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Eur Radiol. 2019 Mar;29(3):1074-1082. doi: 10.1007/s00330-018-5629-2. Epub 2018 Aug 16.

Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively.

Author information

1
Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, No.1838, North Guangzhou Avenue, Guangzhou, 510515, Guangdong Province, China. drchentao@163.com.
2
Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan. drchentao@163.com.
3
School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong Province, China.
4
Medical Image Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China.
5
Department of General Surgery, Guangdong General Hospital, Guangdong Academy of Medical Science, Guangzhou, 510080, Guangdong Province, China.
6
Department of General Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, Guangdong Province, China.
7
Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan.
8
Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, No.1838, North Guangzhou Avenue, Guangzhou, 510515, Guangdong Province, China.
9
Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, No.1838, North Guangzhou Avenue, Guangzhou, 510515, Guangdong Province, China. gzliguoxin@163.com.

Abstract

OBJECTIVE:

To develop and evaluate a radiomics nomogram for differentiating the malignant risk of gastrointestinal stromal tumours (GISTs).

METHODS:

A total of 222 patients (primary cohort: n = 130, our centre; external validation cohort: n = 92, two other centres) with pathologically diagnosed GISTs were enrolled. A Relief algorithm was used to select the feature subset with the best distinguishing characteristics and to establish a radiomics model with a support vector machine (SVM) classifier for malignant risk differentiation. Determinant clinical characteristics and subjective CT features were assessed to separately construct a corresponding model. The models showing statistical significance in a multivariable logistic regression analysis were used to develop a nomogram. The diagnostic performance of these models was evaluated using ROC curves. Further calibration of the nomogram was evaluated by calibration curves.

RESULTS:

The generated radiomics model had an AUC value of 0.867 (95% CI 0.803-0.932) in the primary cohort and 0.847 (95% CI 0.765-0.930) in the external cohort. In the entire cohort, the AUCs for the radiomics model, subjective CT findings model, clinical index model and radiomics nomogram were 0.858 (95% CI 0.807-0.908), 0.774 (95% CI 0.713-0.835), 0.759 (95% CI 0.697-0.821) and 0.867 (95% CI 0.818-0.915), respectively. The nomogram showed good calibration.

CONCLUSIONS:

This radiomics nomogram predicted the malignant potential of GISTs with excellent accuracy and may be used as an effective tool to guide preoperative clinical decision-making.

KEY POINTS:

• CT-based radiomics model can differentiate low- and high-malignant-potential GISTs with satisfactory accuracy compared with subjective CT findings and clinical indexes. • Radiomics nomogram integrated with the radiomics signature, subjective CT findings and clinical indexes can achieve individualised risk prediction with improved diagnostic performance. • This study might provide significant and valuable background information for further studies such as response evaluation of neoadjuvant imatinib and recurrence risk prediction.

KEYWORDS:

Classification; Gastrointestinal stromal tumour; Machine learning; Nomogram; Radiomics

PMID:
30116959
DOI:
10.1007/s00330-018-5629-2
[Indexed for MEDLINE]

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