دورية أكاديمية
Predicting outcomes for locally advanced rectal cancer treated with neoadjuvant chemoradiation with CT-based radiomics.
العنوان: | Predicting outcomes for locally advanced rectal cancer treated with neoadjuvant chemoradiation with CT-based radiomics. |
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المؤلفون: | Wang F; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore. wang.fu.qiang@singhealth.com.sg., Tan BF; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore., Poh SS; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore., Siow TR; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore., Lim FLWT; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore., Yip CSP; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore., Wang MLC; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore., Nei W; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore., Tan HQ; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore. tan.hong.qi@nccs.com.sg. |
المصدر: | Scientific reports [Sci Rep] 2022 Apr 13; Vol. 12 (1), pp. 6167. Date of Electronic Publication: 2022 Apr 13. |
نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
اللغة: | English |
بيانات الدورية: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
أسماء مطبوعة: | Original Publication: London : Nature Publishing Group, copyright 2011- |
مواضيع طبية MeSH: | Neoplasms, Second Primary*/pathology , Rectal Neoplasms*/diagnostic imaging , Rectal Neoplasms*/therapy, Humans ; Neoadjuvant Therapy/methods ; Prognosis ; Rectum/pathology ; Retrospective Studies ; Tomography, X-Ray Computed |
مستخلص: | A feasibility study was performed to determine if CT-based radiomics could play an augmentative role in predicting neoadjuvant rectal score (NAR), locoregional failure free survival (LRFFS), distant metastasis free survival (DMFS), disease free survival (DFS) and overall survival (OS) in locally advanced rectal cancer (LARC). The NAR score, which takes into account the pathological tumour and nodal stage as well as clinical tumour stage, is a validated surrogate endpoint used for early determination of treatment response whereby a low NAR score (< 8) has been correlated with better outcomes and high NAR score (> 16) has been correlated with poorer outcomes. CT images of 191 patients with LARC were used in this study. Primary tumour (GTV) and mesorectum (CTV) were contoured separately and radiomics features were extracted from both segments. Two NAR models (NAR > 16 and NAR < 8) models were constructed using Least Absolute Shrinkage and Selection Operator (LASSO) and the survival models were constructed using regularized Cox regressions. Area under curve (AUC) and time-dependent AUC were used to quantify the performance of the LASSO and Cox regression respectively, using ten folds cross validations. The NAR > 16 and NAR < 8 models have an average AUCs of 0.68 ± 0.13 and 0.59 ± 0.14 respectively. There are statistically significant differences between the clinical and combined model for LRFFS (from 0.68 ± 0.04 to 0.72 ± 0.04), DMFS (from 0.68 ± 0.05 to 0.70 ± 0.05) and OS (from 0.64 ± 0.06 to 0.66 ± 0.06). CTV radiomics features were also found to be more important than GTV features in the NAR prediction model. The most important clinical features are age and CEA for NAR > 16 and NAR < 8 models respectively, while the most significant clinical features are age, surgical margin and NAR score across all the four survival models. (© 2022. The Author(s).) |
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تواريخ الأحداث: | Date Created: 20220414 Date Completed: 20220415 Latest Revision: 20220527 |
رمز التحديث: | 20221213 |
مُعرف محوري في PubMed: | PMC9008122 |
DOI: | 10.1038/s41598-022-10175-2 |
PMID: | 35418656 |
قاعدة البيانات: | MEDLINE |
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