NDLI: Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis

Content Provider	Springer Nature : BioMed Central
Author	Park, Shin-Hyung Hahm, Myong Hun Bae, Bong Kyung Chong, Gun Oh Jeong, Shin Young Na, Sungdae Jeong, Sungmoon Kim, Jae-Chul
Abstract	Background Current chemoradiation regimens for locally advanced cervical cancer are fairly uniform despite a profound diversity of treatment response and recurrence patterns. The wide range of treatment responses and prognoses to standardized concurrent chemoradiation highlights the need for a reliable tool to predict treatment outcomes. We investigated pretreatment magnetic resonance (MR) imaging features of primary tumor and involved lymph node for predicting clinical outcome in cervical cancer patients. Methods We included 93 node-positive cervical cancer patients treated with definitive chemoradiotherapy at our institution between 2006 and 2017. The median follow-up period was 38 months (range, 5–128). Primary tumor and involved lymph node were manually segmented on axial gadolinium-enhanced T1-weighted images as well as T2-weighted images and saved as 3-dimensional regions of interest (ROI). After the segmentation, imaging features related to histogram, shape, and texture were extracted from each ROI. Using these features, random survival forest (RSF) models were built to predict local control (LC), regional control (RC), distant metastasis-free survival (DMFS), and overall survival (OS) in the training dataset (n = 62). The generated models were then tested in the validation dataset (n = 31). Results For predicting LC, models generated from primary tumor imaging features showed better predictive performance (C-index, 0.72) than those from lymph node features (C-index, 0.62). In contrast, models from lymph nodes showed superior performance for predicting RC, DMFS, and OS compared to models of the primary tumor. According to the 3-year time-dependent receiver operating characteristic analysis of LC, RC, DMFS, and OS prediction, the respective area under the curve values for the predicted risk of the models generated from the training dataset were 0.634, 0.796, 0.733, and 0.749 in the validation dataset. Conclusions Our results suggest that tumor and lymph node imaging features may play complementary roles for predicting clinical outcomes in node-positive cervical cancer.
Related Links	https://ro-journal.biomedcentral.com/counter/pdf/10.1186/s13014-020-01502-w.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s13014-020-01502-w
Journal	Radiation Oncology
Issue Number	1
Volume Number	15
Language	English
Publisher	BioMed Central
Publisher Date	2020-04-20
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Radiotherapy Imaging Radiology Cervical cancer Lymph node Chemoradiotherapy Predictive modeling Texture analysis Radiomics
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.6/2023

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