Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learning
Colorectal cancer (CRC) is the second most prevalent cause of oncological mortality, and its diagnostic and therapeutic decision-making processes is complex. Alteration in molecular characteristic expression is closely related to tumor invasiveness and can serve a novel biomarker for predicting canc...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025002324 |
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| Summary: | Colorectal cancer (CRC) is the second most prevalent cause of oncological mortality, and its diagnostic and therapeutic decision-making processes is complex. Alteration in molecular characteristic expression is closely related to tumor invasiveness and can serve a novel biomarker for predicting cancer prognosis. In this study, we aimed to construct radiomic models through machine learning to predict the progression of CRC. We collected the clinical, pathological, and magnetic resonance imaging (MRI) data of 136 CRC patients who underwent direct surgical resection. Immunohistochemistry analysis was performed to detect the expression levels of p53, synaptophysin (Syn), human epidermal growth factor receptor 2 (HER2), perineural invasion (PNI), and vascular invasion (VI) expression levels in CRC tissues. After the manual lesion segmentation, 1781 radiomics features were extracted from the transverse T2-weighted image of MRI (T2W-MRI). We employed Spearman's rank correlation coefficient, greedy recursive deletion strategy, minimum redundancy, maximum relevance, least absolute shrinkage, and selection operator regression were utilized to screen for radiological features. Radiomics and clinical models were constructed using the K-nearest neighbor (KNN). The diagnostic efficiencies of the prediction models were evaluated using receiver operating characteristic curves and quantified employing the area under the curve (AUC). Our research results indicate that compared with the single radioactive model, the clinical radiomics model in the validation cohort showed better diagnostic performance, as indicated by the AUC values (p53 = 0.758, Syn = 0.739, HER2 = 0.786, PNI = 0.835, VI = 0.797). Furthermore, the calibration curve and decision curve analyses showed the clinical benefits. In summary, we developed and validated a clinical radiomics model to preoperative prediction of the biological characteristic expression levels of CRC. The findings of this research may offer a promising noninvasive method for evaluating CRC risk stratification and may lay the groundwork for treatment of this disease. |
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| ISSN: | 2405-8440 |