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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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025002324 |
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| author | Qiao-yi Huang Hui-da Zheng Bin Xiong Qi-ming Huang Kai Ye Shu Lin Jian-hua Xu |
| author_facet | Qiao-yi Huang Hui-da Zheng Bin Xiong Qi-ming Huang Kai Ye Shu Lin Jian-hua Xu |
| author_sort | Qiao-yi Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a829b1ecd0bc47aa8e2138eedac77364 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-a829b1ecd0bc47aa8e2138eedac773642025-02-02T05:28:23ZengElsevierHeliyon2405-84402025-01-01112e41852Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learningQiao-yi Huang0Hui-da Zheng1Bin Xiong2Qi-ming Huang3Kai Ye4Shu Lin5Jian-hua Xu6Department of Gynaecology and Obstetrics, The Second Affiliated Hospital, Fujian Medical University, Quanzhou, Fujian Province, ChinaDepartment of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, ChinaDepartment of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, ChinaDepartment of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, ChinaDepartment of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, ChinaCentre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China; Corresponding author. No.34 North Zhongshan Road, Quanzhou, Fujian Province, 362000, China.Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China; Corresponding author. No.34 North Zhongshan Road, Quanzhou, Fujian Province, 362000, China.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.http://www.sciencedirect.com/science/article/pii/S2405844025002324RadiomicsEnhanced magnetic resonance imagingColorectal cancerBiological characteristicsPreoperative evaluation |
| spellingShingle | Qiao-yi Huang Hui-da Zheng Bin Xiong Qi-ming Huang Kai Ye Shu Lin Jian-hua Xu Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learning Heliyon Radiomics Enhanced magnetic resonance imaging Colorectal cancer Biological characteristics Preoperative evaluation |
| title | Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learning |
| title_full | Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learning |
| title_fullStr | Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learning |
| title_full_unstemmed | Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learning |
| title_short | Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learning |
| title_sort | preoperative prediction of multiple biological characteristics in colorectal cancer using mri and machine learning |
| topic | Radiomics Enhanced magnetic resonance imaging Colorectal cancer Biological characteristics Preoperative evaluation |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025002324 |
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