| Autor: |
Li, Jing, Zhang, Hui‐ling, Yin, Hong‐kun, Zhang, Hong‐kai, Wang, Yi, Xu, Shu‐ning, Ma, Fei, Gao, Jian‐bo, Li, Hai‐liang, Qu, Jin‐rong |
| Předmět: |
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| Zdroj: |
Journal of Magnetic Resonance Imaging; Sep2023, Vol. 58 Issue 3, p907-923, 17p |
| Abstrakt: |
Background: Current radiomics for treatment response assessment in gastric cancer (GC) have focused solely on Computed tomography (CT). The importance of multi‐parametric magnetic resonance imaging (mp‐MRI) radiomics in GC is less clear. Purpose: To compare and combine CT and mp‐MRI radiomics for pretreatment identification of pathological response to neoadjuvant chemotherapy in GC. Study Type: Retrospective. Population: Two hundred twenty‐five GC patients were recruited and split into training (157) and validation dataset (68) in the ratio of 7:3 randomly. Field/Sequence: T2‐weighted fast spin echo (fat suppressed T2‐weighted imaging [fs‐T2WI]), diffusion weighted echo planar imaging (DWI), and fast gradient echo (dynamic contrast enhanced [DCE]) sequences at 3.0T. Assessment: Apparent diffusion coefficient (ADC) maps were generated from DWI. CT, fs‐T2WI, ADC, DCE, and mp‐MRI Radiomics score (Radscores) were compared between responders and non‐responders. A multimodal nomogram combining CT and mp‐MRI Radscores was developed. Patients were followed up for 3–65 months (median 19) after surgery, the overall survival (OS) and progression free survival (PFS) were calculated. Statistical Tests: A logistic regression classifier was applied to construct the five models. Each model's performance was evaluated using a receiver operating characteristic curve. The association of the nomogram with OS/PFS was evaluated by Kaplan–Meier survival analysis and C‐index. A P value <0.05 was considered statistically significant. Results: CT Radscore, mp‐MRI Radscore and nomogram were significantly associated with tumor regression grading. The nomogram achieved the highest area under the curves (AUCs) of 0.893 (0.834–0.937) and 0.871 (0.767–0.940) in training and validation datasets, respectively. The C‐index was 0.589 for OS and 0.601 for PFS. The AUCs of the mp‐MRI model were not significantly different to that of the CT model in training (0.831 vs. 0.770, P = 0.267) and validation dataset (0.797 vs. 0.746, P = 0.137). Data Conclusions: mp‐MRI radiomics provides similar results to CT radiomics for early identification of pathologic response to neoadjuvant chemotherapy. The multimodal radiomics nomogram further improved the capability. Evidence Level: 3 Technical Efficacy: 2 [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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