6P Deep learning radiomics supports perioperative treatment decisions and is associated with LncRNAs in breast cancer: A multicenter study
نویسندگان
چکیده
Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. This study aims to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing underpinning. In this multicenter study, 1,113 nonmetastatic invasive cancer were included, divided into training cohort (n=698), validation (n=171), testing (n=244). included three phases Radiomic DeepSurv Net (RDeepNet). phase 1, RDeepNet was constructed deep learning radiomic features recurrence-free (RFS) prediction. RNA-sequencing performed explore mechanisms of radiomics. 2, correlation variance analyses conducted examine changes after neoadjuvant chemotherapy. The association quantitative relation epigenetic molecular characteristics further analyzed 3. significantly associated RFS (HR 0.03, 95% CI 0.02–0.06, P < 0.001), it achieved AUCs 0.98, 0.94, 0.92 1-, 2-, 3-year RFS. showed 0.91 0.94 cohorts, respectively. generalized by different subtypes therapy regimens (All 0.001). found vary among therapeutic responses Moreover, long non-coding RNAs (lncRNAs) discovered be correlated could quantified findings indicate conveniently used guide treatment decisions lncRNA expression noninvasively cancer.
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ژورنال
عنوان ژورنال: Annals of Oncology
سال: 2022
ISSN: ['0923-7534', '1569-8041']
DOI: https://doi.org/10.1016/j.annonc.2022.10.013