A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer
| Autor: | Yu Guo, Shengnan Mu, Yan Guo, Yunming Xie, Huimao Zhang, Yiying Zhang, Xiangchun Liu, Kan He, Yu Fu, Qi Yang, Chunyu Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Cancer Research medicine.medical_specialty Colorectal cancer Lymphovascular invasion lymphovascular invasion multimodal imaging lcsh:RC254-282 nomogram 03 medical and health sciences 0302 clinical medicine medicine rectal cancer Original Research Rank correlation medicine.diagnostic_test Receiver operating characteristic business.industry computed tomography Magnetic resonance imaging Retrospective cohort study Nomogram lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens medicine.disease 030104 developmental biology Oncology radiomics 030220 oncology & carcinogenesis Radiology business MRI Diffusion MRI |
| Zdroj: | Frontiers in Oncology Frontiers in Oncology, Vol 10 (2020) |
| ISSN: | 2234-943X |
| DOI: | 10.3389/fonc.2020.00457 |
| Popis: | Objective: To explore a new predictive model of lymphatic vascular infiltration (LVI) in rectal cancer based on magnetic resonance (MR) and computed tomography (CT). Methods: A retrospective study was conducted on 94 patients with histologically confirmed rectal cancer, they were randomly divided into training cohort (n = 65) and validation cohort (n = 29). All patients underwent MR and CT examination within 2 weeks before treatment. On each slice of the tumor, we delineated the volume of interest on T2-weighted imaging, diffusion weighted imaging, and enhanced CT images, respectively. A total of 1,188 radiological features were extracted from each patient. Then, we used the student t-test or Mann–Whitney U-test, Spearman's rank correlation and least absolute shrinkage and selection operator (LASSO) algorithm to select the strongest features to establish a single and multimodal logic model for predicting LVI. Receiver operating characteristic (ROC) curves and calibration curves were plotted to determine how well they explored LVI prediction performance in the training and validation cohorts. Results: An optimal multi-mode radiology nomogram for LVI estimation was established, which had significant predictive power in training (AUC, 0.884; 95% CI, 0.803–0.964) and validation (AUC, 0.876; 95% CI, 0.721–1.000). Calibration curve and decision curve analysis showed that the multimodal radiomics model provides greater clinical benefits. Conclusion: Multimodal (MR/CT) radiomics models can serve as an effective visual prognostic tool for predicting LVI in rectal cancer. It demonstrated great potential of preoperative prediction to improve treatment decisions. |
| Databáze: | OpenAIRE |
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