Response Prediction to Concurrent Chemoradiotherapy in Esophageal Squamous Cell Carcinoma Using Delta-Radiomics Based on Sequential Whole-Tumor ADC Map

An, Dianzheng; Cao, Qiang; Su, Naichuan; Li, Wanhu; Li, Zhe; Liu, Yanxiao; Zhang, Yuxing; Li, Baosheng

doi:10.3389/fonc.2022.787489

Cited by 5 publications

(3 citation statements)

References 46 publications

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“…More importantly, the best time point for imaging remains controversial. In our study, there were eleven (23.90%) studies that acquired radiomic features in images from pretreatment and post-treatment scans, and only two studies (4.30%) explored the changes between radiomic features extracted from pretreatment and mid-treatment images 43 , 50 . The features derived from the images during treatment rather than the baseline images could discriminate local tumor control from other confounding surroundings, such as inflammation or residual tumor bulk.…”

Section: Discussionmentioning

confidence: 99%

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis

Yang

Gong

et al. 2023

International Journal of Surgery

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Background: Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting outcomes of EC patients after chemoradiotherapy. Materials and Methods: PubMed, Embase and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the RQS, IBSI guideline and TRIPOD statement, as well as the modified QUADAS-2 tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented. Results: Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70% and 43.17%, respectively. Ultimately, twenty-four studies were included in the meta-analysis, of which sixteen studies had a pooled sensitivity, specificity and AUC of 0.83 (0.76-0.89), 0.83 (0.79-0.86) and 0.84 (0.81-0.87) in NCRT datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93) and 0.93 (0.90-0.95) in DCRT datasets, respectively. Moreover, radiomics could distinguish patients from the low- and high-risk groups with different DFS (pooled HR: 3.43, 95% CI 2.39-4.92) and OS (pooled HR: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size and usage of DL signature. Conclusions: Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis and open science categories. Improved model reporting of study objectives, blind assessment and image processing steps are required to help promote real clinical applications of radiomics in EC research.

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Section: Discussionmentioning

confidence: 99%

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis

Yang

Gong

et al. 2023

International Journal of Surgery

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“…As the feature dimension increases, the difficulty of classification will increase, which may lead to the accuracy of the combined CT and PET model for predicting LNM being lower than that of the PET model alone. In order to maximize the correlation between features and classification variables, and minimize the correlation between features, mRMR was applied in this study for optimal feature selection [ 27 ].LR was applied due to its advantage in training speed and interpretability in model construction [ 11 ].…”

Section: Discussionmentioning

confidence: 99%

Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics

Lei,

Cao,

et al. 2023

Insights Imaging

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Background Preoperative stratification is critical for the management of patients with esophageal cancer (EC). To investigate the feasibility and accuracy of PET-CT-based radiomics in preoperative prediction of clinical and pathological stages for patients with EC. Methods Histologically confirmed 100 EC patients with preoperative PET-CT images were enrolled retrospectively and randomly divided into training and validation cohorts at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) was applied to select optimal radiomics features from PET, CT, and fused PET-CT images, respectively. Logistic regression (LR) was applied to classify the T stage (T1,2 vs. T3,4), lymph node metastasis (LNM) (LNM(−) vs. LNM(+)), and pathological state (pstage) (I–II vs. III–IV) with features from CT (CT_LR_Score), PET (PET_LR_Score), fused PET/CT (Fused_LR_Score), and combined CT and PET features (CT + PET_LR_Score), respectively. Results Seven, 10, and 7 CT features; 7, 8, and 7 PET features; and 3, 6, and 3 fused PET/CT features were selected using mRMR for the prediction of T stage, LNM, and pstage, respectively. The area under curves (AUCs) for T stage, LNM, and pstage prediction in the validation cohorts were 0.846, 0.756, 0.665, and 0.815; 0.769, 0.760, 0.665, and 0.824; and 0.727, 0.785, 0.689, and 0.837 for models of CT_LR_Score, PET_ LR_Score, Fused_ LR_Score, and CT + PET_ LR_Score, respectively. Conclusions Accurate prediction ability was observed with combined PET and CT radiomics in the prediction of T stage, LNM, and pstage for EC patients. Critical relevance statement PET/CT radiomics is feasible and promising to stratify stages for esophageal cancer preoperatively. Key points • PET-CT radiomics achieved the best performance for Node and pathological stage prediction. • CT radiomics achieved the best AUC for T stage prediction. • PET-CT radiomics is feasible and promising to stratify stages for EC preoperatively. Graphical Abstract

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“…The ADC is a reliable and reproducible value that serves as a promising noninvasive indicator that can be used to assess tumor aggressiveness as well as tumor responses to CRT ( 14 , 15 ). The potential of the ADC value as a helpful marker has been well documented in various studies, which have highlighted its ability to predict treatment response and the survival probabilities of patients with esophageal squamous cell carcinoma (ESCC) ( 16 ).…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of imaging methods for prognosis assessment in esophageal squamous cell carcinoma: focus on diffusion-weighted magnetic resonance imaging, computed tomography and esophagography

Li,

Su,

Shang

et al. 2024

Front. Oncol.

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ObjectiveTo identify the most sensitive imaging examination method to evaluate the prognosis of esophageal squamous cell carcinoma (ESCC).Materials and methodsThirty patients with esophageal squamous cell carcinoma (ESCC) participated in the study and underwent chemoradiotherapy (CRT). They were divided into two groups based on their survival status: the survival group and non-survival group. The diagnostic tests were utilized to determine the most effective imaging examination method for assessing the prognosis.Results1. There were no significant differences in tumor length shown on esophagography or computed tomography (CT) or the maximal esophageal wall thickness shown on CT at the specified time points between the two groups. 2. The tumor length on diffusion-weighted imaging (DWI) in the survival group was significantly lower than in the non-survival group at the end of the sixth week of treatment (P=0.001). The area under the ROC curve was 0.840 (P=0.002), and the diagnostic efficiency was moderately accurate. 3. The apparent diffusion coefficient (ADC) values of the survival group were significantly higher than those in the non-survival group at the end of the fourth week and sixth week of treatment (both P<0.001). Areas under the curve were 0.866 and 0.970, with P values of 0.001 and <0.001 and good diagnostic accuracy. Cox regression analyses indicated the ADC at the end of the sixth week of treatment was an independent risk factor.ConclusionsCompared with esophagography and CT, DW-MRI has certain advantages in predicting the prognosis of ESCC.

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Response Prediction to Concurrent Chemoradiotherapy in Esophageal Squamous Cell Carcinoma Using Delta-Radiomics Based on Sequential Whole-Tumor ADC Map

Cited by 5 publications

References 46 publications

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis

Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics

Comparative evaluation of imaging methods for prognosis assessment in esophageal squamous cell carcinoma: focus on diffusion-weighted magnetic resonance imaging, computed tomography and esophagography

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