rectal cancer. T2WI score, DWI score, and CT score were calculated via the radiomics features selection and optimization. Discrimination, calibration, and clinical benefit ability were used to evaluate the performance of the radiomics scores in both training and testing datasets. CT score and T2WI score were independent risk predictors [CT score, OR (95% CI) = 4.218 (1.070–16.620); T2WI score, OR (95% CI) = 105.721 (3.091–3615.790)]. The concise score which combined CT score and T2WI score, showed the best performance [training dataset, AUC (95% CI) = 0.906 (0.833–0.979); testing dataset, AUC (95% CI) = 0.884 (0.761–1.000)] and good calibration (P > 0.05 in the Hosmer–Lemeshow test for the training and testing datasets). Decision curve analysis showed that the multi-modality radiomics nomogram had a higher clinical net benefit. The multi-modality radiomics score could be used to preoperatively assess PNI status in rectal cancer.Yu GuoQuan WangYan GuoYiying ZhangYu FuHuimao ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yu Guo Quan Wang Yan Guo Yiying Zhang Yu Fu Huimao Zhang Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer
description	Abstract Perineural invasion (PNI) as a grossly underreported independent risk predictor in rectal cancer is hard to identify preoperatively. We aim to predict PNI status in rectal cancer using multi-modality radiomics. In total, 396 radiomics features were extracted from T2-weighted images (T2WIs), diffusion-weighted images (DWIs), and portal venous phase of contrast-enhanced CT (CE-CT) respectively of 94 consecutive patients with histologically confirmed rectal cancer. T2WI score, DWI score, and CT score were calculated via the radiomics features selection and optimization. Discrimination, calibration, and clinical benefit ability were used to evaluate the performance of the radiomics scores in both training and testing datasets. CT score and T2WI score were independent risk predictors [CT score, OR (95% CI) = 4.218 (1.070–16.620); T2WI score, OR (95% CI) = 105.721 (3.091–3615.790)]. The concise score which combined CT score and T2WI score, showed the best performance [training dataset, AUC (95% CI) = 0.906 (0.833–0.979); testing dataset, AUC (95% CI) = 0.884 (0.761–1.000)] and good calibration (P > 0.05 in the Hosmer–Lemeshow test for the training and testing datasets). Decision curve analysis showed that the multi-modality radiomics nomogram had a higher clinical net benefit. The multi-modality radiomics score could be used to preoperatively assess PNI status in rectal cancer.
format	article
author	Yu Guo Quan Wang Yan Guo Yiying Zhang Yu Fu Huimao Zhang
author_facet	Yu Guo Quan Wang Yan Guo Yiying Zhang Yu Fu Huimao Zhang
author_sort	Yu Guo
title	Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer
title_short	Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer
title_full	Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer
title_fullStr	Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer
title_full_unstemmed	Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer
title_sort	preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/93abc9a59f9e486f9b9e07a90eb38c14
work_keys_str_mv	AT yuguo preoperativepredictionofperineuralinvasionwithmultimodalityradiomicsinrectalcancer AT quanwang preoperativepredictionofperineuralinvasionwithmultimodalityradiomicsinrectalcancer AT yanguo preoperativepredictionofperineuralinvasionwithmultimodalityradiomicsinrectalcancer AT yiyingzhang preoperativepredictionofperineuralinvasionwithmultimodalityradiomicsinrectalcancer AT yufu preoperativepredictionofperineuralinvasionwithmultimodalityradiomicsinrectalcancer AT huimaozhang preoperativepredictionofperineuralinvasionwithmultimodalityradiomicsinrectalcancer
_version_	1718386186872946688

Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer

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