| Autor: |
Qiong Qin, Xiangyu Gan, Peng Lin, Jingshu Pang, Ruizhi Gao, Rong Wen, Dun Liu, Quanquan Tang, Changwen Liu, Yun He, Hong Yang, Yuquan Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-13 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2342 |
| DOI: |
10.1186/s12880-024-01237-0 |
| Popis: |
Abstract Objectives To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model. Materials and methods This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training set of 74 and a validation set of 32 in a 7: 3 ratios. Ultrasomics features were extracted from the tumors’ region of interest of B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images based on PyRadiomics. Mann-Whitney U test, spearman, and least absolute shrinkage and selection operator algorithms were utilized to reduce features dimension. Five models were built with ultrasomics and clinical analysis using multilayer perceptron neural network classifier based on python. Including BUS, CEUS, Combined_1, Combined_2 and Clinical models. The diagnostic performance of models was assessed with the area under the curve (AUC) of the receiver operating characteristic. The DeLong testing algorithm was utilized to compare the models’ overall performance. Results The AUC (95% confidence interval [CI]) of the five models in the validation cohort were as follows: BUS 0.675 (95%CI: 0.481–0.868), CEUS 0.821 (95%CI: 0.660–0.983), Combined_1 0.829 (95%CI: 0.673–0.985), Combined_2 0.893 (95%CI: 0.780-1.000), and Clinical 0.690 (95%CI: 0.509–0.872). The Combined_2 model was the best in the overall prediction performance, showed significantly better compared to the Clinical model after DeLong testing (P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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