Dataset Information

Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study.

ABSTRACT: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (p < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.

SUBMITTER: Xie CY

PROVIDER: S-EPMC8124289 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study.

Xie Chen-Yi CY Hu Yi-Huai YH Ho Joshua Wing-Kei JW Han Lu-Jun LJ Yang Hong H Wen Jing J Lam Ka-On KO Wong Ian Yu-Hong IY Law Simon Ying-Kit SY Chiu Keith Wan-Hang KW Fu Jian-Hua JH Vardhanabhuti Varut V

Cancers 20210429 9

<h4>Purpose</h4>To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery.<h4>Methods</h4>We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiom ...[more]

PMID: 33946826

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Project description:BackgroundEffective diagnostic tools for prompt identification of high-risk locally advanced cervical cancer (LACC) patients are needed to facilitate early, individualized treatment. The aim of this work was to assess temporal changes in tumor radiomics (delta radiomics) from T2-weighted imaging (T2WI) during concurrent chemoradiotherapy (CCRT) in LACC patients, and their association with progression-free survival (PFS). Furthermore, to develop, validate, and compare delta- and pretreatment radiomic signatures for prognostic modeling.MethodsA total of 110 LACC patients undergoing CCRT with MRI at baseline and mid-treatment were divided into training (cohortT: n = 73) and validation (cohortV: n = 37) cohorts. Radiomic features were extracted from tumors segmented on pre-CCRT and mid-CCRT T2WI and radiomic deltas (delta features) were computed. Two radiomic signatures for predicting PFS were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression: Deltarad (from delta features) and Pre-CCRTrad (from pre-CCRT features). Prognostic performance of the radiomic signatures, 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-IV), and baseline MRI-derived maximum tumor diameter (Tumormax: ≤2 cm; >2 and ≤ 4 cm; >4 cm) was evaluated by area under time-dependent receiver operating characteristics (tdROC) curves (AUC) in cohortT and cohortV (AUCT/AUCV). Mann-Whitney U tests assessed differences in radiomic delta features. PFS was evaluated using the Kaplan-Meier method with log-rank tests.ResultsDeltarad (AUCT/AUCV: 0.74/0.79) marginally outperformed Pre-CCRTrad (0.72/0.75) for predicting 5-year PFS, and both signatures clearly surpassed that of FIGO (0.61/0.61) and Tumormax (0.58/0.65). In total, four features within Deltarad and Pre-CCRTrad significantly differed in delta feature values between progressors and non-progressors, being consistently lower in progressors (p ≤ 0.03 for all). High Deltarad and Pre-CCRTrad radiomic scores were associated with poor PFS (p ≤ 0.04 for Deltarad in cohortT/Pre-CCRTrad in both cohorts; p = 0.11 for Deltarad in cohortV).ConclusionsDelta- and pretreatment radiomic signatures equally allow early prognostication in LACC, outperforming FIGO stage and MRI-assessed maximum tumor diameter.

Project description:ObjectivesThis study aimed to evaluate the effectiveness of neoadjuvant immunochemotherapy (NIC) in patients diagnosed with locally advanced esophageal squamous cell carcinoma (LAESCC), by assessing the performance of models that utilize enhanced computed tomography (CT) images at the pre, post, and delta/pre group stages.MethodsA total of 225 patients were included in our study and randomly divided into a training set (n = 157) and test set I (n = 68). In addition, we conducted a test set II involving 60 patients from another center. We obtained omics features from CT images before and after NIC. Then, the delta radiomics features were obtained by calculating the differences between the post and pre group features, which was then divided by the pre group features to obtain the delta/pre group. Imaging and clinicopathological data were collected in the two centers according to the same inclusion and exclusion criteria. The tumor regression grading (TRG) system was used according to the Japanese Esophageal Cancer (11th edition). Three sets of models were built and their performance was assessed using receiver operating characteristic (ROC) curve, confusion matrix, and calibration curve. The clinical utility of the model was evaluated through decision curve analysis and nomogram.ResultsThe area under the curve value of the delta/pre-radiomics (Rad) score model was 0.876 in the training set and 0.827 and 0.749 in the two test sets, which was significantly higher than that in the pre and post Rad score models. The radiomics nomogram was constructed using Rad scores derived from the post model, delta/pre model, Ki67, P53, and the pathological stage of lymph node after neoadjuvant therapy (ypN), demonstrating robust performance. The internal correction curve (apparent) and the external correction curve (bias-corrected) exhibited negligible deviations from the ideal curve, thereby demonstrating a high level of similarity.ConclusionNomogram, based on delta/pre-enhanced CT features and clinical risk indicators, is a non-invasive tool to predict therapeutic effects in patients with LAESCC after NIC.

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Dataset Information

Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study.

Publications

Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study.

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